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Record W4210658948 · doi:10.1115/imece2021-68974

A Heterogenous Nucleation Model for Supercooled Water and Sucrose Solution Droplets Under Ultra-Cold Environments

2021· article· en· W4210658948 on OpenAlexaff
Minghan Xu, Saad Akhtar, Agus P. Sasmito

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMicroencapsulation and Drying Processes
Canadian institutionsMcGill University
Fundersnot available
KeywordsNucleationSupercoolingMaterials scienceDegree (music)ThermodynamicsChemical physicsChemical engineeringChemistryPhysics

Abstract

fetched live from OpenAlex

Abstract With growing food scarcity and high demands for vaccine storage, advancing spray freeze-drying technology has never been more important for prolonging shelf life of biological and pharmaceutical materials. Particularly, the estimation of nucleation behaviour for both pure substances and binary mixtures has become vital to the optimal thermal design and implementation of spay freeze-drying technology. Notwithstanding that past nucleation frameworks could predict nucleation rate and temperature of droplet solidification, few of them considered extreme surrounding conditions, such as very low ambient temperature below −60 degree Celsius. These environments, however, play a significant role in ascertaining the preservation and storage of chemical and pharmaceutical products, e.g., vaccines and protein drugs. It is therefore of great interest to establish accurate and reliable mathematical framework on simulating nucleation during droplet solidification subjected to ultra-cold conditions. This paper develops a semi-analytical heterogeneous nucleation model and anticipates nucleation phenomena of a suspended droplet under ultra-cold environment. Nucleation temperatures calculated from the presented model are validated against a set of experiments on single suspended droplets for a wide array of ambient temperatures from −20 until −160 degree Celsius. Both pure water and 20% w/w sucrose aqueous solution are examined for these droplets. Cumulative probability distributions of nucleation for both types of droplets over nucleation temperatures are also presented and comparisons are made between the model results and recent experimental data from literatures. Our preliminary findings demonstrate that drastic changes in nucleation temperature for ultra-cold surroundings are the aftermath of alterations in interfacial surface tension. Conventionally, the inter-facial surface tension is defined as a function of supercooling degree only, which fails as surrounding temperature is prescribed below −40 degree Celsius. In this study, the interfacial surface tension is linearly optimized using error minimization with experimental data fit, such that it substantially relates to both the supercooling degree and surrounding temperature under a given environment for pure water. As for sucrose aqueous solution (i.e., an example of binary mixtures), their solute concentration is also a dependent variable of interfacial surface tension. The results indicate that our proposed framework is capable of predicting heterogeneous nucleation in a droplet filled with either pure material or binary mixture. Development of this nucleation model for spray freeze-drying can expedite manufacturing process and reduce expenses in handling, transportation and storage of biological products, thus improving the shelf life of pharmaceuticals and availability of foods at large. Our model can be extended on other pure materials and binary mixtures, which will further be used to facilitate the design and implementation of spray freeze-drying technology for preserving and storing more chemicals and pharmaceutical excipients.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.213
Teacher spread0.185 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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