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Record W4320492660 · doi:10.3390/mi14020429

Effect of Red Mud Addition on Electrical and Magnetic Properties of Hemp-Derived-Biochar-Containing Epoxy Composites

2023· article· en· W4320492660 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMicromachines · 2023
Typearticle
Languageen
FieldEngineering
TopicBauxite Residue and Utilization
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiocharRed mudMaterials sciencePyrolysisEpoxyPyrolytic carbonCarbothermic reactionComposite materialAluminiumMetalWaste managementMetallurgy

Abstract

fetched live from OpenAlex

Waste stream valorization is a difficult task where the economic and environmental issues must be balanced. The use of complex metal-rich waste such as red mud is challenging due to the wide variety of metal oxides present such as iron, aluminum, and titanium. The simple separation of each metal is not economically feasible, so alternative routes must be implemented. In this study, we investigated the use of red mud mixed with hemp waste to produce biochar with high conductivity and good magnetic properties induced by the reduction of the metal oxides present in the red mud through carbothermal processes occurring during the co-pyrolysis. The resulting biochar enriched with thermally-reduced red mud is used for the preparation of epoxy-based composites that are tested for electric and magnetic properties. The electric properties are investigated under DC (direct current) regime with or without pressure applied and under AC (alternating current) in a frequency range from 0.5 up to 16 GHz. The magnetic measurements show the effective tailoring of hemp-derived biochar with magnetic structures during the co-pyrolytic process.

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.

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 score0.032
Threshold uncertainty score0.367

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 head0.006
GPT teacher head0.204
Teacher spread0.197 · 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