As we kick off a new year, two topics are sure to dominate the fields that we refer to collectively as materials science: artificial intelligence and sustainability.
One of the most exciting developments is AI’s ability to process vast datasets from materials characterization techniques. Machine learning algorithms are helping researchers uncover hidden patterns and correlations that were previously inaccessible. These insights are guiding the design of next-generation materials, from ultra-efficient semiconductors to sustainable energy storage solutions.
These advancements, though, come with a potential environmental cost. Training and running AI models require substantial computational power, leading to significant energy consumption. The energy-intensive data centers powering AI often rely on non-renewable energy sources.
On the other hand, AI is proving to be a powerful ally in sustainability. Machine learning algorithms help optimize resource use, reduce waste, and accelerate the development of eco-friendly materials. AI can identify pathways to produce carbon-neutral cement or design energy-efficient materials with minimal environmental impact. Moreover, AI-driven predictive modeling can drastically reduce the trial-and-error phases in materials research, saving time, resources, and energy.
By integrating sustainability principles into AI development and deployment—such as using green energy for AI computations or explicitly programming models to prioritize environmental impact—these two domains can become synergistic. Together, they hold the potential to transform materials science while addressing some of our most pressing ecological challenges.
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Alternative Applications Using WDXRF Webinar
Join us for a free 60-minute webinar on Thursday, February 20, 2025, at 3:00 PM CET to explore alternative and innovative applications of WDXRF technology that you may not have considered before.
While WDXRF is widely used for bulk material analysis, it also holds the potential to address a wide variety of sample types and challenges, such as analyzing samples with unknown light element content, solidifying unstable oils for analysis, analyzing water in pre-concentrated forms, working with small sample sizes and loose powders, mapping sample surfaces, and performing vacuum analysis.
Don’t miss this opportunity to learn how to adapt WDXRF for a wider range of sample types, discover practical techniques for handling and analyzing difficult sample forms, and explore solutions for surface mapping and vacuum-based analysis. This webinar is perfect for XRF users looking to expand the capabilities of their instruments, distributors seeking to understand new applications, and anyone interested in exploring the diverse applications of WDXRF technology.
For a crystal-clear view of cutting-edge science, step into Brandon Mercado’s lab in the basement of Kline Chemistry Laboratory.
Brandon Mercado, the director of Yale’s Structural Science Facility, has played a key role in bringing MicroED (microcrystal electron diffraction) to the university. This cutting-edge technology allows researchers to determine the atomic structure of tiny crystals that are too small for traditional X-ray crystallography. With its wide applications in structural biology, chemistry, and materials science, MicroED is opening new doors for analyzing small molecules, proteins, and nanomaterials.
High-performance Direct Excitation EDXRF Spectrometers
Powerful, non-destructive qualitative and quantitative elemental analysis
The Rigaku NEX DE Series are high-performance, direct excitation EDXRF spectrometers that deliver exceptional elemental analysis capabilities. Utilizing a high-powered X-ray tube, these instruments provide superior analytical performance, including higher count rates, improved precision, and the ability to analyze even challenging materials with ease. This versatility allows the NEX DE Series to address a wide range of applications, from demanding quality control needs to those requiring small spot analysis. The series includes NEX DE and NEX DE VS.
The video "Platinum - Periodic Table of Videos" from the University of Nottingham provides an in-depth look at platinum, a precious metal known for its rarity and high value. It explores platinum's unique properties, including its resistance to corrosion and high melting point, which make it invaluable in various industries such as automotive, jewelry, and electronics. The video also delves into the history of platinum, its discovery, and its applications in modern technology.
December 6, 2024: A team of scientists has created a shape-changing polymer that could transform the construction of future soft materials. Made using a liquid crystalline elastomer (LCE), a soft rubber-like material that can be stimulated by external forces like light or heat, the polymer is so versatile that it can move in several directions. This development opens avenues for innovative applications in various fields requiring adaptable materials.
December 18, 2024: Bulky silicon wafers serve as the main scaffold on which high-quality, single-crystalline semiconducting elements are grown. Stackable chips would have to include thick silicon “flooring” as part of each layer, slowing down communication between functional semiconducting layers. MIT engineers have found a way around this hurdle, with a multilayered chip design that doesn’t require any silicon wafer substrates and works at temperatures low enough to preserve the underlying layer’s circuitry.
January 24, 2025: Researchers at Tufts University's Silklab have developed a material combining silk fibroin and dopamine, capable of being shot from a device and solidifying mid-air to form strong, sticky fibers. This innovation mimics Spider-Man's web-slinging abilities and holds potential for applications such as retrieving objects underwater or in challenging environments.
January 27, 2025: Lithium–air batteries have the potential to outstrip conventional lithium-ion batteries by storing significantly more energy at the same weight. A Chinese team has now proposed addition of a soluble catalyst to the electrolyte. It acts as a redox mediator that facilitates charge transport and counteracts passivation of the electrodes.
Featured Application Notes
3D Printed Superalloy
Additive manufacturing, commonly known as 3D printing, is increasingly popular due to its ability to produce complex shapes without geometric constraints. It enables quick and adaptable prototyping while allowing the utilization of a diverse array of materials.
As capabilities grow, so do the complexities and challenges in product design. Therefore, it is essential to use advanced characterization techniques that offer comprehensive evaluations of manufactured products and help optimize the design and process parameters.
X-ray Computed Tomography (CT) is a versatile, non-destructive, 3D volume imaging tool for structural inspection. It can accommodate a wide range of sample sizes at different stages of additive manufacturing. With scan times as short as tens of seconds, CT generates a detailed digital replica of the product, enabling both quantitative analysis of the internal defects and dimensions and property simulation.
XRF is an analysis tool used for non-destructive analysis in industrial forensics to identify and resolve manufacturing issues or contamination within the production and distributor processes. Analysis using XRF allows the operator to determine the elemental composition of foreign material in failure analysis and root cause analysis to optimize quality control and testing procedures.
EDXRF is a fast and simple means of obtaining the elemental composition of samples investigated in industrial forensics. Samples analyzed are often irregularly shaped, small or available in small quantities. Rigaku NEX DE VS EDXRF analyzer is an excellent tool equipped with small spot size measurement, camera image and powerful yet simple-to-use software for the investigation and identification of foreign material.
Small angle X-ray scattering study for investigating 3D nanoparticle packing structure of Pt catalyst on Gd-doped CeO₂ supports for fuel cells
A 3D real-space structural model for fuel cell catalysis systems, consisting of Pt and Gd-doped CeO2 nanoparticles, was constructed to match simulated small angle X-ray scattering (SAXS) intensity and observed SAXS intensity using the reverse Monte–Carlo (RMC) method. The observed SAXS patterns were well reproduced by those of the simulations. The SAXS–RMC simulation results indicated that the number of nanometer-sized Pt particles is much smaller than the introduced amount. This suggests that most Pt particles are not uniformly distributed throughout the catalysts.
Additionally, the coordination number of Pt particles, calculated from the structural model, tends to decrease as the amount of Pt loaded increases, which is consistent with the transmission electron microscopy (TEM) images. 3D pore size distributions using the obtained structure models were compared with the Barrett–Joyner–Halenda (BJH) analysis results for nitrogen gas adsorption data, and the lower quartiles and medians of the pore diameters were reasonably consistent. The presented SAXS-RMC modeling can evaluate both local arrangement of the constituent primary particles and aggregated mesoscale structure.
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