Skip to main content
NCBI home page
RSC Advances logoLink to RSC Advances
. 2026 Aug 7;16(40):46251–46265. doi: 10.1039/d6ra04337b

Cu-supported alkali activated carbon as a green catalyst for organic conversion and environmental remediation

Amira S Hassan a, Ahmed H Ragab b, Saedah Rwede AL-Mhyawi c, Huriyyah Ahmed Alturaifi b, Omar Mbrouk d,✉
PMCID: PMC13449290  PMID: 42569074

Abstract

Green, eco-friendly approaches to designing high-performance catalysts offer a potential solution for environmental remediation, enabling the production of value-adding products and enhancing environmental sustainability. In this study, a green-synthesized Cu-supported active carbon was prepared via a wet impregnation technique. Activated carbon derived from date pits was impregnated with a copper acetate solution at different loadings of 5% for Cu-AS1, 10% for Cu-AS2 and 15% for Cu-AS3. The kinetics of catalytic conversion of hydrocarbon over Cu-supported activated carbon from date pits were studied at 280–380 °C and contact times of 13.63–6.52 min in a flow system under normal pressure. TGA, DTA, X-ray, and BET surface area measurements were used to characterize the treated prepared samples. The liquid and gaseous reaction products were analyzed using gas–liquid chromatography (GLC) to evaluate the catalytic activity and selectivity toward benzene formation. Furthermore, the treated samples were evaluated for their environmental remediation activity in removing various heavy metal ions such as Co2+, Ni2+, Fe3+, and Cd2+ ions. The results revealed that, benzene was identified as the predominant product under the investigated reaction conditions, whereas toluene and xylene were formed via alkylation. The apparent activation energies were 11.05 kcal mol−1 for Cu-AS1, 10.51 kcal mol−1 for Cu-AS2 and 9.75 kcal mol−1 for Cu-AS3; these values are maybe affected by mass-transfer effects. Conversion (%) increased with temperature and the formation rates of gaseous products (H2, CH4, C2H6), indicating enhanced catalytic performance due to higher copper dispersion and improved pore accessibility. The treated samples effectively removed Co2+, Ni2+, Fe3+, and Cd2+ ions at concentrations of 10–30 mg L−1, with adsorption fitting Langmuir and Freundlich models (RL < 1 and n > 1), indicating favorable and efficient adsorption behavior.

Preparation scheme of Cu-supported activated carbon derived from date pits via pretreatment, carbonization, chemical activation, and copper impregnation for enhanced environmental catalytic applications.graphic file with name d6ra04337b-ga.webp

1. Introduction

The heterogeneous catalysis process promotes efficient hydrocarbon transformations, such as the dehydrogenation and aromatization of cyclohexane, to produce useful chemical products such as benzene.1–3 From an environmental perspective, heterogeneous catalysts have the potential to reduce corrosion, separation difficulties, solvent requirements, and energy requirements, thereby facilitating greener, cleaner production processes.4–6 The surface chemistry of heterogeneous catalysts offers more control over the course of the reaction, suppression of side products, and the application of continuous flow reactors, which are widely used in the petrochemical industry.7–9

Environmental challenges and high costs of conventional adsorbents drive the use of sustainable materials derived from renewable resources. Agricultural wastes are considered a good alternative because they are readily available, cost-effective, biodegradable, and high in carbon content. Among the agricultural wastes, date palm pits, also known as date stones, are considered a good alternative, especially in the Middle Eastern and North African countries, because of their huge production and low economic value.10,11 Date pits or date palm seeds (Phoenix dactylifera L.) are lignocellulosic biomass and consist primarily of dietary fibers, carbohydrates (up to 84%), fat content (7–13%), protein content (5–9%), and ash content (1–2%). They are also rich in phenolic compounds and antioxidants, and the oil content is also unique, with high oleic (48–50%) and lauric (10–15%) fatty acids.12,13 The seeds also show good prospects for industrial uses.11,14,15

This composition makes them very suitable precursors for the production of activated carbon, porous catalyst supports, and adsorbents for environmental applications, after activation by thermal or chemical methods. Upon activation, the date pits produce activated carbon that possesses a large surface area (>600–1200 m2 g−1), micro- and mesoporosity, a high adsorption capacity for contaminants, and thermal and chemical stability.10,16 The activated carbon produced from date pits has been documented to be an effective adsorbent for water treatment and contaminant removal due to its porosity and surface functional groups that improve adsorption interactions.16–21 Despite the high adsorption capacity of activated carbon (AC), its standalone use often exhibits limited adsorption efficiency and catalytic activity; therefore, incorporating metals (Cu, Fe, Ni, etc.) into the AC matrix represents a promising strategy to enhance surface reactivity, increase active sites, and improve overall adsorption and heterogeneous catalytic performance.22 In traditional chemical processes, copper nanoparticles are a great substitute for more expensive noble metals like platinum, palladium, gold, and silver.23 Recently, nanostructured and hybrid catalysts have gained significant attention for catalytic and environmental applications due to their high surface area and enhanced surface reactivity.24–28 For instance, Ag–Fe2O3 nanohybrids graphene oxide (FGS)/ZnO nanocomposites and micro-/nano-α-Fe2O3 have shown improved performance in pollutant degradation and environmental remediation processes.29,30

Copper-based catalysts exhibit low activity and may promote side reactions; however, when in nanoparticle or supported form, they are easily recovered and reused. Copper-based catalysts and nanomaterials exhibit high activity, selectivity, stability, and large surface area, while activated carbon, owing to its porous structure and strong adsorption capacity, serves as an excellent support material and one of the most widely used adsorbents for catalytic and environmental applications.31–34 Furthermore, Álvarez et al. reported that CuO-supported activated carbon showed effective phenol degradation, with an activation energy of 78.6 kJ mol−1.35 Yan and his coworkers prepared a Cu/Co–carbon catalyst achieved a CO conversion of 74.8% with a C2+ alcohol selectivity of 58.7%.37 However, the biomass-derived Cu@KF–C/CoFe2O4 reported by Sharma et al.36 exhibited catalyst with high surface area and pore volume, magnetic separability, and reusability in oxidation reactions.37 Wang et al. showed that highly dispersed Cu nanospheres supported on activated carbon exhibited excellent catalytic activity for 4-nitrophenol reduction due to enhanced metal dispersion and porous surface characteristics.38 Additionally, Cu–AC can significantly enhance environmental remediation.39 Shu et al.20 prepared copper-loaded activated carbon (Cu–AC) as an efficient adsorbent for the removal of methylene blue from wastewater.

Despite many research studies, very few attempts have been made in the development of sustainable activated carbon supported by Cu with dual capabilities in catalytic hydrocarbons conversion and heavy metals removal. The current study is therefore focused on using date-pit based activated carbon produced through alkaline activation process as a catalyst support of Cu catalysts remains a promising waste valorisation approach. This approach tends to develop high performance in both heterogeneous catalytic conversion of cyclohexane and removal of heavy-metal ions from aqueous solutions, while enhancing catalytic and adsorption activities with the structural and surface properties of the catalysts. Herein, AC was prepared via a waste valorisation approach from date pit waste, through carbonization followed by chemical activation using NaOH. The copper was then impregnated on the support at varying percentages (5%, 10%, and 15%) using the wet impregnation method to prepare the Cu-AS catalysts. The catalytic activity was assessed using cyclohexane dehydrogenation and alkylation reactions. Further, the efficiency of the treated samples as adsorbents for the removal of heavy metal ions (Co2+, Ni2+, Fe3+, and Cd2+) from aqueous solutions was also explored.

