Kinetics and Mechanism of Turanite Reduction by Hydrogen
Bibliographic record
Abstract
Turanite, Cu<sub>5</sub>(VO<sub>4</sub>)<sub>2</sub>(OH)<sub>4</sub>, is a naturally occurring mineral whose physical and chemical properties are of interest for magnetic data storage, battery applications, and catalysis. In this study, the reduction of nanostructured Cu<sub>5</sub>(VO<sub>4</sub>)<sub>2</sub>(OH)<sub>4</sub> by H<sub>2</sub> is investigated using a combination of scanning transmission electron microscopy–electron energy-loss spectroscopy, X-ray photoelectron spectroscopy, <i>in situ</i> diffuse reflectance infrared Fourier transform spectroscopy, <i>in situ</i> X-ray absorption spectroscopy, first-principles calculations, and kinetic analysis based on thermogravimetry. A two-step mechanism is proposed, in which Cu<sub>5</sub>(VO<sub>4</sub>)<sub>2</sub>(OH)<sub>4</sub> is first reduced, following exponential growth kinetics, to form a stable intermediate consisting of a mix of Cu<sub>3</sub>VO<sub>4</sub>, Cu<sub>2</sub>O, and CuV<sub>2</sub>O<sub>5</sub>. The intermediate phase is then subsequently reduced in a 3-D diffusion-limited process to form two segregated phases consisting of Cu metal and V<sub>2</sub>O<sub>3</sub> particles. The apparent energy barriers for these two steps, obtained from the kinetic analysis, were found to be 52.5 and 71.2 kJ mol<sup>–1</sup> for Stages 1 and 2 of the reduction, respectively. These results highlight the importance of developing a rigorous understanding of the reduction processes involved in metal oxide catalyst preparation.
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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) | 0.023 | 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".