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Record W2333416699 · doi:10.1021/ef300914f

Microwave Absorption Capability of High Volatile Bituminous Coal during Pyrolysis

2012· article· en· W2333416699 on OpenAlexaff
Zhiwei Peng, Jiann-Yang Hwang, Byoung‐Gon Kim, Joe Mouris, Ron Hutcheon

Bibliographic record

VenueEnergy & Fuels · 2012
Typearticle
Languageen
FieldChemistry
TopicMicrowave-Assisted Synthesis and Applications
Canadian institutionsDeep River Science Academy
Fundersnot available
KeywordsMicrowaveCoalPyrolysisBituminous coalReflection lossDielectricMaterials scienceAbsorption (acoustics)Dielectric lossAnalytical Chemistry (journal)Penetration depthChemistryOptoelectronicsOpticsComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

The microwave absorption capability of an eastern high volatile bituminous coal from West Virginia was investigated by measuring the dielectric properties from room temperature to ∼900 °C in ultrahigh purity (UHP) argon, at both 915 and 2450 MHz. The dielectric properties remain relatively constant below 500 °C. As the temperature continues to increase, however, the relative dielectric constant and loss factor increase dramatically. This is due to the release of volatiles, resulting in the increased electrical conductivity and higher microwave loss. The calculation of microwave penetration depth shows that the pyrolysis process significantly improves the microwave absorption capability of the coal at high temperatures. The calculated reflection losses of the coal sample suggest that the maximum microwave absorption with the reflection losses of −41.25 dB and −32.54 dB can be obtained for the coal having thicknesses of 0.14 and 0.20 m at 915 and 2450 MHz, respectively. The sample dimension has a significant effect on the overall performance of microwave absorption of coal during pyrolysis and thus on the efficiency of microwave coal pyrolysis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.011
GPT teacher head0.212
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 source (direct Gemma or distilled Codex), 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

Citations94
Published2012
Admission routes1
Has abstractyes

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