Advances in Electrochemical Biosensors for COVID-19 Detection: Progress, Challenges, and Future Perspectives: A Review
Bibliographic record
Abstract
The COVID-19 pandemic has led to over 770 million confirmed cases and nearly 7 million deaths globally as of early 2024, highlighting the urgent need for rapid, scalable, and accurate diagnostic tools. Electrochemical biosensors have gained increasing attention due to their miniaturization, affordability, and rapid response time. This review presents a structured and critical summary of electrochemical biosensing strategies applied to COVID-19 diagnostics, with an emphasis on genosensors, immunosensors, and label-free detection techniques. Key electrochemical methods—including voltammetry, amperometry, potentiometry, and electrochemical impedance spectroscopy—are discussed in the context of detecting viral proteins, nucleic acids, and antibodies. Notably, over 250 studies published since 2020 have demonstrated biosensors with detection limits as low as 1 fg/mL and response times under 10 min. The integration of nanomaterials such as graphene, carbon nanotubes, and transition metal dichalcogenides is reviewed for their role in enhancing signal output and biorecognition specificity. A bibliometric analysis of 779 articles (1985–2024) highlights global research trends and emerging areas. By consolidating these advancements and limitations, this review aims to guide researchers developing next-generation diagnostic tools for pandemic preparedness and infectious disease control.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".