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Record W2094763478 · doi:10.1021/jp910045u

Water Adsorption on Kaolinite Surfaces Containing Trenches

2010· article· en· W2094763478 on OpenAlexaff
T. Croteau, Allan K. Bertram, G. N. Patey

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2010
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsKaoliniteTrenchAdsorptionMonolayerChemistryChemical physicsMonte Carlo methodMineralogyPhysical chemistryLayer (electronics)Organic chemistry

Abstract

fetched live from OpenAlex

Recent laboratory studies of water adsorption on kaolinite at 296 K, and at relative humidity (RH) values relevant for the atmosphere, have reported coverages ranging up to tens of monolayers. In contrast, recent simulations have suggested that atomistically smooth kaolinite surfaces uptake only monolayers (some slightly overgrown) at similar RH values. In an effort to possibly explain the laboratory data, we have performed water adsorption calculations on kaolinite surfaces containing trenchlike structures using the grand canonical Monte Carlo simulation method at 298 K. The results obtained show that the granularity of the surfaces can play a major role in the adsorption of multiple layers of water. For all trenches considered, multilayers of water were observed over a large range of RH. The narrowest trench investigated remained filled with water even in the very low RH regime (<or=0.0003%). Increasing the trench width resulted in partial or complete trench filling depending on the RH value, with large water mounds growing on the step edges. This strong affinity for water is explained by very attractive water-lattice interactions inside the trenches, especially near the walls. Our calculations suggest that water adsorption in trenches, and possibly in other similar defects, can offer an explanation of the experimental results.

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 score0.003
Threshold uncertainty score0.271

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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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

Citations40
Published2010
Admission routes1
Has abstractyes

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