Graphene–Bacteriophage Hybrid Nanomaterials for Specific and Rapid Electrochemical Detection of Pathogenic Bacteria
Abstract
1. Introduction
2. Search Strategy
3. Results
3.1. Graphene Sources and Sensor Preparation
3.2. Immobilization Methods
3.2.1. Covalent Bonding
3.2.2. Physisorption, Electrostatic Binding, and Affinity Binding
3.3. Signal Transduction and Detection Mechanisms
3.4. Electroanalytical Performance
4. Conclusions and Future Perspectives
- (a)
- Materials Engineering Aspects: The combination of phages with unexplored graphene derivatives—such as heteroatom-doped or graphene–nanoparticle conjugates—and the use of conductivity-enhancing agents (e.g., metal or semiconductor NPs, ionic liquids, or conducting polymers) could further amplify detection capabilities. Improvements in surface modification techniques could lead to enhanced probe immobilization efficiency and optimized phage orientation for better pathogen recognition. These innovations could potentially push GPEBs’ detection limits below 1 CFU·mL−1. While phage display has not yet been applied in reported GPEBs, it holds considerable potential for future integration [148]. By enabling genetically engineered phages with site-specific attachment tags, phage display could enhance immobilization stability, facilitate orientation control, and support modular receptor design for improved selectivity. These advantages may also contribute to improved reproducibility and cost-efficiency in complex sample conditions. For a more detailed overview of synthetic biology tools in phage-based biosensors, readers are referred to recent specialized reviews such as that reported by Wang et al. [91].
- (b)
- Cost and Scalability: Traditional detection methods, such as PCR and ELISA, face significant economic and scalability challenges. PCR, while highly sensitive, requires sophisticated laboratory infrastructure, expensive reagents, and trained personnel, making it impractical for widespread, on-site applications. Similarly, ELISA relies on monoclonal antibodies, which are not only expensive, but are also difficult to produce at scale, further limiting its accessibility. Electrochemical immunosensors and aptasensors offer improved detection limits and portability but also face cost-related drawbacks. Immunosensors rely on monoclonal antibodies, while custom aptamers, although highly specific and versatile, remain prohibitively expensive to synthesize [21,22], limiting their commercial adoption. In comparison, bacteriophages represent a simpler and less expensive alternative as recognition probes. Nonetheless, the labor-intensive and costly process of large-scale phage purification remains a significant bottleneck for commercialization. Improving the efficiency of this step is essential to enable scalable production in the medium term. Employing RBPs, which can be more easily produced through recombinant overexpression [149], also offers a promising solution to overcome this challenge. Additionally, developing strategies for recovering and reusing immobilized phages or RBPs in disposable devices could further reduce material costs and improve economic feasibility.
- (c)
- Sensor Stability and Durability: Long-term stability of immobilized phages under varying environmental conditions remains largely underexplored. Bacterial lysis following recognition and capture can destabilize the sensor signal, particularly in impedance-based platforms [150]. The rupture of large bacterial cells exposes substantial portions of the sensor surface, causing unpredictable signal fluctuations. Exploring phage’s binding proteins as alternative probes to whole phages could address lysis issues while enhancing overall sensor durability in practical applications (thanks to the greater physicochemical stability of RBPs under extreme conditions).
- (d)
- Expanding Multiplexing Capabilities: Early detection of multiple serovars in a single test could help contain outbreaks more rapidly by minimizing diagnostic delays. Except for Y. Ding et al.’s sensor [57], which responded to four S. enterica strains, current GPEBs typically focus on single-pathogen detection, limiting their practicality for broader applications. Future designs should enable simultaneous detection of various species or strains in a single test, significantly reducing time and resource requirements. Multiplexing could be achieved by co-immobilizing phages or RBPs targeting different genera or serovars. The smaller size of RBPs compared to whole phages may facilitate the assembly of a higher density and diversity of probes, further supporting multiplexing efforts. Co-immobilization with complementary recognition probes, such as antibodies or aptamers, is another possibility. Alternatively, multiple single-target GPEBs could be integrated into multichannel platforms.
- (e)
- Miniaturization and Integration: While miniaturized GPEB designs using SPEs have been demonstrated, further standardization is crucial to advance portable, cost-effective devices for real-time, on-site pathogen monitoring. Miniaturization not only facilitates on-site practical application, but also reduces maintenance requirements and enhances sensor durability, particularly in harsh environments.
- (f)
- Smart Integration: Linking GPEBs to cloud networks and integrating AI for real-time data analysis could enable continuous pathogen monitoring in food production, water systems, or healthcare applications. Integrated systems could provide predictive insights for early intervention during outbreaks but require substantial investments in infrastructure and standardization to ensure compatibility and reliability.
