Hydrophobic aerogel-modified hemostatic gauze with thermal management performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Current hemostatic agents or dressings are not efficient under extremely hot and cold environments due to deterioration of active ingredients, water evaporation and ice crystal growth. To address these challenges, we engineered a biocompatible hemostatic system with thermoregulatory properties for harsh conditions by combining the asymmetric wetting nano-silica aerogel coated-gauze ([email protected]) with a layer-by-layer (LBL) structure. Our [email protected] was a dressing with a tunable wettability prepared by spraying the hydrophobic nano-silica aerogel onto the gauze from different distances. The hemostatic time and blood loss of the [email protected] were 5.1 and 6.9 times lower than normal gauze in rat's injured femoral artery model. Moreover, the modified gauze was torn off after hemostasis without rebleeding, approximately 23.8 times of peak peeling force lower than normal gauze. For the LBL structure, consisting of the nano-silica aerogel layer and a n-octadecane phase change material layer, in both hot (70 °C) and cold (−27 °C) environments, exhibited dual-functional thermal management and maintained a stable internal temperature. We further verified our composite presented superior blood coagulation effect in extreme environments due to the LBL structure, the pro-coagulant properties of nano-silica aerogel and unidirectional fluid pumping of [email protected] Our work, therefore, shows great hemostasis potential under normal and extreme temperature environments.
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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 | 0.001 |
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 it