2. Materials and methods

2.1. Materials

Cyclohexane (C6H12, b.p. 80.7 °C, chromatographically pure, Sigma-Aldrich), copper(ii) acetate (Cu(CH3COO)2, anhydrous, M.W. = 181.65, Koch-Light Laboratories), hydrochloric acid (HCl, density = 1.18 g cm−3, 30–32 wt%, BDH Laboratories), and sodium hydroxide (NaOH, M.W. = 40.00, ETWA) were used as received. Ferric(III) nitrate nonahydrate (Fe(NO3)3·9H2O, extra pure, M.W. = 404.00, Oxford Laboratories), cobalt acetate tetrahydrate (Co(CH3COO)2·4H2O, extra pure, M.W. = 249.08, Koch-Light Laboratories), nickel sulfate heptahydrate (NiSO4·7H2O, extra pure, M.W. = 280.88, Koch-Light Laboratories), and cadmium nitrate tetrahydrate (Cd(NO3)2·4H2O, extra pure, M.W. = 308.48, PRS Laboratories) were used for the preparation of heavy-metal ion solutions. Aqueous solutions with concentrations of 10, 20, and 30 mg L−1 were prepared from cobalt acetate (Co(CH3COO)2), nickel sulfate heptahydrate (NiSO4·7H2O), ferric nitrate nonahydrate (Fe(NO3)3·9H2O), and cadmium nitrate tetrahydrate (Cd(NO3)2·4H2O) to obtain the stock solutions of heavy-metal ions. All chemicals were of analytical grade and used without further purification.

2.2. Synthesis of supported activated carbon

Date pits were collected from date palm fruits cultivated in Egypt. The pits were repeatedly washed with agitation and decantation to remove adhering impurities, and the remaining hard stones were used in the experiments.40 The cleaned pits were dried at 130 °C for about 24 h, then crushed using a Pyrex mortar and sieved to obtain the desired particle size.

The preparation of activated carbon was carried out according to the procedure reported by K. Y. Foo and B. H. Hameed (2012)41 with modification, which involves chemical activation of carbon precursors using an alkali activating agent to enhance pore development and surface characteristics. The carbonization process was carried out by loading 85 g of the pre-dried precursor into a furnace and heating it to 600 °C. The resulting char was mixed with NaOH pellets using an impregnation ratio (IR), defined as:

2.2. 1

where wNaOH and wchar represent the dry weights (g) of NaOH pellets and char, respectively. The mixture was shaken to ensure homogeneity and then left at room temperature for 24 h. The particles were subsequently dried at 85 °C for about 24 h to ensure complete drying. The resulting activated carbon was then washed successively with 0.1 M hydrochloric acid and repeatedly with hot and cold distilled water until the filtrate had a neutral pH. Yield was considered as the weight of activated carbon to the weight of char used for activation. Cu-AS(1–3) catalysts were synthesis via wet impregnation technique.

Cu-supported activated carbon catalysts were subsequently prepared by a wet impregnation method.42 Activated carbon was dried at 140 °C for 2 h prior to impregnation. Copper(ii) acetate solutions containing 5, 10, and 15 wt% Cu precursors were added dropwise to the activated carbon under continuous stirring. The impregnated samples were dried at 140 °C for 4 h, gently ground, and sieved. The prepared catalysts were designated as Cu-AS1, Cu-AS2, and Cu-AS3, respectively.

2.3. Characterisation

The X-ray diffraction (XRD) patterns of the solid samples, after calcination at 500 °C for 4 h, were recorded at room temperature using a Philips X'Pert MPD diffractometer (Cu Kα radiation, λ = 1.54 Å) equipped with a graphite monochromator. The BET surface area, pore volume, and average pore diameter of the various samples were determined from nitrogen adsorption–desorption isotherms measured at −195 °C (77 K) using a Micromeritics ASAP 2020 (USA). Prior to measurement, each sample was degassed at 550 °C for 3 h. Data were collected over the range 4° < 2θ < 70° in continuous scan mode. Thermogravimetric analysis (TGA) and differential thermal analysis (DTA) of fresh samples were performed using a Shimadzu 50H thermal analyzer over the temperature range from room temperature to 900 °C at a heating rate of 10 °C min−1 under an air atmosphere. The Scanning Electron Microscope (SEM), JCM-7000 NeoScope™ Benchtop with acceleration voltage 10 kV, was used for taking SEM images and performing elemental analysis.

2.4. Catalytic performance evaluation

A continuous-flow tubular reactor made of silica glass, containing 1.00 mL (0.05 g) of reduced catalyst, was employed in all catalytic cyclohexane conversion experiments. The reactor was heated within an insulated, wider silica-tube jacket,21,42 with temperature controlled to within ±10 °C. Cyclohexane was introduced into the reactor by continuous evaporation, with a feed flow rate ranging from 7.30 to 15.30 mL min−1. The reaction temperature was varied between 280 and 380 °C. The effluent stream passed through a continuously heated zone to prevent condensation of reaction products.43,44

The effluent was analyzed by gas–liquid chromatography (GLC) using a PerkinElmer 8600 gas chromatograph equipped with a double flame ionization detector (FID) and a DB-WAX capillary column (30 m × 0.53 mm i.d., 1 µm film thickness). The injector and detector temperatures were maintained at 200 and 250 °C, respectively, using nitrogen as the carrier gas. Each sample was analyzed in duplicate at each reaction temperature to ensure reproducibility. Catalytic performance was evaluated in terms of hydrocarbon conversion and product selectivity based on chromatographic peak areas.45,46

2.5. Environmental remediation activity test

The treated samples were evaluated for their ability to remove heavy metal ions (Co2+, Ni2+, Fe3+, and Cd2+) from laboratory-prepared aqueous solutions simulating real wastewater. The concentration of the metal ions was analyzed using a UV-vis spectrophotometer with a wavelength range of 340–630 nm. The roles of initial concentration and contact time in the adsorption process were also explored. The effects of operating parameters, including initial metal ion concentration and contact time, were also investigated to determine their influence on adsorption behavior and equilibrium characteristics. Equilibrium adsorption data were analyzed using the Langmuir and Freundlich adsorption isotherm models to evaluate adsorption capacity, adsorption intensity, and surface interaction characteristics of the prepared samples.

3. Results and discussion

3.1. Morphology characterization

As represented in Fig. 1 the XRD spectra for the synthesized (a) activated carbon (AC), (b) CuO, Cu-supported active carbon prepared by wet impregnating activated carbon from date pits with Cu acetate solution at different loading (5, 10, and 15%), producing samples represented as Cu-AS1, Cu-AS2, and Cu-AS3, respectively. The XRD profile of the pristine activated carbon support (sample a) exhibits a prominent, diffuse scattering dome centered around 2θ ≈ 24°, which is characteristic of the (002) plane of amorphous carbon with a disordered graphitic structure (prominent broad diffraction hump) (PDF card: 00-041-1487). The diffractogram of the synthesized pure oxide reference sample (b) demonstrates intense, well-defined reflections at 2θ ≈ 35.5° and 38.7°, corresponding precisely to the (1̄11) and (111) planes of the monoclinic tenorite CuO phase (PDF card: 05-0661), thereby confirming its high crystallinity.

Fig. 1. XRD patterns of (a) AC, (b) CuO, and (c–e) Cu-supported active carbon (Cu-AS1, Cu-AS2, Cu-AS3).

