Tyler O'Neal, Staff Editor ACADEMIA

HZB physicist gains new insights into topological materials for ultrafast spintronics

The laws of quantum physics rule the microcosm. They determine, for example, how easily electrons move through a crystal and thus whether the material is a metal, a semiconductor, or an insulator. Quantum physics may lead to exotic properties in certain materials: In so-called topological insulators, only the electrons that can occupy some specific quantum states are free to move like massless particles on the surface, while this mobility is completely absent for electrons in the bulk. What's more, the conduction electrons in the "skin" of the material are necessarily spin-polarized and form robust, metallic surface states that could be utilized as channels in which to drive pure spin currents on femtosecond time scales (1 fs= 10-15 s). Snapshots of the electronic structure of Sb acquired with femtosecond time-resolution. Note the changing spectral weight above the Fermi energy (EF).

These properties open up exciting opportunities to develop new information technologies based on topological materials, such as ultrafast spintronics, by exploiting the spin of the electrons on their surfaces rather than the charge. In particular, optical excitation by femtosecond laser pulses in these materials represents a promising alternative to realize highly efficient, lossless transfer of spin information. Spintronic devices utilizing these properties have the potential of superior performance, as they would allow increasing the speed of information transport up to frequencies a thousand times faster than in modern electronics.

However, many questions still need to be answered before spintronic devices can be developed. For example, the details of exactly how the bulk and surface electrons from a topological material respond to the external stimulus i.e., the laser pulse, and the degree of overlap in their collective behaviors on ultrashort time scales.

A team led by HZB physicist Dr. Jaime Sánchez-Barriga has now brought new insights into such mechanisms. The team, which has also established a Helmholtz-RSF Joint Research Group in collaboration with colleagues from Lomonosov State University, Moscow, examined single crystals of elemental antimony (Sb), previously suggested to be a topological material. "It is a good strategy to study interesting physics in a simple system because that's where we can hope to understand the fundamental principles," Sánchez-Barriga explains. "The experimental verification of the topological property of this material required us to directly observe its electronic structure in a highly excited state with time, spin, energy, and momentum resolutions, and in this way, we accessed an unusual electron dynamics," adds Sánchez-Barriga.

The aim was to understand how fast excited electrons in the bulk and on the surface of Sb react to the external energy input and to explore the mechanisms governing their response. "By controlling the time delay between the initial laser excitation and the second pulse that allows us to probe the electronic structure, we were able to build up a full time-resolved picture of how excited states leave and return to equilibrium on ultrafast time scales. The unique combination of time and spin-resolved capabilities also allowed us to directly probe the spin-polarization of excited states far out-of-equilibrium," said Dr. Oliver J. Clark.

The data show a "kink" structure in transiently occupied energy-momentum dispersion of surface states, which can be interpreted as an increase in effective electron mass. The authors were able to show that this mass enhancement plays a decisive role in determining the complex interplay in the dynamical behaviors of electrons from the bulk and the surface, also depending on their spin, following the ultrafast optical excitation.

"Our research reveals which essential properties of this class of materials are the key to systematically control the relevant time scales in which lossless spin-polarized currents could be generated and manipulated," explained Sánchez-Barriga. These are important steps on the way to spintronic devices which based on topological materials possess advanced functionalities for ultrafast information processing.

Spanish university develops a machine learning method for computational design of industrial apps without the high computational costs

Tyler O'Neal, Staff Editor ACADEMIA

The study has been selected as an outstanding publication by the academic journal Physics of Fluids Structure of the mix in the microdevice under different designs

In the field of industrial engineering, using simulations to model, predict, and even optimize the response of a system or device is widespread, as it is less expensive and less complex -and, sometimes, less dangerous- than fabricating and testing several prototypes.

This type of simulation study uses numerical methods that, depending on the problem to be addressed -for example, reducing the aerodynamic forces of an aircraft by changing its shape or using the minimum possible amount of material on elements under loading without breaking- require the simulation of a wide variety of possible combinational cases, which entails high computational costs.

The researchers from the School of Industrial Engineering of the University of Malaga in Spain Francisco Javier Granados Ortiz and Joaquín Ortega Casanova have taken a step further by developing a novel computational design optimization method that reduces these simulation costs by using artificial intelligence.

Faster and cost-efficient designs

They have developed a new methodology with Machine Learning algorithms to predict whether a combination of the design parameters of a problem will be useful or not, based on the objective pursued, and thus guide the design process.

