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Record W4372405849 · doi:10.1080/07373937.2023.2207634

Electrohydrodynamic drying: The opportunity for sustainable development

2023· article· en· W4372405849 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

VenueDrying Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsDalhousie University
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsElectrohydrodynamicsSustainabilityExergyEnvironmental scienceExergy efficiencyExploitGreenhouse gasProcess engineeringEnvironmental economicsNatural resource economicsEngineeringComputer scienceEconomics

Abstract

fetched live from OpenAlex

This review presents electrohydrodynamic (EHD) drying as a new advanced technology to increase awareness of professionals on the great opportunity to contribute to the sustainable future of humanity. This emerging technology, which exploits the phenomenon of ionic wind for direct extraction of liquid water from wet materials, has been described considering product quality, energy consumption, efficiency, and environmental protection from the emission of greenhouse gases. The economic analysis of this new technology showed that EHD drying has great potential to reduce food losses in production and distribution chains, as well as minimize waste from restaurants and households. The sustainability of EHD drying, based on capital and operating costs, energy and exergy efficiency, and social implications, is illustrated, using the example of EHD-assisted convective drying.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.237
Teacher spread0.224 · 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