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Record W2625234231 · doi:10.1080/07373937.2017.1326130

Future perspectives for electrohydrodynamic drying of biomaterials

2017· article· en· W2625234231 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

VenueDrying Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrohydrodynamicsAirflowParticle image velocimetryHeat transferMaterials scienceProcess (computing)VelocimetryProcess engineeringMechanical engineeringMechanicsEngineeringComputer sciencePhysicsElectric field

Abstract

fetched live from OpenAlex

Electrohydrodynamic drying (EHD) is a promising technology to dehydrate biomaterials but needs further development for industrial use. Open questions in our understanding of EHD drying are discussed. These include the phenomena driving the EHD drying process, possible dryer configurations for industrial upscaling, and the specific energy consumption of corona discharge and peripheral equipment. Future opportunities for experimental and numerical analysis of EHD drying are also highlighted, including particle image velocimetry and X-ray/neutron tomography. Numerical modeling of EHD airflow and the associated vapor transport, coupled with the transfer processes within the drying material, are considered essential for further process optimization.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.252
Teacher spread0.245 · 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