Fig. 1

Open in a new tab

For the copper-impregnated matrices (Cu-AS1, Cu-AS2, and Cu-AS3; samples c–e), the spectra reveal a distinctive multi-phase, hybrid architecture. The successful anchoring and chemical retention of the amorphous carbon support are verified by the continuous presence of the underlying broad baseline hump in the 2θ = 20°–30° region. The coexistence of two distinct copper-based crystalline phases is clearly resolved within these hybrid frameworks: first, the residual unreacted precursor phase is identified by the sharp, low-angle reflections at 2θ ≈ 13.0° and 18.4°, indexed to the (001) and (002) planes of copper acetate monohydrate (PDF card: 00-027-0145); second, the newly nucleated oxide phase is evidenced by the emerging reflections at higher angles (35.5° and 38.7°) matching the monoclinic CuO phase. This simultaneous appearance of both precursor and oxide signatures provides evidence of a controlled crystalline transition taking place inside the porous network of the amorphous carbon host.32,36

These peaks are attributed to the copper acetate precursor used during the impregnation process, suggesting that the copper exists primarily as a coordinated salt or a complex on the carbon surface rather than a fully converted oxide. A clear correlation is observed between the copper loading percentage (5%, 10%, and 15%) and the diffraction intensity. Sample Cu-AS3 (15%) displays the highest peak intensity, which may be attributed to proportional loading of copper onto the activated carbon matrix. Fig. 2a and b shows the morphology of the prepared activated carbon (AC) at different magnifications at 2000× magnification (10 µm scale bar) and 3000× magnification (5 µm scale bar) respectively. SEM-SE image of activated carbon shows heterogeneous and rough surface morphology with the presence of particles of irregular shape with variable size and agglomeration, with void spaces may be linked to the development of a porosity. Fig. 2c and d Cu-loaded activated carbon sample Cu-AS3 at 2000× magnification (10 µm scale bar) and 3000× magnification (5 µm scale bar) respectively. The SEM images show a heterogeneous morphology where the carbon matrix has an irregular surface with bright spots that represent copper species deposited on the AC surface. The images show that, activated carbon has a rough surface with some cavities, voids, and irregularity, which signifies that it is porous in nature due to activation. The SEM-SE images clearly capture the anchoring of bright-contrast, fine needle-like (acicular) crystalline microstructures alongside clustered aggregates distributed heterogeneously across the external carbon boundary and around the pore channels. These localized needle-like arrays are directly attributed to the crystalline networks of the unreacted or partially coordinated copper acetate precursor complex remaining on the surface rather than converting fully into a bulk oxide phase, which strongly aligns with the sharp low-angle reflections resolved in the XRD spectra.20,32 Some of bright contrast particles observed can be refers to the presence of heavier metallic Cu species. The activated carbon (AC) particles exhibited an average grain size of approximately 1.89 µm, while the Cu particles showed a significantly smaller average size of approximately 568.9 nm (grain size analysis was provided in the SI S1).

Fig. 2. SEM images of (a and b) activated carbon (AC) and (c and d) Cu-loaded activated carbon (Cu-AS3) at 2000× (10 µm) and 3000× (5 µm) magnifications.

Fig. 2

Open in a new tab

The nitrogen adsorption–desorption measurement of (a) Cu-AS1, (b) Cu-AS2, and (c) Cu-AS3 measured at 77 K is represented in Fig. 3. The samples follow the type II isotherm of the Brunauer–Deming–Teller classification of mesoporous solids. The specific surface area (SBET) estimated for the catalysts is given in Table 1 using the conventional BET equation. The pore volume distribution analysis was performed using the modeless method for the three samples calcined at 500 °C. This method is based on calculations depending on the hydraulic radius of pores of any shape, which is half the radius of a cylinder and half the distance between the walls of a parallel plate pore. Two sets of values are calculated, namely the cumulative surface (Scum) and cumulative volume (Vcum), as presented in Table 1. As follows from these results, the SBET values of the prepared solids of Cu supported on AC treated with 5, 10, and 15% NaOH, respectively, depend on the copper content. The V–t plots of all the prepared solids exhibit a downward deviation, which is characteristic of micropores, and the pore size distribution curves also indicate the presence of mesoporous of different sizes.

Fig. 3. Nitrogen adsorption–desorption isotherms of samples (a) Cu-AS1, (b) Cu-AS2, and (c) Cu-AS3 measured at 77 K.

Fig. 3

Open in a new tab

Table 1. Textural properties of the prepared catalysts (Cu-AS1, Cu-AS2, and Cu-AS3).

Catalysts S BET, m2 g−1 S cum, m2 g−1 V cum, cc g−1 e−2 Mean pore radius, AO
Cu-AS1 181.50 234.50 0.04 27.54
Cu-AS2 219.20 280.20 0.06 21.65
Cu-AS3 371.70 468.90 0.10 22.77
Open in a new tab

The TGA and DTA curves of the prepared samples are shown in Fig. 3. The TGA curves of (a) Cu-AS1, (b) Cu-AS2, and (c) Cu-AS3 show two main steps of thermal decomposition. The first step at 200 °C for the three catalysts corresponds to the loss (10.2, 11.6, and 12.6%) of the weight of (a) Cu-AS1, (b) Cu-AS2, and (c) Cu-AS3 catalysts, respectively, as water of hydration. The second step at 655 °C for the three catalysts corresponds to the loss (66.6, 49.9, and 42.9%) of the catalyst weight, respectively. The third step at 900 °C for the three catalysts corresponds to the loss (0, 14.8, and 10.8%) of the catalyst weight, respectively, as combined water or phase transformation, while the weight of the three catalysts remains constant up to 900 °C.

The total weight loss of the three catalysts was (76.8, 76.5, and 64.8%) for Fig. 4a Cu-AS1, 4b Cu-AS2, and 4c Cu-AS3, respectively. From the results, the prepared solids of Cu supported on AC samples with percentage ratios of 5%, 10%, and 15% Cu show stability over the temperature range from 250 °C to above 900 °C. The thermodynamic activation parameters of the decomposition process for (a) Cu-AS1, (b) Cu-AS2, and (c) Cu-AS3, respectively, were calculated using the Coats–Redfern equation47–49 and are presented in Table 2. The thermodynamic parameters provide information about the thermal stability of the system.49 The DTA curves for the three catalysts display two endothermic peaks at (50.19 and 340.05 °C) for (a) Cu-AS1, (b) Cu-AS2, and (c) Cu-AS3, respectively.

Fig. 4. The TGA and DTA curves for (a) Cu-AS1, (b) Cu-AS2, and (c) Cu-AS3, respectively.

Fig. 4

Open in a new tab

Table 2. The thermodynamic parameters of the decomposition process.

Catalysts Decomp. temp., K ΔE, kJ mol−1 ΔS, kJ mol−1 K−1 ΔH, kJ mol−1 ΔG, kJ mol−1
Cu-AS1 303–531 25.94 −290.98 23.42 111.58
531–1173 7.47 −264.76 3.06 143.65
Cu-AS2 302–473 42.60 −146.23 40.08 84.24
473–832 4.42 −291.09 0.48 138.16
Cu-AS3 832–1173 58.92 −130.87 52.01 160.89
296–473 28.94 −216.90 26.47 90.67
Open in a new tab

Stage of weight loss below ∼200 °C is believed to be due to physical adsorption water removal and coordinated hydration water removal for copper acetate species:

Cu(CH3COO)2·xH2O → Cu(CH3COO)2 + xH2O↑ 2

Stage two, decomposition at ∼655 °C is related to decomposition of the strongly attached copper acetate species on the activated carbon support surface. Decomposition temperature of such complexes is much higher compared to that of bulk copper acetate which undergoes thermal decomposition at ∼250–350 °C as was reported. Such significant increase in decomposition temperature points to high interaction between metal and support as well as high dispersion of Cu species in the porous matrix of activated carbon which prevents acetate groups mobility and delays ligands decomposition.

