A novel utilisation of principal component analysis to optimise sorption isotherms and determine diffusion coefficients of five naphthalimide disperse dyes on polyester fibres
Bibliographic record
Abstract
Abstract Experimental data was gathered from more than 700 dyeings of polyester fibres with five naphthalimide disperse dyes. The dyeing process which was the same for all the dyes was carried out separately at five different temperatures, namely: 40°, 60°, 80°, 100° and 120° centigrade. Dye sorption was measured at 10 min time intervals at each temperature until the completion of the dyeing process. Additionally, the raw data was modified by the principal components analysis (PCA) in order to extract the highly informative part of the raw data (i.e. principal components). Such modified data was correlated with the six main sorption models. The results illustrate that unmodified raw data fit a dual combination of sorption models of the Nernst–Freundlich or Nernst–Scatchard type. However, the novel utilisation of PCA‐modified data illustrate that a linear Nernst type sorption model is appropriate, making it less dependent on dye structure and aggregation. The validity of this approach was confirmed by sensitivity analysis, affinity calculations and diffusivity determination. Furthermore, a novel ‘To To’ numerical method worked extremely well in determining diffusion coefficients based on the Hill's approximate solution.
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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 | 0.001 | 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 | 0.000 | 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".