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Fundamental Differences between Magnesium and Alkali Metal Electrowinning

2014· article· en· W2009908963 on OpenAlex
D.S. van Vuuren, Eugene Swanepoel

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced materials research · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsCanadian Society of Intestinal Research
Fundersnot available
KeywordsElectrowinningMagnesiumSpan (engineering)MetallurgyAlkali metalElectrodeAnodeMaterials scienceChemistryEngineeringStructural engineering

Abstract

fetched live from OpenAlex

<span><p><span lang="DE"><span style="font-family: Times New Roman;" face="Times New Roman"><span style="font-size: medium;" size="3">In the Kroll and Hunter processes to produce titanium from TiCl</span> <span style="font-size: small;" size="2">4</span> <span style="font-size: medium;" size="3">, magnesium and sodium are used respectively as reducing agents. These processes are slow and very energy intensive and consequently much work was done over the years to improve the economics of producing these metals. In this regard, more success has been achieved with improving the economics of magnesium electrowinning than with alkali metal electrowinning. </span></span></span><span lang="EN-US"><span style="font-family: Times New Roman; font-size: medium;" face="Times New Roman" size="3">Magnesium electrowinning cells generally have electrodes with a planar shape and alkali metal electrolysis cells have electrodes with a cylindrical shape. Furthermore, recent advances in magnesium electrolysis allowed the introduction of bipolar electrodes, whereas such electrodes have not been introduced in alkali metal electrowinning cells. </span></span><span lang="EN-US"><span style="font-family: Times New Roman; font-size: medium;" face="Times New Roman" size="3">It is conceptually possible to replicate the advances in the construction of magnesium electrowinning cells to improve sodium or other alkali metal electrowinning cells. However, there are underlying fundamental reasons why it would be difficult to do so.</span></span><span lang="EN-US"><span style="font-family: Times New Roman; font-size: medium;" face="Times New Roman" size="3">In this paper the technologies for magnesium and alkali metal electrowinning cells are briefly reviewed. The reasons why it would be difficult to copy the improvements made in magnesium electrowinning technology to alkali metal electrowinning technology are then explained in terms of the implications of the underlying chemical and physical properties of the chemicals involved in the processes.</span></span></p>

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Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.324
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it