Faradaic Impedimetric Immunosensor for Label-Free Point-of-Care Detection of COVID-19 Antibodies Using Gold-Interdigitated Electrode Array
Bibliographic record
Abstract
Label-free electrochemical biosensors have many desirable characteristics in terms of miniaturization, scalability, digitization, and other attributes associated with point-of-care (POC) applications. In the era of COVID-19 and pandemic preparedness, further development of such biosensors will be immensely beneficial for rapid testing and disease management. Label-free electrochemical biosensors often employ [Fe(CN)6]−3/4 redox probes to detect low-concentration target analytes as they dramatically enhance sensitivity. However, such Faradaic-based sensors are reported to experience baseline signal drift, which compromises the performance of these devices. Here, we describe the use of a mecaptohexanoic (MHA) self-assembled monolayer (SAM) modified Au-interdigitated electrode arrays (IDA) to investigate the origin of the baseline signal drift, developed a protocol to resolve the issue, and presented insights into the underlying mechanism on the working of label-free electrochemical biosensors. Using this protocol, we demonstrate the application of MHA SAM-modified Au-IDA for POC analysis of human serum samples. We describe the use of a label-free electrochemical biosensor based on covalently conjugated SARS-CoV-2 spike protein for POC detection of COVID-19 antibodies. The test requires a short incubation time (10 min), and has a sensitivity of 35.4/decade (35.4%/10 ng mL−1) and LOD of 21 ng/mL. Negligible cross reactivity to seasonal human coronavirus or other endogenous antibodies was observed. Our studies also show that Faradaic biosensors are ~17 times more sensitive than non-Faradaic biosensors. We believe the work presented here contributes to the fundamental understanding of the underlying mechanisms of baseline signal drift and will be applicable to future development of electrochemical biosensors for POC applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".