Synergistic Cu‐Zn/Fly Ash Catalysts for Intensified Sorption‐Enhanced CO <sub>2</sub> Hydrogenation to CO Through the RWGS Reaction
Bibliographic record
Abstract
Abstract The reverse water–gas shift (RWGS) reaction is one of the most direct approaches for converting CO 2 into CO, thereby enabling syngas production for downstream synthesis. Yet, its equilibrium limitations restrict CO yield under typical operating conditions, reducing overall process efficiency. Sorption‐enhanced reverse water–gas shift (SERWGS) is a promising intensified approach for syngas production while minimizing environmental impact, significantly increasing the yield of target products (CO) compared to conventional processes. Here, fly ash (FA) was investigated as a catalytic support to develop a novel Cu–Zn catalyst. Alkali/acid pretreatment on FA was very efficient in increasing specific surface area and porosity, promoting effective Cu/Zn dispersion. 15Cu‐7.5 Zn/FA Na–H catalyst prepared via deposition‐precipitation achieved the best performance (26.2% CO 2 conversion and 97.5% CO selectivity at 350 °C). Incorporating zeolitic adsorbents revealed that LTA‐4A provided superior intensification compared to FAU‐13X, enhancing the maximum CO 2 conversion by 131%, and effectively surpassing the thermodynamic limit of the conventional (non‐intensified) process, at 250 °C. These results highlight the combined role of optimized catalyst formulation and selective adsorbents in enhancing RWGS performance and provide key insights for the development of intensified RWGS processes that overcome thermodynamic limitations, offering valuable guidance for future research on sorption‐enhanced CO 2 conversion via catalytic hydrogenation.
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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".