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Record W4408764336 · doi:10.1016/j.nanoms.2025.03.001

Stable temperature-pressure conditions of natural gas hydrates in porous media: A review

2025· review· en· W4408764336 on OpenAlexaff
Yang Yu, Ran Chen, Yun-Peng An, Leyan Wen, Jianjia Yu, Xingyue Wang, Guobin Zhang, Dingyuan Tang, Yajun Deng, Qingxia Liu

Bibliographic record

VenueNano Materials Science · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China-Shenzhen Robotics Research Center ProjectNational Natural Science Foundation of China
KeywordsPorous mediumNatural gasMaterials scienceChemical engineeringClathrate hydratePetroleum engineeringPorosityChemistryComposite materialGeologyHydrateEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Natural gas hydrates are considered a highly promising future energy, however they could also pose risks, such as triggering submarine landslides and exacerbating the greenhouse effect. The stable temperature-pressure conditions of natural gas hydrates in porous media are critical for estimating natural gas hydrate reserves, designing extraction strategies, and addressing challenges such as submarine landslides and the greenhouse effect induced by natural gas hydrates dissociation. In this review, we systematically summarized the literature regarding the stable temperature-pressure conditions of natural gas hydrates in porous media. Focusing on the nanopore confinement effect and surface effect, we reviewed the influencing mechanisms of four types of porous media, i.e., artificial porous media, sand, clay minerals, and actual marine sediments, on the stable temperature-pressure conditions of natural gas hydrates. We also summarized the latest outcomes of advanced technologies, such as nuclear magnetic resonance (NMR), visualization techniques, and differential scanning calorimetry (DSC), in studying the stable temperature-pressure conditions of natural gas hydrates. With these advanced technologies, it is more promising to reveal the mechanisms of which porous media affect the stable temperature-pressure conditions of natural gas hydrates.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.740
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.281
Teacher spread0.270 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2025
Admission routes1
Has abstractyes

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