Microalgae-based biosensing technologies for environmental monitoring and emerging food safety applications: Opportunities and challenges

Summary

Microalgae have long been investigated as biological sensing elements, yet their translation into robust, deployable biosensors remains limited by challenges related to cell viability, analyte selectivity, and transducer integration. Their sensing function is based on pollutant-induced alterations in photosynthesis, respiration, enzyme activity, and metabolism, which can be converted into measurable optical or electrochemical signals. Optical sensors typically monitor changes in chlorophyll fluorescence associated with photosystem II inhibition, while electrochemical systems detect variations in oxygen evolution or enzyme activity through amperometric, potentiometric, or conductometric responses. Despite promising sensitivity, practical deployment is hindered by poor operational stability, limited selectivity, and lack of standardized fabrication methodologies. Emerging advances in cell immobilization, microalgae-derived nanomaterials, microfluidic platforms, and biomimetic recognition elements offer opportunities to enhance sensor performance and reproducibility. This review examines whole-cell and photosynthetic-component algal biosensors, highlighting key biological and engineering challenges that must be addressed to enable reliable environmental and food-safety monitoring applications.

Graphical abstract

Environmental monitoring; Bioelectronics; Food safety

Introduction

Biosensors integrate a biological recognition element, for example, an enzyme, antibody, aptamer, nucleic acid, or a whole cell, within a transducer that converts the biorecognition event into a quantifiable signal^1,2 (Figure 1). Conventional analytical techniques such as high-performance liquid chromatography, gas chromatography, inductively coupled plasma-mass spectrometry, enzyme-linked immunosorbent assays, and molecular assays remain the benchmark for regulatory testing because of their sensitivity, selectivity, and standardization. However, these methods often require specialized instrumentation, trained personnel, and laboratory-based workflows, limiting their use for rapid, on-site monitoring.³ This analytical gap has driven substantial interest in biosensor platforms that can provide faster, lower-cost, and field-deployable detection of contaminants in environmental and food systems.

Figure 1.

Overview of microalgae-based biosensor

The schematic illustrates the fundamental components of a microalgae-based biosensor, including the target analytes, biological sensing element, signal transduction pathway, and end-use applications.

Among whole-cell sensing platforms, microalgae are particularly attractive because they grow rapidly and are tolerant to diverse environmental conditions, including wastewater.⁴ Unlike conventional chemical analysis, which generally quantifies total contaminant concentration, microalgae-based biosensors can capture the biological effects of pollutants by monitoring changes in chlorophyll fluorescence, oxygen evolution, and electrochemical behavior.^2,5,6 This can be done by interfacing with optical and electrochemical transducers, immobilized within hydrogels and alginate beads, and miniaturizing them into portable devices. These responses make microalgae suitable for detecting contaminants such as pesticides, herbicides, heavy metals, and wastewater-derived toxicants, especially when early warning of ecological or food-safety risks is required. In addition, advances in lab-on-a-chip systems are expanding the design space for algal sensing platforms.^7,8,9,10 However, translating these proof-of-concept systems into robust monitoring tools remains limited. Many reported devices are evaluated under laboratory conditions and rarely reflect real environmental or food matrices, where turbidity, organic matter, pH variation, salinity, microbial contamination, and mixed pollutants can alter algal physiology and sensor output. This biological responsiveness is also the central limitation of microalgae-based biosensing.

Many algal responses reflect general physiological stress rather than selective recognition of a specific analyte. For example, inhibition of photosystem II (PS II) may be triggered by several herbicides, pesticides, or heavy metals, producing overlapping signal profiles that complicate analyte identification. Their analytical selectivity often remains inferior to that of antibody-, aptamer-, or enzyme-based platforms. This issue is particularly important for food and environmental regulation, where decisions frequently require not only detection of toxicity but also identification and quantification of individual contaminants. The major barriers to commercialization are as follows: (1) insufficient long-term stability of whole-cell sensing elements; (2) poor analyte selectivity arising from the broad-spectrum photosynthetic toxicity response; (3) the absence of validated fabrication protocols for reproducible, field-deployable device production. These weaknesses have contributed to a persistent gap between the conceptual promise of microalgae-based biosensors and their practical adoption in environmental surveillance or food-quality control.

Recent reviews have summarized advances in algal biosensing and related photosynthetic biointerfaces.^11,12 However, a consolidated comparison of microalgae-based biosensor performance remains limited. Accordingly, this narrative review draws on a structured, non-systematic search of the literature to provide a representative, rather than exhaustive, assessment of the field. The present review addresses this gap by: (1) evaluating the mechanistic foundations of algal biosensing, distinguishing whole-cell toxicity-based systems from component-based formats that use isolated photosynthetic proteins, pigments, enzymes, membranes or microalgae-derived biocompounds; (2) assessing optical, electrochemical, and hybrid transduction strategies in relation to analytical sensitivity and selectivity; (3) Examining immobilization strategies, with emphasis on cell viability and operational stability; (4) evaluating emerging use of microalgae-derived nanomaterials for signal amplification; and (5) identifying engineering and biological bottlenecks that must be resolved to advance the platform toward validated, scalable, and field-deployable monitoring platforms.

Microalgae-based biosensors should not be evaluated solely on their capacity to detect contaminants, but on the specificity and decision-relevance of the information they generate. Their primary value may lie not in replacing chromatographic or mass spectrometric techniques, but in complementing them through rapid, biologically meaningful screening of toxic effects, early warning of contamination events, and the prioritization of samples for confirmatory laboratory analysis. Realizing this potential will require rigorous benchmarking, standardized reporting frameworks, advances in strain and device engineering, enhanced integration with data analytics, and thorough validation under realistic environmental and food-system conditions.

Algal biology relevant to biosensing

Pollutant-cell interactions as signal-generating events

Microalgal biosensors exploit biological responses that occur when contaminants interact with the cell surface or alter intracellular metabolism. These interactions occur mainly through two routes: rapid surface biosorption and slower intracellular bioaccumulation or biotransformation. The resulting changes in photosynthetic activity, chlorophyll fluorescence, oxygen evolution, cellular metabolism, and electrochemical behavior form the basis of signal transduction in whole-cell biosensors. Microalgal cell walls contain polysaccharides, proteins, and lipid components. Ionizable functional groups (carboxyl, hydroxyl, amino, sulfate, and carboxylate) can participate in metal binding via electrostatics interactions.^13,14 Heavy-metal interaction with microalgae is typically biphasic. The first phase is a rapid, metabolism-independent surface adsorption during which ions adhere to the cell surface and its functional groups.¹⁵ This phase is modulated by multiple factors, including pH, temperature, metal concentration, and exposure duration.^16,17 In general, cationic ions bind more effectively under alkaline conditions, whereas anionic species show higher affinity under acidic conditions.^18,19 For instance, cadmium ions (Cd²⁺) can be coordinated through two main pathways: an initial exchange of H⁺ within the carboxyl group (type I) or, under prolonged exposure, the formation of carboxylate complexes (type II).²⁰ The second phase involves slower, energy-dependent intracellular transport and chelation by phytochelatins and metallothioneins.^19,21,22 This process is strongly influenced by temperature and cellular metabolic activity, as active transport requires continuous energy input.²³ To counteract oxidative damage generated during heavy metal exposure, microalgae also activate antioxidant enzymes, such as superoxide dismutase (SOD) and catalase (CAT), to eliminate reactive oxygen species (ROS) and maintain cellular metabolism.²¹ These metal-induced physiological shifts have been used in whole-cell biosensors, where changes in chlorophyll fluorescence or related cellular signals provide rapid indicators of heavy-metal or metallic nanoparticle toxicity.^6,24,25

Pesticides also interact with microalgae through biosorption and biodegradation,^26,27 although their signal-generating mechanisms often involve photosynthetic inhibition or enzymatic transformation. Through biosorption, pesticides bind to the microalgal cell walls passively, facilitated by functional groups, such as carboxyl, hydroxyl, and amine moieties.^13,28 In parallel, some microalgae can biodegrade pesticides through enzyme-mediated pathways, converting complex compounds into less toxic derivatives.²⁹ For instance, Chlorella sorokiniana exposed to malathion showed increased carboxylesterase activity, which promoted malathion degradation and phosphate release during metabolic conversion.³⁰

Because many pesticides interfere directly with photosynthetic electron transport, chlorophyll fluorescence has become a widely used readout for microalgal pesticide biosensors. One notable example is the work by Moro et al.,³¹ who employed fluorescence inhibition as a rapid screening tool to detect marine pesticide contamination. In their study, PS II fluorescence responses of several microalgal species were evaluated following exposure to three common marine pesticides (diuron, simazine, and irgarol), which were known to interfere with photosynthetic processes. Although all species exhibited measurable responses, their sensitivities varied slightly, with C. mirabilis showing the highest susceptibility among the tested microalgae.³¹ Such species-specific variation highlights both the potential and the challenge of microalgal biosensors: they can provide sensitive biological detection, but sensor performance depends strongly on strain selection, pollutant class, and exposure conditions. Overall, pollutant-cell interactions, such as surface binding, PS II disruption, and enzyme inhibition, define the informative signal window for microalgal biosensors and are critical for the rational design of robust microalgae-based biosensors for monitoring environmental and food contaminants.

Photosynthesis in microalgae: Biosensing-relevant mechanisms

Microalgae-based biosensors primarily exploit perturbations of the photosynthetic electron transport chain as signal-generating events (Figure 2). In algal cells, chlorophyll pigments are embedded in thylakoid membranes within chloroplasts^32,33 where they form pigment-protein complexes associated with PSI and PSII.³³ Upon light absorption, excitation energy is transferred to the reaction centers of PSI and PS II, initiating charge separation and electron transport.¹¹ In PSII, excitation of the reaction center transfers electron to the primary quinone acceptors Q_A, which is associated predominantly with the D2 reaction-center complex. The electron is subsequently transferred from reduced Q_A to the secondary quinone acceptor Q_B at the D1 protein.³⁴ In essence, absorbed light energy can be used to initiate photosynthesis, dissipate as heat, or emit as chlorophyll a fluorescence. Hence, PSII fluorescence provides a sensitive, non-invasive indicator of photosynthetic performance.^35,36

Figure 2.

