From Plastic to Silicone: The Novelties in Porous Polymer Fabrications
Bibliographic record
Abstract
Porous polymers are gaining increased interest in several areas due, in great part, to their large surface area and unique physiochemical properties. Porous polymers are conventionally manufactured using specific processes related to the chemical structure of each polymer. With the wide variety of porous polymers that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the preparation processes and fabrication techniques. A detailed comparison between these techniques is also provided. Some of these techniques offer the advantage of controlling the porosity and the possibility to obtain porous 3D polymers. A new generic fabrication process that can be applied to all liquid polymers to texture their outer surfaces with a desired porosity is also presented. The proposed process, which is based on two micromolding steps, offers flexibility in terms of tailoring the texture of the final polymer by simply using porous silicon templates with different pore sizes and configurations. The anticipated process was successfully implemented to texture polyethyl hydrosiloxane (PMHS) using porous silicon and polymethyl methacrylate (PMMA) scaffolds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".