Graphitization of carbon offers a promising route to upcycle waste biomass and plastics into functional carbon nanomaterials for a range of applications including energy storage devices. One challenge to the more widespread utilization of this technology is controlling the carbon nanostructures formed. In this work, we undertake a meta-analysis of graphitization catalyzed by transition metals, examining the available electron microscopy data of carbon nanostructures and finding a correlation between different nanostructures and metal particle size. By considering a thermodynamic description of the graphitization process on transition-metal nanoparticles, we show an energy barrier exists that distinguishes between different growth mechanisms. Particles smaller than ∼25 nm in radius remain trapped within closed carbon structures, while nanoparticles larger than this become mobile and produce nanotubes and ribbons. These predictions agree closely with experimentally observed trends and should provide a framework to better understand and tailor graphitization of waste materials into functional carbon nanostructures.

A statistical method to determine the number of measurements required from nanomaterials to ensure reliable and robust analysis is described. Commercial products utilizing graphene are in their infancy and recent investigations of commercial graphene manufacture have attributed this to the lack of robust metrology and standards by which graphene and related carbon materials can be measured and compared. Raman spectroscopy is known to be a useful tool in carbon nanomaterial characterization, but to provide meaningful information, in particular for quality control or management, multiple spectra are needed. Herein we present a statistical method to quantify the number of different spectra or other microscale measurements that should be taken to reliably characterize a graphene material. We have recorded a large number of Raman measurements and studied the statistical convergence of these data sets. We use a graphical approach to monitor the change in summary statistics and a Monte Carlo based bootstrapping method of data analysis to computationally resample the data demonstrating the effects of underanalyzing a material; for example, graphene nanoplatelets may require over 500 spectra before information about the exfoliation efficiency, particle size, layer number, and chemical functionalization is accurately obtained.

Porous carbon foams hold great promise for supercapacitors and next generation energy storage materials but greater control over the formation of the pore structure would aid the development of these...

The scalable production and dispersion of 2D materials, like graphene, is critical to enable their use in commercial applications. While liquid exfoliation is commonly used, solvents such as N-methyl-pyrrolidone (NMP) are toxic and difficult to scale up. However, the search for alternative solvents is hindered by the intimidating size of the chemical space. Here, we present a computational pipeline informing the identification of effective exfoliation agents. Classical molecular dynamics simulations provide statistical sampling of interactions, enabling the identification of key molecular descriptors for a successful solvent. The statistically representative configurations from these simulations, studied with quantum mechanical calculations, allow us to gain insights onto the chemophysical interactions at the surface–solvent interface. As an exemplar, through this pipeline we identify a potential graphene exfoliation agent 2-pyrrolidone and experimentally demonstrate it to be as effective as NMP. Our workflow can be generalized to any 2D material and solvent system, enabling the screening of a wide range of compounds and solvents to identify safer and cheaper means of producing dispersions.