Affinity-Based Detection of Biomolecules Using Photo-Electrochemical Readout
Bibliographic record
Abstract
Detection and quantification of biologically-relevant analytes using handheld platforms are important for point-of-care diagnostics, real-time health monitoring, and treatment monitoring. Among the various signal transduction methods used in portable biosensors, photoelectrochemcial (PEC) readout has emerged as a promising approach due to its low limit-of-detection and high sensitivity. For this readout method to be applicable to analyzing native samples, performance requirements beyond sensitivity such as specificity, stability, and ease of operation are critical. These performance requirements are governed by the properties of the photoactive materials and signal transduction mechanisms that are used in PEC biosensing. In this review, we categorize PEC biosensors into five areas based on their signal transduction strategy: (a) introduction of photoactive species, (b) generation of electron/hole donors, (c) use of steric hinderance, (d) in situ induction of light, and (e) resonance energy transfer. We discuss the combination of strengths and weaknesses that these signal transduction systems and their material building blocks offer by reviewing the recent progress in this area. Developing the appropriate PEC biosensor starts with defining the application case followed by choosing the materials and signal transduction strategies that meet the application-based specifications.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".