Max-Planck-Institut Für Polymerforschung

Researchers have developed a new method to isolate HIV from samples more easily, potentially making it easier to detect infection with the virus. They focus on peptide nanofibrils (PNFs) on magnetic microparticles, a promising tool and hybrid material for targeted binding and separation of viral particles. Researchers at Leipzig University and Ulm University have developed a new method to isolate HIV from samples more easily, potentially making it easier to detect infection with the virus. They focus on peptide nanofibrils (PNFs) on magnetic microparticles, a promising tool and hybrid material for targeted binding and separation of viral particles. They have published their new findings in the journal Advanced Functional Materials. "The presented method makes it possible to efficiently capture, isolate and concentrate virus particles, which may improve the sensitivity of existing diagnostic tools and analytical tests," says Professor Bernd Abel of the Institute of Technical Chemistry at Leipzig University. The nanofibrils used -- small, needle-like structures -- are based on the EF-C peptide, which was first described in 2013 by Professor Jan Münch from Ulm University and Ulm University Medical Center. EF-C is a peptide consisting of twelve amino acids that forms nanoscale fibrils almost instantaneously when dissolved in polar solvents. These can also be applied to magnetic particles. "Using the EF-C peptide as an example, our work shows how peptide fibrils on magnetic particles can have a completely new functionality -- the more or less selective binding of viruses. Originally, fibrils of this kind were more likely to be associated with neurodegenerative diseases," adds Dr Torsten John, co-first author of the study and former doctoral researcher under Professor Abel at Leipzig University. He is now a junior researcher at the Max Planck Institute for Polymer Research in Mainz, Germany. "Increasing the local concentration and isolating viruses from samples are critical to increasing the sensitivity of diagnosing viral infections," says Professor Münch. The researchers from Ulm and Leipzig have presented such a method for the concentration and isolation of HIV particles. In their study, they show how PNFs can be used effectively to separate HIV particles from solutions without relying on centrifugation. This innovative method uses special magnetic microbeads to specifically bind and magnetically separate virus particles, preserving their activity and infectivity. This is significant for new genetic engineering processes, for example. The study highlights the importance of the new method for HIV research and diagnostics, as well as for other applications in viral research. By improving the efficiency with which virus particles can be concentrated and isolated, this technology and the new hybrid material could help to improve the diagnosis of infections and the monitoring of resistance.

Researchers have demonstrated the use of a specially coated metal mesh to harvest water from fog and simultaneously remove pollutants. People living in foggy areas with low rainfall should benefit from this technology. In countries such as Peru, Bolivia and Chile, it's not uncommon for people who live in foggy areas to hang up nets to catch droplets of water. The same is true of Morocco and Oman. These droplets then trickle down the mesh and are collected to provide water for drinking, cooking and washing. As much as several hundred litres of water can be harvested daily using a fog net only a few square metres in area. For regions with little rain or spring water, but where fog is a common occurrence, this can be a blessing. One crucial drawback with this method, however, is atmospheric pollution, since the hazardous substances also end up in the droplets of water. In many of the world's major cities, the air is so polluted that any water harvested from fog isn't clean enough to be used untreated either for drinking or for cooking. Researchers at ETH Zurich have now developed a method that collects water from fog and simultaneously purifies it. This uses a close-mesh lattice of metal wire coated with a mixture of specially selected polymers and titanium dioxide. The polymers ensure that droplets of water collect efficiently on the mesh and then trickle down as quickly as possible into a container before they can be blown off by the wind. The titanium dioxide acts as a chemical catalyst, breaking down the molecules of many of the organic pollutants contained in the droplets to render them harmless. "Our system not only harvests fog but also treats the harvested water, meaning it can be used in areas with atmospheric pollution, such as densely populated urban centres," Ritwick Ghosh explains. A scientist at the Max Planck Institute for Polymer Research in Mainz, Ghosh conducted this project while on an extended guest stay at ETH Zurich. During this time, he was a member of the group led by Thomas Schutzius, who has since taken up a post as professor at the University of California, Berkeley. Photocatalytic memory Once installed, the technology needs little or no maintenance. Moreover, no energy is required apart from a small but regular dose of UV to regenerate the catalyst. Half an hour of sunlight is enough to reactivate the titanium oxide for a further 24 hours -- thanks to a property known as photocatalytic memory. Following reactivation with UV, the catalyst also remains active for a lengthy period in the dark. With periods of sunlight often rare in areas prone to fog, this is a very useful quality. The new fog collector was tested in the lab and in a small pilot plant in Zurich. Researchers were able to collect 8 percent of the water in artificially created fog and break down 94 percent of the organic compounds that had been added to it. Among the added pollutants were extremely fine diesel droplets and the chemical bisphenol A, a hormonally active agent. Potential use in cooling towers In addition to harvesting drinking water from fog, this technology could also be used to recover water used in the cooling towers. "In the cooling towers, steam escapes up into the atmosphere. In the United States, where I live, we use a great deal of fresh water to cool power plants," says Schutzius. "It would make sense to capture some of this water before it escapes and ensure that it is pure in case you want to return it back to the environment." Past research by Ghosh focused on water recovery from cooling towers. He would now like to advance this technology and explore marketable applications. His hope is to make greater use of fog and steam as a hitherto underutilised source of water and thereby play a role in alleviating the scarcity of this vital resource.

Scientists pioneer a new machine learning model for corrosion-resistant alloy design. In a world where annual economic losses from corrosion surpass 2.5 trillion US Dollars, the quest for corrosion-resistant alloys and protective coatings is unbroken. Artificial intelligence (AI) is playing an increasingly pivotal role in designing new alloys. Yet, the predictive power of AI models in foreseeing corrosion behaviour and suggesting optimal alloy formulas has remained elusive. Scientists of the Max-Planck-Institut für Eisenforschung (MPIE) have now developed a machine learning model that enhances the predictive accuracy by up to 15% compared to existing frameworks. This model uncovers new, but realistic corrosion-resistant alloy compositions. Its distinct power arises from fusing both numerical and textual data. Initially developed for the critical realm of resisting pitting corrosion in high-strength alloys, this model's versatility can be extended to all alloy properties. The researchers published their latest results in the journal Science Advances. Merging texts and numbers "Every alloy has unique properties concerning its corrosion resistance. These properties do not only depend on the alloy composition itself, but also on the alloy's manufacturing process. Current machine learning models are only able to benefit from numerical data. However, processing methodologies and experimental testing protocols, which are mostly documented by textual descriptors, are crucial to explain corrosion,," explains Kasturi Narasimha Sasidhar, lead author of the publication and former postdoctoral researcher at MPIE. The researcher team used language processing methods, akin to ChatGPT, in combination with machine learning (ML) techniques for numerical data and developed a fully automated natural language processing framework. Moreover, involving textual data into the ML framework allows to identify enhanced alloy compositions resistant to pitting corrosion. "We trained the deep-learning model with intrinsic data that contain information about corrosion properties and composition. Now the model is capable of identifying alloy compositions that are critical for corrosion-resistance even if the individual elements were not fed initially into the model," says Michael Rohwerder, co-author of the publication and head of the group Corrosion at MPIE. Pushing boundaries: automated data mining and image processing In the recently devised framework, Sasidhar and his team harnessed manually gathered data as textual descriptors. Presently, their objective lies in automating the process of data mining and seamlessly integrating it into the existing framework. The incorporation of microscopy images marks another milestone, envisioning the next generation of AI frameworks that converge textual, numerical, and image-based data.