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Record W2976565661 · doi:10.3390/ma12193207

Evaluation of Mechanical Properties and Microscopic Structure of Coal Gangue after Aqueous Solution Treatment

2019· article· en· W2976565661 on OpenAlexaff
Yan Zhang, Xiaoyun Yang, Susan Tighe

Bibliographic record

VenueMaterials · 2019
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsGangueCoalMaterials scienceCoal miningAqueous solutionMetallurgyMineralogyWaste managementChemistry

Abstract

fetched live from OpenAlex

Coal gangue, a solid waste produced in coal production, had caused serious environmental pollution due to accumulation on dumps. Embankment filling can solve the problem while significantly consuming the amount of coal waste for mining. The main purpose of this study is to investigate the mechanical properties and microscopic structure of coal gangue when it is subjected to erosion from water environment with different acidity. Using immersion testing to evaluate its stability in different hydro-chemical environments. Mechanical property parameters of coal gangue treated by solutions were investigated. The action microstructure of coal gangue was revealed through a series of X-ray diffraction (XRD), X-ray fluorescence spectrometry (XRF), scanning electron microscopy (SEM), and energy dispersive spectrometry (EDS). The results show that acidic solution behaved better improvement effect on compressive modulus and fraction of coal gangue samples owing to the generation of quartz and the reduction of aluminum, dissolving of some substances, and transforming of small scattered angular grains through soaking treatment. Alkalinity treatment can be chosen to improve cohesion of coal gangue as a result of polymeric silicon aluminum salt, with high viscosity, was produced by chemical reaction during immersion. Therefore, aqueous solution treatment contributes to engineering properties and presents great potential in both supplement road building materials and recycling of coal gangue.

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 score0.001
Threshold uncertainty score0.349

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 head0.022
GPT teacher head0.215
Teacher spread0.193 · 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

Citations29
Published2019
Admission routes1
Has abstractyes

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