Green synthesis of a copper–titanium oxide photocatalyst via liquid-assisted resonant acoustic mixing for hydrogen generation
Bibliographic record
Abstract
Conventional methods for synthesizing catalytic materials usually involve utilizing large volumes of solvents, auxiliary substances, and additives to give the desired properties and reactivity to the final products. Consequently, these routes imply the production of waste with potential negative impacts on the environment and living organisms. This work explored a simple, liquid-assisted resonant acoustic mixing (RAM) process to produce copper oxide (CuO x )-modified titanium dioxide (TiO 2 ) heterostructures that are active in the photocatalytic production of hydrogen (H 2 ) from water. The synthesis involves a mixture of precursors and a neglectable amount of water, resulting in eliminating unnecessary work-up stages. RAM acceleration and water content were adjusted to achieve the most homogenous catalysts that show a significant enhancement in photocatalytic hydrogen generation compared to pristine TiO 2 . These promising results open the possibility to further explore RAM-based methods as greener synthesis routes for the obtention of a wider diversity of materials.
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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".