Toward High‐Performance Piezoresistive Polymer Derived SiOC Ceramics through Masked Stereolithography 3D Printing with β‐Silicon Carbide Nanopowder Reinforcement
Bibliographic record
Abstract
Abstract Enhancing the piezoresistivity of polymer‐derived silicon oxycarbide ceramics (SiOC PDC ) is of great interest in the advancement of highly sensitive pressure/load sensor technology for use in harsh and extreme working conditions. However, a facile, low cost, and scalable approach to fabricate highly piezoresistive SiOC PDC below 1400 °C still remains a great challenge. Here, the fabrication and enhancement of piezoresistive properties of SiOC PDC reinforced with β‐SiC nanopowders (SiC NP ) through masked stereolithography‐based 3D‐printing and subsequent pyrolysis at 1100 °C are demonstrated. The presence of free carbon in SiC NP augments high piezoresistivity in the fabricated SiC NP ‐SiOC PDC composites even at lower pyrolysis temperatures. A gauge factor (GF) in the range of 4385–5630 and 6129–8987 with 0.25 and 0.50 wt% of SiC NP , respectively is demonstrated, for an applied pressure range of 0.5–5 MPa at ambient working conditions. The reported GF is significantly higher compared to those of any existing SiOC PDC materials. This rapid and facile fabrication route with significantly enhanced piezoresistive properties makes the 3D‐printed SiC NP ‐SiOC PDC composite a promising high‐performance material for the detection of pressure/load in demanding applications. Also, the overall robustness in mechanical properties and load‐bearing capability ensures its long‐term stability and makes it suitable for challenging and severe environment 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 | 0.000 |
| 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".