Comparison of Novel Conductive Open- and Closed-Porous PPy-PLA Composites
Bibliographic record
Abstract
This study compared the fabrication techniques and characterization of novel open- and closed-porous structures in PPy-PLA conductive composites. For the open-porous composites, PLA samples were fabricated using compression molding and salt leaching with varying salt-to-polymer mass ratios, which were subsequently coated with PPy by in situ polymerization of pyrrole and iron (III) chloride. For the closed-porous composites, a patterned structure of PPy within PLA was created using compression molding of PPy-coated PLA pellets, followed by gas saturation and foaming techniques in order to create the closed pores. Characterization of both porous composites included their physical, mechanical, and electrical properties. Results showed that the modulus increased with increasing relative density and decreasing open porosity. The open-porous composites had lower relative density values but higher open porosities compared to the closed-porous composites. The average size of the closed pores was approximately an order of magnitude larger than the open pores. Lastly, the open-porous composites had higher conductivity values than the closed-porous composites due to the greater surface area of the continuous conductive pathway. The comparisons between open- and closed-porous composites established their characteristic properties for their future development in 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 | 0.001 |
| 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 | 0.000 |
| 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".