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Peng, Jian

Publications and source records attributed to Peng, Jian.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Chemical Bond Covalency in Superionic Halide Solid‐State Electrolytes

Abstract Halide solid‐state electrolytes (SSEs) are promising superionic conductors with high oxidative stability and ionic conductivity, making them attractive for all‐solid‐state lithium‐ion batteries. However, most studies have focused on ion‐stacking structures, overlooking the role of bond characteristics in ionic transport. Here, we investigate bond dynamics and the superionic transition (SIT) in bromide electrolyte, Li 3 InBr 6 , using synchrotron X‐ray techniques and ab initio molecular dynamics (AIMD) simulations. We demonstrate that the SIT in halide SSEs is driven by a thermally induced transition in bonding character (ionic to covalent) rather than a change in crystal phase. AIMD simulations further reveal enhanced Li⁺ diffusion and collective anion motion at elevated temperatures. Expanding our study to Li 3 LnBr 6 (Ln = Gd, Tb, Ho, Tm, and Lu), we confirm the widespread occurrence of SIT in this material class, with Li 3 GdBr 6 exhibiting the highest ionic conductivity (5.2 mS cm −1 at 298 K). More importantly, the ionic‐covalent transition is highly tunable through electrolyte modifications, such as cation/anion substitution and synthesis methods. Our findings provide a new perspective on ionic transport, highlighting the critical role of chemical bond characteristics in halide SSEs.

Chemistry↗
DOE OSTI2024

Multi-catalytic active site biochar-based catalysts for glucose isomerized to fructose: Experiments and density functional theory study

In this study, this work provides an innovative method for preparing different isomerization catalysts by impregnating different proportions of MgCl 2 and AlCl 3 and combining different K compounds on cellulose-derived biochar, followed by pyrolysis. Results show MgO and Al(OH) 3 existing in 4 Mg- 1 Al-C catalyst can obtain better catalytic effect on glucose isomerization than the singe of Al presenting in 0 Mg- 1 Al-C catalyst. Moreover, the synergism effects of the multi-catalytic active sites such as β-, γ -Al(OH) 3 , KCl, MgO, and K 4 H 2 (CO 3 ) 3 in Mg-Al-KHCO 3 -C catalyst can further lead to an increase in glucose isomerization, compared to the 4 Mg- 1 Al-C catalyst. The X-ray diffraction results present that the value of O/Al in Mg-Al-KHCO 3 -C catalyst is as high as 13.38, which provides many unsaturated acidic catalysis sites and benefits the glucose isomerization. Simultaneously, the TPD results reveal that the main active sites (MgO, Al(OH) 3 , and K 4 H 2 (CO 3 ) 3 ) in Mg-Al-KHCO 3 -C catalyst can provide weakly acidic and basic sites and avoid strongly acidic and basic sites to excessively attack the glucose. Based on the DFT analysis, the results indicate that the MgO has a great effect on the ring-opening reaction to form acyclic glucose, while Al(OH) 3+ has a great effect on promoting acyclic glucose hydrogen transfer isomerized to form fructose. Compared to other carbon-based metal catalysts, the prepared Mg-Al-KHCO 3 -C has excellent catalytic performance, which gives a higher fructose yield (38.7%) and selectivity (87.72%) and glucose conversion (44.12%) at 100 °C in 30 min. In this study, we develop a highly efficient Mg-Al-K-biochar catalyst for glucose isomerization and provide an efficient method for cellulose valorization.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Synthesis of Mg–K-biochar bimetallic catalyst and its evaluation of glucose isomerization

