Synchrotron light reveals the previously unknown crystal structure of dypingite

2025/11/142025/11/20

A team of researchers from the University of Oslo and the ALBA Synchrotron has determined for the first time the crystal structure of dypingite, a naturally occurring hydrated magnesium carbonate mineral. Using synchrotron X-ray diffraction at ALBA, the scientists revealed how humidity triggers subtle but reversible disorder in the mineral’s structure. These findings, published in the Journal of Applied Crystallography, help explain the elusive nature of dypingite’s atomic arrangement and could improve our understanding of carbon mineralization – a natural process with implications for carbon dioxide capture and storage.

Understanding the structure of crystals and their defects has led to a number of surprising innovations across various fields, from modern electronics and computing to high-precision MRI machines and large high-energy accelerators. In light of this, researchers have been studying a number of disordered solid materials and exploring methods to engineer disorder within their crystal structures to gain control over the physical and chemical properties of the compounds. One mineral of growing interest is dypingite, a naturally-occurring hydrated magnesium carbonate mineral that forms through the reaction of magnesium-rich rocks with carbon dioxide and water.

These minerals have been found to play a role in natural carbon sequestration, whereby they lock atmospheric carbon dioxide into stable solid forms over geological timescales. Furthermore, dypingite forms flower-like nanoparticles that could have applications in catalysis and water filtration. Identifying their crystal structure could enable scientists to exploit these properties. Dypingite was first described in the 70’s. However, until now, it has been notoriously difficult to characterize due to its complex layering and sensitivity to moisture.

Read more on the ALBA website

Image: Naturally formed dypingite: (left) microphotograph of a dypingite layer on a serpentine rock; (right) SEM image of dypingite’s layers

ALS Machine Learning Models at Beamtimes around the World

2024/06/212024/07/16

From the types of samples to the techniques used to study them, user experiences at beamlines around the world can vary, but one commonality connects them: beamtime is precious. At different facilities, users encounter different beamline controls, and varying availability of compute infrastructure to process their data. Beyond needing to familiarize themselves with different equipment and software setups, they also need to ensure that they’re collecting meaningful, consistent data no matter where they are. For the past several months, the ALS Computing group has been traveling around the world for beamtime. Their firsthand experience is informing the development of a suite of tools aimed at lowering the barriers of access to advanced data processing for all users.

Today’s beamtime experience

As a beamline scientist at the ALS, Dula Parkinson has helped numerous users with microtomography, a technique that can yield ten gigabytes of data in two seconds. “In many cases, users won’t have done this kind of experiment or analysis before, and they won’t have the computing infrastructure or software needed to analyze the huge amounts of complex data being produced,” he said.

Computational tools and machine-learning models can help the users, from adjusting their experimental setup in real time to processing the data after the experiment has concluded. Eliminating these bottlenecks can make the limited beamtime more efficient and help users glean scientific insights more quickly.

As a former beamline scientist himself, Computing Program Lead Alex Hexemer has first-hand knowledge of the user experience. He was instrumental in the creation of a dedicated computing group at the ALS in 2018, which continues to grow in both staff numbers and diversity of expertise. A current focus for the group is to advance the user experience with intuitive interfaces.

Computing approach to beamtime

Recently, Hexemer and two of his group members, Wiebke Koepp and Dylan McReynolds, traveled to Diamond Light Source, where they worked with Beamline Scientist Sharif Ahmed to test some of their tools during a beamline experiment. “It is always useful to see other facilities from the user’s perspective,” McReynolds said. “We want our software to be usable at many facilities, so getting to test in other environments was very valuable.”

The computational infrastructure is an essential complement to the beamline instrumentation. To standardize their experiments across different microtomography beamlines, the team performed measurements on a reference material—sand with standardized size distributions. Each scan captures a “slice” from the sample; the slices then need to be reconstructed into three-dimensional images that contain 50 to 200 gigabytes of data.

Read more on ALS website

Image: The ALS Computing group performed experiments and tested their machine learning models at Beamline 8.3.2. Clockwise from back left: Tanny Chavez, Dylan McReynolds, Raja Vyshnavi Sriramoju, Seij De Leon, Dula Parkinson, Wiebke Koepp.

Superconducting X-ray laser reaches operating temperature colder than outer space

2022/05/102022/05/11

The facility, LCLS-II, will soon sharpen our view of how nature works on ultrasmall, ultrafast scales, impacting everything from quantum devices to clean energy.

Nestled 30 feet underground in Menlo Park, California, a half-mile-long stretch of tunnel is now colder than most of the universe. It houses a new superconducting particle accelerator, part of an upgrade project to the Linac Coherent Light Source (LCLS) X-ray free-electron laser at the Department of Energy’s SLAC National Accelerator Laboratory.

