Circa 2013 o.o
Katharine Sanderson answers the big questions about the tiny technology on its way to your plate.
Category: nanotechnology – Page 154
The amniotic membrane (Amnio-M) has various applications in regenerative medicine. It acts as a highly biocompatible natural scaffold and as a source of several types of stem cells and potent growth factors. It also serves as an effective nano-reservoir for drug delivery, thanks to its high entrapment properties. Over the past century, the use of the Amnio-M in the clinic has evolved from a simple sheet for topical applications for skin and corneal repair into more advanced forms, such as micronized dehydrated membrane, amniotic cytokine extract, and solubilized powder injections to regenerate muscles, cartilage, and tendons. This review highlights the development of the Amnio-M over the years and the implication of new and emerging nanotechnology to support expanding its use for tissue engineering and clinical applications. Graphical Abstract.
Schran agrees. “This new mechanism of friction is definitely very interesting and exciting,” he says. “But what is missing in my opinion, is a clear benchmark measurement.” Quantifying, for instance, how friction changes based on water’s interaction with single versus multiple layers of carbon atoms could go a long way to fully verifying the new theory, which predicts that greater numbers of electrons in the multilayered carbon will boost friction.
The study team is already progressing along this path and dreaming of what lies beyond. They are hoping to eventually test their theory with flowing liquids other than water, and nanotubes composed of elements besides carbon. In such cases, molecules in the liquid and the electrons within nanotube walls would follow different patterns of interaction, possibly leading to changes in the degree of quantum friction. Lydéric Bocquet says that it may even be possible to control the amount of friction a flowing liquid experiences by constructing nanotubes with electron behavior explicitly in mind.
The new study sets the stage for years of complex exploration by experimental and theoretical physicists alike and, according to Kavokine, also signals a fundamental shift in how physicists should think about friction. “Physicists have long thought that it is different at the nanoscale, but this difference was not so obvious to find and describe,” he says. “They were dreaming about some quantum behavior arising at these scales—and now we have shown how it does.”
Aluminum is a highly reactive metal that can strip oxygen from water molecules to generate hydrogen gas. Now, researchers at UC Santa Cruz have developed a new cost-effective and effective way to use aluminum’s reactivity to generate clean hydrogen fuel.
In a new study, a team of researchers shows that an easily produced composite of gallium and aluminum creates aluminum nanoparticles that react rapidly with water at room temperature to yield large amounts of hydrogen. According to researchers, the gallium was easily recovered for reuse after the reaction, which yields 90% of the hydrogen that could theoretically be produced from the reaction of all the aluminum in the composite.
Easy aluminum nanoparticles split water and generate hydrogen gas rapidly under ambient conditions.
Fully organic rechargeable household batteries are an ideal alternative to traditional metal-based batteries, in particular for reducing pollution to landfill and the environment.
Now researchers at Flinders University, with Australian and Chinese collaborators, are developing an all-organic polymer battery that can deliver a cell voltage of 2.8V—a big leap in improving the energy storage capability of organic batteries.
“While starting with small household batteries, we already know organic redox-active materials are typical electroactive alternatives due to their inherently safe, lightweight and structure-tunable features and, most importantly, their sustainable and environmentally friendly,” says senior lecturer in chemistry Dr. Zhongfan Jia, a research leader at Flinders University’s Institute for Nanoscale Science and Technology.
Building a better supercomputer is something many tech companies, research outfits, and government agencies have been trying to do over the decades. There’s one physical constraint they’ve been unable to avoid, though: conducting electricity for supercomputing is expensive.
Not in an economic sense—although, yes, in an economic sense, too—but in terms of energy. The more electricity you conduct, the more resistance you create (electricians and physics majors, forgive me), which means more wasted energy in the form of heat and vibration. And you can’t let things get too hot, so you have to expend more energy to cool down your circuits.
With the advent of Big Data, current computational architectures are proving to be insufficient. Difficulties in decreasing transistors’ size, large power consumption and limited operating speeds make neuromorphic computing a promising alternative.
Neuromorphic computing, a new brain-inspired computation paradigm, reproduces the activity of biological synapses by using artificial neural networks. Such devices work as a system of switches, so that the ON position corresponds to the information retention or “learning,” while the OFF position corresponds to the information deletion or “forgetting.”
In a recent publication, scientists from the Universitat Autònoma de Barcelona (UAB), the CNR-SPIN (Italy), the Catalan Institute of Nanoscience and Nanotechnology (ICN2), the Institute of Micro and Nanotechnology (IMN-CNM-CSIC) and the ALBA Synchrotron have explored the emulation of artificial synapses using new advanced material devices. The project was led by Serra Húnter Fellow Enric Menéndez and ICREA researcher Jordi Sort, both at the Department of Physics of the UAB, and is part of Sofia Martins Ph.D. thesis.
In a paper published by Science, DeepMind demonstrates how neural networks can improve approximation of the Density Functional (a method used to describe electron interactions in chemical systems). This illustrates deep learning’s promise in accurately simulating matter at the quantum mechanical.
In a paper published in the scientific journal Science, DeepMind demonstrates how neural networks can be used to describe electron interactions in chemical systems more accurately than existing methods.
Density Functional Theory, established in the 1960s, describes the mapping between electron density and interaction energy. For more than 50 years, the exact nature of mapping between electron density and interaction energy — the so-called density functional — has remained unknown. In a significant advancement for the field, DeepMind has shown that neural networks can be used to build a more accurate map of the density and interaction between electrons than was previously attainable.
By expressing the functional as a neural network and incorporating exact properties into the training data, DeepMind was able to train the model to learn functionals free from two important systematic errors — the delocalization error and spin symmetry breaking — resulting in a better description of a broad class of chemical reactions.
A nanomaterials-engineered penetrating sealer developed by Washington State University researchers is able to better protect concrete from moisture and salt—the two most damaging factors in crumbling concrete infrastructure in northern states.
The novel sealer showed a 75% improvement in repelling water and a 44% improvement in reducing salt damage in laboratory studies compared to a commercial sealer. The work could provide an additional way to address the challenge of aging bridges and pavements in the U.S.
“We focused on one of the main culprits that compromises the integrity and durability of concrete, which is moisture,” said Xianming Shi, professor in the Department of Civil and Environmental Engineering who led the work. “If you can keep concrete dry, the vast majority of durability problems would go away.”