Hexbyte Glen Cove New strategy puts evolution of microscopic structures on fast track thumbnail

Hexbyte Glen Cove New strategy puts evolution of microscopic structures on fast track

Hexbyte Glen Cove

Engineers at Rice University and Lawrence Livermore National Laboratory are using neural networks to accelerate the prediction of how microstructures of materials evolve. This example predicts snowflake-like dendritic crystal growth. Credit: Mesoscale Materials Science Group/Rice University

The microscopic structures and properties of materials are intimately linked, and customizing them is a challenge. Rice University engineers are determined to simplify the process through machine learning.

To that end, the Rice lab of materials scientist Ming Tang, in collaboration with physicist Fei Zhou at Lawrence Livermore National Laboratory, introduced a technique to predict the evolution of microstructures—structural features between 10 nanometers and 100 microns—in materials.

Their open-access paper in the Cell Press journal Patterns shows how (computer models that mimic the brain’s neurons) can train themselves to predict how a structure will grow under a certain environment, much like a snowflake forms from moisture in nature.

In fact, snowflake-like, dendritic crystal structures were one of the examples the lab used in its proof-of-concept study.

“In modern material science, it’s widely accepted that the microstructure often plays a critical role in controlling a material’s properties,” Tang said. “You not only want to control how the atoms are arranged on lattices, but also what the microstructure looks like, to give you good performance and even new functionality.

“The holy grail of designing materials is to be able to predict how a microstructure will change under given conditions, whether we heat it up or apply stress or some other type of stimulation,” he said.

Tang has worked to refine microstructure prediction for his entire career, but said the traditional equation-based approach faces significant challenges to allow scientists to keep up with the demand for new materials.

“The tremendous progress in machine learning encouraged Fei at Lawrence Livermore and us to see if we could apply it to materials,” he said.






Fortunately, there was plenty of data from the traditional method to help train the team’s neural networks, which view the early evolution of microstructures to predict the next step, and the next one, and so on.

“This is what machinery is good at, seeing the correlation in a very complex way that the human mind is not able to,” Tang said. “We take advantage of that.”

The researchers tested their neural networks on four distinct types of microstructure: plane-wave propagation, grain growth, spinodal decomposition and dendritic crystal growth.

In each test, the networks were fed between 1,000 and 2,000 sets of 20 successive images illustrating a material’s microstructure evolution as predicted by the equations. After learning the evolution rules from these data, the was then given from 1 to 10 images to predict the next 50 to 200 frames, and usually did so in seconds.

The new technique’s advantages quickly became clear: The neural networks, powered by graphic processors, sped the computations up to 718 times for grain growth, compared to the previous algorithm. When run on a standard central processor, they were still up to 87 times faster than the old method. The prediction of other types of evolution showed similar, though not as dramatic, speed increases.

Comparisons with images from the traditional simulation method proved the predictions were largely on the mark, Tang said. “Based on that, we see how we can update the parameters to make the prediction more and more accurate,” he said. “Then we can use these predictions to help design materials we have not seen before.

“Another benefit is that it’s able to make predictions even when we do not know everything about the material properties in a system,” Tang said. “We couldn’t do that with the equation-based method, which needs to know all the parameter values in the equations to perform simulations.”

Tang said the computation efficiency of neural networks could accelerate the development of novel materials. He expects that will be helpful in his lab’s ongoing design of more efficient batteries. “We’re thinking about novel three-dimensional structures that will help charge and discharge batteries much faster than what we have now,” Tang said. “This is an that is perfect for our new approach.”



More information:
Kaiqi Yang et al, Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks, Patterns (2021). DOI: 10.1016/j.patter.2021.100243

Citation:
New strategy puts evolution of microscopic structures on fast track (2021, April 30)
retrieved 2 May 2021
from https://phys.org/news/2021-04-strategy-evolution-microscopic-fast-track.html

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Hexbyte Glen Cove The DALI experiment: Searching for the axion, a proposed component of dark matter thumbnail

Hexbyte Glen Cove The DALI experiment: Searching for the axion, a proposed component of dark matter

Hexbyte Glen Cove

The boxes show how filaments and superclusters of galaxies grow over time, from billions of years after the Big Bang to current structures. Credit: Modification of work by CXC/MPE/V. Springel

The detection of the axion would mark a key episode in the history of science. This hypothetical particle could resolve two fundamental problems of Modern Physics at the same time: the problema of Charge and Parity in the strong interaction, and the mystery of dark matter. However, in spite of the high scientific interest in finding it, the search at high radio frequency—above 6 GHz—has been almost left aside for the lack of the high sensitivity technology which could be built at reasonable cost. Until now.