Photosynthesis process and electron transport chain in microalgae

Light energy absorbed by antenna chlorophyll-protein complexes is funneled to PSII, where it drives water oxidation, generating O₂, protons, and electrons. Electron transfer from the primary quinone acceptor Q_A to the secondary acceptor Q_B, mediated by the D1 protein, represents the key redox step exploited in most herbicide biosensors.

This mechanism is particularly relevant for detecting PSII-targeting herbicides, such as triazines (atrazine, simazine, and terbutryn), phenylureas (diuron and isoproturon), and diazines (bromacil). Many PSII-targeting herbicides interact with Q_B binding region on D1 protein, preventing electron transfer from Q_A to Q_B and halting linear electron flow.³⁷ This produces two directly transducible signal changes: increased chlorophyll a fluorescence due to reduced photochemical quenching³⁸ and decreased oxygen evolution as photosynthetic electron transport is suppressed. Both responses can correlate with inhibitor concentration within defined ranges, enabling quantitative detection using optical or electrochemical transducers.

However, chlorophyll fluorescence is not a universal toxicity signal. Its response is most directly interpretable for contaminants that interfere with PSII. Compounds targeting PSI (paraquat), inhibiting carbon fixation pathways (e.g., glyphosate)³⁹ or disrupting membrane integrity may produce weak or ambiguous fluorescence responses despite causing substantial cellular damage.³⁹ This selectivity boundary explains the growing interest in dual electro-optical biosensing platforms, where chlorophyll fluorescence is combined with oxygen evolution, electrochemical activity, or metabolic indicators to broaden detection capability.

Heavy metals also interfere with photosynthetic activity, but their effects are often more complex than those of PSII-specific herbicides. Cadmium, for example, has a pronounced toxic effect on the photosynthetic machinery by targeting light-harvesting complexes and both photosystems (PSI and PSII).⁴⁰ Mercury can disrupt both the light-dependent and light-independent reactions, and may compromise pigment integrity through its effects on chlorophyll pigments and photosynthetic proteins.⁴¹ Lead further illustrates the indirect nature of metal toxicity. Rather than acting solely on PSII, lead can inhibit enzymes involved in chlorophyll biosynthesis and the Calvin cycle, thereby reducing photosynthetic efficiency through multiple biochemical routes.^40,42 Consequently, heavy metal biosensing requires careful interpretation, as changes in fluorescence, oxygen evolution, or electrochemical output may reflect combined effects on pigment integrity, electron transport, antioxidant stress, and metabolic activity. These pollutant-induced disturbances provide useful biosensing signals, but robust quantification requires multi-point calibration, appropriate exposure windows and, where possible, complementary optical and electrochemical readouts.

Design principles of algae-based biosensors

Employing microalgae as biosensing elements has been recognized as a cost-effective and environmentally friendly strategy for monitoring environmental pollutants.^2,43,44,45 Designing an effective biosensor requires several critical steps, as illustrated in Figure 1: (1) selecting an appropriate bioreceptor, (2) selecting an appropriate immobilization approach, (3) choosing a compatible transducer, (4) amplifying the generated signal, and (5) validating the device under realistic conditions. The choice of each component influences sensitivity, selectivity, stability, and overall performance.

Biorecognition elements

Whole-cell and enzyme-based biosensors

Whole cell microalgal biosensors use intact algal cells immobilized on a supporting matrix as the biorecognition element. Compared with enzyme-based biosensors, they preserve native photosynthetic and metabolic machinery, which can improve operational stability and enable the detection of diverse pollutant classes with high sensitivity.² Most reported microalgae-based biosensors adopt this format, in which contaminant-induced changes in chlorophyll fluorescence, photosynthetic efficiency, respiration, oxygen evolution, electrical conductivity, electrochemical behavior, or growth are converted into optical or electrochemical signals. In addition to whole-cell physiological responses, several systems measure the activity of enzymes associated with the cell surface. The reported enzymes include alkaline phosphatase (ALP, EC 3.1.3.1),⁴⁶ esterase, and acetylcholinesterase. These enzymes provide in situ biorecognition without enzyme extraction, purification, or supplementation with external cofactors. Their enzymatic activity can be monitored using chromogenic, fluorogenic, conductometric, or amperometric measurements.⁴⁷

Among these reporters, ALP is the most widely used because it transforms phosphorus compounds into inorganic orthophosphate.⁴⁸ Surface-associated ALP activity has been reported in microalgae, such as C. vulgaris^47,49 and Synura uvella.⁵⁰ A representative optical ALP-based format uses methylumbelliferyl phosphate (MUP) as the enzymatic substrate.⁴⁹ ALP converts MUP into methylumbelliferone (MUF), a fluorescent product, and inorganic phosphate. This reaction can be measured either in microplate assays containing C. vulgaris cells or in a flow-through algal biosensor.⁴⁹ Durrieu et al.⁴⁷ extended this principle by combining membrane-bound ALP and esterase activities in C. vulgaris to evaluate the toxicity of freshwater pollutants. The ALP-based response was sensitive to heavy metals, whereas esterase inhibition was associated with organophosphorus pesticides such as methyl paraoxon.⁴⁷ These results demonstrated differential contaminant-class sensitivity, rather than specific identification of an individual heavy metal or pesticide.

Electrochemical enzyme-mediated formats have also been reported. Iravani et al.² developed a chronoamperometric biosensor using immobilized Scenedesmus sp. to detect Cd²⁺ in freshwater. In this system, ALP hydrolyzed p-nitrophenyl phosphate (p-NPP) to p-nitrophenol (p-NP). Inhibition by Cd²⁺ reduced the amperometric signal, allowing quantitative assessment of metal-induced enzyme inhibition. The authors reported an IC₅₀ value of 0.26 ppb and a limit of detection (LOD) of 0.1 ppb under optimized conditions. However, selectivity studies on binary mixtures containing competing divalent metal ions showed interference in the presence of Hg²⁺. The applicability was further validated using freshwater samples, confirming its reliability, reproducibility, and effectiveness for monitoring cadmium contamination in aquatic environments.² Similarly, Chouteau et al.⁵¹ designed a conductometric biosensor using immobilized C. vulgaris. Conductivity changes resulting from ALP and acetylcholinesterase enzyme reactions were measured as separate enzyme endpoints. This dual-enzyme configuration enabled differential responses: ALP was strongly inhibited by heavy metals, whereas acetylcholinesterase was sensitive to carbamate and organophosphorus pesticides.⁵¹

Among enzymatic reporters, ALP is preferred because it catalyzes reactions rapidly, accepts a broad range of substrates, and can be incorporated into analytical platforms with minimal complexity.⁵² Critically, enzyme-mediated whole-cell formats improve functional sensitivity. ALP inhibition is intrinsically non-selective because several heavy metal ions can generate overlapping ALP concentration-response profiles. This enzyme-level promiscuity is the mechanistic root of the selectivity challenge described in Section advances in microalgae-based sensors. Multi-enzyme arrays combining ALP (heavy metal-sensitive), esterase (organophosphate-sensitive), and acetylcholinesterase (carbamate/organophosphate-sensitive) can partially distinguish contaminant classes by exploiting differential inhibition patterns.^47,51 However, cross-reactivity among enzyme-analyte pairs remains a major obstacle to reliable single-analyte identification, particularly in complex environmental or food matrices.

Component-based biosensors: Photosynthetic subcomponents

Component-based biosensors utilize isolated biological elements, such as thylakoid membranes, PS II, reaction centers, chlorophyll-protein complexes, or purified enzymes, as the sensing interface rather than intact cells (Table 1). Most experimental progress in this area has come from higher plants and cyanobacteria rather than isolated components from microalgae. Plant-derived thylakoid membranes, isolated PS II, and chlorophyll extracts from spinach and cyanobacteria have been incorporated into optical, electrochemical, and photoelectrochemical platforms for detecting contaminants.^53,54,56,58 The examples are spinach-derived thylakoid membranes immobilized on screen-printed carbon electrodes for electrochemical monitoring of PS II activity,⁵⁶ as well as the development of a component-based photoelectrochemical biosensor using thylakoid membranes isolated from spinach to detect herbicides diuron and atrazine.⁵⁴ These studies demonstrate that isolated photosynthetic components can retain transduction competence outside the native cell and can be coupled to engineered transducers.

Table 1.

Overview of photosynthetic biological components from higher plants and microorganisms used as sensing elements

Photosynthetic element	Source	Analyte	Immobilization	Transduction mechanisms	Reference
Thylakoid membrane	Spinach	Diuron, Pb2+ and Cd2+	Albumin-glutaraldehyde matrix	Electrochemical measurement of generated photocurrent	Carpentier et al.53
	Spinach	Diuron, atrazine	Polyvinyl alcohol matrix modified with styrylpyridinium groups	Electrochemical detection of hydrogen peroxide	Rouillon et al.54
	Spinacia oleracea L., Senecio vulgaris	Diuron, atrazine, simazine, terbuthylazine	Screen-printed graphite using BSA-glutaraldehyde matrix	Amperometric detection of artificial electron acceptor DQ photoreduction rate	Touloupakis et al.55
	Spinach	Diuron, ioxynil, atrazine, Cd2+	Carbon-based screen-printed electrodes	Electrochemical detection of artificial electron acceptor DQ oxidation	Bettazzi et al.56
Photosystem II	Synechococcus elongatus	Triazine- and phenylurea-type herbicides	Screen-printed graphite electrode	Amperometric detection of photoreduction rate	Koblízek et al.57
Chlorophyll-zinc complex	Brassica oleracea L.	Dissolved oxygen	–	Optical detection based on fluorescence quenching of the chlorophyll-zinc complex by dissolved oxygen	Silva et al.58

Published examples of studies based on the purified or reconstituted photosynthetic subcomponents isolated specifically from microalgae remain comparatively underexplored as active sensing elements. Whole-cell microalgae platforms are increasingly reported and have demonstrated high sensitivity for herbicides, pesticides, or heavy metals under defined laboratory conditions.^59,60 These findings do not indicate that whole-cell platforms are universally more stable or sensitive than component-based systems. Whole-cell formats have progressed further toward integrated prototype devices, while corresponding data for isolated microalgal subcomponents, such as thylakoids, remain comparatively sparse.

