Abstract
This contribution offers a comprehensive guide to understanding and applying heterogeneous catalysis for transforming biomass‐derived hydrocarbons and oxygenates into high‐value added products at the laboratory scale. Analysis of molecular foundations, catalyst selection criteria, experimental setups, and characterization techniques establishes a methodological basis for designing catalytic processes relevant to basic research and pre‐industrial applications. Key catalytic mechanisms, including cracking, isomerization, C─C coupling, hydrogenation, and oxidation, are addressed, emphasizing the specific roles of active sites and how their acidic, basic, metallic, or redox nature influences system activity and selectivity. The comparison between batch and continuous flow reactors underscores the importance of their suitability to control the needed operating variables, including reproducibility and extrapolating kinetic data to industrial conditions. Likewise, rigorous mass balances and advanced analytical techniques are essential for comprehensively evaluating catalytic performance. The strategic relevance of heterogeneous catalysis is demonstrated in petroleum refining, biofuel production, plastic waste valorization, and renewable fuel synthesis. In these contexts, using bifunctional catalysts, including modified mesoporous materials and hybrid systems, has enabled significant improvements in conversion, selectivity, and operational process stability. This guide is a practical starting point for researchers entering the field, supporting the development of experimental projects and in‐depth studies in heterogeneous catalysis.
Keywords: Catalysis, Guide, Hydrocarbons, Researcher, Student
A focused overview of the catalytic processing of hydrocarbons to transform them into more valuable compounds is presented. This work is intended to assist BSc, MSc, and PhD students, as well as junior researchers, in selecting and performing appropriate heterogeneous catalytic processes for specific heterogeneous catalytic reactions.

1. Introduction
Heterogeneous catalysis involves a catalyst in a different phase (typically solid) from the reactants (often gases or liquids). Reactant molecules adsorb onto active sites on the catalyst surface, undergo a chemical transformation, and then desorb as products. This adsorption–reaction–desorption cycle lowers activation energy and accelerates reaction rates compared to uncatalyzed pathways.[ 1 , 2 ] The catalyst remains active after each cycle, enabling repeated reactions. Key factors influencing heterogeneous catalysis include adsorption type (physisorption versus chemisorption), surface reaction kinetics, and transport processes (heat and mass transfer), which affect overall reaction rates. Catalytic activity often depends on the availability of active sites, which may consist of specific atoms, ions, or functional groups where reactants bind and react.[ 3 , 4 ]
Effective heterogeneous catalysts require careful engineering of their physicochemical properties across multiple scales. At the atomic level, catalysts are designed with specific active sites that bind reactants optimally strongly enough to activate bonds but not so strongly that products adhere irreversibly. This principle leads to “volcano curves,” where intermediate binding energies yield the highest catalytic activity.[ 5 , 6 , 7 , 8 ] Researchers employ surface science and computational modeling to fine‐tune catalyst compositions and structures. For instance, scaling relations between adsorption energies help screen materials, while breaking these linear trends can surpass conventional activity–selectivity limits.[ 9 , 10 ] At the nano and microscale, catalyst design maximizes surface area and tailors pore structures for molecular accessibility. Most catalysts are porous solids (e.g., oxides, zeolites, and supported metals) with high surface areas, as reactions occur at surfaces. Supports and promoters help disperse active phases, prevent metal sintering, enhance stability, or introduce functionalities like acid sites or redox couples.[ 11 ] For example, metal nanoparticles on oxide supports combine the metal's activity with the support's acid or textural properties. Additionally, the pore size and shape in materials like zeolites can induce shape‐selectivity, favoring certain products by steric constraints.[ 12 ] Overall, catalyst design is a multidimensional optimization of composition, structure, and morphology to achieve the desired activity, selectivity, and stability.
Heterogeneous catalysis plays a pivotal role in the large‐scale production of fuels and value added chemicals, especially in processes utilizing hydrocarbons and biomass feedstocks. Nearly 90% of industrial chemicals by volume are produced using solid catalysts, underscoring the economic and technological significance of this field.[ 13 ] In petroleum refining, catalytic processes such as catalytic cracking, hydrodesulfurization, and reforming have largely supplanted thermal methods due to their superior efficiency, selectivity, and ability to yield cleaner fuels like high‐octane gasoline with fewer byproducts.[ 14 ] Likewise, biomass upgrading through catalytic pyrolysis, hydrodeoxygenation, and Fischer–Tropsch synthesis is gaining traction as part of the global shift toward renewable energy systems. The durability and reusability of heterogeneous catalysts offer distinct economic advantages, particularly in continuous flow operations where extended catalyst lifetimes help reduce operational costs.[ 15 ] Overall, heterogeneous catalysis remains a cornerstone of modern chemical manufacturing and the energy transition, bridging surface science and process engineering to support the development of sustainable fuel and chemical platforms.
An important distinction exists between sensible and insensible reactions, which aids in understanding and classifying catalytic processes. This connection involves concepts from thermodynamics, kinetics, and surface science.[ 16 ] Sensible reactions are those that result in observable and measurable changes within a system. These changes may include variations in temperature (due to the release or absorption of heat), pressure, volume, or the chemical composition of the reactants and products. Because these changes can be detected, sensible reactions are relatively easy to monitor using standard laboratory techniques. A classic example is the hydrogenation of ethylene to ethane, where the consumption of gas and the release of heat can be easily measured and correlated with the progress of the reaction. Insensible reactions, on the other hand, are defined by the lack of easily measurable changes. These reactions typically take place at the catalyst's surface and may involve subtle processes such as the rearrangement of adsorbed species, surface migration, or internal transformations. These processes do not significantly alter the overall properties of the system. Because of this, they are much harder to detect and measure using conventional methods. For example, the movement of a hydrogen atom across a metal surface or the isomerization of an adsorbed intermediate can occur without causing any noticeable changes in pressure or temperature. Understanding this distinction is vital for both students and researchers, as it underscores the limitations of experimental observation and the necessity of advanced characterization techniques in catalysis. It also highlights the importance of choosing the right analytical tools based on the specific nature of the reaction under study. In catalyst design and selection, it is important to identify whether a reaction is sensible or insensible. This classification can help guide the choice of materials and the development of strategies for monitoring and optimizing catalytic performance. Including this distinction in educational materials assists students in developing a deeper understanding of how reactions occur on catalytic surfaces and why some processes are more challenging to study than others.[ 16 , 17 , 18 ]
Heterogeneous catalysis is a well‐established field with a vast literature.[ 1 , 2 , 3 , 4 , 17 , 18 ] Due to the extensive nature of the subject and the numerous catalytic reactions involved, it is challenging to address all aspects comprehensively in a single article. This work aims to provide a focused overview of a specific area: the catalytic processing of hydrocarbons to transform them into more valuable compounds. This guide is intended to assist BSc, MSc, and PhD students, as well as junior researchers, in selecting and performing appropriate heterogeneous catalytic processes for specific reactions.
2. Fundamentals of Heterogeneous Catalysis: Selection of Reactants and Catalysts
Selecting reactants and catalysts is a fundamental step in hydrocarbon conversion research, ensuring the desired product formation under controlled reaction conditions. The process requires careful consideration of feedstock composition, catalyst properties, and reactor characteristics to optimize conversion efficiency, selectivity, and stability.[ 19 , 20 , 21 ]
2.1. Selection of Reactants (Feedstock Considerations)
Depending on the feedstock choice the reaction outcome will change. The reactants can be classified into model feedstocks (pure compounds with well‐defined structures) and complex feedstocks (mixtures with varying compositions), as shown in Figure 1. The selection depends on the research objectives, such as mechanistic studies, catalytic performance evaluations, or process optimization for industrial applications.[ 22 , 23 , 24 ]
Figure 1.

Selection of feedstock: model versus complex mixtures.
2.1.1. Model Feedstocks
Model feedstocks are chemically pure compounds, such as n‐alkanes, iso‐alkanes, olefins, naphthenes, aromatics, or oxygenated hydrocarbons (e.g., heptane, cyclohexane, phenol, anisole, and guaiacol). These compounds allow researchers to precisely study reaction mechanisms, catalytic activity, and selectivity under well‐defined conditions.[ 23 , 25 , 26 , 27 ] The advantages of using model feedstocks include the use of a simple molecule which will react to more easily detectable products leading to a precise evaluation of catalyst performance. This could be a good starting point to continue a research project with a more complex feedstock. Having a less number of products and a single model molecule, it is possible to have an accurate determination of product distribution and reaction pathways. The use of a model feedstock eliminates uncertainties associated with complex feedstock variability.
For longer‐chain hydrocarbons (C > 7), secondary reactions such as cracking, isomerization, dehydrogenation, hydrogenation, and cyclization may lead to a broader range of reaction products (C1‐7+).[ 28 ]
In the case of gaseous feeds (methane, ethane, and ethylene, etc.), purity is a critical factor, as even trace impurities can influence catalytic performance.[ 29 ] For liquid and solid hydrocarbons, compositional analysis includes the quantification of n‐alkanes, alkenes, alkynes, isomers, and oxygenates. Regarding oxygenated model compounds, the most relevant for refineries include triglycerides,[ 30 ] free fatty acids,[ 31 ] and lignin model compounds such as phenol, anisole, or guaiacol,[ 32 ] as well as alcohols.[ 33 ] However, other oxygenated compounds, such as ketones or aldehydes, can also be utilized. It is essential to know their composition before performing the reaction.
To ensure that model compounds adequately represent real feedstocks, selection was guided by criteria such as: i) molecular similarity to key constituents in complex mixtures, ii) comparable functional group chemistry and reactivity, iii) thermal and chemical stability under reaction conditions, and iv) prevalence in relevant industrial or environmental matrices. For instance, when simulating petroleum derivatives or biomass‐derived streams, model compounds like guaiacol, phenol, or furfural are often selected due to their structural and reactive resemblance to lignin‐derived phenolics or carbohydrate degradation products. These criteria allow for mechanistic extrapolation and catalytic behavior prediction under simplified but representative conditions. In addition, practical and regulatory factors must also be considered. These include: v) safety in handling and operation; vi) compliance with national and institutional regulations; and vii) suitability for the intended scale of experimentation, from laboratory‐scale to pilot or industrial‐scale process development. It is important to note, however, that at pilot or industrial scales, real feedstocks are typically used instead of model compounds.[ 34 , 35 , 36 , 37 ]
2.1.2. Complex Feedstocks
Complex feedstocks, such as petroleum fractions and biomass‐derived liquids, require detailed characterization to assess their suitability for catalytic conversion.[ 38 , 39 ] These include:
Gas Mixtures: Composed of hydrocarbons, heteroatoms, and impurities (e.g., N2, O2, CO2, SOx, NOx, and VOCs). Analyzed via gas chromatography (GC/FID‐TCD).[ 40 ]
Liquid Mixtures: Hydrocarbon fractions categorized by boiling point ranges.[ 41 ]
Naphtha (C5–C10)
Kerosene (C10–C16)
Gas Oil (C14–C20)
Heavier Oils (C20–C70)
Residues (C70+)
Supplier specifications for these feedstocks include density, viscosity, elemental composition (C, H, N, S, and O), and metal content. For oxygenated feedstocks, such as biomass‐derived fractions, compositional analysis focuses on free fatty acids, triglycerides, and lignin‐derived phenolics. Depending on the complexity and the supplier information, the sample composition could be detailed with compound‐specific analysis or grouped analyses, such as paraffins, naphthenes, and aromatics (mono‐, di‐, and poly‐aromatics).[ 42 ] Ash content may also be included. Refineries, whether fossil‐based, renewable, or hybrid (co‐processing fossil and renewable feeds), primarily produce fuels.[ 43 ] However, their scope extends beyond fuels, refining raw materials into valuable chemical products, including liquids (lease condensates, natural gas plant liquids, and liquefied gases) and unfinished oils (naphtha, lighter oils, kerosene, light/heavy gas oils, and residuum). In biorefineries, feedstocks consist of oxygenated compounds derived from biomass, which can undergo fermentation, pyrolysis, or deoxygenation to produce hydrocarbons. These processes enable converting renewable resources into fuels and high‐value chemicals, supporting a transition to sustainable energy and chemical production.[ 44 ]
Although the focus is on hydrocarbon‐type feedstocks, using oxygenated compound mixtures, such as biomass, biowaste, and waste residues, is a current and relevant topic. Many studies address the use of these feedstocks to produce fuels and other valuable compounds, including hydrocarbons.[ 45 , 46 , 47 ] Another topic is the gasification of biomass or waste to produce synthetic gas, which is then used in Fischer–Tropsch synthesis (FTS) to produce paraffins.[ 48 , 49 , 50 , 51 ] These paraffins can subsequently be refined to obtain suitable products. However, a complete process involving gasification, syngas production, FTS synthesis, and refining may not be feasible for a laboratory student research project.
2.2. Catalyst Selection Criteria for Hydrocarbon Reactions
The selection of catalysts for hydrocarbon transformations depends on intrinsic activity, selectivity, and stability, which are influenced by their physicochemical properties.[ 52 , 53 , 54 ] Key factors include surface area, porosity, active site chemistry, and operational durability.[ 55 , 56 ] Porous solid acids, such as zeolites, possess high surface areas and well‐defined micropore networks that facilitate the trapping and cracking of large hydrocarbon molecules. Their acidic sites promote carbocation‐mediated reactions, making them ideal for catalytic cracking and isomerization.[ 57 , 58 ] In contrast, metal catalysts are typically employed in hydrogenation, dehydrogenation, and coupling reactions due to their ability to adsorb and activate H2 or facilitate C─C bond formation.[ 59 ] A classic example is hydrocracking bifunctional catalysts, combining metal sites for hydrogenation–dehydrogenation with acidic supports (e.g., silica–alumina or zeolites) for cracking and isomerization. The metal sites saturate intermediates via hydrogenation, while the acid sites break large molecules, enabling the conversion of heavy feedstocks into lighter fuels under hydrogen atmospheres.[ 60 ] Sulfur tolerance is a key practical consideration. Base metal sulfides (e.g., Ni–Mo or Co–Mo) are commonly used for sulfur‐rich refinery feeds. In contrast, sulfur‐sensitive noble metals (e.g., Pt, Pd) require feed pretreatment to prevent sulfur poisoning of active sites.[ 61 ]
Selecting the right catalyst requires a thorough understanding of mass transfer, as it is crucial for accurately interpreting catalytic activity. There are two main types of mass transfer limitations that can impact reaction rates: external diffusion and internal diffusion. External diffusion involves the transport of reactants from the surrounding fluid to the outer surface of the catalyst particle. If this process is slow, it can limit the overall reaction rate, even if the catalyst itself is highly active. Internal diffusion, on the other hand, pertains to the movement of reactants within the porous structure of the catalyst to reach the active sites. When internal diffusion plays a significant role, it can result in lower observed reaction rates due to restricted access to these active sites. To measure the impact of internal diffusion, we use the Thiele modulus (ϕ). This modulus relates the rate of reaction to the rate of diffusion occurring within the catalyst pores. A high Thiele modulus indicates significant diffusion limitations. In addition, the effectiveness factor (η) assesses the ratio of the actual reaction rate to the rate that would be achieved if the entire catalyst volume were fully accessible. An effectiveness factor of less than one indicates that diffusion is negatively affecting the catalyst's performance.[ 62 ]
Together, these concepts help researchers and students identify whether observed catalytic behavior is governed by intrinsic kinetics or masked by transport limitations, guiding better catalyst design and reactor operation.
