Real-time monitoring and hydrodynamic scaling of shear exfoliated graphene

Shear-assisted liquid exfoliation is a primary candidate for producing defect-free two-dimensional (2D) materials. A range of approaches that delaminate nanosheets from layered precursors in solution have emerged in recent years. Diverse hydrodynamic conditions exist across these methods, and combined with low-throughput, high-cost characterization techniques, strongly contribute to the wide variability in performance and material quality. Nanosheet concentration and production rate are usually correlated against operating parameters unique to each production method, making it difficult to compare, optimize and predict scale-up performance. Here, we reveal the shear exfoliation mechanism from precursor to 2D material and extract the derived hydrodynamic parameters and scaling relationship that are key to nanomaterial output and common to all shear exfoliation processes. Our investigations use conditions created from two different hydrodynamic instabilities—Taylor vortices and interfacial waves—and combine materials characterization, fluid dynamics experiments and numerical simulations. Using graphene as the prototypical 2D material, we find that scaling of concentration of few-layer nanosheets depends on local strain rate distribution, relationship to the critical exfoliation criterion, and precursor residence time. We report a transmission-reflectance method to measure concentration profiles in real-time, using low-cost optoelectronics and without the need to remove the layered precursor material from the dispersion. We show that our high-throughput, in situ approach has broad uses by controlling the number of atomic layers on-the-fly, rapidly optimizing green solvent design to maximize yield, and viewing live production rates. Combining the findings on the hydrodynamics of exfoliation with this monitoring technique, we unlock targeted process intensification, quality control, batch traceability and individually customizable 2D materials on-demand.