Preparation of <scp>SiO<sub>2</sub></scp>/<scp>Si<sub>3</sub>N<sub>4</sub>ws</scp>/<scp>PU</scp> reinforced coating and its reinforcement mechanism for <scp>SLS</scp>‐molded <scp>TPU</scp> materials
Bibliographic record
Abstract
Abstract To address the shortcomings of large porosity and insufficient mechanical properties of skeletonized Thermoplastic polyurethane (TPU) fabrications during the preparation of composite materials by selective laser sintering (SLS) technology. In this paper, SiO 2 @Si 3 N 4 ws‐GPTMS core‐shell structure filler was successfully prepared by first synthetically growing silicon dioxide (SiO 2 ) nanospheres on the surface of silicon nitride whiskers (Si 3 N 4 ws) in situ by sol–gel method, and then modified with silane coupling agent Silane coupling agent (GPTMS). Then the filler was incorporated into the inner and outer surfaces of TPU skeletonized structural parts by vacuum dip coating using a water‐based polyurethane (PU) coating post‐treatment enhancement method, and the surface coating coating of TPU skeletonized structural parts was achieved by warming and curing. This composite filler coating structure greatly improved the mechanical properties of the SLS parts (tensile strength of 20.3 MPa and elongation at break of 338.9%, 163% higher tensile strength, 17.7% higher elongation at break, and 672% higher rebound dimension compared with the pure TPU sintered parts). In addition, the thermal stability of the SLS sintered parts was improved, which greatly enhanced the application of the sintered manufactured parts prepared by SLS technology in the field of rebound‐absorbing artificial wear.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".