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One step closer to bomb-sniffing cyborg locusts

If you want to enhance a locust to be used as a bomb-sniffing bug, there are a few technical challenges that need solving before sending it into the field.

Is there some way to direct the locust — to tell it where to go to do its sniffing? And because the locusts can’t speak (yet), is there a way to read the brain of these cyborg bugs to know what they’re smelling?

For that matter, can locusts even smell explosives?

Yes and yes to the first two questions. Previous research from Washington University in St. Louis has demonstrated both the ability to control the locusts and the ability to read their brains, so to speak, to discern what it is they are smelling. And now, thanks to new research from the McKelvey School of Engineering, the third question has been settled.

The answer, again: ‘yes.’

In a pre-proof published online Aug. 6 in the journal Biosensors and Bioelectronics: X, researchers showed how they were able to hijack a locust’s olfactory system to both detect and discriminate between different explosive scents — all within a few hundred milliseconds of exposure.

They were also able to optimize a previously developed biorobotic sensing system that could detect the locusts’ firing neurons and convey that information in a way that told researchers about the smells the locusts were sensing.

“We didn’t know if they’d be able to smell or pinpoint the explosives because they don’t have any meaningful ecological significance,” said Barani Raman, professor of biomedical engineering. “It was possible that they didn’t care about any of the cues that were meaningful to us in this particular case.”

Previous work in Raman’s lab led to the discovery that the locust olfactory system could be decoded as an ‘or-of-ands’ logical operation. This allowed researchers to determine what a locust was smelling in different contexts.

With this knowledge, the researchers were able to look for similar patterns when they exposed locusts to vapors from TNT, DNT, RDX, PETN and ammonium nitrate — a chemically diverse set of explosives. “Most surprisingly,” Raman said, “we could clearly see the neurons responded differently to TNT and DNT, as well as these other explosive chemical vapors.”

With that crucial piece of data, Raman said, “We were ready to get to work. We were optimized.”

Now they knew that the locusts could detect and discriminate between different explosives, but in order to seek out a bomb, a locust would have to know from which direction the odor emanated. Enter the “odor box and locust mobile.”

“You know when you’re close to the coffee shop, the coffee smell is stronger, and when you’re farther away, you smell it less? That’s what we were looking at,” Raman said. The explosive vapors were injected via a hole in the box where the locust sat in a tiny vehicle. As the locust was driven around and sniffed different concentrations of vapors, researchers studied its odor-related brain activity.

The signals in the bugs’ brains reflected those differences in vapor concentration.

The next step was to optimize the system for transmitting the locusts’ brain activity. The team, which included Shantanu Chakrabartty, the Clifford W. Murphy Professor in the Preston M. Green Department of Electrical & Systems Engineering, and Srikanth Singamaneni, the Lilyan & E. Lisle Hughes Professor in the Department of Mechanical Engineering & Materials Science, focused the breadth of their expertise on the tiny locust.

In order to do the least harm to the locusts, and to keep them stable in order to accurately record their neural activity, the team came up with a new surgical procedure to attach electrodes that didn’t hinder the locusts’ movement. With their new instrumentation in place, the neuronal activity of a locust exposed to an explosive smell was resolved into a discernible odor-specific pattern within 500 milliseconds.

“Now we can implant the electrodes, seal the locust and transport them to mobile environments,” Raman said. One day, that environment might be one in which Homeland Security is searching for explosives.

The idea isn’t as strange as it might first sound, Raman said.

“This is not that different from in the old days, when coal miners used canaries,” he said. “People use pigs for finding truffles. It’s a similar approach — using a biological organism — this is just a bit more sophisticated.”

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Machine learning can predict market behavior

Machine learning can assess the effectiveness of mathematical tools used to predict the movements of financial markets, according to new Cornell research based on the largest dataset ever used in this area.

The researchers’ model could also predict future market movements, an extraordinarily difficult task because of markets’ massive amounts of information and high volatility.

“What we were trying to do is bring the power of machine learning techniques to not only evaluate how well our current methods and models work, but also to help us extend these in a way that we never could do without machine learning,” said Maureen O’Hara, the Robert W. Purcell Professor of Management at the SC Johnson College of Business.

O’Hara is co-author of “Microstructure in the Machine Age,” published July 7 in The Review of Financial Studies.