Cu(CH3COO)2 → CuO + CO2↑ + CH3COCH3↑ + H2O↑ 3

In parallel, the carbon support may participate in secondary redox processes depending on local oxygen availability, resulting in partial reduction of CuO:

2CuO + C → Cu2O + CO2↑ 4

or

CuO + C → Cu + CO↑ 5

This step involves decarboxylation and acetate group fragmentation resulting in the formation of copper oxides and formation of gaseous products (CO2, CO, H2O, and acetone-like molecules).

Third step that occurs close to ∼900 °C can be explained by the process of gradual structural modification and stabilization of the carbon matrix along with well-bound copper residues that result in a nearly stable mass.

3.2. Catalytic activity

Different series of experiments were done at different times of duration (13.63–6.52 min) and at a specific temperature as reported previously.50 The experiments were repeated at reaction temperatures 280, 300, 330, 350, and 380 °C. The results of catalytic tests are shown in Fig. 4a. The catalysts Cu supported on AC treated by NaOH are also different in the catalytic activity (expressed by the cyclohexane conversion) and stability.

The gaseous and the liquid reaction products were analyzed using gas chromatography (GC). The gaseous products were H2, CH4 and C2H6. The dehydrogenation reaction predominantly produced benzene and small traces of cyclohexene; meanwhile, toluene and xylene51 are detected as alkylation products. The conversion of cyclohexane over the Cu-AS3 catalyst was higher than that over the Cu-AS2 and Cu-AS1 catalysts. Moreover, the Cu-AS3 exhibits much higher activity and stability than the other catalysts. The activity of metal catalysts depends on the surface area (as listed in Table 1) of the support, the well-dispersed nature, and the size of metal particles. The selectivity (%) variation for the Cu-AS1, Cu-AS2, and Cu-AS3 catalysts as a function of reaction time is shown in Fig. 5b. The results obtained clearly show that the selectivity in product formation depends significantly on catalyst composition and properties. Cu-AS1 catalyst had the highest selectivity compared to the other two catalysts used in the reactions reaching approximately 43% at 30 min and increasing to a maximum value of about 46% at 90 min. This reduction may be attributed to catalyst deactivation phenomena, such as coke deposition or partial blockage of active sites, which negatively affect the selective formation of the desired products.52,53

Fig. 5. (a) Cyclohexane conversion (%), and (b) selectivity of benzene formation over Cu-AS1, Cu-AS2, and Cu-AS3 catalysts.

Fig. 5

Open in a new tab

Cu-AS2 exhibited a relatively low selectivity at the initial stage (≈22% at 30 minutes), then increased over time, and reached the maximum selectivity (≈38%) at 120 minutes, and decreased slightly at 180 minutes. Thus, increasing selectivity with the reaction time indicates that Cu-AS2 needs more activation time for optimal catalytic performance due to gradual stabilization of Cu active sites. Cu-AS3 proved to have the most stable catalytic performance among all other studied catalysts. Although the selectivity initially showed average results (≈31% at 30 minutes), the catalyst maintained the same relatively constant selectivity throughout the reaction and obtained the highest selectivity result (≈39%) after 180 minutes. Thus, Cu-AS3 proved to be highly resistant to catalyst deactivation during the reaction process. The better catalytic performance is associated with good metal dispersion, interaction between metal and support, and high surface area.

3.2.1. Effect of contact time

Fig. 6a illustrates the rate of gaseous products (mL min−1) for the three prepared samples. Fig. 6b and c illustrate the effect of contact time (τ) on cyclohexane conversion rate over Cu-AS1 catalysts and rate of gaseous products, respectively. It follows from these findings that: the reaction rate was influenced by the cyclohexane flow rate over Cu-AS1, Cu-AS2, and Cu-AS3 catalysts at temperatures ranging from 280 to 380 °C. It was also affected by the nature of the catalysts and the prevailing kinetic conditions. Furthermore, the reaction rate increased with time, and the system exhibited first-order kinetics.

Fig. 6. Effect of contact time variation on the catalytic conversion of cyclohexane for (a) rate of gaseous products, (b) Cu-AS1, and (c) Cu-AS3.

Fig. 6

Open in a new tab

3.2.2. Effect of temperature

Fig. 7 and 8 illustrates the relation between the reaction temperatures and the conversion% of cyclohexane over the Cu-AS1, Cu-AS2 and Cu-AS3. From the figures, it can be seen that as the temperature increases, the kinetic energy of the molecules also rises, thereby increasing the proportion of molecules capable of engaging in successful collisions. Consequently, the overall reaction rate increases and the order of catalytic activities of the current catalysts for cyclohexane dehydrogenation is: Cu-AS1 > Cu-AS2 > Cu-AS3. For cyclohexane alkylation, the order of catalytic activity was as follows: Cu-AS3 > Cu-AS2 > Cu-AS1. According to Lam and Sinfelt's explanation,52 dehydrogenation is a structure-insensitive reaction catalyzed by metallic sites. Pore size distribution data (Table 1) indicate that catalysts with larger pores facilitate the formation of active intermediates involved in alkylation reactions, such as toluene. Additionally, the selectivity toward benzene formation can be explained based on the findings of Desai and Richardson,43 who reported that dehydrogenation is favored on smaller crystallites (as evidenced by XRD patterns) and is predominant at higher metal dispersion.

Fig. 7. Effect of temperature on cyclohexane conversion over Cu-AS1, Cu-AS2, and Cu-AS3 catalysts.

Fig. 7

Open in a new tab
Fig. 8. Converted products of cyclohexane over Cu-AS1, Cu-AS2, and Cu-AS3 catalysts: (a) dehydrogenation (%) and (b) alkylation (%).

Fig. 8

Open in a new tab

3.2.3. Arrhenius equation

The mathematical expression of the Arrhenius equation is given by:

k = A e−Ea/RT 6

where, k denotes the reaction rate constant, A represents the pre-exponential (frequency) factor. Ea refers to the activation energy, expressed in kcal mol−1. R is the universal gas constant (8.314 J mol−1 K−1), and T is the absolute temperature (kelvin).

The apparent Ea was calculated according to the Arrhenius equation (Fig. 9a and b) and was found to be 11.05, 10.51, and 9.75 kcal mol−1 for Cu-AS1, Cu-AS2, and Cu-AS3, respectively. The progressive reduction in activation energy from Cu-AS1 to Cu-AS3 indicates that the alkylation process is energetically more favorable over Cu-AS3, which accounts for its superior alkylation activity. On the other hand, although its activation energy remains relatively high, the dehydrogenation process is significantly favored by Cu-AS1. This behavior may be attributed to metal dispersion, crystal size, and the nature of the Cu active sites, which play a central role in promoting dehydrogenation.

Fig. 9. Arrhenius plots for (a) Cu-AS1 and (b) Cu-AS3 catalysts.

Fig. 9

Open in a new tab

3.2.4. Reaction mechanism

The catalytic conversion of cyclohexane proceeds via two alternative pathways: a structure-sensitive pathway, namely alkylation, and a structure-insensitive pathway, namely dehydrogenation.54,55 Cyclohexane molecules are first adsorbed onto Cu active sites that are uniformly dispersed over the alkali-activated carbon surface. The high surface area and mesoporous structure facilitate adsorption and improve reactant accessibility.

The reaction mechanism can be schematically represented as shown in Fig. 10. The dehydrogenation pathway mainly leads to benzene formation and follows the mechanism proposed by Germain.56 In this mechanism, involves the adsorption of cyclohexane onto the active sites, followed by the stepwise removal of hydrogen atoms to form a π-system, ultimately yielding benzene as the final product. In contrast, alkylation and condensed, condensed products (polyaromatization) can be explained by a polymolecular mechanism.50

nC6H12 + k ⇄ {(C6H12)}adsk 7

where k indicates the catalyst surface.

Fig. 10. Proposed mechanism for cyclohexane dehydrogenation (pathway I) and alkylation (pathway II) over Cu–AC catalyst.

