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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Automated self-optimisation of multi-step reaction and separation processes using machine learning
Adam D. Clayton, Artur M. Schweidtmann, Graeme Clemens, et al.
Chemical Engineering Journal (2019) Vol. 384, pp. 123340-123340
Open Access | Times Cited: 128

Showing 1-25 of 128 citing articles:

Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
Maarten R. Dobbelaere, Pieter Plehiers, Ruben Van de Vijver, et al.
Engineering (2021) Vol. 7, Iss. 9, pp. 1201-1211
Open Access | Times Cited: 191
A Brief Introduction to Chemical Reaction Optimization
Connor J. Taylor, Alexander Pomberger, Kobi Felton, et al.
Chemical Reviews (2023) Vol. 123, Iss. 6, pp. 3089-3126
Open Access | Times Cited: 183
Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors
Li‐Tao Zhu, Xizhong Chen, Bo Ouyang, et al.
Industrial & Engineering Chemistry Research (2022) Vol. 61, Iss. 28, pp. 9901-9949
Open Access | Times Cited: 133
Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform
Anirudh M. K. Nambiar, C. Breen, Travis Hart, et al.
ACS Central Science (2022) Vol. 8, Iss. 6, pp. 825-836
Open Access | Times Cited: 110
Toward Machine Learning-Enhanced High-Throughput Experimentation
Natalie S. Eyke, Brent A. Koscher, Klavs F. Jensen
Trends in Chemistry (2021) Vol. 3, Iss. 2, pp. 120-132
Open Access | Times Cited: 106
Accelerated Chemical Reaction Optimization Using Multi-Task Learning
Connor J. Taylor, Kobi Felton, Daniel Wigh, et al.
ACS Central Science (2023) Vol. 9, Iss. 5, pp. 957-968
Open Access | Times Cited: 59
Recent progress in hybrid conducting polymers and metal oxide nanocomposite for room-temperature gas sensor applications: A review
Lemma Tirfie Zegebreal, Newayemedhin A. Tegegne, Fekadu Gashaw Hone
Sensors and Actuators A Physical (2023) Vol. 359, pp. 114472-114472
Closed Access | Times Cited: 45
Quid Pro Flow
Andrea Laybourn, Karen Robertson, Anna G. Slater
Journal of the American Chemical Society (2023) Vol. 145, Iss. 8, pp. 4355-4365
Open Access | Times Cited: 37
Self-Driving Laboratories for Chemistry and Materials Science
Gary Tom, Stefan P. Schmid, Sterling G. Baird, et al.
Chemical Reviews (2024) Vol. 124, Iss. 16, pp. 9633-9732
Open Access | Times Cited: 19
A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization
Florian Wagner, Peter Sagmeister, Clemens E. Jusner, et al.
Advanced Science (2024) Vol. 11, Iss. 13
Open Access | Times Cited: 13
Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine
Edgar A. Galan, Haoran Zhao, Xukang Wang, et al.
Matter (2020) Vol. 3, Iss. 6, pp. 1893-1922
Open Access | Times Cited: 113
Gas sensing based on organic composite materials: Review of sensor types, progresses and challenges
Abdelghaffar Nasri, Mathieu Pétrissans, Vanessa Fierro, et al.
Materials Science in Semiconductor Processing (2021) Vol. 128, pp. 105744-105744
Open Access | Times Cited: 69
Artificial intelligence and automation in computer aided synthesis planning
Amol Thakkar, Simon Johansson, Kjell Jorner, et al.
Reaction Chemistry & Engineering (2020) Vol. 6, Iss. 1, pp. 27-51
Closed Access | Times Cited: 68
Sampling and Analysis in Flow: The Keys to Smarter, More Controllable, and Sustainable Fine‐Chemical Manufacturing
Mathieu Morin, Wenyao Zhang, Debasis Mallik, et al.
Angewandte Chemie International Edition (2021) Vol. 60, Iss. 38, pp. 20606-20626
Closed Access | Times Cited: 59
Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery
Rebecca L. Greenaway, Kim E. Jelfs
Advanced Materials (2021) Vol. 33, Iss. 11
Open Access | Times Cited: 58
Autonomous Multi‐Step and Multi‐Objective Optimization Facilitated by Real‐Time Process Analytics
Peter Sagmeister, F. F. Ort, Clemens E. Jusner, et al.
Advanced Science (2022) Vol. 9, Iss. 10
Open Access | Times Cited: 58
Maximizing information from chemical engineering data sets: Applications to machine learning
Alexander Thebelt, Johannes Wiebe, Jan Kronqvist, et al.
Chemical Engineering Science (2022) Vol. 252, pp. 117469-117469
Open Access | Times Cited: 56
Machine learning directed multi-objective optimization of mixed variable chemical systems
Oliver J. Kershaw, Adam D. Clayton, Jamie A. Manson, et al.
Chemical Engineering Journal (2022) Vol. 451, pp. 138443-138443
Open Access | Times Cited: 53
Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
Yunchao Xie, Kianoosh Sattari, Chi Zhang, et al.
Progress in Materials Science (2022) Vol. 132, pp. 101043-101043
Open Access | Times Cited: 53
Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation
Stephen T. Knox, Sam J. Parkinson, Clarissa. Y. P. Wilding, et al.
Polymer Chemistry (2022) Vol. 13, Iss. 11, pp. 1576-1585
Open Access | Times Cited: 52
Bayesian Self‐Optimization for Telescoped Continuous Flow Synthesis
Adam D. Clayton, Edward O. Pyzer‐Knapp, Mark Purdie, et al.
Angewandte Chemie International Edition (2022) Vol. 62, Iss. 3
Open Access | Times Cited: 52
Autonomous chemical science and engineering enabled by self-driving laboratories
Jeffrey A. Bennett, Milad Abolhasani
Current Opinion in Chemical Engineering (2022) Vol. 36, pp. 100831-100831
Open Access | Times Cited: 40
A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.0
Chasheng He, Chengwei Zhang, Tengfei Bian, et al.
Processes (2023) Vol. 11, Iss. 2, pp. 330-330
Open Access | Times Cited: 25
Challenges and opportunities for SERS in the infrared: materials and methods
Chiara Deriu, Shaila Thakur, Olimpia Tammaro, et al.
Nanoscale Advances (2023) Vol. 5, Iss. 8, pp. 2132-2166
Open Access | Times Cited: 20
Autonomous reaction Pareto-front mapping with a self-driving catalysis laboratory
Jeffrey A. Bennett, Negin Orouji, Muhammad Babar Khan, et al.
Nature Chemical Engineering (2024) Vol. 1, Iss. 3, pp. 240-250
Open Access | Times Cited: 10
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