Zwitter-repel: An anti-fouling coating promoting electrochemical biosensing in biological fluids
Bibliographic record
Abstract
Most electrochemical biosensors require sample processing to reduce biofouling induced by the complex composition of biological samples that typically causes a decrease in the target-to-blank signal. Herein, we report a zwitterionic copolymer bearing sulfobetaine, carboxylic, aldehyde, and thiol groups as a thin (∼16 nm) anti-fouling coating for electrochemical biosensing platforms. The resulting polymer-coated electrodes reduced protein adsorption by ∼ 67 % compared to the bare-gold surface when incubated with radiolabeled human serum albumin (HSA) protein-spiked human plasma, while cyclic voltammetry yielded a 5 % increase in anodic current signal after incubation in 1 % HSA for 1 h compared to the 83 % decrease in anodic current observed with bare gold electrodes. The polymer-coated electrode facilitated the detection of redox-labeled DNA in buffer, as well as in unprocessed and undiluted plasma with detection limits of 23 nM and 21 nM, respectively; detection of 104 cp mL−1 lentivirus pseudotyped with the Omicron spike protein of SARS-CoV-2 in unfiltered 50 % saliva was also achieved within 5 min with improved target-to-blank ratios and reproducibility relative to the well-established PEG-based biosensing platform for detecting COVID-19. On this basis, the Zwitter-repel coating offers potential to sensitively detect other disease biomarkers/analytes while eliminating or reducing the need for sample pre-processing and/or the use of additional backfilling/blocking agents.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| 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".