Synthesis of vacancy-rich titania particles suitable for the additive manufacturing of ceramics
Bibliographic record
Abstract
Abstract In the last decades, titania (or TiO2) particles played a crucial role in the development of photo-catalysis and better environmentally-friendly energy-harvesting techniques. In this work, we engineer a new generation of TiO2 particles rich in oxygen vacancies using a modified sol–gel synthesis. By design, these vacancy-rich particles efficiently absorb visible light to allow carefully-controlled light-induced conversion to the anatase or rutile crystalline phases. FTIR and micro-Raman spectroscopy reveal the formation of oxygen vacancies during conversion and explain this unique laser-assisted crystallization mechanism. We achieve low-energy laser-assisted crystallization in ambient environment using a modified filament 3D printer equipped with a low-power laser printhead. Since the established high-temperature treatment necessary to convert to crystalline TiO2 is ill-suited to additive manufacturing platforms, this work removes a major fundamental hurdle and opens whole new vistas of possibilities towards the additive manufacturing of ceramics, including carefully-engineered crystalline TiO2 substrates with potential applications for new and better photo-catalysis, fuel cells and energy-harvesting technologies.
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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 | 0.000 | 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".