A Pulse Voltage Application in Electrochemical Reduction of Solid CaWO<sub>4 </sub>Powder
Bibliographic record
Abstract
Among more than thirty known tungsten containing minerals, only scheelite (CaWO 4 ) and wolframite ((Fe, Mn)WO 4 ) are industrially important [1]. Scheelite is the most abundant mineral of tungsten, but wolframite is used more than scheelite due to easier dissolution in alkaline solutions used in present processing methods [2]. An alternative method based on electrochemical reduction of CaWO 4 by direct current applications in molten salt solutions was recently reported [3-5]. In this study, pulse voltage and constant voltage reduction mechanisms were compared and an optimization of reduction kinetics was achieved. Studies have shown that faster reduction rates could be achieved during pulse voltage applications compared to constant voltage applications, when average voltage value of pulse voltage application was the same as its constant counterpart. Furthermore, analysis of charge, energy and theoretical reduction graphs showed that reduction of calcium tungstate occurs at higher potential differences than 2.2 V, between calcium tungstate and graphite. References: [1] Brown, T. and Pitfield, P. (2013) ‘Tungsten’, in Gunn/Critical Metals Handbook. Wiley-Blackwell, pp. 385–413. [2] Tang, D., Xiao, W., Yin, H., Tian, L., Wang, D. and and, L. T. (2012) ‘Dingding Tang’, Journal of The Electrochemical Society, 159(6), p. E139. doi: 10.1149/2.113206jes. [3] Karakaya, I. and Erdogan, M. (2009)‘Production of Tungsten and Tungsten Alloys from Tungsten Bearing Compounds by Electrochemical Methods’, WIPO PCT application WO 2009/054819A1. [4] Karakaya, I. and Erdogan, M. (2013) ‘Production of tungsten and tungsten alloys from tungsten bearing compounds by electrochemical methods’, CA 2703400Canadian Intellectual Property Office. [5] Erdogan, M. and Karakaya, I. (2010) ‘Electrochemical Reduction of Tungsten Compounds to Produce Tungsten Powder’, Metallurgical and Materials Transactions B, 41(4), pp. 798–804. doi: 10.1007/s11663-010-9374-4.
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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 | 0.001 |
| 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 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".