Enhancing the Sensitivity of Photoelectrochemical DNA Biosensing Using Plasmonic DNA Barcodes and Differential Signal Readout
Bibliographic record
Abstract
Photoelectrochemical biosensors hold great promise for sensitive bioanalysis; however, similar to their electrochemical analogues, they are highly affected by the variable backgrounds caused by biological matrices. We developed a new PEC biosensing strategy that uses differential signal generation, combining signals from two separate but correlated binding events on the biosensor, for improving the limit-of-detection, sensitivity, and specificity of PEC DNA biosensors in biological samples. In this assay, the binding of unlabeled target DNA is followed by the capture of a signal amplification barcode featuring a plasmonic nanoparticle. The interaction of the plasmonic barcode with the semiconductive building blocks of the biosensor results in significant signal amplification, and together with differential signal processing enhances the limit of detection and sensitivity of the assay by up to 15- and three-fold, respectively, compared to the previously-used PEC assays with a single binding event, demonstrating a limit of detection of 3 fM.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".