Fig. 10

Open in a new tab

According to this mechanism, the intermediate decomposes immediately in several ways giving the different products. These intermediates rapidly decompose through several competing pathways, generating different products such as toluene, xylene, and heavier aromatic compounds.

3.3. Environmental remediation

A series of experiments was conducted at different contact times (0.5–24 h) and initial metal ion concentrations (10, 20, and 30 mg L−1) at room temperature. The results of the adsorption tests are presented in Table 3. The Cu-supported activated carbon catalysts treated with NaOH exhibited varying adsorption capacities toward heavy metal ions (HMIs), including Ni(ii), Co(ii), Cd(ii), and Fe(iii), from aqueous solutions.

Table 3. Heavy metal ions (HMIs) removal adsorption activities of the prepared samples.

Biosorbent Initial ions conc., g L−1 Co2+ ions Ni2+ ions Fe3+ ions Cd2+ ions
q e, mg g−1 Removal, % q e, mg g−1 Removal, % q e, mg g−1 Removal, % q e, mg g−1 Removal, %
Cu-AS1 0.03 13.88 46.29 15.33 51.11 19.85 66.17 19.99 66.66
0.02 11.66 58.33 15.00 75.00 15.68 78.43 15.00 75.00
0.01 9.00 90 7.70 77.00 9.40 94.04 8.20 82.00
Cu-AS2 0.03 17.64 58.82 22.5 75.00 23.82 79.41 25.2 84.00
0.02 15.90 79.54 17.50 87.50 17.50 87.50 18.00 90
0.01 8.33 83.33 9.00 90 9.00 90 9.64 96.47
Cu-AS3 0.03 23.62 78.75 26.40 88.00 26.40 88.00 27.99 93.33
0.02 18.75 93.75 18.00 90 18.36 91.80 19.00 95.00
0.01 9.46 96.42 9.25 92.50 9.99 99.99 9.78 97.80
Open in a new tab

As shown in Table 3, Cu-AS3 demonstrated the highest adsorption capacity compared to Cu-AS2 and Cu-AS1. This enhanced performance can be attributed to its higher surface area, as indicated by the measurements listed in Table 1.

The adsorption behavior of metal ions onto the Cu-AS1 adsorbent (selected as a model system) is illustrated in Fig. 11, where the amount adsorbed (qe, mg g−1) is plotted against the equilibrium concentration (Ce, mg L−1). The isotherm initially rises sharply at low Ce and qe values, indicating the availability of abundant active adsorption sites. As adsorption progresses, the curve approaches a plateau, suggesting saturation of the adsorbent surface. The gradual decrease in the curvature of the isotherm indicates a tendency toward monolayer adsorption. To optimize the design of the adsorption system for efficient removal of metal ions from aqueous media, it is essential to establish an appropriate model that accurately describes the equilibrium isotherm. The adsorption isotherm parameters obtained from the Langmuir and Freundlich models for the adsorption of HMI's onto Cu-AS1, Cu-AS2, and Cu-AS3 are presented in Tables 4 , 5 and 6 respectively.

Fig. 11. Adsorption isotherms of heavy metal ions (HMIs) over Cu-AS1 catalysts.

Fig. 11

Open in a new tab

Table 4. Parameters of Langmuir and Freundlich isotherm models for the adsorption of HMI's onto Cu-AS1.

Co2+ ions Ni2+ ions Fe3+ ions Cd2+ ions
Langmuir constants and statistical parameters
q m b R 2 q m b R 2 q m b R 2 q m b R 2
14.49 0.93 0.99 20 4.13 0.98 2.17 0.10 0.99 6.66 5.00 0.98
 
Freundlich constants and statistical parameters
K F n R 2 K F n R 2 K F n R 2 K F n R 2
8.70 7.14 0.98 6.60 2.94 0.88 10.71 3.84 0.98 6.02 1.92 0.99
Open in a new tab

Table 5. Parameters of Langmuir and Freundlich isotherm models for the adsorption of HMI's onto Cu-AS2.

Co2+ ions Ni2+ ions Fe3+ ions Cd2+ ions
Langmuir constants and statistical parameters
q m b R 2 q m b R 2 q m b R 2 q m b R 2
20.83 0.5 0.98 28.57 0.53 0.9 3.44 3.81 0.99 30.30 0.10 0.99
 
Freundlich constants and statistical parameters
K F n R 2 K F n R 2 K F n R 2 K F n R 2
7.58 2.77 0.93 9.77 2.27 0.95 9.33 1.88 0.98 13.80 2.77 0.99
Open in a new tab

Table 6. Parameters of Langmuir and Freundlich isotherm models for the adsorption of HMI's onto Cu-AS3.

Co2+ ions Ni2+ ions Fe3+ ions Cd2+ ions
Langmuir constants and statistical parameters
q m b R 2 q m b R 2 q m b R 2 q m b R 2
26.31 1.80 0.99 52.63 0.27 0.99 27.02 4.11 0.98 37.03 1.42 0.96
 
Freundlich constants and statistical parameters
K F n R 2 K F n R 2 K F n R 2 K F n R 2
14.45 3.33 0.99 11.22 1.51 0.98 19.95 10.00 0.99 19.49 2.12 0.99
Open in a new tab

The experimental data were analyzed using Langmuir and Freundlich isotherm models.57 The linearized form of the Langmuir isotherm58 was applied to characterize the adsorption of heavy metal ions onto the prepared adsorbents and to determine the maximum adsorption capacity:

3.3. 8

where qe = amount of metal absorbed (mg g−1) at equilibrium, qmax = maximum of Langmuir monolayer adsorption capacity, Ce = equilibrium concentration of the metal ions in the solution, b = Langmuir constant, b and qmax constants related to adsorption efficiency and energy of adsorption.

The Freundlich isotherm model, one of the earliest empirical models describing adsorption, is expressed as follows:

log qe = log KF + (1/n)log Ce 9

where KF = Freundlich adsorption constant, n = adsorption intensity constant.

K F is an indication of the adsorbents, n, indicates the effect of concentration on the adsorption capacity and represents adsorption intensity.59KF measure adsorbent capacity which calculated from linear plot of log qe against log Ce also calculated separation factor according to the equation:

R = 1/1 + KFC 10

The results indicate that the adsorption capacity (qmax) of activated carbon significantly increased upon Cu incorporation, enhancing the adsorption of heavy metal ions. The obtained b values suggest that the adsorption process is endothermic in nature. Moreover, the values of the separation factor (RL) were found to lie between 0 and 1, confirming that the adsorption process is favorable.60,61

Furthermore, the values of Freundlich parameter (n) greater than unity indicate favorable adsorption conditions and strong interaction between the adsorbent and metal ions.60,61

