Computers Math

In the transition toward clean, renewable energy, there will still be a need for conventional power sources, like coal and natural gas, to ensure steady power to the grid. Researchers across the world are using unique materials and methods that will make those conventional power sources cleaner through carbon capture technology.
Creating accurate, detailed models is key to scaling up this important work. A recent paper led by the University of Pittsburgh Swanson School of Engineering examines and compares the various modeling approaches for hollow fiber membrane contactors (HFMCs), a type of carbon capture technology. The group analyzed over 150 cited studies of multiple modeling approaches to help researchers choose the technique best suited to their research.
“HFMCs are one of the leading technologies for post-combustion carbon capture, but we need modeling to better understand them,” said Katherine Hornbostel, assistant professor of mechanical engineering and materials science, whose lab led the analysis. “Our analysis can guide researchers whose work is integral to meeting our climate goals and help them scale up the technology for commercial use.”
A hollow fiber membrane contactor (HFMC) is a group of fibers in a bundle, with exhaust flowing on one side and a liquid solvent on the other to trap the carbon dioxide. The paper reviews state-of-the-art methods for modeling carbon capture HFMCs in one, two and three dimensions, comparing them in-depth and suggesting directions for future research.
“The ideal modeling technique varies depending on the project, but we found that 3D models are qualitatively different in the nature of information they can reveal,” said Joanna Rivero, graduate student working in the Hornbostel Lab and lead author. “Though cost limits their wide use, we identify 3D modeling and scale-up modeling as areas that will greatly accelerate the progress of this technology.”
Grigorios Panagakos, research engineer and teaching faculty in Carnegie Mellon University’s Department of Chemical Engineering, brought his expertise in analyzing the modeling of transport phenomena to the review paper, as well.

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Materials provided by University of Pittsburgh. Note: Content may be edited for style and length. More

A team of researchers from the Technical University of Munich, the Walther-Meissner-Institute of the Bavarian Academy of Sciences and Humanities, and the Norwegian University of Science and Technology in Trondheim has discovered an exciting method for controlling spin carried by quantized spin wave excitations in antiferromagnetic insulators.
Elementary particles carry an intrinsic angular momentum known as their spin. For an electron, the spin can take only two particular values relative to a quantization axis, letting us denote them as spin-up and spin-down electrons. This intrinsic two-valuedness of the electron spin is at the core of many fascinating effects in physics.
In today’s information technology, the spin of an electron and the associated magnetic momentum are exploited in applications of information storage and readout of magnetic media, like hard disks and magnetic tapes.
Antiferromagnets: future stars in magnetic data storage?
Both, the storage media and the readout sensors utilize ferromagnetically ordered materials, where all magnetic moments align parallel. However, the moments may orient in a more complex way. In antiferromagnets, the “antagonist to a ferromagnet,” neighboring moments align in an anti-parallel fashion. While these systems look “non-magnetic” from outside, they have attracted broad attention as they promise robustness against external magnetic fields and faster control. Thus, they are considered as the new kids on the block for applications in magnetic storage and unconventional computing.
One important question in this context is, whether and how information can be transported and detected in antiferromagnets. Researchers at the Technical University of Munich, the Walther-Meissner-Institute and the Norwegian University of Science and Technology in Trondheim studied the antiferromagnetic insulator hematite in this respect.