The Instituto de Astrofísica de Canarias (IAC) will participate in an to develop the DALI (Dark-photons & Axion-Like particles Interferometer) experiment, an astro-particle telescope for dark matter whose scientific objective is the search for axions and paraphotons in the 6 to 60 GHz band. The prototype, proof of concept, is currently in the design and fabrication phase at the IAC. The white-paper describing the experiment has been accepted for publication in the Journal of Cosmology and Astroparticle Physics (JCAP).

Predicted by theory in the 1970’s, the axion is a hypothetical low mass particle which interacts weakly with standard particles such as nucleons and electrons, as well as with photons. These proposed interactions are studied to try to detect the axion with different types of instruments. One promising technique is to study the interaction of axions with standard photons.

“Axions ‘mix’ with photons under the action of a strong external magnetic field, such as those produced by the in particle detectors or those used for medical diagnostics by magnetic resonance, and produce a weak radio or microwave signal. This signal has been looked for in a variety of experiments since the end of the 80’s, and it is just the signal that we want to detect now with DALI, although in a new almost unexplored range of parameters, which will be accessible for the first time thanks to this experiment”, explains Javier De Miguel, an IAC researcher and the first author of the study.

The first axion detectors, made in the 80’s and 90’s, used a resonant cavity which, inside a super-magnet, amplified the weak predicted from the axion, trying to bring it into a power range detectable by scientific instruments. Unfortunately, the size of the cavity is inversely proportional to the scanning frequency and, for the axion, the cavies were too small to be made for frequencies greater than some 6 GHz.

For this reason, the new experiment brings together the most promising techniques for scanning at high frequencies, and includes it in a practical design to which is also added the capacity of astro- for axionic dark matter. In this way, DALI comprises a powerful superconducting magnet, an axion detector with a novel resonator to make the weak signal caused by the axions detectable, and an altazimuth mount to allow it to scan objects and regions in the sky looking for dark matter.

This way, DALI could help in the detection of the , a pseudo-scalar particle whose nature is similar to that of the Higgs boson, discovered in 2012 at CERN, and a promising candidate for . Dark matter is a fundamental constituent of the Universe which interacts very weakly with ordinary matter, and so is very difficult to detect directly, but whose discovery would allow us to explain the rotation curves of the spiral galaxies, and why the formation of structure in the Universe has developed in the way it has until now, among other mysteries.



More information:
Javier De Miguel, A dark matter telescope probing the 6 to 60 GHz band, Journal of Cosmology and Astroparticle Physics (2021). DOI: 10.1088/1475-7516/2021/04/075

Citation:
The DALI experiment: Searching for the axion, a proposed component of dark matter (2021, April 30)
retrieved 1 May 2021
from https://phys.org/news/2021-04-dali-axion-component-dark.html

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Hexbyte Glen Cove 'Pokemonas': Bacteria related to lung parasites discovered, named after Pokémon thumbnail

Hexbyte Glen Cove ‘Pokemonas’: Bacteria related to lung parasites discovered, named after Pokémon

Hexbyte Glen Cove

Light microscope image and illustration of a Thecofilosea amoeba with intracellular Legionellales bacteria (‘Ca. Pokemonas kadabra’). The bacteria were stained red by so-called ‘fluorescence in situ hybridization’. Credit: Marcel Dominik Solbach

A research team at the University of Cologne has discovered previously undescribed bacteria in amoebae that are related to Legionella and may even cause disease. The researchers from Professor Dr. Michael Bonkowski’s working group at the Institute of Zoology have named one of the newly discovered bacteria ‘Pokemonas’ because they live in spherical amoebae, comparable to Pokémon in the video game, which are caught in balls. The results of their research have been published in the journal Frontiers in Cellular and Infection Microbiology.

