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Record W4386165893 · doi:10.1002/cjce.25069

Feasibility and reaction process for recovery of Si and Pb from high‐calcium zinc extraction residue by alkali roasting

2023· article· en· W4386165893 on OpenAlexvenueno aff
Shanshan Zhang, Xiaoyi Shen, Hongmei Shao, Mingyu Han, Jianshe Chen

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsnot available
FundersMajor Science and Technology Projects in Yunnan ProvinceNational Natural Science Foundation of China
KeywordsRoastingAlkali metalZincExtraction (chemistry)Residue (chemistry)QuartzChemistryNuclear chemistryActivation energyMineralogyMaterials scienceInorganic chemistryMetallurgyChromatographyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract To explore the feasibility of Si and Pb recovery from high‐calcium zinc extraction residues via alkali roasting, NaOH molten roasting is performed and the reaction process is discussed. Under the optimal reaction conditions—residue–alkali ratio of 1:5.0, reaction time of 1.75 h, and reaction temperature of 500°C—the Si and Pb extraction ratios remained stable at 74.00% and 40.00%, respectively. The X‐ray diffraction patterns of the specimens roasted at different temperatures indicated that the main factor affecting the extraction process was the presence of Na 2 CaSiO 4 , Ca 2 PbO 4 , and quartz. Further, the phase transformations and reaction processes of Si and Pb were determined. In the final phases, the specimen roasted at 550°C included quartz, Na 4 SiO 4 , Na 2 CaSiO 4 , Ca 2 PbO 4 , and Na 2 SO 4 . The kinetics data calculated using the unreacted shrinking core model showed that the roasting process followed a mixed control mechanism with an apparent activation energy of 16.99 kJ · mol −1 .

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 score0.113
Threshold uncertainty score0.348

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 head0.026
GPT teacher head0.245
Teacher spread0.219 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

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