Aurelia Butler

Quantum cryptography is one of the most promising quantum technologies of our time: Exactly the same information is generated at two different locations, and the laws of quantum physics guarantee that no third party can intercept this information. This creates a code with which information can be perfectly encrypted.
The team of Prof. Marcus Huber from the Atomic Institute of TU Wien developed a new type of quantum cryptography protocol, which has now been tested in practice in cooperation with Chinese research groups: While up to now one normally used photons that can be in two different states, the situation here is more complicated: Eight different paths can be taken by each of the photons. As the team has now been able to show, this makes the generation of the quantum cryptographic key faster and also significantly more robust against interference. The results have now been published in the scientific journal Physical Review Letters.
Two states, two dimensions
“There are many different ways of using photons to transmit information,” says Marcus Huber. “Often, experiments focus on their photons’ polarisation. For example, whether they oscillate horizontally or vertically — or whether they are in a quantum-mechanical superposition state in which, in a sense, they assume both states simultaneously. Similar to how you can describe a point on a two-dimensional plane with two coordinates, the state of the photon can be represented as a point in a two-dimensional space.”
But a photon can also carry information independently of the direction of polarization. One can, for example, use the information about which path the photon is currently travelling on. This is exactly what has now been exploited: “A laser beam generates photon pairs in a special kind of crystal. There are eight different points in the crystal where this can happen,” explains Marcus Huber. Depending on the point at which the photon pair was created, each of the two photons can move along eight different paths — or along several paths at the same time, which is also permitted according to the laws of quantum theory.
These two photons can be directed to completely different places and analysed there. One of the eight possibilities is measured, completely at random — but as the two photons are quantum-physically entangled, the same result is always obtained at both places. Whoever is standing at the first measuring device knows what another person is currently detecting at the second measuring device — and no one else in the universe can get hold of this information. More

Rutgers researchers and their collaborators have found that learning — a universal feature of intelligence in living beings — can be mimicked in synthetic matter, a discovery that in turn could inspire new algorithms for artificial intelligence (AI).
The study appears in the journal PNAS.
One of the fundamental characteristics of humans is the ability to continuously learn from and adapt to changing environments. But until recently, AI has been narrowly focused on emulating human logic. Now, researchers are looking to mimic human cognition in devices that can learn, remember and make decisions the way a human brain does.
Emulating such features in the solid state could inspire new algorithms in AI and neuromorphic computing that would have the flexibility to address uncertainties, contradictions and other aspects of everyday life. Neuromorphic computing mimics the neural structure and operation of the human brain, in part, by building artificial nerve systems to transfer electrical signals that mimic brain signals.
Researchers from Rutgers, Purdue and other institutions studied how the electrical conductivity of nickel oxide, a special type of insulating material, responded when its environment was changed repeatedly over various time intervals.
“The goal was to find a material whose electrical conductivity can be tuned by modulating the concentration of atomic defects with external stimuli such as oxygen, ozone and light,” said Subhasish Mandal, a postdoctoral associate in the Department of Physics and Astronomy at Rutgers-New Brunswick. “We studied how this material behaves when we dope the system with oxygen or hydrogen, and most importantly, how the external stimulation changes the material’s electronic properties.”
The researchers found that when the gas stimulus changed rapidly, the material couldn’t respond in full. It stayed in an unstable state in either environment and its response began to decrease. When the researchers introduced a noxious stimulus such as ozone, the material began to respond more strongly only to decrease again.
“The most interesting part of our results is that it demonstrates universal learning characteristics such as habituation and sensitization that we generally find in living species,” Mandal said. “These material characteristics in turn can inspire new algorithms for artificial intelligence. Much as collective motion of birds or fish have inspired AI, we believe collective behavior of electrons in a quantum solid can do the same in the future.
“The growing field of AI requires hardware that can host adaptive memory properties beyond what is used in today’s computers,” he added. “We find that nickel oxide insulators, which historically have been restricted to academic pursuits, might be interesting candidates to be tested in future for brain-inspired computers and robotics.”
The study included Distinguished Professor Karin Rabe from Rutgers and researchers from Purdue University, the University of Georgia and Argonne National Laboratory.
Story Source:
Materials provided by Rutgers University. Original written by John Cramer. Note: Content may be edited for style and length. More