- (g)
- Matrix Effects in Real Samples: Matrix effects remain a significant challenge when applying GPEBs to complex food and environmental samples. High protein or fat content, dense tissue matrices, and interfering compounds can hinder pathogen accessibility to immobilized phages or RBPs, reducing detection capabilities and analytical accuracy [146,147]. Addressing these effects will likely require the implementation of sample pre-treatment strategies, involving processes such as dilution, filtration, emulsion, selective enrichment, or matrix cleanup, to reduce the presence of interfering species without compromising target detection [151]. However, such approaches must be carefully optimized, as sample processing could affect the concentration of bacterial analytes. Signal modulation approaches may help correct for background noise and variability in complex matrices. In addition to internal standards or matrix-specific calibration models, software-based techniques such as chemometric data processing, machine learning algorithms, and wavelet-based signal filtering have shown promise in enhancing signal-to-noise ratios without requiring hardware modification [152,153]. Although antifouling coatings (such as sol-gel and PEG-based or zwitterionic layers) could minimize nonspecific adsorption [154], they may also hinder bacterial access to the sensing surface or interfere with phage orientation, potentially introducing further disadvantages in whole-cell biosensing applications.
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
Au | Gold |
AFM | Atomic Force Microscopy |
APTES | (3-Aminopropyl)triethoxysilane |
APTMS | (3-Aminopropyl)trimethoxysilane |
BC | Bacterial Cellulose |
BPs | Bacterial Pathogens |
BSA | Bovine Serum Albumin |
CB | Carbon Black |
CFU | Colony-Forming Units |
CPE | Carbon Paste Electrode |
CRGO | Carboxyl-Rich Graphene Oxide |
CV | Cyclic Voltammetry |
DPV | Differential Pulse Voltammetry |
EBs | Electrochemical Biosensors |
EDC | 1-Ethyl-3-(3-dimethylaminopropyl) Carbodiimide |
EFSA | European Food Safety Agency |
EIS | Electrochemical Impedance Spectroscopy |
ELISA | Enzyme-Linked Immunosorbent Assay |
EOPG | Electro-Oxidized Pristine Graphene |
FC | Flow Cytometry |
FET | Field-Effect Transistor |
GCE | Glassy Carbon Electrode |
GEBs | Graphene-Based Electrochemical Biosensors |
GFET | Graphene-Based Field-Effect Transistor |
GNMs | Graphene Nanomaterials |
GO | Graphene Oxide |
GPEBs | Graphage-Based Electrochemical Biosensors |
LOD | Limit of Detection |
LCR | Low-Frequency Capacitance/Inductance |
MES | 2-(N-Morpholino)ethanesulfonic Acid (MES buffer) |
NCBI | National Center for Biotechnology Information |
NHS | N-Hydroxysuccinimide |
NPs | Nanoparticles |
PCR | Polymerase Chain Reaction |
PD | Phage Display |
PEBs | Phage-Based Electrochemical Biosensors |
PEI | Polyethylenimine |
PI-5-CA | Polyindole-5-carboxylic acid |
PLL | Poly-L-Lysine |
PPy | Polypyrrole |
PVA | Polyvinyl Alcohol |
PDADMAC | Poly(Diallyldimethylammonium Chloride) |
qPCR | Quantitative Polymerase Chain Reaction |
RBPs | Receptor Binding Proteins |
rGO | Reduced Graphene Oxide |
SAMs | Self-Assembled Monolayers |
SEM | Scanning Electron Microscopy |
SPEs | Screen-Printed Electrodes |
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GNM | Phage | Immobilization | Incubation Times | Biosensor Structure—Electrode | Real Samples | Transduction | Ref. |
---|---|---|---|---|---|---|---|
Electrochemically Oxidized Pristine Graphene (EOPG) | Anti-Staphylococcus arlettae | Covalent | 2 h | EOPG/phage—SPE | River Water and Apple Juice | Impedance | [51] |
GO | Anti-Salmonella Typhimurium | Covalent | 6 h | GO/phage—SPE | None | Impedance | [56] |
Carboxyl Rich GO (CRGO) | RBP 41 from Anti-Salmonella Typhimurium T102 | Covalent | 2 h | CRGO/AuNPs/phage—RBP—GCE | Milk and Lettuce | Voltammetry | [57] |
