Lifeboat Foundation

A team of researchers working at Tsinghua University in China has created a sub-1-nm gate in a MoS₂ transistor. In their paper published in the journal Nature, the group outlines how they created the super tiny gate and explains why they believe it will be difficult for anyone to beat their record.

For most of the history of microcomputing, Moore’s Law has held up—researchers and engineers have managed to double the speed and capability of computers regularly by reducing the size of their components. But more recently, it has grown increasingly difficult to make components smaller as scientists now run into physical limitations. In this new effort, the researchers believe they may have bumped up against the ultimate limit—they have built a gate that is just one atom in length.

At their most basic, transistors are a source and a drain, with a gate controlling the flow of electricity between them. It switches on and off depending on how much electricity is applied. The push to reduce the size of the components has led to the testing of materials such as carbon nanotubes, which are approximately 1nm, for use as gates. In this new effort, the researchers have unrolled the carbon nanotube and used its graphene edge as the gate—reducing its length to just 0.34 nm.

In the future, decarbonized societies that use internet of things (IoT) devices will become commonplace. But to achieve this, we need to first realize highly efficient and stable sources of renewable energy. Solar cells are considered a promising option, but their electrical contacts suffer from a “tradeoff” relationship between surface passivation and conductivity. Recently, researchers from Japan have developed a new type of electrical contact that can overcome this problem.

The most recent type of commercial photovoltaic cell (solar cell) uses stacked layers of crystalline silicon (c-Si) and an ultrathin layer of silicon oxide (SiO_x) to form an electrical contact. The SiO_x is used as a “passivating” film—an unreactive layer that improves the performance, reliability, and stability of the device. But that does not mean that simply increasing the thickness of this passivating layer will lead to improved solar cells. SiO_x is an electrical insulator and there is a trade-off relationship between passivation and the conductivity of the electrical contact in solar cells.

In a new study, published in ACS Applied Nano Materials, a research team led by Assistant Professor Kazuhiro Gotoh and Professor Noritaka Usami from Nagoya University has developed a novel SiO_x layer that simultaneously allows high passivation and improved conductivity. Named NAnocrystalling Transport path in Ultrathin dielectrics for REinforcing passivating contact (NATURE contact), the new electrical contact consists of three-layer structures made up of a layer of silicon nanoparticles sandwiched between two layers of oxygen-rich SiO_x. “You can think of a passivating film as a big wall with gates in it. In the NATURE contact, the big wall is the SiO_x layer and the gates are Si nanocrystals,” explains Dr. Gotoh.

Physicists have discovered a new way to coat soft robots in materials that allow them to move and function in a more purposeful way. The research, led by the UK’s University of Bath, is described today in Science Advances.

Authors of the study believe their breakthrough modeling on ‘active matter’ could mark a turning point in the design of robots. With further development of the concept, it may be possible to determine the shape, movement and behavior of a soft solid not by its natural elasticity but by human-controlled activity on its surface.

The surface of an ordinary soft material always shrinks into a sphere. Think of the way water beads into droplets: the beading occurs because the surface of liquids and other soft material naturally contracts into the smallest surface area possible—i.e. a sphere. But active matter can be designed to work against this tendency. An example of this in action would be a rubber ball that’s wrapped in a layer of nano-robots, where the robots are programmed to work in unison to distort the ball into a new, pre-determined shape (say, a star).

Researchers at North Carolina State University have developed a new computational tool that allows users to conduct simulations of multi-functional magnetic nanoparticles in unprecedented detail. The advance paves the way for new work aimed at developing magnetic nanoparticles for use in applications from drug delivery to sensor technologies.

“Self-assembling magnetic nanoparticles, or MNPs, have a lot of desirable properties,” says Yaroslava Yingling, corresponding author of a paper on the work and a Distinguished Professor of Materials Science and Engineering at NC State. “But it has been challenging to study them, because computational models have struggled to account for all of the forces that can influence these materials. MNPs are subject to a complicated interplay between external magnetic fields and van der Waals, electrostatic, dipolar, steric, and hydrodynamic interactions.”

Many applications of MNPs require an understanding of how the nanoparticles will behave in complex environments, such as using MNPs to deliver a specific protein or drug molecule to a targeted cancer affected cell using external magnetic fields. In these cases, it is important to be able to accurately model how MNPs will respond to different chemical environments. Previous computational modeling techniques that looked at MNPs were unable to account for all of the chemical interactions MNPs experience in a given colloidal or biological environment, instead focusing primarily on physical interactions.

