Atomically dispersed Pd on nanostructured TiO<sub>2</sub> for NO removal by solar light
Bibliographic record
Abstract
Reducing the particle size of noble metals on ceramic supports can maximize noble metal performance and minimize its use. Here Pd clusters onto nanostructured TiO 2 particles are prepared in one step by scalable flame aerosol technology while controlling the Pd cluster size from a few nanometers to that of single atoms. Annealing such materials at appropriate temperatures leads to solar photocatalytic NO x removal in a standard ISO reactor up to 10 times faster than that of commercial TiO 2 (P25, Evonik). Such superior performance can be attained by only 0.1 wt.% Pd loading on TiO 2 . Annealing these flame‐made powders in air up to 600 °C decreases the amorphous TiO 2 fraction and increases its crystal and particle sizes as observed by x‐ray diffraction (XRD) and N 2 adsorption. The growth of single Pd atoms to Pd clusters on TiO 2 prepared at different Pd loading and annealing conditions was investigated by scanning transmission electron microscopy and XRD. The single Pd atoms and clusters on TiO 2 are stable up to, at least, 600 °C for 2 h in air but at 800 °C they grow into PdO nanoparticles whose fraction is comparable with the nominal Pd loading. Hence, most of Pd atoms are on the TiO 2 surface where at 800 °C they diffuse and coalesce. Diffuse reflectance infrared Fourier transform spectroscopy reveals NO adsorption on single, double, three and fourfold coordinated Pd atoms depending on their synthesis and annealing conditions. The peak intensity of NO adsorption sites involving multiple Pd atoms is substantially lower in TiO 2 containing 0.1 wt.% than 1 wt.% Pd but that intensity from single Pd atoms is comparable. This indicates the dominance of isolated Pd atoms compared to clusters in Pd/TiO 2 containing 0.1 wt.% Pd that match or exceed the photocatalytic NO x removal of Pd/TiO 2 of higher Pd contents. © 2016 American Institute of Chemical Engineers AIChE J , 63: 139–146, 2017
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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".