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Record W3080696250 · doi:10.3390/min10090761

Molecular Dynamics Simulation of Cetyl Phosphate Adsorption in Flotation of Magnesite and Pertinent Chemical Aspects

2020· article· en· W3080696250 on OpenAlexaff
Yuan Tang, Jin Yao, Wanzhong Yin, Ş. Kelebek

Bibliographic record

VenueMinerals · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsQueen's University
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsMagnesiteAdsorptionElectrokinetic phenomenaMagnesiumChemistryPhosphateWettingChemical engineeringSuspension (topology)Inorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Magnesite ores are important resources in the production of value-added magnesium materials. Generally, low selectivity of conventional collectors and the requirement of a large amount of depressant has been a motivation for researchers to identify alternate collectors. In this work, the role of potassium cetyl phosphate (PCP) as a new collector in magnesite flotation is investigated using molecular dynamics (MD) simulations and chemical equilibria, electrokinetics and wettability. The results indicate that PCP exhibits a strong collecting ability for magnesite particles even with low concentrations. The presence of PCP leads to significant alterations in the electric double layer and contact angle behavior of magnesite, which results in rapid adsorption of PCP on magnesite surface. The results from chemical computations show that the monoanionic forms of PCP are the dominant species in the weakly acidic pH range, where monohydroxy magnesium species and the ion concentration of magnesite in suspension can be controlled by adjusting pH. The adsorption models indicate that the stable adsorption of PCP on magnesite surfaces occurs spontaneously, supporting the potentiality for selective magnesite flotation in its separation from other carbonate minerals.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.292

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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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