Inexpensive yet sensitive and specific biomarker detection is a critical bottleneck in diagnostics, monitoring, and surveillance of infectious diseases such as COVID‐19. Multiplexed detection of several biomarkers can achieve wider diagnostic applicability, accuracy, and ease‐of‐use, while reducing cost. Current biomarker detection methods often use enzyme‐linked immunosorbent assays (ELISA) with optical detection which offers high sensitivity and specificity. However, this is complex, expensive, and limited to detecting only a single analyte at a time. Here, it is found that biomarker‐bound enzyme‐labeled probes act synergistically with nanostructured catalytic surfaces and can be used to selectively reduce a soluble silver substrate to generate highly dense and conductive, localized surface silver metallization on microelectrode arrays. This enables a sensitive and quantitative, simple, direct electronic readout of biomarker binding without the use of any intermediate optics. Furthermore, the localized and dry‐phase stable nature of the metallization enables multiplexed electronic measurement of several biomarkers from a single drop (<10 µL) of sample on a microchip.This method is applied for the multiplexed point‐of‐care (POC) quantitative detection of multiple COVID‐19 antigen‐specific antibodies. Combining a simple microchip and an inexpensive, cellphone‐interfaced, portable reader, the detection and discrimination of biomarkers of prior infection versus vaccination is demonstrated.

A novel, integrated experimental and modeling framework was applied to an inhibition-based bi-enzyme (IBE) electrochemical biosensor to detect acetylcholinesterase (AChE) inhibitors that may trigger neurological diseases. The biosensor was fabricated by co-immobilizing AChE and tyrosinase (Tyr) on the gold working electrode of a screen-printed electrode (SPE) array. The reaction chemistry included a redox-recycle amplification mechanism to improve the biosensor’s current output and sensitivity. A mechanistic mathematical model of the biosensor was used to simulate key diffusion and reaction steps, including diffusion of AChE’s reactant (phenylacetate) and inhibitor, the reaction kinetics of the two enzymes, and electrochemical reaction kinetics at the SPE’s working electrode. The model was validated by showing that it could reproduce a steady-state biosensor current as a function of the inhibitor (PMSF) concentration and unsteady-state dynamics of the biosensor current following the addition of a reactant (phenylacetate) and inhibitor phenylmethylsulfonylfluoride). The model’s utility for characterizing and optimizing biosensor performance was then demonstrated. It was used to calculate the sensitivity of the biosensor’s current output and the redox-recycle amplification factor as a function of experimental variables. It was used to calculate dimensionless Damkohler numbers and current-control coefficients that indicated the degree to which individual diffusion and reaction steps limited the biosensor’s output current. Finally, the model’s utility in designing IBE biosensors and operating conditions that achieve specific performance criteria was discussed.

Electrochemical immunosensors (EIs) integrate biorecognition molecules (e.g., antibodies) with redox enzymes (e.g., horseradish peroxidase) to combine the advantages of immunoassays (high sensitivity and selectivity) with those of electrochemical biosensors (quantitative electrical signal). However, the complex network of mass-transfer, catalysis, and electrochemical reaction steps that produce the electrical signal makes the design and optimization of EI systems challenging. This paper presents an integrated experimental and modeling framework to address this challenge. The framework includes (1) a mechanistic mathematical model that describes the rate of key mass-transfer and reaction steps; (2) a statistical-design-of-experiments study to optimize operating conditions and validate the mechanistic model; and (3) a novel dimensional analysis to assess the degree to which individual mass-transfer and reaction steps limit the EI’s signal amplitude and sensitivity. The validated mechanistic model was able to predict the effect of four independent variables (working electrode overpotential, pH, and concentrations of catechol and hydrogen peroxide) on the EI’s signal magnitude. The model was then used to calculate dimensionless groups, including Damkohler numbers, novel current-control coefficients, and sensitivity-control coefficients that indicated the extent to which the individual mass-transfer or reaction steps limited the EI’s signal amplitude and sensitivity.

Multiplexed biomarker detection can play a critical role in reliable and comprehensive disease diagnosis and prediction of outcome. Enzyme-linked immunosorbent assay (ELISA) is the gold standard method for immunobinding-based biomarker detection. However, this is currently expensive, limited to centralized laboratories, and usually limited to the detection of a single biomarker at a time. We present a low-cost, smartphone-based portable biosensing platform for high-throughput, multiplexed, sensitive, and quantitative detection of biomarkers from single, low-volume drops (<1 μL) of clinical samples. Biomarker binding to spotted capture antigens is converted, via enzymatic metallization, to the localized surface deposition of amplified, dry-stable, silver metal spots whose darkness is proportional to biomarker concentration. A custom smartphone application is developed, which uses real-time computer vision to enable easy optical detection of the deposited metal spots and sensitive and reproducible quantification of the biomarkers. We demonstrate the use of this platform for high-throughput, multiplexed detection of multiple viral antigen-specific antibodies from convalescent COVID-19 patient serum as well as vaccine-elicited antibody responses from uninfected vaccine-recipient serum and show that distinct multiplexed antibody fingerprints are observed among them.