Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

Y Xie, K Sattari, C Zhang, J Lin - Progress in Materials Science, 2023 - Elsevier
The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …

AI for nanomaterials development in clean energy and carbon capture, utilization and storage (CCUS)

H Chen, Y Zheng, J Li, L Li, X Wang - ACS nano, 2023 - ACS Publications
Zero-carbon energy and negative emission technologies are crucial for achieving a carbon
neutral future, and nanomaterials have played critical roles in advancing such technologies …

Growth modes of single-walled carbon nanotubes on catalysts

F Yang, H Zhao, R Li, Q Liu, X Zhang, X Bai… - Science …, 2022 - science.org
Understanding the growth mechanism of single-walled carbon nanotubes (SWCNTs) and
achieving selective growth requires insights into the catalyst structure-function relationship …

[HTML][HTML] Machine learning methods for aerosol synthesis of single-walled carbon nanotubes

DV Krasnikov, EM Khabushev, A Gaev, AR Bogdanova… - Carbon, 2023 - Elsevier
This work is devoted to the strategy towards the optimal development of multiparametric
process of single-walled carbon nanotube (SWCNT) synthesis. Here, we examine the …

Statistical patterns in high-throughput growth of single-wall carbon nanotubes from Co/Pt/Mo ternary catalysts

ZH Ji, L Zhang, DM Tang, YM Zhao, MK Zou, RH Xie… - Carbon, 2023 - Elsevier
Designed alloy catalysts have shown promise in structure-controlled growth of single-wall
carbon nanotubes (SWCNTs). However, due to the high-dimensional growth parameter …

Forecasting carbon nanotube diameter in floating catalyst chemical vapor deposition

JS Bulmer, AWN Sloan, M Glerum, J Carpena-Núñez… - Carbon, 2023 - Elsevier
Carbon nanotube (CNT) diameter control is essential for many conductive and structural
applications; to date, a general diameter methodology applicable to all floating catalyst …

Predicting stress–strain behavior of carbon nanotubes using neural networks

V Košmerl, I Štajduhar, M Čanađija - Neural computing and applications, 2022 - Springer
Artificial neural networks are employed to predict stress–strain curves for all single-walled
carbon nanotube configurations with diameters up to 4 nm. Three model architectures are …

Addressing the Trade-Off between Crystallinity and Yield in Single-Walled Carbon Nanotube Forest Synthesis Using Machine Learning

D Lin, S Muroga, H Kimura, H Jintoku, T Tsuji, K Hata… - ACS …, 2023 - ACS Publications
Synthetic trade-offs exist in the synthesis of single-walled carbon nanotube (SWCNT)
forests, as growing certain desired properties can often come at the expense of other …

A comprehensive review of carbon nanotubes: growth mechanisms, preparation and applications

P Wang, Q Dong, C Gao, W Bai, D Chu… - … , Nanotubes and Carbon …, 2024 - Taylor & Francis
Carbon nanotubes (CNTs) have a wide range of applications in many fields, such as
electronic devices, composites, sensors, catalysts, hydrogen storage materials, biomedicine …

Improved understanding of carbon nanotube growth via autonomous jump regression targeting of catalyst activity

R Waelder, C Park, A Sloan, J Carpena-Núñez, J Yoho… - Carbon, 2024 - Elsevier
Catalyst control is critical to carbon nanotube (CNT) growth and scaling their production. In
supported catalyst CNT growth, the reduction of an oxidized metal catalyst enables growth …