Extrapolating from plants and cyanobacteria to microalgae is scientifically well justified, as algae possess conserved photosynthetic electron transport machinery, including thylakoid membranes, PSI, PSII, and associated light-harvesting complexes. However, direct translation requires experimental validation. Microalgal cell disruption must be sufficiently efficient to release photosynthetic components while preserving the structural and functional properties required for sensing. Harsh sonication, bead milling, or chemical extraction can damage photosynthetic complexes, whereas overly gentle disruption methods may yield low recovery and poor reproducibility. In addition, biosensor performance depends on the orientation of isolated membranes on electrode surfaces, access of redox mediators, photostability under repeated illumination, and long-term storage stability.⁵⁶ Some plant-derived biosensing platforms have demonstrated satisfactory stability and performance, but equivalent evidence for microalgae-derived components is not yet available.

A priority for the field is therefore to move beyond feasibility arguments and establish quantitative performance benchmarks for microalgae-derived photosynthetic components. These criteria should include retained PS II activity following extraction, batch-to-batch reproducibility, operational lifetime, storage stability, LOD, response time, and performance in real environmental or food matrices. Without such benchmarks, component-based microalgal biosensors will remain conceptually promising yet difficult to compare with whole-cell systems or established plant-derived biosensing platforms. Importantly, component-based biosensors should not be seen as superior alternatives to whole-cell microalgal sensors. Whole-cell systems retain intrinsic repair and metabolic functions, whereas isolated photosynthetic subcomponents offer a more controlled and mechanistically interpretable interface, particularly for pollutants that directly target photosynthetic electron transport. Future research should therefore focus on identifying conditions under which microalgal subcomponents deliver measurable advantages, such as enhanced reproducibility, faster response times, stronger signal coupling, improved miniaturization, or clearer mechanistic attribution of photosynthesis-disrupting contaminants.

Immobilization techniques

Immobilization of the biological recognition element is critical to biosensor performance, governing analyte access, cell viability, signal stability, and device regenerability.^11,61,62 An effective immobilization strategy must balance a few competing requirements: retention of cellular or enzymatic activity, efficient diffusion of analytes and reaction products, and stable coupling between the biological element and the transducer during repeated measurement cycles.

Passive immobilization is the most used approach. It relies on physical adsorption or entrapment of cells within biocompatible matrices such as calcium alginate, agarose, chitosan, sol-gel silica, or polyvinyl alcohol, without the use of chemical linkers that may compromise viability.⁶¹ These matrices provide mechanical support and provide a three-dimensional, tissue-like microenvironment that facilitates cell growth and proliferation. Despite their ability to mimic native tissue structures, these passive matrices typically are not electronically conductive and do not participate in signal transduction.⁶¹ Membrane thickness and gel porosity are the primary determinants of analyte transport kinetics and must be empirically optimized for each analyte-cell combination.⁶³

Active immobilization employs covalent or affinity-based linkers to anchor cells directly to electrode surfaces, nanostructured supports, or microfluidic substrates. These approaches can reduce diffusion path lengths and improve transducer proximity.⁶¹ The principal risk is membrane disruption by cross-linking reagents (e.g., glutaraldehyde); reagent concentration and exposure time must be optimized to balance attachment efficiency against cell viability. Recent advances in immobilization techniques favor hybrid electrode-matrix configurations over direct covalent cell attachment to enhance biocompatibility, stability, and performance. For instance, immobilization of Chlamydomonas reinhardtii whole cells onto paper-based screen-printed electrodes enhanced with carbon black nanomaterials demonstrated excellent functionality in standard solutions and real water samples, achieving very low detection limits in the pico- to nanomolar range, offering high repeatability, retaining operational stability for up to 10 h, and showing good storage stability for approximately three weeks.⁶⁴ Carbonized lignin has also been evaluated as a bio-derived electrode material for microalgal biosensing. In this system, Chlamydomonas reinhardtii was immobilized onto carbonized-lignin screen-printed electrodes and incorporated into a photoelectrochemical transducer for detecting nanoformulated atrazine. The biosensor also maintained operational stability for approximately 10 h.⁶⁵ Overall, the choice of immobilization strategy involves a trade-off between cell viability, mass transport efficiency, signal transduction, and operational stability, and remains one of the most important factors governing the overall performance of algae-based biosensors.

Microfluidic and lab-on-a-chip integration

Recent work has also shifted toward lab-on-a-chip (LOC) microfluidic platforms, which bring the bioreceptor into closer contact with the analyte while minimizing physical barriers, thereby improving detection performance.¹¹ Advances in microfluidic engineering have enabled the integration of sampling, detection, reagent storage, and pretreatment into compact LOC devices. These systems support portable, user-friendly, and real-time analyses, and can also be integrated with IoT-enabled tools for remote environmental monitoring. LOC technologies are especially well-suited for routine field screening of agrifood and environmental samples, reducing the need for laboratory-based testing.⁶⁶

Microfluidic platforms, typically composed of networks of channels and microchambers, offer advantages such as enhanced specificity, reduced reagent consumption, and the ability to combine multiple operational units in a single device¹⁰ (Figure 3). Common fabrication approaches include continuous-flow, droplet-based, and digital microfluidics, many of which are produced via soft lithography.⁷ Continuous-flow systems rely on controlled fluid movement through microscale channels, whereas droplet-based designs minimize sample volumes by creating isolated reaction compartments. Digital microfluidics represents the newest generation, enabling precise manipulation of discrete droplets without pumps or valves, thereby providing high accuracy and low sample requirements.^7,8,71,72,73 Lefèvre et al.⁸ developed a miniaturized fluorescent-based algae sensor by integrating an organic light emitting diode (LED), an organic photodetector (OPD), and custom optical filters into a (poly)dimethylsiloxane (PDMS) microfluidic chip. Chlorophyll fluorescence from Chlamydomonas reinhardtii was used to evaluate photosynthetic inhibition by diuron, which showed 50% photosynthetic inhibition at concentrations as low as 11 nM.⁸ Next, a soft-lithography-fabricated microfluidic device hosting Chlamydomonas reinhardtii contained five reaction chambers connected by microchannels and integrated into an optoelectronic module. This platform demonstrated fast, multiplex pesticide detection in the nanomolar range and simultaneously monitored oxygen evolution, carbon dioxide consumption, and algal fluorescence.⁷⁴

Figure 3.

Schematic representation of examples of microalgal immobilization techniques

(i) Xurography-based fabrication procedure of a microfluidic chip with algae inserted by capillarity for algal immobilization in a portable station.⁶⁷ Reproduced with permission. Copyright 2018, Elsevier. (ii) Immobilization of microalgae between two filter membranes: black mixed cellulose ester and Omnipore.⁶⁸ (iii) Microarray chip featuring an in situ-printed microgel trap for cell immobilization.⁶⁹ Reproduced with permission. Copyright 2023, Elsevier. (iv) Immobilization on a Ta₂O₅ ion-sensitive field-effect transistor.⁷⁰ Reproduced with permission. Copyright 2025, Elsevier.

Bartholomeusz et al.⁷⁵ proposed xurography as a more accessible and streamlined alternative for fabricating microfluidic channels and chips, offering a pathway that bypasses many of the limitations associated with traditional methods.⁷⁵ Xurography, which uses thin films and cutter plotters, offers a simpler, low-cost, and scalable alternative. Xurographic devices offer high resolution, optical transparency, and gas permeability, and have already been used to develop disposable microfluidic chips incorporating LEDs for reading algal chlorophyll fluorescence. Such devices have been successfully tested in real environmental samples, including urban and hospital wastewater, demonstrating their potential as early warning systems for aquatic pollution.^67,76 Gosset et al. developed a disposable microfluidic algal biosensor based on xurography for monitoring urban water pollution. Prior to application, key operational parameters, including algal cell density and light intensity, were optimized using the PS II-inhibiting herbicide diuron as a model toxicant. The biosensor exhibited species-dependent sensitivity, with C. reinhardtii demonstrating excellent analytical performance, characterized by the lowest LOD and EC₅₀ values.⁶⁷

In summary, microfluidic architectures improve sample handling and miniaturization, yet introduce new challenges associated with long-term cell maintenance and device fouling. A critical priority for the field is therefore to report immobilization performance using standardized metrics, including cell viability after immobilization, retained photosynthetic activity, analyte diffusion time, signal drift, operational lifetime, storage stability, regeneration capacity, and reproducibility across independently fabricated devices.

Transduction in microalgae-based biosensors

For biosensing applications, microalgae interact with target analytes, including heavy metal ions, pesticides, herbicides, and other contaminants. The interaction induces biochemical responses that underpin the sensing mechanism, which are subsequently translated into measurable outputs. Using optical or electrochemical transducers, these alterations can be detected as variations in fluorescence, current, potential, or other physicochemical signals.¹²

Optical transducers

Optical biosensors exploit changes in chlorophyll a fluorescence from PSII-inhibited algal cells as the primary analytical signal (Table 2; Figure 4). A source-study list and data-extraction sheet supporting the information compiled in Tables 2 and 3 are provided in supplemental information (Tables S1 and S2). Fluorescence assays are non-destructive and can be measured using organic LEDs and photodetectors.^25,91 For optical biosensors, the selected responsive materials are typically those that exhibit distinctive UV-vis absorption, infrared responses, or fluorescence characteristics.⁹² An optical biosensor based on C. vulgaris was developed to detect herbicides in aquatic environments by monitoring chlorophyll fluorescence emission, corresponding to chlorophyll b absorption and the PS II fluorescence band. Exposure to PS II-inhibiting herbicides, including diuron, atrazine, simazine, and isoproturon, resulted in increased fluorescence intensity due to disruption of photosynthetic electron transport. The C. vulgaris-based biosensing platform was effective for detecting PS II inhibitors and was also applicable to detect other herbicides, such as dinitro-o-cresol (DNOC), a poor inhibitor of PSII electron transport.⁶⁰ Rathnayake et al.⁴⁴ created an optical whole-cell biosensor using the microalgae Scenedesmus subspicatus MM1, immobilized within inorganic silica hydrogels, to measure bioavailable Cd²⁺, Cu²⁺, and Zn²⁺ in freshwater environments. Chlorophyll a fluorescence was monitored at excitation and emission wavelengths of 680 and 440 nm, respectively, over a 96 h incubation period. The limits of detection for the system were calculated as 9.0 × 10⁻¹ mg/L for Cd, 9.1 × 10⁻¹ mg/L for Cu, and 8.8 × 10⁻¹ mg/L for Zn, as well as maintained its functionality for up to 4 weeks of storage.⁴⁴

Table 2.

