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Record W3018510216 · doi:10.1016/j.isci.2020.101099

Solid-State Protein Junctions: Cross-Laboratory Study Shows Preservation of Mechanism at Varying Electronic Coupling

2020· article· en· W3018510216 on OpenAlexafffund
Sabyasachi Mukhopadhyay, Senthil Kumar Karuppannan, Cunlan Guo, Jerry A. Fereiro, Adam Johan Bergren, Vineetha Mukundan, Xinkai Qiu, Olga E. Castañeda Ocampo, Xiaoping Chen, Ryan C. Chiechi, Richard L. McCreery, Israel Pecht, Mordechai Sheves, Rupali Reddy Pasula, Sierin Lim, Christian A. Nijhuis, Ayelet Vilan, David Cahen

Bibliographic record

VenueiScience · 2020
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Alberta
FundersCouncil for Higher EducationNational Research Council CanadaAzrieli FoundationDepartment of Science and Technology, Ministry of Science and Technology, IndiaIsrael Science FoundationMinistry of EducationMinerva FoundationUniversity of Alberta
KeywordsCoupling (piping)Chemical physicsElectronicsNanotechnologySubstrate (aquarium)Solid surfaceSurface finishChemistryBiophysicsMaterials scienceBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.013
GPT teacher head0.256
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations49
Published2020
Admission routes2
Has abstractyes

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