Antimicrobial treatments with chitosan microencapsulated angelica ( <i>Angelica archangelica</i> ) and marsh Labrador tea ( <i>Rhododendron tomentosum</i> ) supercritical CO <sub>2</sub> extracts in linen-cotton jacquard woven textiles
Bibliographic record
Abstract
In this study antimicrobial linen-cotton jacquard textiles were manufactured using green chemistry methods. The functionalization of the fabrics was executed by impregnating chitosan microencapsulated bio-based oils from angelica ( Angelica archangelica L.) (AAC) and marsh Labrador tea ( Rhododendron tomentosum Harmaja) (MLTC) obtained with pilot scale supercritical carbon dioxide extraction. The chemical compositions of the extracts of angelica and marsh Labrador tea were analyzed by a combination of gas chromatography and mass spectrometry. The antimicrobial activities of the extracts, AAC and MLTC microcapsules, and the microencapsulated textiles (AAC and MLTC textiles) were analyzed against gram-positive Staphylococcus aureus and gram-negative Escherichia coli bacteria, dimorphic yeast Candida albicans and filamentous mold Aspergillus brasiliensis. The AAC textile proved 40% inhibition against S. aureus, whereas the MLTC textile demonstrated 43.8% and 51.7% inhibition against both S. aureus and E. coli, respectively. Although the chitosan shell material itself indicated mild activity against both bacterial strains, the extracts increased the antibacterial activities in microencapsulated textiles. In addition, the antifungal impact of the MLTC textile was demonstrated against A. brasiliensis. According to the Fourier transform infrared spectroscopy with attenuated total reflection and field emission scanning electron microscopy analyses, covalent bonding between the microcapsules and textile fibers was established with citric acid as a cross-linker. The antimicrobial activity was also shown to persist in the MLTC textiles after six domestic washing cycles.
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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".