Machine Learning Guided Synthesis of Flash Graphene

Abstract

nology, nanomaterials must be synthesized by rapid and scalable processes that do not deleteriously affect their properties. To address this challenge, we and others recently reported the synthesis of graphene, [1][2][3] as well as mixed-phase MoS 2 and WS 2 , [4] high-entropy alloy NPs, [5,6] nanodiamond, [7] and other nanomaterials using the electrothermal flash Joule heating effect. The graphene product was called "flash graphene" after the intense black body radiation produced during the electrical discharge. Flash Joule heating permits the conversion of amorphous carbon, including waste such as pyrolyzed rubber tires, [8] ash by-products from plastic recycling, [9] or landfill-grade mixed plastic waste, [10] into graphene crystals. Furthermore, flash graphene crystals are turbostratic and exhibit varying degrees of layer-to-layer misorientation along the c-axis. [1] Such turbostratic graphene possesses nanostructure-dependent properties, including enhanced solubility in surfactant solutions [1] and altered band structure. [11] The scalable and environmentally friendly nature of the Joule heating process, as well as the turbostratic nature of the synthesized product, make flash Joule heating an intriguing synthetic technique that warrants further study and analysis.Although flash Joule heating has immense practical utility, it is intrinsically difficult to study. The flash graphene formation process occurs in just hundreds of milliseconds. Furthermore, present-day flash Joule heating reactors do not offer control over the current discharge profile, adding a stochastic element to each reaction that depends on momentary fluctuations in circuit-to-sample contact. These fluctuations are difficult to control experimentally, making it challenging to map processstructure-property relationships by a traditional grid-search. Due to these factors, the parameters that drive bulk nanocrystal formation during flash Joule heating remain ambiguous.At the same time, an emerging body of literature indicates that machine learning (ML) is a powerful tool for fundamental studies in materials science. [12][13][14][15][16][17][18] While ML is classically considered an industrial tool for process failure prevention, the use of ML to interrogate large parameter spaces can yield insights on new technologies at a low time-cost. For example, Tang et al. used ML to explore the process-structure-property relationships governing well-understood processes, such as chemical vapor deposition and quantum dot synthesis, and argued based on their results that ML would allow researchers to investigate Advances in nanoscience have enabled the synthesis of nanomaterials, such as graphene, from low-value or waste materials through flash Joule heating. Though this capability is promising, the complex and entangled variables that govern nanocrystal formation in the Joule heating process remain poorly understood. In this work, machine learning (ML) models are constructed to explore the factors that drive the transformation of amorphous carbon into gra...

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