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Record W3174097487 · doi:10.1080/07373937.2021.1942899

Alternating versus direct current in electrohydrodynamic drying

2021· article· en· W3174097487 on OpenAlexaff
Alex Martynenko, Tadeusz Kudra

Bibliographic record

VenueDrying Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrodeEnergy consumptionElectrohydrodynamicsMaterials scienceDirect currentRange (aeronautics)Power consumptionAnalytical Chemistry (journal)Specific energyEfficient energy usePower (physics)Environmental scienceVoltageElectrical engineeringChemistryComposite materialThermodynamicsChromatographyPhysicsEngineering

Abstract

fetched live from OpenAlex

Although DC power is claimed to be more energy-efficient than AC power for EHD drying, experimental studies on this subject are still insufficient. This research compares the efficiency of DC versus AC (60 Hz frequency) power for EHD drying by using 1 × 1 and 2 × 2 multi-pin discharge electrodes. Energy indicators were determined from the measurements of electric power consumption at the corresponding drying rate of the wet tissue paper. The experiments revealed the key benefits of DC drying, such as enhanced drying rate, higher energy efficiency, and lower specific energy consumption. For the comparable drying flux of 0.2 g/(m2 s), the specific energy consumption of DC drying was in the range from 350 J/g (2 × 2) to 750 J/g (1 × 1) as compared with 2000 J/g (2 × 2) to 5000 J/g (1 × 1) for AC. The specific energy consumption of a 2 × 2 discharge electrode was consistently smaller than the 1 × 1 one, indicating the role of emitters spacing in the process efficiency. The specific energy consumption of both electrodes increased with the drying rate.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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