Challenges in engineering conductive protein fibres: Disentangling the knowledge
Bibliographic record
Abstract
Abstract Conductive protein materials are promising candidates for next‐generation bioelectronics due to their genetically‐customizable functionalities, biocompatibility, and bioactivity. We envision that they could be used in a variety of bio‐friendly functional devices, including bio‐electronic interfaces, bio‐energy devices, and sensors. However, their practical uses are limited by gaps in our understanding of charge transport in proteins, and by challenges in establishing reliable data collection methods. Moreover, characterization protocols are not always designed with applications in mind, which hinders engineering developments. Here, we review the effects of sample preparation, environmental conditions (ie, hydration level, pH, temperature), measurement scale (nano, micro, and macro), and geometrical considerations, on the measured electrical properties of proteins. We emphasize the need for standardized methods and collaborations across fields for the design of conductive protein materials, keeping in mind their end goal applications. Our objective for this review is to disentangle the knowledge on protein conductivity, and to clarify the current challenges, limitations, and future possibilities for these biological conductors.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".