New research from the Giannis (Yanni) Mpourmpakis and his team introduces the first universal adsorption model that accounts for detailed nanoparticle structural characteristics, metal composition and different adsorbates, making it possible to not only predict adsorption behavior on any metal nanoparticles but screen their stability, as well. The research combines computational chemistry modeling with machine learning to fit a large number of data and accurately predict adsorption trends on nanoparticles that have not previously been seen. By connecting adsorption with the stability of nanoparticles, nanoparticles can now be optimized in terms of their synthetic accessibility and application property behavior. This improvement will significantly accelerate nanomaterials design and avoid trial and error experimentation in the lab. Their work was published in Science Advances on Sept. 13, 2019.
The ability to combine continuously tunable narrow-band terahertz (THz) generation that can access both the far-infrared and mid-infrared regimes with nanometer-scale spatial resolution is highly promising for identifying underlying light-matter interactions and realizing selective control of rotational or vibrational resonances in nanoparticles or molecules. Here, we report selective difference frequency generation with over 100 THz bandwidth via femtosecond optical pulse shaping. The THz emission is generated at nanoscale junctions at the interface of LaAlO3/SrTiO3 (LAO/STO) that is defined by conductive atomic force microscope lithography, with the potential to perform THz spectroscopy on individual nanoparticles or molecules. Numerical simulation of the time-domain signal facilitates the identification of components that contribute to the THz generation. This ultra-wide-bandwidth tunable nanoscale coherent THz source transforms the LAO/STO interface into a promising platform for integrated lab-on-chip optoelectronic devices with various functionalities.
The recently published paper in journal of ACS NANO on Ultrafast Microscopy of Spin-Momentum Locked Surface Plasmon Polaritons is an essential research for designing optical elements to control spin-polarized SPP (surface plasmon polaritons) fields on the nano femto scale. Hrvoje Petek and his colleagues have shown two-photon photoemission electron microscopy images formed by coupling and propagation of longitudinal and transverse components of SPP fields of light. Further, they have also shown the spin-momentum locked SPP wave packets launched with circularly polarized excitation propagate at the same phase and group velocities as for the linearly polarized excitation using time-resolved experiments.
Michael Widom and his colleagues showed what happens at the grain boundaries of one particular alloy of the metals nickel and bismuth that makes it brittle in their paper published in Science. Using advanced electron microscopes, Widom’s collaborators at Lehigh University scrutinized these microscopic grain boundaries at an atomic level. In a "very heroic experimental program" they discovered that when grains met, the bismuth and nickel atoms realigned into lattices to form layered superstructures at the grain boundaries. These superstructures had previously been thought to exist only rarely in some alloys. Finding it at many different boundaries led the team to conclude that these superstructures are probably much more common than many people had thought. 
Owing to high surface to volume ratios and chemical potential, nanoparticles possess unique optical, electrical, and thermal properties, which constitute the basis of novel applications in sensing, catalysis, nanoelectronics, bio-tagging etc. Despite the great advances in the synthesis, the total structure determination of nanoclusters still remains to be a major challenge. Recently Hyung J. Kim and their colleagues have reported the synthesis and crystal structure of a nanocluster composed of 23 silver atoms capped by 8 phosphine and 18 phenylethanethiolate ligands in the journal of Nature Communications.
In the recently published paper in Scientific Reports, Sara A. Majetich and their colleagues have demonstrated the engineering of spin canting across a Magnetic nanoparticles (MNP) via the Dzyaloshinskii-Moriya interaction (DMI). In this paper, they have shown that strong DMI can lead to magnetic frustration within the shell and cause canting of the net particle moment. These results have illuminated how core/shell nanoparticle systems can be engineered for spin canting across the whole of the particle, rather than solely at the surface.
In the recently accepted paper in Physical Review Letter, Hrvoje Petek and his colleagues investigate the coherent electron transfer from an interface state that forms upon chemisorption of Ag nanoclusters onto graphite to a σ symmetry interlayer band of graphite. Interfacial charge transfer is a fundamental process in heterogeneous and plasmonically enhanced catalysis. The charge transfer, however, is thought to be restrained by an interfacial potential barrier, such as a Schottky barrier at a metal-semiconductor interface. With optical excitation, the interfacial charge transfer can also be efficiently completed by coherent dipole coupling between the donor and acceptor bands. In this study Petek and his colleagues advance the time-resolved multidimensional multiphoton photoemission spectroscopy technique to not only pump the donor band and probe the acceptor band, but also directly image the coherent polarization dynamics between them. They discover a direct resonant ... 
QM simulations to identify improved photovoltaic materials
Noa Marom leads a Carnegie Mellon University team in an Argonne Early Science Project with plans to use Aurora, Argonne's exascale supercomputer, to find materials that can increase the efficiency of solar cells. They use machine learning tools extensively in their research and are working with the developers of BerkeleyGW, SISSO, and Dragonfly software to prepare to run on the Aurora system.
According to Marom, “The goal of our research is to find new materials that make photovoltaic solar cells more efficient. The quest for any new materials that can enable new technologies is challenging. The materials we are researching have unique properties that make them suitable for use in solar cells, and these properties are very rare and difficult to find out of the wide array of possible materials. We are trying to accelerate the process of material discovery through computer simulation on high-performance computers (HPC) using sophisticated quantum-mechanical simulation software and machine learning (ML) tools. We are excited that our project has been accepted as one of the projects that will run on the future Aurora supercomputer as part of the Argonne ESP program. Our multi-institution team is currently modifying algorithms and workflows so they will be able to run on Aurora.”
Sangyeop Lee, PhD, assistant professor of mechanical engineering and materials science, received a $500,000 CAREER Award from the National Science Foundation (NSF) for research that would utilize machine learning to model thermal transport in polycrystalline materials. The research seeks to create a computer model that can predict the conductive properties of a material in real life, providing guidance to engineer defects for desired thermal properties.
