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Chen, Junhong

Publications and source records attributed to Chen, Junhong.

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Selective single-atom adsorption for precision separation of lead ions in tap water via capacitive deionization

Capacitive deionization (CDI) offers a cost-effective and low-energy method for selective removal of Pb 2+ from drinking water. Modifying CDI electrode surfaces with functional groups presents a versatile approach to enhancing selective ion adsorption capacity. However, a comprehensive understanding of the selectivity and removal efficiency of Pb 2+ among diverse functional groups remains unexplored. Here, we investigated the effects of different functional groups (-SH, -COOH, and -NH 2 ) attached to the graphene oxide (GO) electrode surfaces on Pb 2+ selectivity and removal efficiency. Surprisingly, GO-COOH demonstrated single-atom adsorption of Pb 2+ , displaying superior removal efficiency and selectivity compared with -SH and -NH 2 , although -SH possesses significant chelation capability for Pb 2+ . Both density functional theory (DFT) calculations and X-ray pair distribution function (PDF) analyses confirmed that Pb 2+ exhibits a theoretically higher affinity to -COOH. Further, this research deepens our understanding of the interactions between functional groups and heavy metal ions, enabling selective and rapid separation of target cations for water purification.

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

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

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

Environmentally sustainable lithium-ion battery cathode binders based on cellulose nanocrystals

Aqueous binders as environmentally sustainable alternatives to conventional polyvinylidene difluoride (PVDF) binders have not yet been successful for cathodes in lithium-ion batteries (LIBs). Here, carboxylic acid functionalized cellulose nanocrystals (CNC-COOHs) have been obtained from Miscanthus × giganteus (M×G) biomass and evaluated as aqueous binders for LIB cathodes.

Cellulose nanocrystal↗
DOE OSTI2023

Data-Driven Discovery of Linear Molecular Probes with Optimal Selective Affinity for PFAS in Water

Approaches to tackle the wide and growing variety of highly persistent per- and polyfluoroalkyl substances (PFAS) are of pressing global need because of their detrimental human health effects, such as cancer, birth defects, and hormone imbalance. Sensitive, selective, and easy-to-use real-time sensors to monitor and detect PFAS and sorbents to extract them are critical to meeting government-mandated environmental concentrations. In this work, we combine all-atom molecular dynamics simulations, enhanced sampling, deep representational learning, and Bayesian optimization to perform high-throughput virtual screening for highly sensitive and selective molecular probes. Our molecular design space consists of 3850 linear hydrocarbon chains with varying degrees of halogenation with and without amine- and phosphine-based headgroups. By employing a data-driven search process, we efficiently explore the molecular design space to optimize the sensitivity to perfluorooctanesulfonic acid (PFOS) as a prototypical PFAS analyte and selectivity relative to a sodium dodecyl sulfate (SDS) interferent. We calculate 504 Gibbs free energies of probe-analyte and probe-interferent interactions and identify probes with PFOS association free energies of up to (-ΔG PFOS ) = 9.8 ± 0.2 kJ/mol and selectivities relative to SDS of (-ΔΔG PFOS–SDS ) = 3.1 ± 1.5 kJ/mol. A C 11 Br 23 P(CH 3 ) 2 probe containing 11 backbone brominated carbons and a tertiary phosphine headgroup possesses the most sensitive binding constant to PFOS within the defined search space of K b PFOS = 177.4 ± 12.7, and a semibrominated probe C 5 H 11 C 7 Br 14 N(CH 3 ) 2 containing 12 backbone carbons and a tertiary amine headgroup possesses the highest selectivity relative to SDS of K b PFOS /K b SDS = 4.6 ± 1.7. A retrospective analysis of our data to extract interpretable design rules reveals that the sensitivity of linear hydrogenated probes increases by approximately 1 kJ/mol per C–C bond. The addition or removal of halogen atoms and amine or phosphine headgroups produces nonmonotonic changes in both sensitivity and selectivity with changes to the sensitivity of up to 2.5 kJ/mol. Finally, this work places empirical limitations on the performance of a wide range of linear probes for PFOS detection and offers a generic strategy for high-throughput computational screening to promote selective and sensitive binding.