Overview of microalgae-integrated optical biosensors

Microalgae	Biological recognition element	Analyte	Immobilization	Limit of detection	Linear range	Reference
Scenedesmus subspicatus MM1	Whole cell	•Cadmium •Copper •Zinc	Sol–gel matrices	Cd2+: 900 μg/L Cu2+: 910 μg/L Zn2+: 880 μg/L	Cd: 50–49,000 μg/L Cu: 50–49,000 μg/L Zn: 50–100,000 μg/L	Rathnayake et al.44
Scenedesmus acutus and Pseudokirchneriella subcapitata	Whole cell	•Glyphosate •Copper •Chromium •Atrazine	1% calcium alginate beads	Technical-grade glyphosate: 10000 μg/L Glyphosate-based formulations: 15000 μg/L Atrazine: 50 μg/L Copper: 7500 μg/L Chromium: 250 μg/L	Not reported	Prudkin-Silva et al.43
Chlorella vulgaris	Whole cell	•Cadmium •Lead	Detachable membrane immobilization (optical fiber tip)	Not reported	Cadmium and lead: 10–1,000 μg/L	Durrieu et al.49
Chlorella vulgaris	Whole cell	•Titanium dioxide •Silver nanoparticles	1% agarose matrix (w/v)	1 μg/L	Not reported	Thenarasu et al.24
Microalgae (Chlorella vulgaris) and ciliate (Tetrahymena pyriformis)	Whole cell	•Simazine •Atrazine •Diuron	Calcium-alginate gel, and custom microfluidic cartridges	Herbicide mixture: 0.13 μg/L Free Simazine: 1.35 μg/L Atrazine: 0.44 μg/L Diuron: 0.25 μg/L Immobilized Simazine: 1.55 μg/L Atrazine: 0.89 μg/L Diuron: 0.39 μg/L	Simazine: 1–100 μg/L Atrazine: 1–60 μg/L Diuron: 1–40 μg/L	Turemis et al.77
Chlorella vulgaris and Pseudokirchneriella subcapitata	Whole cell	•Diuron •Atrazine •Isoproturon	Alginate-silica hydrogel	10 μg/L (C. vulgaris exposed to diuron)	Not reported	Gosset et al.78
Chlorella vulgaris, Pseudokirchneriella subcapitata, and Chlamydomonas reinhardtii	Whole cell	•Diuron	Physical entrapment within microfluidic chambers	P. subcapitata: 1 μg/L C. vulgaris: 10 μg/L C. reinhardtii: 1 μg/L	Not reported	Gosset et al.67
Chlorella vulgaris, Chlamydomonas sp., Pseudokirchneriella subcapitata, Tetraselmis cordiformis, Eudorina elegans, Staurodesmus convergens, Synechocystis sp., and Scherffelia dubia	Whole cell	•Atrazine •Simazine •Diuron •Isoproturon •Paraquat	Cellulose-supported membrane filters	Not reported	Atrazine 1–50 μg/L: C. vulgaris, T. cordiformis, E. elegans, S. convergens 2–50 μg/L: Chlamydomonas sp., P. subcapitata, Synechocystis sp., S. dubia Diuron 0.1–10 μg/L: C. vulgaris, P. subcapitata 0.1–5 μg/L: E. elegans, Synechocystis sp., S. dubia, S. convergens, Chlamydomonas sp. 0.1–2 μg/L: T. cordiformis Simazine 5–100 μg/L: S. dubia, Chlamydomonas sp. 2–100 μg/L: C. vulgaris, T. cordiformis, Synechocystis sp. 1–100 μg/L: P. subcapitata, S. convergens, E. elegans Paraquat 2–50 μg/L: S. dubia Isoproturon 0.1–10 μg/L: C. vulgaris, E. elegans, S. convergens, T. cordiformis 0.5–10 μg/L: Synechocystis sp., S. dubia 2–10 μg/L: Chlamydomonas sp. 2–50 μg/L: P. subcapitata	Podola et al.79
Pseudokirchneriella subcapitata	Whole cell	•Diuron •Simetryn •Simazine •Atrazine	Dual-layer membrane biofilm (mixed cellulose ester and Omnipore®)	Diuron: 1 μg/L Simetryn, simazine, and atrazine: 10 μg/L	Not reported	Kashem et al.68
Platymonas subcordiformis, Platymonas helgolandica var. tsingtaoensis, Isochrysis galbana, and Isochrysis zhanjiangensis sp.nov.	Whole cell	•Copper •Lead •Phenol	No immobilization used (Cell suspension)	Copper: 41.30 μg/L Lead: 393.70 μg/L Phenol: 268214 μg/L Nonylphenol: 1150.20 μg/L	Not reported	Han et al.80
Dictyosphaerium chlorelloides	Whole cell	•Simazine	Porous silicone films	12 μg/L	50–800 μg/L	Haigh-Flórez et al.81

Figure 4.

Various optical microalgal biosensor configurations for pollutants and herbicide detection

Schematic illustration of (i) the microalgal dual-head fiber-optic biosensor, which uses a two-chamber aluminum flow cell containing microalgae membranes illuminated by a blue LED through a sapphire windowOxygen is measured using a luminescent O₂-sensing film based on phase-sensitive detection.⁸¹ Reproduced with permission. Copyright 2014, Elsevier. (ii) Optical sensor-based naked-eye observation of the response of P. subcapitata to increasing pollutant concentrations (left to right): (A) technical-grade glyphosate acid, (B) glyphosate-based formulation, (C) atrazine, and (D) chromium⁴³ (Reproduced with permission. Copyright 2021, Elsevier).

Table 3.

Overview of electrochemical biosensors developed using microalgae as the sensing element

Microalgae	Biological recognition element	Analyte	Transduction	Immobilization	Limit of detection	Linear range	Reference
Scenedesmus sp. MN738556	Whole cell	•Cadmium	Chronoamperometry	BSA matrix cross-linked by glutaraldehyde	0.1 μg/L	10 to 1000 μg/L	Iravani et al.2
Arthrospira (Spirulina) platensis	Whole cell	•Cadmium •Mercury	Conductometry	Ceramic part of gold interdigitated transducer	Cadmium: 1.12 × 10−12 μg/L Mercury: 2.01 × 10−12 μg/L	Not reported	Tekaya et al.82
Chlorella vulgaris	Whole cell	•Mercury •Chromium •Cadmium •Lead •Arsenic	Oxygen evolution measurement	No immobilization used	Not reported	Not reported	Eom et al.83
Chlorella vulgaris	Whole cell	•Cd2+ •Zn2+ •Pb2+ •Paraoxon-methyl •Carbofuran	Conductometry	Glutaraldehyde-crosslinked bovine serum albumin matrices on interdigitated gold electrodes	Cd 2+, Zn 2+:10 μg/L	Not reported	Chouteau et al.51
Chlorella sp.	Whole cell	•Acephate •Triazophos •Malathion •Chlorpyrifos	Potentiometry	Ta2O5 Ion-Sensitive Field Effect Transistor (ISFET) electrode	Acephate and triazophos: 0.018 μg/L, 0.031 μg/L.	Acephate: 0.0183–1.83 × 106 μg/L Triazophos: 0.0313– 3.13 × 106 μg/L	Patial et al.70
Chlorella pyrenoidosa	Whole cell	•Atrazine •Propazine	Amperometry	Magnetic-assisted screen-printed electrodes	Atrazine: 151 μg/L Propazine: 91.9 μg/L	Atrazine: 1.94 × 102–1.60 × 104 μg/L Propazine: 1.38 × 102–2.76 × 104 μg/L.	Zamaleeva et al.84
Chlorella vulgaris	Whole cell	•Acephate •Triazophos	Amperometry	Glassy carbon electrodes	Not reported	Acephate: 18.3–1.83 × 106 μg/L Triazophos: 0.313–3.13 × 106 μg/L	Patial et al.85
Chlorella vulgaris	Whole cell	•Atrazine •DCMU •Toluene •Buenzene	Amperometry	Alginate/polyion on indium tin oxide electrode	Atrazine Alginate gel: 215.68 μg/L; Polyion complex: 10.78 μg/L	Not reported	Shitanda et al.86
Chlorella vulgaris	Whole cell	p-nitrophenylphosphate (pNPP)	Amperometry	Alginate gel or pyrrole-alginate matrix	Not reported	Not reported	Ionescu et al.59
Scenedesmus acutus and Monoraphidium contortum	Whole cell	Atrazine	Amperometry	Screen-printed graphite electrode with polyelectrolyte-surfactant-carbon nanotube matrix	Monoraphidium contortum: 23.7 μg/L Scenedesmus acutus: 293.3 μg/L	Not reported	Boron et al.87
Chlorella sp.	Whole cell	•Chromium •Cadmium •Manganese •Iron	Electrochemical measurement of photocurrent	Physical entrapment within a microfluidic chamber	Cadmium, iron, chromium, and manganese: 5.62, 2.79, 2.60, 2.75 μg/L.	Not reported	Roxby et al.1
Chlorella vulgaris	Whole cell	•Cadmium •Zinc •Methyl paraoxon	Conductometry	Glass microfiber filter (GF/C membrane)	Methyl paraoxon: 10 μg/L	Not reported	Durrieu et al.47
Chlamydomonas reinhardtii UV180 mutant	Whole cell	Nanoformulated atrazine	Amperometry	Calcium/alginate matrix	Atrazine-zein: 0.194 μg/L Atrazine-PCL-Ch: 0.237 μg/L	4.3–647 μg/L.	Antonacci et al.65
Chlamydomonas reinhardtii	Whole cell	Diuron	Chronoamperometry	Physical confinement within microfluidic LOC	23.3 μg/L	Not reported	Tsopela et al.88
Chlamydomonas reinhardtii	Whole cell	•Linuron •Simazine	Chronoamperometry	Alginate gel entrapment	Linuron: 1.49 μg/L Simazine: 18.15 μg/L	Not reported	Husu et al.89
Chlorella vulgaris, Chlamydomonas reinhardtii, and Pseudokirchneriella subcapitata	Whole cell	•Diuron	Optical-electrochemical hybrid system	LOC fluidic system	Not reported	23–233 μg/L	Sekli Belaïdi et al.90

