Nanofibrous Photocatalytic Membranes Based on Tailored Anisotropic Gold/Ceria Nanoparticles
Bibliographic record
Abstract
The combination of plasmonic nanoparticles with semiconductor photocatalysts is a good strategy for synthesizing highly efficient photocatalysts. Such binary nanoparticles have demonstrated enhanced catalytic activity in comparison to either plasmonic catalysts or semiconductor catalysts. However, problematic recovery and limited long-term colloidal stability of those nanoparticles in suspension limit their wide use in catalysis. To palliate to such limitations, we embedded binary nanoparticles in polymer fibers to design photocatalytic membranes. First, we used the selective over-growth of crystalline cerium oxide on the gold nanoparticle template with distinct shapes. Gold nanospheres, gold nanorods, and gold nanotriangles were used as the template for the growth of the cerium oxide domains. Then, the resulting nanoparticles were used to catalyze model reactions in suspensions. The gold nanoparticles covered with patches of cerium oxide outperformed the fully covered and naked nanoparticles in terms of catalytic efficiency. Finally, the most efficient binary nanostructures were successfully embedded in nanofibrous membranes by colloidal electrospinning and used in water remediation experiments in a flow-through reactor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".