Enhanced hybrid photocatalytic dry reforming using a phosphated Ni-CeO2 nanorod heterostructure
Bibliographic record
Abstract
Abstract Operating the dry reforming reaction photocatalytically presents an opportunity to produce commodity chemicals from two greenhouse gases, carbon dioxide and methane, however, the top-performing photocatalysts presented in the academic literature invariably rely on the use of precious metals. In this work, we demonstrate enhanced photocatalytic dry reforming performance through surface basicity modulation of a Ni-CeO 2 photocatalyst by selectively phosphating the surface of the CeO 2 nanorod support. An optimum phosphate content is observed, which leads to little photoactivity loss and carbon deposition over a 50-hour reaction period. The enhanced activity is attributed to the Lewis basic properties of the PO 4 3− groups which improve CO 2 adsorption and facilitate the formation of small nickel metal clusters on the support surface, as well as the mechanical stability of CePO 4 . A hybrid photochemical-photothermal reaction mechanism is demonstrated by analyzing the wavelength-dependent photocatalytic activities. The activities, turnover numbers, quantum efficiencies, and energy efficiencies are shown to be on par with other dry-reforming photocatalysts that use noble metals, representing a step forward in understanding how to stabilize ignoble nickel-based dry reforming photocatalysts. The challenges associated with comparing the performance of photocatalysts reported in the academic literature are also commented on.
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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".