Computers Math

In 2016, a supercomputer beat the world champion in Go, a complicated board game. How? By using reinforcement learning, a type of artificial intelligence whereby computers train themselves after being programmed with simple instructions. The computers learn from their mistakes and, step by step, become highly powerful.
The main drawback to reinforcement learning is that it can’t be used in some real-life applications. That’s because in the process of training themselves, computers initially try just about anything and everything before eventually stumbling on the right path. This initial trial-and-error phase can be problematic for certain applications, such as climate-control systems where abrupt swings in temperature wouldn’t be tolerated.
Learning the driver’s manual before starting the engine
The CSEM engineers have developed an approach that overcomes this problem. They showed that computers can first be trained on extremely simplified theoretical models before being set to learn on real-life systems. That means that when the computers start the machine-learning process on the real-life systems, they can draw on what they learned previously on the models. The computers can therefore get on the right path quickly without going through a period of extreme fluctuations. The engineers’ research has just been published in IEEE Transactions on Neural Networks and Learning Systems.
“It’s like learning the driver’s manual before you start a car,” says Pierre-Jean Alet, head of smart energy systems research at CSEM and a co-author of the study. “With this pre-training step, computers build up a knowledge base they can draw on so they aren’t flying blind as they search for the right answer.”
Slashing energy use by over 20%
The engineers tested their approach on a heating, ventilation and air conditioning (HVAC) system for a complex 100-room building using a three-step process. First, they trained a computer on a “virtual model” built from simple equations that roughly described the building’s behavior. Then they fed actual building data (temperature, how long blinds were open, weather conditions, etc.) into the computer, to make the training more accurate. Finally, they let the computer run its reinforcement-learning algorithms to find the best way to manage the HVAC system. Broad applications
This discovery could open up new horizons for machine learning by expanding its use to applications where large fluctuations in operating parameters would have important financial or security costs.

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Materials provided by Swiss Center for Electronics and Microtechnology – CSEM. Note: Content may be edited for style and length. More

High-definition roll-up televisions or foldable smartphones may soon no longer be unaffordable luxury goods that can be admired at international electronics trade fairs. High-performance organic transistors are a key necessity for the mechanically flexible electronic circuits required for these applications. However, conventional horizontal organic thin-film transistors are very slow due to the hopping-transport in organic semiconductors, so they cannot be used for applications requiring high frequencies. Especially for logic circuits with low power consumption, such as those used for Radio Frequency Identification (RFID), it is mandatory to develop transistors enabling high operation frequency as well as adjustable device characteristics (i.e., threshold-voltage). The research group Organic Devices and Systems (ODS) at the Dresden Integrated Center for Applied Photophysics (IAPP) of the Institute of Applied Physics headed by Dr. Hans Kleemann has now succeeded in realizing such novel organic devices.
“Up to now, vertical organic transistors have been seen as lab curiosities which were thought too difficult to be integrated in an electronic circuit. However, as shown in our publication, vertical organic transistors with two independent control electrodes are perfectly suited to realize complex logic circuits while keeping the main benefit of vertical transistors devices, namely the high switching frequency,” says Dr. Hans Kleemann.
The vertical organic transistors with two independent control electrodes are characterized by a high switching frequency (a few nanoseconds) and an adjustable threshold voltage. Thanks to these developments, even single transistors can be used to represent different logical states (AND, NOT, NAND). Furthermore, the adjustable threshold voltage ensures signal integrity (noise margin) and low power consumption.
With this, the research group has set a milestone with regard to the vision of flexible and printable electronics. In the future, these transistors could make it possible to realize even sophisticated electronic functions such as wireless communication (RFID) or high-resolution flexible displays completely with organic components, thus completely dispensing with silicon-based electronic components.