“Trying to estimate these sorts of things using standard techniques gets very tricky, because the databases are so big. The beauty of machine learning is that it’s a different way to analyze the data,” O’Hara said. “The key thing we show in this paper is that in some cases, these microstructure features that attach to one contract are so powerful, they can predict the movements of other contracts. So we can pick up the patterns of how markets affect other markets, which is very difficult to do using standard tools.”

Markets generate vast amounts of data, and billions of dollars are at stake in mining that data for patterns to shed light on future market behavior. Companies on Wall Street and elsewhere employ various algorithms, examining different variables and factors, to find such patterns and predict the future.

In the study, the researchers used what’s known as a random forest machine learning algorithm to better understand the effectiveness of some of these models. They assessed the tools using a dataset of 87 futures contracts — agreements to buy or sell assets in the future at predetermined prices.

“Our sample is basically all active futures contracts around the world for five years, and we use every single trade — tens of millions of them — in our analysis,” O’Hara said. “What we did is use machine learning to try to understand how well microstructure tools developed for less complex market settings work to predict the future price process both within a contract and then collectively across contracts. We find that some of the variables work very, very well — and some of them not so great.”

Machine learning has long been used in finance, but typically as a so-called “black box” — in which an artificial intelligence algorithm uses reams of data to predict future patterns but without revealing how it makes its determinations. This method can be effective in the short term, O’Hara said, but sheds little light on what actually causes market patterns.

“Our use for machine learning is: I have a theory about what moves markets, so how can I test it?” she said. “How can I really understand whether my theories are any good? And how can I use what I learned from this machine learning approach to help me build better models and understand things that I can’t model because it’s too complex?”

Huge amounts of historical market data are available — every trade has been recorded since the 1980’s — and vast volumes of information are generated every day. Increased computing power and greater availability of data have made it possible to perform more fine-grained and comprehensive analyses, but these datasets, and the computing power needed to analyze them, can be prohibitively expensive for scholars.

In this research, finance industry practitioners partnered with the academic researchers to provide the data and the computers for the study as well as expertise in machine learning algorithms used in practice.

“This partnership brings benefits to both,” said O’Hara, adding that the paper is one in a line of research she, Easley and Lopez de Prado have completed over the last decade. “It allows us to do research in ways generally unavailable to academic researchers.”

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Breaking molecular traffic jams with finned nanoporous materials

Thousands of chemical processes used by the energy industry and for other applications rely on the high speed of catalytic reactions, but molecules frequently are hindered by molecular traffic jams that slow them down. Now an entirely new class of porous catalysts has been invented, using unique fins to speed up the chemistry by allowing molecules to skip the lines that limit the reaction.

This discovery was published in Nature Materials.

The breakthrough focused on reducing barriers for molecules accessing the interior pores of catalysts, called zeolites — aluminosilicates with pores smaller than a nanometer. Zeolites are widely used in commercial processes as solid catalysts for the production of gasoline and value-added chemicals and other products.

In these applications, chemistry within the zeolite pores first requires molecules to find the small number of openings on the outside surface of catalyst particles. This creates a queue of molecules that must “wait in line” to enter the particle, diffuse to the active site involved in the chemical reaction, and then exit the particle.

One approach to address these transport problems has been to synthesize small nanoparticles. As zeolites become smaller, the amount of surface area exposing the pores increases per amount of catalyst material, which grants increased access for molecules entering the pores. Smaller particles also reduce the internal distance molecules must travel through the particle.

However, the synthesis of these smaller zeolite particles is expensive, and the resulting particles are often too inefficient for practical applications.

Researchers at the University of Houston, led by Jeffrey Rimer, Abraham E. Dukler Professor of chemical and biomolecular engineering, developed a way to induce larger catalyst particles to behave like nanoparticles — that is, to allow molecules to enter, spark a reaction and exit quickly, by growing protrusions, or fins, on the surfaces of catalyst particles. By adding nanoscale fins that protrude from the external surface of large particles, the roughened exterior of the particle significantly increased in surface area, granting molecules increased access and reducing the transport limitations that frequently plague conventional zeolite materials.

“Our new synthesis approach capitalizes on work we have been doing in our group for many years, focused on controlling zeolite crystallization in ways that enable the growth of fins,” Rimer said. “This new class of materials bypasses the need to directly synthesize nanoparticles, creating a new paradigm in zeolite catalyst design.”

