Solid-State Protein Junctions: Cross-Laboratory Study Shows Preservation of Mechanism at Varying Electronic Coupling
Bibliographic record
Abstract
Successful integration of proteins in solid-state electronics requires contacting them in a non-invasive fashion, with a solid conducting surface for immobilization as one such contact. The contacts can affect and even dominate the measured electronic transport. Often substrates, substrate treatments, protein immobilization, and device geometries differ between laboratories. Thus the question arises how far results from different laboratories and platforms are comparable and how to distinguish genuine protein electronic transport properties from platform-induced ones. We report a systematic comparison of electronic transport measurements between different laboratories, using all commonly used large-area schemes to contact a set of three proteins of largely different types. Altogether we study eight different combinations of molecular junction configurations, designed so that A geo of junctions varies from 10 5 to 10 −3 μm 2 . Although for the same protein, measured with similar device geometry, results compare reasonably well, there are significant differences in current densities (an intensive variable) between different device geometries. Likely, these originate in the critical contact-protein coupling (∼contact resistance), in addition to the actual number of proteins involved, because the effective junction contact area depends on the nanometric roughness of the electrodes and at times, even the proteins may increase this roughness. On the positive side, our results show that understanding what controls the coupling can make the coupling a design knob. In terms of extensive variables, such as temperature, our comparison unanimously shows the transport to be independent of temperature for all studied configurations and proteins. Our study places coupling and lack of temperature activation as key aspects to be considered in both modeling and practice of protein electronic transport experiments.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".