A comparative study of nano-fillers to improve toughness and modulus of polymer-derived ceramics
Bibliographic record
Abstract
Abstract Brittleness is a major limitation of polymer-derived ceramics (PDCs). Different concentrations of three nanofillers (carbon nanotubes, Si3N4and Al2O3nanoparticles) were evaluated to improve both toughness and modulus of a commercial polysilazane (PSZ) PDC. The PSZs were thermally cross-linked and pyrolyzed under isostatic pressure in nitrogen. A combination of mechanical, chemical, density, and microscopy characterizations was used to determine the effects of these fillers. Si3N4and Al2O3nanoparticles (that were found to be active fillers) were more effective than nanotubes and improved the elastic modulus, hardness, and fracture toughness (JIC) of the PDC by ~ 1.5 ×, ~ 3 ×, and ~ 2.5 ×, respectively. Nanotubes were also effective in maintaining the integrity of the samples during pyrolysis. The modulus and hardness of PDCs correlated positively with their apparent density; this can provide a fast way to assess future PDCs. The improvement in fracture toughness was attributed to crack deflection and bridging observed in the micro-indentation cracks in the modified PDCs. The specific toughness of the modified PDCs was 4 × higher than that of high-purity alumina, and its specific modulus reached that of commercially available technical ceramics. These PDCs can also easily take different shapes and therefore are of interest in protective armor, propulsion, thermal protection, device packaging and biomaterial systems.
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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".