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Cheng, Yongqiang

Publications and source records attributed to Cheng, Yongqiang.

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Hydrogen Storage with Aluminum Formate, ALF: Experimental, Computational, and Technoeconomic Studies

Long-duration storage of hydrogen is necessary for coupling renewable H2 with stationary fuel cell power applications. In this presentation, I will discuss how aluminum formate, Al(HCOO)3 (ALF), which adopts an ReO3-type structure, is shown to have remarkable H2 storage performance at non-cryogenic (> 120 K) temperatures and low pressures. The most promising performance of ALF is found between 120 K and 160 K and at 10 bar to 20 bar. The talk will cover and illustrate the H2 adsorption performance of ALF over the 77 K to 296 K temperature range using gas isotherms, in situ neutron powder diffraction, and DFT calculations, as well as technoeconomic analysis (TEA), illustrating ALF’s competitive performance for long-duration storage versus compressed hydrogen and leading metal–organic frameworks. In the TEA, it is shown that ALF’s storage capacity, when combined with a temperature/pressure swing process, has advantages versus compressed H2 at a fraction of the pressure (15 bar versus 350 bar). Given ALF’s performance in the 10 bar to 20 bar regime under moderate cooling, it is particularly promising for use in safe storage systems serving fuel cells, and is currently the only MOF that works in this moderate temperature range/ low pressure regime to be cost competitive with compressed H2 gas for large scale H2 storage.[1]

Chemistry↗
DOE OSTI2024

Linking NH$^+_4$ motion to magnetism in molecular multiferroic (NH 4 ) 2 ⁢[FeCl 5 ⁢(H 2 ⁢O)]: A neutron vibrational spectroscopy study

Here, we present a neutron vibrational spectroscopy study to investigate the influence of NH$^+_4$ motion on the magnetism in [(NH 4 ) 1–x K x ] 2 ⁢[FeCl 5 ⁢(H 2 ⁢O)]. The parent compounds, (NH 4 ) 2 ⁢[FeCl 5 ⁢(H 2 ⁢O)] (x = 0) and K 2 ⁡[FeCl 5 ⁢(H 2 ⁢O)]⁢(x = 1) are isostructural at room temperature, yet displaying drastically different magnetic and multiferroic behavior. K 2 ⁡[FeCl 5 ⁢(H 2 ⁢O)] is nonmultiferroic with type-A collinear antiferromagnetic structure below T N ≈ 14.06 K, whereas (NH 4 ) 2 ⁢[FeCl 5 ⁢(H 2 ⁢O)] is a type-II multiferroic with incommensurate cycloidal spin structure below T FE ≈ 6.8 K. A recent study of the dielectric, structure, and magnetic properties in the mixed [(NH 4 ) 1–x ⁢K x ] 2 ⁢[FeCl 5 ⁢(H 2 ⁢O)] shows that a small amount of potassium substitution to replace NH$^+_4$ transforms the spin structure from incommensurate cycloidal (x ≤ 0.06) into commensurate collinear antiferromagnetic (x ≥ 0.15), indicating NH$^+_4$ is essential to the emergent phenomena observed in this molecular multiferroic compound. Our vibrational spectroscopy study reveals that NH$^+_4$ libration and torsion motion exhibit substantial temperature dependence at low temperatures. The intensity of NH$^+_4$ libration and torsion modes increases slightly at 5 K in comparison with data at 25 K behaving like a magnon, indicating that they are coupled to the magnetism in (NH 4 ) 2 ⁢[FeCl 5 ⁢(H 2 ⁢O)]. Comparing data of x=0, 0.06, 0.09, and 0.15 samples further illustrates that the strength of the increased signal in NH$^+_4$ libration mode is very sensitive to potassium concentration. The signal diminishes quickly with increasing potassium concentration and vanishes in the x = 0.15 sample corresponding to the magnetic structure change for x ≥ 0.15. The results directly link the anomalous behavior in NH$^+_4$ libration motion to the magnetism in [(NH 4 ) 1–x ⁢K x ] 2 ⁢[FeCl 5 ⁢(H 2 ⁢O)], providing new insights into the crucial role NH$^+_4$ plays in the coupled phenomena in (NH 4 ) 2 ⁢[FeCl 5 ⁢(H 2 ⁢O)]. The unique information opens a new door to go through in searching for new multifunctional materials by incorporation of NH 4 via a material-by-design approach.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Low-temperature dechlorination of polyvinyl chloride (PVC) for production of H 2 and carbon materials using liquid metal catalysts