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Materials provided by Technische Universität Dresden. Note: Content may be edited for style and length. More

All animals great and small live every day in an uncertain world. Whether you are a human being or an insect, you rely on your senses to help you navigate and survive in your world. But what drives this essential sensing?
Unsurprisingly, animals move their sensory organs, such as eyes, ears and noses, while they are searching. Picture a cat swiveling its ears to capture important sounds without needing to move its body. But the precise position and orientation these sense organs take over time during behavior is not intuitive, and current theories do not predict these positions and orientations well.
Now a Northwestern University research team has developed a new theory that can predict the movement of an animal’s sensory organs while searching for something vital to its life.
The researchers applied the theory to four different species which involved three different senses (including vision and smell) and found the theory predicted the observed sensing behavior of each animal. The theory could be used to improve the performance of robots collecting information and possibly applied to the development of autonomous vehicles where response to uncertainty is a major challenge.
“Animals make their living through movement,” said Malcolm A. MacIver, who led the research. “To find food and mates and to identify threats, they need to move. Our theory provides insight into how animals gamble on how much energy to expend to get the useful information they need.”
MacIver is a professor of biomedical and mechanical engineering in Northwestern’s McCormick School of Engineering and a professor of neurobiology (courtesy appointment) in the Weinberg College of Arts and Sciences.

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The new theory, called energy-constrained proportional betting provides a unifying explanation for many enigmatic motions of sensory organs that have been previously measured. The algorithm that follows from the theory generates simulated sensory organ movements that show good agreement to actual sensory organ movements from fish, mammals and insects.
The study was published today (Sept. 22) by the journal eLife. The research provides a bridge between the literature on animal movement and energetics and information theory-based approaches to sensing.
MacIver is the corresponding author. Chen Chen, a Ph.D. student in MacIver’s lab, is the first author, and Todd D. Murphey, professor of mechanical engineering at McCormick, is a co-author.
The algorithm shows that animals trade the energetically costly operation of movement to gamble that locations in space will be informative. The amount of energy (ultimately food they need to eat) they are willing to gamble, the researchers show, is proportional to the expected informativeness of those locations.
“While most theories predict how an animal will behave when it largely already knows where something is, ours is a prediction for when the animal knows very little — a situation common in life and critical to survival,” Murphey said.
The study focuses on South American gymnotid electric fish, using data from experiments performed in MacIver’s lab, but also analyzes previously published datasets on the blind eastern American mole, the American cockroach and the hummingbird hawkmoth. The three senses were electrosense (electric fish), vision (moth) and smell (mole and roach).
The theory provides a unified solution to the problem of not spending too much time and energy moving around to sample information, while getting enough information to guide movement during tracking and related exploratory behaviors.
“When you look at a cat’s ears, you’ll often see them swiveling to sample different locations of space,” MacIver said. “This is an example of how animals are constantly positioning their sensory organs to help them absorb information from the environment. It turns out there is a lot going on below the surface in the movement of sense organs like ears and eyes and noses.”
The algorithm is a modified version of one Murphey and MacIver developed five years ago in their bio-inspired robotics work. They took observations of animal search strategies and developed algorithms to have robots mimic those animal strategies. The resulting algorithms gave Murphey and MacIver concrete predictions for how animals might behave when searching for something, leading to the current work.

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Materials provided by Northwestern University. Original written by Megan Fellman. Note: Content may be edited for style and length. More

As head of the educational outreach arm of the Florida State University-headquartered National High Magnetic Field Laboratory, Roxanne Hughes has overseen dozens of science camps over the years, including numerous sessions of the successful SciGirls Summer Camp she co-organizes with WFSU .
In a new paper published in the Journal of Research in Science Teaching, Hughes and her colleagues took a much closer look at one of those camps, a coding camp for middle school girls.
They found that nuanced interactions between teachers and campers as well as among the girls themselves impacted how girls viewed themselves as coders.
The MagLab offers both co-ed camps and summer camps for girls about science in general as well as about coding in particular . Hughes, director of the MagLab’s Center for Integrating Research and Learning , wanted to study the coding camp because computer science is the only STEM field where the representation of women has actually declined since 1990.
“It’s super gendered in how it has been advertised, beginning with the personal computer,” Hughes said. “And there are stereotypes behind what is marketed to girls versus what is marketed to boys. We wanted to develop a conceptual framework focusing specifically on coding identity — how the girls see themselves as coders — to add to existing research on STEM identity more broadly.”
This specific study focused on the disparate experiences of three girls in the camp. The researchers looked at when and how the girls were recognized for their coding successes during the camp, and how teachers and peers responded when the girls demonstrated coding skills.