Apart from chlorophyll fluorescence measurements, optical biosensors can also detect enzyme-generated products, where contaminant-induced enzymatic reactions produce measurable colorimetric or fluorescent signals. Durrieu and Tran-Minh⁴⁹ utilized C. vulgaris to develop a biosensor to detect heavy metals, in which the microalgal cells were immobilized onto detachable membranes positioned in front of an optical fiber bundle housed within a custom-designed microcell.⁴⁹ Singh et al.⁶ also reported the development of a chlorophyll fluorescence-based biosensor employing C. vulgaris to detect heavy metal contamination in aquatic environments. Chlorophyll a fluorescence was monitored (440/680 nm excitation/emission) over a 96 h exposure period, revealing a concentration- and time-dependent increase in fluorescence intensity with increasing metal stress. C. vulgaris exhibited a pronounced fluorescence response to heavy metal stress, with increases in Kautsky fluorescence of 67.82% for Cr(VI), 67.10% for Cd(II), and 35.27% for Hg. Exposure to mixtures of heavy metals induced stronger fluorescence responses than single-metal treatments, suggesting synergistic toxic effects arising from enhanced oxidative stress, altered metal uptake, and disruption of cellular homeostasis, highlighting its potential for monitoring heavy-metal contamination in aquatic system.⁶ Another study demonstrated the fabrication of a biosensor using C. vulgaris to detect metal contaminants in water. The sensing strategy utilized changes in the microalgal bioluminescence spectrum (350–650 nm) in response to exposure to heavy metals (Cu, Pb, and Cd) and light metals (Na, Al, and Li). The biosensor was capable of detecting metal concentrations ranging from 0.001 to 10,000 nmol L⁻¹ within 15 min of exposure.⁹³

Photosynthetic oxygen evolution can also be measured optically. A microalgae-based optical biosensor was developed using immobilized Pseudokirchneriella subcapitata integrated with a fluorescence oxygen-sensing microarray for pesticide detection. The sensing platform employed ruthenium-containing fluorescent spots fabricated on modified glass substrates, enabling indirect monitoring of photosynthetic oxygen evolution. As dissolved oxygen produced by the microalgae quenched the fluorescence signal, inhibition of photosynthetic activity by pesticides could be quantified through changes in fluorescence intensity. The biosensor exhibited high sensitivity toward PSII-targeting herbicides, particularly diuron, with a detection limit of 1 ppb, while simetryn, simazine, and atrazine were detected at concentrations as low as 10 ppb. In contrast, mefenacet and pendimethalin produced minimal responses.⁶⁸

Fluorescence-based microalgal biosensors do not require an additional external reporter agent. Because these systems can be coupled with fiber-optic components, they benefit from advantages such as reduced electrical noise, safer operation in wet environments, and easier miniaturization. When a pollutant specifically interferes with PS II, fluorescence measurements often achieve lower detection limits than oxygen-based approaches. However, they are subject to four key limitations: (1) signal stabilization delays of up to 30 min before steady-state baseline is achieved^39,60,81; (2) optical matrix interference in turbid or colored samples (inner filter effect)⁸¹; (3) reduced sensitivity to analytes acting downstream of PSII or on non-photosynthetic targets; and (4) susceptibility to ambient light fluctuations in field settings.³⁹ These limitations must be explicitly quantified and addressed in any field-deployable optical algal biosensor.

Electrochemical transducers

Electrochemical biosensors operate by converting biological effects into quantifiable electrical signals. Electrochemical sensors are attractive due to their simple operation, low cost, fast analytical response, and high sensitivity and selectivity for detecting target analytes. Electrochemical biosensors differ in how they translate biological activity into electrical signals. Conductometric sensors detect changes in the electrolyte’s electrical conductivity resulting from the production or consumption of ionic species during cellular metabolic activity. Potentiometric sensors measure changes in electrochemical potential at the sensing interface relative to the target analytes under zero-current conditions. In contrast, amperometric sensors monitor variations in current generated from the oxidation or reduction of electroactive species at the electrode surface. During measurement, a fixed potential is applied between the working and reference electrodes, and the resulting current is proportional to the concentration of the electroactive analyte or to metabolite production by the biological recognition element. Table 3 summarizes representative algae-based electrochemical biosensors reported in the literature. It is evident that most electrochemical biosensors are based on oxygen evolution monitoring during photosynthesis^83,88,89,90 or enzyme inhibition mechanisms (Figure 5).^2,47 When target analytes interact with microalgal cells, they inhibit enzymatic pathways, suppress metabolic and respiratory activity, and disrupt intracellular electron transfer processes. These disturbances lead to detectable changes in the electrochemical current.^{94,95,96,97,98} However, conventional electrochemical sensors, particularly oxygen sensors, are prone to fouling and require regular maintenance, which restricts their long-term and in situ applicability.⁹⁹ For this reason, optical oxygen transducers are often preferred: they resist contamination, require minimal calibration, and can be readily miniaturized, making them more suitable for continuous monitoring in environmental and biological systems.¹⁰⁰ Electrochemical biosensors are broadly categorized into three main types based on their mode of signal transduction, which are potentiometric, conductometric, and amperometric or voltammetric detection.

Figure 5.

Electrochemical microalgal biosensor platforms for monitoring and detection of pesticide and pollutants

(i) Design and development of a microalgae biosensor chip integrated with a microarray oxygen sensor for pesticide sensing.⁶⁸ (ii) Photosynthetic microalgae biosensor with nanocavity where Chlorella sp. is placed in a microfluidic chip with copper tape electrodes and excited by violet light, allowing metal ions to be detected through generated photocurrents. A Fabry-Perot-like nanocavity formed between copper nanoparticles and the copper film enhances plasmon coupling and photocurrent signals.¹ Reproduced with permission. Copyright 2020, Elsevier. (iii) Control-engaged DMF sensor technology that consists of a DMF diluter chip, actuation electronics, and a detector for programmable droplet manipulation and sensing. The system enables droplet splitting, dilution, and fluorescence-based analysis, while microalgae movement is tracked using image analysis.⁸⁰ Reproduced with permission. Copyright 2019, Elsevier. (iv) Schematic illustration of an amperometric biosensor utilizing Chlorella sp. for the detection of organophosphorus pesticides.⁸⁵ Reproduced with permission. Copyright 2025, Elsevier.

Potentiometric transducers

Potentiometric sensors measure open-circuit potential at the algal cell-modified electrode relative to a reference under zero-current conditions. Signals can arise from enzyme-reaction-generated ionic changes (e.g., proton release during ALP-catalyzed phosphate hydrolysis) or metabolic perturbation-induced shifts in extracellular ion activity. The potentiometric transducers that are commonly employed are ion-selective electrodes (ISEs) and ion-sensitive field-effect transistors (ISFETs).

Potentiometric biosensors typically operate through two main detection principles: (1) Changes in ionic concentration generated by enzymatic reactions within the microorganism, where the analytes may serve as a substrate for the enzyme or act as an inhibitor, altering proton or ion release in the microenvironment adjacent to the electrode surface; (2) Alterations in cellular metabolism or receptor activity which can manifest as shifts in extracellular acidification rates. ISFETs function as transducers that convert ion-induced changes in surface potential into electrical signals, typically through shifts in threshold voltage or drain current. Analyte specificity is not an intrinsic property of the transistor but is achieved through the incorporation of selective sensing layers or biological recognition components. ISFETs have become increasingly popular as transducers in potentiometric biosensing due to their high sensitivity, and compatibility with miniaturized, portable systems.⁷⁰ Patial et al.⁷⁰ developed a portable potentiometric biosensor based on Chlorella sp. immobilized onto a Ta₂O₅-gated ISFET for the detection of organophosphorus pesticides. The sensing mechanism was based on the inhibition of ALP activity. When ALP is inhibited, the enzymatic conversion of 2-phospho-L-ascorbic acid to ascorbic acid is suppressed, leading to measurable changes in proton release, which serve as the analytical signal. Changes in proton concentration alter the surface potential of the Ta₂O₅ gate, resulting in corresponding changes in the threshold voltage and output signal of the ISFET. The biosensor demonstrated exceptionally high sensitivity, achieving detection limits as low as 10⁻¹⁰ M for acephate and triazophos, with linear responses spanning 10⁻¹⁰ to 10⁻² M. Malathion produced lower sensitivity and inconsistent response trends.⁷⁰

Amperometric transducers

Amperometric sensors apply a fixed potential and monitor the resulting steady-state current over time from the oxidation or reduction at the working electrode. In microalgae biosensor, the measured current may arise through several mechanisms, including oxygen reduction current, mediator-assisted current or direct photocurrent generation. Specifically, oxygen-reduction currents originate from the electrochemical reduction of dissolved oxygen produced during photosynthesis, mediator-assisted currents arise from electron transfer mediated by artificial redox species, whereas direct photocurrents result from the transfer of photosynthetic electrons from the microalgal cells to the electrode surface. Changes in the recorded current reflect the expansion or contraction of the diffusion layer adjacent to the electrode surface, providing time-dependent information about the analyte concentration. Amperometric biosensors typically monitor biological processes. These include oxygen consumption or evolution associated with cellular respiration or photosynthesis, the production or depletion of specific metabolites formed during the biotransformation of an analyte.¹⁰¹

