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Liu, Juejing

Publications and source records attributed to Liu, Juejing.

12 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Optimizing iodine adsorption in functionalized metal-organic frameworks via an unprecedented positional isomerism strategy

Porous metal–organic frameworks (MOFs) have emerged as highly promising adsorbents for capturing radioiodine, a predominant fission product released during nuclear fuel reprocessing. However, systematic investigations into the correlation between MOF structure and iodine uptake capacity remain scarce. Here, we present a novel approach to enhance the iodine adsorption capacity of MOFs by optimizing linker functionalization. Using ligand-functionalized thorium-based MOFs as a structural platform, we demonstrate that ortho-amino-substitution near the node of the dicarboxylate linker significantly increases iodine adsorption capacity compared to meta-amino-substitution, where the amino groups are directed away from the node. Specifically, ortho-substituted Th-UiO-68-3,3”-(NH 2 ) 2 exhibits higher iodine uptake capacities than the meta-substituted Th-UiO-68-2,2”-(NH 2 ) 2 via both vapor diffusion-based (2.042 vs. 1.087 g/g) and solution-based (0.841 vs. 0.784 g/g) processes. Notably, the I 2 vapor adsorption capacity (2.042 g/g) of Th-UiO-68-3,3”-(NH 2 ) 2 represents the second highest among all reported Th-MOFs. Pair distribution function (PDF) studies reveal that the superior iodine uptake performance of ortho-functionalized MOFs can be attributed to the reduced steric hindrance of the amino groups compared with the meta-substituted variants. Finally, this research highlights how positional isomerism and its subtle alterations can significantly influence host–guest interactions, extending beyond simple structural considerations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Accurate and Data‐Efficient Micro X‐ray Diffraction Phase Identification Using Multitask Learning: Application to Hydrothermal Fluids

Traditional analysis of highly distorted micro X‐ray diffraction (μ‐XRD) patterns from hydrothermal fluid environments is a time‐consuming process, often requiring substantial data preprocessing and labeled experimental data. Herein, the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations is demonstrated. MTL models are trained to identify phase information in μ‐XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models show superior accuracy compared to binary classification convolutional neural networks. Additionally, introducing a tailored cross‐entropy loss function improves MTL model performance. Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieve close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor‐intensive experimental datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Tailoring the Coordination Micro‐Environment in Nanotraps for Efficient Platinum/Palladium Separation

Abstract Recovering platinum group metals from secondary resources is crucial to meet the growing demand for high‐tech applications. Various techniques are explored, and adsorption using porous materials has emerged as a promising technology due to its efficient performance and environmental beingness. However, the challenge lies in effectively recovering and separating individual platinum group metals (PGMs) given their similar chemical properties. Herein, a breakthrough approach is presented by sophisticatedly tailoring the coordination micro‐environment in a series of aminopyridine‐based porous organic polymers, which enables the creation of platinum‐specific nanotraps for efficient separation of binary PGMs (platinum/palladium). The newly synthesized POP‐o2NH 2 ‐Py demonstrates record uptakes and selectivity toward platinum over palladium, with the amino groups adjacent to the pyridine moieties being vital in improving platinum binding performance. Further breakthrough experiments underline its remarkable ability to separate platinum and palladium. Spectroscopic analysis reveals that POP‐o2NH 2 ‐Py offers a more favorable coordination fashion to platinum ions compared to palladium ions owing to the greater interaction between N and Pt 4+ and stronger intramolecular hydrogen bonding between the amino groups and four coordinating chlorines at platinum. These findings underscore the importance of fine‐tuning the coordination micro‐environment of nanotraps through subtle modifications that can greatly enhance the selectivity toward the desired metal ions.

