Lifeboat Foundation

With a new nanoparticle that converts light to heat, a team of researchers has found a promising technology for clearing water of pollutants.

Trace amounts of contaminants such as pesticides, pharmaceuticals and perfluorooctanoic acid in drinking water sources have posed significant health risks to humans in recent years. These micropollutants have eluded conventional treatment processes, but certain chemical processes that typically involve ozone, hydrogen peroxide or UV light have proven effective. These processes, however, can be expensive and energy-intensive.

A new nanoparticle created by Yale University engineers as part of an effort for the Rice-based Nanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment (NEWT) could lead to technologies that get around those limitations. The particle is described in a study published this week in the Proceedings of the National Academy of Sciences.

Rapid progress has been made in recent years to build these tiny machines, thanks to supramolecular chemists, chemical and biomolecular engineers, and nanotechnologists, among others, working closely together. But one area that still needs improvement is controlling the movements of swarms of molecular robots, so they can perform multiple tasks simultaneously.

Researchers at Empa and EPFL have created one of the smallest motors ever made. It’s composed of just 16 atoms, and at that tiny size it seems to function right on the boundary between classical physics and the spooky quantum realm.

Like its macroscopic counterparts, this mini motor is made up of a moving part (the rotor) and a fixed part (the stator). The stator in this case is a cluster of six palladium atoms and six gallium atoms arranged in a rough triangular shape. Meanwhile, the rotor is a four-atom acetylene molecule, which rotates on the surface of the stator. The whole machine measures less than a nanometer wide.

Continue reading “Nano-motor of just 16 atoms runs at the boundary of quantum physics” »

A research team from Empa and EPFL has developed a molecular motor which consists of only 16 atoms and rotates reliably in one direction. It could allow energy harvesting at the atomic level. The special feature of the motor is that it moves exactly at the boundary between classical motion and quantum tunneling — and has revealed puzzling phenomena to researchers in the quantum realm.

The smallest motor in the world—consisting of just 16 atoms: this was developed by a team of researchers from Empa and EPFL. “This brings us close to the ultimate size limit for molecular motors,” explains Oliver Gröning, head of the Functional Surfaces Research Group at Empa. The motor measures less than one nanometer—in other words it is around 100,000 times smaller than the diameter of a human hair.

Continue reading “The smallest motor in the world” »

Circa 2015

Move over, graphene — you’re not the only miracle material in town. Australian researchers have discovered that diamond nanothreads (one-dimensional diamond crystals capped with hydrogen) could be extremely strong. While scientists thought they were brittle when announced just a month ago, it turns out that they become supremely flexible (and thus durable) when you introduce the right kinds of defects. You could create nanoscopic structures that are just as strong as you need them to be, with a ‘perfect’ mix of bendy and rigid shapes.

The next major revolution in computer chip technology is now a step closer to reality. Researchers have shown that carbon nanotube transistors can be made rapidly in commercial facilities, with the same equipment used to manufacture traditional silicon-based transistors – the backbone of today’s computing industry.

The ability to restore sight to the blind is one of the most profound acts of healing medicine can achieve, in terms of the impact on the affected patient’s life — and one of the most difficult for modern medicine to achieve. We can restore vision in a limited number of scenarios and there are some early bionic eyes on the market that can restore limited vision in very specific scenarios. Researchers may have taken a dramatic step towards changing that in the future, with the results of a new experiment to design a bionic retina.

The research team in question has published a paper in Nature detailing the construction of a hemispherical retina built out of high-density nanowires. The spherical shape of the retina has historically been a major challenge for biomimetic devices.

A technological advancement that may prove crucial in the long-term success of dental implants has been developed by University of Queensland researchers.

Dr. Karan Gulati, NHMRC Early Career Fellow from the UQ School of Dentistry, said modifying dental implants with ‘nanopores’ will help protect against one of the leading causes of implant failure.

“Poor integration between the implant and the surrounding tissue is one of the leading causes of dental implant failure,” Dr. Gulati said. “If the sealing between the implant and the surrounding gum tissue fails it can result in bacteria entering the implant and causing infection.”

Researchers at the Nanoscience Center and at the Faculty of Information Technology at the University of Jyväskylä in Finland have demonstrated that new distance-based machine learning methods developed at the University of Jyväskylä are capable of predicting structures and atomic dynamics of nanoparticles reliably. The new methods are significantly faster than traditional simulation methods used for nanoparticle research and will facilitate more efficient explorations of particle-particle reactions and particles’ functionality in their environment. The study was published in a Special Issue devoted to machine learning in the Journal of Physical Chemistry on May 15, 2020.

The new methods were applied to ligand-stabilized metal nanoparticles, which have been long studied at the Nanoscience Center at the University of Jyväskylä. Last year, the researchers published a method that is able to successfully predict binding sites of the stabilizing ligand molecules on the nanoparticle surface. Now, a new tool was created that can reliably predict potential energy based on the atomic structure of the particle, without the need to use numerically heavy electronic structure computations. The tool facilitates Monte Carlo simulations of the atom dynamics of the particles at elevated temperatures.

Potential energy of a system is a fundamental quantity in computational nanoscience, since it allows for quantitative evaluations of system’s stability, rates of chemical reactions and strengths of interatomic bonds. Ligand-stabilized metal nanoparticles have many types of interatomic bonds of varying chemical strength, and traditionally the energy evaluations have been done by using the so-called density functional theory (DFT) that often results in numerically heavy computations requiring the use of supercomputers. This has precluded efficient simulations to understand nanoparticles’ functionalities, e.g., as catalysts, or interactions with biological objects such as proteins, viruses, or DNA. Machine learning methods, once trained to model the systems reliably, can speed up the simulations by several orders of magnitude.