Zamaleeva et al.⁸⁴ developed a whole-cell amperometric herbicide biosensor using C. pyrenoidosa cells functionalized with magnetic nanoparticles. The magnetized microalgae were retained on a screen-printed electrode using a permanent magnet, forming a stable, and reversible sensing layer. The biosensor measured ferricyanide reduction currents (mediator-assisted amperometric currents) under alternating light and dark cycles. It successfully detected atrazine (0.9–74 μM) and propazine (0.6–120 μM), with detection limits of 0.7 and 0.4 μM, respectively. Amperometric measurements were optimized at −600 mV, where photosynthetic current differences were greatest, and oxygen-reduction interference was minimal. Light exposure produced rapid current responses, stabilizing within seconds and remaining consistent for extended periods. Slightly longer stabilization times than those of LED-based systems were attributed to weaker daylight illumination.⁸⁴

The study by Patial et al.⁸⁵ also reported an amperometric biosensor based on Chlorella sp. that detects organophosphorus pesticides, which was attributed to direct transfer of photosynthetic electrons from the microalgal cells to the electrode surface. The PS II inhibition reduced the flux of electrons to the electrode, resulting in a proportional decrease in the recorded amperometric signal and providing the basis for analytical detection. The biosensor was evaluated in two configurations, suspended cells and cells immobilized on glassy carbon electrodes, using chronoamperometry at −0.7 V under continuous white LED illumination to sustain PS II activity. In the suspended-cell format, acephate decreased current while triazophos increased it, each showing linear behavior from 10⁻⁸ to 10⁻² M. After immobilization via glutaraldehyde cross-linking, both analytes produced consistent current decreases, with linear ranges of 10⁻⁷ to 10⁻² M for acephate and 10⁻⁹ to 10⁻² M for triazophos.⁸⁵

In contrast, the Scenedesmus-based electrochemical biosensor reported by Iravani et al.² also relied on enzyme-based chronoamperometry, where the current generated by ALP activity was continuously monitored over time. Unlike the photosynthesis-based amperometric systems discussed earlier, the measured current in this case originated from enzyme-catalyzed reactions rather than oxygen reduction, mediator-assisted electron transfer, or direct photocurrent generation. Exposure to Cd²⁺ inhibited ALP activity, resulting in a measurable decrease in the chronoamperometric current. The magnitude of current suppression was proportional to cadmium concentration. Under optimized conditions, the biosensor achieved an IC₅₀ value of 0.26 ppb and LOD of 0.1 ppb. The chronoamperometric approach offers several advantages, including rapid signal acquisition, simple instrumentation, high sensitivity, and suitability for real-time and on-site environmental analysis. Lastly, Tsopela et al.⁸⁸ constructed microfluidic lab-on-chip electrochemical biosensor for herbicide monitoring based on the photosynthetic activity of microalgae. The sensor employed chronoamperometry to measure oxygen-reduction currents arising from dissolved oxygen produced during photosynthesis. Exposure to the herbicide diuron suppressed photosynthetic oxygen evolution, resulting in a concentration-dependent decrease in the chronoamperometric signal. The device achieved a detection limit of approximately 0.1 μM diuron and was capable of analyzing small sample volumes without extensive sample preparation.⁸⁸

Conductometric transducers

Conductometric biosensors measure electrolyte conductance changes arising from enzymatic production or consumption of ionic species within the immobilized cell layer.¹⁰² A representative example is the bi-enzymatic whole-cell conductometric biosensor developed by Chouteau et al.⁵¹ C. vulgaris cells were immobilzed within cross-linked bovine serum albumin films and deposited on interdigitated electrodes. Sensor responses were based on conductivity changes resulting from ALP and acetylcholinesterase-catalyzed reactions. The platform enabled detection of heavy metals via ALP inhibition and organophosphate pesticides via acetylcholinesterase inhibition. High sensitivity was observed for Cd²⁺ and Zn²⁺, with detection limits of 10 ppb after short exposure periods, whereas Pb²⁺ showed minimal interference. The biosensor also demonstrated rapid response times, good reproducibility, and analytical performance comparable with that of conventional bioassays, despite substantially reduced analysis time. Although the two enzyme responses improved discrimination between contaminant classes, cross-reactivity, and interference from the sample’s background conductivity limited identification of individual analytes.⁵¹

In a nutshell, electrochemical microalgal biosensors are often best regarded as platforms for measuring pollutant-induced physiological or enzymatic effects. By exploiting changes in photosynthetic electron transport, enzyme activity, oxygen evolution, or cellular metabolism, these biosensors provide an integrated assessment of contaminant toxicity at the biological level. Their high sensitivity, rapid response, low cost, and compatibility with miniaturized and portable devices make them attractive tools for environmental monitoring. However, achieving high analyte specificity remains a challenge. Future developments should focus on advanced immobilization strategies, nanomaterial-enhanced electrodes, microfluidic integration, and multi-species sensing platforms to improve selectivity, stability, and real-time monitoring capabilities.

Dual electrochemical-optical transduction

Since optical fluorescence responses are maximally sensitive to PSII-acting analytes while electrochemical monitoring of photosynthetically generated dissolved oxygen reflects overall photosynthetic activity, integrating both signals in a single platform provides complementary, corroborating information about microalgal physiological responses, thereby improving the reliability of analyte detection. Antonacci et al.⁶⁴ demonstrated this principle with a dual electro-optical Chlamydomonas reinhardtii biosensor immobilized on carbon black-modified screen-printed electrodes. The device achieved simultaneous fluorescence and amperometric diuron detection, characterized by high sensitivity (pico-/nanomolar detection limits), strong repeatability (RSD of 5% for n = 100 measurements), long-term operational performance (10 h), and storage stability of up to 3 weeks.⁶⁴ Belaïdi et al. investigated an LOC device integrating three-electrode electrochemical microcells (ElecCell) with OPDs to detect the herbicide Diuron. The electrochemical component monitors oxygen evolution, whereas the optical component measures chlorophyll fluorescence. Using three microalgal species (Chlamydomonas reinhardtii, Pseudokirchneriella subcapitata, and C. vulgaris), this multimodal microsensor platform measured changes in chlorophyll fluorescence and oxygen-evolution rates. Exposure to the PSII-inhibiting herbicide diuron resulted in increased fluorescence emission and reduced oxygen evolution, with detectable responses at nanomolar concentrations.⁹⁰

Overall, dual electro-optical configurations represent a major step forward for algae-based biosensing. Their complementary measurement capabilities improve diagnostic confidence and expand field-deployment potential, paving the way for more versatile, sensitive, and scalable environmental monitoring technologies. Future studies should therefore demonstrate whether the additional transduction channel produces a statistically significant improvement in sensitivity, contaminant classification or resistance to matrix interference compared with either measurement alone.

Microalgae-derived nanomaterials for biosensor enhancement

Nanomaterials in general are widely employed in biosensor development owing to their high surface area, excellent electrical conductivity, and unique optical, electronic, and catalytic properties. These characteristics facilitate the immobilization of biorecognition elements and enhance signal transduction, thereby improving the sensitivity and overall analytical performance of biosensors. Microalgae synthesize metal nanoparticles using endogenous biomolecules (polyphenols, proteins, pigments, polysaccharides) as reducing, capping, and stabilizing agents, enabling eco-friendly production of nanoparticles (silver, gold, copper, and zinc oxide), and carbon-based nanomaterials (Figure 6).^{103,104,105,106,107,108,109} A growing number of microalgae species, including Chlorella sp., Desmodesmus sp., Tetraselmis sp., etc., have been explored for the synthesis of metallic or metal oxide nanoparticles with notable antimicrobial and functional properties for various technological applications (Table S3). As shown in Table S3, most metal nanoparticles synthesized from microalgae have been predominantly investigated for medicinal applications, particularly for their antibacterial properties, while their use in biosensing applications remains comparatively limited. Microalgae-mediated nanoparticle formation generally involves three interconnected steps: nucleation, particle growth and stabilization, and size or shape control. These processes are facilitated by a wide range of biomolecules naturally present in microalgae cells, which act as reducing, capping, and stabilizing agents, such as polyphenols, proteins, enzymes, pigments, polysaccharides, antioxidants, and phenolic compounds, which act collectively to convert metal ions into stable nanosized particles.^110,111,112

Figure 6.

Overview of microalgae-mediated nanoparticle synthesis mechanisms

Biological nanoparticles synthesis method involving microalgae in which metal ions act as the precursor and interact with microalgal extracts. Microalgae-based biomolecules act as natural reducing, stabilizing, and capping agents, enabling the controlled formation of metal or metal-oxide nanoparticles.

Microalgae can generate nanoparticles either intracellularly or extracellularly. Intracellular synthesis occurs when positively charged metal ions bind to the algal cell wall or enter the cytoplasm, where reductase enzymes, using NADH or NADPH as electron donors, convert them into metallic nuclei that can transform into nanoparticles.^113,114 Particle size, shape, and localization depend on metal speciation, pH, exposure concentration, incubation time and the physiological state of the cells. On the other hand, extracellular synthesis takes place outside the cell or cell-free extracts, driven by metabolites, pigments, enzymes, nucleic acids, and other biomolecules secreted by the algae. Algal biomass is usually washed or homogenized prior to extraction, and extracellular proteins often play a central role in metal ion reduction.^113,114 for the synthesis of quantum dots. Singh et al.¹¹⁵ synthesized nitrogen- and phosphorus-doped carbon dots (A-NPCDs) from Dunaliella salina using a green hydrothermal approach. The A-NPCDs demonstrated strong fluorescence and effectively detected Hg (II) and Cr (VI) at highly sensitive levels, with an LOD of 0.018 μM, below drinking-water limits. They were also biocompatible, enabling successful live-cell imaging and intracellular detection of these metal ions, highlighting their potential as alternatives to chemically derived carbon dots (CDs).¹¹⁵ Another study by Zhang et al.¹¹⁶ demonstrated that microalgae cells can be used as natural bioreactors to immobilize cadmium ions and biosynthesize CdSe quantum dots through a simple, eco-friendly, and cost-effective process. The resulting CdSe QDs displayed excellent optical characteristics, high crystallinity, good water solubility, and uniform particle sizes of approximately 5–6 nm. Due to their strong fluorescence-quenching properties, these biosynthesized QDs functioned effectively as a fluorescent nanoprobe for imatinib detection, offering a wide linear range and a low detection limit of 0.014 μg mL^-1.¹¹⁶

