Enhancing Antimicrobial and Fastness Properties of Silk and Lyocell Fabric by Dyeing with Azadirachta indica and Mordanting with Citrus limon Extract
Why this work is in the frame
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Bibliographic record
Abstract
Antimicrobial resistance presents a substantial global health challenge, prompting the exploration of innovative strategies to combat pathogenic bacteria. Within this context, considerable attention has been directed towards textile materials treated with antimicrobial agents for their potential to mitigate the spread of infectious diseases. This research sought to investigate the influence of Neem dyeing and Lemon mordanting on enhancing the innate antimicrobial properties of Silk and Lyocell fabric. Textile materials underwent mordanting exclusively with natural agents, such as citrus lemon extract, to enhance dye adherence. The dyeing process involved the use of Azadirachta indica dye obtained through aqueous boiling, applied to lyocell and silk fabrics under various parameters. The key findings revealed a significant augmentation in the antimicrobial effectiveness of lyocell and silk fabrics following Neem dyeing and Lemon mordanting. Optimized conditions, including prolonged dyeing time, elevated temperatures, and constant dye concentrations, notably improved inhibition percentages against common pathogens like Staphylococcus aureus and Klebsiella pneumoniae. Furthermore, the study assessed the washing and perspiration fastness properties of silk and lyocell fabrics in accordance with established standards. In conclusion, the integration of Neem dyeing and Lemon mordanting emerges as a promising method to significantly enhance the antimicrobial efficacy of Lyocell and silk fabrics. This approach opens avenues for the development of textiles with heightened infection control properties, particularly in sectors focused on public health and hygiene. The evaluation of fastness properties also yielded excellent results, further supporting the viability of this method in academic and practical contexts. Considering the global challenge of antimicrobial resistance, adopting such innovative strategies in textile treatment can contribute significantly to the development of effective and sustainable solutions. Future research endeavours may explore additional applications and variations of this method, ensuring its adaptability and widespread implementation in diverse settings.
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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) | 0.001 | 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 | 0.000 |
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 it