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Record W4383988553 · doi:10.1126/sciadv.adf3902

Hydration layer structure modulates superlubrication by trivalent La <sup>3+</sup> electrolytes

2023· article· en· W4383988553 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRubbingLubricationLubricantAqueous solutionElectrolyteLayer (electronics)Materials scienceTribologyAdsorptionSurface finishChemical physicsIonChemical engineeringBoundary lubricationNanotechnologyComposite materialChemistryPhysical chemistryOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Water-based lubricants provide lubrication of rubbing surfaces in many technical, biological, and physiological applications. The structure of hydrated ion layers adsorbed on solid surfaces that determine the lubricating properties of aqueous lubricants is thought to be invariable in hydration lubrication. However, we prove that the ion surface coverage dictates the roughness of the hydration layer and its lubricating properties, especially under subnanometer confinement. We characterize different hydration layer structures on surfaces lubricated by aqueous trivalent electrolytes. Two superlubrication regimes are observed with friction coefficients of 10 −4 and 10 −3 , depending on the structure and thickness of the hydration layer. Each regime exhibits a distinct energy dissipation pathway and a different dependence to the hydration layer structure. Our analysis supports the idea of an intimate relationship between the dynamic structure of a boundary lubricant film and its tribological properties and offers a framework to study such relationship at the molecular level.

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.

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 score0.044
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.284
Teacher spread0.278 · 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