An international team has developed a new method for generating quantum-entangled photons in a previously inaccessible spectral range, making the encryption of satellite-based communications much more secure in the future.

A 15-member research team from the UK, Germany, and Japan has developed a new method for generating and detecting quantum-entangled photons at a wavelength of 2.1 micrometers. In practice, entangled photons are used in encryption methods such as quantum key distribution to completely secure telecommunications between two partners against eavesdropping attempts. The research results are presented to the public for the first time in a recent issue of Science Advances.

Until now, it has been possible to implement such encryption mechanisms only in the near-infrared range of 700 to 1550 nanometers. These shorter wavelengths have disadvantages, especially in satellite-based communication, because they are disturbed by light-absorbing gases in the atmosphere as well as the background radiation of the sun. With the existing technology, end-to-end encryption of transmitted data can only be guaranteed at night.

Entangled photon pairs at two micrometers wavelength are significantly less influenced by the solar background radiation. Also, a so-called “transmission window” exists in the earth’s atmosphere for wavelengths of two micrometers.  That means these photons are less absorbed by atmospheric gases, allowing more effective communication.

For their experiment, the researchers used a nonlinear crystal made of lithium niobate. When they sent ultrashort light pulses from a laser into the crystal a nonlinear interaction produced entangled photon pairs with a new wavelength of 2.1 micrometers.

The next crucial step will be to miniaturize this system by converting it into photonic integrated devices, making it suitable for mass production and use in other application scenarios.

References
Science Advances, March 27, 2020, “Two-Photon Quantum Interference and Entanglement at 2.1 μm,” by Shashi Prabhakar et al. ⓒ 2020 American Association for the Advancement of Science. All rights reserved.

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https://advances.sciencemag.org/content/6/13/eaay5195

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Researchers at the University of Georgia’s Regenerative Bioscience Center have found that “natural killer” white blood cells could guard against the cascade of cellular changes that lead to Parkinson’s disease and help stop its progression.

Natural killer (or NK) cells are white blood cells that can kill tumors without being “told” to do so by the body. NK cells provide the first line of defense against invasion by a bacterium or a virus. They are equipped with activating receptors that can sense cellular stress and identify cells that have been altered due to infection.

Right now, there’s no available therapy to modify or stop the progression of Parkinson’s.  This study, published in a recent issue of Proceedings of the National Academy of Sciences, is the first to show the possibility of actually stopping the disease.

It shows that NK cells may be critical for regulating and restraining inflammation of brain tissue and protein clumping - hallmarks of Parkinson’s and other neurodegenerative disorders. The report also found that NK cell depletion in a mouse model significantly exaggerated the disease condition. This led to the discovery that, without NK cells, the nervous system was left vulnerable to attack.

We believe that NK cells exert protection by their ability to reduce inflammation in the brain and clear proteins that misfold and create toxic clumps. In their absence, proteins were left unchecked, and the researchers saw a substantial decrease in viral resistant cells, confirming that NK cells are a significant source of signaling proteins that boost the immune system response.

The researchers caution that the Parkinson’s work was done in animal models, but they are optimistic about future immunotherapy discoveries. They cited recent human trials that tested immunotherapies against an aggressive form of brain cancer called glioblastoma, indicating that NK cells contribute to the elimination of tumor cells and release messages in support of the defense of the immune system.

The preliminary data suggest that the number and function of NK cells are decreased in aged animals, which display impaired ability to perform their normal functions.  The researchers would like to look deeper at age-related changes associated with NK cell biology and the wider implications for the health and well-being of older adults.

References
Proceedings of the National Academy of Sciences, NK Cells clear A-Synuclein and the Depletion of NK Cells Exacerbates Synuclein Pathology in a Mouse Model of α-Synucleinopathy,” by Rachael H. Earls, et al. ⓒ 2020 National Academy of Sciences.  All rights reserved.

To view or purchase this article, please visit:
https://www.pnas.org/content/117/3/1762

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When searching through theoretical lists of possible new materials for particular applications, such as batteries or other energy-related devices, there are often millions of potential materials that could be considered and multiple criteria that need to be met and optimized at once. Now, researchers at MIT have found a way to dramatically streamline the discovery process, using a machine learning system. Those findings were reported recently in the journal ACS Central Science.

As a demonstration, the team identified a set of eight highly promising materials, out of nearly 3 million candidates, for an energy storage system called a flow battery. This process would have taken 50 years by conventional analytical methods, but they accomplished it in five weeks.

The study looked at a set of materials called transition metal complexes. These can exist in a vast number of different forms. The only way to understand why they work the way they do is to study them using quantum mechanics.

To predict the properties of any one of millions of materials would require either time-consuming and resource-intensive spectroscopy or slow, highly complex physics-based computer modeling for each possible candidate material or combination of materials. Each such study could consume days of work.

Instead, the MIT team took a small number of different possible materials. It used them to teach an advanced machine-learning neural network about the relationship between the materials’ chemical compositions and their physical properties. That knowledge was then applied to generate suggestions for the next generation of possible materials to be used for the next round of training of the neural network. Through four successive iterations of this process, the neural network improved each time significantly, until reaching a point where it was clear that further iterations would not yield any further improvements.

This iterative optimization system significantly streamlined the process of arriving at potential solutions that satisfied the two conflicting criteria being sought. This kind of process of finding the best solutions in situations where improving one factor tends to worsen the other, is known as a Pareto frontier, representing a graph of the points such that any further improvement of one element would make the other worse. In other words, the graph represents the best possible compromise points, depending on the relative importance assigned to each factor.

Training typical neural networks require very large data sets, ranging from thousands to millions of examples. Still, the MIT team was able to use this iterative process, based on the Pareto frontier model, to streamline the process and provide reliable results using only a few hundred samples.

In the case of screening for the flow battery materials, the desired characteristics conflicted, as is often the case: The optimum material would have high solubility and a high energy density,  which is the ability to store energy for a given weight. But increasing solubility tends to decrease the energy density, and vice versa.

Not only was the neural network able to rapidly come up with promising candidates, but it also was able to assign levels of confidence to its different predictions through each iteration, which helped to allow the refinement of the sample selection at each step. The team developed a best-in-class uncertainty quantification technique for really knowing when these models were going to fail.

Apart from the specific transition metal complexes suggested for further investigation using this system, the method itself could have much broader applications. Think of it as a framework that can be applied to any materials design challenge where you’re trying to address multiple objectives at once. Today, all of the most exciting materials design challenges are ones where you have one thing, you’re trying to improve but improving that worsens another. The redox flow battery was just a good demonstration of where researchers can go with machine learning and accelerated materials discovery.

For example, optimizing catalysts for various chemical and industrial processes is another kind of such sophisticated material search. Presently used catalysts often involve rare and expensive elements, so finding similarly effective compounds based on abundant and inexpensive materials could be a significant advantage.

This study represents the first application of multidimensional directed improvement in the chemical sciences. But the long-term significance of the work is in the methodology itself, because of things that might not be possible at all otherwise.  As the MIT researchers explained, “You start to realize that even with parallel computations, these are cases where we wouldn’t have come up with a design principle in any other way. And these are not ideas that were already known from the literature or that an expert would have been able to point you to.”

References
ACS Central Science, March 11, 2020, “Accurate Multiobjective Design in the space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization,” by Jon Paul Janet et al. ⓒ 2020 American Chemical Society. All rights reserved.

To view or purchase this article, please visit:
https://pubs.acs.org/doi/10.1021/acscentsci.0c00026#