MétaCan
Menu
Back to cohort
Record W3188533116 · doi:10.1021/acs.jced.1c00399

A Calorimetric Study on the Phase Behavior of Tetra-<i>n</i>-butyl Phosphonium Bromide + CO<sub>2</sub> Semiclathrate Hydrate and Evaluation of CO<sub>2</sub> Consumption─Impact of a Surfactant

2021· article· en· W3188533116 on OpenAlexaff
Feng-Mei Xie, Xi‐Yue Li, Dong‐Liang Zhong, Peter Englezos, Guo-Xiang Lu

Bibliographic record

VenueJournal of Chemical & Engineering Data · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsHydrateChemistryClathrate hydratePhosphoniumInorganic chemistryBromideMineralogyOrganic chemistry

Abstract

fetched live from OpenAlex

This work presents data on the phase behavior of tetra-n-butyl phosphonium bromide (TBPB) semiclathrate hydrate formed in the presence of CO2 at a stoichiometric concentration (2.57 mol % TBPB). A high pressure microdifferential scanning calorimeter (HP μ-DSC) was employed to measure the phase equilibrium data and identify the dissociation behaviors of TBPB + CO2 semiclathrate hydrate. It was found that the phase equilibrium conditions for TBPB + CO2 semiclathrate hydrate formed at 2.57 mol % TBPB were lower than CO2 hydrate formed in pure water and TBPB + CO2 semiclathrate hydrate formed at 0.5 mol % TBPB. The coexistence of CO2 hydrate (sI) and TBPB + CO2 semiclathrate were identified in the 2.57 mol % TBPB solution. When adding 500 ppm sodium dodecyl sulfate (SDS) into the 2.57 mol % TBPB solution, CO2 hydrate disappeared and more TBPB + CO2 semiclathrate hydrate was formed. However, under the same temperature and pressure conditions, CO2 consumption during the formation of TBPB + CO2 semiclathrate hydrate was reduced when 500 ppm of SDS was added to the 2.57 mol % TBPB solution. Therefore, future work should aim to increase the CO2 storage capacity of TBPB semiclathrate hydrate.

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 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 score0.008
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.312
Teacher spread0.267 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Chemical & Engineering DataSame topicMethane Hydrates and Related PhenomenaFrench-language works237,207