Researchers have designed a 4D-printed soft robot that self-assembles when heated and can take on challenging tasks like rolling uphill and navigating a bumpy and unpredictable landscape. The prototype, which is tube-shaped, appears September 22nd in the journal Matter.
“Like an insect with antennae, the robot can surmount a small obstacle. But when the obstacle is too high, it will turn back,” says senior author Wei Feng, a materials scientist at Tianjin University in China. “The whole process is spontaneous without human interference or control.”
The robot starts off as a flat, rectangular sheet of a 3D-printed liquid crystal elastomer, a type of stretchy plastic material. When the surface beneath it is heated, the robot spontaneously twists up to form a tubule resembling a spring. The change in shape under external stimulation adds time as a fourth dimension to the printing process, making it 4D.
Once the robot forms a tubule, the contact from the hot surface induces a strain in the material, which causes it to roll in one direction. The driving force behind this motion is so strong that the robot can climb up a 20° incline or even carry a load 40 times its own weight. The length of the robot affects its velocity, with longer robots rolling faster than their shorter counterparts.
The researchers captured videos showing off the robot’s skills, including a race between differently sized robots and another robot carrying a cart. The videos also show how its behavior changes based on its surroundings, with the robot either climbing up a step or changing directions when encountering an insurmountable obstacle.
For Feng, the behavior of the robot came as a surprise. “We processed the liquid crystal elastomers into samples of various shapes through 4D printing and stimulated these samples with light, heat, and electricity to observe their response,” he says. “We found many interesting driving phenomena besides deformation.”
In the future, these soft robots may be used to perform work in small, confined places like in a pipe or under extreme conditions like a 200? surface. “We hope that soft robots will no longer be limited to simple actuators, which can only change shape in a fixed position,” says Feng.
This work was supported by the State Key Program of National Natural Science Foundation of China, National Key R&D Program of China, and National Natural Science Foundation of China.
Story Source:
Materials provided by Cell Press. Note: Content may be edited for style and length. More

Northwestern University engineers have added a new capability to electronic microchips: flight.
About the size of a grain of sand, the new flying microchip (or “microflier”) does not have a motor or engine. Instead, it catches flight on the wind — much like a maple tree’s propeller seed — and spins like a helicopter through the air toward the ground.
By studying maple trees and other types of wind-dispersed seeds, the engineers optimized the microflier’s aerodynamics to ensure that it — when dropped at a high elevation — falls at a slow velocity in a controlled manner. This behavior stabilizes its flight, ensures dispersal over a broad area and increases the amount of time it interacts with the air, making it ideal for monitoring air pollution and airborne disease.
As the smallest-ever human-made flying structures, these microfliers also can be packed with ultra-miniaturized technology, including sensors, power sources, antennas for wireless communication and embedded memory to store data.
The research is featured on the cover of the Sept. 23 issue of Nature.
“Our goal was to add winged flight to small-scale electronic systems, with the idea that these capabilities would allow us to distribute highly functional, miniaturized electronic devices to sense the environment for contamination monitoring, population surveillance or disease tracking,” said Northwestern’s John A. Rogers, who led the device’s development. “We were able to do that using ideas inspired by the biological world. Over the course of billions of years, nature has designed seeds with very sophisticated aerodynamics. We borrowed those design concepts, adapted them and applied them to electronic circuit platforms.”
A pioneer in bioelectronics, Rogers is the Louis Simpson and Kimberly Querrey Professor of Materials Science and Engineering, Biomedical Engineering and Neurological Surgery in the McCormick School of Engineeringand Feinberg School of Medicineand director of the Querrey Simpson Institute for Bioelectronics. Yonggang Huang, the Jan and Marcia Achenbach Professor of Mechanical Engineering at McCormick, led the study’s theoretical work. More

Scientists have developed a method using machine learning to better analyze data from a powerful scientific tool: nuclear magnetic resonance (NMR). One way NMR data can be used is to understand proteins and chemical reactions in the human body. NMR is closely related to magnetic resonance imaging (MRI) for medical diagnosis.
NMR spectrometers allow scientists to characterize the structure of molecules, such as proteins, but it can take highly skilled human experts a significant amount of time to analyze that data. This new machine learning method can analyze the data much more quickly and just as accurately.
In a study recently published in Nature Communications, the scientists described their process, which essentially teaches computers to untangle complex data about atomic-scale properties of proteins, parsing them into individual, readable images.
“To be able to use these data, we need to separate them into features from different parts of the molecule and quantify their specific properties,” said Rafael Brüschweiler, senior author of the study, Ohio Research Scholar and a professor of chemistry and biochemistry at The Ohio State University. “And before this, it was very difficult to use computers to identify these individual features when they overlapped.”
The process, developed by Dawei Li, lead author of the study and a research scientist at Ohio State’s Campus Chemical Instrument Center, teaches computers to scan images from NMR spectrometers. Those images, known as spectra, appear as hundreds and thousands of peaks and valleys, which, for example, can show changes to proteins or complex metabolite mixtures in a biological sample, such as blood or urine, at the atomic level. The NMR data give important information about a protein’s function and important clues about what is happening in a person’s body.
But deconstructing the spectra into readable peaks can be difficult because often, the peaks overlap. The effect is almost like a mountain range, where closer, larger peaks obscure smaller ones that may also carry important information. More