Catalyst selection is primarily guided by reaction type (Figure 2).
Cracking of Large Hydrocarbons: Strongly acidic solids are preferred as they generate carbocation intermediates, facilitating C─C bond scission. Zeolites and silica–alumina catalysts are commonly used.[ 63 , 64 ]
Alkene Polymerization and Coupling Reactions: Catalysts that form surface alkyl or carbene species, such as supported titanium chloride in Ziegler–Natta polymerization or metal oxides in oxidative coupling are effective.[ 65 ]
Hydrogenation Reactions: Group VIII transition metals (e.g., Ni, Pd, and Pt) efficiently dissociate H2 and hydrogenate multiple bonds. Supports like activated carbon or alumina disperse the metals, increasing active surface area.[ 65 ]
Oxidation Reactions: Metal oxides or mixed‐metal oxides (e.g., vanadium, molybdenum, and cobalt oxides) cycle between oxidation states, providing lattice oxygen for hydrocarbon oxidation. These catalysts often follow the Mars–van Krevelen mechanism, where lattice oxygen oxidizes hydrocarbons and is subsequently replenished by O2 from the gas phase, enabling continuous operation.[ 66 , 67 ]
Figure 2.

Catalytic reactions scheme in a (bio, renewable, standard)‐refinery.
Selecting a catalyst requires consideration of activity, thermal stability, mechanical strength, and resistance to deactivation. Catalysts degrade over time due to coke deposition, sintering, or poisoning by impurities, needing tailored formulations or additives to mitigate these effects.[ 68 , 69 ] The regeneration capacity for the catalyst is a crucial condition for catalysts used in catalytic cracking. For example, these catalysts are periodically or continuously regenerated by burning off their coke deposits. This step grants the optimal catalyst balance activity, selectivity, and durability, ensuring efficient performance under industrial conditions while remaining economically viable over its operational lifespan.[ 70 , 71 ]
From a research perspective, new catalysts are commonly used in novel research projects. In this case, a new synthesis method must be detailed in the experimental section. The synthesis can follow established methods or involve the development of a new approach. The description of the catalyst synthesis should include the chemicals used. Commonly, catalysts are metal‐supported materials, such as metals supported on Al2O3, SiO2, ZrO2, or TiO2, in various mixtures and structures. Supports can be either commercial or synthesized. Typical structures include amorphous solids, zeolites,[ 72 , 73 ] and mesostructured materials.[ 74 ] Researchers may also opt for other heterogeneous catalysts (or even homogeneous ones), such as layered materials or composites.
In addition to considering physicochemical properties and practical aspects, understanding the reaction mechanism is crucial for selecting catalysts, model compounds, and reaction conditions. Initial hypotheses about how a reaction occurs—such as identifying intermediate species, determining the rate‐limiting step, or understanding the nature of the active sites—can greatly enhance the rational design of catalytic systems. The decision to include acidic or metallic components in a catalyst often depends on the nature of the reaction and whether it involves carbocation, radical, or organometallic intermediates. Understanding these mechanisms helps in selecting suitable model compounds. These model compounds not only resemble real feedstocks structurally but also demonstrate similar reactivity, allowing researchers to study specific reaction pathways under controlled conditions. By combining mechanistic insights with experimental data, researchers can enhance their ability to predict catalyst performance, which contributes to the design of optimized catalytic processes for both academic research and industrial applications.[ 75 , 76 ]
2.3. Detailed Reaction Mechanisms in Hydrocarbon Conversions
Heterogeneous catalytic reactions generally proceed via multi‐step molecular mechanisms on the catalyst surface. For hydrocarbon transformations, the specific mechanism varies with reaction type, but all involve the formation of intermediate adsorbed species and transition states that lower the energy barriers (Figure 3) of the overall conversion.[ 77 ]
Figure 3.

Examples for hydrocarbon reactions scheme summary.[ 78 , 79 , 80 , 81 ]
2.3.1. Catalytic Cracking Mechanisms
Catalytic cracking refers to the cleavage of large hydrocarbon molecules into smaller fragments, primarily to produce gasoline‐range fuels and light olefins. The process follows an ionic mechanism involving carbocation intermediates on solid acid catalysts such as zeolites. The reaction begins when a long‐chain hydrocarbon adsorbs onto a Brønsted acid site of the catalyst, leading to protonation and the formation of a carbocation (e.g., R─CH2─CH2─⁺CH─R'). This positively charged intermediate undergoes β‐scission, splitting into a smaller carbocation and an alkene. For instance, cracking a C16 paraffin on H‐zeolite may generate a Cn + carbocation and an olefin. The carbocation can further fragment or stabilize through deprotonation, producing another olefin.[ 82 , 83 ]
Because these steps involve ionic intermediates, catalytic cracking occurs at moderate temperatures, significantly lower than thermal cracking, which relies on free‐radical mechanisms at higher temperatures. The solid acid catalyst facilitates a lower‐energy pathway through proton transfer, carbocation rearrangement, and subsequent product desorption, often accompanied by isomerization, which produces branched hydrocarbons. In contrast, cracking over nonacid catalysts (e.g., metals in catalytic steam cracking) may involve free‐radical mechanisms. Still, industrial catalytic cracking predominantly relies on solid acids for their higher efficiency and selectivity.[ 14 , 23 ] Fluid catalytic cracking (FCC) is the most widely used process in refineries, employing fine acidic catalyst particles in a fluidized bed reactor to convert heavy oils into valuable fuels. The reaction mechanism in each catalyst particle follows the ionic route described above. The catalyst is regenerated in a separate regenerator vessel to ensure continuous operation, where deposited coke is burned off, restoring catalytic activity. The ability to regenerate catalysts makes FCC an economically viable and sustainable process for maximizing gasoline and light olefin yields in modern petroleum refining.[ 84 ]
2.3.2. Coupling and C─C Bond Formation Mechanisms
Coupling reactions construct larger hydrocarbons by forming new C─C bonds, often requiring bifunctional catalysts or special conditions due to the inherent challenge of C─C bond formation. One notable example is the oxidative coupling of methane (OCM), where methane (CH4), a relatively inert molecule, is activated on the surface of oxide catalysts at high temperatures in the presence of O2. The proposed mechanism involves surface oxygen abstracting a hydrogen atom from CH4, generating a surface ─OH group and a methyl radical (·CH3). This radical desorbs into the gas phase, where two methyl radicals couple to form ethane (C2H6), which then dehydrogenates either catalytically or thermally, yielding ethylene (C2H4). The heterogeneous homogeneous interplay of surface activation and gas‐phase radical coupling is a defining feature of OCM.[ 85 , 86 , 87 , 88 ]
Another major C─C coupling mechanism occurs in Fischer–Tropsch (FT) synthesis, where CO and H2 react on metal catalysts (typically iron or cobalt) to produce long‐chain hydrocarbons. In this process, CO dissociates on the metal surface, forming CH x species that undergo stepwise hydrogenation and polymerization. The mechanism follows a chain‐growth sequence, where surface carbon or ─CH2─units successively insert into growing hydrocarbon chains, culminating in hydrogenation to release alkanes. The Anderson–Schulz–Flory model describes the product distribution in FT synthesis, dictated by the probability of chain propagation versus termination.[ 89 , 90 ] Other heterogeneous C─C coupling reactions include aldol condensations of oxygenates on basic catalysts and olefin oligomerization on acid catalysts (e.g., converting propylene or butenes into gasoline‐range hydrocarbons). Regardless of the pathway, the catalyst must stabilize C─C bond formation, whether through radical combination, metal‐alkyl coupling, or carbon insertion mechanisms.[ 91 , 92 , 93 ]
2.3.3. Isomerization Mechanisms
Isomerization transforms a molecule into another isomer by rearranging atoms without altering its molecular formula. A key industrial example is the isomerization of straight‐chain alkanes into branched isomers, which enhances gasoline octane ratings. In heterogeneous catalysis, alkane isomerization typically follows a carbenium ion mechanism, similar to catalytic cracking.[ 94 , 95 ] For instance, n‐butane isomerization involves: 1) dehydrogenation of the alkane to an alkene on metal sites (in bifunctional catalysts), 2) protonation of the alkene on an acid site to form a carbenium ion, 3) skeletal rearrangement to generate a branched carbenium ion, and 4) deprotonation to produce the isomerized alkene, which is subsequently hydrogenated back to the alkane. In purely acidic zeolite catalysts, alkane activation can proceed via hydride abstraction or protolysis, directly generating the carbenium ion.[ 96 , 97 ]
Key intermediates in alkane isomerization are carbenium (carbocation) species, which rearrange to form branched isomers. For example, n‐hexane isomerization into iso‐hexanes occurs through secondary → tertiary carbocation rearrangements. Studies confirm that n‐alkane isomerization over solid acids follows a rate‐determining step involving carbocation rearrangements. Since these intermediates are also involved in cracking, isomerization and cracking often occur together. Cracking can be considered an “over‐cracking” of isomerized products, with bifunctional hydrocracking catalysts balancing the two reactions: acid sites crack long chains, but at intermediate conversions, they favor isomerization a balance controlled by catalyst acidity and reaction conditions[ 98 , 99 ] Another class, olefin isomerization, occurs on metal surfaces via π‐allyl or alkyl intermediates. For example, double‐bond migration in olefins (e.g., 1‐butene to 2‐butene) readily proceeds on metal catalysts, forming σ‐bonded alkyls.[ 100 ] In general, heterogeneous isomerization mechanisms typically involve an intermediate capable of rearrangement carbocations on acids or metallacycles/π‐allyl species on metals, leading to the new isomer before stabilization into the final product.[ 101 ]
2.3.4. Oxidation Mechanisms
Heterogeneous catalytic oxidation of hydrocarbons typically follows redox mechanisms on oxide catalyst surfaces, with one of the most well‐known pathways being the Mars–van Krevelen mechanism, common in transition metal oxides.[ 102 , 103 ] In this mechanism, lattice oxygen from the catalyst oxidizes the hydrocarbon. For instance, in n‐butane oxidation to maleic anhydride over vanadium phosphorus oxide, an oxygen atom from the oxide lattice reacts with n‐butane, forming an intermediate that subsequently yields maleic anhydride, leaving behind an oxygen vacancy. Molecular O2 then reoxidizes the catalyst, refilling the vacancy and completing the cycle. A key piece of evidence for this mechanism is that oxidation can proceed even in the absence of gas‐phase O2 (for a short duration), utilizing lattice oxygen, after which the catalyst requires regeneration via O2 exposure.[ 104 , 105 , 106 ]
In contrast, oxidation on metal catalysts, such as platinum in catalytic converters, often follows a Langmuir–Hinshelwood mechanism, where both reactants adsorb onto the surface. For example, in CO oxidation on Pt, O2 dissociates into O* atoms, which then react with adsorbed CO* to form CO2, which subsequently desorbs. For hydrocarbons, oxidation typically involves initial dehydrogenation, forming surface carbonaceous fragments, followed by oxidation to CO2. Selective oxidation requires precise control to prevent total combustion.[ 107 , 108 ] For example, ethylene epoxidation to ethylene oxide on silver catalysts proceeds via a surface oxametallacycle intermediate. At the same time, undesired total combustion to CO2 must be suppressed by optimal catalyst selection and reaction conditions.[ 109 ] In this context, catalytic oxidation mechanisms involve either lattice oxygen transfer (characteristic of oxide catalysts, favoring partial oxidation) or adsorbed oxygen reaction (typical of noble metal catalysts, often leading to total oxidation). These processes frequently generate radical or ionic intermediates (e.g., alkoxy, peroxy, or carbocation species). Controlling these mechanisms to favor partial oxidation over total oxidation remains a central challenge in oxidation catalysis.
2.3.5. Hydrogenation Mechanisms
Hydrogenation is the addition of H2 to unsaturated bonds (C═C, C≡C, C═O, aromatics, etc.) to form saturated products. Heterogeneous hydrogenation typically employs metal catalysts (Ni, Pd, Pt, and Ru), following the Horiuti–Polányi mechanism. In this mechanism, H2 dissociatively adsorbs on the metal surface, splitting into two adsorbed H atoms (H). The unsaturated substrate also adsorbs onto the surface.[ 110 , 111 , 112 ] The reaction proceeds stepwise:
One H atom transfers to the adsorbed substrate, forming a half‐hydrogenated intermediate bound to the surface.
A second H atom transfers, saturating the bond.
The saturated product desorbs from the surface.
For example, in ethylene (C2H4) hydrogenation on Pd:
H2 → 2 H* (dissociative adsorption of H2)
C2H4 adsorbs onto Pd
C2H4–Pd + H → C2H5–Pd* (formation of ethyl species)
C2H5–Pd + H → C2H6 desorbs* (product formation)
This stepwise addition via surface hydrides explains cis/trans isomerization as a side reaction since the half‐hydrogenated intermediate can rotate before receiving the second H atom.[ 113 ]
In some cases, particularly on less reactive metal surfaces or under specific conditions, a non Horiuti–Polányi mechanism may occur, where H2 and the substrate react concertedly without prior H2 dissociation. However, in most heterogeneous hydrogenations, H2 dissociates rapidly on active metals, and the surface continuously supplies atomic H for selective hydrogenation. This mechanism also explains selectivity trends for example, in partial hydrogenation of alkynes to alkenes, once the alkene forms, it binds weaklier and tends to desorb before further hydrogenation.[ 113 , 114 ] Proper catalyst selection, such as Lindlar catalyst, ensures alkynes bind strongly while alkenes leave before full saturation.[ 115 ]
Additionally, hydrogenation on metals is structure‐sensitive certain crystal faces or particle sizes may favor specific intermediates or adsorption strengths. This extends to hydrogenation of aromatics, carbonyls, and nitriles, where distinct intermediates may form.[ 116 , 117 ] Overall, metal‐catalyzed hydrogenations are highly efficient because metals can dissociate H2 and deliver atomic hydrogen to chemisorbed molecules in a controlled manner, making them essential in industrial hydrogenation processes.