The performance of the catalyst, and its characteristics were compared with previously reported carbon-supported copper systems. Early configurations, such as CuO on commercial activated carbon (AC), suffered from notable copper leaching (<1.5 mg L−1).62 Subsequent research utilized nanostructured carbon matrices, including carbon nanotubes (CNTs),63,64 and MOF-derived carbon architectures.65–68 While these platforms achieved high specific surface areas (up to 695 m2 g−1) and stable active sites,66,68 their findings remained restricted to electrocatalysis. On the other hand, environmental applications have relied either on unfunctionalized biomass—such as date-pit AC, which exhibits high adsorption capacities but lacks catalytic activity,61,69 or copper-loaded composites tailored exclusively for single-target remediation, such as dye removal,70 disinfection,71 or organic degradation,72 without addressing heavy-metal co-contamination. Furthermore,CuO@ATT/AC composites successfully minimized copper leaching to (0.03 mg L−1) during antibiotic degradation, but remained ineffective for heavy-metal removal.72 While, the present findings introduce a distinct paradigm shift by engineering a high-performance, multifunctional catalyst based on copper-supported alkali-activated date-pit carbon, effectively bridging the gap between high catalytic conversion and metal remediation. Structurally, our alkali activation protocol successfully expanded the surface area up to 371.7 m2 g−1 upon optimizing the copper loading (5–15 wt%). This unique micro-environment achieved superior catalytic kinetics, driving organic conversions with low activation energy 9.75 kcal mol−1 (40.8 kJ mol−1) and a selective benzene yield of ∼46%. Significantly, while existing literature focuses on single-utility applications either optimizing adsorption at the expense of catalysis61,69 or degrading organics while ignoring heavy metals.71,72 In this work, the optimized catalyst (Cu-AS3) functions as a robust dual-benefit platform. It transforms an agricultural waste byproduct into a sustainable, high-value catalyst that simultaneously drives organic synthesis and removes heavy metals with exceptional efficiencies: Fe3+ (99.9%); Cd2+ (97.8%); Co2+ (96.4%); Ni2+ (92.5%). Table 7 highlighted the efficacy of the synthesized catalyst, compares its catalytic parameters with various previously reported Cu/carbon systems.

Table 7. Comparison between previously reported Cu/carbon matrices and the current work.

Category Catalyst/material Method Carbon source Cu loading (wt%) BET (m2 g−1) Main application Key quantitative results Year Ref.
Cu/activated carbon CuO/AC Wet impregnation Commercial AC 5 — Wet air oxidation of phenol Cu leaching < 1.5 mg L−1 2002 62
Cu/CNT (CuCNT-Nw) Cu nanowire deposition onto CNTs CNTs 10 wt% 23.1–64.3 Electrocatalytic CO2 reduction Cu NPs = 2–3.8 nm; high catalyst stability (24 h, ∼8% activity loss) 2017 63
Cu@NC NT/CF In situ growth thermal annealing (500 °C) CNT — — Electrocatalytic H2 evolution (HER) Encapsulated Cu nanoparticles; optimum annealing at 500 °C; stable for 10 h under continuous operation 2017 64
Cu–N–C (porous nanodisks) MOF-derived pyrolysis N-doped carbon 4.5 wt% 293 Electrocatalysis (ORR) Dispersed Cu single atoms; hierarchical porous structure; 87.3% activity 2019 65
Green Cu-supported carbon catalyst Cu/Cu2O-MFC60 Wet impregnation Mesoporous fullerene carbon (MFC60) 5, 10, 15 and 20 wt% 213–657 Electrocatalytic ORR Optimum at 15 wt% Cu; ordered mesoporous carbon with finely dispersed Cu/Cu2O nanoparticles 2020 66
RGO@CuO2 Green synthesis using malachite Reduced graphene oxide (RGO) 15–30% — Decarboxylative tandem coupling for C(sp3)–H activation Uniformly dispersed nanocrystalline CuO on RGO; catalyst performance dependent on CuO dispersion 2018 67
Cu NPs-loaded PDMC (Cu-PDMC) Wet impregnation PANI-derived N-doped mesoporous carbon 7.5% 695 Electrocatalyst Inclusion of Cu into carbon reduced the overpotential and increase the current density of the reaction 2018 68
Biomass-derived AC Date-pit AC Green synthesis carbonization Date pits — 316.9 Pb adsorption Pb(ii) adsorption capacity = 101.35 mg g−1 (experimental); Langmuir qmax = 128.21 mg g−1, Pb removal = 99.4% 2019 69
Date-pit AC Steam activation at 800 °C Date pits — 702 Heavy-metal adsorption Langmuir adsorption capacities (mg g−1): Zn2+ = 1594, Fe3+ = 1555, Co2+ = 1318, Pb2+ = 1261; 99–100% removal of metal ions from groundwater sample 2013 61
Cu-supported biomass/AC AC–Fe0/Cu Wet impregnation Fava bean biomass (Vicia faba L.) — — Removal of methyl orange dye from wastewater Methyl orange removal = 97.2%; adsorption capacity = 72.9 mg g−1 2024 70
Cu–AC Wet impregnation Commercial activated carbon — — Water disinfection Disinfection of water containing 104 CFU mL−1E. coli; optimum contact time = 25 min 2024 71
CuO@ATT/AC Wet impregnation Attapulgite (ATT) — PMS activation for degradation of sulfadiazine (SDZ) 96.5% SDZ removal within 60 min; Cu leaching = 0.03 mg L−1; focused on organic pollutant degradation; no heavy-metal adsorption 2025 72
Cu-supported alkali-activated carbon (Cu/AC) Wet impregnation Date pits 5, 10 and 15 wt% 181.5, 219.2, 371.7 Catalytic organic conversion and heavy-metal removal ET increased to 371.7 m2 g−1 (Cu-AS3); lowest activation energy = 9.75 kcal mol−1 (40.8 kJ mol−1); benzene selectivity reached ≈46% (Cu-AS1); alkylation activity: Cu-AS2 > Cu-AS3 > Cu-AS1; maximum heavy-metal removal by Cu-AS3 reached Co2+ 96.4%, Ni2+ 92.5%, Fe3+ 99.9%, and Cd2+ 97.8% as multifunctional catalyst integrating catalytic conversion and environmental remediation This work
Open in a new tab

4. Conclusion

Cu-supported activated carbon catalysts treated with NaOH at 5, 10, and 15 wt% (denoted as Cu-AS1, Cu-AS2, and Cu-AS3, respectively) were successfully prepared via the wet impregnation technique and characterized using TGA, DTA, X-ray diffraction (XRD), and BET surface area analysis. The catalysts exhibited mesoporous structures with specific surface areas (SBET) of 181.50, 219.20, and 371.70 m2 g−1 for Cu-AS1, Cu-AS2, and Cu-AS3, respectively. The increase in surface area was attributed to improved CuO dispersion and structural modification of the NaOH-treated activated carbon support.

The catalytic performance of the prepared materials was evaluated for catalytic conversion of cyclohexane over the temperature range of 280–380 °C using a continuous-flow reactor, and both liquid and gaseous products were analyzed by gas–liquid chromatography. Among the catalysts, Cu-AS1 exhibited relatively lower catalytic activity. The order of catalytic activity for benzene formation (via dehydrogenation) was found to be: Cu-AS1 > Cu-AS3 > Cu-AS2. In contrast, for cyclohexane alkylation, the activity followed the order: Cu-AS2 > Cu-AS3 > Cu-AS1.

In terms of adsorption performance, the adsorption capacity increased with decreasing initial metal ion concentration. The calculated values of the separation factor (RL) and adsorption intensity (n) indicated that the prepared adsorbents are highly effective and favorable for the removal of Co2+, Ni2+, Fe3+, and Cd2+ ions from aqueous media.

Author contributions

Conceptualization and primary manuscript drafting were led by Amira S. Hassan and Omar Mbrouk, made the study design, interpretation, and integration of all manuscript sections. Ahmed H. Ragab, Saedah Rwede AL-Mhyawi, and Huriyyah Ahmed Alturaifi contributed to data analysis, literature review, manuscript revision, and critical intellectual feedback. All authors reviewed and provided feedback on each version of the manuscript throughout its preparation, making necessary revisions. Finally, all authors have read and approved the final published version of the manuscript.

Conflicts of interest

The authors declare no conflict of interest.

Supplementary Material

RA-016-D6RA04337B-s001

Acknowledgments

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through large group research project under number RGP2/261/47.

Data availability

All the data investigated in this research are accessible to the corresponding author upon reasonable request.

Supplementary information (SI) is available. See DOI: https://doi.org/10.1039/d6ra04337b.

