High catalytic efficiency of <scp>Pd</scp> nanoparticles immobilized on <scp>TiO</scp><sub>2</sub> nanorods‐coated ceramic membranes
Bibliographic record
Abstract
In this work, we designed and constructed an efficient and reusable membrane catalyst by loading palladium (Pd) nanoparticles on TiO 2 nanorods (NRs)‐coated ceramic membrane. The morphology and structure analysis show that the TiO 2 NRs with a width of 200 nm–1000 nm and a length of about 4000 nm can be successfully synthesized on the ceramic membrane by a two‐step hydrothermal method. The Pd nanoparticles with an average particle size of 4 nm can be uniformly distributed on the TiO 2 NRs‐coated ceramic membrane. The catalytic performance evaluation highlights that higher catalytic activity and stability are observed for the Pd nanoparticles immobilized on the TiO 2 NRs‐coated ceramic membrane compared to the ones loaded on the unmodified ceramic membrane in the liquid‐phase p ‐nitrophenol hydrogenation. The TiO 2 NRs can provide more areas for the deposition of Pd nanoparticles, and then more Pd nanoparticles with better dispersion can be loaded on the membrane, leading to superior catalytic activity. The interaction between Pd nanoparticles and TiO 2 provides less leaching of Pd nanoparticles and higher catalytic stability.
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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 | 0.001 | 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".