Carbon Nanotube Reinforced Porous Gels of Poly(methyl methacrylate) with Nonsolvents as Porogens
Bibliographic record
Abstract
We fabricated porous organogels of poly(methyl methacrylate) (PMMA) using nonsolvents as porogens. In contrast to the use of inorganic materials, surfactants, etc., as porogens, we used the nonsolvents for PMMA such as water, methanol, propanol, and cyclohexane. This offers the advantage of not having to extract the porogen after gel formation. The nonsolvent simply evaporates. We find that the pore size can be controlled by matching the solubility parameters of the solvent/nonsolvent mixtures and that of PMMA. Incorporation of CNT itself does not lead to porous morphology in this case, unless a nonsolvent is added. Introducing carbon nanotubes during the addition of nonsolvent for gelation enhances the elongation ratio of the PMMA gel. We show that the CNT forms a network around the pores. Infrared and Raman spectra show no specific interaction between the CNT and PMMA. Hence, the former simply acts as a reinforcing filler.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".