Evidence for direct integration of microalgae-mediated nanomaterials into food- and environmental-sensing devices remains limited. Most reported microalgae-mediated metallic nanoparticles have been evaluated for antimicrobial, antioxidant, or biomedical applications. The use of externally fabricated nanomaterials has gained attention as a complementary strategy to enhance biosensor performance. One example is the study by Roxby et al.¹ that integrated Chlorella-based photoelectrochemical sensing with copper nanoparticle-electrode nanocavity architectures. Cu nanoparticles and the underlying copper electrode created nanocavity structures that resulted in resonance. Strong coupling between the photosynthetic fluorescence and the cavity modes facilitates energy transfer within the nanostructure, resulting in amplified photoelectrochemical responses. When the fluorescence emitted by the photosynthetic pigments of Chlorella sp. spectrally overlaps with the resonant wavelength of these nanocavities, photoelectrochemical response is significantly amplified. Under optimized resonance conditions, signal intensity increased more than two orders of magnitude and enabled rapid detection of multiple metal ions, including Cd, Fe, Cr, and Mn, at nanomolar concentrations.¹ These findings underscore the growing importance of nanomaterial-assisted signal amplification strategies in next-generation microalgal biosensors. Although this study does not involve the synthesis of algae-derived nanoparticles, it underscores the broader potential of nanomaterial-assisted transducer engineering to enhance microalgal biosensing through advanced transducer engineering. A microalgae-based biosensor was developed for atrazine detection by immobilizing microalgae cells, Scenedesmus acutus and Monoraphidium contortum, in an alginate matrix combined with polyallylamine-dodecylsulfate and multiwalled carbon nanotubes. The sensing mechanism relies on changes in photosynthetic activity caused by herbicide exposure. Among them, M. contortum exhibited superior analytical performance, providing greater sensitivity and a lower detection limit for atrazine. The incorporation of multiwalled carbon nanotubes as electrocatalytic materials in the electrode decreased the overpotential required for oxygen reduction and increased the resulting current response, as demonstrated by cyclic voltammetry and comparison with unmodified graphite electrodes. This facilitated oxygen-reduction kinetics, enhanced signal generation, and improved sensor sensitivity. The biosensor was successfully applied to environmental water samples and maintained its functionality for up to five months when stored under appropriate conditions, demonstrating its potential for long-term environmental monitoring.⁸⁷

Environmental monitoring applications

Microalgal biosensors have been most extensively developed for aquatic environmental monitoring, targeting heavy metals, herbicides, and organophosphate pesticides (Tables 2 and 3). Microalgae form the primary trophic level in aquatic food webs, so their physiological impairments, manifested as inhibited enzyme activity, altered chlorophyll fluorescence, and disrupted PS II performance, serve as indicators of water toxicity. The natural localization of key enzymes, such as ALP, esterase, and acetylcholinesterase on the algal cell surface enables direct interaction with toxic ions or pollutants, eliminating the need for enzyme extraction or purification while preserving native catalytic behavior. Furthermore, microalgae can be immobilized on a wide range of solid supports, facilitating the fabrication of compact, low-cost, and disposable sensing units suitable for in situ or portable monitoring applications. Their robustness, ability to remain metabolically active for extended periods, and low maintenance requirements make them ideal candidates for environmental surveillance. Beyond simple detection, microalgae-based biosensors provide insight into the biological impact of contaminants by more accurately mimicking natural exposure scenarios than chemical assays, thereby bridging the gap between analytical quantification and ecological relevance. With increasing concerns over water pollution, eutrophication, and the accumulation of trace-level toxicants, microalgal biosensors offer unique advantages for broad-spectrum aquatic toxicity screening. The strategic positioning of algal biosensors should therefore be as first-pass broad-toxicity screening tools, complementing rather than replacing chromatographic confirmation methods.

Food safety

Although microalgae-based biosensors have been extensively explored for aquatic environmental monitoring, their application to food safety remains comparatively underdeveloped, reflecting the greater complexity of food matrices and associated analytical challenges. Food matrices contain proteins, lipids, carbohydrates, pigments, and other food particles that may interfere with optical or electrochemical measurements. Besides, the practical application of microalgae-based biosensors in food analysis is further complicated by the need to maintain physiological conditions that support cell viability, including adequate hydration and appropriate pH. Looking ahead, microalgae-based biosensors may be useful for rapid screening of liquid food samples, such as agricultural wastewater, food-processing effluents, beverages, and clarified food extracts. By monitoring endpoints such as changes in chlorophyll fluorescence, oxygen evolution, or enzyme activity, these platforms could provide an early warning of contamination. However, due to the limited analyte specificity of many microalgae-based sensing responses, positive findings would require confirmation by analytical methods such as liquid chromatography-mass spectrometry or gas chromatography-mass spectrometry.

Microalgae- and cyanobacteria-derived pigments have also attracted attention for intelligent food packaging applications. Phycocyanin-rich extracts from Spirulina sp. exhibit pH-dependent color and fluorescence changes. These properties can be exploited in packaging films to indicate the accumulation of volatile alkaline compounds generated during food spoilage, such as ammonia, a spoilage marker for fish and meat. It is useful for evaluating perishable meat, fish, or ready-to-eat products. Nevertheless, these systems function primarily as colorimetric freshness indicators, since their responses arise from non-specific physicochemical changes in the pigment rather than analyte-specific biological recognition. For meats and seafood, colorimetric freshness indicators have been used for real-time monitoring by detecting spoilage-related metabolites such as total volatile basic nitrogen, hydrogen sulfide, biogenic amines (e.g., histamine, putrescine), ammonia, and pH changes. Additionally, freshness monitoring for fruits and vegetables focuses on detecting carbon dioxide, ethylene, aldehydes, and pH changes, key markers associated with respiration, ripening, and microbial deterioration.¹¹⁷

Najafi and Kahyaoglu¹¹⁸ developed multifunctional, biodegradable smart packaging films from pectin, phycocyanin-rich Spirulina extract, and citric-acid-derived CDs. The film exhibited strong ammonia-responsive colorimetric sensing, with a detection limit of 9.04 mg N/100 g and maintained stability for 2 months. In real-food applications, the film reliably monitored fish freshness at 23 °C, suggesting that microalgae have potential as biological indicators for evaluating product freshness. The reported system functions primarily as a colorimetric indicator rather than a fully developed biosensor, as it does not incorporate a defined biorecognition event coupled with a calibrated transduction mechanism. Ammonia calibration demonstrates quantitative indicator performance but does not establish selective recognition because other volatile bases, including trimethylamine and additional biogenic amines, may produce similar color changes. To advance this approach toward a true biosensor format, integration with an appropriate transducer, quantitative calibration against specific food spoilage analytes, and validation in real food matrices would be required. These aspects represent important directions for future research because their sensitivity to spoilage-induced environmental changes suggests considerable potential for the development of next-generation biosensing systems for food safety and quality assessment.

The extension of food-relevant microalgal biosensing to the detection of pesticide residues (e.g., organophosphates and triazines) and mycotoxins represents an ambitious objective. Direct analysis would require matrix-compatible extraction protocols that retain algal cell viability. Validation parameters, including stability, selectivity, cross-reactivity, LOD, and performance also need to be compared and benchmarked against appropriate reference methods across multiple food matrices. Addressing these challenges will be critical for translating microalgal biosensors from proof-of-concept systems into practical tools for food safety monitoring and regulatory surveillance. Although still in its infancy, microalgal biosensing represents a promising and largely untapped area for future food-industry applications.

Advances in microalgae-based sensors

Despite several decades of research, no commercial microalgal biosensor has reached the market. The principal technical barriers are selectivity, operational stability, and sensitivity—each requiring targeted design strategies rather than incremental improvements to existing approaches. This limitation highlights the need for improved sensor performance that enhance recovery, robustness, and long-term operational performance. Hence, achieving high sensitivity, specificity, stability, and selectivity in the electrode response remains essential for reliable performance. In addition, the cost of large-scale production is a significant factor when considering practical deployment.

Selectivity

Analytical selectivity remains one of the most important challenges faced by microalgae-based biosensors. This challenge arises because a wide variety of pollutants can disrupt microalgal metabolism in similar ways, making it difficult to attribute the observed response to a single contaminant. Most microalgae-based biosensors used in environmental monitoring are designed to measure overall effects rather than identify a single pollutant. These systems generally rely on a single microalgal strain, which provides a cumulative response to all contaminants present in the sample. As a result, they indicate the total toxic effect but cannot distinguish which specific compound is responsible.¹¹

In contrast, biosensors developed for the selective detection of a particular contaminant require a fundamentally different design strategy. Several strategies have been proposed to address this limitation, including the use of sensitive and resistant strain pairs, multi-strain sensor arrays, engineered microalgae, biomimetic recognition elements, and multiparametric sensing platforms that combine different physiological endpoints. Firstly, instead of testing one strain against multiple pollutants, as is common in most published studies, selective biosensors rely on multiple pre-characterized microalgal strains with differential sensitivities to a given group of contaminants, as demonstrated in the study by Podola et al.⁷⁹ By comparing herbicide specific response patterns, such as variations in chlorophyll fluorescence, it is possible to improve contaminant discrimination and potentially identify individual analytes with greater specificity.⁷⁹ In this study, a multi-strain algal biosensor array demonstrated the ability to detect several photosynthesis-inhibiting herbicides, including atrazine, simazine, diuron, isoproturon, and paraquat, within minutes at concentrations ranging from 0.5 to 100 μg L⁻¹. Sensitivity varied considerably among strains, with Tetraselmis cordiformis and Scherffelia dubia exhibiting the lowest EC₅₀ values, while C. vulgaris, Chlamydomonas sp., and Pseudokirchneriella subcapitata were less sensitive. Importantly, no single strain exhibited universal sensitivity or resistance, highlighting the value of multi-strain sensor arrays for generating characteristic response patterns that can improve contaminant discrimination and biosensor selectivity.⁷⁹ This offers a promising route toward improving the selectivity of effect-based biosensors while retaining the biological relevance of whole-cell sensing. Besides, Haigh-Flórez et al.,⁸¹ for example, developed a dual-head fiber-optic biosensor containing simazine-sensitive and simazine-resistant Dictyosphaerium chlorelloides strains immobilized in porous silicone films. Photosynthetic activity was monitored using a luminescence-based oxygen sensor. In the presence of simazine, the sensitive strain exhibited a marked decline in oxygen production, whereas the resistant strain showed no significant change. The resistant strain was generated through a modified Luria-Delbrück fluctuation scheme combined with ratchet selection cycles. These contrasting responses enabled selective detection of the target herbicide, as oxygen evolution was inhibited only in the sensitive strain, whereas herbicides not associated with the resistance mechanism affected both strains similarly. The platform successfully discriminated simazine from isoproturon, demonstrating the potential of strain-based response profiling to improve biosensor selectivity. However, validation against a limited number of compounds is insufficient to establish broad selectivity, which must ultimately be demonstrated using a broader range of structurally related contaminants, complex mixtures, and real environmental samples. This limitation highlights a key challenge in the field: developing microalgal biosensors that reliably distinguish specific analytes in complex environmental and food matrices.

