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Record W4323536522 · doi:10.1002/cjce.24897

Advancements in droplet reactor systems represent new opportunities in chemical reactor engineering: A perspective

2023· article· en· W4323536522 on OpenAlexvenueno aff
Meng Zhang, Asher E. Vokoun, Boyuan Chen, Weichen Deng, Robert L. Dupont, Yang Xu, Xiaoguang Wang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsnot available
FundersOhio State University
KeywordsChemical reactorReactor designProcess engineeringChemical reaction engineeringThroughputContinuous reactorChemical processBiochemical engineeringComputer scienceNanotechnologyNuclear engineeringMaterials scienceChemical engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

Abstract Traditional chemical reactors, such as batch reactors, continuous reactors, and semi‐batch reactors, have been extensively studied and frequently act as central components of modern chemical plants. Recently, various advances in reaction times, surface‐to‐volume ratios, required amounts of reagents, and throughput have led to new directions in the design of miniaturized chemical reactors. In this Perspective, we provide an overview of the progress from traditional to miniaturized chemical reactors by summarizing the characteristics and applications of different types of reactors. Furthermore, we compare classical chemical reactors and miniaturized droplet reactors to highlight advancements in the design of droplet reactor systems based on open functional surfaces. Finally, we provide an outlook on the research directions of miniaturized droplet reactors.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.031
GPT teacher head0.237
Teacher spread0.206 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations14
Published2023
Admission routes1
Has abstractyes

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Same venueThe Canadian Journal of Chemical EngineeringSame topicInnovative Microfluidic and Catalytic Techniques InnovationFrench-language works237,207