Enhancing the Photoelectrochemical Response of DNA Biosensors Using Wrinkled Interfaces
Bibliographic record
Abstract
Photoelectrochemical (PEC) biosensors, with optical biasing and electrochemical readout, are expected to enhance the limit-of-detection of electrochemical biosensors by lowering their background signals. However, when PEC transducers are functionalized with biorecognition layers, their current significantly decreases, which reduces their signal-to-noise ratio and dynamic range. Here, we develop and investigate a wrinkled conductive scaffold for loading photoactive quantum dots into an electrode. The wrinkled photoelectrodes demonstrate an order of magnitude enhancement in the magnitude of the transduced PEC current compared to their planar counterparts. We engineer PEC biosensors by functionalizing the wrinkled photoelectrodes with nucleic acid capture probes. We challenge the sensitivity of the wrinkled and planar biosensors with various concentrations of DNA target and observe a 200 times enhancement in the limit-of-detection for wrinkled versus planar electrodes. In addition to enhanced sensitivity, the wrinkled PEC biosensors are capable of distinguishing between fully complementary and targets with a single base-pair mismatch, demonstrating the suitability of these biosensors for use in clinical diagnostics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".