Advances and challenges of ceramic pigments for inkjet printing
Bibliographic record
Abstract
In the last decades, the development of new ceramic pigments has been a very pursuit goal, mainly since the emergence of the inkjet technology applied to ceramic tiles. The digital decoration of industrial ceramics has contributed to a notable reduction of pigment consumption and an aesthetical decoration improvement, which makes more flexible the production processes, enhancing the reproducibility and cost savings. The nanopigment requirements of the inkjet technology demand submicronic particle size, ink rheology, stability, drop resolution, etc. Although the up-down procedure by micromilling contributed firstly to the particle size reduction of existing pigments, novel approaches are developed to obtain directly high-quality particles of suitable size with the aim of providing a higher optical efficiency. Thus, in this work, a comprehensive review about the existing crystalline structures, novel compositions, and synthesis methods as well as new coloring mechanisms is addressed, giving an overview of all these advances while considering the four-color process required for inkjet technology. The inkjet technology has the challenge of evolving towards a sustainable technology by eliminating the use of critical raw materials, removing the use of synthesis aid fluxes, and reducing the energy consumption, to approximate to a circular economy which is predominant in this sector.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".