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Effect of Ammonium Fluoride Doping on Nitrogen, Oxygen, and Methane Clathrate Hydrates

2022· article· en· W4293802150 on OpenAlexaff
Byeonggwan Lee, Kyuchul Shin, Saman Alavi, John A. Ripmeester

Bibliographic record

VenueEnergy & Fuels · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersKyungpook National University
KeywordsClathrate hydrateMethaneNitrogenChemistryFluorideInorganic chemistryDopingOxygenAmmoniumHydrateMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The van der Waals diameters of N 2, O 2, and CH 4 are almost identical; however, they have different electrostatic charge distributions, and their preferred hydrate structures are different. The O 2 and N 2 molecules form structure II (sII) clathrate hydrates under moderate pressure conditions up to 1 kbar, while the CH 4 molecule forms a structure I (sI) clathrate under these pressure conditions. In this work, we investigated the effect of NH 4 F doping on N 2, O 2, and CH 4 hydrates with powder X-ray diffraction (PXRD) measurement. From the PXRD pattern analyses, the lattice parameter decreased for all three hydrates as the concentration of NH 4 F doping in the framework increased. The sizes and electrostatic charge distributions within hydrate cages were “tuned” by the NH 4 F doping, and the transition of the preferred clathrate structure of N 2 hydrate from sII to sI occurred at doping concentrations greater than 5 mol %. This transition was not observed in O 2 and CH 4 hydrates. The findings in this work reveal that the guest–host van der Waals and electrostatic interactions can be adjusted by the NH 4 F doping to the host framework and suggest that the crystal engineering of the hydrate lattice can be an alternative to improve hydrate-based gas separation technologies.

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 score0.006

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 head0.006
GPT teacher head0.212
Teacher spread0.207 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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