Intensified processes for CO2 capture and valorization by catalytic conversion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Energy and environmental issues are today's major concerns. To solve huge energy needs, the increasing use of fossil fuels leads to significant amounts of CO 2 emissions, which have major negative effects on the environment. An urgent reduction in CO 2 emissions is therefore an absolute priority to minimize the actual global warming. Carbon capture & utilization (CCU) has been introduced as a sustainable avenue. Viewing CO 2 as a resource (renewable feedstock) rather than a waste, its conversion into different value-added products offers an attractive and efficient alternative to CO 2 storage via chemical recycling. However, CO 2 is a very stable molecule whose conversion is a very difficult and complex task. On the other hand, from a sustainable development perspective, CO 2 conversion by catalytic hydrogenation reactions requires hydrogen derived from renewable sources. Because of numerous benefits, our group has been focussing high attention to the application of different process intensification tools to proposed technologies for CO 2 capture in gas/liquid contactors (including membrane separation and enzymatic processes), highly pure hydrogen production with in-situ CO 2 capture, and CO 2 conversion by catalytic hydrogenation, which will be reviewed in the present paper.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 it