Aqueous-Phase Reforming of Methanol for Hydrogen Production on Nitrogen-Doped Ceria: The Effect of the Doping Method
Bibliographic record
Abstract
This study first compares the effects of three nitrogen doping methods, namely, solvothermal, hydrothermal, and coheat treatments, on the catalytic performance of Pt/CeO 2 catalysts for aqueous-phase reforming (APR) of methanol to produce hydrogen. Characterization techniques (X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), H 2 temperature-programmed reduction (H 2 -TPR), temperature-programmed desorption of O 2 (O 2 -TPD), and Fourier transform infrared spectroscopy (FT-IR)) were used to analyze the impact of the N-dopant type and content on oxygen vacancy formation, Ce 3+ ratio, crystal structure, and active sites. Compared to the undoped sample, N-doping significantly enhanced the catalytic performance, increasing the turnover frequency (TOF) from 773 to 1290/h at 200 °C with 0.5 wt % Pt and a methanol-to-water molar ratio of 1:1. Hydrothermal treatment generated more oxygen vacancies due to Ce–N–O bond formation, while coheat treatment produced both Ce–N–O and Ce–N bonds. Triethanolamine (TEA) proved effective for hydrothermal N-doping, promoting oxygen vacancies via ethanol derivative formation. In contrast, solvothermal treatment was less effective due to inadequate N-doping and disruption of the CeO 2 nanorods. This study highlights the importance of selecting suitable N-doping strategies to improve the activity and stability of Pt/CeO 2 catalysts for hydrogen production.
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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".