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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.

The increasing occurrence of antibiotic-resistant bacteria and the dwindling antibiotic research and development pipeline have created a pressing global health crisis. Here, we report the discovery of a distinctive antibacterial therapy that uses visible (405 nanometers) light-activated synthetic molecular machines (MMs) to kill Gram-negative and Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus , in minutes, vastly outpacing conventional antibiotics. MMs also rapidly eliminate persister cells and established bacterial biofilms. The antibacterial mode of action of MMs involves physical disruption of the membrane. In addition, by permeabilizing the membrane, MMs at sublethal doses potentiate the action of conventional antibiotics. Repeated exposure to antibacterial MMs is not accompanied by resistance development. Finally, therapeutic doses of MMs mitigate mortality associated with bacterial infection in an in vivo model of burn wound infection. Visible light–activated MMs represent an unconventional antibacterial mode of action by mechanical disruption at the molecular scale, not existent in nature and to which resistance development is unlikely.

The mechanism for carbon-based superoxide dismutase (SOD) nanomimetics is not known, hindering the optimization of this potentially clinically useful feature. Here we studied oxidized activated charcoal (OAC) prepared by fuming nitric acid oxidation of activated charcoal and characterized its properties. The OAC nanoparticles have sizes 5−30 nm and are highly water-soluble. The OACs are strong SOD mimetics with a k cat = 2.1 × 10 5 s −1 at pH 12.7 and ∼10 8 M −1 s −1 rate constants at pH 8.5, having great potential in therapeutic application for disorders with pathological superoxide levels. Electron paramagnetic resonance (EPR) indicates that resting OACs are fully oxidized, exhibiting a stoichiometric level of an intrinsic radical. The OACs can be reduced with superoxide, leading to a decreased level of the intrinsic radical; however, the reduction is incomplete even at high superoxide levels. This outcome was predicted by a simple two-step SOD reaction mechanism using the species containing the intrinsic radical as the fully oxidized state. Pretreatment of the OACs with superoxide causes little change to its SOD activity, indicating that a stoichiometric amount of the intrinsic radical is not mandatory for full activity. This study indicates the direct participation of the intrinsic radical in the catalytic turnover of a highly active SOD-like nanozyme.