Simultaneous Low-Cost Carbon Sources and CO<sub>2</sub> Valorizations through Catalytic Gasification
Bibliographic record
Abstract
The inherent CO 2 gasification reactivities of chars derived from a series of plentiful carbon sources, including petroleum coke (petcoke), coal with various ranks, biomass, and even municipal solid wastes, have been systematically investigated in this work. Among these, switchgrass char exhibits the best activity, while petcoke char behaves the worst toward CO production. The results from sample characterizations indicate that the inherent char reactivity during CO 2 gasification is closely related to its physical properties such as the alkali metal and oxygen contents as well as the H/C atomic ratio. In addition, the effect of the reaction conditions (i.e., temperature, pressure, space velocity, particle size of char, and CO and CO 2 concentrations in the gas feedstock) on char CO 2 gasification has also been explored. Moreover, a set of supported catalysts have been developed to further promote the char reactivity toward CO 2 gasification at moderate temperature, among which K–Ca/ZnO–CeO 2 ranks at the top in terms of CO 2 conversion and CO production and demonstrates excellent stability during a long-term cumulative run. Through careful analyses of the collected catalyst characterization results, a novel catalyst design composed of two redox metal/metal oxide pairs supported on an oxygen ion conductor, based on the reported K–Ca/ZnO–CeO 2 system, has been proposed for future catalyst development with even better CO 2 gasification performance for fluidized bed applications with much easier catalyst recovery and thus minimized catalyst loss.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".