Rimer worked with a team of international experts in materials synthesis, characterization and modeling to demonstrate the capability of finned zeolites to improve the performance of this unique family of solid catalysts. By comparing finned zeolites with conventional catalytic materials, they showed that zeolites with fins lasted almost eight times longer. Rimer said the incorporation of fins leads to shorter internal diffusion pathways and ensures molecules efficiently reach the reaction sites while reducing the propensity of carbon-based species to become immobilized. That build up ultimately deactivates the catalyst.

Xiaodong Zou, professor of inorganic and structural chemistry at Stockholm University, and members of her laboratory conducted advanced 3D electron microscopy characterization to unravel the pore structures of the finned crystals and confirmed that the fins were extensions of the underlying crystal and did not create impediments for internal diffusion.

“It is amazing to see how well all these hundreds of individual nanofins are aligned with the parent crystal,” Zou said.

Additional state-of-the-art techniques for characterizing zeolite catalysts in real time were performed at Utrecht University by the research group of Bert Weckhuysen, professor of catalysis, energy and sustainability. These measurements confirmed the exceptional ability of finned zeolites to prolong catalyst activity well beyond that of larger catalysts.

Weckhuysen said the use of operando spectroscopy clearly showed how the introduction of fins lowered the amount of external coke deposits during catalysis. “That substantially increased the lifetime of finned zeolite crystals,” he said.

Jeremy Palmer, assistant professor of chemical and biomolecular engineering at UH, used computational methods to model finned materials and explain how the new design works to improve catalysis.

Researchers had expected the fins would perform better than a standard-sized zeolite catalyst, he said. “But we found it was not just a 10% or 20% improvement. It was a tripling of efficiency. The magnitude of the improvement was a real surprise to us.”

Additional work at the University of Minnesota by the research group of Paul Dauenhauer, professor of chemical engineering and materials science, and by Michael Tsapatsis, professor of chemical and biomolecular engineering at Johns Hopkins University, confirmed the enhanced mass transport properties of finned zeolites. Using a new method to track molecule diffusion by infrared light, the UM researchers demonstrated that the fins enhanced molecule transport between 100 and 1,000 times faster than conventional particles.

“The addition of fins allows molecules to get inside the channels of zeolites where the chemistry happens, but it also lets molecules quickly get out of the particle, which lets them operate for a much longer period of time,” Dauenhauer said.

The discovery has immediate relevance to industry for a host of applications, including the production of fuels, chemicals for plastics and polymers, and reactions that make molecules for food, medicine and personal care products.

“The beauty of this new discovery is its potential generalization to a wide range of zeolite materials, using techniques that are easy to incorporate in existing synthesis processes,” Rimer said. “The ability to control the properties of fins could allow for much greater flexibility in the rational design of zeolite catalysts.”

This work was supported by and is part of a larger mission of the U.S. Department of Energy, with additional support provided by various international funding agencies.

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Methanol synthesis: Insights into the structure of an enigmatic catalyst

Methanol is one of the most important basic chemicals used, for example, to produce plastics or building materials. To render the production process even more efficient, it would be helpful to know more about the copper/zinc oxide/aluminium oxide catalyst deployed in methanol production. To date, however, it hasn’t been possible to analyse the structure of its surface under reaction conditions. A team from Ruhr-Universität Bochum (RUB) and the Max Planck Institute for Chemical Energy Conversion (MPI CEC) has now succeeded in gaining insights into the structure of its active site. The researchers describe their findings in the journal Nature Communications from 4 August 2020.

In a first, the team showed that the zinc component of the active site is positively charged and that the catalyst has as many as two copper-based active sites. “The state of the zinc component at the active site has been the subject of controversial discussion since the catalyst was introduced in the 1960s. Based on our findings, we can now derive numerous ideas on how to optimise the catalyst in the future,” outlines Professor Martin Muhler, Head of the Department of Industrial Chemistry at RUB and Max Planck Fellow at MPI CEC. For the project, he collaborated with Bochum-based researcher Dr. Daniel Laudenschleger and Mülheim-based researcher Dr. Holger Ruland.

Sustainable methanol production

The study was embedded in the Carbon-2-Chem project, the aim of which is to reduce CO2 emissions by utilising metallurgical gases produced during steel production for the manufacture of chemicals. In combination with electrolytically produced hydrogen, metallurgical gases could also serve as a starting material for sustainable methanol synthesis. As part of the Carbon-2-Chem project, the research team recently examined how impurities in metallurgical gases, such as are produced in coking plants or blast furnaces, affect the catalyst. This research ultimately paved the way for insights into the structure of the active site.

