Laser‐Assisted, Large‐Area Selective Crystallization and Patterning of Titanium Dioxide Polymorphs
Bibliographic record
Abstract
Although ubiquitous in multiple industrial applications, the widespread use of solution‐based precursors for crystalline titanium dioxide (TiO2) for optoelectronic device integration remains limited due to its high processing temperature. This limitation generates material compatibility issues and complicates the fabrication steps, especially for low‐temperature substrates used in flexible hybrid electronics and low‐cost photovoltaics. It is currently possible to crystallize TiO2 at lower processing temperatures, but it requires a carefully controlled atmosphere or metallic doping of the amorphous precursor and can only achieve a low‐yield conversion of the precursor. Herein, a qualitative method is presented for the processing of an amorphous photosensitive precursor to achieve high‐yield conversion to highly crystalline TiO2 at room temperature and in ambient environment without added dopants using a low‐energy laser. Moreover, it demonstrates the ability to controllably convert precursor solutions to anatase or rutile TiO2 only by adjusting the laser power density. A real potential for the additive manufacturing of TiO2 structures for photocatalysis, printable flexible hybrid electronics, and low‐cost photovoltaics using low‐energy laser processing that is compatible with heat‐sensitive materials and flexible substrates is shown.
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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) | 0.002 | 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".