Automated robotic platforms in design and development of formulations

Article Properties
  • Language
    English
  • DOI (url)
    10.1002/aic.17248
  • Publication Date
    2021/03/03
  • Journal
    AIChE Journal
  • Indian UGC (journal)
  • Refrences
    187
  • Citations
    8
  • Liwei Cao Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UKCambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. Singapore ORCID (unauthenticated)
  • Danilo Russo Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
  • Alexei A. Lapkin Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UKCambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. Singapore ORCID (unauthenticated)
Abstract
Cite
Cao, Liwei, et al. “Automated Robotic Platforms in Design and Development of Formulations”. AIChE Journal, vol. 67, no. 5, 2021, https://doi.org/10.1002/aic.17248.
Cao, L., Russo, D., & Lapkin, A. A. (2021). Automated robotic platforms in design and development of formulations. AIChE Journal, 67(5). https://doi.org/10.1002/aic.17248
Cao L, Russo D, Lapkin AA. Automated robotic platforms in design and development of formulations. AIChE Journal. 2021;67(5).
Journal Categories
Science
Chemistry
Technology
Chemical technology
Chemical engineering
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Refrences
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New approach to the design of multifactor experiments 1977
Smart Innovation, Systems and Technologies 2018
Green Chemical Engineering 2018
Citations
Title Journal Journal Categories Citations Publication Date
Predicting surfactant phase behavior with a molecularly informed field theory Journal of Colloid and Interface Science
  • Science: Chemistry: Physical and theoretical chemistry
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 4 2023
Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation Progress in Materials Science
  • Science: Chemistry
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials
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  • Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials
 21 2023
Autonomous Nanocrystal Doping by Self‐Driving Fluidic Micro‐Processors

 Advanced Intelligent Systems
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware
  • Technology: Mechanical engineering and machinery: Control engineering systems. Automatic machinery (General)
  • Technology: Mechanical engineering and machinery
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Mechanical engineering and machinery
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 13 2022
Computer-aided design of formulated products Current Opinion in Colloid & Interface Science
  • Science: Chemistry: Physical and theoretical chemistry
  • Science: Chemistry: Physical and theoretical chemistry
  • Science: Chemistry
 5 2022
Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors Industrial & Engineering Chemistry Research
  • Technology: Chemical technology: Chemical engineering
  • Technology: Chemical technology: Chemical engineering
  • Science: Chemistry
 67 2022
Citations Analysis
Category Category Repetition
Science: Chemistry6
Technology: Chemical technology: Chemical engineering2
Science: Chemistry: Physical and theoretical chemistry2
Science: Chemistry: General. Including alchemy1
Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials1
Technology: Chemical technology1
The category Science: Chemistry 6 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors and was published in 2022. The most recent citation comes from a 2023 study titled Predicting surfactant phase behavior with a molecularly informed field theory. This article reached its peak citation in 2022, with 6 citations. It has been cited in 8 different journals. Among related journals, the Journal of Colloid and Interface Science cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year