Lifeboat Foundation

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.”

Membrane filters don’t require much energy to purify water, making them popular for wastewater treatment. To keep these materials in tip-top condition, they’re commonly cleaned with large amounts of strong chemicals, but some of these agents destroy the membranes in the process. Now, researchers reporting in ACS Applied Materials & Interfaces have developed reusable nanoparticle catalysts that incorporate glucose to help efficiently break down contaminants inside these filters without damaging them.

Typically, dirty wastewater filters are unclogged with strong acids, bases or oxidants. Chlorine-containing oxidants such as bleach can break down the most stubborn organic debris. But they also damage polyamide membranes, which are in most commercial nanofiltration systems, and they produce toxic byproducts. A milder alternative to bleach is hydrogen peroxide, but it decomposes contaminants slowly.

Previously, scientists have combined hydrogen peroxide with iron oxide to form hydroxyl radicals that improve hydrogen peroxide’s efficiency in a process known as the Fenton reaction. Yet in order for the Fenton reaction to clean filters, extra hydrogen peroxide and acid are needed, increasing financial and environmental costs. One way to avoid these additional chemicals is to use the enzyme glucose oxidase, which simultaneously forms hydrogen peroxide and gluconic acid from glucose and oxygen. So, Jianquan Luo and colleagues wanted to combine glucose oxidase and iron oxide nanoparticles into a system that catalyzes the Fenton-based breakdown of contaminants, creating an efficient and delicate cleaning system for membrane filters.

Hard drives and flash storage have gotten more reliable over the years, but only on a human timescale. What if we need data storage that lasts longer? Decades? Millennia? The key to that vision might be 5D optical storage, which has a data density 10,000 times that of a Blu-ray disc. But it’s always been far too slow to write data onto glass plates in this way—until now. A new technique developed at the University of Southampton speeds up the process dramatically, without impacting the reliability of the data.

This type of data storage uses three layers of nanoscale dots in a glass disc. The size, orientation, and position (in three dimensions) of the dots gives you the five “dimensions” used to encode data. Researchers say that a 5D disc could remain readable after 13.8 billion years, but it would be surprising if anyone was even around to read them at that point. In the shorter term, 5D optical media could also survive after being heated to 1,000 degrees Celsius. You can see an earlier, smaller version of the disc above.

This is not the first time 5G optical data storage has popped up. It was just impractically slow before. Data is added to the discs with lasers, but if the laser moves too fast, the disc’s structural integrity is compromised. The technique devised by doctoral researcher Yuhao Lei uses a femtosecond laser with a high repetition rate. The process starts with a seeding pulse that creates a nanovoid, but the fast pulse doesn’t need to actually write any data. The repeated weak pulses leverage a phenomenon known as near-field enhancement to sculpt the nanostructures in a more gentle way.

Circa 2013

Can the food industry avoid the mistakes of GM and put nanoketchup on the UK’s menu?