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Record W3198888384 · doi:10.3390/min11030239

Surface Chemical Heterogeneity of Low Rank Coal Characterized by Micro-FTIR and Its Correlation with Hydrophobicity

2021· article· en· W3198888384 on OpenAlexaff
Wei Wang, Long Liang, Yaoli Peng, Maria Holuszko

Bibliographic record

VenueMinerals · 2021
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsUniversity of British Columbia
FundersPriority Academic Program Development of Jiangsu Higher Education Institutions
KeywordsFourier transform infrared spectroscopyCoalChemistryContact angleCarbon fibersRank (graph theory)Analytical Chemistry (journal)Functional groupMaterials scienceMathematicsOrganic chemistryChemical engineeringComposite numberComposite material

Abstract

fetched live from OpenAlex

Micro-Fourier transform infrared (micro-FTIR) spectroscopy was used to correlate the surface chemistry of low rank coal with hydrophobicity. Six square areas without mineral impurities on low rank coal surfaces were selected as testing areas. A specially-designed methodology was applied to conduct micro-FTIR measurements and contact angle tests on the same testing area. A series of semi-quantitative functional group ratios derived from micro-FTIR spectra were correlated with contact angles, and the determination coefficients of linear regression were calculated and compared in order to identify the structure of the functional group ratios. Finally, two semi-quantitative ratios composed of aliphatic carbon hydrogen, aromatic carbon hydrogen and two different types of carbonyl groups were proposed as indicators of low rank coal hydrophobicity. This work provided a rapid way to predict low rank coal hydrophobicity through its functional group composition and helped us understand the hydrophobicity heterogeneity of low rank coal from the perspective of its surface chemistry.

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 score0.016
Threshold uncertainty score0.522

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

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