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“Each girl received different levels of recognition, which affected their coding identity development,” Hughes said. “We found that educators play a crucial role in amplifying recognition, which then influences how those interactions reinforce their identities as coders.”
Positive praise often resulted in a girl pursuing more challenging activities, for example, strengthening her coding identity.
Exactly how teachers praised the campers played a role in how that recognition impacted the girls. Being praised in front of other girls, for example, had more impact than a discreet pat on the back. More public praise prompted peer recognition, which further boosted a girl’s coding identity.
The type of behavior recognized by teachers also appeared to have different effects. A girl praised for demonstrating a skill might feel more like a coder than one lauded for her persistence, for example. Lack of encouragement was also observed: One girl who sought attention for her coding prowess went unacknowledged, while another who was assisting her peers received lots of recognition, responses that seem to play into gender stereotypes, Hughes said. Even in a camp explicitly designed to bolster girls in the sciences, prevailing stereotypes can undermine best intentions.
“To me, the most interesting piece was the way in which educators still carry the general gender stereotypes, and how that influenced the behavior they rewarded.” Hughes said. “They recognized the girl who was being a team player, checking in on how everyone was feeling — all very stereotypically feminine traits that are not necessarily connected to or rewarded in computing fields currently.”
Messaging about science is especially important for girls in middle school, Hughes said. At that developmental stage, their interest in STEM disciplines begins to wane as they start to get the picture that those fields clash with their other identities.

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The MagLab study focused on three girls — one Black, one white and one Latina — as a means to develop a framework for future researchers to understand coding identity. Hughes says this is too small a data set to tease out definitive conclusions about roles of race and gender, but the study does raise many questions for future researchers to examine with the help of these findings.
“The questions that come out of the study to me are so fascinating,” Hughes said. “Like, how would these girls be treated differently if they were boys? How do the definitions of ‘coder’ that the girls develop in the camp open or constrain opportunities for them to continue this identity work as they move forward?”
The study has also prompted Hughes to think about how to design more inclusive, culturally responsive camps at the MagLab.
“Even though this is a summer camp, there is still the same carryover of stereotypes and sexism and racism from the outer world into this space,” she said. “How can we create a space where girls can behave differently from the social gendered expectations?”
The challenge will be to show each camper that she and her culture are valued in the camp and to draw connections between home and camp that underscore that. “We need to show that each of the girls has value — in that camp space and in science in general,” Hughes said.
Joining Hughes as co-authors on the study were Jennifer Schellinger of Florida State University and Kari Roberts of the MagLab.
The National High Magnetic Field Laboratory is funded by the National Science Foundation and the State of Florida, and has operations at Florida State University, University of Florida and Los Alamos National Laboratory. More

