Highly Sensitive Method to Isolate Photocurrent Signals from Large Background Redox Currents on Protein‐Modified Electrodes
Bibliographic record
Abstract
Abstract The high quantum efficiency in converting light energy into a charge‐separated state is a major advantage in using photosynthetic proteins in biophotovoltaic applications. Photocurrents are typically measured at open circuit potential (OCP), where the electrochemical redox or faradaic currents are minimized. However, at potentials far from the OCP, the photocurrents produced by the proteins may be impossible to measure against the large background current, owing to electrochemical redox reactions of charge‐transfer mediators and/or sacrificial electron donors. Demonstrated here is a highly sensitive method using a sinusoidal‐modulated intensity of an LED excitation light source to isolate the protein‐based photocurrent component from the total current irrespective of electrode surface coverage. Using a genetically modified photochemical reaction center from Rhodobacter sphaeroides as a proof‐of‐concept, photocurrents up to 104–105 orders of magnitude smaller than the background electrochemical redox current (due to redox reactions directly on the electrode surface) were measured at applied voltages >0.4 V from the OCP. The phase relationship between the optical excitation and photocurrent response was also measured and shown to be analytically useful.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".