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Understanding Interactions between Clay and Model Coal Surfaces in Electrolyte Solutions by a Quartz Crystal Microbalance with Dissipation Study

2017· article· en· W2768755654 on OpenAlexaff
Qian Chen, Tiantian Cao, Yong Xiong, Chen Wang, Zehui Lin, Zihui Chen, Shengming Xu, Zhenghe Xu

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsQuartz crystal microbalanceCoalChemical engineeringMontmorilloniteElectrolyteCarbon fibersDeposition (geology)ChemistryClay mineralsMineralogyMaterials scienceAdsorptionGeologyOrganic chemistryElectrodeComposite materialComposite number

Abstract

fetched live from OpenAlex

Clay slime coating is known to have a negative impact on coal flotation. It is therefore of great importance to study interactions between clay and coal under flotation conditions. In this study, the effect of electrolytes on the interactions of montmorillonite (MMT) clays with carbon or asphaltenes on sensor surfaces of quartz crystal microbalance with dissipation (QCM-D) as model coal was investigated. Ion beam sputtering deposition and spin coating methods were used to prepare carbon and asphaltene (a class of natural polyaromatic compounds) surfaces. The prepared surfaces were characterized by Raman spectroscopy. QCM-D was used to determine in situ interactions between MMT clays and model coal surfaces in electrolyte aqueous solutions. The MMT clays were shown to deposit significantly on the model coal surfaces only in the presence of NaCl or CaCl 2 as a result of the compression of electrical double layers. Moreover the enhanced deposition by calcium ions is stronger than by sodium ions. The results from this fundamental study provide a better understanding of the critical role of water chemistry in modulating the interactions of clays with coal surfaces and scientific guidance to achieve better performance of fine coal flotation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.628

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 communication0.0000.001
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 head0.052
GPT teacher head0.292
Teacher spread0.240 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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