Metal–Organic-Framework-Based Optical Biosensors: Recent Advances in Pathogen Detection and Environmental Monitoring
Abstract
1. Introduction
2. Fundamental Mechanisms Underpinning MOF-Based Optical Biosensors
2.1. Structural and Chemical Tunability
2.2. Optical Biosensing Strategies Using MOFs (e.g., Fluorescence, Colorimetry, SERS)
2.3. Functionalization Strategies for Specificity and Sensitivity
3. MOF-Based Electrochemical and Optical Biosensors for Pathogen Detection
3.1. MOF-Based Biosensors for Microbial Detection
3.1.1. Optical Biosensing Platforms
3.1.2. Electrochemical Biosensing Platforms
3.1.3. Dual-Mode and Multiplex Biosensing Platforms
3.2. Detection Platforms (Label-Free vs. Labeled)
3.2.1. Label-Free Detection Platforms
3.2.2. Labeled Detection Platforms
3.3. Performance Metrics (Sensitivity, Selectivity, LOD)
4. MOF-Based Optical Biosensors for Environmental Monitoring
4.1. Detection of Pollutants (Heavy Metals, Pesticides, Toxins, etc.)
4.2. Detection of Pesticides and Herbicides
4.3. Detection of Microbial Pathogens
4.4. Detection of Biochemical and Volatile Environmental Contaminants
5. Integration with Advanced Technologies
5.1. Portable-Based Sensing
5.2. Data Processing with AI/ML for Signal Interpretation
6. Challenges and Opportunities
6.1. Stability, Reusability, Cost-Effectiveness
6.2. Bridging Lab-Scale and Real-World Application
6.3. Opportunities in Multimodal Sensing and MOF-Hybrids
7. Conclusions and Future Perspective
- Looking forward, a few directions seem particularly worth exploring though, admittedly, none of them are simple fixes. These are described as follows. Greener, scalable MOF synthesis: There is a real push toward eco-friendly methods that use less energy and fewer harsh solvents. If successful, this could make industrial-scale production not only more sustainable but also more practical for widespread deployment.
- AI and digital integration: Pairing MOF sensors with machine learning or AI systems could allow real-time signal analysis or even the ability to detect multiple analytes simultaneously. Still, these setups might be challenging to implement outside high-tech labs, so careful design and validation will be key.
- Regulatory and point-of-care readiness: Sensors need standardized testing and safety checks to meet the rules for clinical, food safety, or environmental applications. Without this, even the most sensitive device may never see actual use.
- Operational robustness: Developing MOFs that can reliably handle environmental stressors, humidity, heat, or light exposure would go a long way toward making these sensors field-ready.
- Miniaturized, multimodal devices: Compact, low-cost sensors that can measure several things at once could be game-changing, especially in remote or resource-limited locations. However, balancing sensitivity with simplicity is often easier said than done.
- Functional diversity and biocompatibility: Expanding the range of detectable targets, from microbes to volatile compounds, while keeping them compatible with biological samples, remains a delicate engineering challenge.
- Opportunities abound in developing multimodal sensing platforms that combine multiple optical transduction methods, enabling improved accuracy and reliability. Furthermore, coupling MOF sensors with digital technologies such as artificial intelligence and real-time data analytics offers exciting prospects for smart, autonomous monitoring systems. By addressing current limitations and leveraging emerging trends, MOF-based biosensors are poised to play a vital role in advancing global health, food safety, and environmental sustainability.
