Electrically tunable giant Nernst effect in two-dimensional van der Waals heterostructures.

The Nernst effect, a transverse thermoelectric phenomenon, has attracted significant attention for its potential in energy conversion, thermoelectrics and spintronics. However, achieving high performance and versatility at low temperatures remains elusive. Here we demonstrate a large and electrically tunable Nernst effect by combining the electrical properties of graphene with the semiconducting characteristics of indium selenide in a field-effect geometry.

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS.

Machine learning and signal processing on the edge are poised to influence our everyday lives with devices that will learn and infer from data generated by smart sensors and other devices for the Internet of Things. The next leap toward ubiquitous electronics requires increased energy efficiency of processors for specialized data-driven applications. Here, we show how an in-memory processor fabricated using a two-dimensional materials platform can potentially outperform its silicon counterparts in both standard and nontraditional Von Neumann architectures for artificial neural networks.

Superconducting 2D NbS Grown Epitaxially by Chemical Vapor Deposition.

Metallic two-dimensional (2D) transition metal dichalcogenides (TMDCs) are attracting great attention because of their interesting low-temperature properties such as superconductivity, magnetism, and charge density waves (CDW). However, further studies and practical applications are being slowed down by difficulties in synthesizing high-quality materials with a large grain size and well-determined thickness. In this work, we demonstrate epitaxial chemical vapor deposition (CVD) growth of 2D NbS crystals on a sapphire substrate, with a thickness-dependent structural phase transition.

Logic-in-memory based on an atomically thin semiconductor.

The growing importance of applications based on machine learning is driving the need to develop dedicated, energy-efficient electronic hardware. Compared with von Neumann architectures, which have separate processing and storage units, brain-inspired in-memory computing uses the same basic device structure for logic operations and data storage, thus promising to reduce the energy cost of data-centred computing substantially. Although there is ample research focused on exploring new device architectures, the engineering of material platforms suitable for such device designs remains a challenge.

Self-sensing, tunable monolayer MoS nanoelectromechanical resonators.

Excellent mechanical properties and the presence of piezoresistivity make single layers of transition metal dichalcogenides (TMDCs) viable candidates for integration in nanoelectromechanical systems (NEMS). We report on the realization of electromechanical resonators based on single-layer MoS with both piezoresistive and capacitive transduction schemes. Operating in the ultimate limit of membrane thickness, the resonant frequency of MoS resonators is primarily defined by the built-in mechanical tension and is in the very high frequency range.