Bibliographic record
Abstract
Carbon dioxide hydrates were synthesized in a 0.10m I.D. and 1.22m tall bubble column equipped with a cooling jacket for heat removal. Visual observations at different driving forces (pressures between 2.75 and 3.60 MPa and temperatures between 0 and 8ºC) were recorded with a digital camera through a sight glass of 118.8 by 15.6 mm. The superficial gas velocity was varied from 20 to 50 mm/s to attain different levels of turbulence in the liquid. The growth rate was found to be dependent on the sequence/method used to reach the operating temperature and pressure. A greater supersaturation was obtained when the system temperature and pressure were reached with very low or no bubble-induced mixing. As a result, hydrates nucleated and grew immediately when starting the gas flow with the reactor volume being quickly filled with hydrates. Moreover, the hydrate growth rate and solution final density were higher when operating conditions partially condensed CO2 resulting in greater interphase mass transfer rates. In parallel, since hydrate formation is an exothermic process and the reaction is often limited by the rate of heat removal, heat transfer measurements were achieved in a simulated hydrate environment. The instantaneous heat transfer coefficient and related statistics gave insight on the role of bubbles on heat transfer and hydrodynamics.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".