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Chemists have created nanorobots propelled by magnets that remove pollutants from water. The invention could be scaled up to provide a sustainable and affordable way of cleaning up contaminated water in treatment plants.

Martin Pumera at the University of Chemistry and Technology, Prague, in the Czech Republic and his colleagues developed the nanorobots by using a temperature-sensitive polymer material and iron oxide. The polymer acts like tiny hands that can pick up and dispose of pollutants in the water, while the iron oxide makes the nanorobots magnetic. The researchers also added oxygen and hydrogen atoms to the iron oxide that can attach onto target pollutants.

The robots are about 200 nanometres wide and are powered by magnetic fields, which allow the team to control their movements.

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Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Watch now.

A supercomputer, providing massive amounts of computing power to tackle complex challenges, is typically out of reach for the average enterprise data scientist. However, what if you could use cloud resources instead? That’s the rationale that Microsoft Azure and Nvidia are taking with this week’s announcement designed to coincide with the SC22 supercomputing conference.

Nvidia and Microsoft announced that they are building a “massive cloud AI computer.” The supercomputer in question, however, is not an individually-named system, like the Frontier system at the Oak Ridge National Laboratory or the Perlmutter system, which is the world’s fastest Artificial Intelligence (AI) supercomputer. Rather, the new AI supercomputer is a set of capabilities and services within Azure, powered by Nvidia technologies, for high performance computing (HPC) uses.

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The global COVID-19 epidemic has spread rapidly around the world and caused the death of more than 5 million people. It is urgent to develop effective strategies to treat COVID-19 patients. Here, we revealed that SARS-CoV-2 infection resulted in the dysregulation of genes associated with NAD+ metabolism, immune response, and cell death in mice, similar to that in COVID-19 patients. We therefore investigated the effect of treatment with NAD+ and its intermediate (NMN) and found that the pneumonia phenotypes, including excessive inflammatory cell infiltration, hemolysis, and embolization in SARS-CoV-2-infected lungs were significantly rescued. Cell death was suppressed substantially by NAD+ and NMN supplementation. More strikingly, NMN supplementation can protect 30% of aged mice infected with the lethal mouse-adapted SARS-CoV-2 from death.

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A tool for estimating the local entropy production rate of a system enables the visualization and quantification of the out-of-equilibrium regions of an active-matter system.

A movie of a molecule jostling around in a fluid at equilibrium looks the same when played forward and backward. Such a movie has an “entropy production rate”—the parameter used to quantify this symmetry—of zero; most other movies have a nonzero value, meaning the visualized systems are out of equilibrium. Researchers know how to compute the entropy production rate of simple model systems. But measuring this parameter in experiments is an open problem. Now Sungham Ro of the Technion-Israel Institute of Technology, Buming Guo of New York University, and colleagues have devised a method for making local measurements of the entropy production rate [1]. They demonstrate the technique using simulations and bacteria observations (Fig. 1).

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Researchers at Penn Engineering have created a chip that outstrips the security and robustness of existing quantum communications hardware. Their technology communicates in “qudits,” doubling the quantum information space of any previous on-chip laser.

Liang Feng, Professor in the Departments of Materials Science and Engineering (MSE) and Electrical Systems and Engineering (ESE), along with MSE postdoctoral fellow Zhifeng Zhang and ESE Ph.D. student Haoqi Zhao, debuted the technology in a recent study published in Nature. The group worked in collaboration with scientists from the Polytechnic University of Milan, the Institute for Cross-Disciplinary Physics and Complex Systems, Duke University and the City University of New York (CUNY).

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