Soft phonon modes in strongly anharmonic crystals are often neglected in calculations of phonon-related properties. Herein, we experimentally measure the temperature effects on the band gap of cubic SrTiO 3 , and compare with first-principles calculations by accounting for electron−phonon coupling using harmonic and anharmonic phonon modes. The harmonic phonon modes show an increase in the band gap with temperature using either Allen−Heine−Cardona theory or finite-displacement approach, and with semilocal or hybrid exchange-correlation functionals. This finding is in contrast with experimental results that show a decrease in the band gap with temperature. We show that the disagreement can be rectified by using anharmonic phonon modes that modify the contributions not only from the significantly corrected soft modes, but also from the modes that show little correction in frequencies. Our results confirm the importance of soft-phonon modes that are often neglected in the computation of phonon-related properties and particularly in electron−phonon coupling.

Nanostructured superclear, superomniphobic glass fabricated by a statistical machine learning procedure.

The latest digital revolution involves the rise of smart devices composed of sensor hardware and artificial intelligence (AI) software for performing intelligent tasks. Smart sensors have become ubiquitous in our lives with varied applications ranging from voice-enabled home devices (Google Home, Alexa, etc.) to the Industrial Internet of Things (IIoT). This revolution has been fueled by 1) miniaturization of sensing hardware, 2) easy access to cloud and high-performance computing, 3) development of big data storage and analytics technologies, and 4) the latest breakthroughs in machine learning (ML) and AI technologies. The emergence of AI since 2012 and its major breakthroughs can be attributed to the research and development (R&D) in deep learning, a subfield of ML that uses biologically inspired neural networks to perform learning tasks. [1] The performance of conventional ML algorithms depends on the individual selection of specific features, while deep neural networks (DNN) automatically generate features as part of the learning process. Deep learningbased AI technologies are increasingly showing performance

To gain fundamental understanding of the high-temperature optical gas-sensing and light-energy conversion materials, we comparatively investigate the temperature effects on the band gap and optical properties of rutile and anatase TiO 2 experimentally and theoretically. Given that the electronic structures of rutile and anatase are fundamentally different, i.e. direct band gap in rutile and indirect gap in anatase, it is not clear whether these materials exhibit different electronic structure renormalizations with temperature. Using ab initio methods, we show that the electron-phonon interaction is the dominant factor for temperature band gap renormalization compared to the thermal expansion. As a result of different contributions from the acoustic and optical phonons, the band gap is found to widen with temperature up to 300 K, and to narrow at higher temperatures. Our calculations suggest that the band gap is narrowed by about 147 meV and 128 meV at 1000 K for rutile and anatase, respectively. Experimentally, for rutile and anatase TiO 2 thin films we conducted UV-Vis transmission measurements at different temperatures, and analyzed band gaps from the Tauc plots. For both TiO 2 phases, the band gap is found to decrease for temperature above 300 K quantitatively, agreeing with our theoretical results. The temperature effects on the dielectric functions, the refractive index, the extinction coefficient as well as the optical conductivity are also investigated. Rutile and anatase show generally similar optical properties, but differences exist in the long wavelength regime above 600 nm, where we found that the dielectric function of rutile decreases while that of anatase increases with temperature increase.

Understanding the temperature dependence of functional properties in high-temperature gas sensors is vital for applications in combustion environments. Temperature effect on the electronic structure due to electron-phonon coupling is a key property of interest as this influences other responses of sensors. In this work, we assess the impact of temperature on band gap renormalization of pristine and oxygen-vacant LaCrO3−δ perovskite employing Allen–Heine–Cardona theory with first-principles simulations and corroborate with experimental observation. Antiferromagnetic cubic LaCrO3 shows a direct ground-state band gap of 2.62 eV that is reduced by over 1 eV due to the presence of oxygen vacancies, which can form endothermically. We find excellent agreement in temperature-dependent band gap shift in LaCrO3 between theory and an in-house experiment, proving that the theory can adequately predict renormalization on the band gap in a magnetic system. Band gaps in cubic LaCrO3−δ are found to monotonically narrow by 1.13 eV in pristine and by around 0.62 eV in oxygen-vacant structures as temperature increases from 0 to 1500 K. The predicted band gap variations are rationalized using an analytical model. The experimental zero-temperature band gaps are extracted from the model fits that can provide useful insights on the simulated band gaps.

Coupled plasmonic and Drude response of gold-nanoparticle incorporated LSTO demonstrates visible and NIR fiber-based sensing of hydrogen at high-temperature (600–800 °C).

Transparent polymer substrates have recently received increased attention for various flexible optoelectronic devices. Optoelectronic applications such as solar cells and light emitting-diodes would benefit from substrates with both high transparency and high haze, which increase how much light scatters into or out of the underlying photoactive layers. In this letter, we demonstrate a new flexible nanograss plastic substrate that displays the highest combination of transparency and haze in the literature for polyethylene terephthalate (PET). As opposed to other nanostructures that increase haze at the expense of transparency, our nanograss demonstrates the potential to improve both haze and transparency. Furthermore, the monolithic nanograss may be fabricated in a facile scalable maskless reactive ion etching process without the need for additional lithography or synthesis of nanostructures. Our 9 μm height nanograss sample exhibits a transparency and haze of 92.4% and 89.4%, respectively, and our 34 μm height nanograss displays a transparency and haze of 91.0% and 97.1%, respectively. We also performed durability experiments that demonstrate these nanostructured PET substrates are robust from bending and show similar transmission and haze values after 5000 cycles of bending.

Eliminating light reflection from the top glass sheet in optoelectronic applications is often desirable across a broad range of wavelengths and large variety of angles. In this paper, we report on a combined simulation and experimental study of single-layer films, nanowire arrays, and nanocone arrays to meet these antireflection (AR) needs. We demonstrate the application of Bayesian learning to the multiobjective optimization of these structures for broadband and broad angle AR and show the superior performance of Bayesian learning to genetic algorithms for optimization. Our simulations indicate that nanocone structures have the best AR performance of these three structures, and we additionally provide physical insight into the AR performance of different structures. Simulations suggest nanocone arrays are able to achieve a solar integrated normal and 65° incidence angle reflection of 0.15% and 1.25%, respectively. A simple and scalable maskless reactive ion etching process is used to create nanocone structures, and etched samples demonstrate a solar integrated normal and 65° reflection of 0.4% and 4.9%, respectively, at the front interface.