Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

Abstract

The parameter space of CNT forest synthesis is vastand multidimensional, making experimental and/or numericalexploration of the synthesis prohibitive. We propose a morepractical approach to explore the synthesis-process relationshipsof CNT forests using machine learning (ML) algorithms toinfer the underlying complex physical processes. Currently, nosuch ML model linking CNT forest morphology to synthesisparameters has been demonstrated. In the current work, weuse a physics-based numerical model to generate CNT forestmorphology images with known synthesis parameters to trainsuch a ML algorithm. The CNT forest synthesis variablesof CNT diameter and CNT number densities are varied togenerate a total of 12 distinct CNT forest classes. Images of theresultant CNT forests at different time steps during the growthand self-assembly process are then used as the training dataset.Based on the CNT forest structural morphology, multiplesingle and combined histogram-based texture descriptors areused as features to build a random forest (RF) classifier topredict class labels based on correlation of CNT forest physicalattributes with the growth parameters. The machine learningmodel achieved an accuracy of up to 83.5% on predicting thesynthesis conditions of CNT number density and diameter.These results are the first step towards rapidly characterizingCNT forest attributes using machine learning. Identifying therelevant process-structure interactions for the CNT forests usingphysics-based simulations and machine learning could rapidlyadvance the design, development, and adoption of CNT forestapplications with varied morphologies and properties.

Multi-Objective Optimization of Solid Oxide Fuel Cell/GT Combined Heat and Power System: A comparison between Particle Swarm and Genetic Algorithms

“…The quantity of hydrogen and oxygen necessary for the electrochemical reaction can be calculated at this identified current amount. Later, the fuel utilization as well as the air utilization can be calculated by equations (4,5), followed by determining the inlet molar flow rates of anode and cathode employing the mass balance equations for each gas component equations (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). Then, the cell voltage may be found by Nernst equation 21, also the polarization equations at given operating cell temperature and pressure can be calculated by solving the set of equations (21)(22)(23)(24)(25)(26)(27)(28)(29).…”

“…In such circumstances, multi-objective optimization (MOO) turns out a promising approach for finding various Pareto optimal solutions, to comprehend the quantitative tradeoffs between the objectives and also to acquire the optimal values of decision variables [6,9,10]. However, various arrangements of SOFC-GT combined systems have been studied with respect to several design parameters [11][12][13][14][15]. The SOFC-GT hybrid systems are usually fueled by natural gas, converted by means of an internal reformer.…”

“…A synthesis/design optimization of an integrated GT-SOFC power plant has been performed by Calinse et al [14], where a classical single-level method created on the GA basis has been developed. A thermo-economic optimization that employs a Lagrange Multiplier Approach (LMA) has been examined by Cheddie [15]. In this work, an indirectly coupled integrated SOFC-GT power plant was considered where sizing and operating conditions were employed as the decision variables to obtain the minimum lifecycle cost of the system.…”

Effect of Temperature on the Mechanical Properties of Carbon Composites

“…Furthermore, the electrical conductivity of CNT is high [2]. Hence, recently, for enhancing the knowledge regarding the CNTs, a few studies to further analyze the mechanics and forest synthesis of CNT have been published [3][4][5][6]. Additionally, for evaluating the effect of CNTs on the mechanical and/or thermal properties of composite materials, many studies have been provided [7][8][9][10].…”

Evaluation of process-structure-property relationships of carbon nanotube forests using simulation and deep learning

“…For instance, it takes about 2.5 hours to grow a forest of 55 m height with a pitch of 100 nm between nucleation cites while such forest is grown in less than 2 minutes experimentally [161]. Therefore, the growth rate of each CNT within the population was assigned stochastically from a 250 CNTs -OD=15 nm (e) class4: 250 CNTs -OD=20 nm (e) class5: 500 CNTs -OD=5 nm (f) class6: 500 CNTs -OD=10 nm (g) class7: 500 CNTs -OD=15 nm (h) class8: 500 CNTs OD=20 nm (i) class9: 750 CNTs -OD=5 nm (j) class10: 750 CNTs -OD=10 nm (k) class11: 750 CNTs -OD=15 nm (l) class12: 750 CNTs -OD=20 nm [158].…”