Active site deactivated for analysis

The researchers had identified nitrogen-containing molecules- ammonia and amines — as impurities that act as catalyst poisons. They deactivated the catalyst, but not permanently: if the impurities disappear, the catalyst recovers by itself. Using a unique research apparatus that was developed in-house, i.e. a continuously operated flow apparatus with an integrated high-pressure pulse unit, the researchers passed ammonia and amines over the catalyst surface, temporarily deactivating the active site with a zinc component. Despite the zinc component being deactivated, another reaction still took place on the catalyst: namely the conversion of ethene to ethane. The researchers thus detected a second active site operating in parallel, which contains metallic copper but doesn’t have a zinc component.

Since ammonia and the amines are bound to positively charged metal ions on the surface, it was evident that zinc, as part of the active site, carries a positive charge.

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Using artificial intelligence to smell the roses

A pair of researchers at the University of California, Riverside, has used machine learning to understand what a chemical smells like — a research breakthrough with potential applications in the food flavor and fragrance industries.

“We now can use artificial intelligence to predict how any chemical is going to smell to humans,” said Anandasankar Ray, a professor of molecular, cell and systems biology, and the senior author of the study that appears in iScience. “Chemicals that are toxic or harsh in, say, flavors, cosmetics, or household products can be replaced with natural, softer, and safer chemicals.”

Humans sense odors when some of their nearly 400 odorant receptors, or ORs, are activated in the nose. Each OR is activated by a unique set of chemicals; together, the large OR family can detect a vast chemical space. A key question in olfaction is how the receptors contribute to different perceptual qualities or percepts.

“We tried to model human olfactory percepts using chemical informatics and machine learning,” Ray said. “The power of machine learning is that it is able to evaluate a large number of chemical features and learn what makes a chemical smell like, say, a lemon or a rose or something else. The machine learning algorithm can eventually predict how a new chemical will smell even though we may initially not know if it smells like a lemon or a rose.”

According to Ray, digitizing predictions of how chemicals smell creates a new way of scientifically prioritizing what chemicals can be used in the food, flavor, and fragrance industries.

“It allows us to rapidly find chemicals that have a novel combination of smells,” he said. “The technology can help us discover new chemicals that could replace existing ones that are becoming rare, for example, or which are very expensive. It gives us a vast palette of compounds that we can mix and match for any olfactory application. For example, you can now make a mosquito repellent that works on mosquitoes but is pleasant smelling to humans.”

The researchers first developed a method for a computer to learn chemical features that activate known human odorant receptors. They then screened roughly half a million compounds for new ligands — molecules that bind to receptors — for 34 odorant receptors. Next, they focused on whether the algorithm that could estimate odorant receptor activity could also predict diverse perceptual qualities of odorants.

“Computers might help us better understand human perceptual coding, which appears, in part, to be based on combinations of differently activated ORs,” said Joel Kowalewski, a student in the Neuroscience Graduate Program working with Ray and the first author of the research paper. “We used hundreds of chemicals that human volunteers previously evaluated, selected ORs that best predicted percepts on a portion of chemicals, and tested that these ORs were also predictive of new chemicals.”

Ray and Kowalewski showed the activity of ORs successfully predicted 146 different percepts of chemicals. To their surprise, few rather than all ORs were needed to predict some of these percepts. Since they could not record activity from sensory neurons in humans, they tested this further in the fruit fly (Drosophila melanogaster) and observed a similar result when predicting the fly’s attraction or aversion to different odorants.

“If predictions are successful with less information, the task of decoding odor perception would then become easier for a computer,” Kowalewski said.

Ray explained that many items available to consumers use volatile chemicals to make themselves appealing. About 80% of what is considered flavor in food actually stems from the odors that affect smell. Fragrances for perfuming cosmetics, cleaning products, and other household goods play an important role in consumer behavior.

“Our digital approach using machine learning could open up many opportunities in the food, flavor, and fragrance industries,” he said. “We now have an unprecedented ability to find ligands and new flavors and fragrances. Using our computational approach, we can intelligently design volatile chemicals that smell desirable for use and also predict ligands for the 34 human ORs.”

The study was partially funded by UCR and the National Science Foundation.

