Room-Temperature Selective Detection of H<sub>2</sub> by Pd Nanoparticle-Decorated SnO<sub>2</sub>@WO<sub>3</sub> Core–Shell Hollow Structures
Bibliographic record
Abstract
Sensitive H 2 sensors play key roles in the large-scale and safe applications of H 2 . In this study, we developed novel ternary Pd-loaded SnO 2 @WO 3 core–shell structures by hydrothermal and in situ reduction methods. The compositions of the optimized ternary core–shell structures (Pd-SW-2) are prepared on the basis of the optimal binary core–shell structures (SW-X) according to the sensing performances to H 2 . Gas sensing tests reveal that the sensing performances (e.g., sensing response and response/recovery time) to H 2 are gradually improved after the formation of core–shell structures and the modification of Pd nanoparticles. 10Pd-SW-2 exhibits the highest response (370 times and 204 times higher than those of SnO 2 and SW-2, respectively) and the shortest response and recovery time (19/53 s) to 100 ppm of H 2 at 25 °C among the as-prepared ternary and binary composites. Combined with the morphology, XPS, electrochemical, H 2 -TPR, and O 2 -TPD analyses, the underlying reasons for the improved sensing performance of 10Pd-SW-2 are attributed to (1) the unique core–shell hollow structure and appropriate Pd particle sizes and distribution, (2) abundant oxygen vacancies, (3) the electron sensitization resulting from the energy band structure, and (4) the excellent chemical sensitization originated from the interaction between Pd/PdHx/PdO and H 2 .
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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".