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Record W3118689547 · doi:10.1039/d0cp06436j

On the physicochemical origin of nanoscale friction: the polarizability and electronegativity relationship tailoring nanotribology

2021· article· en· W3118689547 on OpenAlexaff
Leonardo M. Leidens, M.E.H. Maia da Costa, Neileth S. Figueroa, Rodrigo A. Barbieri, F. Alvarez, Alexandre F. Michels, Carlos A. Figueroa

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsMicrosemi (Canada)
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado do Rio Grande do SulFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsElectronegativityPolarizabilityNanotribologyNanoscopic scaleChemical physicsNanotechnologyChemistryComputational chemistryMaterials scienceAtomic force microscopyOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

Friction is a ubiquitous manifestation of nature, and when it is studied at the nanoscale, complex and interesting effects arise from fundamental physical and chemical surface properties. Surprisingly, and probably due to the complexity of nanofriction studies, this aspect has not been completely discussed in prior studies. To fully consider the physicochemical influence in nanoscale friction, amorphous carbon films with different amounts of hydrogen and fluorine were prepared, chemically characterized, and evaluated via lateral force microscopy. Hydrogen and fluorine were selected because although they exhibit different physicochemical properties, both contribute to frictional force reduction. Indeed, to explain the experimental behavior, it is necessary to propose a new damping constant unifying both polarizability (physical) and electronegativity (chemical) properties. The satisfactory agreement between theory and experiments may encourage and enhance deeper discussion and new experiments that take into account the chemical peculiarities of frictional behavior relating to nanoscale elastic regimes.

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 score0.059
Threshold uncertainty score0.654

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 integrity0.0000.001
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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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