There is growing interest in transitioning electronic components and circuitry from stiff and rigid substrates to more flexible and stretchable platforms, such as thin plastics, textiles, and foams. In parallel, the push for more sustainable, biocompatible, and cost-efficient conductive inks to coat these substrates, has led to the development of formulations with novel nanomaterials. Among these, 2D materials, and particularly graphenes and MXenes, have received intense research interest due to their increasingly facile and scalable production, high electrical conductivity, and compatibility with existing manufacturing techniques. They enable a range of electronic devices, including strain and pressure sensors, supercapacitors, thermoelectric generators, and heaters. These new flexible and stretchable electronic devices developed with 2D material coatings are poised to unlock exciting applications in the wearable, healthcare and Internet of Things sectors. This review has surveyed key data from more than 200 articles published over the last 6 years, to provide a quantitative analysis of recent progress in the field and shade light on future directions and prospects of this technology. We find that despite the different chemical origins of graphenes and MXenes, their shared electrical properties and 2D morphology, guarantee intriguing performance in end applications, leaving plenty of space for shared progress and advancements in the future.

A significant challenge for graphene nanoplatelet (GNP) suppliers is the characterisation of platelet morphology in industrial environments. This challenge is further exacerbated to platelet surface chemistry when scalable functionalisation processes, such as plasma treatment, are used to modify the GNPs to improve the filler-matrix interphase in nanocomposites. The costly and complex suite of analytical equipment necessary for a complete material description makes quality control and process optimisation difficult. Raman spectroscopy is a facile and accessible characterisation technique, with recent advancements unlocking fast mapping for rapid data collection. In this study, we develop novel techniques to better characterise GNP morphology and changes in surface chemistry using Raman maps of bulk powders. Providing a bespoke algorithmic framework for the analysis of these advanced materials. An unsupervised peak fitting and processing algorithm was used to extract crystallinity data and correlate it with laser-diffraction-derived lateral size values for a commercial set of GNPs rapidly and accurately. Classical machine learning was used to identify the most informative Raman features for classifying the plasma-functionalised GNPs. The initial material properties were found to affect the peak features that were the most useful for classification. In low defect density and low specific surface area GNPs, the D peak FWHM is found to be the most useful, whereas the I2D/IG ratio is the most useful in the opposite case. Finally, a convolutional neural network was trained to discern between different GNP grades with 86% accuracy. This work demonstrates how computer vision could be deployed for rapid and accurate quality control on the factory floor.

Copper plating of industrial components is common. However, the application of copper to moving parts is limited due to its poor tribological properties. In this study, a novel nickel-flashed, graphene nanoplatelet-reinforced copper matrix coating (referred to as ML(Ni)) on AISI 52100 steel exhibited stable and long-lasting friction coefficients (μ < 0.2) that were substantially lower than from similarly-prepared coatings that omitted nickel and graphitic material. Chemical, structural and mechanical analysis of the wear tracks on ML(Ni) after pin-on-disk testing identified key contributions that promote lubrication synergy. Moreso, the roles of individual components (Ni, Cu and GnP) within ML(Ni) were probed in detail to demonstrate that its hybrid composite structure is a promising candidate for use in high-load engineering applications.