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Record W4388291897 · doi:10.1039/bk9781837670277-00347

Non-uniform Microwave Heating of Heterogeneous Systems: How to Turn Problems into Opportunities

2023· book-chapter· en· W4388291897 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.

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldChemistry
TopicMicrowave-Assisted Synthesis and Applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMicrowaveProcess engineeringMicrowave heatingEfficient energy useEnergy consumptionMaterials scienceHydrogenCrackingProduct (mathematics)CatalysisNanotechnologyBiochemical engineeringEnvironmental scienceComputer scienceEngineeringChemistryTelecommunicationsElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

Turning problematic, non-uniform microwave heating into an opportunity is a promising approach to enhancing energy efficiency and reducing environmental impacts due to the chemical processing of heterogeneous systems. Selective microwave heating of materials provides an adequate temperature for the occurrence of an efficient chemical reaction at desired sites while the surrounding media remain unheated, diminishing heat loss and enhancing the energy efficiency. Moreover, the side reactions at non-desired sites, including reactor walls and the surrounding media, are avoided owing to the lower bulk temperature compared to that of the target material, which likely improves the quality of the product. Applying this approach in various microwave-assisted catalytic and non-catalytic processes, including pyrolysis, cracking, and hydrogen production, confirmed an extraordinary improvement in product quality and reduction in energy consumption. Highlighting these advantages will likely increase the demand for scaling up microwave-assisted processes.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.581
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.059
GPT teacher head0.239
Teacher spread0.180 · 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