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Record W3145972188 · doi:10.1002/smll.202008218

Inelastic Electron Tunneling Spectroscopic Analysis of Bias‐Induced Structural Changes in a Solid‐State Protein Junction

2021· article· en· W3145972188 on OpenAlexfundno aff
Jerry A. Fereiro, Israel Pecht, Mordechai Sheves, David Cahen

Bibliographic record

VenueSmall · 2021
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsnot available
FundersAzrieli FoundationIsrael Science FoundationDeutsche Forschungsgemeinschaft
KeywordsAzurinProtein filamentQuantum tunnellingMaterials scienceChemical physicsElectron transport chainMolecular vibrationCrystallographyElectronBendingChemistryElectron transferMoleculePhysicsOptoelectronicsPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract A central issue in protein electronics is how far the structural stability of the protein is preserved under the very high electrical field that it will experience once a bias voltage is applied. This question is studied on the redox protein Azurin in the solid‐state Au/protein/Au junction by monitoring protein vibrations during current transport under applied bias, up to ≈1 GV m −1 , by electrical detection of inelastic electron transport effects. Characteristic vibrational modes, such as CH stretching, amide (NH) bending, and AuS (of the bonds that connect the protein to an Au electrode), are not found to change noticeably up to 1.0 V. At >1.0 V, the NH bending and CH stretching inelastic features have disappeared, while the AuS features persist till ≈2 V, i.e., the proteins remain Au bound. Three possible causes for the disappearance of the NH and CH inelastic features at high bias, namely, i) resonance transport, ii) metallic filament formation, and iii) bond rupture leading to structural changes in the protein are proposed and tested. The results support the last option and indicate that spectrally resolved inelastic features can serve to monitor in operando structural stability of biological macromolecules while they serve as electronic current conduit.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.062
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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 teacher head, 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

Citations11
Published2021
Admission routes1
Has abstractyes

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