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Quasi-One-Dimensional Free-Electron-Like States Selected by Intermolecular Hydrogen Bonds at the Glycine/Cu(100) Interface*

2020· article· en· W3110054761 on OpenAlexaff
Linwei Zhou, Chenguang Wang, Zhixin Hu, Xianghua Kong, Zhong-Yi Lu, Hong Guo, Wei Ji

Bibliographic record

VenueChinese Physics Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicSurface Chemistry and Catalysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsIntermolecular forceHydrogen bondMoleculeMaterials scienceChemical physicsMonolayerAnisotropyAb initioMetalCrystallographyNanotechnologyChemistryPhysics

Abstract

fetched live from OpenAlex

We carry out ab initio density functional theory calculations to study manipulation of electronic structures of self-assembled molecular nanostructures on metal surfaces by investigating the geometric and electronic properties of glycine molecules on Cu(100). It is shown that a glycine monolayer on Cu(100) forms a two-dimensional hydrogen-bonding network between the carboxyl and amino groups of glycine using a first principles atomistic calculation on the basis of a recently found structure. This network includes at least two hydrogen-bonding chains oriented roughly perpendicular to each other. Through molecule–metal electronic hybridization, these two chains selectively hybridized with the two isotropic degenerate Cu(100) surface states, leading to two anisotropic quasi-one-dimensional surface states. Electrons occupying these two states can near-freely move from a molecule to its adjacent molecules directly through the intermolecular hydrogen bonds, rather than mediated by the substrate. This results in the experimentally observed anisotropic free-electron-like behavior. Our results suggest that hydrogen-bonding chains are likely candidates for charge conductors.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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