Chemistry↗
DOE OSTI2024

Tailoring the d-band center on Ru 1 Cu single-atom alloy nanotubes for boosting electrochemical non-enzymatic glucose sensing

The development of cost-effective and highly efficient electrocatalysts is critical to help electrochemical non-enzymatic sensors achieve high performance. Here, in this work, a new class of catalyst, Ru single atoms confined on Cu nanotubes as a single-atom alloy (Ru 1 Cu NTs), with a unique electronic structure and property, was developed to construct a novel electrochemical non-enzymatic glucose sensor for the first time. The Ru 1 Cu NTs with a diameter of about 24.0 nm showed a much lower oxidation potential (0.38 V) and 9.0-fold higher response (66.5 μA) current than Cu nanowires (Cu NWs, oxidation potential 0.47 V and current 7.4 μA) for glucose electrocatalysis. Moreover, as an electrochemical non-enzymatic glucose sensor, Ru1Cu NTs not only exhibited twofold higher sensitivity (54.9 μA mM −1 cm −2 ) and wider linear range (0.5–8 mM) than Cu NWs, but also showed a low detection limit (5.0 μM), excellent selectivity, and great stability. According to theoretical calculation results, the outstanding catalytic and sensing performance of Ru 1 Cu NTs could be ascribed to the upshift of the d-band center that helped promote glucose adsorption. This work presents a new avenue for developing highly active catalysts for electrochemical non-enzymatic sensors.

electrochemistry↗
DOE OSTI2023

pnnl/mlxrd

A multi-task neural network for the simultaneous identification of multiple compounds from XRD data obtained in a synchrotron-based hydrothermal fluid system. To enhance the alignment between the training and test datasets, random noise is introduced into the synthetic training dataset, and minor labeled experimental data is incorporated. Additionally, we employ a weighted cross-entropy loss function to address data imbalances and ensure smoother model training.

Li, Ang↗
DOE OSTI2023

Machine learning assisted phase and size-controlled synthesis of iron oxide particles

Synthesis of iron oxides with specific phases and particle sizes is a crucial challenge in various fields, including materials science, energy storage, biomedical applications, environmental science, and earth science. However, despite significant advances in this area, much of the current palette of particle outcomes has been based on time-consuming trial-and-error exploration of synthesis conditions. The present study was designed to explore a very different approach to 1) predict the outcome of synthesis from specified reaction parameters based on using machine learning (ML) techniques, and 2) correlate sets of parameters to obtain products with desired outcomes by a newly designed recommendation algorithm. To achieve this, four ML algorithms were tested, namely random forest, logistic regression, support vector machine, and k-nearest neighbor. Among the models, random forest outperformed the others, attaining 96% and 81% accuracy when predicting the phase and size of iron oxide particles in the test dataset. Surprisingly, the permutation feature importance analysis revealed that volume, which may strongly relate to pressure, was one of the important features, along with precursor concentration, pH, temperature, and time, influencing the phase and size of iron oxide particles during synthesis. To verify the robustness of the random forest models, prediction and experimental results were compared based on 24 randomly generated methods in additive and non-additive systems not included in the datasets. The predictions of product phase and particle size from the models agreed well with the experimental results. Furthermore, a searching and ranking algorithm was developed to recommend potential synthesis parameters for obtaining iron oxide products with the desired phase and particle size from previous studies in the dataset. Furthermore, this study lays the foundation for a closed-loop approach in materials synthesis and preparation, beginning with suggesting potential reaction parameters from the dataset and predicting potential outcomes, followed by conducting experiments and analyses, and ultimately enriching the dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine Learning Automated Analysis of Enormous Synchrotron X-ray Diffraction Datasets

X-ray diffraction (XRD) data analysis can be a time-consuming and laborious task. Deep neural network (DNN) based models trained with synthetic XRD patterns have been proven to be a highly efficient, accurate, and automated method for analyzing common XRD data collected from solid samples in ambient environments. However, it remains unclear whether synthetic XRD-based models can be effective in solving micro(μ)-XRD mapping data for in situ experiments involving liquid phases, which always have lower quality and significant artifacts. In this study, we collected μ-XRD mapping data from a LaCl 3 -calcite hydrothermal fluid system and trained two categories of models to analyze the experimental XRD patterns. Here, the models trained solely with synthetic XRD patterns showed low accuracy (as low as 64%) when solving experimental μ-XRD mapping data. However, the accuracy of the DNN models significantly improved (90% or above) when we trained them with a data set containing both synthetic and a small number of labeled experimental μ-XRD patterns. This study highlights the importance of labeled experimental patterns in training DNN models to solve μ-XRD mapping data from in situ experiments involving liquid phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Boosting Electrochemical Catalysis and Nonenzymatic Sensing Toward Glucose by Single‐Atom Pt Supported on Cu@CuO Core–Shell Nanowires