Bacteria of the order Legionellales have long been of scientific interest because some of these bacteria are known to cause lung disease in humans and animals—such as “Legionnaires’ disease,” which is caused by the species Legionella pneumophila and can sometimes be fatal. Legionellales bacteria live and multiply as intracellular parasites in the cells of organisms as hosts. In particular, the hosts of Legionellales are . The term ‘amoeba’ is used to describe a variety of microorganisms that are not closely related, but share a variable shape and crawling locomotion by means of pseudopods. “We wanted to screen amoebae for Legionellales and chose a group of amoebae for our research that had no close relationship to the hosts that were previously studied. The choice fell on the amoeba group Thecofilosea, which is often overlooked by researchers,” explains Marcel Dominik Solbach.

And indeed, the spherical Thecofilosea serve as host organisms for Legionellales. In Thecofilosea amoebae from environmental samples, the scientists were able to detect various Legionellales species, including two previously undescribed genera and one from the genus Legionella. “The results show that the range of known host organisms of these bacteria is considerably wider than previously thought. In addition, these findings suggest that many more amoebae may serve as hosts for Legionellales—and thus potentially as vectors of disease. To investigate this further, we are now sequencing the complete genome of these bacteria,” said Dr. Kenneth Dumack, who led the project.

In the future, these new findings should help to better understand how Legionellales bacteria are related amongst each other, and clarify their interactions with their hosts as well as the routes of infection in order to prevent outbreaks of the diseases in humans.

Light microscope image and illustration of a Thecofilosea amoeba with intracellular Legionellales bacteria (‘Ca. Pokemonas kadabra’). The bacteria were stained red by so-called ‘fluorescence in situ hybridization’. Credit: Marcel Dominik Solbach

The researchers named one of the genera of bacteria they discovered “Pokemonas.” The genus name “Pokemonas’ is a play on words based on the franchise “Pokémon,” which celebrates its 25th anniversary this year and which most schoolchildren, students, and their parents should be familiar with. The name alludes to the intracellular lifestyle of the in the ball-shaped Thecofilosea amoebae, because in the “Pokémon’ series games, little monsters are caught in balls, much like “Pokemonas’ in the Thecofilosea.



More information:
Marcel Dominik Solbach et al. Novel Endosymbionts in Rhizarian Amoebae Imply Universal Infection of Unrelated Free-Living Amoebae by Legionellales, Frontiers in Cellular and Infection Microbiology (2021). DOI: 10.3389/fcimb.2021.642216

Citation:
‘Pokemonas’: Bacteria related to lung parasites discovered, named after Pokémon (2021, April 30)
retrieved 1 May 2021
from https://phys.org/news/2021-04-pokemonas-bacteria-lung-parasites-pokmon.html

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Hexbyte Glen Cove Self-organization of nanoparticles and molecules in periodic Liesegang-type structures thumbnail

Hexbyte Glen Cove Self-organization of nanoparticles and molecules in periodic Liesegang-type structures

Hexbyte Glen Cove

by Thamarasee Jeewandara , Phys.org

Polarizing Optical Microscopy (POM) images of CNC, TA/CNC, and TA films. POM images of films formed from (A) a CNC suspension; (B to D) TA/CNC suspensions with (left to right) R of 4.0, 5.0, and 6.0; and (E) a TA solution. Films were formed at 22°C and RH = 23%. The concentration of CNCs in (A) to (D) was 3 wt %, and the concentration of TA solution in (E) was 11.3 wt % (750 mM). All films were dried for 24 hours. Scale bars, 420 μm. Credit: Science Advances, doi: 10.1126/sciadv.abe3801

Chemical organization in reaction-diffusion systems offer a strategy to generate materials with ordered morphologies and architecture. Periodic structures can be formed using molecules or nanoparticles. An emerging frontier in materials science aims to combine nanoparticles and molecules. In a new report on Science Advances, Amanda J. Ackroyd and a team of scientists in chemistry, physics and nanomaterials in Canada, Hungary and the U.S. noted how solvent evaporation from a suspension of cellulose nanocrystals (CNCs) and L-(+)-tartaric acid [abbreviated L-(+)-TA] caused the phase separation of precipitation to result in the rhythmic alteration of CNC-rich, L-(+)-TA rings. The CNC-rich regions maintained a cholesteric structure, while the L-(+)-TA-rich bands formed via radially elongated bundles to expand the knowledge of self-organizing reaction-diffusion systems and offer a strategy to design self-organizing materials.

