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Record W2490418899 · doi:10.5539/jmsr.v5n4p1

Performance of Sulfuric Acid Leaching of Titanium from Titanium-Bearing Electric Furnace Slag

2016· article· en· W2490418899 on OpenAlexvenueno aff
Xiaoming Qu, Yufeng Guo, Fuqiang Zheng, Tao Jiang, Guanzhou Qiu

Bibliographic record

VenueJournal of Materials Science Research · 2016
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsnot available
FundersProgram for New Century Excellent Talents in UniversityChina Scholarship Council
KeywordsSulfuric acidLeaching (pedology)TitaniumMaterials scienceMetallurgyNuclear chemistryGround granulated blast-furnace slagChemistryCement

Abstract

fetched live from OpenAlex

The sulfuric acid leaching of titanium from titanium-bearing electric furnace slag (TEFS) was investigated under different experimental conditions. In the sulfuric acid leaching process, the MxTi3-xO5(0≤x≤2) and diopside could react with sulfuric acid. The optimum conditions of sulfuric acid leaching process were particle size at < 0.045mm, sulfuric acid concentration at 90 wt.%, acid/slag mass ratio at 1.6:1, feeding temperature at 120 °C, reaction temperature at 220 °C, reaction time at 120minute, curing at 200°C for 120 minute. The [TiO2] concentration of the water leaching was 150 g/L, and leaching temperature at 60℃for 120 minute. Ti extraction could reach 84.29 %. F of titanium-bearing solution was 2.15, and the Ti3+/TiO2 of the titanium-bearing solution was 0.068. The TiO2 content of the leaching residue was 18.32 wt.%. The main mineral phases of the leaching residue were calcium sulphate, spinel, diopside and little MxTi3-xO5.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.046
GPT teacher head0.320
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2016
Admission routes1
Has abstractyes

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