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Huang, Yu

Publications and source records attributed to Huang, Yu.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Mitigation of hydrogen crossover in liquid alkaline water electrolysers using gas recombination catalysts

The rising demand for hydrogen calls for improvements in the efficiency of liquid alkaline water electrolysers (LAWEs), which can be fulfilled by advanced electrodes or separators. Nevertheless, they also intensify hydrogen crossover and safety concerns, thus mandating efficient mitigation strategies. Here we studied the correlation between cathodes and hydrogen crossover behaviours and mitigated safety risks by designing a gas recombination catalyst (GRC). We attribute the elevated hydrogen crossover associated with platinum-based cathodes to their preferential utilization for the hydrogen evolution reaction that creates elevated hydrogen supersaturation, as evidenced by direct measurements of dissolved hydrogen concentration. Varying the placement of platinum layers relative to the cathode–separator interface also supports this conclusion. The implementation of a GRC reduces hydrogen crossover by 95% without affecting LAWE performance and functions for over 1,000 h at 1 A cm−2. This study provides insights into hydrogen supersaturation and the crossover mechanism, as well as offering a promising pathway to enhance the efficiency and reliability of alkaline water electrolysis.

Liu, Haotian↗
DOE OSTI2026

Complete Phase Transformation of Ir Nanowire Network into Defect-Rich Oxide Catalyst for High-Performance PEM Water Electrolysis

Iridium-based catalysts remain the most reliable option for the oxygen evolution reaction (OER) in proton exchange membrane water electrolyzers (PEMWEs). However, their high cost and limited performance represent critical barriers to the commercialization of this green hydrogen production technology. Herein, we report the creation of a metallic Ir nanowire network (IrNWN), which exhibits superior OER performance through its in-situ transition into an oxide structure with high intrinsic activity. At a low loading of 0.25 mgIr/cm2 in PEMWEs, IrNWN achieved a current density of 3.13 A/cm2 at a cell voltage of 1.8 V, outperforming the commercial Ir-based catalyst and surpassing the Department of Energy (DOE) 2026 technical target. Moreover, the high activity of IrNWN was maintained for 900 hours in a durability test at 2 A/cm2, showing a low degradation rate of 0.042 mV/hour. Structural analysis of the electrochemically oxidized IrNWN revealed the presence of mixed Ir oxidation states and a high density of surface terminal oxygen groups (μ1-O), which contributed to a reduced energy barrier for the rate-determining O-O coupling step.

oxygen evolution reaction↗
DOE OSTI2024

Atomic-scale identification of active sites of oxygen reduction nanocatalysts

Heterogeneous nanocatalysts play a crucial role in both the chemical and energy industries. Despite substantial advancements in theoretical, computational and experimental studies, identifying their active sites remains a major challenge. Here we utilize atomic electron tomography to determine the three-dimensional atomic structure of PtNi and Mo-doped PtNi nanocatalysts for the electrochemical oxygen reduction reaction. We then employ the experimental atomic structures as input to first-principles-trained machine learning to identify the active sites of the nanocatalysts. Through the analysis of the structure–activity relationships, we formulate an equation termed the local environment descriptor, which balances the strain and ligand effects to provide physical and chemical insights into active sites in the oxygen reduction reaction. The ability to determine the three-dimensional atomic structure and chemical composition of realistic nanoparticles, combined with machine learning, could transform our fundamental understanding of the active sites of catalysts and guide the rational design of optimal nanocatalysts.