Highly efficient isomerization of glucose to fructose is essential for valorizing cellulose fraction of biomass to value-added chemicals. This work provided an innovative method for preparing Mg-biochar and Mg–K-biochar catalysts by impregnating either MgCl 2 alone or in combination with different K compounds (Ding et al. in Bioresour Technol 341:125835, 2021, https://doi.org/10.1016/j.biortech.2021.125835 and KHCO 3 ) on cellulose-derived biochar, followed by hydrothermal carbonization and pyrolysis. Single active substance MgO existing in the 10 Mg–C could give better catalytic effect on glucose isomerization than the synergy of MgO and KCl crystalline material present in 10 Mg–KCl–C. But the catalytic effect of 10 Mg–C was decreased when the basic site of MgO was overloaded. Compared to other carbon-based metal catalysts, 10 Mg–KHCO3–C with 10 wt% MgCl 2 loading had excellent catalytic performance, which gave a higher fructose yield (36.7%) and selectivity (74.54%), and catalyzed excellent glucose conversion (53.99%) at 100 °C in 30 min. Scanning electron microscope–energy dispersive spectrometer and X-Ray diffraction revealed that the distribution of Mg 2+ and K+ in 10 Mg–KHCO 3 –C was uniform and the catalytic active substances (MgO, KCl and K2CO 3 ) were more than 10 Mg–C (only MgO). The synergy effects of MgO and K 2 CO 3 active sites enhanced the pH of reaction system and induced H 2 O ionization to form considerable OH– ions, thus easily realizing a deprotonation of glucose and effectively catalyzing the isomerization of glucose. In this study, we developed a highly efficient Mg–K-biochar bimetallic catalyst for glucose isomerization and provided an efficient method for cellulose valorization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

System and method to fuse multiple sources of optical data to generate a high-resolution, frequent and cloud-/gap-free surface reflectance product

Aspects of the subject disclosure may include, for example, performing, by a processing system, image fusion using two or more groups of images to generate predicted images, wherein each group of the two or more groups has one of a different resolution, a different frequency temporal pattern or a combination thereof than another of the two or more groups. Gap filling can be performed by the processing system to correct images of the two or more groups. Additional embodiments are disclosed.

Guan, Kaiyu↗
DOE OSTI2023

Regulating the properties of activated carbon for supercapacitors: impact of particle size and degree of aromatization of hydrochar

Hydrochar (HC), obtained by the hydrothermal carbonization (HTC) of biomass, is an excellent precursor for preparing activated carbon (AC). However, the effects of the intrinsic properties of HC on the microstructure and electrochemical properties of AC are largely unknown. Here, this study investigates the impact of particle size and the degree of aromatization of in-situ HC on the microstructure and electrochemical properties of AC. Our results show that a large particle size and a high degree of aromatization help protect the HC from overactivation by ZnCl 2 , resulting in an large specific surface area (SSA > 2000 m 2 /g) and high mesopore to micropore volume ratio value ( V mes /V mic > 0.3) of the AC. Electrochemical performance measurements show a maximum specific capacitance of 218 F/g at 0.5 A/g was achieved in all samples where the AC (AC180-10 h) was prepared by activating HC180-10 h with a large size, a high degree of aromatization, and abundant surface oxygen-containing functional groups. After AC180-10 h was assembled into a supercapacitor, the specific capacitance of AC180-10 h still reached 158 F/g at 0.5 A/g, and it showed excellent cycling stability. This study advances the understanding of the impact of HC particle size and degree of aromatization on AC properties, providing new insights into tailoring HC qualities so that improving improve the properties of AC.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A machine learning approach to predict thermal expansion of complex oxides

Although it is of scientific and practical importance, the state-of-the-art of predicting the thermal expansion of oxides over broad temperature and composition ranges by physics-based atomistic simulations is currently limited to qualitative agreements. We present an emerging machine learning (ML) approach to accurately predict the thermal expansion of cubic oxides with a dataset consisting of experimentally measured lattice parameters while using the metal cation polyhedron and temperature as descriptors. High-fidelity ML models that can accurately predict temperature- and composition-dependent lattice parameters of cubic oxides with isotropic thermal expansions have been successfully trained. The ML-predicted thermal expansions of oxides not included in the training dataset have shown good agreement with available experiments. The limitations of the current approach and challenges to go beyond cubic oxides with isotropic thermal expansion are also briefly discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Growth mechanism of glucose-based hydrochar under the effects of acid and temperature regulation

