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Record W4406071123 · doi:10.1016/j.isci.2024.111732

A particle-scale study showing microwave energy can effectively decarbonize process heat in fluidization industry

2025· article· en· W4406071123 on OpenAlexafffund
Mehdi Salakhi, Murray J. Thomson

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldChemistry
TopicMicrowave-Assisted Synthesis and Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaCMC Microsystems
KeywordsFluidizationProcess (computing)Scale (ratio)Process engineeringParticle (ecology)NanotechnologyEnvironmental scienceMaterials scienceFluidized bedComputer scienceThermodynamicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Microwave heating converts electromagnetic energy directly into thermal energy within the heated material, thereby overcoming the limitations of traditional indirect heat transfer methods. However, microwaves are well-known to have limited penetration depth, which remains a significant challenge that inhibits the use of microwaves in processes requiring uniform heating. Here, we show that fluidized beds of particles with sufficient electrical conductivity break the limitations imposed by microwave penetration depth, enabling uniform heating in large-scale reactors. Results suggest that the alternating magnetic field penetrates the entire studied reactor to induce eddy currents everywhere, causing each particle to be heated. The power absorption density for Geldart A and B particles across the bed is uniform, with no evidence of exponential attenuation, introducing unexpected penetration depth under the magnetic field component. Utilizing microwave energy, sourced by clean electricity, to heat fluidized beds offers a transformative solution to decarbonize industry, significantly reducing greenhouse gas emissions.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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

Citations21
Published2025
Admission routes2
Has abstractyes

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