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

Atomic dynamics of electrified solid–liquid interfaces in liquid-cell TEM

Electrified solid–liquid interfaces (ESLIs) play a key role in various electrochemical processes relevant to energy, biology and geochemistry. The electron and mass transport at the electrified interfaces may result in structural modifications that markedly influence the reaction pathways. For example, electrocatalyst surface restructuring during reactions can substantially affect the catalysis mechanisms and reaction products. Despite its importance, direct probing the atomic dynamics of solid–liquid interfaces under electric biasing is challenging owing to the nature of being buried in liquid electrolytes and the limited spatial resolution of current techniques for in situ imaging through liquids. Here, with our development of advanced polymer electrochemical liquid cells for transmission electron microscopy (TEM), we are able to directly monitor the atomic dynamics of ESLIs during copper (Cu)-catalysed CO 2 electroreduction reactions (CO 2 ERs). Our observation reveals a fluctuating liquid-like amorphous interphase. It undergoes reversible crystalline–amorphous structural transformations and flows along the electrified Cu surface, thus mediating the crystalline Cu surface restructuring and mass loss through the interphase layer. Furthermore, the combination of real-time observation and theoretical calculations unveils an amorphization-mediated restructuring mechanism resulting from charge-activated surface reactions with the electrolyte. Our results open many opportunities to explore the atomic dynamics and its impact in broad systems involving ESLIs by taking advantage of the in situ imaging capability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Platinum Surface Water Orientation Dictates Hydrogen Evolution Reaction Kinetics in Alkaline Media

The fundamental understanding of sluggish hydrogen evolution reaction (HER) kinetics on a platinum (Pt) surface in alkaline media is a topic of considerable debate. Herein, we combine cyclic voltammetry (CV) and electrical transport spectroscopy (ETS) approaches to probe the Pt surface at different pH values and develop molecular-level insights into the pH-dependent HER kinetics in alkaline media. The change in HER Tafel slope from ~110 mV/decade in pH 7–10 to ~53 mV/decade in pH 11–13 suggests considerably enhanced kinetics at higher pH. The ETS studies reveal a similar pH-dependent switch in the ETS conductance signal at around pH 10, suggesting a notable change of surface adsorbates. Fixed-potential calculations and chemical bonding analysis suggest that this switch is attributed to a change in interfacial water orientation, shifting from primarily an O-down configuration below pH 10 to a H-down configuration above pH 10. This reorientation weakens the O–H bond in the interfacial water molecules and modifies the reaction pathway, leading to considerably accelerated HER kinetics at higher pH. Finally, our integrated studies provide an unprecedented molecular-level understanding of the nontrivial pH-dependent HER kinetics in alkaline media.

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

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to MLexperts because of lack of training data and relevant, easy-to-use workflows. Wepresent ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res2) and code(https://leap-stc.github.io/ClimSim)arereleasedopenlytosupport the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

artificial intelligence, machine learning↗
DOE OSTI2023

Amorphous nickel hydroxide shell tailors local chemical environment on platinum surface for alkaline hydrogen evolution reaction

In analogy to natural enzymes, an elaborated design of catalytic systems with a specifically tailored local chemical environment could substantially improve reaction kinetics, effectively combat catalyst poisoning effect and boost catalyst lifetime under unfavourable reaction conditions. Here we report a unique design of ‘Ni(OH) 2 -clothed Pt-tetrapods’ with an amorphous Ni(OH) 2 shell as a water dissociation catalyst and a proton conductive encapsulation layer to isolate the Pt core from bulk alkaline electrolyte while ensuring efficient proton supply to the active Pt sites. This design creates a favourable local chemical environment to result in acidic-like hydrogen evolution reaction kinetics with a lowest Tafel slope of 27 mV per decade and a record-high specific activity and mass activity in alkaline electrolyte. The proton conductive Ni(OH) 2 shell can also effectively reject impurity ions and retard the Oswald ripening, endowing a high tolerance to solution impurities and exceptional long-term durability that is difficult to achieve in the naked Pt catalysts. Furthermore, the markedly improved hydrogen evolution reaction activity and durability in an alkaline medium promise an attractive catalyst material for alkaline water electrolysers and renewable chemical fuel generation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Implicit learning of convective organization explains precipitation stochasticity

