Lithium-metal solid-state batteries are a promising technology, but the deposition (plating) of lithium metal on electrode surfaces remains a significant technical hurdle. Here, researchers used micro-computed tomography data to train an artificial intelligence model to identify characteristics vital to improving battery performance. Read more »
An Organic Transistor That Can Sense, Process, and Remember
Traditional AI hardware employs physically separated information sensing, processing, and memory architecture, a configuration that suffers from large energy and time overhead. Now, researchers have fabricated an organic transistor device that can simultaneously act as the sensor and processing core of a streamlined AI hardware system. Read more »
Shaping X-Ray Mirrors Using Machine Learning
Researchers used machine learning to predict and control the shape of an x-ray mirror’s surface with exquisite accuracy and precision. The work represents a key step toward mirrors that can fully exploit the x-rays from upgraded, state-of-the-art light sources and could also enable the engineering of x-ray beams with novel characteristics. Read more » 
Liquid Heterostructures: Generation of Liquid–Liquid Interfaces in Free-Flowing Liquid Sheets
Microscope image of a microfluidic nozzle producing a liquid heterostructure: a layered flat liquid sheet with outer toluene layers and an inner water layer. The colored bands arise from thin film interference, indicating the presence of buried liquid‒liquid interfaces and submicron layer thicknesses. Read more »
A Machine-Learning Approach to Better Batteries
Researchers extracted the relationship between strain and composition in a battery material by applying machine-learning methods to atomic-scale images. The work could lead to more durable batteries and also highlights the potential of integrating microscopy techniques with machine learning to gain insights into complex materials. Read more »
Dynamic Measurements of Antiferromagnetically Aligned Spins
Researchers developed a technique that enables time-resolved, direct detection of spin currents in either ferromagnetic or antiferromagnetic materials at GHz frequencies. Studying the dynamic properties of antiferromagnetic spintronic effects could lead to greater stability and faster intrinsic switching speeds compared to conventional spintronics. Read more »
When Timing Isn’t Everything: Spontaneous Chemical Dynamics
Researchers combined aspects of x-ray photoelectron spectroscopy (XPS) with correlation spectroscopy—a statistical method capable of detecting patterns in microscopic fluctuations across space and time. The new technique, called time-correlation XPS, allows researchers to monitor dynamics without the need for a timed trigger. Read more »
Machine-Learning Team Receives 2021 Halbach Award
This year’s Halbach Award for Innovative Instrumentation at the ALS went to a team of accelerator physicists and computer scientists who were able to use machine-learning techniques to solve a problem that has plagued third-generation light sources for a long time: fluctuations in beam size due to the motion of insertion devices. Read more »
Autonomous Data Acquisition for Scientific Discovery
Researchers at large scientific facilities such as the ALS have applied a robust machine-learning technique to automatically optimize data gathering for a variety of experimental techniques. The work promises to enable experiments with large, complex datasets to be run more quickly, efficiently, and with minimal human intervention. Read more »
Coral Skeleton Reveals Hidden Structures under Multimodal Scrutiny
A powerful new microscope combining ptychography with x-ray linear dichroism provides nanoscale insight into the biomineral strength and resilience of a coral skeleton. The technique’s previously unachievable spatial resolution and contrast will open up new lines of research for users of x-ray microscopy at the ALS. Read more »