Aurelia Butler

A new electrode that could free up 20% more light from organic light-emitting diodes has been developed at the University of Michigan. It could help extend the battery life of smartphones and laptops, or make next-gen televisions and displays much more energy efficient.
The approach prevents light from being trapped in the light-emitting part of an OLED, enabling OLEDs to maintain brightness while using less power. In addition, the electrode is easy to fit into existing processes for making OLED displays and light fixtures.
“With our approach, you can do it all in the same vacuum chamber,” said L. Jay Guo, U-M professor of electrical and computer engineering and corresponding author of the study.
Unless engineers take action, about 80% of the light produced by an OLED gets trapped inside the device. It does this due to an effect known as waveguiding. Essentially, the light rays that don’t come out of the device at an angle close to perpendicular get reflected back and guided sideways through the device. They end up lost inside the OLED.
A good portion of the lost light is simply trapped between the two electrodes on either side of the light-emitter. One of the biggest offenders is the transparent electrode that stands between the light-emitting material and the glass, typically made of indium tin oxide (ITO). In a lab device, you can see trapped light shooting out the sides rather than traveling through to the viewer.
“Untreated, it is the strongest waveguiding layer in the OLED,” Guo said. “We want to address the root cause of the problem.”
By swapping out the ITO for a layer of silver just five nanometers thick, deposited on a seed layer of copper, Guo’s team maintained the electrode function while eliminating the waveguiding problem in the OLED layers altogether. More

A new University of Liverpool study could help scientists mitigate the future spread of zoonotic and livestock diseases caused by existing viruses.
Researchers have used a form or artificial intelligence (AI) called machine-learning to predict more than 20,000 unknown associations between known viruses and susceptible mammalian species. The findings, which are published in Nature Communications, could be used to help target disease surveillance programmes.
Thousands of viruses are known to affect mammals, with recent estimates indicating that less than 1% of mammalian viral diversity has been discovered to date. Some of these viruses such as human and feline immunodeficiency viruses have a very narrow host range, whereas others such as rabies and West Nile viruses have very wide host ranges.
“Host range is an important predictor of whether a virus is zoonotic and therefore poses a risk to humans. Most recently, SARS-CoV-2 has been found to have a relatively broad host range which may have facilitated its spill-over to humans. However, our knowledge of the host range of most viruses remains limited,” explains lead researcher Dr Maya Wardeh from the University’s Institute of Infection, Veterinary and Ecological Sciences.
To address this knowledge gap, the researchers developed a novel machine learning framework to predict unknown associations between known viruses and susceptible mammalian species by consolidating three distinct perspectives — that of each virus, each mammal, and the network connecting them, respectively.
Their results suggests that there are more than five times as many associations between known zoonotic viruses and wild and semi-domesticated mammals than previously thought. In particular, bats and rodents, which have been associated with recent outbreaks of emerging viruses such as coronaviruses and hantaviruses, were linked with increased risk of zoonotic viruses.
The model also predicts a five-fold increase in associations between wild and semi-domesticated mammals and viruses of economically important domestic species such as livestock and pets.
Dr Wardeh said: “As viruses continue to move across the globe, our model provides a powerful way to assess potential hosts they have yet to encounter. Having this foresight could help to identify and mitigate zoonotic and animal-disease risks, such as spill-over from animal reservoirs into human populations.”
Dr Wardeh is currently expanding the approach to predict the ability of ticks and insects to transmit viruses to birds and mammals, which will enable prioritisation of laboratory-based vector-competence studies worldwide to help mitigate future outbreaks of vector-borne diseases.
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Using methods from conservation science, a new analysis suggests that the first case of COVID-19 arose between early October and mid-November, 2019 in China, with the most likely date of origin being November 17. David Roberts of the University of Kent, U.K., and colleagues present these findings in the open-access journal PLOS Pathogens.
The origins of the ongoing COVID-19 pandemic remain unclear. The first officially identified case occurred in early December 2019. However, mounting evidence suggests that the original case may have emerged even earlier.