Due to continual improvements in transportation technology, people travel more extensively than ever before. Although this strengthened connection between faraway countries comes with many benefits, it also poses a serious threat to disease control and prevention. When infected humans travel to regions that are free of their particular contagions, they might inadvertently transmit their infections to local residents and cause disease outbreaks. This process has occurred repeatedly throughout history; some recent examples include the SARS outbreak in 2003, the H1N1 influenza pandemic in 2009, and — most notably — the ongoing COVID-19 pandemic.
Imported cases challenge the ability of nonendemic countries — countries where the disease in question does not occur regularly — to entirely eliminate the contagion. When combined with additional factors such as genetic mutation in pathogens, this issue makes the global eradication of many diseases exceedingly difficult, if not impossible. Therefore, reducing the number of infections is generally a more feasible goal. But to achieve control of a disease, health agencies must understand how travel between separate regions impacts its spread.
In a paper publishing on Tuesday in the SIAM Journal of Applied Mathematics, Daozhou Gao of Shanghai Normal University investigated the way in which human dispersal affects disease control and total extent of an infection’s spread. Few previous studies have explored the impact of human movement on infection size or disease prevalence — defined as the proportion of individuals in a population that are infected with a specific pathogen — in different regions. This area of research is especially pertinent during severe disease outbreaks, when governing leaders may dramatically reduce human mobility by closing borders and restricting travel. During these times, it is essential to understand how limiting people’s movements affects the spread of disease.
To examine the spread of disease throughout a population, researchers often use mathematical models that sort individuals into multiple distinct groups, or “compartments.” In his study, Gao utilized a particular type of compartmental model called the susceptible-infected-susceptible (SIS) patch model. He divided the population in each patch — a group of people such as a community, city, or country — into two compartments: infected people who currently have the designated illness, and people who are susceptible to catching it. Human migration then connects the patches. Gao assumed that the susceptible and infected subpopulations spread out at the same rate, which is generally true for diseases like the common cold that often only mildly affect mobility.
Each patch in Gao’s SIS model has a certain infection risk that is represented by its basic reproduction number (R0) — the quantity that predicts how many cases will be caused by the presence of a single contagious person within a susceptible population. “The larger the reproduction number, the higher the infection risk,” Gao said. “So the patch reproduction number of a higher-risk patch is assumed to be higher than that of a lower-risk patch.” However, this number only measures the initial transmission potential; it can rarely predict the true extent of infection.
Gao first used his model to investigate the effect of human movement on disease control by comparing the total infection sizes that resulted when individuals dispersed quickly versus slowly. He found that if all patches recover at the same rate, large dispersal results in more infections than small dispersal. Surprisingly, an increase in the amount by which people spread can actually reduce R0 while still increasing the total amount of infections.
The SIS patch model can also help elucidate how dispersal impacts the distribution of infections and prevalence of the disease within each patch. Without diffusion between patches, a higher-risk patch will always have a higher prevalence of disease, but Gao wondered if the same was true when people can travel to and from that high-risk patch. The model revealed that diffusion can decrease infection size in the highest-risk patch since it exports more infections than it imports, but this consequently increases infections in the patch with the lowest risk. However, it is never possible for the highest-risk patch to have the lowest disease prevalence.
Using a numerical simulation based on the common cold — the attributes of which are well-studied — Gao delved deeper into human migration’s impact on the total size of an infection. When Gao incorporated just two patches, his model exhibited a wide variety of behaviors under different environmental conditions. For example, the dispersal of humans often led to a larger total infection size than no dispersal, but rapid human scattering in one scenario actually reduced the infection size. Under different conditions, small dispersal was detrimental but large dispersal ultimately proved beneficial to disease management. Gao completely classifies the combinations of mathematical parameters for which dispersal causes more infections when compared to a lack of dispersal in a two-patch environment. However, the situation becomes more complex if the model incorporates more than two patches.
Further investigation into Gao’s SIS patch modeling approach could reveal more nuanced information about the complexities of travel restrictions’ impact on disease spread, which is relevant to real-world situations — such as border closures during the COVID-19 pandemic. “To my knowledge, this is possibly the first theoretical work on the influence of human movement on the total number of infections and their distribution,” Gao said. “There are numerous directions to improve and extend the current work.” For example, future work could explore the outcome of a ban on only some travel routes, such as when the U.S. banned travel from China to impede the spread of COVID-19 but failed to block incoming cases from Europe. Continuing research on these complicated effects may help health agencies and governments develop informed measures to control dangerous diseases.

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Materials provided by Society for Industrial and Applied Mathematics. Original written by Jillian Kunze. Note: Content may be edited for style and length. More