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Researchers 3D print endoscopic imaging device for improved cardio care

An international group of scientists led by the University of Adelaide and the University of Stuttgart has used 3D microprinting to develop an Optical Coherence Tomography (OCT) endoscope.  The research team’s novel probe fabrication technique uses side-facing freeform micro-optics (less than 130 µm in diameter) to 3D print directly onto single-mode fibers. Measuring just 0.48mm, the resulting […]

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Chemists make tough plastics recyclable

Thermosets, which include epoxies, polyurethanes, and rubber used for tires, are found in many products that have to be durable and heat-resistant, such as cars or electrical appliances. One drawback to these materials is that they typically cannot be easily recycled or broken down after use, because the chemical bonds holding them together are stronger than those found in other materials such as thermoplastics.

MIT chemists have now developed a way to modify thermoset plastics with a chemical linker that makes the materials much easier to break down, but still allows them to retain the mechanical strength that makes them so useful.

In a study appearing today in Nature, the researchers showed that they could produce a degradable version of a thermoset plastic called pDCPD, break it down into a powder, and use the powder to create more pDCPD. They also proposed a theoretical model suggesting that their approach could be applicable to a wide range of plastics and other polymers, such as rubber.

“This work unveils a fundamental design principle that we believe is general to any kind of thermoset with this basic architecture,” says Jeremiah Johnson, an professor of chemistry at MIT and the senior author of the study.

Peyton Shieh, an American Cancer Society Postdoctoral Fellow at MIT, is the first author of the paper.

Hard to recycle

Thermosets are one of the two major classes of plastics, along with thermoplastics. Thermoplastics include polyethylene and polypropylene, which are used for plastic bags and other single-use plastics like food wrappers. These materials are made by heating up small pellets of plastic until they melt, then molding them into the desired shape and letting them cool back into a solid.

Thermoplastics, which make up about 75 percent of worldwide plastic production, can be recycled by heating them again until they become liquid, so they can be remolded into a new shape.

Thermoset plastics are made by a similar process, but once they are cooled from a liquid into a solid, it is very difficult to return them to a liquid state. That’s because the bonds that form between the polymer molecules are strong chemical attachments called covalent bonds, which are very difficult to break. When heated, thermoset plastics will typically burn before they can be remolded, Johnson says.

“Once they are set in a given shape, they’re in that shape for their lifetime,” he says. “There is often no easy way to recycle them.”

The MIT team wanted to develop a way to retain the positive attributes of thermoset plastics — their strength and durability — while making them easier to break down after use.

In a paper published last year, with Shieh as the lead author, Johnson’s group reported a way to create degradable polymers for drug delivery, by incorporating a building block, or monomer, containing a silyl ether group. This monomer is randomly distributed throughout the material, and when the material is exposed to acids, bases, or ions such as fluoride, the silyl ether bonds break.

The same type of chemical reaction used to synthesize those polymers is also used to make some thermoset plastics, including polydicyclopentadiene (pDCPD), which is used for body panels in trucks and buses.

Using the same strategy from their 2019 paper, the researchers added silyl ether monomers to the liquid precursors that form pDCPD. They found that if the silyl ether monomer made up between 7.5 and 10 percent of the overall material, pDCPD would retain its mechanical strength but could be broken down into a soluble powder upon exposure to fluoride ions.

“That was the first exciting thing we found,” Johnson says. “We can make pDCPD degradable while not hurting its useful mechanical properties.”

New materials

In the second phase of the study, the researchers tried to reuse the resulting powder to form a new pDCPD material. After dissolving the powder in the precursor solution used to make pDCPD, they were able to make new pDCPD thermosets from the recycled powder.

“That new material has nearly indistinguishable, and in some ways improved, mechanical properties compared to the original material,” Johnson says. “Showing that you can take the degradation products and remake the same thermoset again using the same process is exciting.”

The researchers believe that this general approach could be applied to other types of thermoset chemistry as well. In this study, they showed that using degradable monomers to form the individual strands of the polymers is much more effective than using degradable bonds to “cross-link” the strands together, which has been tried before. They believe that this cleavable strand approach could be used to generate many other kinds of degradable materials.

If the right kinds of degradable monomers can be found for other types of polymerization reactions, this approach could be used to make degradable versions of other thermoset materials such as acrylics, epoxies, silicones, or vulcanized rubber, Johnson says.

The researchers are now hoping to form a company to license and commercialize the technology. MIT has also granted Millipore Sigma a non-exclusive license to manufacture and sell the silyl ether monomers for research purposes.

Patrick Casey, a new product consultant at SP Insight and a mentor with MIT’s Deshpande Center for Technological Innovation, has been working with Johnson and Shieh to evaluate the technology, including performing some preliminary economic modeling and secondary market research.

