Development of a biodegradable and eco-friendly novel printing composite using biomaterials on textile substrate and assessing the characterization
Bibliographic record
Abstract
The use of biomaterials has grown in interest over the past few years for their unique properties and diverse applications. The textile printing industry is one of the appropriate sectors to introduce biomaterials that can replace synthetic printing ingredients and reduce environmental threats. In this research, a novel textile printing process was developed using a combination of eco-friendly printing ingredients and later applied with screen printing technology on different textile substrates such as cotton, poly-cotton (65% polyester and 35% cotton), and linen. To evaluate the performance of different printed fabrics, spectrophotometric and color fastness properties were assessed and compared against the conventionally printed control samples. The CIE L*a*b* values showed a slight difference in color intensity between conventional and sustainable printing. For non-washed samples, a prominent fastness result of the highest rating was observed for sustainable-printed specimens. The results were also consistent with the number of washings, as 10 times washed samples also showed comparatively better results in the fastness property and color staining. This eco-friendly printing can replace detrimental synthetic ingredients without compromising the environment, performance, or effectiveness and sustainable printing is a potentially feasible and suitable alternative to conventional printing.
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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 | 0.001 | 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".