Fairy circles are one of nature’s greatest enigmas and most visually stunning phenomena. An international research team led by the University of Göttingen has now, for the first time, collected detailed data to show that Alan Turing’s model explains the striking vegetation patterns of the Australian fairy circles. In addition, the researchers showed that the grasses that make up these patterns act as “eco-engineers” to modify their own hostile and arid environment, thus keeping the ecosystem functioning. The results were published in the Journal of Ecology.
Researchers from Germany, Australia and Israel undertook an in-depth fieldwork study in the remote Outback of Western Australia. They used drone technology, spatial statistics, quadrat-based field mapping, and continuous data-recording from a field-weather station. With the drone and a multispectral camera, the researchers mapped the “vitality status” of the Triodia grasses (how strong and how well they grew) in five one-hectare plots and classified them into high- and low-vitality.
The systematic and detailed fieldwork enabled, for the first time in such an ecosystem, a comprehensive test of the “Turing pattern” theory. Turing’s concept was that in certain systems, due to random disturbances and a “reaction-diffusion” mechanism, interaction between just two diffusible substances was enough to allow strongly patterned structures to spontaneously emerge. Physicists have used this model to explain the striking skin patterns in zebrafish or leopards for instance. Earlier modelling had suggested this theory might apply to these intriguing vegetation patterns and now there is robust data from multiple scales which confirms that Alan Turing’s model applies to Australian fairy circles.
The data show that the unique gap pattern of the Australian fairy circles, which occur only in a small area east of the town of Newman, emerges from ecohydrological biomass-water feedbacks from the grasses. In fact, the fairy circles — with their large diameters of 4m, clay crusts from weathering and resultant water run-off — are a critical extra source of water for the dryland vegetation. Clumps of grasses increased shading and water infiltration around the nearby roots. With increasing years after fire, they merged more and more at the periphery of the vegetation gaps to form a barrier so that they could maximize their water uptake from the fairy circle’s runoff. The protective plant cover of grasses could reduce soil-surface temperatures by about 25°C at the hottest time of the day, which facilitates the germination and growth of new grasses. In summary, the scientists found evidence both at the scale of the landscape and at much smaller scales that the grasses, with their cooperative growth dynamics, redistribute the water resources, modulate the physical environment, and thus function as “ecosystem engineers” to modify their own environment and better cope with the arid conditions.
Dr Stephan Getzin, Department of Ecosystem Modelling at the University of Göttingen, explains, “The intriguing thing is that the grasses are actively engineering their own environment by forming symmetrically spaced gap patterns. The vegetation benefits from the additional runoff water provided by the large fairy circles, and so keeps the arid ecosystem functional even in very harsh, dry conditions.” This contrasts with the uniform vegetation cover seen in less water-stressed environments. “Without the self-organization of the grasses, this area would likely become desert, dominated by bare soil,” he adds. The emergence of Turing-like patterned vegetation seems to be nature’s way of managing an ancient deficit of permanent water shortage.
In 1952 when the British mathematician, Alan Turing, published his ground-breaking theoretical paper on pattern formation, he had most likely never heard of the fairy circles before. But with his theory he laid the foundation for generations of physicists to explain highly symmetrical patterns like sand ripples in dunes, cloud stripes in the sky or spots on an animal’s coat with the reaction-diffusion mechanism. Now, ecologists have provided an empirical study to extend this principle from physics to dryland ecosystems with fairy circles.

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Although less than one per cent of all water in the world is freshwater, it is what we drink and use for agriculture. In other words, it’s vital to human survival. York University researchers have just created a publicly available water quality database for close to 12,000 freshwater lakes globally — almost half of the world’s freshwater supply — that will help scientists monitor and manage the health of these lakes.
The study, led by Faculty of Science Postdoctoral Fellow Alessandro Filazzola and Master’s student Octavia Mahdiyan, collected data for lakes in 72 countries, from Antarctica to the United States and Canada. Hundreds of the lakes are in Ontario.
“The database can be used by scientists to answer questions about what lakes or regions may be faring worse than others, how water quality has changed over the years and which environmental stressors are most important in driving changes in water quality,” says Filazzola.
The team included a host of graduate and undergraduate students working in the laboratory of Associate Professor Sapna Sharma in addition to a collaboration with Assistant Professor Derek Gray of Wilfrid Laurier University, Associate Professor Catherine O’Reilly of Illinois State University and York University Associate Professor Roberto Quinlan.
The researchers reviewed 3,322 studies from as far back as the 1950s along with online data repositories to collect data on chlorophyll levels, a commonly used marker to determine lake and ecosystem health. Chlorophyll is a predictor of the amount of vegetation and algae in lakes, known as primary production, including invasive species such as milfoil.
“Human activity, climate warming, agricultural, urban runoff and phosphorus from land use can all increase the level of chlorophyll in lakes. The primary production is most represented by the amount of chlorophyll in the lake, which has a cascading impact on the phytoplankton that eat the algae and the fish that eat the phytoplankton and the fish that eat those fish,” says Filazzola. “If the chlorophyll is too low, it can have cascading negative effects on the entire ecosystem, while too much can cause an abundance of algae growth, which is not always good.”
Warming summer temperatures and increased solar radiation from decreased cloud cover in the northern hemisphere also contributes to an increase in chlorophyll, while more storm events caused by climate change contribute to degraded water quality, says Sharma. “Agricultural areas and urban watersheds are more associated with degraded water quality conditions because of the amount of nutrients input into these lakes.”
The researchers also gathered data on phosphorus and nitrogen levels — often a predictor of chlorophyll — as well as lake characteristics, land use variables, and climate data for each lake. Freshwater lakes are particularly vulnerable to changes in nutrient levels, climate, land use and pollution.
“In addition to drinking water, freshwater is important for transportation, agriculture, and recreation, and provides habitats for more than 100,000 species of invertebrates, insects, animals and plants,” says Sharma. “The database can be used to improve our understanding of how chlorophyll levels respond to global environmental change and it provides baseline comparisons for environmental managers responsible for maintaining water quality in lakes.”
The researchers started looking only at Ontario lakes, but quickly expanded it globally as although there are thousands of lakes in Ontario a lot of the data is not as readily available as it is in other regions of the world.
“The creation of this database is a feat typically only accomplished by very large teams with millions of dollars, not by a single lab with a few small grants, which is why I am especially proud of this research,” says Sharma.