Author Contributions
Funding
Conflicts of Interest
Abbreviations
2D | Two-Dimensional |
3-O-C10-HL | N-(3-oxodecanoyl)-L-homoserine lactone |
AAS | Atomic Absorption Spectroscopy |
ABTS | 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) |
AChE | Acetylcholinesterase |
AI | Artificial Intelligence |
AIA | 2-Aminoisonicotinic Acid |
AIEgen | Aggregation-Induced Emission Luminogen |
ALP | Alkaline Phosphatase |
ATP | Adenosine Triphosphate |
Au NPs | Gold Nanoparticles |
CD | Carbon Nanodot |
CFU | Colony-Forming Unit |
mL | Milliliter |
CMOS | Complementary Metal–Oxide–Semiconductor |
COF | Covalent Organic Framework |
Cr(VI) | Hexavalent Chromium |
MOF | Metal–Organic Framework |
Cu-MOF | Copper-Based Metal–Organic Framework |
DNA | Deoxyribonucleic Acid |
DPV | Differential Pulse Voltammetry |
dsDNA | Double-Stranded Deoxyribonucleic Acid |
E. coli | Escherichia coli |
ELISA | Enzyme-Linked Immunosorbent Assay |
FcMBL | Fragment Crystallizable Mannose-Binding Lectin |
FRET | Fluorescence Resonance Energy Transfer |
GBM | Glioblastoma Multiforme |
GO | Graphene Oxide |
HCR | Hybridization Chain Reaction |
HER2 | Human Epidermal Growth Factor Receptor 2 |
hCG | Human Chorionic Gonadotropin |
HPLC | High-Performance Liquid Chromatography |
HRP | Horseradish Peroxidase |
IFE | Inner Filter Effect |
IoT | Internet of Things |
LOD | Limit of Detection |
LSPR | Localized Surface Plasmon Resonance |
miRNA | MicroRNA |
ML | Machine Learning |
MXene | Transition Metal Carbide |
MWCNT | Multi-Walled Carbon Nanotube |
NIR | Near-Infrared |
NMOF | Nanoscale MOF |
NS | Nanosheet |
OTA | Ochratoxin A |
PCR | Polymerase Chain Reaction |
PEDOT:PSS | Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) |
PET | Photoinduced Electron Transfer |
POCT | Point-of-Care Testing |
PSA | Prostate-Specific Antigen |
PtNP | Platinum Nanoparticle |
PTS | Pregnancy Test Strip |
RAA | Recombinase-Aided Amplification |
RNA | Ribonucleic Acid |
S. aureus | Staphylococcus aureus |
SARS-CoV-2 | Severe Acute Respiratory Syndrome Coronavirus 2 |
SERS | Surface-Enhanced Raman Scattering |
SGI | SYBR Green I |
SPR | Surface Plasmon Resonance |
TcP | Tetraphenylporphyrin |
TMB | 3,3′,5,5′-Tetramethylbenzidine |
UFD-DEC | Urease–Fe-cdDNA Dual-Enzyme Cascade |
UV–vis | Ultraviolet–Visible Spectroscopy |
VOC | Volatile Organic Compound |
ZIF | Zeolitic Imidazolate Framework |
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Design Strategy | Target Microorganism/Analyte | Detection Method | Advantages | Ref. |
---|---|---|---|---|
MOF with hairpin DNA for gated dye release | Brucella (wild vs. vaccine strain) | Fluorescence | DNA-specific release; low cross-reactivity | [45] |
Zn-MOFs with modifiable chemistry | PSA | Fluorescence | Enhanced stability and selectivity | [46] |
Cu-MOF with antigen and click-reaction signal amplification | Aflatoxin B1 | Fluorescence | Strong proximity-triggered signal amplification | [54] |
CD/Co-MOF nanocoral (ratiometric probe) | Alkaline Phosphatase (ALP) | Fluorescence | Dual-signal output; low background noise | [56] |
MIL-53(Al)-NH2 with FcMBL and aptamer-functionalized beads | Bacillus cereus | Fluorescence | Dual recognition; fast detection | [61] |
