Experimental methods in chemical engineering: Barrier properties
Bibliographic record
Abstract
Abstract Up to half of food spoils or goes to waste; packaging is one element that can extend shelf life and reduce the landfill burden. However, plastic packaging contributes to landfills and microparticles in the environment and, as a consequence, society has mandated industry and academics to identify sustainable materials to replace petroleum derived plastics. Oxygen, water, and CO2 permeability are among the physico‐chemical properties we measure to identify the suitability of new polymer formulations. Other application of gas permeability include petroleum engineering, carbon capture, water purification, and biological systems. Here we concentrate on the basic concepts of gas transport through polymeric film as well as the effect of structural and environmental parameters. We then describe common instrumentation and data they produce with a specific focus on reference standards. To identify the major research areas, we compiled 4271 articles indexed by Web of Science since 2017 with film and polymer as keywords. The VOSViewer software tool classified the 100 most frequent keywords from these articles into six clusters: nanofilteration, thin film composites, and reverse osmosis; nanocomposites, morphology, and polyvinyl alcohol; mechanical, barrier, and physicochemical properties; permeability, membranes, and transport properties; chitosan and antimicrobial and antioxidant properties; and, films, nanoparticles, and drug delivery. Barrier property research will continue to focus on developing biobased polymers and analyzers capable of measuring multiple compounds simultaneously with dozens of samples while minimizing time.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".