Crews have successfully cooled the accelerator to minus 456 degrees Fahrenheit – or 2 kelvins – a temperature at which it becomes superconducting and can boost electrons to high energies with nearly zero energy lost in the process. It is one of the last milestones before LCLS-II will produce X-ray pulses that are 10,000 times brighter, on average, than those of LCLS and that arrive up to a million times per second – a world record for today’s most powerful X-ray light sources.

“In just a few hours, LCLS-II will produce more X-ray pulses than the current laser has generated in its entire lifetime,” says Mike Dunne, director of LCLS. “Data that once might have taken months to collect could be produced in minutes. It will take X-ray science to the next level, paving the way for a whole new range of studies and advancing our ability to develop revolutionary technologies to address some of the most profound challenges facing our society.”

With these new capabilities, scientists can examine the details of complex materials with unprecedented resolution to drive new forms of computing and communications; reveal rare and fleeting chemical events to teach us how to create more sustainable industries and clean energy technologies; study how biological molecules carry out life’s functions to develop new types of pharmaceuticals; and peek into the bizarre world of quantum mechanics by directly measuring the motions of individual atoms.

A chilling feat

LCLS, the world’s first hard X-ray free-electron laser (XFEL), produced its first light in April 2009, generating X-ray pulses a billion times brighter than anything that had come before. It accelerates electrons through a copper pipe at room temperature, which limits its rate to 120 X-ray pulses per second.

Read more on the SLAC website

Secrets of skyrmions revealed

2021/10/142021/10/14

Why skyrmions could have a lot in common with glass and high-temperature superconductors

Spawned by the spins of electrons in magnetic materials, these tiny whirlpools behave like independent particles and could be the future of computing. Experiments with SLAC’s X-ray laser are revealing their secrets.

Scientists have known for a long time that magnetism is created by the spins of electrons lining up in certain ways. But about a decade ago, they discovered another astonishing layer of complexity in magnetic materials: Under the right conditions, these spins can form little vortexes or whirlpools that act like particles and move around independently of the atoms that spawned them.

The tiny whirlpools are called skyrmions, named after Tony Skyrme, the British physicist who predicted their existence in 1962. Their small size and sturdy nature – like knots that are hard to undo – have given rise to a rapidly expanding field devoted to understanding them better and exploiting their strange qualities.

“These objects represent some of the most sophisticated forms of magnetic order that we know about,” said Josh Turner, a staff scientist at the Department of Energy’s SLAC National Accelerator Laboratory and principal investigator with the Stanford Institute for Materials and Energy Sciences (SIMES) at SLAC.

Diamond-II programme set to transform UK science

2021/07/162021/07/28

Diamond Light Source has established itself as a world-class synchrotron facility enabling research by leading academic and industrial groups in physical and life sciences. Diamond has pioneered a model of highly efficient and uncompromised infrastructure offered as a user-focussed service driven by technical and engineering innovation.

To continue delivering the world-changing science that Diamond leads and enables, Diamond-II is a co-ordinated programme of development that combines a new machine and new beamlines with a comprehensive series of upgrades to optics, detectors, sample environments, sample delivery capabilities and computing. The user experience will be further enhanced through access to integrated and correlative methods as well as broad application of automation in both instrumentation and analysis. Diamond-II will be transformative in both spatial resolution and throughput and will offer users streamlined access to enhanced instruments for life and physical sciences.

Read more on the Diamond website

Image: Diamond’s synchrotron building

Credit: Diamond Light Source

Germanium telluride’s hidden properties revealed

2020/11/052020/11/10

Germanium Telluride is an interesting candidate material for spintronic devices. In a comprehensive study at BESSY II, a Helmholtz-RSF Joint Research Group has now revealed how the spin texture switches by ferroelectric polarization within individual nanodomains.

Germanium telluride (GeTe) is known as a ferrolectric Rashba semiconductor with a number of interesting properties. The crystals consist of nanodomains, whose ferrolectric polarization can be switched by external electric fields. Because of the so-called Rashba effect, this ferroelectricity can also be used to switch electron spins within each domain. Germanium telluride is therefore an interesting material for spintronic devices, which allow data processing with significantly less energy input.

Russian German Cooperation

Now a team from HZB and the Lomonosov Moscow State University, which has established a Helmholtz-RSF Joint Research Group, has provided comprehensive insights into this material at the nanoscale. The group is headed by physical chemist Dr. Lada Yashina (Lomonosov State University) and HZB physicist Dr. Jaime Sánchez-Barriga. “We have examined the material using a variety of state-of-the-art methods to not only determine its atomic structure, but also the internal correlation between its atomic and electronic structure at the nanoscale,” says Lada Yashina, who produced the high-quality crystalline samples in her laboratory.

