Effect of thermoplastic polyurethane filament on the cellular ceramics structures obtained from material extrusion and polymer-derived ceramic
Bibliographic record
Abstract
Cellular ceramic structures were fabricated via 3D printing of thermoplastic polyurethane (TPU) followed by impregnation with polysilazane, and pyrolysis. The 3D printing was performed using fused filament fabrication (FFF), while the ceramic was obtained through the polymer derived ceramic (PDC) process starting from a commercially available polysilazane, Durazane 1800. We investigated the role of ester- and ether-based TPUs with two different Shore hardness (90A vs 80A) on the impregnation of polysilazane. Regardless of the TPU type and Shore hardness, impregnation of the TPU 3D structure was successful and resulted in dense, non-hollow ceramic struts after pyrolysis. All polyester- and polyether-based TPUs showed a similar mass and volume increase after impregnation with high deviation. The mass loss during pyrolysis was also very similar for all the TPUs. The behavior of these TPUs was then compared with one commercial TPU filament (Ninjaflex with a Shore hardness of 85A). While the Ninjaflex 3D-printed structures showed a greater increase in mass and volume after impregnation, the pyrolysis outcome was almost identical to that of the samples fabricated with both ester- and ether-based TPUs, resulting in dense, non-hollow ceramic struts. Supplementary Information: The online version contains supplementary material available at 10.1007/s40964-025-01243-w.
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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".