Evolution of red ceramic pigments: from hazardous compounds to environmentally friendly alternatives
Bibliographic record
Abstract
Synthesis of red ceramic pigments is a challenging task in the ceramic industry. Most classic reds are based on severely toxic materials including lead, arsenic, mercury, selenium, and cadmium, which are forbidden in many countries. On the other hand, the red color is super sensitive to the synthesis parameters, heat treatment conditions (atmosphere and temperature), particle size, etc. Therefore, achieving a bright true red shade and its stability at high temperatures is crucial. There has been a massive attempt to find a sustainable high-temperature resistant alternative for these hazardous compounds. Iron oxide is one of the first red pigments in history, but it cannot produce a bright red shade and its color is mostly red-brown. Ce2S3 is another red pigment with a beautiful red color. But it cannot stand the temperature above 350 °C in an oxidizing atmosphere. Doping lanthanides in the perovskites or entrapping the toxic beautiful chromophores in the core-shell structures are among the strategies to achieve safe bright red pigments. This review outlines the recent progress of hazardous classic reds to environmentally friendly ceramic red pigments. Various compounds and dopants, applied to develop sustainable reds, from simple iron-oxides to composites, solid solutions, core-shell structures, or even purified wastes have been covered in this review.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".