Field-driven transport systems offer the possibility of biofunctionalized carriers for microrobotics, biomedicine, and cell delivery. Here, researchers show how magnetic fields may selectively manipulate and drive microrobotics along a patterned micromagnet. Different-sized magnetic carriers move in multiple directions, including selective rotation and bidirectional movement. Such steering systems can direct the delivery of drugs or cells into artificial microvascular channels. Read more »
A Deep-Learning Analysis of Lithium-Plating Dynamics in Batteries
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 »
Ptychography Reduces Spectral Distortions Intrinsic to Conventional Zone-Plate-Based X-Ray Spectromicroscopy
The point spread function (PSF) of a conventional zone-plate-based microscope limits the achievable spatial resolution and results in spatially resolved spectra that do not accurately reflect the spatial heterogeneity of the samples when the scale of the detail approaches the probe size. X-ray ptychography, a coherent-scattering-based imaging scheme that effectively removes the probe from the image data, returns accurate spectra from regions smaller than the probe size. 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 »