Mechanical properties of polymer-derived ceramics modified by active nanoparticles
Bibliographic record
Abstract
<p>Polymer-derived ceramics (PDCs) address the shaping limitations of traditional ceramic processing techniques and result in near-net-shape manufacturing of ceramics with complex geometries. One of the major limitations of PDCs is their brittleness. Four different types of nanoparticles were evaluated as fillers to improve the mechanical properties of a commercial PDC, polysilazane (PSZ). Different concentrations of either active fillers (silicon nitride and alumina nano-particle) or passive fillers (boron nitride nanotubes and carbon nanotubes) were added to the PSZ, which was subsequently thermally cross-linked and pyrolyzed under hydrostatic pressure. Nano/micro indentation measurements show that adding silicon nitride nano-powders improves the fracture toughness of the PDCs up to ~ 3 times and adding alumina nano-powders improves the modulus and hardness of the PDCs up to ~ 1.5 and 2.6 times, respectively. The ceramics made with sufficient concentration of active fillers have clearly lower void content than unfilled PSZ, which is attributed to the reaction of these nanoparticles with pyrolysis by-products. Addition of nanotubes, which are passive fillers not expected to react, leads to some mechanical improvement but not the increased density or decreased instance of voids that was achieved with the active fillers. The ceramics developed in this study show interesting combinations of properties and can easily take different shapes. Therefore, they are of interest in protective armor, propulsion, thermal protection, device packaging and bio-material systems.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".