Ultrasonic-assisted sustainable extraction and dyeing of organic cotton fabric using natural dyes from Dillenia indica leaf
Bibliographic record
Abstract
As a means of preventing the environmental damage caused by synthetic dyes, eco-friendly textile dyeing with natural dyes is gaining popularity worldwide. This research focused on the extraction of natural dyes from Dillenia Indica leaf (DI L.) using an ultrasonic (US) extraction technique and applied on the dyeing of organic cotton fabrics (OCFs). The US method was used for both extractions of DI L. dyes and dyeing of OCFs. Here, the amount of DI L. powder was used 5% and 6.67% for producing light and dark shade, respectively. The investigation of the color fastness to washing, rubbing, and light for the dyed OCFs indicated an excellent rating. The spectrophotometric analysis revealed the L*, a*, b*, C*, h*, R%, and K/S that expressed the correct shade of the dyed OCFs sample. To understand the interaction between DI L. dye and OCFs, different characterization including Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were performed. The characterization results confirmed the successful deposition of DI L. dyes on the OCFs. The other comparable testing results such as bursting strength, air permeability, and thermogravimetric analysis (TGA) of dyed and undyed OCFs were in the acceptable range. One of the important findings of this research was no chemicals were utilized during the extraction and dyeing of OCFs. This process can be referred to as completely chemical-free and advantageous for the environment because no chemicals were needed during extraction or dyeing. Therefore, the natural dye extracted from DI L. is extremely promising and could be a viable option for the sustainable dyeing of OCFs in the textile dyeing industry.
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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".