Improving the tailorability of hydrochar synthesis is an effective way to enhance its performance and utilization efficiency. In this study, the growth rate, morphology, and molecular structure of hydrochar were controlled by regulating the pH and temperature of the hydrothermal carbonization process. Growth process analysis indicates that hydrochar has three growth periods: induction, rapid, and stable growth periods. It is mainly controlled by 5-hydroxymethylfurfural (HMF), which is formed by converting glucose and its transformation products. Further, the regulation of acid can significantly shorten the induction period of hydrochar, even under low-temperature conditions (<180 °C), and increase the growth rate of hydrochar. However, the degree of hydrochar adhesion varies: the lower the temperature, the greater the degree of its adhesion. Molecular structural analysis demonstrates that hydrochar mainly consists of furan structural domains and aromatic clusters, and its surface is rich in oxygen-containing functional groups. The degree at which hydrochar was aromatized was improved by increasing the reaction temperature (160–220 °C); whereas the regulation of acid reduced it and increased the content of oxygen-containing functional groups on the hydrochar surface. Based on these results, it is proposed that hydrochar formation has five stages and three growth periods with or without acid regulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Competitive effects of glucan’s main hydrolysates on biochar formation: A combined experiment and density functional theory analysis

We report the complexity of polysaccharide hydrothermal products increases the difficulty of exploring the formation of biochar, limiting the development of biochar. This work clarifies the completive effects of glucan’s main hydrolysates on biochar formation from three aspects: experimental, thermodynamic, and kinetic. The products distribution illustrates that 5-HMF, FA, and LA are mainly involved in the formation of biochar. Biochar mainly includes furan ring, ether group, and ester group by the analysis of magic-angle-spinning nuclear magnetic resonance, X-ray photoelectron spectroscopy, and elemental analysis. Combined experiments and density functional theory analysis, the etherification reaction of 5-HMF itself is most likely to occur and is key to form biochar, followed by the esterification of FA with 5-HMF, and then the etherification of 5-HMF and LA. The further verified experiments also manifest these results. This work will develop a foundation for exploring the complex formation mechanism of cellulose-based biochar.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing

Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to exploit soil hyperspectral reflectance to predict SOC concentration, and the potential performance of airborne and satellite data for predicting surface SOC at large scales remain relatively underknown. Here, this study utilized a continental-scale soil laboratory spectral library (37,540 full-pedon 350–2500 nm reflectance spectra with SOC concentration of 0–780 g·kg –1 across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration. Furthermore, by using the coupled soil-vegetation-atmosphere radiative transfer model, we simulated twelve airborne and spaceborne hyper/multi-spectral remote sensing data from surface bare soil laboratory spectra to evaluate their potential for estimating SOC concentration of surface bare soils. Results show that LSTM achieved best predictive performance of quantifying SOC concentration for the whole data sets (R 2 = 0.96, RMSE = 30.81 g·kg –1 ), mineral soils (SOC ≤ 120 g·kg –1 , R 2 = 0.71, RMSE = 10.60 g·kg –1 ), and organic soils (SOC > 120 g·kg –1 , R 2 = 0.78, RMSE = 62.31 g·kg –1 ). Spectral data preprocessing, particularly the first-order derivative, improved the performance of PLSR, RF, Ridge, KNN, and ANN, but not LSTM or CNN. We found that the SOC models of mineral and organic soils should be distinguished given their distinct spectral signatures. Finally, we identified that the shortwave infrared is vital for airborne and spaceborne hyperspectral sensors to monitor surface SOC. This study highlights the high accuracy of LSTM with hyperspectral/multispectral data to mitigate a certain level of noise (soil moisture <0.4 m 3 ·m –3 , green leaf area < 0.3 m 2 ·m –2 , plant residue <0.4 m 2 ·m –2 ) for quantifying surface SOC concentration. Forthcoming satellite hyperspectral missions like Surface Biology and Geology (SBG) have a high potential for future global soil carbon monitoring, while high-resolution satellite multispectral fusion data can be an alternative.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Abstract Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants ( k p ). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys

