Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
Graphene and related two-dimensional (2D) materials have been at the core of intense research and development for over fifteen years, however the market penetration of products based on this technology has lagged behind expectations. At Nature Communications we wish to support research providing insights into the path towards the industrialisation of 2D materials. We introduce a Collection that encapsulates the recent progress and outstanding challenges faced by research on atomically thin materials, and we focus, in particular, on the potential of 2D technologies for future impact at the commercial level.
Graphene and related two-dimensional (2D) materials have been at the core of intense research and development for over fifteen years, however the market penetration of products based on this technology has lagged behind expectations. At Nature Communications we wish to support research providing insights into the path towards the industrialisation of 2D materials. We introduce a Collection that encapsulates the recent progress and outstanding challenges faced by research on atomically thin materials, and we focus, in particular, on the potential of 2D technologies for future impact at the commercial level.
The importance of statistical analyses on 2D materials-based electronic devices and circuits is sometimes overlooked. Here the authors discuss the most pressing integration issues for such devices and emphasize the need for yield, variability, reliability, and stability benchmarking, and outline viable strategies resulting in research papers that are useful for the industry.
Graphene and related two-dimensional (2D) materials have remained an active field of research in science and engineering for over fifteen years. Here, the authors investigate why the transition from laboratories to fabrication plants appears to lag behind expectations, and summarize the main challenges and opportunities that have thus far prevented the commercialisation of these materials.
The fast-growing interest for two-dimensional (2D) nanomaterials is undermined by their natural restacking tendency, which severely limits their practical application. Novel porous heterostructures that coordinate 2D nanosheets with monolayered mesoporous scaffolds offer an opportunity to greatly expand the library of advanced materials suitable for electrochemical energy storage technologies.
The industrial application of two-dimensional (2D) materials strongly depends on the large-scale manufacturing of high-quality 2D films and powders. Here, the authors analyze three state-of-the art mass production techniques, discussing the recent progress and remaining challenges for future improvements.
The lack of scalable, high-quality insulators is a major problem hindering the progress on electronic devices built from 2D materials. Here, the authors review the current state-of-the-art and the future prospects of suitable insulators for 2D technologies.
This review presents an overview of scenarios where van der Waals (vdW) materials provide unique advantages for nanophotonic biosensing applications. The authors discuss basic sensing principles based on vdW materials, advantages of the reduced dimensionality as well as technological challenges.
Antimicrobial resistance is a growing global problem and low dimensional materials have emerged as a potential solution. Here, the authors review the progress which has been made on low dimensional antimicrobials looking at the materials synthesis, modes of action and currently applications.
Two-dimensional materials are receiving increasing interest as they could pave the way to a paradigm shift in nano-electronics. Here, the authors demonstrate a 1-bit implementation of a microprocessor consisting of 115 transistors, using atomically thin MoS2.
Here, the authors perform a benchmark study of field-effect transistors (FETs) based on 2D transition metal dichalcogenides, i.e., 230 MoS2 and 160 WS2 FETs, and track device-to-device variations to gauge the technological viability in future integrated circuits.
Here, the authors report a method to fabricate reconfigurable electronic devices based on 2D materials by using polyvinyl alcohol as substrate. This technique enables repeatable disassembling and reassembling of van der Waals heterostructures with different functionalities.
The fabrication of van der Waals heterostructures of atomically thin materials often relies on the search, manual transferring, and alignment of suitable flakes. Here, the authors develop a robotic system capable of identifying exfoliated 2D crystals and assembling them in complex heterostructures.
The existing integration approaches for 2D materials often degrade material properties and are not compatible with industrial processing. Here, the authors devise an adhesive wafer bonding strategy to transfer and stack monolayers, suitable for back end of the line integration of 2D materials.
Here, three-dimensional hafnium oxide and two-dimensional hexagonal boron nitride are integrated in the insulating section of double-layer graphene optical modulators, leading to a maximum bandwidth of 39 GHz and enhanced modulation efficiency.
Here, the authors demonstrate the application of machine learning to optimize the device fabrication process for wafer-scale 2D semiconductors, and eventually fabricate digital, analog, and optoelectrical circuits.
Silicon-based contaminants are ubiquitous in natural graphite, and they are thus expected to be present in exfoliated graphene. Here, the authors show that such impurities play a non-negligible role in graphene-based devices, and use high-purity parent graphite to boost the performance of graphene sensors and supercapacitor microelectrodes.
