Evidence map›Paper›PMID 39337724›Full record

ReviewMicromachines2024

Democratizing Microreactor Technology for Accelerated Discoveries in Chemistry and Materials Research.

Tomomi Sato, Koji Masuda, Chikako Sano, Keiji Matsumoto, Hidetoshi Numata, Seiji Munetoh, Toshihiro Kasama, Ryo Miyake

Abstract readReview
In one paragraph

Review in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Tomomi SatoGraduate School of Engineering, The University of Tokyo, Kawasaki 212-0032, Japan.
Koji MasudaDepartment of Physics and Astronomy, University of Exeter, Exeter EX4 4QL, UK.
Chikako SanoIBM Semiconductors, IBM Research-Tokyo, Kawasaki 212-0032, Japan.
Keiji MatsumotoIBM Semiconductors, IBM Research-Tokyo, Kawasaki 212-0032, Japan.
Hidetoshi NumataIBM Semiconductors, IBM Research-Tokyo, Kawasaki 212-0032, Japan.ORCID 0009-0000-9393-4424
Seiji MunetohIBM Semiconductors, IBM Research-Tokyo, Kawasaki 212-0032, Japan.
Toshihiro KasamaGraduate School of Engineering, The University of Tokyo, Kawasaki 212-0032, Japan.ORCID 0000-0001-6753-1509
Ryo MiyakeGraduate School of Engineering, The University of Tokyo, Kawasaki 212-0032, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microreactor technologies have emerged as versatile platforms with the potential to revolutionize chemistry and materials research, offering sustainable solutions to global challenges in environmental and health domains. This survey paper provides an in-depth review of recent advancements in microreactor technologies, focusing on their role in facilitating accelerated discoveries in chemistry and materials. Specifically, we examine the convergence of microfluidics with machine intelligence and automation, enabling the exploitation of the cyber-physical environment as a highly integrated experimentation platform for rapid scientific discovery and process development. We investigate the applicability and limitations of microreactor-enabled discovery accelerators in various chemistry and materials contexts. Despite their tremendous potential, the integration of machine intelligence and automation into microreactor-based experiments presents challenges in establishing fully integrated, automated, and intelligent systems. These challenges can hinder the broader adoption of microreactor technologies within the research community. To address this, we review emerging technologies that can help lower barriers and facilitate the implementation of microreactor-enabled discovery accelerators. Lastly, we provide our perspective on future research directions for democratizing microreactor technologies, with the aim of accelerating scientific discoveries and promoting widespread adoption of these transformative platforms.

Indexed as

accelerated discoveryadvanced integrated microreactor development platformartificial intelligenceflow chemistrymachine learningmicroreactor

Identifiers

PMID39337724
PMCPMC11434323

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.