3D Printing Auxetic Architectures for Hypertrophic Scar Therapy
Bibliographic record
Abstract
Abstract Fitting the human body for different purposes such as flexible electronics and functional garments can be a challenging task. Pressure garments are used to treat hypertrophic scars (HSs)pr. However, they have often failed to provide consistent pressure during joint movement. To increase the therapy efficacy, the application is proposed of 3D printed thermoplastic polyurethane (TPU) with an auxetic architecture insert for pressure therapy. Auxetic material can undergo an out‐of‐plane bending into a synclastic curvature, which can easily accommodate the contours of the human body. In this study, the synclastic effect of the auxetic structure under out‐of‐plane bending is illustrated through finite element analysis (FEA) first. Next, the formability, structural deformation, and auxetic response of re‐entrant (RE) and double‐arrowhead (DAH) auxetic structures when loading by a spherical surface in out‐of‐plane direction are unprecedentedly evaluated experimentally and numerically. It can be observed the internal angle of auxetic structure plays an important role regarding shape formability. Nevertheless, the result of wear trial reveals this design facilitates a stable level of pressure during the body motion which promotes the recovery of HS. It is believed the characterized result of auxetic architectures not only contribute to HS therapy, but also any type of biomedical devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".