To help clarify the timing of the onset of the pandemic, Roberts and colleagues repurposed a mathematical model originally developed by conservation scientists to determine the date of extinction of a species, based on recorded sightings of the species. For this analysis, they reversed the method to determine the date when COVID-19 most likely originated, according to when some of the earliest known cases occurred in 203 countries.
The analysis suggests that the first case occurred in China between early October and mid-November of 2019. The first case most likely arose on November 17, and the disease spread globally by January 2020. These findings support growing evidence that the pandemic arose sooner and grew more rapidly than officially accepted.
The analysis also identified when COVID-19 is likely to have spread to the first five countries outside of China, as well as other continents. For instance, it estimates that the first case outside of China occurred in Japan on January 3, 2020, the first case in Europe occurred in Spain on January 12, 2020, and the first case in North America occurred in the United States on January 16, 2020.
The researchers note that their novel method could be applied to better understand the spread of other infectious diseases in the future. Meanwhile, better knowledge of the origins of COVID-19 could improve understanding of its continued spread.
Roberts adds, “The method we used was originally developed by me and a colleague to date extinctions, however, here we use it to date the origination and spread of COVID-19. This novel application within the field of epidemiology offers a new opportunity to understand the emergence and spread of diseases as it only requires a small amount of data.”
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‘Precision agriculture’ where farmers respond in real time to changes in crop growth using nanotechnology and artificial intelligence (AI) could offer a practical solution to the challenges threatening global food security, a new study reveals.
Climate change, increasing populations, competing demands on land for production of biofuels and declining soil quality mean it is becoming increasingly difficult to feed the world’s populations.
The United Nations (UN) estimates that 840 million people will be affected by hunger by 2030, but researchers have developed a roadmap combining smart and nano-enabled agriculture with AI and machine learning capabilities that could help to reduce this number.
Publishing their findings today in Nature Plants, an international team of researchers led by the University of Birmingham sets out the following steps needed to use AI to harness the power of nanomaterials safely, sustainably and responsibly: Understand the long-term fate of nanomaterials in agricultural environments — how nanomaterials can interact with roots, leaves and soil; Assess the long-term life cycle impact of nanomaterials in the agricultural ecosystem such as how how repeated application of nanomaterials will affect soils; Take a systems-level approach to nano-enabled agriculture — use existing data on soil quality, crop yield and nutrient-use efficiency (NUE) to predict how nanomaterials will behave in the environment; and Use AI and machine learning to identify key properties that will control the behaviour of nanomaterials in agricultural settings.Study co-author Iseult Lynch, Professor of Environmental Nanosciences at the University of Birmingham, commented: “Current estimates show nearly 690 million people are hungry — almost nine per cent of the planet’s population. Finding sustainable agricultural solutions to this problem requires us to take bold new approaches and integrate knowledge from diverse fields, such as materials science and informatics.
“Precision agriculture, using nanotechnology and artificial intelligence, offers exciting opportunities for sustainable food production. We can link existing models for nutrient cycling and crop productivity with nanoinformatics approaches to help both crops and soil perform better — safely, sustainably and responsibly.”
The main driver for innovation in agritech is the need to feed the increasing global population with a decreasing agricultural land area, whilst conserving soil health and protecting environmental quality. More

Researchers have developed a method to make previously hardly accessible properties in quantum systems measurable. The new method for determining the quantum state in quantum simulators reduces the number of necessary measurements and makes work with quantum simulators much more efficient.
In a few years, a new generation of quantum simulators could provide insights that would not be possible using simulations on conventional supercomputers. Quantum simulators are capable of processing a great amount of information since they quantum mechanically superimpose an enormously large number of bit states. For this reason, however, it also proves difficult to read this information out of the quantum simulator. In order to be able to reconstruct the quantum state, a very large number of individual measurements are necessary. The method used to read out the quantum state of a quantum simulator is called quantum state tomography. “Each measurement provides a ‘cross-sectional image’ of the quantum state. You then put these cross-sectional images together to form the complete quantum state,” explains theoretical physicist Christian Kokail from Peter Zoller’s team at the Institute of Quantum Optics and Quantum Information at the Austrian Academy of Sciences and the Department of Experimental Physics at the University of Innsbruck. The number of measurements needed in the lab increases very rapidly with the size of the system. “The number of measurements grows exponentially with the number of qubits,” the physicist says. The Innsbruck researchers have now succeeded in developing a much more efficient method for quantum simulators.
