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Record W4205184307 · doi:10.1002/cjce.24373

Investigation of charcoal and activated charcoal for microwave absorbers

2022· article· en· W4205184307 on OpenAlexvenueno aff
Gabriela Gil de Oliveira, Marina Seixas Pereira, Carlos Henrique Ataíde

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsnot available
Fundersnot available
KeywordsActivated carbonActivated charcoalMicrowaveCharcoalMaterials scienceRaw materialPorosityCarbon fibersMicrowave heatingComposite materialSpecific surface areaAbsorption (acoustics)ChemistryAdsorptionMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The effects of the microwave heating process and the behaviour as absorbent materials of charcoals obtained from Eucalyptus urograndis wood, macauba endocarp, and lignin, in the activated and non‐activated forms, were investigated. The dielectric properties were measured with the coaxial probe method, and the values of loss tangent obtained for all the materials characterize them as absorbers. Heating tests were performed with materials in the raw form and also mixed with sand, a transparent material to microwave radiation. A factorial design of experiments was performed, and the effect of microwave power and mass concentration of materials over heating rate was studied. Both factors had a significant and positive influence on the heating rate of the seven analyzed materials. The carbon activation process increased the absorption and heating potential, due to the greater surface area and porosity generated by the process. The highest values of heating rate were obtained in the tests carried out with Eucalyptus ‐activated carbon.

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 score0.001
Threshold uncertainty score0.265

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 head0.013
GPT teacher head0.191
Teacher spread0.178 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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