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Record W4386456407 · doi:10.1021/acs.langmuir.3c01200

Effects of Interfacial Molecular Structures on Pressure-Driven Brine Flow in Silica Mesopores

2023· article· en· W4386456407 on OpenAlexafffund
Lian Duan, Mingshan Zhang, Yiling Nan, Zhehui Jin

Bibliographic record

VenueLangmuir · 2023
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaChina Scholarship Council
KeywordsHydrogen bondCounterionMesoporous materialBrineAdsorptionProtonationChemical engineeringChemistryChemical physicsDeprotonationMesoporous silicaInorganic chemistryMaterials scienceMoleculeIonOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

In this work, we conduct molecular dynamics simulations to investigate pressure-driven brine flow in silica mesopores under typical reservoir conditions (323 K and 20 MPa). While surface counterions accumulate strongly in the vicinity of fully deprotonated silica surfaces, water forms multilayer structures due to hydrogen bonding, counterion hydration, and excluded-volume effect. Brine flow behaviors exhibit adsorption, transition, and bulk-like regions in fully deprotonated silica mesopores, while the transition region is negligible in fully protonated ones. In the adsorption region, strong surface hydrogen bonding and a high degree of counterion hydration collectively hinder water mobility. Even without surface hydrogen bonding, persistent ion hydration impedes water flow, leading to the transition region in fully deprotonated silica mesopores and higher viscosity of brine (with 10 wt % NaCl) in the bulk region. This work elucidates the collective effects of surface chemistry and interfacial water structures on brine flow behaviors in silica mesopores from molecular perspectives.

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 score0.003
Threshold uncertainty score0.007

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 head0.005
GPT teacher head0.215
Teacher spread0.210 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

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