Synergistic Effect of the Electronic Structure and Defect Formation Enhances Photocatalytic Efficiency of Gallium Tin Oxide Nanocrystals
Bibliographic record
Abstract
The design of photocatalysts with enhanced efficiency is pivotal to sustainable environmental remediation and renewable energy technologies. Simultaneous optimization of different factors affecting the performance of a photocatalyst, including the density of active surface sites, charge carrier separation, and valence and conduction band redox potentials, remains challenging. Here, we report the synthesis of ternary gallium tin oxide (GTO) nanocrystals (NCs) with variable composition and investigate the role of Ga³⁺ dopants in altering the electronic structure of rutile-type SnO₂ NC lattice using steady-state and time-resolved photoluminescence spectroscopies. Substitutional incorporation of Ga³⁺ increases the band gap of SnO₂ NCs, imparting the reducing power to the conduction band electrons, and causes the formation of acceptor states, which, in conjunction with electron trapping by donors (oxygen vacancies), leads to stabilization of the photoexcited carriers. Combination of a decrease in the charge recombination rate and adjustment of the conduction band reduction potential to more negative values synergistically promote the photocatalytic efficiency of the GTO NCs. The apparent rate constant for the photocatalytic degradation of rhodamine-590 dye by optimally prepared GTO NCs is 0.39 min–¹, more than 2 times greater than that by benchmark AEROXIDE TiO₂ P25 photocatalyst. The results of this work highlight the concept of using rational aliovalent doping of judiciously chosen metal oxide NC lattices to simultaneously manipulate multiple photocatalytic parameters, enabling the design of versatile and highly efficient photocatalysts.
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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) | 0.000 | 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".