Au NPs@ZIF-MOF functionalized with aptamers | S. aureus | Fluorescence | Wide dynamic range; built-in signal calibration | [62] |
Au/Ir@Cu/Zn-MOF with anti-S. aureus antibodies | S. aureus | Immunoassay | High sensitivity; multifunctional | [63] |
Ru(bpy)32+@Zr-MOF with dsDNA and SGI | ATP (viable bacterial marker) | Fluorescence | Real-time detection; smartphone compatible | [64] |
Au@CuMOF and Au@PbMOF functionalized with DNA probes | S. typhimurium and L. monocytogenes | Electrochemical | Dual-pathogen detection; high selectivity; broad range | [65] |
Materials | Target Microorganisms | Method | LOD (µg/L) | Linear Range | Ref. |
---|---|---|---|---|---|
Zr-MOF + DNA aptamer | E. coli O157:H7 | Fluorescence | – | – | [70] |
MOFs + AuNPs + antibodies | SARS-CoV-2 | Colorimetric | – | – | [71] |
MOFs + lectins | Candida albicans | SERS | – | – | [72] |
Fe-MOF@SalmpYZU47 | S. enterica | Colorimetric | 5.5 | 1.0 × 102 to 1.0 × 108 CFU/mL | [73] |
MOF + PTS | E. coli O157:H7 | Visual | 265 | – | [74] |
GO + Cu–MOF | M. pneumoniae, L. pneumophila | Electrochemical | 0.001 | 1 pg/mL to 100 ng/mL | [75] |
CoFe-MOFs@Nafion | Salmonella | Electrochemical | 69 | 1.38 × 102 to 1.38 × 108 CFU/mL | [76] |
CoFe-MOFs + MWCNTs + AuNPs | Salmonella | Electrochemical | 1445 | 1.04 × 104 to 1.04 × 108 CFU/mL | [77] |
MCOF + AuNPs + AIEgens | S. typhimurium | Colorimetric/fluorescent | 500/5 | – | [78] |
Cu-MOF | CRP | Colorimetric/fluorescent | 0.04/0.240 | – | [79] |
Zn-MOF | HER2 (non-microbial) | Fluorescence | 0.12 | – | [80] |
UiO-66-NH2 MOF + MB + TMB | let-7a, miRNA-21 | Electrochemical | 0.0022, 0.005 | – | [81] |
Porphyrinic COFs + AgNPs | CRP | Photoelectrochemical | – | – | [82] |
Polyaniline@Ni-MOF | HCV RNA | Electrochemical | 0.0048 | 1 fM to 100 nM | [87] |
Zr-MOF + methylene blue | GBM exosomes | Electrochemical | 0.0041 | 9.5 × 103 to 1.9 × 107 particles/μL | [87] |
Pt-COF nanozyme + Chromotrope 2R | L. monocytogenes, S. typhimurium | Colorimetric | 0.655, 0.805 | – | [89] |
CoFe-MOFs + graphene + Au–NH2 | Salmonella | Electrochemical | 60 | 2.4 × 102 to 2.4 × 108 CFU/mL | [90] |
Ultrathin MOF-NSs | Multiple bacterial DNAs | Fluorescence | 0.009 | – | [91] |
Fe-MIL-88NH2 + PtNPs | Salmonella | Colorimetric | 46.5 | – | [92] |
Cu-MOF/PEDOT:PSS | E. coli O157:H7 | Electrochemical | 3.7 | 3 × 102 to 3 × 108 CFU/mL | [93] |
Enzyme-loaded ZIF-8 | E. coli O157:H7 | Colorimetric | 0.5 | – | [94] |
MIL-88@TcP | S. typhimurium | Colorimetric | 84 | – | [95] |
ZIF-8 + FLS + CRISPR/Cas12a + RAA | S. typhimurium | Fluorescence | 65 | – | [96] |
Cu-MOF + streptavidin | E. coli | Colorimetric | 1 | 16 to 1.6 × 106 CFU/mL | [97] |
Pt@ZIF-8 | S. typhimurium | Colorimetric | 5.5 | 101–104 CFU/mL | [98] |
MOF@B(OH)2 + Ni mesh | Salmonella | Fluorescence | 9.4 | – | [99] |
PtNPs + Co/Zn-MOF + MWCNTs | S. typhimurium | Electrochemical | 47 | 1.3 × 102 to 1.3 × 108 CFU/mL | [100] |
MOF-based micromotors | E. coli | Theranostic | – | – | [101] |
Materials | Target Analytes | Methods | LOD (µg/L) | Real-World Application Challenges | Ref. |
---|---|---|---|---|---|
Zr-based MOF | Cu2+, Pb2+, Hg2+ | Fluorescence | <2 | Humic substances, turbidity quenching fluorescence; fouling of sensing sites in natural waters | [108] |
