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Record W2963272693 · doi:10.1021/acsomega.9b01173

Study on the Interaction between Galena and Sphalerite During Grinding Based on the Migration of Surface Components

2019· article· en· W2963272693 on OpenAlexaff
Bo Huang, Hao Lai, Jiushuai Deng, Hongxiang Xu, Guixia Fan

Bibliographic record

VenueACS Omega · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversity of Toronto
FundersChina Postdoctoral Science FoundationFok Ying Tong Education FoundationNational Natural Science Foundation of China
KeywordsSphaleriteGalenaGrindingZincMetallurgyMineralSecondary ion mass spectrometryChemistryMineralogyMaterials scienceIonPyrite

Abstract

fetched live from OpenAlex

In Pb-Zn ore flotation, unintentional activation of sphalerite often leads to difficult separation of Pb and Zn minerals, during which grinding plays a key role in unintentional activation. Therefore, the aim of this study was to evaluate the surface component changes of two different mineral particles and to propose the interaction between galena and sphalerite during mixed grinding using time-of-flight secondary ion mass spectrometry (ToF-SIMS). The results show that after mixed grinding of the galena and sphalerite, the Pb content on the sphalerite surface increased with the decrease of Zn and Fe contents on the sphalerite surface. The lead ions from galena were obviously absorbed onto the sphalerite surface, while the zinc and iron ions from sphalerite were not obviously migrated to the galena surface. Principal component analysis (PCA) of a dataset composed of 206 positive ion peaks of galena and sphalerite indicates that the surface components of galena and sphalerite migrated from either side to different degrees. This study successfully identified an important factor for unintentional activation of lead and zinc minerals during flotation: homogenization of surface components of different minerals during grinding.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.161

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 head0.036
GPT teacher head0.276
Teacher spread0.239 · 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 designObservational
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

Citations19
Published2019
Admission routes1
Has abstractyes

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