3. Advanced Experimental Methodologies: Batch versus Flow Reactors
Understanding and optimizing heterogeneous catalytic reactions requires appropriate experimental setups. Two fundamental laboratory reactor types are batch reactors and continuous flow reactors, each with distinct methodologies and design considerations. In all cases, careful control of operating conditions (temperature, pressure, reactant concentrations/flow, reactant–catalyst ratios) and attention to transport phenomena (mixing, heat removal, and mass transfer) are crucial for obtaining meaningful kinetic data and maximizing performance.[ 118 , 119 , 120 ]
3.1. Batch Reactor Techniques
In a batch reactor, the catalyst and reactants are loaded into a sealed vessel. A common setup in heterogeneous catalysis is a stirred autoclave, where a solid catalyst is either suspended in a liquid phase or contacted by gases under agitation. The reactor can be pressurized and heated as needed.[ 121 , 122 ] Operation involves charging the reactants and catalyst, ramping to the desired temperature, and stirring to ensure good contact. The reaction proceeds for a determined time, with periodic sampling to monitor conversion and selectivity. Since batch reactors are transient systems, reactant and product concentrations continuously change over time. Kinetic analysis typically involves measuring concentration versus time and fitting data to rate laws using initial rate methods or time–course analysis.[ 123 , 124 , 125 ]
To minimize mass transfer limitations, vigorous stirring and small catalyst particles help ensure uniform conditions across particles. In the case of using big particles such as pelleted catalysts, a basket for catalysts could be used. Batch reactors are favored in laboratories for their flexibility and simplicity. They allow for testing multiple reaction conditions sequentially within the same vessel and can be used for high‐pressure reactions. They are particularly useful for reactions with long induction periods or when in situ catalyst activation is required.[ 126 , 127 , 128 ] However, batch reactors have limitations. Because conditions evolve over time, side reactions or catalyst deactivation may complicate data interpretation. Additionally, product isolation requires stopping the reaction and separating the catalyst, which can be cumbersome, especially for fine catalysts or when sampling disturbs the solid phase. In research, batch reactors are often used for catalyst screening and mechanistic studies.[ 129 , 130 ] For performance optimization, the reactor is run long enough to reach the desired conversion or selectivity endpoint, but not so long as to cause excessive deactivation. Turnover frequency (TOF) can be calculated if the quantity of active sites is known, while selectivity is assessed based on product distribution at a given conversion.[ 131 ] Kozuch and Martin[ 132 ] explained that TOF is a concept frequently used in catalysis, but its definition is not always clear. They arguement that TOF should be understood as a rate per active site, measured at a specific moment rather than averaged over time. This precise definition helps to avoid confusion, particularly when comparing different catalysts. Additionally, they propose a standard TOF (TOF°) that is calculated under fixed conditions, such as temperature and concentration, to ensure that comparisons are fair and consistent.
3.2. Continuous Flow (Fixed‐Bed) Reactor Techniques
Continuous flow reactors allow for the steady‐state operation of chemical reactions, where reactants are continuously fed, and products are continuously removed. A common setup for heterogeneous catalysis is the fixed‐bed reactor, where a catalyst is packed in a tube, and reactant gases flow through the catalyst bed. The reactor operates at a controlled temperature and pressure, with precise flow rates ensuring the desired contact time over the catalyst.[ 129 , 133 ] After an initial start‐up period, the system reaches steady state, where the effluent composition remains constant over time. This makes continuous reactors ideal for long‐term catalyst performance evaluation under conditions that mimic industrial operations (Table 1). In gas‐phase flow reactors, flow behavior depends on the operating regime plug flow or laminar flow conditions are often assumed, especially in microreactors or bench‐scale tubular reactors, enabling kinetic modeling using plug‐flow reactor (PFR) equations.[ 134 , 135 , 136 , 137 ]
Table 1.
Examples of reaction conditions for various processes. This table provides examples of reaction conditions for different processes such as reforming, hydrodesulfurization (HDS), cracking, hydrotreatment, oxidation, dehydrogenation, and Fischer–Tropsch Synthesis (FTS), using either a) fixed bed or b) fluidized bed reactors.
| Catalyst | Reaction | Temperature (°C) | Pressure (bar) | Reactor | Refereces |
|---|---|---|---|---|---|
| Platinum (Pt, Pt–Re) | Reforming | 450–550 | 15–40 | (a) | [ 138 , 139 ] |
| Mo, Ni–Mo, Co–Mo | HDS | 300–400 | 30–60 | (a) | [ 140 ] |
| V2O5 | C3‐4 oxidation | 250–650 | 1–5 | (a) | [ 141 ] |
| Zeolite | Cracking | 400–550 | 1–3 | (b) | [ 142 , 143 ] |
| Ni–W, Ni–Mo | Hydrocracking | 380–395 | 100 | (a) | [ 144 ] |
| Co, Fe | FTS | 200–350 | 20–40 | (a), (b) | [ 145 ] |
| Ni–Mo, Co–Mo | Hydrotreatment | 290–430 | 7–180 | (a) | [ 146 ] |
| Pt, Ni, Ni–Mo | (Hydro/dehydro)genation | 170–450 | 1–100 | (a) | [ 147 , 148 ] |
Fixed‐bed reactors are designed to optimize gas–solid contact while minimizing heat and mass transfer limitations. Tube diameters are carefully selected to prevent channeling, and catalysts are often shaped into pellets or extrudates to reduce pressure drop. In laboratory setups, catalyst beds are typically small, and mass flow controllers and pumps ensure precise feed ratios. Temperature control is crucial, especially for highly exothermic reactions. Catalytic dilution or multi‐zone furnaces may be used to prevent hot spots. The ability to independently control feed composition and space velocity allows for systematic process optimization.[ 149 , 150 ]
Online analytical tools are often integrated to provide continuous monitoring of reaction outputs. Compared to batch processes, continuous flow reactors offer several advantages: they closely replicate industrial conditions, facilitate steady‐state kinetic studies, and allow long‐term catalyst stability testing. Unlike batch reactors, they eliminate repeated start‐up/shutdown cycles and reduce sample handling, enabling uninterrupted data collection.[ 151 , 152 , 153 ]
Flow reactors are particularly advantageous:[ 154 , 155 ]
The feedstock is continuously supplied, ensuring stable reaction conditions.
The system can be set to operate at the desired space velocity to have the suitable conversion and selectivities.
The system time on stream (TOS) or reaction time can be precisely controlled. It is easier compared to batch reactors. For batch reactors, the temperature and stirring can be stopped, but the reaction mixture is still inside of the reactor, needing some time up to a suitable colder temperature or lower pressure.
Handling of gaseous reactants is involved, as flow reactors allow for better gas‐phase control.
The thermodynamical equilibrium limits the total feedstock conversion in batch reactors compared to flow reactors because the products and reactants are in the same place.
Continuous reactors generally offer superior control over reaction conditions in batch versus flow comparisons, particularly for scale‐up. Operating parameters established in a small tubular reactor can often be directly translated to larger or multi‐tube systems in industrial applications, preserving similar heat and flow dynamics.[ 156 ] In contrast, scaling up batch processes presents greater challenges due to mixing, heat transfer, and reaction kinetics variations. Optimizing reactor performance in both batch and flow systems demands precise temperature control, contact time, and catalyst efficiency. Crucially, accurate reaction analysis and process optimization depend on ensuring that measured reaction rates reflect intrinsic kinetics rather than being limited by mass transfer phenomena.[ 157 , 158 ] In batch reactors, techniques such as vigorous stirring, smaller catalyst particles, or basket stirrers are employed to minimize external diffusion resistances. In flow reactors, higher flow rates or diluted catalyst beds help prevent local equilibrium limitations and mitigate heat accumulation along the reactor bed.[ 150 , 159 ] To determine whether a system is free from external diffusion constraints, variations in particle size or flow rate are assessed if the reaction rate per unit mass of catalyst remains constant, it is likely that external mass transfer does not limit the reaction.[ 160 ]
4. Experimental Considerations and Analytical Techniques
4.1. Experimental Setup and Catalytic Test
The configuration and selection of the system to use for each reaction will depend on the type of process conditions, reactants, and catalysts. This section outlines the types of reactors typically used in the laboratory, some instructions to execute catalytic reactions for having an accurate mass balance and product analysis.
4.1.1. Laboratory Equipment and Reactors
Depending on the reaction characteristics, several reactor systems are commonly employed. Batch and flow reactors are ideal for kinetic studies, catalyst screening, and reactions requiring flexible operation in the laboratory. Continuous flow reactors are preferred for industrially relevant processes (also used in the laboratory) due to their steady‐state operation, enhanced mass and heat transfer, and ease of scalability. Fixed‐bed reactors are commonly used in gas‐phase reactions and allow for controlled catalyst loading and stable flow conditions. Slurry reactors are suitable for liquid‐phase reactions using finely dispersed catalysts.[ 161 , 162 ]
To properly understand how fast a reaction happens in a catalytic reactor, it is important to consider not only the chemical reaction itself but also how the reactants move through the system (mass transfer). Mass transfer can influence the observed reaction rate. There are two main types: external diffusion, which is the movement of reactants from the surrounding fluid to the surface of the catalyst, and internal diffusion, which is the movement of reactants inside the small pores of the catalyst to reach the active sites. To evaluate whether these movements are limiting the reaction, tools like the Thiele modulus, which compares the rate of reaction to the rate of diffusion inside the catalyst, and the effectiveness factor, which shows how much the internal diffusion slows down the overall reaction, are used. The use of these parameters helps to understand whether the reaction is controlled by the chemistry itself (intrinsic kinetics) or by how the reactants move (mass transfer limitations), which is essential for accurately evaluating and improving the catalytic performance.
The experimental setup for catalytic reactions generally follows four key stages (Figure 4). The first stage is reactor preparation, which involves selecting and assembling the reactor components, loading the catalyst, and securing all seals and connections. The system is typically flushed with an inert gas, such as nitrogen or argon, to eliminate oxygen and moisture in batch reactors. The second stage is reaction execution. Here, the reactants are introduced under controlled temperature and pressure conditions. Key variables such as flow rate, temperature profile, and pressure must be monitored continuously. Feed streams must be stabilized before product sampling can begin in flow systems. The third stage involves reaction termination. The reaction is quenched by depressurization or rapid cooling, and the reactor is safely isolated from the system. Finally, gas, liquid, and solid samples are collected in the sampling and cleaning stage using sealed vessels or inline sampling ports. For autoclaves, samples may be extracted post‐reaction. It is essential to thoroughly clean the reactor after sampling to prevent contamination in subsequent experiments.[ 163 , 164 ]
Figure 4.

Example for the two basic types of reactors: a) batch reactor and b) continuous flow reactor.
4.1.2. Mass Balances
Mass balance is a fundamental concept that ensures the conservation of mass within a system. In practical terms, this means that the total mass of all inputs into a reactor must equal the total mass of all outputs. These outputs include products, unreacted materials, by‐products, and any losses that may occur during the process. The concept is particularly crucial in systems involving multiple phases—gas, liquid, and solid. To perform a complete mass balance, it is essential to measure the fractions of each phase. The gas phase products are typically quantified using gas chromatography or flow meters, while liquid phase products and unreacted reactants are collected and analyzed using techniques such as liquid chromatography or gravimetric methods. The solid phase, especially coke, is measured by weighing the catalyst before and after the reaction, or by using thermogravimetric analysis to determine the amount of carbon deposited.
For instance, in many catalytic reactions, coke (a solid carbonaceous residue) can form and deposit on the catalyst. This coke represents a portion of the reactants that is no longer in the gas or liquid phase and must be accounted for in the overall mass balance.
When performing mass balances, it's important to consider that measurement errors, sampling inconsistencies, and equipment limitations can introduce uncertainty. Therefore, conducting an error analysis is a critical component of mass balance calculations. This analysis helps validate the reliability of the data and ensures that the conclusions drawn from the experiment are robust.
One commonly used metric in this context is mass balance closure, calculated as follows: Closure (%) = (Total Output Mass / Total Input Mass) × 100.
In an ideal scenario, the closure should be 100%. However, in practice, values between 95% and 105% are often observed, depending on the complexity of the system. Additionally, incorporating standard deviations and confidence intervals into the analysis further enhances the credibility of the results.
Mass balances are for validating experimental results and accurately quantifying the product. In batch reactors, the reactor is weighed before and after adding catalyst and reactants and again following the reaction to estimate gas generation. In a typical hydrocarbon catalytic process, gaseous products are analyzed using gas chromatography (GC) equipped with either thermal conductivity detectors (TCD) or flame ionization detectors (FID). Additionally, pressure measurements taken before and after the reaction, while maintaining a constant temperature, help estimate the total volume of gas produced.
In batch reactors, the reactor can be weighed before and after adding the catalyst and reactants, and again following the reaction to estimate gas generation. In continuous flow reactors, calibrated flowmeters are used to measure the inlet feed, while the collected products are weighed to determine the output mass. Gas and liquid products are analyzed using GC systems, and conversion and selectivity are determined by comparing the mass of reactants introduced to the mass of products collected over time.[ 165 , 166 , 167 ]
The final experimental setup varies based on the specific transformation being studied. Table 2 summarizes typical operating conditions for various hydrocarbon transformation reactions, including essential parameters such as catalyst type, reactor configuration, temperature and pressure ranges, and feed‐to‐catalyst ratios. The processes covered include cracking, hydrocracking, ring opening, cyclization, alkylation, oligomerization, etherification, and esterification, all of which play a significant role in refining operations and the production of synthetic chemicals.
Table 2.
Reactions, reactor type (continuous flow reactor (FR)), and batch reactor (BR)) and reaction conditions examples.
| Reaction type | Catalyst | Reactor | Temperature (°C) | Pressure (bar) | Feed catalyst ratio | Refences |
|---|---|---|---|---|---|---|
| Cracking | Zeolite, SiO2–Al2O3 | FR | 450–550 | 1–5 | 5–10 | [ 168 ] |
| Hydrocracking | Ni–Mo, Zeolite | FR | 300–450 | 30–100 | 10–20 | [ 169 ] |
| Opening ring | Acid catalysts (e.g., H2SO₄, AlCl₃), Pt, Ru, Rh, Ir‐supported catalysts. | BR or FR | 100–320 | 1–10 | 5–15 | [ 170 , 171 ] |
| Cyclization | Zeolite, phosphoric acid | BR or FR | 200–300 | 1–10 | 5–10 | [ 172 ] |
| Alkylation | HF, H2SO4 | BR or FR | 0–100 | 1–10 | 10–50 | [ 173 ] |
| Oligomerization | Zeolite, solid H3PO4 | FR | 200–300 | 10–50 | 10–20 | [ 174 ] |
| Etherification | Acidic ion‐exchange resins | BR or FR | 100–150 | 1–5 | 5–20 | [ 175 ] |
| Esterification | H2SO4, solid acid catalysts | BR or FR | 60–250 | 1–5 | 10–30 | [ 176 ] |
Cracking involves the thermal or catalytic decomposition of long‐chain hydrocarbons into shorter, more valuable fractions.[ 168 ] Hydrocracking integrates catalytic cracking with hydrogenation to reduce aromatic content and improve fuel quality.[ 169 ] Ring‐opening reactions target cyclic hydrocarbons, using acidic or metallic catalysts to convert them into linear or branched structures.[ 170 , 171 ] Cyclization facilitates the formation of ring compounds, including aromatics, often through acid catalysis or thermal methods.[ 172 ] Alkylation combines light olefins with isobutane to produce high‐octane gasoline components,[ 173 ] while oligomerization transforms smaller olefins into longer‐chain hydrocarbons for fuels and lubricants.[ 174 ] Etherification and esterification are widely used for producing fuel additives (e.g., MTBE) and biodiesel, respectively.[ 175 , 176 ]
Other key transformations include hydrogenation and dehydrogenation, which convert olefins to paraffins or facilitate aromatization processes.[ 83 , 177 ] Partial oxidation (POx) is employed in the production of synthesis gas (syngas) and hydrogen.[ 178 ] Deoxygenation plays a critical role in biofuel production, removing oxygen from triglycerides or fatty acids to yield diesel‐range hydrocarbons.[ 179 ] Isomerization is commonly used to improve fuel properties, such as increasing gasoline octane or enhancing the cold‐flow performance of diesel, kerosene, and waxes.[ 180 ] The complexity of product analysis depends greatly on the feedstock. Model compounds, such as pure n‐decane, produce predictable and simpler product distributions, making them easier to analyze. In contrast, complex feedstocks like biomass‐derived oils generate a broad spectrum of products, requiring advanced analytical techniques for accurate characterization. Reactor design must align with the selected reaction pathway, desired product distribution, and applicable safety considerations. Effective control of temperature, pressure, and catalyst‐to‐reactant ratios ensures optimal reactor performance, reproducibility, and reliable kinetic or yield data.
