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Record W3212243826 · doi:10.1002/aic.17526

Design of the spherical agglomerate size in crystallization by developing a two‐step bridging mechanism and the model

2021· article· en· W3212243826 on OpenAlex
Changyou Yu, Menghui Yao, Yiming Ma, Yanbo Liu, Shilin Guo, Shijie Xu, Sohrab Rohani, Mingyang Chen, Junbo Gong

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

VenueAIChE Journal · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCrystallization and Solubility Studies
Canadian institutionsWestern University
FundersNational Natural Science Foundation of China
KeywordsAgglomerateCrystallizationParticle sizeMaterials scienceChemical physicsChemical engineeringMechanicsThermodynamicsChemistryComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract Spherical agglomeration technology can produce high‐performance spherical particles in a single crystallization unit, although it is still challenging to control the particle size and shape. To solve this issue, a two‐step bridging (TSB) mechanism containing a preconditioning period, size period, and shape period is proposed. The dynamic balance among the forces of adhesion, dispersion, and capillary action in the multi‐liquid phases plays a key role. This is fully considered to establish the TSB‐based thermodynamic size model and particle design framework by weighting the force action regions in multi‐liquid phases with dynamic composition. The spherical agglomerates of benzoic acid, celecoxib, and salicylic acid with narrow particle size distributions and tunable particle size ranges of 2000–5000, 800–3500, and 1500–4500 μm, respectively, were designed and prepared successfully, showing good correlation with the calculation, which is superior to the reported methods and indicates that the mechanism has certain universality and guiding significance.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score0.203

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.023
GPT teacher head0.257
Teacher spread0.234 · 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