Design of polymeric materials: Experiences and prescriptions
Bibliographic record
Abstract
Abstract As process engineering has matured, research interest has shifted towards polymer product quality. In the past 20 years or so, the shift has progressed even further, as interest in polymer product quality has morphed into polymer product design. Product design is intended to be a targeted pursuit of optimal conditions that will yield polymers with desirable properties for a specific application. This can be achieved by following a systematic design framework that employs sequential, iterative steps informed by prior knowledge and experience. This overview provides some background information regarding the need for design (including some examples from previous experience), especially in terms of structure‐property relationships. When links between kinetics (synthesis conditions), polymer structure, and application properties are well‐understood, it becomes possible to essentially reverse‐engineer the polymeric material; the researcher can start with known application requirements and synthesize polymers with tailor‐made properties using an optimized recipe (according to the polymerization kinetics). A suggested design approach is presented herein, followed by the application of the design approach to two large case studies. The number of applications for polymeric materials is essentially limitless; the current work provides typical examples of a systematic polymeric material design framework (and related case studies).
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".