Polyvinyl chloride (PVC) is ubiquitous in everyday life; however, it is not recycled because it degrades uncontrollably into toxic products above 250°C. Therefore, it is of interest to controllably dechlorinate PVC at mild temperatures to generate narrowly distributed carbon materials. We present a catalytic route to dechlorinate PVC (~90% reduction of Cl content) at mild temperature (200°C) to produce gas H 2 (with negligible coproduction of corrosive gas HCl) and carbon materials using Ga as a liquid metal (LM) catalyst. A LM was used to promote intimate contact between PVC and the catalytic sites. During dechlorination of PVC, Cl is sequestrated in the carbonaceous solid product. Later, chlorine is easily removed with an acetone wash at room temperature. The Ga LM catalyst is reusable, outperforms a traditional supported metal catalyst, and successfully converts (untreated) discarded PVC pipe.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Virtual node graph neural network for full phonon prediction

Understanding the structure-property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection. However, substantial challenges remain, including the generalizability of models and prediction of properties with materials-dependent output dimensions. Here we present the virtual node graph neural network to address the challenges. By developing three virtual node approaches, we achieve Γ-phonon spectra and full phonon dispersion prediction from atomic coordinates. We show that, compared with the machine-learning interatomic potentials, our approach achieves orders-of-magnitude-higher efficiency with comparable to better accuracy. This allows us to generate databases for Γ-phonon containing over 146,000 materials and phonon band structures of zeolites. Additionally, our work provides an avenue for rapid and high-quality prediction of phonon band structures enabling materials design with desired phonon properties. The virtual node method also provides a generic method for machine-learning design with a high level of flexibility. In this study, the authors present a virtual node graph neural network to enable the prediction of material properties with variable output dimensions. This method offers fast and accurate predictions of phonon band structures in complex solids.

36 MATERIALS SCIENCE↗
DOE OSTI2024

INSPIRED: Inelastic neutron scattering prediction for instantaneous results and experimental design

Inelastic neutron scattering (INS) has unique advantages in probing how atoms vibrate and how the vibrations propagate and interact. Such dynamic information is crucial in understanding various material properties, from heat capacity, thermal conductivity, phase transitions, and chemical reactions to more exotic quantum behavior. The analysis and interpretation of the INS spectra often start from a model structure of the sample, followed by a series of calculations to obtain the simulated spectra to compare with experiments. The conventional way to perform such calculations usually requires significant time, computing resources, and specialized expertise. Here, we present a new program named INSPIRED (Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design), which enables users to perform rapid INS simulations in several different ways on their personal computers in just a few clicks, with the crystal structure as the only input file. Specifically, the users can choose a pre-trained symmetry-aware neural network (coupled with an autoencoder) to predict the phonon density of states (DOS), 1D S(E) and 2D S(|Q|,E) spectra for any given structure. One can also choose an existing density functional theory (DFT) calculation from a database (containing over 12,000 crystals), and quickly obtain the simulated INS spectra for single crystals and powders. It is also possible to use pre-trained universal machine learning force fields to relax a given crystal structure, calculate the phonon dispersion and DOS, and, subsequently, the INS spectra. All these functions are implemented with a PyQt graphic user interface. Finally, we expect these new tools will benefit broad user communities and significantly improve the efficiency of experiment design, execution, and data analysis for INS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