Efficient method that delivers new insights
Insights from quantum field theory allow quantum state tomography to be much more efficient, i.e., to be performed with significantly fewer measurements. “The fascinating thing is that it was not at all clear from the outset that the predictions from quantum field theory could be applied to our quantum simulation experiments,” says theoretical physicist Rick van Bijnen. “Studying older scientific papers from this field happened to lead us down this track.” Quantum field theory provides the basic framework of the quantum state in the quantum simulator. Only a few measurements are then needed to fit the details into this basic framework. Based on this, the Innsbruck researchers have developed a measurement protocol by which tomography of the quantum state becomes possible with a drastically reduced number of measurements. At the same time, the new method allows new insights into the structure of the quantum state to be obtained. The physicists tested the new method with experimental data from an ion trap quantum simulator of the Innsbruck research group led by Rainer Blatt and Christian Roos. “In the process, we were now able to measure properties of the quantum state that were previously not observable in this quality,” Kokail recounts.
Verification of the result
A verification protocol developed by the group together with Andreas Elben and Benoit Vermersch two years ago can be used to check whether the structure of the quantum state actually matches the expectations from quantum field theory. “We can use further random measurements to check whether the basic framework for tomography that we developed based on the theory actually fits or is completely wrong,” explains Christian Kokail. The protocol raises a red flag if the framework does not fit. Of course, this would also be an interesting finding for the physicists, because it would possibly provide clues for the not yet fully understood relationship with quantum field theory. At the moment, the physicists around Peter Zoller are developing quantum protocols in which the basic framework of the quantum state is not stored on a classical computer, but is realized directly on the quantum simulator.
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A challenging frontier in science and engineering is controlling matter outside of thermodynamic equilibrium to build material systems with capabilities that rival those of living organisms. Research on active colloids aims to create micro- and nanoscale “particles” that swim through viscous fluids like primitive microorganisms. When these self-propelled particles come together, they can organize and move like schools of fish to perform robotic functions, such as navigating complex environments and delivering “cargo” to targeted locations.
A Columbia Engineering team led by Kyle Bishop, professor of chemical engineering, is at the forefront of studying and designing the dynamics of active colloids powered by chemical reactions or by external magnetic, electric, or acoustic fields. The group is developing colloidal robots, in which active components interact and assemble to perform dynamic functions inspired by living cells.
In a new study published today by Physical Review Letters, Bishop’s group, working with collaborators at Northwestern University’s Center for Bio-Inspired Energy Science (CBES), report that they have demonstrated the use of DC electric fields to drive back-and-forth rotation of micro-particles in electric boundary layers. These particle oscillators could be useful as clocks that coordinate the organization of active matter and even, perhaps, orchestrate the functions of micron-scale robots.
“Tiny particle oscillators could enable new types of active matter that combine the swarming behaviors of self-propelled colloids and the synchronizing behaviors of coupled oscillators,” says Bishop. “We expect interactions among the particles to depend on their respective positions and phases, thus enabling richer collective behaviors — behaviors that can be designed and exploited for applications in swarm robotics.”
Making a reliable clock at the micron-scale is not as simple as it may sound. As one can imagine, pendulum clocks don’t work well when immersed in honey. Their periodic motion — like that of all inertial oscillators — drags to a halt under sufficient resistance from friction. Without the help of inertia, it is similarly challenging to drive the oscillatory motion of micron-scale particles in viscous fluids.
“Our recent observation of colloidal spheres oscillating back and forth in a DC electric field presented a bit of mystery, one we wanted to solve,” observes the paper’s lead author, Zhengyan Zhang, a PhD student in Bishop’s lab who discovered this effect. “By varying the particle size, field strength, and fluid conductivity, we identified experimental conditions needed for oscillations and uncovered the mechanism underlying the particles’ rhythmic dynamics.”
