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Record W2794005977 · doi:10.1007/s11663-018-1215-x

A XANES Study of Sulfur Speciation and Reactivity in Cokes for Anodes Used in Aluminum Production

2018· article· en· W2794005977 on OpenAlexfundno aff
Gøril Jahrsengene, Hannah C. Wells, Stein Ro̸rvik, Arne Petter Ratvik, R. Haverkamp, Ann Mari Svensson

Bibliographic record

VenueMetallurgical and Materials Transactions B · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicMolten salt chemistry and electrochemical processes
Canadian institutionsnot available
FundersNational Research Council CanadaWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchElkem CarbonNorges ForskningsrådUniversity of SaskatchewanCanadian Light Source
KeywordsSulfurXANESReactivity (psychology)ChemistryAnodeCarbon fibersInorganic chemistryMetallurgyMaterials scienceOrganic chemistryElectrodeSpectroscopy

Abstract

fetched live from OpenAlex

Abstract Availability of anode raw materials in the growing aluminum industry results in a wider range of petroleum cokes being used to produce carbon anodes. The boundary between anode grade cokes and what previously was considered non-anode grades are no longer as distinct as before, leading to introduction of cokes with higher sulfur and higher trace metal impurity content in anode manufacturing. In this work, the chemical nature of sulfur in five industrial cokes, ranging from 1.42 to 5.54 wt pct S, was investigated with K-edge XANES, while the reactivity of the cokes towards CO 2 was measured by a standard mass loss test. XANES identified most of the sulfur as organic sulfur compounds. In addition, a significant amount is identified (16 to 53 pct) as S-S bound sulfur. A strong inverse correlation is observed between CO 2 -reactivity and S-S bound sulfur in the cokes, indicating that the reduction in reactivity is more dependent on the amount of this type of sulfur compound rather than the total amount of sulfur or the amount of organic sulfur.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

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)0.000
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 score0.008
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.253
Teacher spread0.235 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueMetallurgical and Materials Transactions BSame topicMolten salt chemistry and electrochemical processesFrench-language works237,207