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Record W4293116035 · doi:10.11159/mmme22.128

Development of an Oxidizing-Distillation Technology for the Extraction of Tellurium from a Tellurium-Containing Middling

2022· article· en· W4293116035 on OpenAlexvenueno aff
Alina Nitsenko, Xeniya Linnik, В. Н. Володин, Н. М. Бурабаева

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicField-Flow Fractionation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsTelluriumOxidizing agentDistillationExtraction (chemistry)ChemistryChromatographyInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

In this paper, the results of studying aimed at tellurium extraction from its compound with copper in the form of oxides by the pyrometallurgical method are presented.Technical copper telluride of Kazakhmys Corporation LLP containing crystalline phases, %: Cu 7 Te 4 -36.5;Cu 5 Te 3 -28.5;Cu 2 Te -12.9;Cu 2.5 SO 4 (OH) 3 •2H 2 O -16.2 and Cu 3 (SO 4 )(OH) 4 -6.0 was used as an object of research.As a result of the physical and chemical research and technological experiments, the fundamental possibility of processing technical copper telluride by oxidative distillation roasting with the extraction of tellurium into a separate product has been shown.Air oxygen was used as an oxidant.It has been established that a pressure decrease at the same temperature entails an increase in the degree of tellurium extraction.However, from a technological point of view, the value of the degree of tellurium extraction (93.0-98.0%) at all pressures (within 1 hour) is achieved at a temperature of 1100 °C.Increasing the exposure to 3 hours has a minor beneficial effect.The derived condensate is a free-flowing mixture of crystalline phases of tellurium dioxide and tellurium oxysulfate.This condensate is a middling product for further production of elemental tellurium.

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 score0.039
Threshold uncertainty score0.531

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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