Earlier work has demonstrated how similar particles can rotate steadily by a process known as Quincke rotation. Like a water wheel filled from above, the Quincke instability is driven by the accumulation of electric charge on the particle surface and its mechanical rotation in the electric field. However, existing models of Quincke rotation — and of overdamped water wheels — do not predict oscillatory dynamics. More

Most models explaining how viruses are transmitted focus on viral particles escaping one person to infect a nearby person. A study on the role of microscopic particles in how viruses are transmitted suggests pollen is nothing to sneeze at.
In Physics of Fluids, by AIP Publishing, Talib Dbouk and Dimitris Drikakis investigate how pollen facilitates the spread of an RNA virus like the COVID-19 virus. The study draws on cutting-edge computational approaches for analyzing fluid dynamics to mimic the pollen movement from a willow tree, a prototypical pollen emitter. Airborne pollen grains contribute to the spread of airborne viruses, especially in crowded environments.
“To our knowledge, this is the first time we show through modeling and simulation how airborne pollen micrograins are transported in a light breeze, contributing to airborne virus transmission in crowds outdoors,” Drikakis said.
The researchers noticed a correlation between COVID-19 infection rates and the pollen concentration on the National Allergy Map. Each pollen grain can carry hundreds of virus particles at a time. Trees alone can put 1,500 grains per cubic meter into the air on heavy days.
The researchers set to work by creating all the pollen-producing parts of their computational willow tree. They simulated outdoor gatherings of roughly 10 or 100 people, some of them shedding COVID-19 particles, and subjected the people to 10,000 pollen grains.
“One of the significant challenges is the re-creation of an utterly realistic environment of a mature willow tree,” said Dbouk. “This included thousands of tree leaves and pollen grain particles, hundreds of stems and a realistic gathering of a crowd of about 100 individuals at about 20 meters from the tree.”
Tuning the model to the temperature, windspeed, and humidity of a typical spring day in the U.S., the pollen passed through the crowd in less than one minute, which could significantly affect the virus load carried along and increase the risk of infection.
The authors said the 6-foot distance often cited for COVID-19 recommendations might not be adequate for those at risk for the disease in crowded areas with high pollen. New recommendations based on local pollen levels could be used to manage the infection risk better.
While calling attention to other forms of COVID-19 transmission, the authors hope their study stokes further interest in the fluid dynamics of plants.
Next, they look to better understand the mechanisms underlying the interaction between airborne pollen grains and the human respiratory system under different environmental conditions.
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From amoebas to zebras, all living things evolve. They change over time as pressures from the environment cause individuals with certain traits to become more common in a population while those with other traits become less common.
Cancer is no different. Within a growing tumor, cancer cells with the best ability to compete for resources and withstand environmental stressors will come to dominate in frequency. It’s “survival of the fittest” on a microscopic scale.
But fitness — how well suited any particular individual is to its environment — isn’t set in stone; it can change when the environment changes. The cancer cells that might do best in an environment saturated with chemotherapy drugs are likely to be different than the ones that will thrive in an environment without those drugs. So, predicting how tumors will evolve over time, especially in response to treatment, is a major challenge for scientists.
A new study by researchers at Memorial Sloan Kettering in collaboration with researchers at the University of British Columbia/BC Cancer in Canada suggests that one day it may be possible to make those predictions. The study, published June 23, 2021, in the journal Nature, was led by MSK computational biologist Sohrab Shah and BC Cancer breast cancer researcher Samuel Aparicio. The scientists showed that a machine-learning approach, built using principles of population genetics that describe how populations change over time, could accurately predict how human breast cancer tumors will evolve.
“Population genetic models of evolution match up nicely to cancer, but for a number of practical reasons it’s been a challenge to apply these to the evolution of real human cancers,” says Dr. Shah, Chief of Computational Oncology at MSK. “In this study, we show it’s possible to overcome some of those barriers.”
Ultimately, the approach could provide a means to predict whether a patient’s tumor is likely to stop responding to a particular treatment and identify the cells that are likely to be responsible for a relapse. This could mean highly tailored treatments, delivered at the optimal time, to produce better outcomes for people with cancer. More