References

  1. Wei J. Yao R. Han Y. Ge Q. Sun J. Chem. Soc. Rev. 2021;50:10764–10805. doi: 10.1039/D1CS00260K. [DOI] [PubMed] [Google Scholar]
  2. Sanoja-Lopez K. A. Balu A. M. Carmona H. D. P. Hájek M. Luque R. Herrador J. M. H. Chem.–Asian J. 2025;20:e00635. doi: 10.1002/asia.202500635. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Liu X. Chen J. Ma T. Org. Biomol. Chem. 2018;16:8662–8676. doi: 10.1039/C8OB02351D. [DOI] [PubMed] [Google Scholar]
  4. Ansari M. Jamali H. Ghanbari R. Ehrampoush M. H. Zamani P. Hatami B. Chem. Eng. J. Adv. 2025;22:100718. doi: 10.1016/j.ceja.2025.100718. [DOI] [Google Scholar]
  5. Parangi T. Rev. Inorg. Chem. 2025;45:831–861. doi: 10.1515/revic-2024-0047. [DOI] [Google Scholar]
  6. Liu J. Burciaga R. Tang S. Ding S. Ran H. Zhao W. et al. . Innovation Mater. 2024;2:100090. doi: 10.59717/j.xinn-mater.2024.100090. [DOI] [Google Scholar]
  7. Králik M. Koóš P. Markovič M. Lopatka P. Molecules. 2025;30:3279. doi: 10.3390/molecules30153279. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Ahorsu R. Constanti M. Medina F. Ind. Eng. Chem. Res. 2021;60:18612–18626. doi: 10.1021/acs.iecr.1c02789. [DOI] [Google Scholar]
  9. Glotov A. Karakhanov E. Catalysts. 2021;11:602. doi: 10.3390/catal11050602. [DOI] [Google Scholar]
  10. Surkatti R., Ibrahim M. H. and El-Naas M. H., in Sorbents Materials for Controlling Environmental Pollution, Elsevier, 2021, pp. 135–161 [Google Scholar]
  11. Faiad A. Alsmari M. Ahmed M. M. Bouazizi M. L. Alzahrani B. Alrobei H. Sustainability. 2022;14:1134. doi: 10.3390/su14031134. [DOI] [Google Scholar]
  12. Besbes S. Blecker C. Deroanne C. Drira N. E. Attia H. Food Chem. 2004;84:577–584. doi: 10.1016/S0308-8146(03)00281-4. [DOI] [Google Scholar]
  13. Nehdi I. Omri S. Khalil M. I. Al-Resayes S. I. Ind. Crops Prod. 2010;32:360–365. doi: 10.1016/j.indcrop.2010.05.016. [DOI] [Google Scholar]
  14. Alkhoori M. A. Kong A. S. Y. Aljaafari M. N. Abushelaibi A. Erin Lim S. H. Cheng W. H. et al. . Biomolecules. 2022;12:1626. doi: 10.3390/biom12111626. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Ashraf Z. Hamidi-Esfahani Z. Food Rev. Int. 2011;27:101–133. doi: 10.1080/87559129.2010.535231. [DOI] [Google Scholar]
  16. Danish M. Hashim R. Ibrahim M. M. Sulaiman O. Biomass Bioenergy. 2014;61:167–178. doi: 10.1016/j.biombioe.2013.12.008. [DOI] [Google Scholar]
  17. Bouchelta C. Medjram M. S. Bertrand O. Bellat J. P. J. Anal. Appl. Pyrolysis. 2008;82:70–77. doi: 10.1016/j.jaap.2007.12.009. [DOI] [Google Scholar]
  18. Jha M. K. Joshi S. Sharma R. K. Kim A. A. Pant B. Park M. Pant H. R. Nanomaterials. 2021;11:3140. doi: 10.3390/nano11113140. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Tian L. Liu S. Tahir N. Huang L. Tao H. Zhao D. et al. . J. Anal. Appl. Pyrolysis. 2024;183:106771. doi: 10.1016/j.jaap.2024.106771. [DOI] [Google Scholar]
  20. Shu J. Cheng S. Xia H. Zhang L. Peng J. Li C. Zhang S. RSC Adv. 2017;7:14395–14405. doi: 10.1039/C7RA00287D. [DOI] [Google Scholar]
  21. Vassilev I. Yao H. Shakeel S. Tuominen M. Heponiemi A. Bergna D. et al. . Bioresour. Technol. 2025;418:131914. doi: 10.1016/j.biortech.2024.131914. [DOI] [PubMed] [Google Scholar]
  22. Guiza M. Abdedayem A. Ghouma I. Ouederni A. J. Chem. Technol. Metall. 2017;52:836–851. [Google Scholar]
  23. Gawande M. B. Goswami A. Felpin F. X. Asefa T. Huang X. Silva R. et al. . Chem. Rev. 2016;116:3722–3811. doi: 10.1021/acs.chemrev.5b00482. [DOI] [PubMed] [Google Scholar]
  24. Ramasamy B. Paul J. M. P. Raman K. Sundaram R. RSC Adv. 2026;16:29918–29931. doi: 10.1039/D6RA02955H. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Ajibade P. A. Andrew F. P. Mbuyazi T. B. Papo T. R. Rajeshwar K. RSC Adv. 2026;16:1848–1862. doi: 10.1039/D5RA04748J. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. AlZubaidi A. Pandit V. Jawale V. Gupta M. Late D. Front. Catal. 2025;5:1655324. doi: 10.3389/fctls.2025.1655324. [DOI] [Google Scholar]
  27. Uttam Pandit V. R. Parshuram Jadhav G. K. Sakharam Jawale V. M. Dubepatil R. Gurao R. Late D. J. RSC Adv. 2024;14:29099–29105. doi: 10.1039/D4RA04575K. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Mithari J. A. Pandit V. R. U. Gaikwad S. J. Indian Chem. Soc. 2026:102678. doi: 10.1016/j.jics.2026.102678. [DOI] [Google Scholar]
  29. Mithari J. Pandit V. Gaikwad S. MRS Commun. 2026:1–7. [Google Scholar]
  30. Nenavathu B. P. Kandula S. Verma S. RSC Adv. 2018;8:19659–19667. doi: 10.1039/C8RA02237B. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Boucherdoud A. Seghier A. Kherroub D. E. Douinat O. Dahmani K. Bestani B. Benderdouche N. Mater. Sci. Eng., B. 2025;314:118015. doi: 10.1016/j.mseb.2025.118015. [DOI] [Google Scholar]
  32. Dawange G. P. Pandit V. U. ChemistrySelect. 2026;11:e73402. doi: 10.1002/slct.73402. [DOI] [Google Scholar]
  33. Liu S. L. Wang Y. N. Lu K. T. J. Porous Mater. 2014;21:459–466. doi: 10.1007/s10934-014-9792-9. [DOI] [Google Scholar]
  34. Nabais J. V. Laginhas C. Carrott M. R. Carrott P. J. M. Amorós J. C. Gisbert A. N. Appl. Surf. Sci. 2013;265:919–924. doi: 10.1016/j.apsusc.2012.11.164. [DOI] [Google Scholar]
  35. Álvarez P. M. McLurgh D. Plucinski P. K. Ind. Eng. Chem. Res. 2002;41:2153–2159. doi: 10.1021/ie010447w. [DOI] [Google Scholar]
  36. Sharma S. Kaur M. Sharma C. Choudhary A. Paul S. ACS Omega. 2021;6:23266–23280. doi: 10.1021/acsomega.1c01830. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Yan G. Pršlja P. Chen G. Kang J. Liu Y. Caro M. A. et al. . ACS Catal. 2025;15:2492–2503. doi: 10.1021/acscatal.4c07156. [DOI] [Google Scholar]