Chemical organization

The process of self-organization and self-assembly occurs universally in non-equilibrium systems of living matter, geochemical environments, materials science and in industry. Existing experiments that lead to can be divided into two groups including the classical Liesegang-type experiments and chemical organization via periodic precipitation to generate materials with ordered morphologies and structural hierarchy. In this work, Ackroyd et al. developed a strategy for solvent evaporation to phase separate an aqueous solution of tartaric acid/cellulose nanocrystals [L-(+)-TA/CNC or TA/CNC] for its subsequent precipitation to result in a rhythmic alternation of CNC-rich or CNC-depleted ring-type regions. The team developed a kinetic model which agreed with the quantitatively. The work expands the range of self-organizing reaction-diffusion systems to pave the way for periodically structured functional materials.







Drying of composite TA/CNC film with R = 4.5 at RH ≈ 21%. The movie recorded using POM shows the formation of equidistant, periodic rings. The ring growth starts at a nucleation point and grows towards the outer edge of the film with a finite, constant velocity. The movie was generated by increasing the speed of the original movie 100-fold, with 20 frames per second. Scale bar, 1 mm. Credit: Science Advances, doi: 10.1126/sciadv.abe3801

Experiments

Ackroyd et al. deposited mixed suspensions as droplets on glass slides and immediately placed them in a humidity chamber. Using a polarizing optical microscope (POM), they formed images of the drying films with varying TA/CNC (tartaric acid/cellulose nanocrystals) compositions. Films formed by drying the tartaric acid solution maintained a spherulite morphology with a needle-like structure. Using images of drying TA/CNC, the team noted the formation of rings beginning from a nucleation point close to the film center from which periodic and grew radially towards the edge of the film. They then characterized the ring patterns in the films, where the increasing relative humidity, increased the value of their period. To understand the growth dynamics of the formation of periodic rings, Ackroyd et al. recorded the evolution of the spatio-temporal patterns of water evaporation for the liquid films. They labeled CNCs with a covalently attached fluorescein isothiocyanate (FITC) dye, to characterize the composition of alternating rings in the composite film. Based on the POM (polarizing optical microscope) images, they noted the CNC-enriched and CNC-deprived periodic bands in the composite film.

Characterization of ring patterns in TA/CNC films. (A and B) POM images of films formed at R of 4.5 (A) and 5.5 (B). (C) Variation in the average period, P, of the ring pattern, plotted as a function of R. In (A) to (C), films were formed at RH = 33%. (D and E) POM images of films formed at RH = 23% (D) and 33% (E). (F) Variation in the average period, P, of the ring pattern, plotted as a function of RH. (D to F) Films were formed at R = 5.0. Error bars in (C) and (F) represent SDs for nine samples. Scale bars (A, B, D, and E), 300 μm. (G to J) POM images of a liquid TA/CNC film (R = 4.5, RH ≈ 21%), taken at various drying times. The white dashed lines show the outline of the drying droplet circumference. (K) Variation in the distance, r, from the nucleation point to the outer edge of the drying film, plotted as a function of time. Scale bars (G to J), 500 μm. Credit: Science Advances, doi: 10.1126/sciadv.abe3801

Characterizing the composite film.

To characterize the composite films further, the scientists acquired spectra under differential transmission of circularly polarized light of opposite handedness. Using scanning electron microscopy, they obtained images of the film cross-section of the CNC-rich and TA-rich regions. To understand the topography of the surface of the composite film, they used atomic force microscopy. Using high-magnification POM images, Ackroyd et al. noted the TA-rich regions in yellow and light orange, while the CNC-rich regions appeared red and green in color. The team also conducted polarimetry imaging to map the variation in the polarization state of transmitted light. To accomplish this, they illuminated a film with a 532 nm linearly polarized light with a light polarization state set parallel to the vertical edge of the images. Based on both POM and polarimetry experiments, Ackroyd et al revealed the orientation order in TA-rich ring-banded regions relative to the chemical composition of the film. The structural features formed by CNCs and TA provided an interesting example of complex, out-of-equilibrium organization, of interest for future studies. To probe the TA/CNC films in the transmission mode, the scientists also used small-angle X-ray scattering, where an X-ray beam size of 220 x 50 µm allowed an entire film to be scanned for mapping with the technique.

