The chronic nature and associated complications of nonhealing wounds have led to the emergence of nanotechnology-based therapies that aim at facilitating the healing process and ultimately repairing the injured tissue. A number of engineered nanotechnologies have been proposed demonstrating unique properties and multiple functions that address specific problems associated with wound repair mechanisms. In this outlook, we highlight the most recently developed nanotechnology-based therapeutic agents and assess the viability and efficacy of each treatment, with emphasis on chronic cutaneous wounds. Herein we explore the unmet needs and future directions of current technologies, while discussing promising strategies that can advance the wound-healing field.
An international team of scientists have restored the vision in blind rats using a nanoparticle-based artificial retina prosthesis that can be injected directly into the eye. The scientific advance has been successfully demonstrated for a period of eight months without the need for surgery. While it is still early days for the research, it suggests it might one day be possible to use the conjugated polymer nanoparticle (P3HT-NP) treatment in humans to correct eye problems –ranging from hereditary retinal dystrophies to the incredibly common age-related macular degeneration.
“In our ‘liquid retina device,’ P3HT nanoparticles spread out over the entire subretinal space and promoted light-dependent activation of spared inner retinal neurons, recovering subcortical, cortical and behavioral visual responses,” Fabio Benfenati, research director at the Italian Institute of Technology, told Digital Trends. “We think that P3HT-NPs provide a new avenue in retinal prosthetics.”
Retinal prostheses refer to implantable devices that are designed to help restore sight in patients with retinal degeneration. They work by introducing visual information into the retina through the electrical stimulation of surviving retinal neurons. While promising, current retinal prostheses have so far been shown to only return low-resolution vision: Useful for things like distinguishing between light and dark or recognizing simple shapes and objects. This new nanotech approach appears far more promising, offering significantly higher resolution. After just one injection, activity in the rats’ visual cortex and visual acuity were the same as those found in healthy rats.
Hunting for lightweight dark matter particles requires detectors with much lower signal thresholds than traditional experiments. This requirement has prompted novel detection techniques, including probing the faint interactions that occur between sub-MeV particles and electrons. In a 180-hour-long experiment, Yonit Hochberg of the Hebrew University of Jerusalem and her colleagues demonstrate a device that distinguishes hypothetical sub-MeV dark matter from background noise with record sensitivity [1]. Their experiment places the strongest constraints yet on interactions between lightweight dark matter and regular matter.
Hochberg and her colleagues etched an array of nanowires in a 7-nm-thick tungsten-silicide film to produce a superconducting nanowire single-photon detector, a sensor that is sensitive to extremely small energy inputs. When energy above some threshold is deposited on a superconducting nanowire, the wire briefly becomes a regular conductor, resulting in a voltage pulse.
The team circulated a fixed current through their device and sealed it in a light-tight box for 180 hours. They counted four voltage pulses, each corresponding to a deposited energy of at least 0.73 eV. Absent any other detectable energy source, these dark counts could be attributed to cosmic-ray-generated muons or high-energy particles excited by radioactive decay.
A research team from Skoltech, Aalto University, and Kurnakov Institute has recently developed a new, versatile and simple approach to using carbon nanotubes for manufacturing carbon nanotube-polymer nanocomposites. The method is reported in Carbon and involves making briquettes—dense packages of carbon nanotube powders. Nanocomposites made with briquettes perform equally well as those made from the more expensive masterbatches, which are also polymer-specific—that is, less versatile.
“We believe the use of dense briquettes of carbon nanotubes can significantly facilitate the development of the carbon nanotube composite industry. This technique is cheap and applicable to a broad variety of polymer matrices, without sacrificing any of the electrical and thermal properties of the final material,” the lead author of the study, Skoltech Ph.D. student Hassaan Butt, stated.
Carbon nanotubes have been intensively investigated for decades by researchers from academia and industry because of their unique combination of electrical, thermal, and mechanical properties. Meanwhile, polymer-based nanocomposites have come to be the largest carbon nanotube application and the one closest to widespread integration into everyday life. It is easy to understand why: The smallest amounts of nanotubes added to a polymer endow the material with fundamentally new properties, such as electrical conductivity and piezoresistivity, as well as crucially enhancing its thermal and mechanical properties.
Lawrence Livermore National Laboratory (LLNL) scientists are scaling up the production of vertically aligned single-walled carbon nanotubes (SWCNT) that could revolutionize diverse commercial products ranging from rechargeable batteries, automotive parts and sporting goods to boat hulls and water filters. The research appears in the journal Carbon.
Most CNT production today is used in bulk composite materials and thin films, which rely on unorganized CNT architectures. For many uses, organized CNT architectures such as vertically aligned forests provide important advantages for exploiting the properties of individual CNTs in macroscopic systems.
“Robust synthesis of vertically-aligned carbon nanotubes at large scale is required to accelerate deployment of numerous cutting-edge devices to emerging commercial applications,” said LLNL scientist and lead author Francesco Fornasiero. “To address this need, we demonstrated that the structural characteristics of single-walled CNTs produced at wafer scale in a growth regime dominated by bulk diffusion of the gaseous carbon precursor are remarkably invariant over a broad range of process conditions.”
