(Invited) Development of New Strategies for Bringing Photoelectrochemical Biosensing to the Point-of-Need
Bibliographic record
Abstract
Photoelectrochemistry combines light excitation with electrochemical readout for lowering the bias voltage needed for performing electrochemical reactions. As a result, when used in biosensing, photoelectrochemical signal readout reduces the background signals, lowering the limit-of-detection of such biosensors. To enable photoelectrochemical (PEC) signal readout to be applied to point-of-need biosensing, we have taken a three tiered approach focused on improving the understanding of signal transduction in PEC Biosensing, developing label-free assays, and creating handheld readout platforms. In this work, we developed a system using DNA as a nano-ruler to control the distance between plasmonic nanoparticles and PEC electrodes. This system was used to rationally-design PEC material systems for signal-on biosensing. Using this materials architecture, we developed a signal-on biosensor without target labeling for detecting DNA hybridization. This assay uses sequential DNA hybridization to generate a PEC signal. First, the DNA target is captured on probe-modified photoelectrodes. This is followed by hybridization of the unbound probes with DNA strands modified with plasmonic labels. The plasmonic label modulates the PEC signal, increasing the measured PEC current at low target concentrations. To enable biosensing at the point-of-need, we also developed a handheld PEC reader. The integration of plasmonic nanoparticles with PEC electrodes, label-free DNA assays, and handheld PEC readout paves the way toward bringing point-of-need PEC Biosensing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.021 |
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".