Graphene‐Based Femtogram‐Level Sensitive Molecularly Imprinted Polymer of SARS‐CoV‐2
Bibliographic record
Abstract
Abstract Rapid distribution of viral‐induced diseases and weaknesses of common diagnostic platforms for accurate and sensitive identification of infected people raises an urgent demand for the design and fabrication of biosensors capable of early detection of viral biomarkers with high specificity. Accordingly, molecularly imprinted polymers (MIPs) as artificial antibodies prove to be an ideal preliminary detection platform for specific identification of target templates, with superior sensitivity and detection limit (DL). MIPs detect the target template with the “lock and key” mechanism, the same as natural monoclonal antibodies, and present ideal stability at ambient temperature, which improves their practicality for real applications. Herein, a 2D MIP platform consisting of decorated graphene oxide with the interconnected complex of polypyrrole‐boronic acid is developed that can detect the trace of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) antigen in aquatic biological samples with ultrahigh sensitivity/specificity with DL of 0.326 and 11.32 fg mL –1 using voltammetric and amperometric assays, respectively. Additionally, the developed MIP shows remarkable stability, selectivity, and accuracy toward detecting the target template, which paves the way for developing ultraspecific and prompt screening diagnostic configurations capable of detecting the antigen in 1 min or 20 s using voltammetric or amperometric techniques.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".