Advances in CO<sub>2</sub>‐assisted oxidative dehydrogenation of light alkanes to light alkenes
Bibliographic record
Abstract
Abstract The CO2‐assisted oxidative dehydrogenation (ODH) of light alkanes offers a promising route for converting underutilized resources into valuable chemical feedstocks while addressing environmental challenges associated with CO2 emissions. CO2 plays a dual role in ODH by acting as a mild oxidant that enhances product selectivity and catalyst stability while preventing carbon deposition through the Reverse Water‐Gas Shift (RWGS) and Boudouard reactions. The review has elucidated a variety of catalyst design and optimization strategies that may guide the future development of novel CO2‐assisted ODH catalysts with improved alkane conversion, superior alkene selectivity, and long‐term stability. It provides a comprehensive analysis of the structural characteristics, catalytic performances, and reaction mechanisms of typical catalysts, including transition metal catalysts (e. g., Cr‐based, Co‐based, V‐based), metal oxide catalysts (e. g., Ga‐based, In‐based), noble metal catalysts (e. g., Pt‐based, Ru‐based), and bimetallic catalysts. Special attention is given to the structure‐performance relationship of these catalysts, emphasizing how changes in promoters, supports, and morphology affect critical properties such as redox behavior, acidity‐basicity balance, dispersion of active components, and catalyst‐support interactions. Finally, future research directions and perspectives for the CO2‐assisted ODH of ethane and propane are proposed, with a focus on advancing catalyst design and optimization strategies. This review aims to serve as a comprehensive reference for researchers exploring the potential of CO2‐assisted ODH in promoting sustainable production of light alkenes.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".