scholarly journals Response to Comment on Predicting Aqueous Adsorption of Organic Compounds onto Biochars, Carbon Nanotubes, Granular Activated Carbons, And Resins with Machine Learning

2020 ◽  
Vol 54 (18) ◽  
pp. 11638-11639
Author(s):  
Kai Zhang ◽  
Shifa Zhong ◽  
Huichun Zhang
Keyword(s):  
Machine Learning ◽  
Carbon Nanotubes ◽  
Organic Compounds ◽  
Activated Carbons

Confining H2 and D2 by adsorption in microporous carbons (single-wall carbon nanotubes and activated carbons) doped by K or Li

2005 ◽  
Vol 30 (4) ◽  
pp. 393-400 ◽  
Author(s):  
Szymon Los ◽  
Philippe Azais ◽  
Roland JM Pellenq ◽  
Yannick Breton ◽  
Olivier Isnard ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Activated Carbons ◽  
Single Wall Carbon Nanotubes ◽  
Single Wall Carbon ◽  
Microporous Carbons

scholarly journals Advanced machine learning decision policies for diameter control of carbon nanotubes

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Rahul Rao ◽  
Jennifer Carpena-Núñez ◽  
Pavel Nikolaev ◽  
Michael A. Susner ◽  
Kristofer G. Reyes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Carbon Nanotubes ◽  
Radial Breathing Mode ◽  
Expected Improvement ◽  
Synthesis Conditions ◽  
Relative Closeness ◽  
Diameter Control ◽  
Decision Policies ◽  
Walled Carbon Nanotubes ◽  
Growth Experiments

AbstractThe diameters of single-walled carbon nanotubes (SWCNTs) are directly related to their electronic properties, making diameter control highly desirable for a number of applications. Here we utilized a machine learning planner based on the Expected Improvement decision policy that mapped regions where growth was feasible vs. not feasible and further optimized synthesis conditions to selectively grow SWCNTs within a narrow diameter range. We maximized two ranges corresponding to Raman radial breathing mode frequencies around 265 and 225 cm−1 (SWCNT diameters around 0.92 and 1.06 nm, respectively), and our planner found optimal synthesis conditions within a hundred experiments. Extensive post-growth characterization showed high selectivity in the optimized growth experiments compared to the unoptimized growth experiments. Remarkably, our planner revealed significantly different synthesis conditions for maximizing the two diameter ranges in spite of their relative closeness. Our study shows the promise for machine learning-driven diameter optimization and paves the way towards chirality-controlled SWCNT growth.

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