Abstract It is critical to develop high‐performance electrocatalyst for electrochemical nonenzymatic glucose sensing. In this work, a single‐atom Pt supported on Cu@CuO core–shell nanowires (Pt 1 /Cu@CuO NWs) for electrochemical nonenzymatic glucose sensor is designed. Pt 1 /Cu@CuO NWs exhibit excellent electrocatalytic oxidation toward glucose with 70 mV lower onset potential (0.131 V) and 2.4 times higher response current than Cu NWs. Sensors fabricated using Pt 1 /Cu@CuO NWs also show high sensitivity (852.163 µA mM −1 cm −2 ), low detection limit (3.6 µM), wide linear range (0.01–5.18 µM), excellent selectivity, and great long‐term stability. The outstanding sensing performance of Pt 1 /Cu@CuO NWs, investigated by experiments and density functional theory (DFT) calculations, is attributed to the synergistic effect between Pt single atoms and Cu@CuO core–shell nanowires that generates strong binding energy of glucose on the nanowires. The work provides a new pathway for exploring highly active SACs for electrochemical nonenzymatic glucose sensor.

Chemistry↗
DOE OSTI2022

Energetics of oxidation and formation of uranium mononitride

Uranium mononitride (UN) is an advanced nuclear fuel currently being considered for use in several generation IV fast and thermal neutron spectrum core designs, with additional applications to thermal and electric nuclear propulsion reactors. Here, to better understand the thermal behavior and thermodynamic stability of UN, we investigated the bulk thermal oxidation process and thermochemical reactions, including the enthalpy of oxidation and standard enthalpy of formation, by conducting thermalgravimetric analysis – differential scanning calorimetry coupled with mass spectrometry (TGA-DSC-MS), and high temperature transposed temperature drop and oxide melt drop solution calorimetry. The bulk oxidation of UN (containing a small amount of α-UN 1.5+x ) in air was found to follow a step-wise process characterized by consecutive oxidative reactions UN-UN 1.5+x -UO 2 → UO 2 -UO 3 —N k → UO 3 —N k → UO 3 → U 3 O 8 . TGA results support that the UO 2 – U 2 N 3+x passivating layer delays the onset of rapid bulk oxidation of UN in air up to 662 K. Synchrotron X-ray diffraction (XRD) and extended X-ray absorption fine structure (EXAFS) analyses were performed to characterize UN and its final oxidized product. The standard enthalpy of formation (ΔH° f ) of UN was determined to be –144.4 ± 5.9 kJ/mol·atom, in good agreement with previously determined values from Pt encapsulation and bomb calorimetric experiments. Lastly, a negative linear correlation between ΔH° f and the N/U molar ratio was established based on the thermochemical data obtained in this work and previously reported enthalpies of formations of β-UN 1.5-x and α-UN 1.5+x .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Single-Atom Pt Boosting Electrochemical Nonenzymatic Glucose Sensing on Ni(OH) 2 /N-Doped Graphene

Conventional nanomaterials in electrochemical non-enzymatic sensing face huge challenge due to their complex size-, surface- and composition- dependent catalytic properties and low active site density. In this work, we designed a single-atom Pt supported on Ni(OH) 2 nanoplates/nitrogen-doped graphene (Pt 1 /Ni(OH) 2 /NG) as the first example for constructing a single-atom catalyst based electrochemical non-enzymatic glucose sensor. The resulting Pt 1 /Ni(OH) 2 /NG exhibited a low anode peak potential of 0.48 V and high sensitivity of 220.75 μA mM -1 cm -2 towards glucose, which are 45 mV lower and 12 times higher than those of Ni(OH) 2 , respectively. The catalyst also showed excellent selectivity for several important interferences, short response time of 4.6 s, and high stability over 4 weeks. Experimental and density functional theory (DFT) calculated results reveal that the improved performance of Pt 1 /Ni(OH) 2 /NG could be attributed to stronger binding strength of glucose on single-atom Pt active centers and their surrounding Ni atoms, combined with fast electron transfer ability by the adding of the highly conductive NG. Finally, this research sheds light on the applications of SACs in the field of electrochemical non-enzymatic sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