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Record W3090523562 · doi:10.1021/acssuschemeng.0c06048

Properties of Bio-pitch and Its Wettability on Coke

2020· article· en· W3090523562 on OpenAlexafffund
Ying Lu, Asem Hussein, Dazhi Li, Xianai Huang, Roozbeh Mollaabbasi, Donald Picard, Thierry Ollevier, Houshang Alamdari

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsNatural Resources CanadaUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesUniversité LavalAlcoa
KeywordsCokeWettingCoal tarSurface tensionMaterials scienceContact anglePyrolysisCoalChemical engineeringEnvironmentally friendlyBiomass (ecology)Pulp and paper industryComposite materialChemistryOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Bio-pitch, driven from biomass, is a potential green alternative of coal-tar-pitch in the production of carbon anodes for the aluminum electrolysis process. Information on the wetting capacity of bio-pitch on the surface of the coke particle is of great interest in assessing its possible use as a renewable and environment-friendly binder. In this study, the wettability of various bio-pitches and one reference coal-tar-pitch on the same type of coke was investigated at a temperature of 178 °C using a sessile-drop setup. Bio-oil, the parental material of the bio-pitch, was produced by commercial pyrolysis from woody biomass. Postpyrolysis treatment was run to synthesize 10 different pitch samples from bio-oil, i.e., bio-pitches. Various techniques were used to analyze the physical and chemical characteristics of the bio-pitch samples and their interactions with coke. It was shown that the wettability of bio-pitch is highly influenced by its viscosity, surface tension, surface chemical functional groups, amount of quinoline insoluble, and molecular weight distribution.

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 score0.007
Threshold uncertainty score0.443

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 head0.176
Teacher spread0.167 · 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

Citations21
Published2020
Admission routes2
Has abstractyes

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