Application of Ni–Spinel in the Chemical-Looping Conversion of CO<sub>2</sub> to CO via Induction-Generated Oxygen Vacancies
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Bibliographic record
Abstract
We demonstrate the technical feasibility of a novel and efficient method for the valorization of CO2 produced by the reverse water gas shift reaction (rWGS), while using an extruded NiFe2O4 as catalyst and self-controlled heating medium induced by magnetic heating. First, oxygen vacancies (δ) were generated by flowing an Ar/H2 mixture over the catalyst for 1 h at ca. 400 °C. Then, an Ar/CO2 mixture was flowed over the activated catalyst (NiFe2O4-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 NiFe2O4) but slowly reduces upon H2 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 H2 and CO2 allowed a higher production of CO when compared to chemical looping, up to 7.74 ± 0.67 μmol/g of NiFe2O4 in a 1-h experiment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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