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Record W3028142305 · doi:10.1016/j.jngse.2020.103378

Population-balance modeling of CO2 foam for CCUS using nanoparticles

2020· article· en· W3028142305 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

VenueJournal of Natural Gas Science and Engineering · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of Calgary
FundersNorges Forskningsråd
KeywordsCoalescence (physics)Carbon nanofoamRheologyPorous mediumBubblePopulationMaterials sciencePulmonary surfactantChemical engineeringPorosityShear thinningComposite materialMechanicsEngineering

Abstract

fetched live from OpenAlex

Foam implementation for carbon capture, utilization and storage (CCUS) can greatly improve CO2 mobility control, resulting in enhanced hydrocarbon production and carbon storage capacity. The use of nanoparticles (NP) to create robust foam structures has recently gained attention. Local foam generation and coalescence dynamics can be described by mathematical models. Here we address knowledge gaps for NP foam in porous media by tracking the bubble density (nf) of NP foam data spatially and temporally using an established surfactant (SF) population-balance model. We suggest a reduced shear-thinning effect, compared to SF, to accurately model NP CO2 foam flow in both the high- and low-quality regime. A NP foam rheological transition appeared at gas fraction fg = 0.85. The nf parameter increased linearly with distance from inlet for NP foam, with reduced CO2 mobility and improved displacement efficiency compared to co-injections of water and CO2.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.165

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.029
GPT teacher head0.263
Teacher spread0.234 · 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