“We have discussed this technology with some leading industry players, who tell us it promises to be good for stakeholders throughout the value chain,” Casey says. “Parts fabricators get a stream of low-cost recycled materials; equipment manufacturers, such as automotive companies, can meet their sustainability objectives; and recyclers get a new revenue stream from thermoset plastics. The consumers see a cost saving, and all of us get a cleaner environment.”

The research was funded by the National Science Foundation and the National Institutes of Health.

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Electron cryo-microscopy: Using inexpensive technology to produce high-resolution images

Biochemists at Martin Luther University Halle-Wittenberg (MLU) have used a standard electron cryo-microscope to achieve surprisingly good images that are on par with those taken by far more sophisticated equipment. They have succeeded in determining the structure of ferritin almost at the atomic level. Their results were published in the journal PLOS ONE.

Electron cryo-microscopy has become increasingly important in recent years, especially in shedding light on protein structures. The developers of the new technology were awarded the Nobel Prize for Chemistry in 2017. The trick: the samples are flash frozen and then bombarded with electrons. In the case of traditional electron microscopy, all of the water is first extracted from the sample. This is necessary because the investigation takes place in a vacuum, which means water would evaporate immediately and make imaging impossible. However, because water molecules play such an important role in biomolecules, especially in proteins, they cannot be examined using traditional electron microscopy. Proteins are among the most important building blocks of cells and perform a variety of tasks. In-depth knowledge of their structure is necessary in order to understand how they work.

The research group led by Dr Panagiotis Kastritis, who is a group leader at the Centre for Innovation Competence HALOmem and a junior professor at the Institute of Biochemistry and Biotechnology at MLU, acquired a state-of-the-art electron cryo-microscope in 2019. “There is no other microscope like it in Halle,” says Kastritis. The new “Thermo Fisher Glacios 200 kV,” financed by the Federal Ministry of Education and Research, is not the best and most expensive microscope of its kind. Nevertheless, Kastritis and his colleagues succeeded in determining the structure of the iron storage protein apoferritin down to 2.7 ångströms (Å), in other words, almost down to the individual atom. One ångström equals one-tenth of a nanometre. This puts the research group in a similar league to departments with far more expensive equipment. Apoferritin is often used as a reference protein to determine the performance levels of corresponding microscopes. Just recently, two research groups broke a new record with a resolution of about 1.2 Å. “Such values can only be achieved using very powerful instruments, which only a handful of research groups around the world have at their disposal. Our method is designed for microscopes found in many laboratories,” explains Kastritis.

Electron cryo-microscopes are very complex devices. “Even tiny misalignments can render the images useless,” says Kastritis. It is important to programme them correctly and Halle has the technical expertise to do this. But the analysis that is conducted after the data has been collected is just as important. “The microscope produces several thousand images,” explains Kastritis. Image processing programmes are used to create a 3D structure of the molecule. In cooperation with Professor Milton T. Stubbs from the Institute of Biochemistry and Biotechnology at MLU, the researchers have developed a new method to create a high-resolution model of a protein. Stubbs’ research group uses X-ray crystallography, another technique for determining the structure of proteins, which requires the proteins to be crystallised. They were able to combine a modified form of an image analysis technique with the images taken with the electron cryo-microscope. This made charge states and individual water molecules visible.

“It’s an attractive method,” says Kastritis. Instead of needing very expensive microscopes, a lot of computing capacity is required, which MLU has. Now, in addition to using X-ray crystallography, electron cryo-microscopy can be used to produce images of proteins — especially those that are difficult to crystallise. This enables collaboration, both inside and outside the university, on the structural analysis of samples with medical and biotechnological potential.

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NYU Tandon researchers use machine learning to expose vulnerability behind 3D printed composites 

Researchers from New York University’s Tandon School of Engineering have successfully used Machine Learning (ML) tools to reverse engineer glass and carbon fiber 3D printed components.  By applying ML tools to CT scans of a 3D printed part, the NYU team were effectively able to “steal” the printing toolpaths behind its structural strength, flexibility, and […]

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Scheurer partners with ETH Zurich students to 3D print “Rowesys” weeding robot 

Engineering company Scheurer Swiss GmbH has used its 3D printing expertise to help a group of ETH Zürich students develop their “Rowesys” automated robotic weeding system. Working with the Zurich team, Scheurer Swiss supplied and produced several 3D printed carbon-reinforced components, which enabled the robot’s construction, and enhanced its performance. The compact weed killing bot, […]

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