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Record W4413391443 · doi:10.1115/omae2025-157164

Quantitative Characterization of Mineral Properties and Microstructures in Deep Coal-Rock

2025· article· en· W4413391443 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeomechanics and Mining Engineering
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsCharacterization (materials science)GeologyCoalMineralMineralogyMining engineeringMaterials scienceMetallurgyChemistryNanotechnology

Abstract

fetched live from OpenAlex

Abstract In this study, a comprehensive and practical experimental method was employed to quantitatively characterize mineral properties and microstructure characteristics of deep coal-rock (DCR). Experimentally, core samples were collected from two study areas in a DCR reservoir, and the petrological and mineral characteristics of the DCR were determined by performing proximate analysis, vitrinite reflectance, maceral components, scanning electron microscopy (SEM) and X-ray diffraction (XRD) experiments. Additionally, other tests such as physisorption and high-pressure mercury injection (HPMI) were conducted to quantitatively characterize the nano- to micro-microstructures. The microscopic components of the DCR are primarily composed of vitrinite, followed by inertinite, whereas liptinite and mineral matter occur in comparatively lesser quantities. The storage space within the DCR primarily consists of cellular pores and fractures, with the majority of these pores being occupied by clay minerals, and the crystalline materials present in the DCR contribute to an increase in the specific surface area of the coal rocks to a certain extent. Although the difference in total pore volume (PV) between the micropores and mesopores of Samples Q and J is relatively minor, such a modest increase in total PV contributes to a substantial rise in specific surface area (SSA), leading to a pronounced discrepancy in SSA between these two types of samples. Despite the presence of microcracks in Sample Q, micropores remain the predominant pore type, contributing over 99% to the SSA, thereby serving as the primary space for adsorbed gas. However, macropores and microcracks significantly contribute to the total PV, providing essential flow channels for desorption, diffusion, and seepage of adsorbed gas. Such quantitatively characterized mineral properties and microstructures of DCR will provide valuable insights for assessing the presence of free gas and adsorbed gas.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.191

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 head0.009
GPT teacher head0.192
Teacher spread0.182 · 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

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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