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Record W2334685570 · doi:10.1021/jp509380y

Behavior of Two-Dimensional Hydrogen-Bonded Networks under Shear Conditions: A First-Principles Molecular Dynamics Study

2014· article· en· W2334685570 on OpenAlexafffund
Stephanie A. Whyte, Nicholas J. Mosey

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and Science
KeywordsMolecular dynamicsChemical physicsMaterials scienceSlip (aerodynamics)Hydrogen bondGraphiteIonic bondingHydrogenCovalent bondShear (geology)QuantumNanotechnologyMoleculeChemistryComposite materialComputational chemistryPhysicsThermodynamicsIon

Abstract

fetched live from OpenAlex

Static quantum chemical calculations and first-principles molecular dynamics simulations are used to examine the behavior of two-dimensional hydrogen-bonded systems under sliding conditions, with the goal of assessing whether such systems may be useful as lubricants. The results demonstrate that these systems can be effective lubricants if the hydrogen bonds (HBs) in the system are of moderate strength, evenly distributed in the system, and restricted to reside within the layers. One system that meets these conditions was found to exhibit friction forces and friction coefficients that are comparable to layered systems consisting of sheets of atoms connected via covalent or ionic bonds, such as graphite and MoS 2 . The results also show that the flexibility associated with the HBs allows this system to reversibly undergo large structural deformations. This ability allowed this system to undergo a slip mechanism in which the layers buckled, which was found to reduce the slip barrier. The ability to reversibly accommodate structural changes may represent an advantage of systems comprising sheets consisting of covalently or ionically bonded components, which can be damaged irreversibly as a result of large structural deformations.

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 score0.064
Threshold uncertainty score0.402

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 head0.008
GPT teacher head0.280
Teacher spread0.272 · 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

Citations9
Published2014
Admission routes2
Has abstractyes

Explore more

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