Machine learning (ML) can offer many advantages in predicting material properties over traditional materials development methods based solely on limited experimental investigations or physical-based simulations with the capability to reduce development cost, risk, and time. However, so far, limited efforts have been made to predict alloy oxidation kinetics and spallation behavior via ML due to the lack of consistently measured and sufficient experimental data and the inherent complexity in oxidation behavior of multicomponent high-temperature alloys. A previous study reported the ability of ML to predict oxidation kinetics of NiCr-based alloys as a function of alloy composition and operating conditions. Here, the performance of a ML model in predicting rate constants and spallation probability was evaluated in light of the roles of the data distribution of the experimental dataset (data analytics), the alloy composition, the exposure environment and the chosen oxidation approach to extracting kinetic values from the measured mass changes (but using either a simple parabolic law or a statistical cyclic oxidation model). Potential strategies to improve the predictions and enhance the extrapolative capability of the previously trained model will be discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Assessing Marginal Land Availability Based on Land Use Change Information in the Contiguous United States

Utilization of marginal land for growing dedicated bioenergy crops for second-generation biofuels is appealing to avoid conflicts with food production. This study develops a novel framework to quantify marginal land for the Contiguous United States (CONUS) based on a history of satellite-observed land use change (LUC) over the 2008–2015 period. Frequent LUC between crop and noncrop is assumed to be an indicator of economically marginal land; this land is also likely to have a lower opportunity cost of conversion from food crop to bioenergy crop production. We first present an approach to identify cropland in transition using the time series of Cropland Data Layer (CDL) land cover product and determine the amount of land that can be considered marginal with a high degree of confidence vs with uncertainty across the CONUS. We find that the biophysical characteristics of this land and its productivity and environmental vulnerability vary across the land and lie in between that of permanent cropland and permanent natural vegetation/bare areas; this land also has relatively low intrinsic value and agricultural profit but a high financial burden and economic risk. We find that the total area of marginal land with confidence vs with uncertainty is 10.2 and 58.4 million hectares, respectively, and mainly located along the 100th meridian. Only a portion of this marginal land (1.4–2.2 million hectares with confidence and 14.8–19.4 million hectares with uncertainty) is in the rainfed region and not in crop production and, thus, suitable for producing energy crops without diverting land from food crops in 2016. These estimates are much smaller than the estimates obtained by previous studies, which consider all biophysically low-quality land to be marginal without considering economical marginality. The estimate of marginal land for bioenergy crops obtained in this study is an indicator of the availability of economically marginal land that is suitable for bioenergy crop production; whether this land is actually converted to bioenergy crops will depend on the market conditions. Here, we note the inability to conduct field-level validation of cropland in transition and leave it to future advances in technology to ground-truth land use change and its relationship to economically marginal land.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Defect-free-induced Na + disordering in electrode materials

For reaching high-performance of electrode materials, it is generally believed that understanding the structure evolution and heterogeneous alignment effect is the key. Presently, a very simple and universally applicable self-healing method is investigated to prepare defect-free Prussian blue analogs (PBAs) that reach their theoretical capacity as cathode materials for sodium-ion batteries (SIBs). For direct imaging of the local structure and the dynamic process at the atomic scale, we deliver a fast ion-conductive nickel-based PBA that enables rapid Na + extraction/insertion within 3 minutes and a capacity retention of nearly 100% over 4000 cycles. This guest-ion disordered and quasi-zero-strain nonequilibrium solid–solution reaction mechanism provides an effective guarantee for realizing long-cycle life and high-rate capability electrode materials that operate via reversible two-phase transition reaction. Unconventional materials and mechanisms that enable reversible insertion/extraction of ions in low-cost metal–organic frameworks (MOFs) within minutes have implications for fast-charging devices, grid-scale energy storage applications, material discovery, and tailored modification.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Computational thermodynamic study of SiC chemical vapor deposition from MTS-H 2

