Mixed Ionic and Electronic Charge Transport in Conductive Protein Fibers Revealed with DC Electrical Measurements
Bibliographic record
Abstract
Naturally conductive protein nanowires have inspired efforts to engineer electrical conductivity into synthetic fibrous proteins for the development of bioelectronic materials and devices. A comprehensive analysis of charge transport in these systems requires a combination of various measurement methods, instruments and electrode designs. Measurements under direct current (DC) typically focus on charge transport without distinguishing between charged species, requiring alternating current (AC) and electrochemical methods to probe additional phenomena. Here, ionic and electronic charge transport mechanisms are separately studied within nanowires on interdigitated micro-electrodes under DC. This study also deconvolutes the effects of humidity, salts and polyethylene glycol (PEG) on protein conductivity. As a model system, the M13 bacteriophage, a filamentous protein assembly that is an ideal scaffold for engineering charge transport is used. The M13 phage is also compared with two previously studied conductive protein fibers, Geobacter-derived protein nanowires (e-PN) and engineered aromatic curli fibers. This study observes both transient ionic charge transport and steady-state electronic conductivity in the M13 phage and curli fibers, whereas e-PN materials predominantly exhibited electronic charge transport. The results show that transient and steady-state examinations of sensitive DC measurements in protein fibers help better understand mixed transport in these materials with particularly low conductivity.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".