Application of Ni–Spinel in the Chemical-Looping Conversion of CO<sub>2</sub> to CO via Induction-Generated Oxygen Vacancies
Bibliographic record
Abstract
We demonstrate the technical feasibility of a novel and efficient method for the valorization of CO 2 produced by the reverse water gas shift reaction (rWGS), while using an extruded NiFe 2 O 4 as catalyst and self-controlled heating medium induced by magnetic heating. First, oxygen vacancies (δ) were generated by flowing an Ar/H 2 mixture over the catalyst for 1 h at ca. 400 °C. Then, an Ar/CO 2 mixture was flowed over the activated catalyst (NiFe 2 O 4-δ ) in similar conditions, leading to CO generation and oxygen restocking. We study the impact of heating method (conventional or induction), gas feeding, and number of cycles on the catalyst performance. We show that the catalyst retains activity during multiple cycles (1.37 ± 0.07 μmol/g of NiFe 2 O 4 ) but slowly reduces upon H 2 exposure. Extensive catalyst characterization suggests that (Ni,Fe) clusters forming on the surface of the Ni–ferrite nanoparticle result from the segregation of metal atoms recruited from octahedral sites of the Ni–ferrite. Such change in the chemistry and structure of the catalyst has a profound impact on the activity of the catalyst and the total CO production. Induction heating excelled in thermally activating the catalyst in a short time; however, it suffers from an uneven distribution of the temperature along the bed, which led to the reduction of overheated zones of the catalyst bed. Finally, simultaneous feeding of H 2 and CO 2 allowed a higher production of CO when compared to chemical looping, up to 7.74 ± 0.67 μmol/g of NiFe 2 O 4 in a 1-h experiment.
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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) | 0.001 | 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".