An Overview of Label-free Electrochemical Protein Sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electrochemical-based protein sensors offer sensitivity, selectivity and reliabilityat a low cost, making them very attractive tools for protein detection. Although the sensorsuse a broad range of different chemistries, they all depend on the solid electrode surface,interactions with the target protein and the molecular recognition layer. Traditionally, redoxenzymes have provided the molecular recognition elements from which target proteins haveinteracted with. This necessitates that the redox-active enzymes couple with electrodesurfaces and usually requires the participation of added diffusional components, or assemblyof the enzymes in functional chemical matrices. These complications, among many others,have seen a trend towards non-enzymatic-based electrochemical protein sensors. Severalelectrochemical detection approaches have been exploited. Basically, these have fallen intotwo categories: labeled and label-free detection systems. The former rely on a redox-activesignal from a reporter molecule or a label, which changes upon the interaction of the targetprotein. In this review, we discuss the label-free electrochemical detection of proteins,paying particular emphasis to those that exploit intrinsic redox-active amino acids.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it