Ink Dyes for Washable Leathers
Bibliographic record
Abstract
Inks, contain pigments or dyes in liquid or paste form, are used to color an image, text, or design. Although some patents describing the ink jet printing methods for textile and leather were found, no report was found addressing the application of ink dyes in leather dying process and determination of washing and other fastness properties. In this study, the potential application of ink dyes in leather industry was investigated as an alternative to conventional dyes for the production of washable leathers in dying process. For this purpose, leather-dyeing recipes were developed by using two different ink dyes and applied at dying process of metis type crust leathers in order to produce washable leathers. The quality performance of dyed leathers were investigated in terms of color measurements, to-and-fro rubbing fastness, crockmeter rubbing fastness, and light fastness characteristics before and after washing leathers in a washing machine under specified conditions according to ISO 15702 standard. The color measurements prior and subsequent to washing process were determined with Minolta CM-3600A spectrophotometer. Toand-fro rubbing, crockmeter and light fastness properties were examined in accordance with ISO 11640, ASTM D5053 and ISO 105-B02 standards respectively. The results of the study showed that ink dyes could be used as a newly adapted dye group and applied successfully in leather dyeing process for the production of washable leather goods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".