P1@BMOF | Cu2+ | Fluorescence | 12.71 | UV/temperature induced degradation of azobenzene; limited stability outdoors | [109] |
Eu@UiO-MOFs | Cd2+ | Fluorescence | 114 | Defect-rich sites prone to hydrolysis at high humidity or pH extremes | [110] |
ZIF-67 | Cr(VI) | Evanescent | 1 | Biofilm growth on fiber tip reduces light throughput; requires cleaning | [111] |
SM-1 | Ag+, Cd2+, Hg2+ | Fluorescence | - | Unclear durability under repeated adsorption desorption in field use | [112] |
MOF-5 | Pb2+ | Plasmonic | 0.5 | Ionic strength variations affect plasmonic shifts; turbidity interference | [113] |
NH2-MIL-101(Fe)-mAb | Pb2+ | Fluorescence | 9.51 | Antibody denaturation at high temperatures; cold-chain requirements | [114] |
CoPc-PT-COF@Cu-MOF | Cr3+ | PEC | 7.54 × 10−7 | High background ion interference; PEC components sensitive to fouling | [115] |
Bi2CuO4 | Cd2+ | Electrochemical | 2.25 × 10−6 | Cross-contamination between analytes; electrode surface fouling | [116] |
Cu-MOF | Glyphosate | Fluorescence | 5.58 | Suspended particles scatter light; binding site blockage in field runoff | [117] |
ZIF-8@Cellulose | Dichlorvos | Colorimetric | 0.29 | Enzyme leaching, reduced activity in humid storage | [118] |
Tb-BTC MOF | E. coli | Fluorescence | 0.003 | Antibody degradation with temperature/UV; matrix background fluorescence | [119] |
ZrPr-MOF | S. typhimurium | Colorimetric | 0.037 | Paper background color interference; aptamer storage stability | [120] |
CAU-17/Bi2S3 | 3-O-C10-HL | Optoelectronic | 1.12 × 10−4 | Salinity changes affect aptamer binding; marine biofouling | [121] |
ZIF-8 | H2O2 | NIR-induced | - | Dust, waxes affect coating uniformity; outdoor weather effects | [122] |
AuNPs/HKUST-1 | VOCs | LSPR | - | VOC mixture overlap; humidity impact on refractive index | [123] |
Urease@ZIF-8 | Urea | Refractive index | 6006 | Enzyme instability in heat; long-term wet storage challenges | [124] |
UFD-DEC | Urea | Colorimetric | 7207.2 | Hydrogel dehydration or overhydration alters response | [125] |
MOF-CMOS | CO2 | Colorimetric | 26,000 | Skin oils/sweat contamination; long-term adhesion to skin | [126] |
Cr(III)-MOF-NPs | Morphine | Fluorescence | 4.76 × 10−2 | Autofluorescent backgrounds in biological samples | [127] |
Tb-BTC MOF | Hemoglobin | Optical | - | MXene oxidation over time; interference from complex fluids | [128] |
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Kidanemariam, A.; Cho, S. Metal–Organic-Framework-Based Optical Biosensors: Recent Advances in Pathogen Detection and Environmental Monitoring. Sensors 2025, 25, 5081. https://doi.org/10.3390/s25165081
Kidanemariam A, Cho S. Metal–Organic-Framework-Based Optical Biosensors: Recent Advances in Pathogen Detection and Environmental Monitoring. Sensors. 2025; 25(16):5081. https://doi.org/10.3390/s25165081
Chicago/Turabian StyleKidanemariam, Alemayehu, and Sungbo Cho. 2025. "Metal–Organic-Framework-Based Optical Biosensors: Recent Advances in Pathogen Detection and Environmental Monitoring" Sensors 25, no. 16: 5081. https://doi.org/10.3390/s25165081
APA StyleKidanemariam, A., & Cho, S. (2025). Metal–Organic-Framework-Based Optical Biosensors: Recent Advances in Pathogen Detection and Environmental Monitoring. Sensors, 25(16), 5081. https://doi.org/10.3390/s25165081