"This method enables us to obtain faster-optimized designs by discarding simulations of little or no interest, thus saving not only physical prototype fabrication costs but also those related to simulation," explained the researchers of the Area of Fluid Mechanics. The researchers Francisco Javier Granados and Joaquin Ortega, authors of this study Particularly, this procedure has been applied to the design of a mechanical mixer that produces a significant increase in heat/mass transfer between two fluids thanks to vortex shedding, which results in an oscillating flow. "Based on the design parameters of the mixer, with our method we have verified that this flow can be controlled and achieve an efficient increase in mixing, but, at the same time, a decrease in pressure drop within it," said Ortega Casanova.

COVID-19 origins still a mystery

Tyler O'Neal, Staff Editor ACADEMIA

Study finds virus was 'highly human adapted'

Scientists using supercomputer modeling to study SARS-CoV-2, the virus that caused the COVID-19 pandemic, have discovered the virus is most ideally adapted to infect human cells - rather than bat or pangolin cells, again raising questions of its origin.

Australian scientists have described how they used high-performance computer modeling of the form of the SARS-CoV-2 virus at the beginning of the pandemic to predict its ability to infect humans and a range of 12 domestic and exotic animals.

Their work aimed to help identify any intermediate animal vector that may have played a role in transmitting a bat virus to humans, and to understand any risk posed by the susceptibilities of companion animals such as cats and dogs, and commercial animals like cows, sheep, pigs, and horses. Professor Nikolai Petrovsky, Flinders University.

From Flinders University and La Trobe University, the scientists used genomic data from the 12 animal species to painstakingly build computer models of the key ACE2 protein receptors for each species. These models were then used to calculate the strength of binding of the SARS-CoV-2 spike protein to each species' ACE2 receptor.

Surprisingly, the results showed that SARS-CoV-2 bound to ACE2 on human cells more tightly than any of the tested animal species, including bats and pangolins. If one of the animal species tested was the origin, it would normally be expected to show the highest binding to the virus.

"Humans showed the strongest spike binding, consistent with the high susceptibility to the virus, but very surprised if an animal was the initial source of the infection in humans," says La Trobe University Professor David Winkler.

"The computer modeling found the virus's ability to bind to the bat ACE2 protein was poor relative to its ability to bind human cells. This argues against the virus being transmitted directly from bats to humans. Hence, if the virus has a natural source, it could only have come to humans via an intermediary species which has yet to be found," says Flinders affiliated Professor Nikolai Petrovsky.

The team's supercomputer modeling shows the SARS-CoV-2 virus also bound relatively strongly to ACE2 from pangolins, a rare exotic ant-eater found in some parts of South-East Asia with occasional instances of use as food or traditional medicines. Professor Winkler says pangolins showed the highest spike binding energy of all the animals the study looked at - significantly higher than bats, monkeys and snakes.

"While it was incorrectly suggested early in the pandemic by some scientists that they had found SARS-CoV-2 in pangolins, this was due to a misunderstanding and this claim was rapidly retracted as the pangolin coronavirus they described had less than 90% genetic similarity to SARS-CoV-2 and hence could not be its ancestor," Professor Petrovsky says.

However, this study and others have shown that the specific part of the pangolin coronavirus spike protein that binds ACE2 was almost identical to that of the SARS-CoV-2 spike protein.

"This sharing of the almost identical spike protein almost certainly explains why SARS-CoV-2 binds so well to pangolin ACE2. Pangolin and SARS-CoV-2 spike proteins may have evolved similarities through a process of convergent evolution, genetic recombination between viruses, or through genetic engineering, with no current way to distinguish between these possibilities," Professor Petrovsky says.

"Overall, putting aside the intriguing pangolin ACE2 results, our study showed that the COVID-19 virus was very well adapted to infect humans."

"We also deduced that some domesticated animals like cats, dogs, and cows are likely to be susceptible to SARS-CoV-2 infection too," Professor Winkler adds. Professor David Winkler, La Trobe University, Australia.

The extremely important and open question of how the virus came to infect humans has two main explanations currently. The virus may have passed to humans from bats through an intermediary animal yet to be found (zoonotic origin), but it cannot yet be excluded that it was released accidentally from a virology lab. A thorough scientific, evidence-based investigation is needed to determine which of these explanations is correct.

How and where the SARS-CoV-2 virus adapted to become such an effective human pathogen remains a mystery. The researchers conclude, adding that finding the origins of the disease will help protect humanity against future coronavirus pandemics.