Anti‐Fouling Polymer or Peptide‐Modified Electrochemical Biosensors for Improved Biosensing in Complex Media
Bibliographic record
Abstract
Abstract Electrochemical biosensing represents a highly effective technology for detecting disease biomarkers given its high sensitivity, low and clinically relevant limit of detection, and cost effectiveness. However, in complex media such as urine, blood, sweat or saliva, biosensing performance can be significantly impacted by electrode biofouling by proteins, cells, lipids, and other matrix components. Such biofouling leads to reduced signal from the target analyte coupled with an elevated background signal, resulting in poor signal‐to‐noise ratios (SNRs), reduced sensitivity, and lower specificity. This comprehensive review describes the design of anti‐fouling polymers and peptides as a potential solution to prevent or suppress electrochemical biosensor fouling. Various anti‐fouling polymers and peptides developed for improved biosensing in complex media are summarized in the context of their mechanism(s) of anti‐fouling, methods of deposition, and practical applications. Recent advances and persistent challenges in the field are also reviewed to provide perspectives on new directions toward enhancing anti‐fouling in electrochemical biosensors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".