Here, the authors report the realization of an active pixel image sensor array composed by 64 pairs of switching transistors and phototransistors, based on wafer-scale bilayer MoS2. The device exhibits sensitive photoresponse under RGB light illumination, showing the potential of 2D MoS2 for image sensing applications.
Reducing circuit redundancy represents a priority for the scalability of parallel computing hardware. Here, the authors report the realization of pixel processing units consisting of single 2D WSe2 transistors implementing electrically-switchable logic functions. This strategy enables the fabrication of an image processing array with ~16% transistor consumption compared to traditional circuits.
The main limitation to the areal storage density of hard disk drives (HDDs) is the thickness of carbon overcoats protecting the disk media. Here, <2-nm-thick graphene overcoats with improved corrosion, wear and irradiation resistance are shown to meet the requirements for the realization of 4–10 Tb/in2 HDDs with heat assisted magnetic recording.
The authors demonstrate wafer-scale, graphene-based ion sensitive field effect transistors arrays for simultaneous concentration measurement of K+, Na+, NH4+, NO3−, SO42−, HPO42− and Cl−, and use their technology for real-time ion concentration measurements in an aquarium with lemnoideae lemna over a period of three weeks.
Here, the authors report ultrasensitive negative capacitance phototransistors based on MoS2 regulated by a layer of ferroelectric hafnium zirconium oxide film to demonstrate a hysteresis-free ultra-steep subthreshold slope of 17.64 mV/dec and specific detectivity of 4.75 × 1014 cm Hz1/2 W−1 at room temperature.
There is emerging interest in photodetectors in the mid-infrared driven by increasing need to monitor the environment for security and healthcare purposes. Sassiet al. show a thermal photodetector, based on the coupling between graphene and a pyroelectric crystal, which shows high temperature sensitivity.
2D materials represent a promising platform for machine vision and edge computing applications, although usually limited to ultraviolet and visible wavelengths. Here, the authors report the realization of a programmable image sensor based on black phosphorus, implementing multispectral imaging and analog in-memory computing functionalities in the near- to mid-infrared range.
Designing efficient and low power memristors-based neuromorphic systems remains a challenge. Here, the authors present graphene-based multi-level (>16) and non-volatile memristive synapses with arbitrarily programmable conductance states capable of weight assignment based on k-means clustering.
In standard computing architectures, memory and logic circuits are separated, a feature that slows matrix operations vital to deep learning algorithms. Here, the authors present an alternate in-memory architecture and demonstrate a feasible approach for analog matrix multiplication.
Designing efficient bio-inspired visual recognition system remains a challenge. Here the authors present a curved neuromorphic image sensor array based on a heterostructure of MoS2 and pV3D3 integrated with a plano-convex lens for efficient image acquisition and data pre-processing.
Continued device miniaturization and feasibility of integrating two-dimensional materials into circuits have enabled flexible and transparent optoelectronic memories. Here, the authors show a WSe2–hBN-based heterostructure memory with switching ratio of ~1.1 × 106, ensuring over 128 distinct storage states and retention time of ~4.5 × 104 s.
The implementation of spiking neural network in future neuromorphic hardware requires hardware encoder analogous to the sensory neurons. The authors show a biomimetic dual-gated MoS2 field effect transistor capable of encoding analog signals into stochastic spike trains at energy cost of 1–5 pJ/spike.
Ferroelectric devices with dielectric layers to modulate channel conductance have limited endurance and miniaturization. Here, the authors demonstrate a 2D ferroelectric channel transistor that integrates memory and computation capabilities, that will support the development of memory and computing fusion systems.
Designing large-scale hardware implementation of Probabilistic Neural Network for energy efficient neuromorphic computing systems remains a challenge. Here, the authors propose an hardware design based on MoS2/BP heterostructures as reconfigurable Gaussian synapses enabling EEG patterns recognition.
Artificial neural networks can emulate the human vision because of their spike-based operation by employing memristors as synapses. Here, Seo et al. integrate synaptic and optical sensing functions in a single heterostructure, which enables accurate and energy-efficient recognition of colored patterns.
A facile and cost-effective synthesis of MXenes is not yet available. Here, the authors propose a one-pot molten salt-based method of MXenes synthesis from elemental precursors in an air atmosphere. Li-ion storage properties of the MXenes are also reported and discussed.