Genetic engineering and synthetic biology may provide additional routes to enhance specificity. Genetic engineering technologies such as CRISPR/Cas, transcription activator-like effector nucleases (TALENs), and zinc-finger nucleases (ZFNs), have begun transforming algal biotechnology by enabling the construction of advanced functional traits and efficient modification of target genes for biosensing applications¹¹⁹ (Figure 7). Among these, CRISPR/Cas has emerged as the most versatile platform owing to its high precision, multiplex editing capability, and ease of design. The growing availability of algae genome sequences and genetic resources has significantly expanded the range of genetically modified microalgae species beyond traditional model organisms such as Chlamydomonas reinhardtii, Phaeodactylum tricornutum, and Thalassiosira pseudonana.^120,121 Despite these advances, no validated algal strain engineered specifically for enhanced biosensor selectivity has been reported. Genome editing tools may provide opportunities to tailor metabolic pathways (photosynthesis), modify photosynthetic components, and introduce analyte-responsive genetic traits that could improve sensor sensitivity, selectivity, and stability. For example, genetic modification of photosynthesis-related genes, including CpSRP54, CpSRP53, CpSRP43, and ChlM, has been investigated to enhance photosynthetic efficiency under adverse conditions such as high light intensity and oxidative stress.^122,123,124 These improvements could contribute to the development of more robust microalgal strains capable of maintaining stable biosensing performance under variable environmental conditions in the future. Furthermore, CRISPR/Cas technology may ultimately enable the engineering of customized microalgal bioreceptors with enhanced responsiveness capabilities for specific contaminants and emerging analytes, thereby expanding the applicability of algae-based biosensors across environmental, food, and biomedical sectors.¹¹ CRISPR-based strategies are most viable for contained, bioreactor-housed biosensors, but field deployment of genetically modified organisms (GMO) strains requires a separate risk-assessment framework.⁸¹ Nevertheless, key challenges include low transformation efficiency in industrially relevant species, the risk of off-target edits that destabilize biosensing performance, and regulatory barriers to environmental release of GMOs.

Figure 7.

Genome editing tools in microalgae for biosensor applications

Lastly, an alternative strategy involves microalgae-inspired biomimetic recognition elements. Computational design has been used to create artificial peptides with improved selectivity and stability for biosensing applications. Scognamiglio et al.¹²⁵ computationally designed peptides mimicking the PS II D1 protein Q_B binding niche of C. reinhardtii. The mutant peptide D1pepMut adopted a stable α-helical conformation and bound atrazine with high affinity in biophysical assays. This work demonstrated the feasibility of translating biological recognition into non-living sensing components. Nevertheless, this system remains at the recognition-element stage and cannot be considered true biosensors until integrated with suitable transducers and evaluated under practical application conditions.¹²⁵

In sum, achieving higher selectivity requires integration of biological differentiation with appropriate transducer and data-driven classification strategies. Future research platforms should be evaluated through blind testing, mixture analysis, and validation in real environmental and food matrices to demonstrate real-world analytical performance.

Sensor designs and fabrication strategies

Ensuring stable microalgal immobilization is essential for consistent analyte detection, as the long-term performance of a biosensor depends heavily on the stability of its biological component. However, with conventional immobilization approaches, such as sol-gel and hydrogel matrices, issues such as signal attenuation, background noise, and unintended cell leaching or diffusion from the matrix are frequently encountered.⁶¹ To address these limitations, increasing attention has been directed toward the integration of functional materials, particularly nanomaterials to enhance stability, signal transduction, and overall sensing performance. Incorporating nanomaterials into biosensor designs can enhance analytical performance, particularly by improving sensitivity and lowering detection limits.¹²⁶ For instance, magnetic nanoparticles have been employed as cell immobilization agents in microalgae-based biosensors. Functionalization of C. pyrenoidosa with biocompatible magnetic nanoparticles enabled efficient and reversible attachment of living cells onto electrode surfaces without affecting cell viability or photosynthetic activity. The use of magnetic nanoparticles improved biosensor construction and stability while maintaining sensitive herbicide detection, demonstrating their potential for enhancing the performance of whole-cell biosensors.⁸⁴ Carbon nanotubes have been incorporated into microalgae biosensors to enhance electron transfer and electrochemical signal generation. In a Chlamydomonas reinhardtii-based herbicide biosensor, carbon nanotube-modified screen-printed electrodes outperformed conventional graphite and gold electrodes, resulting in improved sensitivity and low detection limits for herbicides. The excellent conductivity and high surface area of carbon nanotubes make them attractive nanomaterials for improving the analytical performance of algae-based biosensors.⁸⁹

Metal-organic frameworks (MOFs) represent another emerging class of materials with potential relevance to microalgae-based biosensors. Their high surface area, structural modularity, tunable porosity, and versatile physicochemical properties may support and enhance biosensing performance through improved signal transduction, analyte interaction, and sensor stability.^127,128 The integration of MOFs into modified electrodes has expanded rapidly, enabling the monitoring of a wide range of contaminants.¹²⁹ Although MOF-based sensing platforms have been widely explored, their application in microalgae-based biosensing remains limited and unvalidated. These advances highlight the potential for integrating functional materials, such as MOFs, with microalgae-sensing elements in future systems. Although MOF-microalgae systems have not yet been widely explored for biosensing applications, successful MOF assembly on the surface of Chlorella pyrenoidosa cells has been demonstrated to enhance photosynthetic carbon fixation.¹³⁰ This provides preliminary evidence that MOFs can be integrated with living microalgae, thereby creating opportunities for future biosensor development. Future studies will also require rigorous controls to distinguish between responses arising from the individual MOF and those generated by the microalgae in response to the target analyte.

Future work should also focus on improving extraction and stabilization strategies for isolated microalgal biomolecules and photosynthetic components after removal from their native cellular environment. Parameters such as biological loading, fabrication yield, device-to-device variability, cell leakage, operational lifetime, and storage stability should be reported. Functional materials should be benchmarked against unmodified platforms to establish their contribution to sensor performance. Scalable manufacturing methods, including screen printing, paper-based fabrication, and xurography, offer attractive routes toward low-cost production, but translation will depend on demonstrating batch-to-batch reproducibility and reliable operation in real environmental and food-related samples rather than solely in laboratory-prepared systems. This approach may enhance sensor reproducibility, sensitivity, and device integration while also supporting the development of efficient biosensing platforms by reducing biological waste, lowering maintenance requirements, and more efficiently utilizing renewable microalgal resources. The continued optimization of microalgae biosensor technologies will enable more streamlined, rapid, and efficient strategies for developing a robust and reliable biosensing platform. Collectively, multi-strain differential sensor arrays, genetic-engineered biosensing strains, biomimetic peptide biorecognition elements, and MOF-algal hybrid architectures represent a coherent portfolio of complementary strategies for the next generation of algal biosensors. Realizing their potential requires systematic device integration testing and performance validation under real-life conditions, which is essential for translating promising laboratory concepts into reliable and deployable next-generation microalgal biosensors.¹³¹

Conclusion and future perspectives

This review has critically evaluated the scientific foundations, transduction mechanisms, analytical performance, and translational bottlenecks of microalgae-based biosensors. Microalgal biosensors are technically feasible and scientifically well-grounded. The sensitivity of the PSII electron transport chain to a broad range of chemical stressors provides a versatile, instrument-accessible signal source that has been exploited across optical, amperometric, potentiometric, and conductometric platforms. Key design milestones—from early oxygen-electrode systems to microfluidic, LOC devices and dual electro-optical sensors—demonstrate steady technical progress. Despite significant advances in immobilization strategies, microfluidic integration, and dual-mode sensing architectures, most systems remain at the proof-of-concept stage. Microalgae-based biosensors are mostly studied for environmental applications, namely, the screening of heavy metals, pesticides, and herbicides in water and wastewater samples. In contrast to environmental monitoring, food-related biosensing applications of microalgae remain largely unexplored and represent an emerging area of research. Key challenges include insufficient selectivity, biological variability, limited operational stability, and the lack of standardized fabrication and validation protocols. These constraints currently restrict translation into routine environmental and food-safety monitoring applications. Priority research directions are follows: (1) isolation and functional stabilization of microalgal photosynthetic subcomponents (thylakoid membranes, and purified PSII) as biorecognition elements, applying and adapting established higher-plant protocols to microalgal systems; (2) systematic characterization and device integration of microalgae-derived nanomaterials with defined optical or electrochemical properties for signal amplification; (3) design of microfluidic platforms that manage photosynthetic requirements (illumination, CO₂, and temperature) within deployable, field-ready devices; and (4) benchmarked validation in real-sample conditions. Only by addressing these bottlenecks in an integrated, performance-benchmarked framework can microalgal biosensors advance from laboratory demonstrations to field-deployable, regulatory-compliant monitoring tools.

Acknowledgments

This research was supported by the Food Security and Technology Center (FSTC), Khalifa University of Science and Technology, through KU-FSTC, Project ID: KU-INT-FSTC-2026-8474000576.

Author contributions

D.Y.Y.T., conceptualization, visualization, data curation, and writing – original draft; F.B., supervision and writing – review and editing; S.M., supervision and writing – review and editing; O.E.E., supervision and writing – review and editing; M.K.S., supervision and writing – review and editing; S.E., conceptualization, supervision, and writing – review and editing.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.