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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:

Accelerated Development of Colloidal Nanomaterials Enabled by Modular Microfluidic Reactors: Toward Autonomous Robotic Experimentation
Amanda A. Volk, Robert W. Epps, Milad Abolhasani
Advanced Materials (2020) Vol. 33, Iss. 4
Open Access | Times Cited: 89

Showing 1-25 of 89 citing articles:

Nanoparticle synthesis assisted by machine learning
Huachen Tao, Tianyi Wu, Matteo Aldeghi, et al.
Nature Reviews Materials (2021) Vol. 6, Iss. 8, pp. 701-716
Closed Access | Times Cited: 295
Nanomaterials Synthesis through Microfluidic Methods: An Updated Overview
Adelina-Gabriela Niculescu, Cristina Chircov, Alexandra Cătălina Bîrcă, et al.
Nanomaterials (2021) Vol. 11, Iss. 4, pp. 864-864
Open Access | Times Cited: 121
Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis
Marcus H. Reis, Filipp Gusev, Nicholas G. Taylor, et al.
Journal of the American Chemical Society (2021) Vol. 143, Iss. 42, pp. 17677-17689
Closed Access | Times Cited: 102
Microfluidic Devices: A Tool for Nanoparticle Synthesis and Performance Evaluation
Sara Gimondi, Helena Ferreira, Rui L. Reis, et al.
ACS Nano (2023) Vol. 17, Iss. 15, pp. 14205-14228
Open Access | Times Cited: 90
AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning
Amanda A. Volk, Robert W. Epps, Daniel T. Yonemoto, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 76
Emerging Trends in Machine Learning: A Polymer Perspective
Tyler B. Martin, Debra J. Audus
ACS Polymers Au (2023) Vol. 3, Iss. 3, pp. 239-258
Open Access | Times Cited: 70
Recent developments in microfluidic technology for synthesis and toxicity-efficiency studies of biomedical nanomaterials
Akhilesh Bendre, Mahesh P. Bhat, Kyeong–Hwan Lee, et al.
Materials Today Advances (2022) Vol. 13, pp. 100205-100205
Open Access | Times Cited: 65
Machine learning for nanoplasmonics
Jean‐François Masson, John S. Biggins, Emilie Ringe
Nature Nanotechnology (2023) Vol. 18, Iss. 2, pp. 111-123
Closed Access | Times Cited: 54
Modular microfluidics for life sciences
Jialin Wu, Hui Fang, Jun Zhang, et al.
Journal of Nanobiotechnology (2023) Vol. 21, Iss. 1
Open Access | Times Cited: 40
Visual self-powered platform for ultrasensitive heavy metal detection designed on graphdiyne/graphene heterojunction and DNAzyme-triggered DNA circuit strategy
Jing Xu, Yinbing liu, Xinqi Luo, et al.
Chemical Engineering Journal (2024) Vol. 485, pp. 150151-150151
Closed Access | Times Cited: 15
High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications
Jianhua Zhou, Jianpei Dong, Hongwei Hou, et al.
Lab on a Chip (2024) Vol. 24, Iss. 5, pp. 1307-1326
Open Access | Times Cited: 14
Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis
Linbo Liu, Mingcheng Bi, Yunhua Wang, et al.
Nanoscale (2021) Vol. 13, Iss. 46, pp. 19352-19366
Closed Access | Times Cited: 65
Facile Access to Fabricate Carbon Dots and Perspective of Large‐Scale Applications
Lintao Chen, Cai‐Feng Wang, Chang Liu, et al.
Small (2022) Vol. 19, Iss. 31
Closed Access | Times Cited: 62
The case for data science in experimental chemistry: examples and recommendations
Junko Yano, Kelly J. Gaffney, John M. Gregoire, et al.
Nature Reviews Chemistry (2022) Vol. 6, Iss. 5, pp. 357-370
Open Access | Times Cited: 60
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
Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning
Xueye Chen, Honglin Lv
NPG Asia Materials (2022) Vol. 14, Iss. 1
Open Access | Times Cited: 46
Intelligent control of nanoparticle synthesis through machine learning
Honglin Lv, Xueye Chen
Nanoscale (2022) Vol. 14, Iss. 18, pp. 6688-6708
Closed Access | Times Cited: 36
Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation
Xiliang Yan, Tongtao Yue, David A. Winkler, et al.
Chemical Reviews (2023) Vol. 123, Iss. 13, pp. 8575-8637
Closed Access | Times Cited: 30
Assessing the reproducibility and up-scaling of the synthesis of Er,Yb-doped NaYF4-based upconverting nanoparticles and control of size, morphology, and optical properties
Elina Andresen, Fahima Islam, Carsten Prinz, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 27
Visible Microfluidic Deprotonation for Aramid Nanofibers as Building Blocks of Cascade‐Microfluidic‐Processed Colloidal Aerogels
Yafei Ding, Qing‐Qing Cheng, Jing Lyu, et al.
Advanced Materials (2024) Vol. 36, Iss. 25
Closed Access | Times Cited: 6
Automated nanoarchitectonics for networks filling by Prussian blue single crystals through an intermittentto-flow conversion
Zixuan Wan, Wei Zhang, Tianzi Li, et al.
Chemical Engineering Journal (2025) Vol. 505, pp. 159525-159525
Closed Access
3D printing and artificial intelligence tools for droplet microfluidics: Advances in the generation and analysis of emulsions
Sibilla Orsini, Marco Lauricella, Andrea Montessori, et al.
Applied Physics Reviews (2025) Vol. 12, Iss. 1
Closed Access
Microfluidic fabrication of fluorescent nanomaterials: A review
Liangliang Lin, Yijian Yin, Sergey A. Starostin, et al.
Chemical Engineering Journal (2021) Vol. 425, pp. 131511-131511
Closed Access | Times Cited: 49
Process intensification 4.0: A new approach for attaining new, sustainable and circular processes enabled by machine learning
Enrique A. López-Guajardo, Fernando Delgado‐Licona, A. Alvarez, et al.
Chemical Engineering and Processing - Process Intensification (2021) Vol. 180, pp. 108671-108671
Closed Access | Times Cited: 47
Accelerated AI development for autonomous materials synthesis in flow
Robert W. Epps, Amanda A. Volk, Kristofer G. Reyes, et al.
Chemical Science (2021) Vol. 12, Iss. 17, pp. 6025-6036
Open Access | Times Cited: 46
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