This study focuses on the computational thermodynamic analysis of the chemical vapor deposition (CVD) of SiC from the methyltrichlorosilane-hydrogen (MTS-H 2 ) using up-to-date thermodynamic databases. High-resolution computation has been performed with the fine intervals of temperature and pressure at the various H 2 /MTS ratios of interest to systematically investigate the deposition condition range (800 to 1600°C, 0 to 26 664 Pa, and H 2 /MTS ratios of 0.1 to 100) to guide experimental exploration. The influence of deposition parameters on the compositions and phase stabilities of the deposit and gas phase pertinent to vapor processing is elucidated. Low pressure and medium temperatures (1000 to 1400°C) are beneficial to reaching a higher SiC deposition efficiency and provide an optimal window for preparing a high-purity (>99 wt.% SiC) deposit. This optimal processing window expands significantly with an increasing H 2 /MTS ratio (<20). These results are supported by a number of previous theoretical and experimental observations. The mass fraction of SiC in deposit is proposed as an additional perspective to understand the discrepancy between thermodynamic calculation and experimental observation of pure CVD SiC at low H 2 /MTS ratios.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Uncertainty Quantification of Machine Learning Predicted Creep Property of Alumina-Forming Austenitic Alloys

The development of machine learning (ML) approaches in materials science offers the opportunity to exploit existing engineering and developmental alloy datasets, such as Oak Ridge National Laboratory (ORNL)’s consistently measured creep-rupture dataset for alumina-forming austenitic (AFA) alloys, to accelerate their further development. As a first step toward achieving ML insights for improved alloy design, the potential sources of uncertainty and their impacts on ML output are examined. It is observed that the selection of algorithms and features as well as data sampling significantly affects the performance of ML models, either positively or negatively. Further, the performance of various ML models in predicting the creep properties of AFA alloys is compared, with further evaluation by assessment of a small set of new developmental AFA alloys that were not part of the training dataset. The present study demonstrates that uncertainty quantification (UQ) is essential in materials science for evaluating the performance of ML algorithms with specifically selected feature sets and obtaining a comprehensive understanding of their limitations and the resultant capability of effective prediction in complex materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product

Remote sensing datasets with both high spatial and high temporal resolution are critical for monitoring and modeling the dynamics of land surfaces. However, no current satellite sensor could simultaneously achieve both high spatial resolution and high revisiting frequency. Therefore, the integration of different sources of satellite data to produce a fusion product has become a popular solution to address this challenge. Many methods have been proposed to generate synthetic images with rich spatial details and high temporal frequency by combining two types of satellite datasets—usually frequent coarse-resolution images (e.g., MODIS) and sparse fine-resolution images (e.g., Landsat). In this paper, we introduce STAIR 2.0, a new fusion method that extends the previous STAIR fusion framework, to fuse three types of satellite datasets, including MODIS, Landsat, and Sentinel-2. In STAIR 2.0, input images are first processed to impute missing-value pixels that are due to clouds or sensor mechanical issues using a gap-filling algorithm. The multiple refined time series are then integrated stepwisely, from coarse- to fine- and high-resolution, ultimately providing a synthetic daily, high-resolution surface reflectance observations. We applied STAIR 2.0 to generate a 10-m, daily, cloud-/gap-free time series that covers the 2017 growing season of Saunders County, Nebraska. Moreover, the framework is generic and can be extended to integrate more types of satellite data sources, further improving the quality of the fusion product. View Full-Text

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Status Report on the Molten Salt Thermodynamic Database (MSTDB) Development (FY20)

The current status of the Molten Salt Thermodynamic Database (MSTDB) is reported. While a series of informational exchange meetings with Moten Salt Reactor (MSR) developers has given the NEAMS program insight into the systems of interest, the current effort is devoted to mining data already available in the literature for continued development of MSTDB. Many relevant systems have not been previously studied. Therefore, once the data from the literature is completely reviewed, curated, and used as inputs for MSTDB, new thermodynamic values will need to be generated using computational and experimental approaches.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