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Record W2077898366 · doi:10.1088/0957-4484/21/1/015202

Bias-dependent amino-acid-induced conductance changes in short semi-metallic carbon nanotubes

2009· article· en· W2077898366 on OpenAlexafffund
G. B. Abadir, Konrad Walus, D.L. Pulfrey

Bibliographic record

VenueNanotechnology · 2009
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConductanceCarbon nanotubevan der Waals forceMaterials scienceAdsorptionAmino acidChemical physicsAb initioMolecular dynamicsMetalDensity functional theoryNanotubeComputational chemistryNanotechnologyMoleculePhysical chemistryOrganic chemistryChemistryCondensed matter physicsPhysicsBiochemistry

Abstract

fetched live from OpenAlex

We study the interaction between short semi-metallic carbon nanotubes and different amino acids using molecular dynamics and ab initio (density functional theory/non-equilibrium Green's function) simulations. We identify two different mechanisms of nanotube conductance change upon adsorption of amino acids: one due to the change of the coordinates of the nanotube arising from van der Waals forces of interaction with the adsorbed amino acid; and one due to electrostatic interactions, which appear only in the case of charged amino acids. We also find that the transport mechanism and the changes in the conductance of the tube upon amino acid adsorption are bias dependent.

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 categoriesMeta-epidemiology (narrow)
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 score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.017
GPT teacher head0.225
Teacher spread0.209 · 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.

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

Citations19
Published2009
Admission routes2
Has abstractyes

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