4.2. Analytical Techniques for Catalyst and Product Characterization
Characterizing catalysts and reaction products is essential for comprehensively understanding heterogeneous catalysis. A wide range of analytical techniques are employed, broadly categorized into spectroscopic, chromatographic, and thermal methods.
4.2.1. Spectroscopic and Microscopic Characterization of Catalysts
These techniques provide insight into the structure, morphology, and chemical composition of catalysts at atomic to nanometer scales.
X‐ray Diffraction (XRD)
X‐ray diffraction is employed to identify crystalline phases within solid catalysts and estimate crystallite sizes. For instance, XRD can distinguish between γ‐Al2O3 and θ‐Al2O3 phases or identify whether metal nanoparticles exist as alloys or oxides. The Scherrer equation, applied to the width of diffraction peaks, provides an estimate of particle size. Overall, XRD reveals the bulk crystalline structure of heterogeneous catalysts and is critical for confirming the formation of desired phases.[ 181 , 182 ]
Electron Microscopy (SEM/TEM)
Scanning electron microscopy (SEM) provides detailed images of catalyst morphology and surface features, while transmission electron microscopy (TEM) allows for visualization of fine structural details, including the size and dispersion of metal nanoparticles on support materials. High‐resolution TEM can even resolve lattice fringes, confirming nanoparticle crystallinity and structure. These imaging techniques are instrumental in assessing particle dispersion and detecting signs of sintering or agglomeration post‐reaction. When coupled with energy‐dispersive X‐ray spectroscopy (EDS), SEM, and TEM can also map the elemental composition across catalyst surfaces.[ 183 , 184 ]
X‐ray Photoelectron Spectroscopy (XPS)
XPS is a surface‐sensitive technique that provides elemental composition and chemical states of elements on the catalyst surface (typically probing the top ∼5 nm). It is especially useful for determining oxidation states (e.g., distinguishing Cu° from Cu+ or Cu2+) and identifying surface species such as chemisorbed oxygen or carbides. For instance, XPS can quantify the fraction of active metal remaining in an oxidized form after reduction treatment, aiding in the understanding of surface chemistry and potential active site states.[ 185 ]
Vibrational Spectroscopies (FTIR, Raman)
Fourier‐transform infrared spectroscopy (FTIR) is often used with probe molecules to characterize catalyst surface functionality. For example, pyridine adsorbed on a solid acid catalyst is analyzed by FTIR to quantify Brønsted and Lewis acid sites (distinct bands at ∼1540 cm−1 and ∼1450 cm−1, respectively). FTIR can also monitor reactants or intermediates on catalyst surfaces (in situ or operando IR spectroscopy) to elucidate mechanisms, such as detecting adsorbed carbonyl species or isocyanate during CO hydrogenation. Raman spectroscopy complements IR by detecting vibrational modes of oxides or carbon species (e.g., distinguishing graphitic from amorphous carbon deposits by their D/G‐band in Raman). Certain catalysts, such as those with symmetric metal‐oxo species (e.g., M═O bonds in polyoxometalates), are Raman‐active and can be characterized accordingly.[ 186 , 187 ]
X‐ray Absorption Spectroscopy (XAS)
This includes XANES and EXAFS techniques used at synchrotrons to probe the local geometric and electronic structure around specific elements in the catalyst. XAS can reveal coordination numbers and atom distances around a catalytic metal, indicating nanoparticle size or oxidation state changes under reaction conditions. Operando XAS is powerful for observing how a catalyst's active site structure (e.g., a Cu species in a CO2 hydrogenation catalyst) evolves when exposed to the reactant mixture at temperature.[ 188 , 189 ]
Other spectroscopies
Depending on the catalyst, techniques such as UV–vis spectroscopy (for electronic transitions, e.g., to determine whether a supported metal is in nanoparticle form or as ionic species,[ 190 ] Mössbauer spectroscopy for Fe‐ or Sn‐containing catalysts (providing information on the oxidation state and electronic environment,[ 191 ] and NMR spectroscopy (solid‐state NMR, such as 2⁷Al NMR to probe framework aluminum in zeolites or 13C MAS NMR to detect surface species during reactions can be employed.[ 192 ] These advanced methods offer deeper insight into the structural and electronic properties of catalysts.
4.2.2. Surface Area and Porosity Measurements
Surface area and porosity characteristics are essential parameters in understanding the interaction between contaminants and mangrove sediments, especially when evaluating phytoremediation capacity. These properties were determined through nitrogen adsorption desorption isotherms at 77 K using the Brunauer–Emmett–Teller (BET) method for specific surface area and the Barrett–Joyner–Halenda (BJH) method for pore size distribution.[ 193 ]
4.2.3. Chromatographic and Spectrometric Analysis of Reaction Products
To quantify and identify the wide range of products from hydrocarbon reactions, chromatography serves as a primary tool.
Gas Chromatography (GC)
Gas chromatography is the main analytical technique for volatile products (C1–C20 hydrocarbons and light oxygenates). A GC equipped with a flame ionization detector (FID) provides highly sensitive quantification of hydrocarbons. Detailed hydrocarbon analysis (DHA) methods use capillary GC columns to separate complex mixtures (such as gasoline) into individual components. GC is widely used to analyze hydrocarbon gases and liquids for example, refinery gases are routinely tested by GC to measure hydrogen and C1–C4 hydrocarbons. Retention times and reference standards allow compound identification. In many catalysis labs, an on‐line GC periodically samples reactor effluent, enabling real‐time monitoring of conversion and selectivity.[ 194 , 195 ]
Gas Chromatography–Mass Spectrometry (GC–MS)
Coupling GC with MS provides both separation and identification of products through mass spectra. GC–MS is especially valuable for identifying unknown byproducts or in mechanistic studies where unusual intermediates may form in small amounts. The MS offers structural information to confirm compound identities. For hydrocarbon reactions, GC–MS can detect trace oxygenates or nitrogen compounds that GC‐FID may not clearly distinguish. Modern GC–MS libraries facilitate matching spectra with known compounds.[ 196 ]
High‐Performance Liquid Chromatography (HPLC)
For heavy (nonvolatile) or polar products not suitable for GC, HPLC is used. For example, in studies of catalytic pyrolysis oils or biodiesel production, HPLC can separate heavy oxygenated oligomers or glycerides. Although less common in hydrocarbon refining (since most products are GC‐compatible), HPLC with refractive index or UV detectors is useful in petrochemical research for analyzing aromatic compounds, heavy polyaromatics, and similar products from catalytic processes.[ 197 ]
4.2.4. In‐Situ/Operando Reaction Monitoring
Beyond post‐reaction product analysis, modern catalysis research uses spectroscopic methods to monitor reactions in real‐time. For example, Fourier‐transform infrared spectroscopy can be coupled to a reactor to track gas‐phase concentrations of reactants and products (via characteristic IR peaks, such as CO2 at 2349 cm−1) or even surface species using specially designed cells and windows.[ 198 ] Online mass spectrometry (MS) continuously measures outlet gas composition and is particularly useful for detecting H2, O2, CO, and other species not responsive in FID.[ 199 ] Techniques like temporal analysis of products (TAP) reactors, which use pulse‐response experiments, help elucidate reaction mechanisms and intrinsic kinetics by tracking how reactant pulses traverse a catalyst bed under vacuum.[ 200 ] These advanced methods complement GC/HPLC by providing time‐resolved data and insights into catalyst transient behavior.
4.2.5. Thermal Analysis and Temperature‐Programmed Techniques
These methods characterize catalysts and adsorbed species by monitoring changes with temperature.
Thermogravimetric Analysis (TGA)
In TGA, the sample's weight is measured while heating or holding it isothermal under a controlled atmosphere. This can quantify coke on a spent catalyst by heating in air weight loss corresponds to carbon burn‐off. Similarly, TGA during catalyst preparation can track the loss of precursor compounds (e.g., decomposition of catalyst precursors or drying of absorbed species). When combined with mass spectrometry (TGA–MS), it allows identification of evolved gases (CO2, H2O, etc.). TGA also assesses the thermal stability of supports or the decomposition profile of templating agents in synthesized materials.[ 201 , 202 ]
As a practical example, Figure 5 shows a TGA was performed on a used catalyst in an oxygen atmosphere. The analysis reveals several stages of weight loss as the temperature increases, each corresponding to the removal or oxidation of different materials. The initial weight loss observed around 100 °C is typically due to the evaporation of physically adsorbed water and possibly some light organic compounds. As the temperature rises between approximately 200 °C and 400 °C, a more significant weight loss occurs, likely due to the oxidation of more stable organic residues or reaction by‐products that were adsorbed or trapped within the catalyst pores. Between 400 °C and 600 °C, the weight continues to decrease, which is commonly associated with the combustion of carbonaceous deposits such as coke. The presence of oxygen in the atmosphere facilitates the complete oxidation of these materials, making this stage particularly important for assessing catalyst deactivation. Beyond 600 °C, the rate of weight loss slows significantly, indicating that most oxidizable components have been removed, and the remaining mass corresponds to the thermally stable inorganic framework of the catalyst.
Figure 5.

TGA real example. Catalyst used (collected after reaction).
Differential Scanning Calorimetry (DSC)
Often used alongside TGA, DSC measures heat flow into or out of a sample during heating or cooling. It detects endothermic and exothermic events such as phase transitions, adsorption–desorption, or coke oxidation. This is useful for understanding catalyst phase changes. For example, a supported metal may oxidize with an exothermic peak at a specific temperature.[ 203 ]
Temperature‐Programmed Desorption (TPD)
In TPD, a catalyst pre‐loaded with an adsorbate is heated at a controlled rate, and desorbing molecules are detected. NH3‐TPD is a standard method to assess acid site strength distribution on solid acids: ammonia is adsorbed at low temperature, then desorbs as temperature increases, weak acid sites release NH3 earlier, stronger sites at higher temperatures, producing a desorption spectrum that reflects acid strength and quantity. Similarly, CO2‐TPD assesses basic sites. TPD can also reveal how strongly reactants or products bind to the catalyst, providing insight into reaction mechanisms.[ 204 , 205 ]
An example of TPR could be represented in Figure 6, in which a sample is exposed previously to a H2 gas flow at different temperatures.
Figure 6.

Theoretical model H2‐TPR result for the NiO catalyst shows maximum intensity at 400 °C due to the reduction of NiO to Ni.
The amount of gas that is desorbed is usually measured by calculating the area under the desorption curve, which correlates with the number of molecules that have desorbed. This method allows for the estimation of the total number of adsorption sites, expressed in micromoles per gram (µmol/g) or millimoles per gram (mmol/g), provided there is a known stoichiometry between the adsorbate and the surface sites. To convert the signal intensity into absolute quantities, calibration with known gas volumes or standards is often employed.
Temperature‐Programmed Reduction–Oxidation (TPR/TPO)
TPR heats a catalyst in a hydrogen‐containing atmosphere while monitoring H2 consumption to assess reducibility. For example, a CuO/Al2O3 catalyst may show a reduction peak at a certain temperature as CuO is reduced to Cu0. The area under the peak quantifies consumable oxygen, allowing the calculation of metal oxide dispersion. TPO, the reverse process, involves heating in oxygen to oxidize species commonly used to analyze coke on spent catalysts. Carbon deposits are oxidized during TPO, with CO or CO2 evolution peaks indicating a coke nature. These temperature‐programmed techniques are essential for characterizing catalyst reactivity and deactivation, linking thermal behavior to chemical transformations on the catalyst.[ 206 , 207 ]
A general theoretical example of a TPR or TPO could be represented in Figure 5 in which a gas flow (for example) of H2 or O2 is passing continuously through the sample reacting with it. If the sample has NiO, it could be reduced to Ni (usually at 400 °C (H2), generating a signal). If the sample has metallic Ni, it could be oxidized to NiO (O2 flow).
4.2.6. Another Considerations
In addition to the techniques discussed above, specialized methods like chemisorption measurements (e.g., H2 or CO pulse chemisorption to determine metal dispersion and active site count), elemental analysis (ICP‐OES, XRF for bulk catalyst composition), and reactant adsorption calorimetry (to directly measure adsorption heats) are used to complete the catalyst profile. For reaction products, beyond chromatography, spectroscopic techniques such as NMR for liquids (e.g., 1H and 13C NMR to identify products in liquid‐phase alkene oligomerization) or soft ionization MS (such as GC–TOF MS) can be applied to analyze complex mixtures.[ 208 ]
Combining these analytical techniques provides a comprehensive understanding: catalyst structure and surface properties are characterized by microscopy and spectroscopy; acid–base or redox behavior by temperature‐programmed methods; and catalytic performance by chromatographic product analysis. Correlating catalyst characteristics with performance enables researchers to uncover structure‐activity relationships essential for developing improved heterogeneous catalysts. For example, feedstock and product compositions can be determined using GC or LC with MS or with calibrated data from FID or TCD using analytical standards. Components can be grouped by similar properties in complex mixtures where identifying individual molecules is difficult (Figure 7). For highly complex feeds such as bitumen or heavy oil, saturates, aromatics, resins, and asphaltenes analysis (SARA) is used.
Figure 7.

Example of samples obtained after a reaction in a batch reactor and their respective analyses.
Table 3 presents several examples of characterization techniques used in various analytical applications.
Table 3.
