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Record W4384525345 · doi:10.3390/condmat8030060

Theoretical Study of Electronic and Thermal Transport Properties through a Single-Molecule Junction of Catechol

2023· article· en· W4384525345 on OpenAlexfundno aff
Erika Yazmin Soto-Gómez, Judith Helena Ojeda Silva, John A. Gil-Corrales, Daniel Gallego, Mikel Hurtado, A. L. Morales, C.A. Duque

Bibliographic record

VenueCondensed Matter · 2023
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsnot available
FundersInternational Council for Canadian StudiesUniversidad Pedagógica y Tecnológica de Colombia
KeywordsCatecholMoleculeConductanceThermalFigure of meritThermoelectric effectConjugated systemChemical physicsThermal conductivityMaterials scienceFunction (biology)Work (physics)Space (punctuation)Seebeck coefficientNanotechnologyChemistryCondensed matter physicsPolymerComputer scienceOptoelectronicsPhysicsThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

The study of molecular nanoelectronic devices has recently gained significant interest, especially their potential use as functional junctions of molecular wires. Aromatic systems with π-conjugated bonds within their chemical backbones, such as catechol, have attracted particular attention in this area. In this work, we focused on calculating and determining catechol’s electrical and thermal transport properties using the theoretical method of Green’s functions renormalized in a real space domain within a framework of tight-binding approximation to the first neighbors. Thus, we studied two theoretical models of catechol as a function of its geometry, obtaining striking variations in the profiles of electrical and thermal conductance, the Seebeck coefficient, and the figure of merit. The analyses of the results suggest the potential application of catechol as a likely conductive and thermoelectric molecule serving as a novel material to use in molecular electronic devices.

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 score0.034
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.196
Teacher spread0.187 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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