Vertical distribution of size-resolved chemical composition of Arctic aerosol particles was investigated in different cloud layers. Multimodal microspectroscopy analysis reveals a broadening of chemically-specific size distribution above the cloud top. Read more »
Sub-4 nm mapping of donor–acceptor organic semiconductor nanoparticle composition
We report, for the first time, sub-4 nm mapping of donor : acceptor nanoparticle composition in eco-friendly colloidal dispersions for organic electronics. This technology shows great promise for the optimization of organic semiconductor blends for organic electronics and photocatalysis and has further applications in organic core–shell nanomedicines. Read more »
Macromolecular organic matter in samples of the asteroid (162173) Ryugu
We investigated the macromolecular organic matter in samples of the asteroid Ryugu—brought to Earth by the Hayabusa2 spacecraft—measuring its elemental, isotopic, and functional group compositions along with its small-scale structures and morphologies. Analytical methods used included spectro-microscopies, electron microscopy, and isotopic microscopy. Read more »
An automated size and time-resolved aerosol collector platform integrated with environmental sensors to study the vertical profile of aerosols
Researchers present the vertical distribution of size-resolved aerosol composition over an agricultural site by deploying a newly developed lightweight automated size- and time-resolved aerosol collector (STAC) platform integrated with environmental sensors on unmanned aerial systems (e.g., tethered balloon systems). Read more »
New Insight into Titan’s Hazy Atmospheric Chemistry
Researchers simulated the complex chemistry that may be occurring in the hazy atmosphere of Saturn’s largest moon, Titan, and analyzed the reaction products at the ALS. The work provided new insights into what future Titan probes may encounter upon arrival and what the atmosphere of Earth may have been like eons ago. 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 »
The Inside‐Outs of Metal Hydride Dehydrogenation: Imaging the Phase Evolution of the Li‐N‐H Hydrogen Storage System
Hydrogen absorption and release in lithium amide involves chemical and structural change. Scanning transmission x‐ray microscopy visualizes this phase evolution inside particles, showing a core‐shell architecture, with the more hydrogenated species as the shell for hydrogenation and, more surprisingly, for dehydrogenation as well. Read more »
Machine Learning Helps Stabilize Synchrotron Light
Researchers showed that machine learning can predict noisy fluctuations in the size of beams generated by synchrotron light sources and correct them before they occur. The work solves a decades-old problem and will allow researchers to fully exploit the smaller beams made possible by recent advances in light source technology. Read more »
Machine Learning Enhances Light-Beam Performance at the Advanced Light Source
Researchers have successfully demonstrated how machine-learning tools can improve beam-size stability via adjustments that largely cancel out these fluctuations—reducing them from a level of a few percent down to 0.4 percent, with submicron precision. The demonstration shows that the technique could be viable for scientific light sources around the globe. Read more »
Renewed Prospects for Rechargeable Mg Batteries
Contrary to previous reports, it’s possible to create a rechargeable battery using magnesium ions if the electrode material is first conditioned at high temperature. With twice the charge of lithium ions, magnesium ions hold great promise as the basis for high-energy-density batteries suitable for use in electric vehicles. Read more »