Accurate prediction of precipitation intensity is crucial for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is subgrid-scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here, using global storm-resolving simulations and machine learning, we show that, by implicitly learning subgrid organization, we can accurately predict precipitation variability and stochasticity with a low-dimensional set of latent variables. Using a neural network to parameterize coarse-grained precipitation, we find that the overall behavior of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation (R 2 ~ 0.45) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our organization metric, correctly predicting precipitation extremes and spatial variability (R 2 ~ 0.9). The organization metric is implicitly learned by training the algorithm on a high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by subgrid-scale structures. We demonstrate that this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing subgrid-scale convective organization in climate models to better project future changes of water cycle and extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Photocarrier-induced persistent structural polarization in soft-lattice lead halide perovskites

The success of the lead halide perovskites in diverse optoelectronics has motivated considerable interest in their fundamental photocarrier dynamics. Here, in this work, we report the discovery of photocarrier-induced persistent structural polarization and local ferroelectricity in lead halide perovskites. Photoconductance studies of thin-film single-crystal CsPbBr 3 at 10 K reveal long-lasting persistent photoconductance with an ultralong photocarrier lifetime beyond 10 6 s. X-ray diffraction studies reveal that photocarrier-induced structural polarization is present up to a critical freezing temperature. Photocapacitance studies at cryogenic temperatures further demonstrate a systematic local phase transition from linear dielectric to paraelectric and relaxor ferroelectric under increasing illumination. Our theoretical investigations highlight the critical role of photocarrier–phonon coupling and large polaron formation in driving the local relaxor ferroelectric phase transition. Our findings show that this photocarrier-induced persistent structural polarization enables the formation of ferroelectric nanodomains at low temperature, which suppress carrier recombination and offer the possibility of exploring intriguing carrier–phonon interplay and the rich polaron photophysics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

The role of alkali metal cations and platinum-surface hydroxyl in the alkaline hydrogen evolution reaction

The platinum-catalysed hydrogen evolution reaction (HER) generally shows poorer kinetics in alkaline electrolyte and represents a key challenge for alkaline water electrolysis. In the presence of alkali metal cations and hydroxyl anions, the electrode–electrolyte (platinum–water) interface in an alkaline electrolyte is far more complex than that in an acidic electrolyte. Here we combine electrochemical impedance spectroscopy and an electrical transport spectroscopy approach to probe and understand the fundamental role of different cations (Li + , Na + and K + ) in HER kinetics. Our integrated studies suggest that the alkali metal cations play an indirect role in modifying the HER kinetics, with the smaller cations being less destabilizing to the hydroxyl adsorbate (OH ad ) species in the HER potential window, which favours a higher coverage of OH ad on the platinum surface. Here, the surface OH ad species are highly polar and act as both electronically favoured proton acceptors and geometrically favoured proton donors to promote water dissociation in alkaline media, thus boosting the Volmer-step kinetics and the HER activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Determining the hydronium pKα at platinum surfaces and the effect on pH-dependent hydrogen evolution reaction kinetics

Electrocatalytic hydrogen evolution reaction (HER) is critical for green hydrogen generation and exhibits distinct pH-dependent kinetics that have been elusive to understand. A molecular-level understanding of the electrochemical interfaces is essential for developing more efficient electrochemical processes. Here we exploit an exclusively surface-specific electrical transport spectroscopy (ETS) approach to probe the Pt-surface water protonation status and experimentally determine the surface hydronium pK a = 4.3. Quantum mechanics (QM) and reactive dynamics using a reactive force field (ReaxFF) molecular dynamics (RMD) calculations confirm the enrichment of hydroniums (H 3 O + * ) near Pt surface and predict a surface hydronium pK a of 2.5 to 4.4, corroborating the experimental results. Importantly, the observed Pt-surface hydronium pK a correlates well with the pH-dependent HER kinetics, with the protonated surface state at lower pH favoring fast Tafel kinetics with a Tafel slope of 30 mV per decade and the deprotonated surface state at higher pH following Volmer-step limited kinetics with a much higher Tafel slope of 120 mV per decade, offering a robust and precise interpretation of the pH-dependent HER kinetics. These insights may help design improved electrocatalysts for renewable energy conversion.