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

Probing lead acetate in solution using X-ray diffraction

Here, for the purpose of water purification involving toxic metal sensing and removal, high energy X-ray diffraction experiments have been performed on lead acetate Pb(Ac) 2 solutions, liquid and amorphous glutathione disulfide (GSSG), and their mixtures. The data have been interpreted using Empirical Potential Structure Refinement and pair distribution function analysis. At the highest concentration of 1 M Pb(Ac) 2 in water, the lead molecules are found to cluster and the second shell in the water structure becomes slightly more ordered as the water molecules become compressed. Liquid and amorphous GSSG are found to hydrogen bond primarily via OH-O interactions, while NH-O bonds are much more distorted. Aqueous solutions of Pb(Ac) 2 + GSSG show the closest Pb-O (carboxyl) and Pb-N (amine) bonds both at a distance of 2.5 ± 0.1 Å at a concentration of 1 M Pb(Ac) 2 in water. At 0.75 M Pb(Ac) 2 in water, strong Pb-S bonds are found at a distance of 2.8 ± 0.1 Å. The implications for lead removal using capacitive deionization are discussed.

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

Molecular interactions in short-chain perfluoroalkyl carboxylic acids and aqueous solutions

The presence of short-chain per- and polyfluoroalkyl substances in water poses a major health and environmental challenge. Here, we have performed high-energy small- and wide-angle X-ray scattering measurements on CF 3 [CF 2 ] n COOH (where n = 1, 2, 3 represents the chain length) and their aqueous solutions at 10% mole concentrations to characterize their molecular interactions at the atomic and nanometer length scales. The experimental wide-angle structure factors have been modelled using Empirical Potential Structural Refinement. The oxygen–oxygen partial X-ray pair distribution functions show that the coordination number between the hydroxyl oxygen on the acid and surrounding oxygen water molecules increases significantly with acid chain length, rising from 3.2 for n = 1 to 4.1 for n = 3. The small-angle scattering is dominated by a sharp, high-intensity peak at Q 1 ~ 0.2 Å -1 and a smaller peak at Q 2 = 1.2 Å -1 for n = 3, both of which decrease with decreasing chain length. The Q 2 peak is attributed to groups of adjacent non-bonded acid molecules, and Q 1 has contributions from both correlations between acid molecules and water–water interactions. In all cases, the models show nanoscale aggregation occurs in the form of denser channels of winding hydrogen-bonded chains, approximately 20 water molecules in length, surrounding clusters of acid molecules.

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

Fully Inkjet‐Printed, 2D Materials‐Based Field‐Effect Transistor for Water Sensing

Abstract Despite significant progress in solution‐processing of 2D materials, it remains challenging to reliably print high‐performance semiconducting channels that can be efficiently modulated in a field‐effect transistor (FET). Herein, electrochemically exfoliated MoS 2 nanosheets are inkjet‐printed into ultrathin semiconducting channels, resulting in high on/off current ratios up to 10 3 . The reported printing strategy is reliable and general for thin film channel fabrication even in the presence of the ubiquitous coffee‐ring effect. Statistical modeling analysis on the printed pattern profiles suggests that a spaced parallel printing approach can overcome the coffee‐ring effect during inkjet printing, resulting in uniform 2D flake percolation networks. The uniformity of the printed features allows the MoS 2 channel to be hundreds of micrometers long, which easily accommodates the typical inkjet printing resolution of tens of micrometers, thereby enabling fully printed FETs. As a proof of concept, FET water sensors are demonstrated using printed MoS 2 as the FET channel, and printed graphene as the electrodes and the sensing area. After functionalization of the sensing area, the printed water sensor shows a selective response to Pb 2+ in water down to 2 ppb. This work paves the way for additive nanomanufacturing of FET‐based sensors and related devices using 2D nanomaterials.

36 MATERIALS SCIENCE↗