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Record W4401620538 · doi:10.1002/cphc.202400641

Environmental Influence on Stripe Formation at the Graphite‐Water Interface

2024· article· en· W4401620538 on OpenAlexfundno aff
Chenglong Xu, Greg G. Qiao, Lei Bao

Bibliographic record

VenueChemPhysChem · 2024
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsnot available
FundersRMIT UniversityUniversity of AlbertaAustralian Research CouncilOntario Ministry of Natural Resources and Forestry
KeywordsGraphiteChemical physicsAtomic force microscopyNanotechnologyInert gasMaterials scienceInertChemical engineeringChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Understanding the characteristics of graphite-water interfaces is of scientific significance and practical importance. Ordered stripe structures have been observed at this interface, with their origins debated between condensed gas molecules and airborne hydrocarbons. Atomic force microscopy (AFM) studies have revealed variations in the morphology, formation and growth of these ordered structures. Here, we investigate the graphite-water interface under different environmental conditions using PeakForce Quantitative Nanomechanical (PF-QNM) AFM. Our findings reveal that stripe structures with 4 nm width and 0.5 nm periodicity, form and grow under wet laboratory conditions but not in pure inert gas or cleanroom environments. These stripes appear more readily when the graphite surface is immersed in water, with growth associated with gas nanodomains on the surface. This suggests that atmospheric contaminants migrate to the water-graphite interface, potentially facilitated by gas states. These findings underscore the impact of environmental conditions on graphitic materials, providing new insights into the mechanisms underlying stripe formation and growth.

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 score0.055
Threshold uncertainty score0.659

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)0.0000.001

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 head0.190
Teacher spread0.184 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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