An intrinsic magnetic topological insulator MnBi2Te4 has been discovered with a large band gap, making it a promising material platform for fabricating ultra-low-energy electronics and observing exotic topological phenomena.
Hosting both magnetism and topology, ultra-thin (only several nanometers in thickness) MnBi2Te4 was found to have a large band-gap in a Quantum Anomalous Hall (QAH) insulating state, where the material is metallic (ie, electrically conducting) along its one-dimensional edges, while electrically insulating in its interior. The almost zero resistance along the 1D edges of a QAH insulator, make it promising for lossless transport applications and ultra-low energy devices.
HISTORY OF QAH: HOW TO ACHIEVE THE DESIRED EFFECT
Previously, the path towards realising the QAH effect was to introduce dilute amounts of magnetic dopants into ultra-thin films of 3D topological insulators.
However, dilute magnetic doping results in a random-distribution of magnetic impurities, causing non-uniform doping and magnetisation. This greatly suppresses the temperature at which the QAH effect can be observed and limits possible future applications.
A simpler option is to use materials that host this electronic state of matter as an intrinsic property. More

The EVONANO platform allows scientists to grow virtual tumours and use artificial intelligence to automatically optimise the design of nanoparticles to treat them.
The ability to grow and treat virtual tumours is an important step towards developing new therapies for cancer. Importantly, scientists can use virtual tumours to optimise design of nanoparticle-based drugs before they are tested in the laboratory or patients.
The paper, ‘Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment,’ is published today in the Nature journal Computational Materials. The paper is the result of the European project EVONANO which involves Dr Sabine Hauert and Dr. Namid Stillman from the University of Bristol, and is led by Dr Igor Balaz at the University of Novi Sad.
“Simulations enable us to test many treatments, very quickly, and for a large variety of tumours. We are still at the early stages of making virtual tumours, given the complex nature of the disease, but the hope is that even these simple digital tumours can help us more efficiently design nanomedicines for cancer,” said Dr Hauert.
Dr Hauert said having the software to grow and treat virtual tumours could prove useful in the development of targeted cancer treatments.
“In the future, creating a digital twin of a patient tumour could enable the design of new nanoparticle treatments specialised for their needs, without the need for extensive trial and error or laboratory work, which is often costly and limited in its ability to quickly iterate on solutions suited for individual patients,” said Dr Hauert. More

Researchers from SANKEN (The Institute of Scientific and Industrial Research) at Osaka University and JOANNEUM RESEARCH (Weiz, Austria), have shown how exposing an organic polymer to ultraviolet light can precisely modify its electronic properties. This work may aid in the commercialization of flexible electronics that can be used for real-time healthcare monitoring, along with data processing.
While the integrated circuits inside your smart phone are quite impressive, they lack certain important features. Because the electronics are silicon based, they are very rigid, both in the literal sense of being inflexible, as well as having chemical properties that are not easily altered. Newer devices, including OLED displays, are made from carbon-based organic molecules with chemical properties than can be tuned by scientists to produce the most efficient circuits. However, controlling the characteristics of organic transistors usually requires the integration of complex structures made of various materials.
Now, a team of researchers led by Osaka University have used UV light to precisely change the chemical structure of a dielectric polymer called PNDPE. The light breaks specific bonds in the polymer, which can then rearrange into new versions, or create crosslinks between strands. The longer the light is on, the more the polymer get altered. By using a shadow mask, the UV light is applied to just the desired areas, tuning the circuit behavior. This method can pattern transistors of the desired threshold voltage with high spatial resolution using just a single material.
“We have succeeded in controlling the characteristics of organic integrated circuits using persistent light-induced changes in the molecular structure itself,” study corresponding author Takafumi Uemura explains.
In the future, we may see smart versions of almost everything, from medicine bottles to safety vests. “Meeting the computational demands of ‘the Internet of Things’ will very likely require flexible electronic solutions,” senior author Tsuyoshi Sekitani says. In particular, this technology can be applied to manufacturing methods for ultra-lightweight wearable healthcare devices.
Story Source:
Materials provided by Osaka University. Note: Content may be edited for style and length. More