Ultra‐Hydrophobic Gauze Driving Super‐Haemostasis
Bibliographic record
Abstract
Controlling bleeding by applying pressing cotton gauze is the most facile treatment in prehospital emergencies. However, the wettable nature of cotton fibers leads to unnecessary blood loss due to excessive blood absorption, inseparable adhesion-induced pain, and pliable to infection. Here, a kind of ultra-hydrophobic haemostatic anti-adhesive gauze whose surface is loaded with polydimethylsiloxane (PDMS) and hydrophobic-modified cellulose nanocrystals (CNCs), achieving a water contact angle of ≈160° is developed. It is demonstrated that the mechanism by which hydrophobic CNCs promote blood clotting is associated with their ability to activate coagulation factors, contributing to fibrin formation, and promoting platelet activation. The blood-restricting effect results from the low surface energy layer formed by PDMS and then the alkyl chains of hydrophobic CNCs are combined. The produced ultra-hydrophobic gauze resists blood flow and diffusion, decreases blood loss, is effortlessly peelable, and minimizes pathogen adhesion. Compared to the commercial cotton gauze, this gauze achieved effective haemostasis and antiadhesion by reducing blood loss by more than 90%, shortening haemostasis time by more than 75%, lowering peeling force by more than 90% and minifying bacterium attachment by more than 95%. This work presents promising applications in terms of prehospital first aid.
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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".