Summary of characterization techniques used and their analytical applications.
| Technique | Purpose | Applications | References |
|---|---|---|---|
| XRD | Identify crystal phases and estimate crystallite sizes | Distinguish γ‐ vs θ‐Al2O₃; Cu alloy vs. CuO | [ 181 , 182 ] |
| SEM/TEM | Visualize morphology and dispersion of particles | Detect sintering; visualize metal dispersion | [ 183 , 184 ] |
| XPS | Analyze surface composition and oxidation states | Cu⁰ vs Cu2⁺ states; chemisorbed species | [ 185 ] |
| FTIR/Raman | Detect functional groups, monitor species on catalyst | Identify Brønsted vs Lewis acid sites | [ 186 , 187 ] |
| XAS (XANES/EXAFS) | Probe local geometric/electronic structure | Analyze Cu structure during CO2 hydrogenation | [ 188 , 189 ] |
| BET/BJH | Determine surface area and pore distribution | Phytoremediation capacity of sediments | [ 193 ] |
| GC | Quantify volatile hydrocarbons and gases | Refinery gases, C₁–C₄ analysis | [ 194 , 195 ] |
| GC–MS | Identify unknown or trace products via mass spectra | Mechanistic studies, trace oxygenates | [ 196 ] |
| HPLC | Analyze heavy/polar nonvolatile products | Biodiesel intermediates, polyaromatics | [ 197 ] |
| In‐situ/Operando | Track real‐time reaction behavior | Real‐time catalyst performance monitoring | [ 198 ] |
| TGA | Measure weight change with temperature | Coke quantification, stability analysis | [ 201 , 202 ] |
| DSC | Detect thermal events (phase transitions, oxidation) | Coke oxidation, phase detection | [ 203 ] |
| TPD | Assess acidity/basicity via desorption profiles | NH₃‐TPD for acidity; CO2‐TPD for basicity | [ 204 , 205 ] |
| TPR/TPO | Determine reducibility/oxidizability of catalysts | H2 reduction of CuO, O2 oxidation of coke | [ 206 , 207 ] |
G.C. Bond identified several key challenges in understanding and measuring metal dispersion in heterogeneous catalysis. First, accurately determining metal dispersion is difficult, especially for small or irregularly shaped particles, as common techniques like chemisorption can yield misleading results. Second, particle size significantly influences catalytic activity; however, very small particles may exhibit different behaviors due to changes in their electronic properties. Third, strong interactions between the metal and its support can alter the characteristics of the metal's surface, which impacts both dispersion and reactivity. Fourth, catalysts are dynamic under reaction conditions—metal particles can sinter or change oxidation states—making dispersion a constantly shifting target. Finally, Bond emphasized the limitations of relying solely on one characterization method and advocated for a multi‐technique approach to achieve a more reliable understanding of catalyst structure and behavior.[ 209 , 210 ]
5. Case Studies
5.1. Catalysis in Petroleum Refining
Heterogeneous catalysts are crucial in the petrochemical synthesis and oil refining industries. They are used to facilitate a wide range of chemical reactions essential for producing various chemicals and fuels. These catalysts are solid substances in a different phase than the reactants. This allows them to be easily separated and reactivated after the reaction. One of the main benefits of using these catalysts is their versatility. They can be customized to specific reactions by altering their composition, structure, active phase, and surface characteristics. This makes them highly selective and environmentally friendly. Using heterogeneous catalysts also makes the process more efficient and cost‐effective.[ 211 , 212 ]
Examples of this can be found in research such as that by Song et al.[ 213 ] where hydrogenation technologies were developed to convert heavy fractions into high‐value products like gasoline, diesel, and aviation fuel, while meeting increasingly stringent environmental regulations. Bifunctional catalysts are employed, combining metal sites for hydrogenation with acidic supports that promote carbon‐carbon bond cleavage. Various configurations, such as NiW/Beta catalysts, supported MoS2, and mono‐ and bimetallic systems on zeolites, have been explored, achieving conversion efficiencies above 90% in integrated processes under moderate pressure and temperature conditions. Optimal operating ranges depend on reactor type (fixed bed, ebullated bed, or slurry bed) but generally fall between 200–425 °C and 50–800 psi. Control of parameters such as hydrogen pressure, space velocity, and catalyst structure was shown to be critical for maximizing light distillate yields and minimizing coke formation. The reaction mechanism involves aromatic saturation, heteroatom removal (S, N, and O), and controlled fragmentation of long chains. Catalyst structural design was found to directly affect reactivity, selectivity, and stability, with metal–sulfur interfaces (e.g., Ir–S, MoS2) and metals dispersed on LDH‐type supports or zeolites modified with Ce or P providing significant enhancements. From a kinetic perspective, activation energies have been determined through conversion studies of model compounds such as dibenzothiophene (DBT) and phenols over designed catalysts, achieving high turnover frequencies (TOF) under industrial conditions.
Another interesting approach to fuel sulfur removal involves oxidative desulfurization (ODS). This efficient strategy oxidizes sulfur compounds into more easily separable sulfoxides and sulfonic compounds using active catalysts and mild oxidants such as H2O2. In this context, polyoxometalates (POMs) have emerged as a versatile platform due to their multinuclear structures, high Lewis acidity, and multielectron transfer capabilities. However, their application in heterogeneous systems is often limited by high water solubility and low surface area, prompting their hybridization with metal–organic units to create more structured, functional, and reusable materials. Following this approach, two hybrid complexes were designed using Cu and POMs, featuring two‐dimensional structures based on {P2Mo5O23} (Strandberg‐type) and {PMo12O40} (Keggin‐type) anions, yielding the species [{Cu2.5(pytz)2}{P2Mo5O23}]•11H2O and [{Cu3(pytz)4}{PMo12O40}]•5H2O. These structures, assembled via Cu─O─Mo bonds and N,O‐coordination with the triazine–pyridinic ligand (pytz), form 2D sheets with an ordered arrangement of active metal centers and POM units, enhancing catalytic accessibility. Structural characterization by X‐ray diffraction, IR spectroscopy, UV‐Vis, XPS, and thermal analysis confirmed their composition, thermal stability, and mixed Cu+/Cu2+ and Mo6+ oxidation states, which are critical for redox activity. Both materials demonstrated excellent catalytic efficiency in oxidizing methyl phenyl sulfide (MPS) to its corresponding sulfoxide, achieving conversions of 99.1% and 97.5%, with selectivities near 99% in just 30 min at 50 °C using hydrogen peroxide. Kinetic studies revealed first‐order behavior with respect to MPS, while GC‐FID and NMR analyses confirmed selective sulfoxide formation without overoxidation to sulfone. Notably, both catalysts were reusable for at least five cycles without significant activity loss, maintaining structural integrity as confirmed by IR, XRD, and SEM, indicating high operational stability. These findings position Cu–POM hybrids as effective and recyclable platforms for ODS of organosulfur compounds, with promising applications in green fuel refining and the synthesis of high‐value intermediates.[ 214 ]
5.2. Catalysis in Biofuel Production
As the world seeks sustainable alternatives to petroleum, biofuels have emerged, and heterogeneous catalysis plays a key role in converting biomass‐derived feedstocks into hydrocarbon fuels.[ 215 , 216 ] Researchers highlight that heterogeneous catalysts for biodiesel production via transesterification and esterification can replace traditional homogeneous catalysts, which face limitations in product purification and in processing feedstocks with high free fatty acid (FFA) content. For biodiesel preparation, methanol is most commonly used, but ethanol can also be applied.[ 217 , 218 ] A variety of acidic and basic solid catalysts both Brønsted and Lewis have been studied, including metal oxides, calcined hydrotalcites, functionalized polymer resins, and modified mesoporous structures. Experimental setups involved liquid‐phase reactions under varying temperatures (from room temperature to 300 °C), pressures, and methanol/oil molar ratios, using different vegetable oils (such as soybean, rapeseed, and castor) and waste fats with varying acidity levels. Results showed that certain basic catalysts like CaO and MgO, particularly in nanocrystalline or thermally activated forms, exhibit high catalytic activity under supercritical conditions or with microwave assistance, although they pose leaching risks. Calcined hydrotalcites, especially those with optimized Mg/Al ratios or Mg/Fe, offered a favorable balance of activity, selectivity, and stability, and could tolerate high moisture levels in raw materials.[ 219 ] Conversely, solid acid catalysts such as sulfonated resins, Nafion, sulfated oxides (e.g., ZrO2/SO4 2−), and sulfonated carbonaceous materials proved highly efficient for FFA esterification, enabling the use of unrefined or waste oils, though their transesterification activity was generally lower and temperature‐dependent. Furthermore, bifunctional and supported catalysts such as mixed oxides of Ti, Zn, and Al, vanadyl phosphate compounds (VOPO4·2H2O), and double metal cyanides (Fe–Zn) offered the advantage of combining acidic and basic sites or incorporating hydrophobic properties that enhance conversion in high‐acidity, high‐moisture oils. These catalysts demonstrated good operational stability, recyclability, and low leaching, meeting key industrial performance requirements.[ 220 ]
In another study, researchers focused on the catalytic conversion of waste cooking oils (WCO) into liquid biofuels through catalytic thermal cracking, using MeO‐SBA‐15 catalysts prepared via a one‐pot method. Mesoporous catalysts modified with metal oxides (ZnO, La2O3, CeO2, NiO, and MgO) supported on SBA‐15 were developed, and their performance in WCO conversion, as well as the distribution and quality of the resulting products, was evaluated. Reactions were conducted at 460 °C in a reactor under atmospheric pressure. Catalyst characterization by XRD, FTIR, N2 adsorption, and TEM confirmed that the mesoporous structure was preserved after metal incorporation, although surface area and dispersion were affected depending on the oxide used. The results showed that the catalysts significantly enhanced the yield of liquid hydrocarbons, particularly with ZnO‐SBA‐15 (37.3%), while MgO‐SBA‐15 was notable for its low coke formation (2.1%) and the production of biofuel with lower acidity (46.2 mg KOH/g). GC‐MS analysis revealed a reduction in long‐chain fatty acids and an increase in C₇–C15 hydrocarbons, especially in the presence of ZnO and NiO. The physicochemical properties of the biofuel obtained with these catalysts were close to those of conventional diesel, reinforcing its practical applicability. This strategy highlights the potential of MeO‐SBA‐15 as a stable, economical, and effective heterogeneous catalyst for transforming waste oils into renewable fuels with high energy value, opening new opportunities for the development of sustainable waste valorization technologies.[ 221 ]
5.3. Catalysis in Petrochemical Processes
The petrochemical industry heavily relies on heterogeneous catalysis to transform hydrocarbon feedstocks into essential chemicals such as plastics, fertilizers, and solvents. Heterogeneous catalysts facilitate these processes by lowering activation energy and enhancing reaction rates, making them indispensable in large‐scale chemical manufacturing. The following sections outline the key aspects of catalysis in petrochemical processes.[ 211 , 222 ]
The valorization of discarded milk packages made of multilayer plastics (MLP) through thermal pyrolysis in a semi‐batch reactor serves as a representative example. In the study,[ 223 ] a detailed characterization of the thermal, structural, and compositional properties of the residue was conducted, including proximate and ultimate analysis, differential scanning calorimetry, FTIR spectroscopy, and thermogravimetric analysis (TGA–DTG). The results indicated that the packages consist primarily of LLDPE and LDPE, exhibiting high volatility (99.3%) and low ash and moisture content, making them suitable for pyrolysis. Kinetic analysis, using iso‐conversional methods (KAS and FWO) and model fitting (such as F1), determined an average activation energy of ∼304 kJ mol−1 and a frequency factor (A) of 2.60 × 1012 min−1. Analysis of the reaction mechanisms and variation in reaction order (peaking at 0.858 at 500 °C) concluded that the thermal degradation process is complex, multi‐stage, nonspontaneous, and endothermic, as indicated by thermodynamic parameters ΔG (814.94 kJ mol−1), ΔH (135.70 kJ mol−1), and ΔS (−0.904 kJ K−1·mol−1). The pyrolysis process achieved a maximum oil yield of 66.92% at 450 °C, with minimal solid residue formation (1.53%), demonstrating high conversion efficiency. FTIR analysis of the pyrolytic oil revealed the presence of saturated and unsaturated hydrocarbons, including alkanes, alkenes, cycloalkanes, and aromatic compounds. The physical properties of the oil produced at 450 °C density of 0.74 g cm−3, kinematic viscosity of 2.055 cSt, calorific value of 10606 kcal kg−1, flash point of 2 °C, and boiling range of 162–490 °C are comparable to those of conventional fuels, positioning it as a promising plastic‐derived fuel substitute. This research not only offers a comprehensive analysis of the thermal degradation and product quality but also lays the foundation for designing efficient pyrolysis reactors and implementing sustainable technologies for industrial‐scale plastic waste valorization.
Similarly, the catalytic conversion of cyclopentanone to high‐density aviation fuel‐range hydrocarbons via sequential aldol condensation and hydrodeoxygenation (HDO) reactions can be implemented in both two‐pot and one‐pot systems. In one study, a mesoporous NbOPO4 catalyst with strong Brønsted and Lewis acidity was synthesized, characterized, and evaluated for the condensation step. In contrast, various supported metal catalysts including 30% Ni/NbOPO‐4, 30% Ni/SiO2, 30% Ni/ZrO2–SiO2, and 5% Pd/Al2O3 were tested for the HDO stage under solvent‐free conditions. The 30% Ni/NbOPO4 catalyst exhibited the highest conversion (99%) and selectivity toward hydrocarbons (94%), outperforming even the Pd‐based catalyst, attributed to its strong acidity and higher surface area. The one‐pot system yielded comparable results regarding liquid product yield and distribution, although it led to increased coke formation. It was found that Ni loading influenced the balance between acid and metal functionalities, modulating the composition of aromatic, naphthenic, and partially hydrogenated products. Additionally, preliminary tests using cyclopentanone and real bio‐oil mixtures revealed that phenolic compounds markedly reduce conversion and C─C coupling efficiency, inhibiting deoxygenation and hydrocarbon formation. These findings underscore the potential of NbOPO4 as a non‐noble catalytic support for producing high‐density hydrocarbons from biomass‐derived cyclic ketones, while also highlighting the need to optimize process conditions for bio‐oil feedstocks.[ 224 ]
5.4. Future Directions
Heterogeneous catalysis is a mature field, but it continues to evolve rapidly to meet new challenges and to take advantage of emerging science and technology. Below are some future directions, challenges, and research opportunities shaping the field, as can be seen in Table 4.
Table 4.
Future directions and challenges in heterogeneous catalysis.
| Future direction / challenge | Description | Reference |
|---|---|---|
| Single‐atom and single‐cluster catalysts | Single‐atom catalysts (SACs) and few‐atom clusters offer exceptional atom efficiency, tunable coordination environments, and high selectivity due to isolated active sites. These materials bridge homogeneous and heterogeneous catalysis. Key challenges include controlling atomic dispersion, preventing sintering, and achieving stable anchoring on supports under reaction conditions. | [ 225 , 226 ] |
| Advanced catalyst design through computational methods and AI | Density Functional Theory (DFT), microkinetic modeling, and machine learning are increasingly used to screen catalyst surfaces, predict adsorption energies, and guide synthesis of tailored catalytic materials. These tools allow for rational design and accelerate discovery by narrowing the experimental search space. Data‐driven optimization also enhances process control and mechanistic understanding. | [ 227 , 228 ] |
| Catalysis for sustainable processes (CO2 utilization and biomass conversion) | Catalysts for CO2 hydrogenation, biomass deoxygenation, and plastic depolymerization contribute to circular carbon economies. These processes demand high selectivity and tolerance to complex feeds. Bifunctional and oxide‐supported metal catalysts enable tandem reactions for CO2‐to‐fuels and bio‐oil upgrading. Development focuses on improving catalyst lifetime and product specificity. | [ 229 , 230 ] |
| Catalyst durability and deactivation mitigation | Addressing deactivation mechanisms such as coke formation, sintering, and poisoning is essential for sustained catalytic activity. Strategies include hierarchical supports, coke‐resistant materials, redox additives, and self‐regenerating systems. Understanding deactivation pathways through operando studies aids in the design of more robust and long‐lasting catalysts. | [ 231 , 232 ] |
| In situ and operand characterization techniques | Techniques such as X‐ray absorption spectroscopy (XAS), transmission electron microscopy (TEM), and ambient pressure XPS enable real‐time monitoring of catalysts under operating conditions. These tools reveal dynamic structural and electronic changes of active sites, enhancing mechanistic understanding and enabling structure‐activity correlations for rational catalyst development. | [ 233 , 234 ] |
| Process Intensification and novel reactor concepts | Novel reactor technologies, including microreactors, membrane‐assisted systems, and 3D‐printed catalyst supports, improve mass and heat transfer and reduce energy demands. Integration with separations or alternative energy inputs (e.g., electro‐/photocatalysis) allows for intensified, compact, and flexible processes suited for modular or distributed applications. | [ 235 , 236 ] |
6. Summary and Outlook
This work provides a comprehensive basic guide to the understanding and application of heterogeneous catalysis for transforming hydrocarbons and biomass into value‐added products at the laboratory scale. Through an elemental analysis of molecular fundamentals, catalyst selection criteria, experimental setups, and characterization techniques, it establishes a good starting point for developing catalytic processes applicable to both basic research and pre‐industrial scenarios. The reaction mechanisms discussed the topics of cracking, isomerization, hydrogenation, C─C coupling, and oxidation, offering an insight into the specific roles of active sites and how their acid–base, redox, or metallic nature influences catalytic activity and selectivity.
