Experimental techniques for the determination of thermophysical properties to enhance chemical processes
Bibliographic record
Abstract
Abstract It is of utmost importance to have accurate experimental data available in order to develop accurate modeling for scientific and engineering purposes as it is emphasized through several herein-reported discussions with reknown scientists and engineers. Many methods are used to determine phase equilibria. Classification of the methods is not straightforward as several basic principles can be mixed in several different ways. In this paper, we have selected some techniques, developed in our laboratory, to illustrate one type of classification. Several apparatuses are described. The techniques where all phases are analyzed are very often preferred to those relying on partial determinations requiring data treatment through models. The internal analyses by means of spectroscopic or other in situ analysis techniques are not applicable every time. Then, sampling devices are necessary. Sampling devices must be reliable and lead to extract sample amounts small enough not to disturb the equilibrium under study. The ROLSI™ sampler developed at MINES ParisTech is a very powerful device allowing one to work up to 100 MPa, 850 K, with corrosive components and with samples from 1 μg to some mg. Applications of this sampler are described for fundamental research (phase equilibrium measurements) and industrial purposes (process control and monitoring).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".