Read more on the BESSY II website

Image: The Fermi surface of multidomain GeTe (111) bulk single crystal measured with high-resolution angle-resolved photoemission at BESSY II. © HZB

Machine learning enhances light-beam performance at the ALS

2019/11/082019/11/08

Successful demonstration of algorithm by Berkeley Lab-UC Berkeley team shows technique could be viable for scientific light sources around the globe.

Synchrotron light sources are powerful facilities that produce light in a variety of “colors,” or wavelengths – from the infrared to X-rays – by accelerating electrons to emit light in controlled beams.
Synchrotrons like the Advanced Light Source at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) allow scientists to explore samples in a variety of ways using this light, in fields ranging from materials science, biology, and chemistry to physics and environmental science. Researchers have found ways to upgrade these machines to produce more intense, focused, and consistent light beams that enable new, and more complex and detailed studies across a broad range of sample types. But some light-beam properties still exhibit fluctuations in performance that present challenges for certain experiments.

Image: This image shows the profile of an electron beam at Berkeley Lab’s Advanced Light Source synchrotron, represented as pixels measured by a charged coupled device (CCD) sensor. When stabilized by a machine-learning algorithm, the beam has a horizontal size dimension of 49 microns (root mean squared) and vertical size dimension of 48 microns (root mean squared). Demanding experiments require that the corresponding light-beam size be stable on time scales ranging from less than seconds to hours to ensure reliable data.
Credit: Lawrence Berkeley National Laboratory

Translation of ‘Hidden’ Information Reveals Chemistry in Action

2017/10/102017/10/16

New method allows on-the-fly analysis of how catalysts change during reactions, providing crucial information for improving performance.

Chemistry is a complex dance of atoms. Subtle shifts in position and shuffles of electrons break and remake chemical bonds as participants change partners. Catalysts are like molecular matchmakers that make it easier for sometimes-reluctant partners to interact.

Now scientists have a way to capture the details of chemistry choreography as it happens. The method—which relies on computers that have learned to recognize hidden signs of the steps—should help them improve the performance of catalysts to drive reactions toward desired products faster.

The method—developed by an interdisciplinary team of chemists, computational scientists, and physicists at the U.S. Department of Energy’s Brookhaven National Laboratory and Stony Brook University—is described in a new paper published in the Journal of Physical Chemistry Letters. The paper demonstrates how the team used neural networks and machine learning to teach computers to decode previously inaccessible information from x-ray data, and then used that data to decipher 3D nanoscale structures.

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RAW Power!

2017/09/142017/10/16

MacCHESS software brings synchrotron-level data processing to the laptop and home laboratory

Since its introduction by Søren Skou (Nielsen) in 2010, the BioXTAS RAW software has been a familiar interface to the many biomedical scientists collecting data at CHESS beamlines in recent years. From the start, RAW was designed specifically with novice users in mind: when scientists arrive at the beamline, they need something fast and easy to learn in the very limited time available … often late at night.

The program was literally designed by looking over the shoulders of beamline users as they collected data. But rather than simply create an automated data processing pipeline, we opted to give people the power to fully process data on their own computers at home, if they choose. This allows them to use the same software at other beamlines and even on their own home X-ray sources: from initial raw data reduction to final publication. Indeed, with over 4000 downloads in 2017, RAW is now the primary processing software at several other beamlines and lab source facilities worldwide.

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Picture: Richard Gillilan, Jesse Hopkins, and Soren Skou at the annual Amrican Crystallographic Association meeting where they conducted a tutorial in the BioXTAS RAW software.

Emergent magnetism at transition-metal-nanocarbon interfaces

2017/08/282017/10/09

Researchers have shed light on the origin of the magnetism arising at carbon/non-magnetic 3d,5d metal interfaces

These results may allow the manipulation of spin ordering at metallic surfaces using electro-optical signals, with potential applications in computing, sensors, and other multifunctional magnetic devices.

Interfaces are key in solid state and quantum physics, controlling many fundamental properties and enabling emergent interfacial, bi-dimensional like phenomena. Therefore they offer potential opportunities for designing hybrid materials that profit from promising combinatory effects.

In particular, the fine-tuning of spin polarization at metallo–organic interfaces opens a realm of possibilities, from the direct applications in molecular spintronics and thin-film magnetism to biomedical imaging or quantum computing. This interaction at the interface can control the spin polarization in magnetic field sensors, generate magnetization spin-filtering effects in non-magnetic electrodes or even give rise to magnetic ordering when non-magnetic elements such as diamagnetic copper or paramagnetic manganese are put in contact with carbon/fullerenes at such interfaces.

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