Additionally, the comparison between batch and continuous‐flow reactors emphasizes the importance of controlling operating variables, ensuring reproducibility, and enabling the extrapolation of kinetic data to industrial conditions. The implementation of rigorous mass balances and the use of advanced analytical techniques (GC–FID, GC–MS, TPD, TPR, XPS, XRD, among others) are positioned as essential tools to evaluate the catalytic processes performance. The case studies presented confirm the relevance of heterogeneous catalysis in strategic applications such as petroleum refining, biofuel production, plastic waste valorization, and the synthesis of renewable fuels from oxygenated compounds. In these contexts, the use of bifunctional catalysts, modified mesoporous materials, and hybrid systems has demonstrated significant improvements in conversion, selectivity, and operational stability.Main text paragraph.
Conflict of Interests
The authors declare no conflict of interest
Acknowledgments
The authors would like to express their sincere gratitude to the University of ECOTEC (Ecuador), the University of Pardubice (Czechia), the University of La Laguna (Spain), and the University of Córdoba (Spain) for their support in writing this review. The time and resources provided by the institutions, including access to its facilities and instruments, were invaluable in completing this work.
Open access publishing facilitated by Univerzita Pardubice, as part of the Wiley ‐ CzechELib agreement.
Biographies
Kelvin A. Sanoja‐López is a researcher specializing in flow catalysis and biomass valorization at Universidad ECOTEC (Ecuador). His work focuses on the development of sustainable catalytic systems for upgrading lignocellulosic and agro‐industrial waste into high‐value chemicals and biofuels. He has experience in continuous‐flow reactors, heterogeneous catalysis, and green chemistry strategies. Through international collaborations, he contributes to advancing scalable, low‐carbon technologies aimed at promoting circular bioeconomy principles.

Alina M. Balu, PhD in Fine Chemistry (University of Cordoba), specializes in nanomaterials for catalysis in fine chemicals and biofuels. She has developed novel catalysts via advanced techniques like microwave‐assisted deposition and mechanochemistry. Her work spans international collaborations and has earned high‐impact publications and awards, including a Marie Curie Fellowship and Green Talent Award. Currently at University of Cordoba, she leads projects in the FQM‐383 Group and COST Action FP1306, focusing on biomass valorization.

Héctor de Paz Carmona has been an Assistant Professor of Chemical Engineering at the University of La Laguna (Spain) since 2023. After obtaining the PhD at the same institution in 2017, he has been a postdoctoral researcher at ORLEN UniCRE a.s. (Czech Republic) during almost five years. His current research interests focuses on advanced biofuels, catalyst development and 3D printing by FDM in Chemical Engineering applications.

Martin Hájek is an Associate Professor of Physical Chemistry, Faculty of Chemical‐Technology at the University of Pardubice (the Czech Republic) since 2015. His current research is focused on transformation of vegetable oils/animal fats into various petrochemicals, especially esters and epoxides and their further usage for various applications. An integral part of the research is a study of catalysis, i.e., synthesis, various characterisations, stability, and description.

Rafael Luque is the distinguished Bioresources Chair and Head of the B4 group in National University of Science and Technology Polytehnica Bucharest and Distinguished Chair Professor at Universidad ECOTEC (Ecuador). His research spans green chemistry, flow catalysis, and biomass valorization. He has authored over 900 publications. His work focuses on developing low‐cost, scalable catalytic technologies for waste‐to‐chemicals conversion. He coordinates projects promoting circular bioeconomy and sustainable industrial practices worldwide.

José Miguel Hidalgo Herrador is Assistant Professor at the University of Pardubice in Czechia. He obtained his PhD in 2005 at the University of Córdoba in Spain. He worked in a biofuels pilot plant production (2006–2009) and in a private company to research and develop new art products (2009–2011). From 2011 to 2024, he was a researcher at the research institute UniCRE in Czechia. Currently, his research is based on heterogeneous catalysis and water pollutant catalytic oxidation reactions.

Sanoja‐Lopez K. A., Balu A. M., Carmona H. de P., Hájek M., Luque R., Herrador J. M. H., Chemistry - An Asian Journal. 2025, e00635. 10.1002/asia.202500635
Contributor Information
Rafael Luque, Email: rluque@ecotec.edu.ec.
José Miguel Hidalgo Herrador, Email: josemiguel.hidalgoherrador@upce.cz.
Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
References
- 1. Schlögl R., Angew. Chem., Int. Ed. 2015, 54, 3465–3520. [DOI] [PubMed] [Google Scholar]
- 2. Li C., Liu Y., Bridging Heterogeneous and Homogeneous Catalysis: Concepts, Strategies, and Applications 2014.
- 3. Kiani D., Wachs I. E., ACS Catal. 2024, 14, 16770–16784. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Argyle M. D., Bartholomew C. H., Catalysts 2015, 5, 145–269. [Google Scholar]
- 5. Ferrando R., in Frontiers in Nanoscience (Ed.: Ferrando R.), Elsevier, Amsterdam: 2016, pp. 229–243. [Google Scholar]
- 6. Ertl G., Knözinger H., Weitkamp J., Handbook of Heterogeneous Catalysis, Wiley‐VCH, New Jersey: 2008. [Google Scholar]
- 7. Russell W. W., J. Chem. Educ. 1945, 22, 163. [Google Scholar]
- 8. Widegren J. A., Finke R. G., J. Mol. Catal. Chem. 2003, 198, 317–341. [Google Scholar]
- 9. Montemore M. M., Medlin J. W., Catal. Sci. Technol. 2014, 4, 3748–3761. [Google Scholar]
- 10. Adamu H., Yamani Z. H., Qamar M., Mater. Renew. Sustain. Energy 2022, 11, 169–213. [Google Scholar]
- 11. Cao Z., Zhang W., Zhou T., Yan W., Wang K., Molecules 2024, 29, 4562. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Ma P., Zhou H., Li Y., Wang M., Nastase S. A. F., Zhu M., Cui J., Cavallo L., Cheng K., Chowdhury A. D., Chem. Sci. 2024, 15, 11937–11945. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Taseska T., Yu W., Wilsey M. K., Cox C. P., Meng Z., Ngarnim S. S., Müller A. M., Top. Catal. 2023, 66, 338–374. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Akhtar M. S., Ali S., Zaman W., Catalysts 2024, 14, 841. [Google Scholar]
- 15. Fouda‐Mbanga B. G., Onotu O., Tywabi‐Ngeva Z., Green Anal. Chem. 2024, 11, 100156. [Google Scholar]
- 16.“ Kinetics of Heterogeneous Catalytic Reactions | Princeton University Press ,” can be found under https://press.princeton.edu/books/hardcover/9780691640488/kinetics‐of‐heterogeneous‐catalytic‐reactions, 2016.
- 17. Parangi T., Rev. Inorg. Chem. 2025, 10.1515/revic-2024-0047. [DOI] [Google Scholar]
- 18. Hariharan S., Kinge S., Visscher L., J. Chem. Inf. Model. 2025, 65, 472–511. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Isahak W. N. R. W., Hisham M. W. M., Yarmo M. A., Yun Hin T., Renew. Sustain. Energy Rev. 2012, 16, 5910–5923. [Google Scholar]
- 20. Chen X., Liu Y., Wang J., Ind. Eng. Chem. Res. 2020, 59, 17008–17025. [Google Scholar]
- 21. Elliott D. C., Peterson K. L., Muzatko D. S., Alderson E. V., Hart T. R., Neuenschwander G. G., Appl. Biochem. Biotechnol. 2004, 115, 807–825. [DOI] [PubMed] [Google Scholar]
- 22. Cruz P. L., Montero E., Dufour J., Fuel 2017, 196, 362–370. [Google Scholar]
- 23. Ahmed Y., Maya A. A. S., Akhtar P., AlMohamadi H., Mohammad A. W., Ashekuzzaman S. M., Olbert A. I., Uddin M. G., J. Environ. Chem. Eng. 2025, 13, 115068. [Google Scholar]
- 24. Rezaei P. S., Shafaghat H., Daud W. M. A. W., Appl. Catal. Gen. 2014, 469, 490–511. [Google Scholar]
- 25. Masloboishchikova O. V., Vasina T. V., Khelkovskaya‐Sergeeva E. G., Kustov L. M., Zeuthen P., Russ. Chem. Bull. 2002, 51, 249–254. [Google Scholar]
- 26. Yao Y., Tian Y.‐J., Wu H.‐H., Lu S.‐X., Fuel Process. Technol. 2015, 133, 146–151. [Google Scholar]
- 27. Roussel M., Lemberton J.‐L., Guisnet M., Cseri T., Benazzi E., J. Catal. 2003, 218, 427–437. [Google Scholar]
- 28. Gao S.‐B., Zhao Z., Lu X.‐F., Chi K.‐B., Duan A.‐J., Liu Y.‐F., Meng X.‐B., Tan M.‐W., Yu H.‐Y., Shen Y.‐G., Li M.‐C., Pet. Sci. 2020, 17, 1752–1763. [Google Scholar]
- 29. Chiodo V., Maisano S., Zafarana G., Urbani F., Int. J. Hydrog. Energy 2017, 42, 1622–1628. [Google Scholar]
- 30. Kubička D., Tukač V., in Advances in Chemical Engineering (Ed.: Murzin D. Y.), Academic Press, Cambridge: 2013, pp. 141–194. [Google Scholar]
- 31. Safa‐Gamal M., Asikin‐Mijan N., Arumugam M., Khalit W. N. A. W., Nur Azreena I., Hafez F. S., Taufiq‐Yap Y. H., J. Anal. Appl. Pyrolysis. 2021, 160, 105334. [Google Scholar]
- 32. Lahive C. W., Kamer P. C. J., Lancefield C. S., Deuss P. J., ChemSusChem 2020, 13, 4238–4265. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Surisetty V. R., Dalai A. K., Kozinski J., Appl. Catal. Gen. 2011, 404, 1–11. [Google Scholar]
- 34. De Groof V., Coma M., Arnot T., Leak D. J., Lanham A. B., Molecules 2019, 24, 398. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Trojanowicz M., Molecules 2020, 25, 1434. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. He L., Jurs P. C., J. Mol. Graph. Model. 2005, 23, 503–523. [DOI] [PubMed] [Google Scholar]
- 37. Vasile C., Baican M., Polymers 2023, 15, 3177. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Cao M., E R., Yuan C., Rosendahl L. A., Zhang Y., Xu C. C., Wu Y., Kong D., Wang Y., Li J., Liu Z., Nat. Commun. 2025, 16, 722. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Geng T., Wang Y., Yin X.‐L.i, Chen W.u, Gu H.‐W., Crit. Rev. Anal. Chem. 2024, 54, 2827–2849. [DOI] [PubMed] [Google Scholar]
- 40. Winch D., A. Technologies, n.d.
- 41. Navas‐Anguita Z., García‐Gusano D., Iribarren D., Renew. Sustain. Energy Rev. 2019, 112, 11–26. [Google Scholar]
- 42. Speight J. G., Handbook of Petroleum Product Analysis, John Wiley & Sons, Ltd, New Jersey: 2014. [Google Scholar]
- 43.“ Refining crude oil ‐ inputs and outputs ‐ U.S. Energy Information Administration (EIA) ,” can be found under https://www.eia.gov/energyexplained/oil‐and‐petroleum‐products/refining‐crude‐oil‐inputs‐and‐outputs.php, 2024.
- 44.“Product‐Driven Biorefineries ‐ an overview | ScienceDirect Topics,” can be found under https://www.sciencedirect.com/topics/engineering/product‐driven‐biorefineries, 2024.
- 45. Bezergianni S., Dimitriadis A., Kikhtyanin O., Kubička D., Prog. Energy Combust. Sci. 2018, 68, 29–64. [Google Scholar]
- 46. Cheah Y. W., Salam M. A., Sebastian J., Ghosh S., Arora P., Öhrman O., Olsson L., Creaser D., J. Environ. Chem. Eng. 2023, 11, 109614. [Google Scholar]
- 47. Choudhary T. V., Phillips C. B., Appl. Catal. Gen. 2011, 397, 1–12. [Google Scholar]
- 48. James O. O., Chowdhury B., Mesubi M. A., Maity S., RSC Adv. 2012, 2, 7347–7366. [Google Scholar]
- 49. Martinelli M., Gnanamani M. K., LeViness S., Jacobs G., Shafer W. D., Appl. Catal. Gen. 2020, 608, 117740. [Google Scholar]
- 50. Arsalanfar M., Mirzaei A. A., Bozorgzadeh H. R., Samimi A., Phys. Chem. Res. 2014, 2, 179–201. [Google Scholar]
- 51. Davis B. H., Fuel Process. Technol. 2001, 71, 157–166. [Google Scholar]
- 52. Morales‐Leal F., Ancheyta J., Torres‐Mancera P., Alonso F., Rayo P., Fuel 2024, 371, 131938. [Google Scholar]
- 53. Vance B. C., Yuliu Z., Najmi S., Selvam E., Granite J. E., Yu K., Ierapetritou M. G., Vlachos D. G., Chem. Eng. J. 2024, 487, 150468. [Google Scholar]
- 54. Corella J., Monzón A., Butt J. B., Absil R. P. L., J. Catal. 1986, 100, 149–157. [Google Scholar]
- 55. Mahmoudi E., Sayyah A., Farhoudi S., Bahranifard Z., Behmenyar G., Turan A. Z., Delibas N., Niaei A., J. CO2 Util. 2024, 86, 102893. [Google Scholar]
- 56. Murindababisha D., Wang Z., Xiao Z., Yusuf A., Chen G. Z., Li J., He J., J. Environ. Chem. Eng. 2025, 13, 115781. [Google Scholar]
- 57. Galadima A., Muraza O., J. Ind. Eng. Chem. 2018, 61, 265–280. [Google Scholar]
- 58. Verdoliva V., Saviano M., De Luca S., Catalysts 2019, 9, 248. [Google Scholar]
- 59. Song B., Xie L.‐H., J. Phys. Chem. C 2025, 129, 4825–4840. [Google Scholar]
- 60. Ramírez C. X., Torres J. E., de Pérez‐Martínez D. J., Kafarov V., Guzman A., in Computer Aided Chemical Engineering (Eds.: Kravanja Z., Bogataj M.), Elsevier, Amsterdam: 2016, pp. 2271–2276. [Google Scholar]
- 61. Boldrin P., Ruiz‐Trejo E., Mermelstein J., Bermúdez Menéndez J. M., Ramírez Reina T., Brandon N. P., Chem. Rev. 2016, 116, 13633–13684. [DOI] [PubMed] [Google Scholar]
- 62.in Model. Simul. Heterog. Catal. React., John Wiley & Sons, Ltd, 2011, p. I–XVI. [Google Scholar]
- 63. Velázquez H. D., in Catalytic Processes for the Production of Automotive Gasoline, CRC Press, Boca Raton: 2024. [Google Scholar]
- 64. Xiao X., Zhao Z., in Heterogenous Catalytic Sustainable Energy John Wiley & Sons, Ltd, New Jersey: 2022, pp. 203–271. [Google Scholar]
- 65. Dong Q., Li R., Jiao H., ChemCatChem 2024, 16, e202400117. [Google Scholar]
- 66. Bathena T., Phung T., Murugesan V., Goulas K. A., Karakoti A. S., Ramasamy K., J. CO2 Util. 2024, 84, 102848. [Google Scholar]
- 67. Schlögl R., Top. Catal. 2016, 59, 1461–1476. [Google Scholar]
- 68. Sahoo S. K., Ray S. S., Singh I. D., Appl. Catal. Gen. 2004, 278, 83–91. [Google Scholar]
- 69. Tian J., Kong R., Deng B., Cheng Y., Hu K., Zhong Z., Sun T., Tan M., Chen L., Zhao J., Wang Y., Li X., Zhu Y., Angew. Chem. 2024, 136, e202409556. [DOI] [PubMed] [Google Scholar]
- 70. Hossain M.d. Z., Jhawar A. K., Chowdhury M. B. I., Xu W. Z., Charpentier P. A., Fuel 2018, 231, 253–263. [Google Scholar]
- 71. Roy T., Alakari J., Lancelot C., Blanchard P., Poinel L., Lamonier C., Catalysts 2024, 14, 823. [Google Scholar]
- 72. Wu Q., Luan H., Xiao F.‐S., Natl. Sci. Rev. 2022, 9, nwac023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73. Feng M., Kou Z., Tang C., Shi Z., Tong Y., Zhang K., Appl. Clay Sci. 2023, 243, 107087. [Google Scholar]
- 74. Taguchi A., Schüth F., Micropor. Mesopor. Mater. 2005, 77, 1–45. [Google Scholar]
- 75.“ Metal‐Catalyzed Transformations and Mechanistic Insights ,” can be found under https://link.springer.com/collections/aaagigdaac, n.d.
