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Record W7162294474 · doi:10.3997/2214-4609.202522120

Best Practice Approach to Live CO2 Brine Preparation and Solubility Calculation

2025· article· W7162294474 on OpenAlexaff
S. Alimohammadi, L. Abiashue, B. S. Adigun, P. Zhou, G. Nyame, A. Babatunde, O. Mohammadzadeh, L. James

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSolubilityBest practiceBrineCarbon dioxide

Abstract

fetched live from OpenAlex

Summary This study presents a standardized method for preparing and measuring CO2 solubility in brine under geological storage conditions. Accurate CO2 solubility measurements are crucial for laboratory research on CO2 storage and field-scale simulations. The solubility of CO2 in brine is influenced by pressure, temperature, and salinity, with higher pressure increasing solubility, while higher temperature and salinity reduce it. The experimental setup used a floating piston accumulator and a mechanical rocker to mix CO2 with brine at 124 bar and 77°C over durations of 1, 3, and 5 days. Two solubility measurement techniques—mass balance and volume displacement—were compared, with results validated using Duan’s thermodynamic model. Findings indicate that longer mixing durations improve solubility, reaching 0.868 mol/kg after 5 days. The volume displacement method provided more precise results than mass balance. The study highlights the importance of adequate mixing and pressure stabilization in CO2 solubility experiments.

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)0.005
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0040.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.014

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 head0.013
GPT teacher head0.272
Teacher spread0.259 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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