Jiang, De‐en — Page 2

DOE OSTI2021

Understanding Superatomic Ag Nanohydrides

Abstract Bulk Ag hydrides are extremely challenging to make even at very high pressures, but they may become stable as the particle size shrinks to the nanometer regime. Here, the formation and electronic structure of Ag nanohydrides are investigated from a superatomic perspective by density functional theory. It is found that as the coverage increases, adsorption energy of hydrogen atoms on Ag 38 cluster to form Ag 38 H 2 n nanohydride ( n is from 1 to 15) can be energetically favorable with respect to bare Ag 38 and H 2 . Furthermore, the adsorbed hydrogen atoms contribute their 1s electrons to the superatom electron count and behave as a metal instead of a ligand. The electronic structure of the silver nanohydrides follows the superatomic complex model, leading to magic or relatively more stable compositions such as Ag 38 H 2 , Ag 38 H 20 , and Ag 38 H 30 , which correspond to 40‐electron, 58‐electron, and 68‐electron shell closings, respectively. Angular momentum analyses of the superatomic orbitals suggest a convoluted interaction of geometry, symmetry, and orbital splitting.

He, Xiang↗

DOE OSTI2020

Fast Proton Insertion in Layered H 2 W 2 O 7 via Selective Etching of an Aurivillius Phase

H 2 W 2 O 7 , a metastable material synthesized via selective etching of the Aurivillius-related Bi 2 W 2 O 9 , is shown as an electrode for high power proton-based energy storage. Comprehensive structural characterization is performed to obtain a high-fidelity crystal structure of H 2 W 2 O 7 using an iterative approach that combines X-ray diffraction, neutron pair distribution function, scanning transmission electron microscopy, Raman spectroscopy, and density functional theory modeling. Electrochemical characterization shows a capacity retention of ≈80% at 1000 mV s –1 (1.5-s charge/discharge time) as compared to 1 mV s –1 (≈16-min charge/discharge time) with cyclability for over 100 000 cycles. Energetics from density functional theory calculations indicate that proton storage occurs at the terminal oxygen sites within the hydrated interlayer. Last, optical micrographs collected during in situ Raman spectroscopy show reversible, multicolor electrochromism, with color changes from pale yellow to blue, purple, and last, orange as a function of proton content. These results highlight the use of selective etching of layered perovskites for the synthesis of metastable transition metal oxide materials and the use of H 2 W 2 O 7 as an anode material for proton-based energy storage or electrochromic applications.

25 ENERGY STORAGE↗

DOE OSTI2020

Prediction by Convolutional Neural Networks of CO 2 /N 2 Selectivity in Porous Carbons from N 2 Adsorption Isotherm at 77 K

Abstract Porous carbons are an important class of porous materials with many applications, including gas separation. An N 2 adsorption isotherm at 77 K is the most widely used approach to characterize porosity. Conventionally, textual properties such as surface area and pore volumes are derived from the N 2 adsorption isotherm at 77 K by fitting it to adsorption theory and then correlating it to gas separation performance (uptake and selectivity). Here the N 2 isotherm at 77 K was used directly as input (representing feature descriptors for the porosity) to train convolutional neural networks to predict gas separation performance (using CO 2 /N 2 as a test case) for porous carbons. The porosity space for porous carbons was explored for higher CO 2 /N 2 selectivity. Porous carbons with a bimodal pore‐size distribution of well‐separated mesopores (3–7 nm) and micropores (<2 nm) were found to be most promising. This work will be useful in guiding experimental research of porous carbons with the desired porosity for gas separation and other applications.

Wang, Song↗

DOE OSTI2020

Prediction by Convolutional Neural Networks of CO 2 /N 2 Selectivity in Porous Carbons from N 2 Adsorption Isotherm at 77 K

Abstract Porous carbons are an important class of porous materials with many applications, including gas separation. An N 2 adsorption isotherm at 77 K is the most widely used approach to characterize porosity. Conventionally, textual properties such as surface area and pore volumes are derived from the N 2 adsorption isotherm at 77 K by fitting it to adsorption theory and then correlating it to gas separation performance (uptake and selectivity). Here the N 2 isotherm at 77 K was used directly as input (representing feature descriptors for the porosity) to train convolutional neural networks to predict gas separation performance (using CO 2 /N 2 as a test case) for porous carbons. The porosity space for porous carbons was explored for higher CO 2 /N 2 selectivity. Porous carbons with a bimodal pore‐size distribution of well‐separated mesopores (3–7 nm) and micropores (<2 nm) were found to be most promising. This work will be useful in guiding experimental research of porous carbons with the desired porosity for gas separation and other applications.

Wang, Song↗