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Flash Joule heating (FJH) can convert almost any carbon-based precursor into bulk quantities of graphene. This work explores the morphologies and properties of flash graphene (FG) generated from carbon black. It is shown that FG is partially comprised of sheets of turbostratic FG (tFG) that have a rotational mismatch between neighboring layers. The remainder of the FG is wrinkled graphene sheets that resemble nongraphitizing carbon. To generate high quality tFG sheets, a FJH duration of 30−100 ms is employed. Beyond 100 ms, the turbostratic sheets have time to AB-stack and form bulk graphite. Atomistic simulations reveal that generic thermal annealing yields predominantly wrinkled graphene which displays minimal to no alignment of graphitic planes, as opposed to the high-quality tFG that might be formed under the direct influence of current conducted through the material. The tFG was easily exfoliated via shear, hence the FJH process has the potential for bulk production of tFG without the need for pre-exfoliation using chemicals or high energy mechanical shear.

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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...

Heteroatom doping can effectively tailor the local structures and electronic states of intrinsic two-dimensional materials, and endow them with modified optical, electrical, and mechanical properties. Recent studies have shown the feasibility of preparing doped graphene from graphene oxide and its derivatives via some post-treatments, including solid-state and solvothermal methods, but they require reactive and harsh reagents. However, direct synthesis of various heteroatom-doped graphene in larger quantities and high purity through bottom-up methods remains challenging. Here, we report catalyst-free and solvent-free direct synthesis of graphene doped with various heteroatoms in bulk via flash Joule heating (FJH). Seven types of heteroatom-doped flash graphene (FG) are synthesized through millisecond flashing, including single-element-doped FG (boron, nitrogen, oxygen, phosphorus, sulfur), two-element-co-doped FG (boron and nitrogen), as well as three-element-co-doped FG (boron, nitrogen, and sulfur). A variety of low-cost dopants, such as elements, oxides, and organic compounds are used. The graphene quality of heteroatom-doped FG is high, and similar to intrinsic FG, the material exhibits turbostraticity, increased interlayer spacing, and superior dispersibility. Electrochemical oxygen reduction reaction of different heteroatom-doped FG is tested, and sulfur-doped FG shows the best performance. Lithium metal battery tests demonstrate that nitrogen-doped FG exhibits a smaller nucleation overpotential compared to Cu or undoped FG. The electrical energy cost for the synthesis of heteroatom-doped FG synthesis is only 1.2 to 10.7 kJ g–1, which could render the FJH method suitable for low-cost mass production of heteroatom-doped graphene.