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Record W4308159137 · doi:10.1007/s12274-022-5115-7

One droplet reaction for synthesis of multi-sized nanoparticles

2022· article· en· W4308159137 on OpenAlexaff
Bingda Chen, Feifei Qin, Meng Su, Daixi Xie, Zeying Zhang, Qi Pan, Huadong Wang, Yang Xu, Sisi Chen, Jingwei Huang, Dominique Derome, Jan Carmeliet, Yanlin Song

Bibliographic record

VenueNano Research · 2022
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsNanoparticleMaterials scienceNanotechnologyRange (aeronautics)Péclet numberKineticsChemical engineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Reaction kinetics of nanoparticles can be controlled by tuning the Peclet number (Pe) as it is an essential parameter in synthesis of multi-sized nanoparticles. Herein, we propose to implement a self-driven multi-dimension microchannels reactor (MMR) for the one droplet synthesis of multi-sized nanoparticles. By carefully controlling the Pe at the gas—liquid interface, the newly formed seed crystals selectively accumulate and grow to a specific size. By the combination of microchannels of different widths and lengths, one droplet reaction in the same apparatus achieves the synchronous synthesis of diverse nanoparticles. MMR enables precise control of nanoparticle diameter at 5 nm precision in the range of 10–110 nm. The use of MMR can be extended to the synthesis of uniform Ag, Au, Pt, and Pd nanoparticles, opening towards the production and engineering of nanostructured materials. This approach gives the chance to regulate the accumulation probability for precise synthesis of nanoparticles with different diameters.

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 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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.092
GPT teacher head0.349
Teacher spread0.257 · 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 teacher head, 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

Citations4
Published2022
Admission routes1
Has abstractyes

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