Zhong, Guangyan↗
DOE OSTI2022

Deep Learning for Subgrid‐Scale Turbulence Modeling in Large‐Eddy Simulations of the Convective Atmospheric Boundary Layer

Abstract In large‐eddy simulations, subgrid‐scale (SGS) processes are parameterized as a function of filtered grid‐scale variables. First‐order, algebraic SGS models are based on the eddy‐viscosity assumption, which does not always hold for turbulence. Here we apply supervised deep neural networks (DNNs) to learn SGS stresses from a set of neighboring coarse‐grained velocity from direct numerical simulations of the convective boundary layer at friction Reynolds numbers Re τ up to 1243 without invoking the eddy‐viscosity assumption. The DNN model was found to produce higher correlation between SGS stresses compared to the Smagorinsky model and the Smagorinsky‐Bardina mixed model in the surface and mixed layers and can be applied to different grid resolutions and various stability conditions ranging from near neutral to very unstable. The DNN model can capture key statistics of turbulence in a posteriori (online) tests when applied to large‐eddy simulations of the atmospheric boundary layer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Functional Redundancy in Soil Microbial Community Based on Metagenomics Across the Globe

Understanding the contribution of soil microbial communities to ecosystem processes is critical for predicting terrestrial ecosystem feedbacks under changing climate. Our current understanding lacks a consistent strategy to formulate the linkage between microbial systems and ecosystem processes due to the presumption of functional redundancy in soil microbes. Here we present a global soil microbial metagenomic analysis to generalize patterns of microbial taxonomic compositions and functional potentials across climate and geochemical gradient. Our analyses show that soil microbial taxonomic composition varies widely in response to climate and soil physicochemical gradients, while microbial functional attributes based on metagenomic gene abundances are redundant. Among 17 climate zones, microbial taxonomic compositions were more distinct than functional potentials, as climate and edaphic properties showed more significant influence on microbial taxonomic compositions than on functional potentials. Microbial taxonomies formed a larger and more complex co-occurrence network with more module structures than functional potentials. Functional network was strongly inter-connected among different categories, whereas taxonomic network was more positively interactive in the same taxonomic groups. This study provides strong evidence to support the hypothesis of functional redundancy in soil microbes, as microbial taxonomic compositions vary to a larger extent than functional potentials based on metagenomic gene abundances in terrestrial ecosystems across the globe.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Lower functional redundancy in “narrow” than “broad” functions in global soil metagenomics

Understanding the relationship between soil microbial taxonomic compositions and functional profiles is essential for predicting ecosystem functions under various environmental disturbances. However, even though microbial communities are sensitive to disturbance, ecosystem functions remain relatively stable, as soil microbes are likely to be functionally redundant. Microbial functional redundancy may be more associated with “broad” functions carried out by a wide range of microbes than with “narrow” functions in which specific microorganisms specialize. Thus, a comprehensive study to evaluate how microbial taxonomic compositions correlate with broad and narrow functional profiles is necessary. Here, we evaluated soil metagenomes worldwide to assess whether functional and taxonomic diversities differ significantly between the five broad and the five narrow functions that we chose. Our results revealed that, compared with the five broad functions, soil microbes capable of performing the five narrow functions were more taxonomically diverse, and thus their functional diversity was more dependent on taxonomic diversity, implying lower levels of functional redundancy in narrow functions. Co-occurrence networks indicated that microorganisms conducting broad functions were positively related, but microbes specializing in narrow functions were interacting mostly negatively. Our study provides strong evidence to support our hypothesis that functional redundancy is significantly different between broad and narrow functions in soil microbes, as the association of functional diversity with taxonomy was greater in the five narrow than in the five broad functions.

54 ENVIRONMENTAL SCIENCES↗