Designing PEEK-based high-performance ternary systems displaying highly controlled hierarchical morphologies
Bibliographic record
Abstract
In this work, ternary systems of melt-processed poly(ether ether ketone) (PEEK) combined with various combinations of meta -poly(ether imide) ( m -PEI), para -poly(ether imide) ( p -PEI), polycarbonate (PC), or poly(phenyl sulfone) (PPSU) are shown to result in uniquely sophisticated and tunable hierarchical morphologies. By combining, for the first time, concepts of thermodynamics of mixing, and surface and interface thermodynamics, it is possible to comprehensively design, predict and generate highly controlled morphologies in ternary systems. To do so, a selected variety of polymer pairs, showing different states of mixing behavior - from fully miscible, to partially miscible, to completely immiscible, are combined into ternary systems and analyzed using spreading coefficients theory. The structure evolves from a biphasic system, as observed when PEEK is combined with PC and m -PEI, to fully triphasic systems when PEEK is instead combined with PC and p -PEI or PPSU. In all cases, PEEK systematically and fully separates the other two components, as predicted by the spreading coefficients, a unique result for ternary systems displaying low interfacial tensions involving miscible or partially miscible polymer pairs. These insights then enabled the preparation of hierarchically porous PEEK monoliths comprised simultaneously of both macro and meso-porous network structures with tunable pore sizes, and even ultraporous materials containing as little as 5 vol% PEEK. The average pore sizes span a considerable range of nearly 4 orders of magnitude, from a few nanometers to several microns. These results underscore the potential of such blends for designing PEEK porous monoliths for very lightweight, high-temperature applications.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".