MétaCan
Menu
Back to cohort
Record W7125523598 · doi:10.3929/ethz-c-000791049

Acceleration of butane vapor nucleation by carbon dioxide gas

2025· other· en· W7125523598 on OpenAlexaboutno aff
Arnab Choudhury, Felix Graber, Stefan Feusi, Jan Krohn, Jai Khatri, Fernando Torres Hernandez, Chenxi Li, Ruth Signorell

Bibliographic record

VenueRepository for Publications and Research Data (ETH Zurich) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsButaneNucleationCarbon dioxideSaturation (graph theory)TolueneGas phaseNatural gasKinetic energy

Abstract

fetched live from OpenAlex

Carbon dioxide (CO2) gas is known to strongly accelerate nucleation of other gas phase components, such as water and toluene. The acceleration was attributed to the formation of transient heteromolecular dimers and referred to as the chaperon mechanism. In this work, we investigate this phenomenon for butane-CO2 gas mixtures with mass spectrometry in the post-nozzle flow of a Laval expansion at a temperature of 51 K and a pressure of 40 Pa. At moderate CO2 and butane concentrations, we observed an acceleration of butane nucleation by the chaperon mechanism, albeit only by a factor of about two compared to unary butane nucleation. The fact that the chaperon mechanism is less important for butane than for water and toluene can be rationalized by the weaker intermolecular interactions between butane and CO2. At higher CO2 and butane concentrations, nucleation and cluster growth overlap in time, which leads to saturation of the measured total butane concentration. Using a kinetic model, we show that saturation is caused by the formation of heteromolecular butane-CO2 clusters of different sizes and compositions. Studies on nucleation at low temperatures in such systems are relevant for flue or natural gas separation.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.106
GPT teacher head0.400
Teacher spread0.294 · 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
GenreOther

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

Explore more

Same venueRepository for Publications and Research Data (ETH Zurich)French-language works237,207