CRGO | Anti-Escherichia coli EP01 | Covalent | Overnight | CRGO/CB/phage—GCE | Milk and Raw Pork | Impedance | [58] |
rGO | Anti-E. coli M13 | Covalent | 3 h | rGO/phage—FET | Simulated River Water | Resistance | [59] |
rGO | Anti-Yersinia pseudotuberculosis vB_YepM_ZN18 | Physical: Adsorption | 2 h | PI-5-CA/rGO/AuNPs/phage—GCE | River Water | Voltammetry | [60] |
rGO | Anti-E. coli | Physical: Adsorption | Does not apply | rGO/phage—CPE | No | Capacitance | [61] |
rGO | Anti-Salmonella Typhimurium | Physical: Adsorption | Overnight | BC/PPy/rGO/phage–SPE | Milk and Chicken | Voltammetry | [62] |
Ref. | Target | Linear Range (CFU·mL−1) | Detection Method | Response Time | Verified Non-Interferent Species | LOD (CFU·mL−1) | Discerns Live/Dead Cells |
---|---|---|---|---|---|---|---|
[16] | Salmonella Typhimurium * | 1.0 × 10–1.0 × 108 | DPV | ~5 min | 5 S. entérica serovars 5 non-Salmonella bacteria | 10 | No |
[17] | P. aeruginosa ATCC 27853 | 60.0–6.0 × 107 | Amperometry | <10 min | S. aureus * V. cholerae * | 60 | No |
[33] | E. coli K12 | 1.0 × 102–1.0 × 108 | EIS | <30 min | S. Typhimurium DT108 | 104 | Non-tested |
[34] | L. monocytogenes Scott A | 10.0–1.0 × 104 | EIS | <30 min | S. Typhimurium 291RH E. coli O157:H7 | 8 | Non-tested |
[51] | S. arlettae | 2.0 × 102–2.0 × 108 | EIS | 2 min | S. aureus 96 S. lentus 2292 E. coli 614 | 200 | Non-tested |
[56] | Salmonella Typhimurium | 1.0 × 10–1.0 × 108 | EIS | <40 min | None | 12 | Non-tested |
[57] | Salmonella Typhimurium ATCC14028 | 3.0–1.0 × 106 | DPV | ~30 min | 8 Salmonella spp. 4 non-Salmonella bacteria | 2 | Non-tested |
[58] | E. coli O157:H7GXEC-N07 | 1.0 × 102–1.0 × 107 | EIS | <30 min | P. aeruginosa PAI Klebsiella pneumoniae L30 S. enteritidis CVCC1806 | 12 | Non-tested |
[59] | E. coli XL1-blue | 1.0 × 102–1.0 × 107 | FET | 30 min | P. Chlororaphis | 45 | Non-tested |
[60] | Y. pseudotuberculosis | 5.3 × 102–1.1 × 107 | DPV | 35 min | Y. enterocolítica * Y. pekkanenii * S. aureus * E. coli O157:H7 | 3 | Yes |
[61] | E. coli * | 3.3 × 10–3.3 × 102 | LCR | 5 s | S. aureus * Klebsiella * Shigella * V. cholerae * | 12 | Non-tested |
[62] | Salmonella Typhimurium | 1.0–1.0 × 107 | DPV | 30 min | S. aureus* L. monocytogenes * E. coli * Bacillus subtilis * S. Typhimurium (heat-killed) | 1 | Yes |
[133] | E. coli ATCC 25922 | 1.0 × 103–1.0 × 105 | FET | 50 s | Heat-killed: S. Typhimurium Streptococcus pneumonia | 103 | Non-tested |
[134] | Salmonella Typhimurium ATCC14028 | 7.0–7.0 × 105 | qPCR | <3 h | 4 S. Typhimurium serovars 3 other Salmonella spp. 5 non-Salmonella bacteria | 7 | No |
[135] | Salmonella Typhimurium DB7155 and 20 other Salmonella spp. | 1.0 × 105–1.0 × 107 | ELISA | 2 h | 10 non-Salmonella bacteria | 100 | No |
[136] | E. coli and other 9 bacteria | - | Flow Cytometry | 21–64 h | - | 105 | No |
[137] | Salmonella spp. | - | Bacterial Culture (ISO 6579) | 58–74 h | - | 1 | Yes |
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Campiña, J.M.; Silva, A.F.; Pereira, C.M. Graphene–Bacteriophage Hybrid Nanomaterials for Specific and Rapid Electrochemical Detection of Pathogenic Bacteria. Biosensors 2025, 15, 467. https://doi.org/10.3390/bios15070467
Campiña JM, Silva AF, Pereira CM. Graphene–Bacteriophage Hybrid Nanomaterials for Specific and Rapid Electrochemical Detection of Pathogenic Bacteria. Biosensors. 2025; 15(7):467. https://doi.org/10.3390/bios15070467
Chicago/Turabian StyleCampiña, José M., António F. Silva, and Carlos M. Pereira. 2025. "Graphene–Bacteriophage Hybrid Nanomaterials for Specific and Rapid Electrochemical Detection of Pathogenic Bacteria" Biosensors 15, no. 7: 467. https://doi.org/10.3390/bios15070467
APA StyleCampiña, J. M., Silva, A. F., & Pereira, C. M. (2025). Graphene–Bacteriophage Hybrid Nanomaterials for Specific and Rapid Electrochemical Detection of Pathogenic Bacteria. Biosensors, 15(7), 467. https://doi.org/10.3390/bios15070467