Construction of cellulose nanofibers/quaternized chitin/organic rectorite composites and their application as wound dressing materials
Bibliographic record
Abstract
Traumatic injury is a major cause of mortality, and poor wound healing affects millions of people. Thus, the development of effective wound dressings is essential for speeding up wound healing and decreasing mortality. In this study, a suspension of carboxylated brown algae cellulose nanofibers (BACNFs) with a high aspect ratio was freeze dried to prepare a sponge. The sponge showed high porosity and water absorption capacity; thus, it can absorb wound exudates when used as a wound dressing. In addition, quaternized β-chitin (QC) with antibacterial properties was intercalated into the interlayer space of the organic rectorite (OREC) via electrostatic interactions to obtain composite suspensions (QCRs) with improved antimicrobial activity compared to that of QC alone. Subsequently, the BACNF sponge was soaked in the QCR suspension to absorb QCRs via electrostatic interactions and hydrogen bonding from which cellulose nanofiber/quaternized chitin/organic rectorite composite (BACNF/QCR) sponges were constructed via freeze-drying. The in vivo animal tests demonstrated that the BACNF/QCR sponges rapidly induced hemostasis in a rat tail amputation test, making them superior to the traditional hemostatic materials. Furthermore, BACNFs/QCRs could substantially promote collagen synthesis and neovascularization, thereby accelerating wound healing 3 days earlier than gauze. This multi-functional biomedical material, fabricated using natural substances, shows great potential to be used for wound healing.
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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".