- 76. Lee W.‐T., van Muyden A., Bobbink F. D., Mensi M. D., Carullo J. R., Dyson P. J., Nat. Commun. 2022, 13, 4850. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77. Žula M., Grilc M., Likozar B., Chem. Eng. J. 2022, 444, 136564. [Google Scholar]
- 78. Zhang J., Chen T., Yao P., Jiao Y., Wang J., Chen Y., Zhu Q., Li X., Ind. Eng. Chem. Res. 2019, 58, 1823–1833. [Google Scholar]
- 79. Zhang H., Li X., Jiao Y., Wang Z., Zhu Q., Wang J., Li X., Pet. Chem. 2017, 57, 666–672. [Google Scholar]
- 80. Phamhuu C., Ledoux M. J., Guille J., J. Catal. 1993, 143, 249–261. [Google Scholar]
- 81. Wang H., Shiju N. R., React. Chem. Eng. 2025, 10, 768–776. [Google Scholar]
- 82. Speight J. G., in Refiniries in Future (Ed.: Speight J.G.), William Andrew Publishing, Boston, 2011, pp. 181–208, 10.1016/C2009-0-20064-X. [DOI] [Google Scholar]
- 83. Almuqati N. S., Aldawsari A. M., Alharbi K. N., González‐Cortés S., Alotibi M. F., Alzaidi F., Dilworth J. R., Edwards P. P., Fuel 2024, 366, 131270. [Google Scholar]
- 84. Zuo Z., Sha Y., Wang R., Wang L., Song H., Wang P., Da Z., RSC Adv. 2024, 14, 15071–15084. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 85. Ramos‐Yataco J., Notestein J., Catal. Today 2023, 416, 113770. [Google Scholar]
- 86. Docherty J. H., Lister T. M., Mcarthur G., Findlay M. T., Domingo‐Legarda P., Kenyon J., Choudhary S., Larrosa I., Chem. Rev. 2023, 123, 7692–7760. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 87. Deng J., Chen P., Xia S., Zheng M., Song D., Lin Y., Liu A., Wang X., Zhao K., Zheng A., Atmosphere 2023, 14, 1538. [Google Scholar]
- 88. Li Y., Zhang J., Catalysts 2024, 14, 363. [Google Scholar]
- 89. Zhou L., Gao J., Hao X., Yang Y., Li Y., Reactions 2021, 2, 161–174. [Google Scholar]
- 90. Ghogia A. C., Nzihou A., Serp P., Soulantica K., Pham Minh D., Appl. Catal. Gen. 2021, 609, 117906. [Google Scholar]
- 91. Yeboah I., Feng X., Rout K. R., Chen D., Ind. Eng. Chem. Res. 2021, 60, 15095–15105. [Google Scholar]
- 92. Resasco D. E., Wang B., Crossley S., Catal. Sci. Technol. 2016, 6, 2543–2559. [Google Scholar]
- 93. Zhang J., Yoo E., Davison B. H., Liu D., Schaidle J. A., Tao L., Li Z., Green Chem. 2021, 23, 9534–9548. [Google Scholar]
- 94. Dhar A., Vekariya R. L., Bhadja P., Cogent. Chem. 2018, 4, 1514686. [Google Scholar]
- 95. Vogt E. T. C., Whiting G. T., Dutta Chowdhury A., Weckhuysen B. M., in Advances in Catalysis (Ed.: Jentoft F.C.), Academic Press, Cambridge: 2015, pp. 143–314. [Google Scholar]
- 96. Potter M. E., Le Brocq J. J. M., Oakley A. E., McShane E. B., Vandegehuchte B. D., Raja R., Catalysts 2020, 10, 1099. [Google Scholar]
- 97. Hattori H., Ono Y., in Metal Oxides in Heterogenous Oxidation Catalysis (Ed.: Védrine J. C.), Elsevier; Amsterdam: 2018, pp. 133–209. [Google Scholar]
- 98. Campelo J. M., Lafont F., Marinas J. M., Zeolites 1995, 15, 97–103. [Google Scholar]
- 99. Izmailov R. I., Bull. Acad. Sci. USSR Div. Chem. Sci. 1964, 13, 1623–1625. [Google Scholar]
- 100. Kondo J. N., Domen K., Wakabayashi F., Micropor. Mesopor. Mater. 1998, 21, 429–437. [Google Scholar]
- 101. Peil S., Bistoni G., Goddard R., Fürstner A., J. Am. Chem. Soc. 2020, 142, 18541–18553. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 102. Ross J. R. H., in Contemporary Catalysis (Ed.: Ross J. R. H.), Elsevier, Amsterdam, 2019, pp. 161–186. [Google Scholar]
- 103. Védrine J. C., Catalysts 2017, 7, 341. [Google Scholar]
- 104. Faizan M., Aamir E., Kiong T. S., Song H., Catal. Sci. Technol. 2024, 14, 5009–5031. [Google Scholar]
- 105. Centi G., Fornasari G., Trifiro F., Ind. Eng. Chem. Prod. Res. Dev. 1985, 24, 32–37. [Google Scholar]
- 106. Guliants V. V., Benziger J. B., Sundaresan S., Wachs I. E., Hirt A. M., Catal. Lett. 1999, 62, 87–91. [Google Scholar]
- 107. Gustafson J., Balmes O., Zhang C., Shipilin M., Schaefer A., Hagman B., Merte L. R., Martin N. M., Carlsson P.‐A., Jankowski M., Crumlin E. J., Lundgren E., ACS Catal. 2018, 8, 4438–4445. [Google Scholar]
- 108. Vikrant K., Chung M. W., Boukhvalov D. W., Heynderickx P. M., Kim K.‐H., Weon S., Chem. Eng. J. 2023, 475, 146007. [Google Scholar]
- 109. Linic S., Barteau M. A., J. Am. Chem. Soc. 2003, 125, 4034–4035. [DOI] [PubMed] [Google Scholar]
- 110. Lyons T. W., Leibler I. N.‐M., He C. Q., Gadamsetty S., Estrada G. J., Doyle A. G., J. Org. Chem. 2024, 89, 1438–1445. [DOI] [PubMed] [Google Scholar]
- 111. Louis C., Delannoy L., in Advances inCatalysis (Ed.: Song C.), Academic Press, Cambridge: 2019, pp. 1–88. [Google Scholar]
- 112. Zhao X., Chang Y., Chen W.‐J., Wu Q., Pan X., Chen K., Weng B., ACS Omega 2022, 7, 17–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 113. Mattson B., Foster W., Greimann J., Hoette T., Le N., Mirich A., Wankum S., Cabri A., Reichenbacher C., Schwanke E., J. Chem. Educ. 2013, 90, 613–619. [Google Scholar]
- 114. Liu L., Corma A., Chem. Rev. 2018, 118, 4981–5079. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 115. Cherkasov N., Murzin D. Y., Catlow C. R. A., Chutia A., Catal. Sci. Technol. 2021, 11, 6205–6216. [Google Scholar]
- 116. Xiong Y., Chen J., Wang Y., Wang Q., Liu D., Shao Q., Lu J., Nano Lett. 2025, 25, 3383–3390. [DOI] [PubMed] [Google Scholar]
- 117. Delgado J. A., Benkirane O., Claver C., Curulla‐Ferré D., Godard C., Dalton Trans. 2017, 46, 12381–12403. [DOI] [PubMed] [Google Scholar]
- 118. Taylor C. J., Pomberger A., Felton K. C., Grainger R., Barecka M., Chamberlain T. W., Bourne R. A., Johnson C. N., Lapkin A. A., Chem. Rev. 2023, 123, 3089–3126. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 119. Pinkard B. R., Gorman D. J., Tiwari K., Rasmussen E. G., Kramlich J. C., Reinhall P. G., Novosselov I. V., Heliyon 2019, 5, e01269. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 120. Guidi M., Seeberger P. H., Gilmore K., Chem. Soc. Rev. 2020, 49, 8910–8932. [DOI] [PubMed] [Google Scholar]
- 121. Charleux B., Cunningham M., Leiza J. R., in Polymer Science– A Comprehensive Reference (Eds.: Matyjaszewski K., Möller M.), Elsevier, Amsterdam: 2012, pp. 463–499. [Google Scholar]
- 122. Yang C., Mao Z.‐S., in Numerical Simulation of Multiphase Reactors with Continous Liquid Phase (Eds.: Yang C., Mao Z.‐S.), Academic Press, Oxford: 2014, pp. 75–151. [Google Scholar]
- 123. Aziz N., Mujtaba I. M., Chem. Eng. J. 2002, 85, 313–325. [Google Scholar]
- 124. Florit F., Nambiar A. M. K., Breen C. P., Jamison T. F., Jensen K. F., React. Chem. Eng. 2021, 6, 2306–2314. [Google Scholar]
- 125. Bokhari A., Yusup S., Asif S., Chuah L. F., Michelle L. Z. Y., in Bioreactors (Eds.: Singh L., Yousuf A., Mahapatra D. M.), Elsevier, Amsterdam: 2020, pp. 27–42. [Google Scholar]
- 126. Xu Z., Liu W., Yu Z., Liu X., Catalysts 2025, 15, 222. [Google Scholar]
- 127. Ashok A., Chen W., Zaza A., Madrahimov S., Al‐Rawashdeh M., ChemistrySelect 2024, 9, e202400978. [Google Scholar]
- 128. Bhoi R., Singh D., Mahajani S., React. Chem. Eng. 2017, 2, 740–753. [Google Scholar]
- 129. De Vylder A., Lauwaert J., Van Auwenis S., De Clercq J., Thybaut J. W., Catalysts 2019, 9, 755. [Google Scholar]
- 130. Grubecki I., Wójcik M., J. Chem. Eng. Jpn. 2006, 39, 1065–1068. [Google Scholar]
- 131. Zhang M., Wang M., Xu B., Ma D., Joule 2019, 3, 2876–2883. [Google Scholar]
- 132. Kozuch S., Martin J. M. L., ACS Catal. 2012, 2, 2787–2794. [Google Scholar]
- 133. Hernández S., in Biofuels Biorefining (Eds.: Gutiérrez‐Antoni C., Gómez Castro F.I.), Elsevier, Amsterdam: 2022, pp. 1–40. [Google Scholar]
- 134. Oloruntoba A., Zhang Y., Hsu C. S., Energies 2022, 15, 2061. [Google Scholar]
- 135. Lindeque R. M., Woodley J. M., Catalysts 2019, 9, 262. [Google Scholar]
- 136. Zhang J., Li X., Chen H., Qi M., Zhang G., Hu H., Ma X., Int. J. Hydrog. Energy 2017, 42, 19755–19775. [Google Scholar]
- 137. V M., Sengupta T., Narasimhan S., Bhatt N., Ind. Eng. Chem. Res. 2019, 58, 13767–13779. [Google Scholar]
- 138. Matar S., Hatch L. F., Chemistry of Petrochemical Processes 2001.
- 139. Gates B. C., Katzer J. R., Olson J. H., Schuit G. C. A., Chem. Eng. Educ. 1974, 8, 172–175. [Google Scholar]
- 140. Cui T.‐Y., Rajendran A., Fan H.‐X., Feng J., Li W.‐Y., Ind. Eng. Chem. Res. 2021, 60, 3295–3323. [Google Scholar]
- 141. Blasco T., Nieto J. M. L., Appl. Catal. ‐Gen. 1997, 157, 117–142. [Google Scholar]
- 142. Rahimi N., Karimzadeh R., Appl. Catal. ‐Gen. 2011, 398, 1–17. [Google Scholar]
- 143. Nishimura Y., Adv. POROUS Mater. 2017, 5, 17–25. [Google Scholar]
- 144. Dik P. P., Yu Pereyma V., Klimov O. V., Koryakina G. I., Budukva S. V., Leonova K. A., Yu Gerasimov E., Danilova I. G., Noskov A. S., Catal. Ind. 2014, 6, 320–328. [Google Scholar]
- 145. Muleja A. A., Gorimbo J., Masuku C. M., Catalysts 2019, 9, 746. [Google Scholar]
- 146. Ortega E., Back Basics 2021.
