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Record W4319791583 · doi:10.1002/aic.18048

Advanced <scp>3D</scp> and <scp>4D</scp> microstructure study of single granule formation for pharmaceutical powders using synchrotron x‐ray imaging

2023· article· en· W4319791583 on OpenAlexafffund
Sima Zeinali Danalou, Carter Blocka, Jingsi Yang, Ning Zhu, Heather N. Emady, Ellen K. Wasan, Lifeng Zhang

Bibliographic record

VenueAIChE Journal · 2023
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug Solubulity and Delivery Systems
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Saskatchewan
KeywordsGranule (geology)GranulationDissolutionPorosityMicrostructureSynchrotronNucleationMaterials scienceChemical engineeringWettingChemistryCrystallographyMineralogyComposite materialOptics

Abstract

fetched live from OpenAlex

Abstract Monitoring the microstructure of the granule in the wet granulation process could play a decisive role in obtaining high‐quality granules. Due to the complex, fast and opaque nature of wet granulation, it cannot be captured by conventional methods. In this study, synchrotron x‐ray imaging was employed for the first time to investigate the internal real‐time pore evolution during the granule formation process, based on the single droplet impact method. It was found that granules from coarser and more homogenous powders experienced a higher rate of pore evolution during nucleation with a more uniform pore distribution. Dynamic wetting studies showed the granule formation mechanisms, the crater mechanism was found for most binary mixtures with 50 wt. % excipients. According to the physical tests, the granules with lower porosity and finer pores exhibited higher hardness and a slower dissolution 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.099
GPT teacher head0.410
Teacher spread0.311 · 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 designObservational
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

Citations6
Published2023
Admission routes2
Has abstractyes

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