Pd@H<sub>y</sub>WO<sub>3–x</sub> Nanowires Efficiently Catalyze the CO<sub>2</sub> Heterogeneous Reduction Reaction with a Pronounced Light Effect
Bibliographic record
Abstract
The design of photocatalysts able to reduce CO2 to value-added chemicals and fuels could enable a closed carbon circular economy. A common theme running through the design of photocatalysts for CO2 reduction is the utilization of semiconductor materials with high-energy conduction bands able to generate highly reducing electrons. Far less explored in this respect are low-energy conduction band materials such as WO3. Specifically, we focus attention on the use of Pd nanocrystal decorated WO3 nanowires as a heretofore-unexplored photocatalyst for the hydrogenation of CO2. Powder X-ray diffraction, thermogravimetric analysis, ultraviolet–visible-near infrared, and in situ X-ray photoelectron spectroscopy analytical techniques elucidate the hydrogen tungsten bronze, HyWO3–x, as the catalytically active species formed via the H2 spillover effect by Pd. The existence in HyWO3–x of Brønsted acid hydroxyls OH, W(V) sites, and oxygen vacancies (Vo) facilitate CO2 capture and reduction reactions. Under solar irradiation, CO2 reduction attains CO production rates as high as 3.0 mmol gcat–1 hr–1 with a selectivity exceeding 99%. A combination of reaction kinetic studies and in situ diffuse reflectance infrared Fourier transform spectroscopy measurements provide a valuable insight into thermochemical compared to photochemical surface reaction pathways, considered responsible for the hydrogenation of CO2 by Pd@HyWO3–x.
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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) | 0.002 | 0.001 |
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".