  38. Wang S. Gao S. Tang Y. Wang L. Jia D. Liu L. J. Solid State Chem. 2018;260:117–123. doi: 10.1016/j.jssc.2018.01.025. [DOI] [Google Scholar]
  39. Meryemoglu B. Irmak S. Hesenov A. Erbatur O. Int. J. Hydrogen Energy. 2012;37:17844–17852. doi: 10.1016/j.ijhydene.2012.09.024. [DOI] [Google Scholar]
  40. Samra S. E. El Sharkawy E. A. Youssef A. M. Colloids Surf., A. 2000;163:199–208. doi: 10.1016/S0927-7757(99)00294-0. [DOI] [Google Scholar]
  41. Foo K. Y. Hameed B. H. Chem. Eng. J. 2012;187:53–62. doi: 10.1016/j.cej.2012.01.079. [DOI] [Google Scholar]
  42. Rioux R. M. Vannice M. A. J. Catal. 2003;216:362–376. doi: 10.1016/S0021-9517(02)00035-0. [DOI] [Google Scholar]
  43. Desai P.H. Richardson J. T. J. Catal. 1986;98(2):392–400. doi: 10.1016/0021-9517(86)90328-3. [DOI] [Google Scholar]
  44. Guo L. Yao J. Bao X. Zhu H. Green Energy Environ. 2025;11(1):105–118. doi: 10.1016/j.gee.2025.09.010. [DOI] [Google Scholar]
  45. Tanuma S. I. Palnichenko A. J. Mater. Res. 1995;10:1120–1125. doi: 10.1557/JMR.1995.1120. [DOI] [Google Scholar]
  46. Dunston K. Lenin A. Kumar P. Veerappan G. Seenivasan G. B. Thakur A. Sivakumar A. Matéria. 2025;30:e20240689. [Google Scholar]
  47. Brunauer S. Emmett P. H. Teller E. J. Am. Chem. Soc. 1938;60:309–319. doi: 10.1021/ja01269a023. [DOI] [Google Scholar]
  48. Coats W. Redfern J. P. Nature. 1964;201:68–69. doi: 10.1038/201068a0. [DOI] [Google Scholar]
  49. Salema A. Afzal M. T. Motasemi F. J. Anal. Appl. Pyrolysis. 2014;105:217–226. doi: 10.1016/j.jaap.2013.11.007. [DOI] [Google Scholar]
  50. Ali R. A. Ezzo E. M. Hassan S. A. El Kherbawi M. A. Hassan A. S. Egypt. J. Chem. 2023;66(special issue 13):1749–1757. doi: 10.21608/ejchem.2023.221828.8232. [DOI] [Google Scholar]
  51. Granata S. Faravelli T. Ranzi E. Combust. Flame. 2003;132:533–544. doi: 10.1016/S0010-2180(02)00465-0. [DOI] [Google Scholar]
  52. Sinfelt J. H. Lam Y. L. Cusumano J. A. Barnett A. E. J. Catal. 1976;42:227–237. doi: 10.1016/0021-9517(76)90257-8. [DOI] [Google Scholar]
  53. Youssef M. El-Sharkawy E. A. Colloids Surf., A. 1998;138:21–27. doi: 10.1016/S0927-7757(98)00226-X. [DOI] [Google Scholar]
  54. Sinfelt J. H., in Advances in Catalysis, Academic Press, 1973, vol. 23, pp. 91–119 [Google Scholar]
  55. Mitrofanova N. Boronin V. S. Poltorak O. M. Russ. J. Phys. Chem. A. 1972;46:32–38. [Google Scholar]
  56. Germain J. E., Catalytic Conversion of Hydrocarbons, Academic Press, London, 1969, p. 170 [Google Scholar]
  57. Mahmoud L. A. M. Rowlandson J. L. Fermin D. J. Ting V. P. Nayak S. RSC Appl. Interfaces. 2025;2:25–55. doi: 10.1039/D4LF00215F. [DOI] [Google Scholar]
  58. Namasivayam C. Muniasamy N. Gayatri K. Rani M. Ranganathan K. Bioresour. Technol. 1996;57:37–43. doi: 10.1016/0960-8524(96)00044-2. [DOI] [Google Scholar]
  59. Reichert H., in Sorpromatic Software ADP 5 Users Manual, Thermo Electron Technical Publications, Rodano, 1st edn, 2005, pp. 39–40 [Google Scholar]
  60. Sheha R. R. J. Colloid Interface Sci. 2007;310:18–26. doi: 10.1016/j.jcis.2007.01.047. [DOI] [PubMed] [Google Scholar]
  61. Awwad N. S. El-Zahhar A. A. Fouda A. M. Ibrahium H. A. J. Environ. Chem. Eng. 2013;1:416–423. doi: 10.1016/j.jece.2013.06.006. [DOI] [Google Scholar]
  62. Álvarez P. M. McLurgh D. Plucinski P. Ind. Eng. Chem. Res. 2002;41:2153–2158. doi: 10.1021/ie010447w. [DOI] [Google Scholar]
  63. Marepally B. C. Ampelli C. Genovese C. Tavella F. Veyre L. Quadrelli E. A. et al. . J. CO2 Util. 2017;21:534–542. doi: 10.1016/j.jcou.2017.08.008. [DOI] [Google Scholar]
  64. Zhang Y. Ma Y. Chen Y. Y. Zhao L. Huang L. B. Luo H. et al. . ACS Appl. Mater. Interfaces. 2017;9:36857–36864. doi: 10.1021/acsami.7b11748. [DOI] [PubMed] [Google Scholar]
  65. Wang T. Yang R. Shi N. Yang J. Yan H. Wang J. et al. . Small. 2019;15:1902410. doi: 10.1002/smll.201902410. [DOI] [PubMed] [Google Scholar]
  66. Saianand G. Gopalan A. I. Lee J. C. Sathish C. I. Gopalakrishnan K. Unni G. E. et al. . Small. 2020;16:1903937. doi: 10.1002/smll.201903937. [DOI] [PubMed] [Google Scholar]
  67. Gulati U. Rajesh U. C. Rawat D. S. ACS Sustainable Chem. Eng. 2018;6:10039–10051. doi: 10.1021/acssuschemeng.8b01376. [DOI] [Google Scholar]
  68. Zhang T. Asefa T. Front. Chem. Sci. Eng. 2018;12:329–338. doi: 10.1007/s11705-018-1741-8. [DOI] [Google Scholar]
  69. Krishnamoorthy R. Govindan B. Banat F. Sagadevan V. Purushothaman M. Show P. L. J. Biosci. Bioeng. 2019;128:88–97. doi: 10.1016/j.jbiosc.2018.12.011. [DOI] [PubMed] [Google Scholar]
  70. Abdel-Aziz B. Mohamed N. El-Taweel R. M. Sh H. Salim A. I. Pal K. et al. . Top. Catal. 2024;67:103–122. doi: 10.1007/s11244-023-01838-z. [DOI] [Google Scholar]
  71. Mukherjee M. Ramachandran S. Bandyopadhyaya R. Water Environ. Res. 2024;96:e11114. doi: 10.1002/wer.11114. [DOI] [PubMed] [Google Scholar]
  72. Huang R. Xia J. Wang Y. Gao Z. Xue Z. Zhu J. et al. . ACS ES&T Water. 2025;5:1416–1425. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

RA-016-D6RA04337B-s001
RA-016-D6RA04337B-s001.pdf (237.5KB, pdf)

Data Availability Statement

All the data investigated in this research are accessible to the corresponding author upon reasonable request.

Supplementary information (SI) is available. See DOI: https://doi.org/10.1039/d6ra04337b.

Articles from RSC Advances are provided here courtesy of Royal Society of Chemistry

RESOURCES