Magnetizing Cellulose Fibers with CoFe<sub>2</sub>O<sub>4</sub> Nanoparticles for Smart Wound Dressing for Healing Monitoring Capability
Bibliographic record
Abstract
In an attempt to address issues accompanying the unnecessary change of wound dressings of patients in traditional wound care management, we are developing smart wound dressing material, based on magnetic nanosensors, for wireless monitoring of the wound healing process. The technology is based on magnetizing the cellulose component of the dressing and tuning the resulting magnetic cellulose to respond to temperature changes of the wound. Here, we report the development of the magnetic cellulose through grafting of magnetic CoFe 2 O 4 nanoparticles (CoFe 2 O 4 NPs) onto cellulose fibers using a layer-by-layer method. Three different methods were used for the synthesis, but the CoFe 2 O 4 NPs with superior properties were obtained through hydrothermal autoclaving followed by annealing. They had 98% match to the XRD reference pattern and rod-like shape (agglomerating into nanowires), with diameter between 30 and 50 nm and length ranging from 582 nm to 5.42 μm and magnetization and demagnetization values of 84.5 emu g –1 and −84.5 emu g –1, respectively. Upon grafting the CoFe 2 O 4 NP onto fibers, the cellulose became magnetic, with magnetization values dependent on the initial concentration of the CoFe 2 O 4 NP in the grafting media. Computational investigation revealed that the CoFe 2 O 4 NPs are covalently bonded onto the cellulose fiber through the formation of −Co–O–C– bonding. In brief, the current findings advanced the development of a wireless wound-healing monitoring technology based on integration of sensory ferrimagnet CoFe 2 O 4 NPs into cellulose fibers of wound dressings.
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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".