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  1. Free, publicly-accessible full text available February 1, 2023
  2. Abstract Optical transmission and scattering spectroscopic microscopy at the visible and adjacent wavelengths denote one of the most informative and inclusive characterization methods in material research. Unfortunately, restricted by the diffraction limit of light, it cannot resolve the nanoscale variation in light absorption and scattering, diagnostics of the local inhomogeneity in material structure and properties. Moreover, a large quantity of nanomaterials has anisotropic optical properties that are appealing yet hard to characterize through conventional optical methods. There is an increasing demand to extend the optical hyperspectral imaging into the nanometer length scale. In this work, we report a super-resolution hyperspectralmore »imaging technique that uses a nanoscale white light source generated by superfocusing the light from a tungsten-halogen lamp to simultaneously obtain optical transmission and scattering spectroscopic images. A 6-nm spatial resolution in the visible to near-infrared wavelength regime (415–980 nm) is demonstrated on an individual single-walled carbon nanotube (SW-CNT). Both the longitudinal and transverse optical electronic transitions are measured, and the SW-CNT chiral indices can be identified. The band structure modulation in a SW-CNT through strain engineering is mapped.« less
    Free, publicly-accessible full text available December 1, 2022
  3. The rapid increase in both quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First,more »we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development; proper model interpretation; and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.« less
    Free, publicly-accessible full text available August 31, 2022
  4. Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields, E ( t ), that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches: (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain ( i.e. , the Fourier transform of E ( t )), and (2) a cross-correlationmore »neural network approach for directly predicting E ( t ) in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models. From these results, we show that machine learning, particularly deep neural networks, can be employed as cost-effective statistical approaches for designing electromagnetic fields to enable desired transitions in these quantum dynamical systems.« less
  5. The basicity constant, or p K b , is an intrinsic physical property of bases that gives a measure of its proton affinity in macroscopic environments. While the p K b is typically defined in reference to the bulk aqueous phase, several studies have suggested that this value can differ significantly at the air–water interface (which can have significant ramifications for particle surface chemistry and aerosol growth modeling). To provide mechanistic insight into surface proton affinity, we carried out ab initio metadynamics calculations to (1) explore the free-energy profile of dimethylamine and (2) provide reasonable estimates of the p Kmore »b value in different solvent environments. We find that the free-energy profiles obtained with our metadynamics calculations show a dramatic variation, with interfacial aqueous dimethylamine p K b values being significantly lower than in the bulk aqueous environment. Furthermore, our metadynamics calculations indicate that these variations are due to reduced hydrogen bonding at the air–water surface. Taken together, our quantum mechanical metadynamics calculations show that the reactivity of dimethylamine is surprisingly complex, leading to p K b variations that critically depend on the different atomic interactions occurring at the microscopic molecular level.« less
  6. Per- and polyfluoroalkyl substances (PFASs) are synthetic chemicals that are harmful to both the environment and human health. Using self-interaction-corrected Born–Oppenheimer molecular dynamics simulations, we provide the first real-time assessment of PFAS degradation in the presence of excess electrons. In particular, we show that the initial phase of the degradation involves the transformation of an alkane-type C–C bond into an alkene-type CC bond in the PFAS molecule, which is initiated by the trans elimination of fluorine atoms bonded to these adjacent carbon atoms.
  7. Abstract

    Trigonal tellurium (Te) is a chiral semiconductor that lacks both mirror and inversion symmetries, resulting in complex band structures with Weyl crossings and unique spin textures. Detailed time-resolved polarized reflectance spectroscopy is used to investigate its band structure and carrier dynamics. The polarized transient spectra reveal optical transitions between the uppermost spin-splitH4andH5and the degenerateH6valence bands (VB) and the lowest degenerateH6conduction band (CB) as well as a higher energy transition at the L-point. Surprisingly, the degeneracy of theH6CB (a proposed Weyl node) is lifted and the spin-split VB gap is reduced upon photoexcitation before relaxing to equilibrium as the carriersmore »decay. Using ab initio density functional theory (DFT) calculations, we conclude that the dynamic band structure is caused by a photoinduced shear strain in the Te film that breaks the screw symmetry of the crystal. The band-edge anisotropy is also reflected in the hot carrier decay rate, which is a factor of two slower along the c-axis than perpendicular to it. The majority of photoexcited carriers near the band-edge are seen to recombine within 30 ps while higher lying transitions observed near 1.2 eV appear to have substantially longer lifetimes, potentially due to contributions of intervalley processes in the recombination rate. These new findings shed light on the strong correlation between photoinduced carriers and electronic structure in anisotropic crystals, which opens a potential pathway for designing novel Te-based devices that take advantage of the topological structures as well as strong spin-related properties.

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