- 147. Garba M. D., Jackson S. D., Appl. Petrochem. Res. 2017, 7, 1–8. [Google Scholar]
- 148. Suganuma S., Katada N., Fuel Process. Technol. 2020, 208, 106518. [Google Scholar]
- 149. Premlall K. C., Lokhat D., Energies 2020, 13, 1971. [Google Scholar]
- 150. Hickman D. A., Degenstein J. C., Ribeiro F. H., Curr. Opin. Chem. Eng. 2016, 13, 1–9. [Google Scholar]
- 151. Taylor C. J., Booth M., Manson J. A., Willis M. J., Clemens G., Taylor B. A., Chamberlain T. W., Bourne R. A., Chem. Eng. J. 2021, 413, 127017. [Google Scholar]
- 152. Roberto M. F., Dearing T. I., Branham C. W., Bleie O., Marquardt B. J., Processes 2014, 2, 24–33. [Google Scholar]
- 153. Baumann M., Moody T. S., Smyth M., Wharry S., Eur. J. Org. Chem. 2020, 2020, 7398–7406. [Google Scholar]
- 154. Hone C. A., Kappe C. O., Chemistry–Methods 2021, 1, 454–467. [Google Scholar]
- 155. Doran P. M., in Bioprocess Engineering Principles– Second Ed. (Ed.: Doran P.M.), Academic Press, London, 2013, pp. 761–852. [Google Scholar]
- 156. Bukhtiyarova M. V., Nuzhdin A. L., Bukhtiyarova G. A., Int. J. Mol. Sci. 2023, 24, 14136. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 157. Zhang J., Smith R., Chem. Eng. Sci. 2004, 59, 459–478. [Google Scholar]
- 158. Al Azri N., Patel R., Ozbuyukkaya G., Kowall C., Cormack G., Proust N., Enick R., Veser G., Chem. Eng. J. 2022, 445, 136775. [Google Scholar]
- 159. Zapater D., Kulkarni S. R., Wery F., Cui M., Herguido J., Menendez M., Heynderickx G. J., Van Geem K. M., Gascon J., Castaño P., Prog. Energy Combust. Sci. 2024, 105, 101176. [Google Scholar]
- 160. Unnikrishnan P., Srinivas D., in Industrial Catalytic Processes for Fine and Specialty Chemicals (Eds.: Joshi S. S., Ranade V. V.), Elsevier, Amsterdam: 2016, pp. 41–111. [Google Scholar]
- 161. Deutschmann O., Ullmann's Encyclopedia of Industrial Chemistry, Wiley, Germany: 2000, https://onlinelibrary.wiley.com/pb‐assets/assets/14356007/Ullmann_LOC_2025%20July‐1753951742507.pdf. [Google Scholar]
- 162. Beenackers A. A. C. M., van Swaaij W. P. M., in Chemical Reactor Design and Technology: Overview of the New Developments of Energy and Petrochemical Reactor Technologies. Projections for the 90's: (Ed.: de Lasa H. I.), Springer Netherlands, Dordrecht: 1986, pp. 463–538. [Google Scholar]
- 163. Hidalgo‐Herrador J. M., Vráblík A., Černý R., Jíša P., Hamerníková J., Chem. Pap. 2017, 71, 1175–1182. [Google Scholar]
- 164. Hidalgo J. M., Kaucký D., Bortnovsky O., Černý R., Sobalík Z., Monatshefte Für Chem. ‐ Chem. Mon. 2014, 145, 1407–1416. [Google Scholar]
- 165. Hidalgo Herrador J. M., Murat M., Tišler Z., Frątczak J., de Paz Carmona H., Materials 2022, 15, 844. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 166. Hidalgo J. M., Horaček J., Matoušek L., Vráblík A., Tišler Z., Černý R., Monatshefte Für Chem. ‐ Chem. Mon. 2018, 149, 1167–1177. [Google Scholar]
- 167. Hidalgo J. M., Tišler Z., Bulánek R., Čičmanec P., Raabová K., Kubička D., React. Kinet. Mech. Catal. 2017, 121, 161–173. [Google Scholar]
- 168. Leffler W. L., Petroleum Refining in Nontechnical Language, PennWell, USA: 2008. [Google Scholar]
- 169. Saab R., Polychronopoulou K., Zheng L., Kumar S., Schiffer A., J. Ind. Eng. Chem. 2020, 89, 83–103. [Google Scholar]
- 170. Kustov L. M., Catal. Ind. 2011, 3, 358–369. [Google Scholar]
- 171. Smith J. M., Ness H. C. V., Abbott M., Introduction to Chemical Engineering Thermodynamics, McGraw‐Hill Education, New York: 2005. [Google Scholar]
- 172. Jones D. S. J., in Elements of Petroleum Processing, Wiley, Germany: 1999. [Google Scholar]
- 173. de Angelis A., Ingallina P., Berti D., Montanari L., Clerici M. G., Catal. Lett. 1999, 61, 45–49. [Google Scholar]
- 174. Zhang Q., Wang T., Li Y., Xiao R., Vitidsant T., Reubroycharoen P., Wang C., Zhang Q., Ma L., Fuel Process. Technol. 2017, 167, 702–710. [Google Scholar]
- 175. Yee K. F., Mohamed A. R., Tan S. H., Renew. Sustain. Energy Rev. 2013, 22, 604–620. [Google Scholar]
- 176. Sirsam R., Hansora D., Usmani G. A., J. Inst. Eng. India Ser. E 2016, 97, 167–181. [Google Scholar]
- 177. Marshall J. L., Lehnherr D., Lindner B. D., Tykwinski R. R., ChemPlusChem 2017, 82, 967–1001. [DOI] [PubMed] [Google Scholar]
- 178. Newson E., Truong T. B., Int. J. Hydrog. Energy 2003, 28, 1379–1386. [Google Scholar]
- 179. Asikin‐Mijan N., Lee H. V., Abdulkareem‐Alsultan G., Afandi A., Taufiq‐Yap Y. H., J. Clean. Prod. 2017, 167, 1048–1059. [Google Scholar]
- 180. Misra P., Alvarez‐Majmutov A., Chen J., Fuel 2023, 351, 128994. [Google Scholar]
- 181. Moulijn J. A., van Leeuwen P. W. N. M., Eds. (van Santen R. A.) , in Studies in Surface Science and Catalysis, Elsevier, Amsterdam: 1993, pp. 363–400. [Google Scholar]
- 182. Holder C. F., Schaak R. E., ACS Nano 2019, 13, 7359–7365. [DOI] [PubMed] [Google Scholar]
- 183. Bijelić L., Ruiz‐Zepeda F., Hodnik N., Inorg. Chem. Front. 2024, 11, 323–341. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 184. Inkson B. J., in Materials Characterization Using Nondestructive Evaluation NDE Methods (Eds.: Hübschen G., Altpeter I., Tschuncky R., Herrmann H.‐G.), Woodhead Publishing, Canada: 2016, pp. 17–43. [Google Scholar]
- 185. Oswald S., in Encyclopaedia of Analytical Chemistry, John Wiley & Sons, Ltd, New Jersey: 2006. [Google Scholar]
- 186. Barzetti T., Selli E., Moscotti D., Forni L., J. Chem. Soc. Faraday Trans. 1996, 92, 1401–1407. [Google Scholar]
- 187. Zholobenko V., Freitas C., Jendrlin M., Bazin P., Travert A., Thibault‐Starzyk F., J. Catal. 2020, 385, 52–60. [Google Scholar]
- 188. Iglesias‐Juez A., Chiarello G. L., Patience G. S., Guerrero‐Pérez M. O., Can. J. Chem. Eng. 2022, 100, 3–22. [Google Scholar]
- 189. Crichton R., in BioInorganic Chemistry– Third Edition (Ed.: Crichton R.), Academic Press, Cambridge: 2019, pp. 149–169. [Google Scholar]
- 190. Kasper J. B., Steen J. D., Hage R., Browne W. R., in Advances in Inorganic Chemistry (Eds.: Hubbard C.D., van Eldik R.), Academic Press, Cambridge: 2021, pp. 143–182. [Google Scholar]
- 191. Zeng Y., Li X., Wang J., Sougrati M. T., Huang Y., Zhang T., Liu B., Chem. Catal. 2021, 1, 1215–1233. [Google Scholar]
- 192. Stepanov A. G., in Zeolites and Zeolite‐ Materials , (Eds.: Sels B.F., Kustov L. M.), Elsevier, Amsterdam: 2016, pp. 137–188. [Google Scholar]
- 193. Medrano V. G. B., Celis V. N., Giraldo R. I., J. Chem. Eng. Data 2022, 68, 2512–2528, 10.26434/chemrxiv-2022-8v4h3. [DOI] [Google Scholar]
- 194. Douglas G. S., Emsbo‐Mattingly S. D., Stout S. A., Uhler A. D., McCarthy K. J., in Introduction to Environment and Forensics–Second Edion (Eds.: Murphy B.L., Morrison R.D.), Academic Press, Burlington: 2007, pp. 311–454. [Google Scholar]
- 195. Mohan S. V., Rohit M. V., Subhash G. V., Chandra R., Devi M. P., Butti S. K., Rajesh K., in Biofuels Algae– Second Edition (Eds.: Pandey A., Chang J.‐S., Soccol C.R., Lee D.‐J., Chisti Y.), Elsevier, Amsterdam: 2019, pp. 287–323. [Google Scholar]
- 196. Maji S. R., Roy C., Sinha S. K., J. Anal. Sci. Appl. Biotechnol. 2023, 5, 72–85. [Google Scholar]
- 197. Hamacher D., Schrader W., Separations 2022, 9, 214. [Google Scholar]
- 198. Jurina T., Cvetnić T. S., Šalić A., Benković M., Valinger D., Kljusurić J. G., Zelić B., Tušek A. J., Catalysts 2023, 13, 690. [Google Scholar]
- 199. van den Broek J., Weber I. C., Güntner A. T., Pratsinis S. E., Mater. Horiz. 2021, 8, 661–684. [DOI] [PubMed] [Google Scholar]
- 200. Gleaves J. T., Yablonsky G., Zheng X., Fushimi R., Mills P. L., J. Mol. Catal. Chem. 2010, 315, 108–134. [Google Scholar]
- 201. Khan M.d. I. H., Karim M. A., Food Res. Int. 2017, 99, 1–14. [DOI] [PubMed] [Google Scholar]
- 202. Leal G. F., Ramos L. A., Barrett D. H., Curvelo A. A. S., Rodella C. B., Thermochim. Acta 2015, 616, 9–13. [Google Scholar]
- 203. Leyva‐Porras C., Cruz‐Alcantar P., Espinosa‐Solís V., Martínez‐Guerra E., Piñón‐Balderrama C. I., Compean Martínez I., Saavedra‐Leos M. Z., Polymers 2020, 12, 5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 204. Ishii T., Kyotani T., in Materials Science and Engineering of Carbon Characterization (Eds.: Inagaki M., Kang F.), Butterworth–Heinemann, Oxford, 2016, pp. 287–305. [Google Scholar]
- 205. Liu C., Lobban L. L., Mallinson R. G., in Studies in Surface Science Catalysis (Eds.: Parmaliana A., Sanfilippo D., Frusteri F., Vaccari A., Arena F.), Elsevier, Amsterdam: 1998, pp. 361–366. [Google Scholar]
- 206. Martens J. H. A., Van ’t Blik H. F. J., Prins R., J. Catal. 1986, 97, 200–209. [Google Scholar]
- 207. Besselmann S., Freitag C., Hinrichsen O., Muhler M., Phys. Chem. Chem. Phys. 2001, 3, 4633–4638. [Google Scholar]
- 208. Seger C., Sturm S., Cells 2022, 11, 3526. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 209. Bond G. C., Metal‐Catalysed Reactions of Hydrocarbons, Springer, USA, 2005. [Google Scholar]
- 210. Burwell R. L., Science 1962, 138, 129–129. [Google Scholar]
- 211. Glotov A., Karakhanov E., Catalysts 2024, 14, 815. [Google Scholar]
- 212. Bravo‐Suárez J. J., Chaudhari R. V., Subramaniam B., in Novel Materials for Catalysis and Fuels Process, American Chemical Society, Washington: 2013, pp. 3–68. [Google Scholar]
- 213. Song Y., Liu S., Sun P., Qiu W., Li Y., Peng C., Results Eng 2025, 25, 103958. [Google Scholar]
- 214. Hou Y., Sun Y., Zhang S., Han P., Li H., Wang X., Chen H., Cheng Y., J. Mol. Struct. 2025, 1328, 141358. [Google Scholar]
- 215. Malode S. J., Prabhu K. K., Mascarenhas R. J., Shetti N. P., Aminabhavi T. M., Energy Convers. Manag. X 2021, 10, 100070. [Google Scholar]
- 216. Mukhtar A., Saqib S., Lin H., Hassan Shah M. U., Ullah S., Younas M., Rezakazemi M., Ibrahim M., Mahmood A., Asif S., Bokhari A., Renew. Sustain. Energy Rev. 2022, 157, 112012. [Google Scholar]
- 217. Černoch M., Skopal F., Hájek M., Eur. J. Lipid Sci. Technol. 2009, 111, 663–668. [Google Scholar]
- 218. Hájek M., Skopal F., Černoch M., Bioresour. Technol. 2012, 110, 288–291. [DOI] [PubMed] [Google Scholar]
- 219. Hájek M., Tomášová A., Kocík J., Podzemná V., Appl. Clay Sci. 2018, 154, 28–35. [Google Scholar]
- 220. Di Serio M., Tesser R., Pengmei L., Santacesaria E., Energy Fuels 2008, 22, 207–217. [Google Scholar]
- 221. Cao X., Li L., Shitao Y., Liu S., Hailong Y., Qiong W., Ragauskas A. J., J. Anal. Appl. Pyrolysis 2019, 138, 137–144. [Google Scholar]
- 222. Atkins P., de Paula J., Keeler J., in Atkins’ Physical Chemistry, Oxford University Press, USA: n.d. [Google Scholar]
- 223. Mahapatra P. M., Aech S., Mandal P. K., Panda A. K., J. Energy Inst. 2024, 115, 101690. [Google Scholar]
- 224. Hart A., Onwudili J. A., Yildirir E., Hashemnezhad S. E., Chem. Eng. J. 2025, 509, 161494. [Google Scholar]
- 225. Li J., Chen C., Xu L., Zhang Y., Wei W., Zhao E., Wu Y., Chen C., JACS Au 2023, 3, 736–755. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 226. Ajmal S., Huang J., Guo J., Tabish M., Mushtaq M. A., Alam M. M., Yasin G., Catalysts 2025, 15, 137. [Google Scholar]
- 227. Hosseini H., Herring C. J., Nwaokorie C. F., Sulley G. A., Montemore M. M., J. Phys. Chem. C 2024, 128, 18144–18157. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 228. Ye R.‐P., Ding J., Gong W., Argyle M. D., Zhong Q., Wang Y., Russell C. K., Xu Z., Russell A. G., Li Q., Fan M., Yao Y.‐G., Nat. Commun. 2019, 10, 5698. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 229. Wei J., Yao R., Han Y., Ge Q., Sun J., Chem. Soc. Rev. 2021, 50, 10764–10805. [DOI] [PubMed] [Google Scholar]
- 230. Martín A. J., Mondelli C., Jaydev S. D., Pérez‐Ramírez J., Chem 2021, 7, 1487–1533. [Google Scholar]
- 231. Lin F., Xu M., Ramasamy K. K., Li Z., Klinger J. L., Schaidle J. A., Wang H., ACS Catal. 2022, 12, 13555–13599. [Google Scholar]
- 232. Weber D., He T., Wong M., Moon C., Zhang A., Foley N., Ramer N. J., Zhang C., Catalysts 2021, 11, 1447. [Google Scholar]
- 233. Fan L., Chen S., Zhang W., Liu Y., Chen Y., Mao X., Chem. – Asian J. 2024, 19, e202301054. [DOI] [PubMed] [Google Scholar]
- 234. Wang C.‐M., J. Mater. Res. 2015, 30, 326–339. [Google Scholar]
- 235. Haase S., Tolvanen P., Russo V., Processes 2022, 10, 99. [Google Scholar]
- 236. Tan J., Ji Y.‐N., Deng W.‐S., Su Y.‐F., Pet. Sci. 2021, 18, 1203–1218. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
