Synthesis of ZnO nanorods for piezoelectric resonators and sensors

Efficiency of the piezoelectric chemisensors may be considerably enhanced by use of zinc oxide nanorods as sensing elements.ZnO nanorod arrays being good piezoelectric materials possess large surface area,which provides extra benefits for chemisorption and photodetection.Highly oriented nanorod arrays are typically prepared onto highly crystalline substrates,whereas the nanorods growth onto metal contacts meets significant technological difficulties.In this paper,we report on carbothermal,electrochemical,and hydrothermal techniques of ZnO nanorod arrays synthesis on metal contacts.The optical and structural properties of the obtained nanorods were studied using scanning electron microscopy,X-ray diffraction(XRD),Raman spectroscopy,and lumi-nescence spectroscopy.A reliable technique was developed for obtaining ohmic contact with the grown nanorods.I-U curves of prepared contact were studied.Carbothermal synthesis made it possible to obtain the most crystallinely perfect,homogeneous,and dense arrays of nanorods and control the concentration of point defects by changing the synthesis parameters over a wide range.The electrochemical synthesis demonstrated excellent results for synthesis of ZnO nanorods on the surface of resonator electrodes.

Platinum single-atom catalyst with self-adjustable valence state for large-current-density acidic water oxidation

The design of active acidic oxygen evolution reaction(OER)catalysts is of paramount importance to achieve efficient large-current-density industrial hydrogen fuel production via water electrolysis.Herein,we develop a Pt-based catalyst with high electrochemical activity for the OER in acidic conditions under a large current.We achieve this by modulating the electronic structure of Pt into a high-valence,electron-accessible Pt1^((2.4+δ)+)(δ=0-0.7)state during the reaction.This electron-accessible Pt1^((2.4+δ)+)single-site catalyst can effectively maintain a large OER current density of 120 mA cm^(-2)for more than 12 h in 0.5 M H_(2)SO_(4) at a low overpotential of 405 mV,and it shows a high mass activity of~3350 A gmetal^(-1)at 10 mA cm^(-2) current density and 232 mV overpotential.Using in situ synchrotron radiation infrared and X-ray absorption spectroscopies,we directly observe in an experiment that a key(*O)-Pt_(1)-C_(2)N_(2) intermediate is produced by the potential-driven structural optimization of square pyramidal Pt_(1)-C_(2)N_(2) moieties;this highly favors the dissociation of H_(2)O over Pt1^(2.4+δ)^(+)sites and prevents over-oxidation and dissolution of the active sites.

Understanding X-ray absorption spectra by means of descriptors and machine learning algorithms

X-ray absorption near-edge structure(XANES)spectra are the fingerprint of the local atomic and electronic structures around the absorbing atom.However,the quantitative analysis of these spectra is not straightforward.Even with the most recent advances in this area,for a given spectrum,it is not clear a priori which structural parameters can be refined and how uncertainties should be estimated.Here,we present an alternative concept for the analysis of XANES spectra,which is based on machine learning algorithms and establishes the relationship between intuitive descriptors of spectra,such as edge position,intensities,positions,and curvatures of minima and maxima on the one hand,and those related to the local atomic and electronic structure which are the coordination numbers,bond distances and angles and oxidation state on the other hand.This approach overcoms the problem of the systematic difference between theoretical and experimental spectra.Furthermore,the numerical relations can be expressed in analytical formulas providing a simple and fast tool to extract structural parameters based on the spectral shape.The methodology was successfully applied to experimental data for the multicomponent Fe:SiO_(2)system and reference iron compounds,demonstrating the high prediction quality for both the theoretical validation sets and experimental data.