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Record W4386913895 · doi:10.1016/j.isci.2023.107981

Electron microscopy approach to the wetting dynamics of single organosilanized mesopores

2023· article· en· W4386913895 on OpenAlexafffund
Chloé Rodriguez, V. Torres‐Costa, Alexander M. Bittner, Sylvie Morin, M. Cascajo Castresana, Serguei Chiriaev, Evgeny Modin, Andrey Chuvilin

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsYork University
FundersEusko JaurlaritzaComisión de Investigaciones CientíficasUniversidad Autónoma de MadridYork UniversityAgencia Estatal de InvestigaciónMinisterio de Ciencia, Innovación y Universidades
KeywordsWettingMesoporous materialGravimetric analysisScanning electron microscopeNanoscopic scaleChemical engineeringMaterials scienceEnvironmental scanning electron microscopeAdsorptionCapillary condensationNanotechnologyChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Columnar mesoporous silicon (PSi) with hydrophobic vs. hydrophilic chemistries was chosen as a model for the local (pore-by-pore) study of water-pore interactions. Tomographic reconstructions provided a 3D view of the ramified pore structure. An in situ study of PSi wetting was conducted for categorized pore diameters by environmental scanning TEM. An appropriate setting of the contrast allows for the normalization of the gray scale in the images as a function of relative humidity (RH). This allows constructing an isotherm for each single pore and a subsequent averaging provides an isotherm for each pore size range. The isotherms systematically point to an initial adsorption through the formation of water adlayers, followed by a capillary filling process at higher RH. The local isotherms correlate with (global) gravimetric determination of wetting. Our results point at the validation of a technique for the study of aging and stability of single-pore nanoscale devices.

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)0.001
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 score0.002
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.258
Teacher spread0.245 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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