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In this system, charge carriers are absent and therefore it is a particularly interesting testbed for the investigation of novel applications, where one aims at avoiding dissipation by a finite electrical resistance. The scientists discovered a new effect unique to the transport of antiferromagnetic excitations, which opens up new possibilities for information processing with antiferromagnets.
Unleashing the pseudospin in antiferromagnets
Dr Matthias Althammer, the lead researcher on the project describes the effect as follows: “In the antiferromagnetic phase, neighboring spins are aligned in an anti-parallel fashion. However, there are quantized excitations called magnons. Those carry information encoded in their spin and can propagate in the system. Due to the two antiparallel-coupled spin species in the antiferromagnet the excitation is of a complex nature, however, its properties can be cast in an effective spin, a pseudospin. We could experimentally demonstrate that we can manipulate this pseudospin, and its propagation with a magnetic field.”
Dr Akashdeep Kamra, the lead theoretician from NTNU in Trondheim adds that “this mapping of the excitations of an antiferromagnet onto a pseudospin enables an understanding and a powerful approach which has been the crucial foundation for treating transport phenomena in electronic systems. In our case, this enables us to describe the dynamics of the system in a much easier manner, but still maintain a full quantitative description of the system. Most importantly, the experiments provide a proof-of-concept for the pseudospin, a concept which is closely related to fundamental quantum mechanics.”
Unlocking the full potential of antiferromagnetic magnons
This first experimental demonstration of magnon pseudospin dynamics in an antiferromagnetic insulator not only confirms the theoretical conjectures on magnon transport in antiferromagnets, but also provides an experimental platform for expanding towards rich electronics inspired phenomena.
“We may be able to realize fascinating new stuff such as the magnon analogue of a topological insulator in antiferromagnetic materials” points out Rudolf Gross, director of the Walther-Meissner-Institute, Professor for Technical Physics (E23) at the Technical University of Munich and co-speaker for the cluster of excellence Munich Center for Quantum Science and Technology (MCQST). “Our work provides an exciting perspective for quantum applications based on magnons in antiferromagnets”
The research was funded by the Deutsche Forschungsgemeinschaft (DFG) via the cluster of excellence Munich Center for Quantum Science and Technology (MCQST) and by the Research Council of Norway. More

Researchers at the University of Tsukuba have created a new carbon-based electrical device, π-ion gel transistors (PIGTs), by using an ionic gel made of a conductive polymer. This work may lead to cheaper and more reliable flexible printable electronics.
Organic conductors, which are carbon-based polymers that can carry electrical currents, have the potential to radically change the way electronic devices are manufactured. These conductors have properties that can be tuned via chemical modification and may be easily printed as circuits. Compared with current silicon solar panels and transistors, systems based on organic conductors could be flexible and easier to install. However, their electrical conductivity can be drastically reduced if the conjugated polymer chains become disordered because of incorrect processing, which greatly limits their ability to compete with existing technologies.
Now, a team of researchers led by the University of Tsukuba have formulated a novel method for preserving the electrical properties of organic conductors by forming an “ion gel.” In this case, the solvent around the poly(para-phenyleneethynylene) (PPE) chains was replaced with an ionic liquid, which then turned into a gel. Using confocal fluorescent microscopy and scanning electron microscopy, the researchers were able to verify the morphology of the organic conductor.
“We showed that the internal structure of our π-ion gel is a nanofiber network of PPE, which is very good at reliably conducting electricity” says author Professor Yohei Yamamoto.
In addition to acting as wires for delocalized electrons, the polymer chains direct the flow of mobile ions, which can help move charge-carriers to the carbon rings. This allows current to flow through the entire volume of the device. The resulting transistor can switch on and off in response to voltage changes in less than 20 microseconds — which is faster than any previous device of this type.
“We plan to use this advance in supramolecular chemistry and organic electronics to design a whole arrange of flexible electronic devices,” explains Professor Yamamoto. The fast response time and high conductivity open the way for flexible sensors that enjoy the ease of fabrication associated with organic conductors, without sacrificing speed or performance.

