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Record W4404764991 · doi:10.1080/21650373.2024.2428987

Biochar and recycled gypsum drywall in concrete: role and effects on compressive behavior, microstructure, and carbon footprint

2024· article· en· W4404764991 on OpenAlexaff
Alireza Jafari, Pedram Sadeghian

Bibliographic record

VenueJournal of Sustainable Cement-Based Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBiocharGypsumMicrostructureCarbon footprintCompressive strengthMaterials scienceWaste managementCarbon fibersEnvironmental sciencePyrolysisComposite materialGreenhouse gasEngineeringGeologyComposite number

Abstract

fetched live from OpenAlex

This research examines the effect of biochar and recycled gypsum drywall (RGD) on the mechanical properties and microstructure of conventional and high-volume fly ash concrete (HVFC) by testing 60 cylinders. Results suggested that although adding biochar increased the porosity of concrete, its effect on concrete’s properties relied on its porosity, pore interconnectivity, and water retention capacity, plus characteristics of binders. The biochar-retained water (BRW) accelerated the hydration of C3S, increasing the dosage of calcium carbonate and calcium aluminate silicate hydrate on the top of biochar pores. The accelerated hydration enhanced early-age strength. Besides the hydration acceleration of OPC, BRW primarily promoted the RGD reactions and fly ash activation in HVFC. Nonetheless, the high porosity, inertness, and potential deterioration of biochar weakened the concrete in the long term and lowered its elastic modulus. Biochar also boosted ductile behavior, especially in HVFC with RGD, and lowered the carbon footprint of the concrete.

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)0.001
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 score0.005
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.228
Teacher spread0.223 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

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