Simultaneous Determination of the Surface Tension and Density of Polystyrene in Supercritical Nitrogen
Bibliographic record
Abstract
In this paper we present a simple procedure for calculating the mass density of polymers at high temperature and pressure conditions. The proposed method entails the integration of a pendant drop volume from an axisymmetric drop image profile; this same pendant drop profile can be used to determine the surface tension using axisymmetric drop shape analysis (ADSA). Our approach yields both density and surface tension data simultaneously and is advantageous since it does not require any additional procedure or module to calculate the density of polymers in a fluid. This integration method provides a set of density data for the sample polymer, polystyrene, in supercritical fluid nitrogen, which is similar to the data derived from the empirical Tait equation and the semiempirical Sanchez−Lacombe equation of state when the temperature is above the polymer glass transition point. The results corroborate the use of all three approaches in determining the mass density of polymers at high temperatures. The comparable surface tension data are obtained at temperatures above the glass transition temperature of polymer with the densities generated by the three density determination approaches. We expect that the approach based on ADSA in conjunction with the mass density determination by the current integration method can be applied to the surface tension measurements of many other polymer melts in various fluid environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".