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A model based solely on the past 40 years of weather events uses 7,000 times less computer power than today’s weather forecasting tools. An A.I.-powered model could someday provide more accurate forecasts for rain, snow and other weather events. More

New research offers clues to what goes on inside the minds of machines as they learn to see. Instead of attempting to account for a neural network’s decision-making on a post hoc basis, their method shows how the network learns along the way, by revealing how much the network calls to mind different concepts to help decipher what it sees as the image travels through successive layers. More

Neuroscientists have found reading computer code does not rely on the regions of the brain involved in language processing. Instead, it activates the ‘multiple demand network,’ which is also recruited for complex cognitive tasks such as solving math problems or crossword puzzles. More

Scientists often look to nature for cues when designing robots – some robots mimic human hands while others simulate the actions of octopus arms or inchworms. Now, researchers have designed a new soft robotic gripper that draws inspiration from an unusual source: pole beans. More

Methods currently used around the world for predicting the development of COVID-19 and other pandemics fail to report precisely on the best and worst case scenarios. Newly developed prediction method for epidemics, published in Nature Physics, solve this problem.
“It is about understanding best and worst-case scenarios — and the fact that worst case is one of the most important things to keep track of when navigating through pandemics — regardless whether it be in Denmark, the EU, the USA or the WHO. If you are only presented with an average estimate for the development of an epidemic — not knowing how bad it possible can get, then it is difficult to act politically,” says Professor Sune Lehmann, one of four authors of the article Fixed-time descriptive statistics underestimate extremes of epidemic curve ensembles just published in Nature Physics.
Researchers Jonas L. Juul, Kaare Græsbøll, Lasse Engbo Christiansen and Sune Lehmann, all from DTU Compute, act as advisors to the National Board of Health in Denmark during the corona crisis. And partly based on their own experience as advisors, they have become aware that the existing methods of projecting the development of epidemics such as COVID-19 have a problem in describing the extremes possibilities of the expected development.
Epidemics are unpredictable
“Disease outbreaks are fundamentally stochastic processes. The same disease introduced in the same population can infect a large number of people or disappear quickly without having a particular prevalence. It depends in part on coincidences,” explains postdoc Jonas L. Juul.
It is precisely the unpredictability of epidemics which makes it so difficult to make the right decisions everywhere in society when it hits. How many beds and respirators will there be a need for? And how much can we reduce this demand by enforcing restrictions?

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However, the general unpredictability is just one of many problems in estimating the development of an epidemic.
“It is not just the unpredictable nature of epidemics that makes it difficult to predict their course — it is also our lack of knowledge about the disease’s characteristics and prevalence in society at any given time. Just to give a few concrete examples of this: there is typically no one who has any idea exactly when an outbreak has started, how many infected we have in an area on any given day, or in which regions the epidemic is getting a foothold right now. The only thing we know for sure is that when the health authorities discover an outbreak, it has been going on for a while, “says Sune Lehmann.
The common way to deal with the lack of information, almost everywhere in the world, is to model many scenarios based on e.g. different numbers of unknown infections and starting times and then summarize by looking at each day separately and assessing the ‘middle’ predictions as the most likely outcomes of the day. If most input parameters give infection numbers of less than 4000 on Christmas Day, more than 4000 new infected are subsequently assessed to be unlikely.
The ‘day-based’ way of making these predictions is used all over the world, and although the link between the development of an epidemic and specific dates is useful in some contexts, it systematically excludes data on how bad or mild the epidemic will be.
If all projections e.g. predict that the epidemic will peak at 4000 infected in one day, but none of the curves shows it on the same day, then on a given day it will be an extreme and therefore not included in any estimate.
“We, therefore, suggest making the summary ‘curve-based’: Instead of assessing which infection rates are probable or unlikely on individual days, we should look at one entire simulation at a time. Is the entire simulated infection curve probable or not? And based on that you can make a summary of the most likely curves for the development of the epidemic, “says Jonas L. Juul.
“By looking at entire prediction curves instead of individual days, you will get a more realistic estimate of how bad the epidemic can become. It is especially useful if you are trying to avoid the hospital system being overloaded,” concludes Sune Lehmann.

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Materials provided by Technical University of Denmark. Original written by Jesper Spangsmark Nielsen. Note: Content may be edited for style and length. More