Thermodynamic and kinetic study of fluorinated gas hydrates for water purification
Bibliographic record
Abstract
Abstract This study investigated and compared the thermodynamic stability, kinetic behaviour, and effectiveness of a water purification process using pentafluoroethane (HFC125a) and 1,1,1,2‐tetrafluoroethane (HFC134a) as guest molecules. The hydrate phase equilibria of each fluorinated gas (F‐gas) in pure water and NaCl solution were predicted using the Hu‐Lee‐Sum correlation, which agreed well with the experimental results from our previous studies. Under the same subcooling temperature of 3 K (at 0.3 MPa), the rate of hydrate growth with HFC134a was faster than that of HFC125a in the absence or presence of NaCl. In situ Raman spectroscopy confirmed that the HFC134a and HFC125a molecules occupy only a large cage of structure II hydrate. The Raman shifts of CH and CC bands in all phases (gas, liquid, and hydrate phases) of HFC125a shifted to higher wavelengths than those of HFC134a due to the increase in the number of fluorine atoms. The change in the salinity was studied to evaluate the effectiveness of an F‐gas hydrate‐based water purification process. In addition, the desalination efficiency of the HFC134a and HFC125a hydrates was compared by separating hydrate crystals from the slurries. The results showed that the desalination efficiency (or total dissolved solids removal efficiency) of HFC134a hydrate was higher than that of HFC125a hydrate. This study proves the importance of the water purification process using hydrates.
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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) | 0.000 | 0.000 |
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.
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".