  • Characterization of the composition of periodic bands in the composite film. (A) Fluorescence microscopy and (B) POM images of TA/FITC-CNC films formed at R = 5.0 and RH = 33%. Scale bars (A and B), 150 μm. (C and D) The variation in ΔE of the TA-rich bands (labeled as 1, 3, and 5) and CNC-rich bands (labeled as 2, 4, and 6) in (C). The ΔE spectra in (D) are collected from the regions marked in (C). Scale bar (C), 100 μm. a.u., arbitrary units. Credit: Science Advances, doi: 10.1126/sciadv.abe3801
  • Characterization of local anisotropy of the TA/CNC film by SAXS. (A) Schematic illustration of the SAXS rastering measurement for SAXS mapping of the film. (B) A typical SAXS pattern with a definition of the azimuthal angle ω. (C) 2D ODF f(ω), calculated from the SAXS pattern in (B), shows the anisotropic features along the most probable angle, denoted by ω0, which provides information about the orientation within the film. The value of f(ω) is fitted using an ad hoc order parameter (red curve), described in section S9. (D) A photograph of the film taken during the SAXS measurement with dashed circles showing the circular edge of the dried droplet and the center of the concentric rings. The green rectangular box in the center of the film represents the size and shape of the x-ray beam. (E) Orientations of anisotropic scatterers, probed by the SAXS measurements and mapped on the entire area of the film. The direction of each arrow indicates the orientation in that location. The color represents the orientation order parameter in 2D, termed S, with the scale shown on the right. The dashed circles correspond to the circular edge of the film and the center of the concentric rings, similar to those shown in (D). The film was prepared at R = 5.0 and RH = 23%. Credit: Science Advances, doi: 10.1126/sciadv.abe3801

Numerical model

The scientists then developed a kinetic model for the phase-separating TA/CNC suspension as applied generally to reaction-diffusion systems. They represented the dynamics of the periodic pattern formation with two types of building blocks using a set of differential equations. The numerical model factored six species of the drying TA/CNC suspension including the (1) dissolved TA, (2) the nuclei of precipitated TA, (3) the crystals of TA in the TA-rich phase and the (4) the suspended individual CNCs, (5) the TA-CNC clusters, and 6) the CNC-enriched phase. The numerical model qualitatively reproduced the experimental findings, and the model predicted a finite constant velocity of the moving front of the edge pattern.

Numerical simulations of ring pattern formation. (A) Spatial distribution of TA-(s), (B) spatial distribution of CNCs, and (C) concentration profiles of TA and CNCs in alternating ring-type bands. In the simulations, the following parameters were used: DA = 10−1, DB = 10−2, DD = 10−2, DE = 10−4, d* = 0.8, and e* = 0.2. The grid spacing (Δr) and time step (Δt) in the numerical simulations were 10−3 and 1.8 × 10−8. All parameters and variables are in dimensionless units. Credit: Science Advances, doi: 10.1126/sciadv.abe3801

Outlook

In this way, Amanda J. Ackroyd and colleagues provided first evidence of a periodic ring-banded structure formed by two components with dimensions differing by several orders of magnitude. The results differed from ring patterns obtained by other phenomena including “coffee ring” patterns. The scientists noted the evaporation of water from the TA/CNC suspension to result in the saturation of CNCs and TA in the mixture. They controlled the morphology of the composite by varying the composition of the TA/CNC suspension and relative humidity. Based on simulations, the team noted that the periodic ring patterns did not qualitatively change with increasing viscosity and therefore reduced the diffusion coefficients of the compounds. They highlighted distinct band structures for the CNC-enriched and TA-enriched ring-banded regions throughout the study. The work will expand the knowledge of self-organizing reaction-diffusion systems and provide strategies to design self-organizing materials.



More information:
Ackroyd J. et al. Self-organization of nanoparticles and molecules in periodic Liesegang-type structures, Science Advances, 10.1126/sciadv.abe3801

Epstein I.R. and Xu B. Reaction–diffusion processes at the nano- and microscales, Nature Nanotechnology, doi.org/10.1038/nnano.2016.41

Anderson L.C. et al. Morphology of poly-L-alanine spherulites. Nature, doi.org/10.1038/216052a0

© 2021 Science X Network

Citation:
Self-organization of nanoparticles and molecules in periodic Liesegang-type structures (2021, April 30)
retrieved 1 May 2021
from https://phys.org/news/2021-04-self-organization-nanoparticles-molecules-periodic-liesegang-type.html

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.

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