Artificial Neuron Networks Enabled Identification and Characterizations of 2D Materials and van der Waals Heterostructures

Two-dimensional (2D) materials and their in-plane and out-of-plane (i.e., van der Waals, vdW) heterostructures are promising building blocks for next-generation electronic and optoelectronic devices. Since the performance of the devices is strongly dependent on the crystalline quality of the materials and the interface characteristics of the heterostructures, a fast and nondestructive method for distinguishing and characterizing various 2D building blocks is desirable to promote the device integrations. In this work, based on the color space information on 2D materials' optical microscopy images, an artificial neural network-based deep learning algorithm is developed and applied to identify eight kinds of 2D materials with accuracy well above 90% and a mean value of 96%. More importantly, this data-driven method enables two interesting functionalities: (1) resolving the interface distribution of chemical vapor deposition (CVD) grown in-plane and vdW heterostructures and (2) identifying defect concentrations of CVD-grown 2D semiconductors. The two functionalities can be utilized to quickly identify sample quality and optimize synthesis parameters in the future. Our work improves the characterization efficiency of atomically thin materials and is therefore valuable for their research and applications.

A Rapid Unraveling of the Activity and Antibiotic Susceptibility of Mycobacteria

The development of antibiotic-resistant bacteria is a worldwide health-related emergency that calls for new tools to study the bacterial metabolism and to obtain fast diagnoses. Indeed, the conventional analysis time scale is too long and affects our ability to fight infections. Slowly growing bacteria represent a bigger challenge, since their analysis may require up to months. Among these bacteria, Mycobacterium tuberculosis, the causative agent of tuberculosis, has caused more than 10 million new cases and 1.7 million deaths in 2016 only. We employed a particularly powerful nanomechanical oscillator, the nanomotion sensor, to characterize rapidly and in real time tuberculous and nontuberculous bacterial species, Mycobacterium bovis bacillus Calmette-Guérin and Mycobacterium abscessus, respectively, exposed to different antibiotics. Here, we show how high-speed and high-sensitivity detectors, the nanomotion sensors, can provide a rapid and reliable analysis of different mycobacterial species, obtaining qualitative and quantitative information on their responses to different drugs. This is the first application of the technique to tackle the urgent medical issue of mycobacterial infections, evaluating the dynamic response of bacteria to different antimicrobial families and the role of the replication rate in the resulting nanomotion pattern. In addition to a fast analysis, which could massively benefit patients and the overall health care system, we investigated the real-time responses of the bacteria to extract unique information on the bacterial mechanisms triggered in response to antibacterial pressure, with consequences both at the clinical level and at the microbiological level.