Multifunctional and diverse artificial neural systems can incorporate multimodal plasticity, memory and supervised learning functions to assist neuromorphic computation. In a new report, Jinran Yu and a research team in nanoenergy, nanoscience and materials science in China and the US., presented a bioinspired mechano-photonic artificial synapse with synergistic mechanical and optical plasticity. The team used an optoelectronic transistor made of graphene/molybdenum disulphide (MoS₂) heterostructure and an integrated triboelectric nanogenerator to compose the artificial synapse. They controlled the charge transfer/exchange in the heterostructure with triboelectric potential and modulated the optoelectronic synapse behaviors readily, including postsynaptic photocurrents, photosensitivity and photoconductivity. The mechano-photonic artificial synapse is a promising implementation to mimic the complex biological nervous system and promote the development of interactive artificial intelligence. The work is now published on Science Advances.

Brain-inspired neural networks.

The human brain can integrate cognition, learning and memory tasks via auditory, visual, olfactory and somatosensory interactions. This process is difficult to be mimicked using conventional von Neumann architectures that require additional sophisticated functions. Brain-inspired neural networks are made of various synaptic devices to transmit information and process using the synaptic weight. Emerging photonic synapse combine the optical and electric neuromorphic modulation and computation to offer a favorable option with high bandwidth, fast speed and low cross-talk to significantly reduce power consumption. Biomechanical motions including touch, eye blinking and arm waving are other ubiquitous triggers or interactive signals to operate electronics during artificial synapse plasticization. In this work, Yu et al. presented a mechano-photonic artificial synapse with synergistic mechanical and optical plasticity.

The human brain, fed on just the calorie input of a modest diet, easily outperforms state-of-the-art supercomputers powered by full-scale station energy inputs. The difference stems from the multiple states of brain processes versus the two binary states of digital processors, as well as the ability to store information without power consumption—non-volatile memory. These inefficiencies in today’s conventional computers have prompted great interest in developing synthetic synapses for use in computers that can mimic the way the brain works. Now, researchers at King’s College London, UK, report in ACS Nano Letters an array of nanorod devices that mimic the brain more closely than ever before. The devices may find applications in artificial neural networks.

Efforts to emulate biological synapses have revolved around types of memristors with different resistance states that act like memory. However, unlike the brain the devices reported so far have all needed a reverse polarity electrical voltage to reset them to the initial state. “In the brain a change in the chemical environment changes the output,” explains Anatoly Zayats, a professor at King’s College London who led the team behind the recent results. The King’s College London researchers have now been able to demonstrate this brain-like behavior in their synaptic synapses as well.

Zayats and team build an array of gold nanorods topped with a polymer junction (poly-L-histidine, PLH) to a metal contact. Either light or an electrical voltage can excite plasmons—collective oscillations of electrons. The plasmons release hot electrons into the PLH, gradually changing the chemistry of the polymer, and hence changing it to have different levels of conductivity or light emissivity. How the polymer changes depends on whether oxygen or hydrogen surrounds it. A chemically inert nitrogen chemical environment will preserve the state without any energy input required so that it acts as non-volatile memory.

The development of functional nanomaterials has been a major landmark in the history of materials science. Nanoparticles with diameters ranging from 5 to 500 nm have unprecedented properties, such as high catalytic activity, compared to their bulk material counterparts. Moreover, as particles become smaller, exotic quantum phenomena become more prominent. This has enabled scientists to produce materials and devices with characteristics that had been only dreamed of, especially in the fields of electronics, catalysis, and optics.

But what if we go smaller? Sub-nanoparticles (SNPs) with particle sizes of around 1 nm are now considered a new class of materials with distinct properties due to the predominance of quantum effects. The untapped potential of SNPs caught the attention of scientists from Tokyo Tech, who are currently undertaking the challenges arising in this mostly unexplored field. In a recent study published in the Journal of the American Chemical Society, a team of scientists from the Laboratory of Chemistry and Life Sciences, led by Dr. Takamasa Tsukamoto, demonstrated a novel molecular screening approach to find promising SNPs.

As one would expect, the synthesis of SNPs is plagued by technical difficulties, even more so for those containing multiple elements. Dr. Tsukamoto explains: “Even SNPs containing just two different elements have barely been investigated because producing a system of subnanometer scale requires fine control of the composition ratio and particle size with atomic precision.” However, this team of scientists had already developed a novel method by which SNPs could be made from different metal salts with extreme control over the total number of atoms and the proportion of each element.

Circa 2017

Arrayed nanochannels can be used to controllably transfect and reprogram tissues in vivo for applications in regenerative medicine and cell-based therapies.

Circa 2014

New research on nanoparticles shows that they could be used to encode information when suspended in a liquid. This could one day allow us to store vast amounts of data in a very small volume of “digital colloid.”