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Record W4406219891 · doi:10.1080/07373937.2024.2442490

A multi-scale multi-stage model for spray freezing of binary solutions

2025· article· en· W4406219891 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 · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsMcGill University
Fundersnot available
KeywordsSpray dryingBinary numberScale (ratio)Materials scienceThermodynamicsSCALE-UPProcess engineeringChemical engineeringEnvironmental scienceChromatographyChemistryMathematicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Spray freezing has found extensive applications in drying processes, drug delivery, and mine ventilation. The technique involves atomizing a liquefied material into a cold medium, where the resulting droplets solidify. This work presents a multi-scale model to study the spray freezing of binary mixtures. Specifically, the freezing model of a binary solution is coupled with a spray-droplet dynamics model. The spray freezing of an aqueous sucrose solution is analyzed using this framework, and parametric studies are conducted to examine the effects of droplet size, reactor length, ambient temperature, and liquid flowrate on the system performance. The results show that increasing the reactor length from 1.25 m to 4.25 m leads to a 79% increase in the final concentration. Lowering the ambient temperature from −15 °C to −25° yields a 2.63-times bigger solid-to-liquid fraction. Additionally, increasing the flowrate from 0.0125 kg/s to 0.0625 kg/s results in a 323% increase in heat rates.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score0.570

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 head0.040
GPT teacher head0.279
Teacher spread0.239 · 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