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Researchers have developed the world’s first photodetector that can see all shades of light, in a prototype device that radically shrinks one of the most fundamental elements of modern technology.
Photodetectors work by converting information carried by light into an electrical signal and are used in a wide range of technologies, from gaming consoles to fibre optic communication, medical imaging and motion detectors. Currently photodetectors are unable to sense more than one colour in the one device.
This means they have remained bigger and slower than other technologies, like the silicon chip, that they integrate with.
The new hyper-efficient broadband photodetector developed by researchers at RMIT University is at least 1,000 times thinner than the smallest commercially available photodetector device.
In a significant leap for the technology, the prototype device can also see all shades of light between ultraviolet and near infrared, opening new opportunities to integrate electrical and optical components on the same chip.
*New possibilities*
The breakthrough technology opens the door for improved biomedical imaging, advancing early detection of health issues like cancer.

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Study lead author, PhD researcher Vaishnavi Krishnamurthi, said in photodetection technologies, making a material thinner usually came at the expense of performance.
“But we managed to engineer a device that packs a powerful punch, despite being thinner than a nanometre, which is roughly a million times smaller than the width of a pinhead,” she said.
As well as shrinking medical imaging equipment, the ultra-thin prototype opens possibilities for more effective motion detectors, low-light imaging and potentially faster fibre optical communication.
“Smaller photodetectors in biomedical imaging equipment could lead to more accurate targeting of cancer cells during radiation therapy,” Krishnamurthi said.
“Shrinking the technology could also help deliver smaller, portable medical imaging systems that could be brought into remote areas with ease, compared to the bulky equipment we have today.”
*Lighting up the spectrum*

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How versatile and useful photodetectors are depends largely on three factors: their operating speed, their sensitivity to lower levels of light and how much of the spectrum they can sense.
Typically, when engineers have tried improving a photodetector’s capabilities in one of those areas, at least one of the other capabilities have been diminished.
Current photodetector technology relies on a stacked structure of three to four layers.
Imagine a sandwich, where you have bread, butter, cheese and another layer of bread — regardless of how good you are at squashing that sandwich, it will always be four layers thick, and if you remove a layer, you’d compromise the quality.
The researchers from RMIT’s School of Engineering scrapped the stacked model and worked out how to use a nanothin layer — just a single atom thick — on a chip.
Importantly, they did this without diminishing the photodetector’s speed, low-light sensitivity or visibility of the spectrum.
The prototype device can interpret light ranging from deep ultraviolet to near infrared wavelengths, making it sensitive to a broader spectrum than a human eye.
And it does this over 10,000 times faster than the blink of an eye.
*Nano-thin technology*
A major challenge for the team was ensuring electronic and optical properties didn’t deteriorate when the photodetector was shrunk, a technological bottleneck that had previously prevented miniaturisation of light detection technologies.
Chief investigator Associate Professor Sumeet Walia said the material used, tin monosulfide, is low-cost and naturally abundant, making it attractive for electronics and optoelectronics.
“The material allows the device to be extremely sensitive in low-lighting conditions, making it suitable for low-light photography across a wide light spectrum,” he said.
Walia said his team is now looking at industry applications for their photodetector, which can be integrated with existing technologies such as CMOS chips.
“With further development, we could be looking at applications including more effective motion detection in security cameras at night and faster, more efficient data storage,” he said. More