Synergistic manipulation of micro-nanostructures and composition: anatase/rutile mixed-phase TiO<sub>2</sub>hollow micro-nanospheres with hierarchical mesopores for photovoltaic and photocatalytic applications
Bibliographic record
Abstract
The construction of nanocrystals with controllable composition and desirable micro-nanostructures is a well-known challenge. A combination of favorable composition and optimized micro-nanostructures can enhance the performance of a material significantly. Using TiO(2) as an example, we demonstrate here a facile approach to prepare anatase/rutile mixed-phase TiO(2) hollow micro-nanospheres with hierarchical mesopores. Our strategy relies on polymer-assisted assembly of ∼ 5 nm nano-building blocks into three-dimensional hierarchical hollow micro-nanospheres in a mixed alcohol-water solution. This superior micro-nanostructure endows the sample with hierarchical mesopores and a high surface area of 106 m(2) g(-1). We also show that, due to the synergetic effects of the mixed-phase composition and the micro-nanostructures, the sample exhibited significantly improved photovoltaic performance and similar photocatalytic performance compared with the commercial Degussa P25. These results suggested that our sample has great potential for future photovoltaic and photocatalytic applications.
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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".