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Record W4401329931 · doi:10.1039/d4cp00032c

Water molecules in boron nitride interlayer space: ice and hydrolysis in super confinement

2024· article· en· W4401329931 on OpenAlexaff
Amin Hosseini, Amir Masoud Yarahmadi, Shahab Azizi, Asghar Habibnejad Korayem, Rouzbeh Savary

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2024
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversité de Sherbrooke
FundersAustralian Research Council
KeywordsBoron nitrideDurabilityMoleculeBoronHydrolysisMaterials scienceNanotechnologyChemical engineeringChemistryChemical physicsComposite materialOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Development of nano-sized channels and filters in the recent years has made the role of water immensely important as water molecules affect their performance and durability. Here, we take advantage of molecular dynamics and density functional theory methods to demonstrate the shift in behavior of water molecules confined between hexagonal boron nitride (HBN) sheets spaced at 3.0 to 6.5 Å. Our results demonstrate that lower interlayer spaces cause higher amounts of charge transferred between the species, while at extreme degrees of confinement, these interactions cause the disintegration of trapped water molecules. Consequently, the inner face of the HBN sheets is functionalized with hydroxyl groups, releasing hydrogens in the form of protons that travel the interlayer space by Grotthuss mechanism. This is the first-hand evidence of a mechanical form of hydrolysis that corresponds with a nucleophilic attack (on boron atoms) to relieve water from extreme confined conditions. This process unveils a previously unknown behavior of water within extremely confined spaces and reveals new considerations concerning nanofilters and nanochannels.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.263
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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