Thin films made of carbon nanotubes hold a lot of promise for advanced optoelectronics, energy and medicine, however with their manufacturing process subject to close supervision and stringent standardization requirements, they are unlikely to become ubiquitous anytime soon.
“A major hindrance to unlocking the vast potential of nanotubes is their multiphase manufacturing process which is extremely difficult to manage. We have suggested using artificial neural networks (ANN) to analyze experimental data and predict the efficiency of single-walled carbon nanotubes synthesis,” explains one of the authors of the study and Skoltech researcher, Dmitry Krasnikov.
In their work published in the prestigious Carbon journal, the authors show that machine learning methods, and, in particular, ANN trained on experimental parameters, such as temperature, gas pressure and flow rate, can help monitor the properties of the carbon nanotube films produced.
Discusses the possibility of Femtotech and the technological possibilities it may unlock. Not long ago nanotechnology was a fringe topic; now it’s a flourishing engineering field, and fairly mainstream. For example, while writing this article, I happened to receive an email advertisement for the “Second World Conference on Nanomedicine and Drug Delivery,” in Kerala, India. It wasn’t so long ago that nanomedicine seemed merely a flicker in the eyes of Robert Freitas and a few other visionaries!
But nano is not as small as the world goes. A nanometer is 10–9 meters – the scale of atoms and molecules. A water molecule is a bit less than one nanometer long, and a germ is around a thousand nanometers across. On the other hand, a proton has a diameter of a couple femtometers – where a femtometer, at 10–15 meters, makes a nanometer seem positively gargantuan. Now that the viability of nanotech is widely accepted (in spite of some ongoing heated debates about the details), it’s time to ask: what about femtotech? Picotech or other technologies at the scales between nano and femto seem relatively uninteresting, because we don’t know any basic constituents of matter that exist at those scales. But femtotech, based on engineering structures from subatomic particles, makes perfect conceptual sense, though it’s certainly difficult given current technology.
The nanotech field was arguably launched by Richard Feynman’s 1959 talk “There’s Plenty of Room at the Bottom.” As Feynman wrote there.
“It is a staggeringly small world that is below. In the year 2000, when they look back at this age, they will wonder why it was not until the year 1960 that anybody began seriously to move in this direction.
A research team of fusion scientists has succeeded in developing “the nano-scale sculpture technique” to fabricate an ultra-thin film by sharpening a tungsten sample with a focused ion beam. This enables the nano-scale observation of a cross-section very near the top surface of the tungsten sample using the transmission electron microscope. The sculpture technique developed by this research can be applied not only to tungsten but also to other hard materials.
Hardened materials such as metals, carbons and ceramics are used in automobiles, aircraft and buildings. In a fusion reactor study, “tungsten,” which is one of the hardest metal materials, is the most likely candidate for the armour material of the device that receives the plasma heat/particle load. This device is called divertor. In any hardened materials, nanometer scale damages or defects can be formed very near the top surface of the materials. For predicting a material lifetime, it is necessary to know the types of the damages and their depth profiles in the material. To do this, we must observe a cross-section of the region very near the top surface of the material with nano-scale level.
For the observation of the internal structure of materials with nano-scale level, transmission electron microscope (TEM), in which accelerated electrons are transmitted through the target materials, is commonly used as a powerful tool. In order to observe a cross-section very near the top surface of the tungsten with TEM, we firstly extract a small piece of the tungsten sample from its surface and then fabricate an ultra-thin film by cutting the extracted sample. The thickness of the film must be below ~100 nm (nanometer) to obtain high resolution due to the high-transmission of the electron beam (IMAGE 1). However, it has been extremely difficult to fabricate such an ultra-thin film for the hard materials such as a tungsten. Therefore, it has been almost impossible to obtain the ~100 nm thickness level by using conventional thin-film fabrication technique.
Sign up for a Curiosity Stream subscription and also get a free Nebula subscription (the streaming platform built by creators) here: https://curiositystream.com/isaacarthur. In the future humanity may build enormous structures, feats of mega-engineering that may rival planets or even be of greater scope. This episode catalogs roughly 100 major types of Megastructure, from those that are cities in space to those that rival galaxies.
Researchers used deep reinforcement learning to steer atoms into a lattice shape, with a view to building new materials or nanodevices.
In a very cold vacuum chamber, single atoms of silver form a star-like lattice. The precise formation is not accidental, and it wasn’t constructed directly by human hands either. Researchers used a kind of artificial intelligence called deep reinforcement learning to steer the atoms, each a fraction of a nanometer in size, into the lattice shape. The process is similar to moving marbles around a Chinese checkers board, but with very tiny tweezers grabbing and dragging each atom into place.
The main application for deep reinforcement learning is in robotics, says postdoctoral researcher I-Ju Chen. “We’re also building robotic arms with deep learning, but for moving atoms,” she explains. “Reinforcement learning is successful in things like playing chess or video games, but we’ve applied it to solve technical problems at the nanoscale.”
