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Record W4411813565 · doi:10.1021/acs.langmuir.5c00646

CO<sub>2</sub> Molecular Behaviors at Kaolinite–Geofluid Interfaces: The Role of Edge Defects in Relation to Geologic Carbon Sequestration

2025· article· en· W4411813565 on OpenAlexafffund
Jiangtao Pang, Hongyi Xu, Qi Li, Zhehui Jin, Fulong Ning

Bibliographic record

VenueLangmuir · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of AlbertaGeomechanica (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsKaoliniteGibbsiteSiloxaneCarbon fibersChemical physicsChemical engineeringDiffusionCarbon sequestrationChemistryMolecular dynamicsMoleculeMaterials scienceMineralogyCarbon dioxideOrganic chemistryComposite materialComputational chemistryPolymer

Abstract

fetched live from OpenAlex

Geologic carbon sequestration (GCS) represents a promising approach to achieve global net-zero carbon targets. Kaolinite, a common component in natural sediments, plays critical roles as a caprock in GCS projects. Herein, molecular dynamics simulations are employed to investigate CO 2 behaviors at the kaolinite–water interface, focusing on natural defects such as nanovalleys. Our findings reveal that the diffusion behavior of CO 2 molecules varies depending on their proximity to different kaolinite surfaces. Specifically, CO 2 near the hydrophobic siloxane surface, which exhibits a stronger affinity for CO 2, demonstrates lower diffusion coefficients compared with molecules near the hydrophilic gibbsite and edge surfaces. This reduced mobility leads to prolonged residence times at the siloxane surface. Additionally, edge sites exhibit greater CO 2 retention than gibbsite surfaces, likely due to their higher surface roughness and reactivity. Furthermore, our results indicate that CO 2 primarily penetrates the nanovalley through the central region and areas adjacent to the siloxane surface. At elevated CO 2 concentrations, nanobubbles begin to form; however, these nanobubbles are unable to enter the nanovalleys due to the presence of substantial breakthrough pressure barriers. This study enhances our understanding of CO 2 behaviors in clay-rich environments and its application within the scope of sustainable chemistry related to the carbon neutrality strategy.

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 score0.232
Threshold uncertainty score0.312

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)0.0000.000

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 head0.251
Teacher spread0.244 · 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
Published2025
Admission routes2
Has abstractyes

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