Bibliographic record
Abstract
Owing to their versatility, iron and steel are among the most widely used engineering materials with about 1.6 billion tonnes produced in 2016. The conventional extraction process based on blast furnaces relies heavily on carbon, thus the production of iron and steel accounts for 4-7% of all anthropogenic CO 2 emissions [1]. Due to recent environmental regulations and global action to mitigate climate change, attention has begun to target the environmental aspects of the steelmaking technology, and to this end research activities have begun around the world to develop breakthrough environmentally sound low carbon alternative processes. Among proposed processes, the most promising is based on molten electrolysis, because electricity has the potential to be carbon-free when produced from renewable resources. Within the electrochemical regime, molten oxides are an attractive medium for extraction of iron for a number of reasons [2]. Their high solubilizing power allows iron ore to be directly fed to the electrolytic cells, while their low vapor pressure allows electrolysis to be conducted above the melting point of iron, which greatly improves throughput, and most importantly, oxide ions in the melt can be discharged as environmentally benign oxygen gas if anode materials are inert. Consequently, there has been considerable effort to develop a molten oxide electrolysis process for iron [3]. However, there remain fundamental uncertainties regarding the electrochemistry of iron in molten oxides [4], thus the process has not yet been commercialized. In this work, we present recent fundamental and experimental investigations into the electrochemical behaviour of iron in molten oxides, with the aim of understanding mechanisms that govern electrolysis. Attention is given to the design and testing of supporting electrolytes to minimize electronic conduction in the melt and improve the current efficiency of the process. At the same time, the performance of supporting electrolytes with respect to viscosity, surface tension, diffusivity, ionic conductivity, and liquidus temperature are considered. The results of fundamental investigations were used to strategically direct experimental investigations. [1] Energy Technology Perspectives 2017 . International Energy Agency: Paris (2017): 76-79. [2] D.R. Sadoway. "New Opportunities for Metal Extraction and Waste Treatment by Electrochemical Processing in Molten Salts." J. Mater. Res. 10 (1995): 487-492. [3] A. Allanore. "Features and Challenges of Molten Oxide Electrolytes for Metal Extraction." J. Electrochem. Soc. 162 (2015): E13-E22. [4] W.D. Judge, A. Allanore, D.R. Sadoway, and G. Azimi. " E -log p O2 diagrams for ironmaking by molten oxide electrolysis." Electrochim. Acta 247 (2017): 1088-1094.
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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".