Tumors that move to the brain are difficult to treat because of the brain-blood barrier that separates the brain from the rest of the body.
Sylvester Comprehensive Cancer Center researchers have developed a nanoparticle that could one day be used to treat brain metastases.
Posted in biotech/medical, nanotechnology
Carbon nanotubes are one of the most elastically strong materials out there.
When I was a kid, I used to take allowance money and occasionally buy rubber-band-powered balsa wood airplanes at a local store. Maybe you’ve seen these. You wind up the rubber band, which stretches the elastomer and stores energy in the elastic strain of the polymer, as in Hooke’s Law (though I suspect the rubber band goes well beyond the linear regime when it’s really wound up, because of the higher order twisting that happens). Rhett Alain wrote about how well you can store energy like this. It turns out that the stored energy per mass of the rubber band can get pretty substantial.
Carbon nanotubes are one of the most elastically strong materials out there. A bit over a decade ago, a group at Michigan State did a serious theoretical analysis of how much energy you could store in a twisted yarn made from single-walled carbon nanotubes. They found that the specific energy storage could get as large as several MJ/kg, as much as four times what you get with lithium ion batteries!
Now, a group in Japan has actually put this to the test, in this Nature Nano paper. They get up to 2.1 MJ/kg, over the lithium ion battery mark, and the specific power (when they release the energy) at about \(10^{6}\) W/kg is not too far away from non-cyclable energy storage media, like TNT. Very cool!
“However, being an indirect semiconductor, its utilization in optoelectronics has been hindered by poor optical properties.”
“While silicon does not naturally emit light in its bulk form, porous and nanostructured silicon can produce detectable light after being exposed to visible radiation.”
Scientists have been aware of this phenomenon for decades, but the precise origins of the illumination have been the subject of debate.
Inside every plant, animal and human cell are billions of molecular machines. They’re made up of proteins, DNA and other molecules, but no single piece works on its own. Only by seeing how they interact together, across millions of types of combinations, can we start to truly understand life’s processes.
In a paper published in Nature, we introduce AlphaFold 3, a revolutionary model that can predict the structure and interactions of all life’s molecules with unprecedented accuracy. For the interactions of proteins with other molecule types we see at least a 50% improvement compared with existing prediction methods, and for some important categories of interaction we have doubled prediction accuracy.
We hope AlphaFold 3 will help transform our understanding of the biological world and drug discovery. Scientists can access the majority of its capabilities, for free, through our newly launched AlphaFold Server, an easy-to-use research tool. To build on AlphaFold 3’s potential for drug design, Isomorphic Labs is already collaborating with pharmaceutical companies to apply it to real-world drug design challenges and, ultimately, develop new life-changing treatments for patients.
Proteins are the molecular machines that sustain every cell and organism, and knowing what they look like will be critical to untangling how they function normally and malfunction in disease. Now researchers have taken a huge stride toward that goal with the development of new machine learning algorithms that can predict the folded shapes of not only proteins but other biomolecules with unprecedented accuracy.
In a paper published today in Nature, Google DeepMind and its spinoff company Isomorphic Labs announced the latest iteration of their AlphaFold program, AlphaFold3, which can predict the structures of proteins, DNA, RNA, ligands and other biomolecules, either alone or bound together in different embraces. The findings follow the tail of a similar update to another deep learning structure-prediction algorithm, called RoseTTAFold All-Atom, which was published in March in Science.
In this study, graduate student Keito Kobayashi and Professor Shunsuke Fukami from Tohoku University, along with Dr. Kerem Camsari from the University of California, Santa Barbara, and their colleagues, developed a near-future heterogeneous version of a probabilistic computer tailored for executing probabilistic algorithms and facile manufacturing.
“Our constructed prototype demonstrated that excellent computational performance can be achieved by driving pseudo random number generators in a deterministic CMOS circuit with physical random numbers generated by a limited number of stochastic nanomagnets,” says Fukami. “Specifically speaking, a limited number of probabilistic bits (p-bits) with a stochastic magnetic tunnel junction (s-MTJ), should be manufacturable with a near-future integration technology.”
The researchers also clarified that the final form of the spintronics probabilistic computer, primarily composed of s-MTJs, will yield a four-order-of-magnitude reduction in area and a three-order-of-magnitude reduction in energy consumption compared to the current CMOS circuits when running probabilistic algorithms.
Nanoscale materials present us with astonishing chemical and physical properties that help materialize applications such as single molecular sensing and minimally invasive photothermal therapy—which were once just theories—into reality.
Nanostructured high entropy alloys—metals made from a chaotic mix of several different elements—show a lot of promise for use in industries such as aerospace and automotive because of their strength and stability at high temperatures compared with regular metals.
Drug delivery researchers at Oregon State University have developed a device with the potential to improve gene therapy for patients with inherited lung diseases such as cystic fibrosis.
In the early 20th century, the development of a catalyst for ammonia synthesis by the Haber-Bosch method took more than 10,000 experiments before it was successful. The development of new materials is a time-consuming and costly process from design to commercialization.
