Engineering topics
Wang, Yan
Publications and source records attributed to Wang, Yan.
Unveiling Morphology and Crystallinity Dynamics in Ni x Mn 1– x CO 3 Cathode Precursors through Batch-Mode Coprecipitation
This study delves into the synthesis and control of Ni x Mn 1–x CO 3 , a critical class of Mn-rich, Co-free precursors vital for cathode-oxide materials in energy storage and conversion technologies. Employing batch-mode coprecipitation, we systematically generated samples with varying Ni concentrations (x = 0, 0.1, 0.3, 0.5, 0.7, and 0.9) and conducted a comprehensive analysis of their compositions, crystallinities, transition-metal distributions, and particle morphologies through both experimental and computational methods. A significant variation in particle size and crystallinity was observed, contingent on the Ni content. Further, a pivotal transition emerged at Ni concentrations above x = ~0.5, transforming uniform morphologies, such as spherical, monodisperse, pseudo-single-crystalline particles, into bimodal, polycrystalline structures. Furthermore, the study highlights the role of Ni–ammonia complexes leading to Ni-deficient precipitates and underscores the importance of ammonia concentration in achieving precise Ni content control. This study unveils critical reaction conditions governing Mn-rich precursor properties that are vital for cathode-oxides, emphasizing the need for meticulous synthetic control and offering the potential for practical applications in advanced energy storage and conversion systems.
MedNAS: Multiscale Training-Free Neural Architecture Search for Medical Image Analysis
Deep neural networks have demonstrated impressive results in medical image analysis, but designing suitable architectures for each specific task is expertise dependent and time consuming. Neural architecture search (NAS) offers an effective means of discovering architectures. It has been highly successful in numerous applications, particularly in natural image classification. Yet, medical images possess unique characteristics, such as small regions and a wide variety of lesion sizes, that differentiate them from natural images. Furthermore, most current NAS methods struggle with high computational costs, especially when dealing with high-resolution image datasets. In this article, we present a novel evolutionary NAS method called multiscale training-free neural architecture search (MSTF-NAS) to address these challenges. Specifically, to accommodate the broad range of lesion region sizes in disease diagnosis, we develop a new reduction cell search space that enables the search algorithm to explicitly identify the optimal scale combination for multiscale feature extraction. Further, to overcome the issue of high computational costs, we utilize training-free indicators as performance measures for candidate architectures, which allows us to search for the optimal architecture more efficiently. More specifically, by considering the capability and simplicity of various networks, we formulate a multiobjective optimization problem that involves two training-free indicators and model complexity for candidate architectures. Extensive experiments on a large medical image benchmark and a publicly available breast cancer detection dataset are conducted. The empirical results demonstrate that our MSTF-NAS outperforms both human-designed architectures and current state-of-the-art NAS algorithms on both datasets, indicating the effectiveness of our proposed method.
Facile etching for single crystal cathode materials
A recycling and synthesis of charge material for secondary batteries generates single-crystal charge materials for producing batteries with greater charge cycle longevity. Charge material particles undergo a heating for fusing or enhancing grain boundaries between polycrystalline particles. The resulting, more well-defined grain boundaries are easily etched by a relatively weak mineral acid solution. The acid solution removes material at the grain boundaries to separate secondary particles into primary particles along the grain boundaries. The resulting single crystal (monocrystalline) charge material particles are washed and filtered, and typically re-sintered to accommodate any needed lithium (lithium carbonate), and result in a charge material with larger surface area, higher lithium diffusivity and lower cation ordering.
Prepare Ground States of Highly Frustrated Magnetic Clusters on Quantum Computers
Solving challenging problems in physical, chemical, and materials sciences is one of the most promising applications of quantum utility that can be realized on current noisy hardware, considering (i) the direct map (encoding) from the quantum particles and their interactions to the qubits and their entangling gates and (ii) the rapidly improved quantum hardware and advanced error-mitigation techniques. Understanding quantum spin liquid in frustrated magnetic materials is a longstanding challenge in condensed matter physics and the nature of the ground-state phases is highly debated among researchers. Using IBM quantum computers with superconducting qubits, we implemented a variational quantum eigensolver (VQE) algorithm to prepare the ground states of two 12-site cluster approximations of these highly frustrated magnetic materials. The interaction graphs of the two corresponding Hamiltonians are (a) the six-pointed star graph (a unit cell of the kagome lattice) and (b) the cuboctahedral graph (the kagome on a sphere). These are also two instances of Quantum Max Cut problem. With the VQE based on the Hamiltonian variational ansatz acting on a valence bond solid initial trial state, we prepared the ground states and obtained the exact ground energy on simulator and high accuracy on noisy hardware. The deep ansatz necessary to reach the ground state of the cuboctahedral graph indicates that it is a hard instance of Quantum Max Cut.
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Towards this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. In conclusion, two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage (case study 1) and the remaining useful life prediction of turbofan engines (case study 2).
Regulating Anionic Redox via Mg Substitution in Mn-Rich Layered Oxide Cathodes Enabling High Electrochemical Stability for Sodium-Ion Batteries
Here, with the limited resources and high cost of lithium-ion batteries (LIBs) and the ever-increasing market demands, sodium-ion batteries (SIBs) gain much interest due to their economical sustainability, and similar chemistry and manufacturing processes to LIBs. As cathodes play a vital role in determining the energy density of SIBs, Mn-based layered oxides are promising cathodes due to their low cost, environmental friendliness, and high theoretical capacity. However, the main challenge is structural instability upon cycling at high voltage. Herein, Mg is introduced into the P2-type Na 0.62 Ni 0.25 Mn 0.75 O 2 cathode to enhance electrochemical stability. By combining electrochemical testing and material characterizations, it is found that substituting 10 mol% Mg can effectively alleviate the P2–O2 phase transition, Jahn-Teller distortion, and irreversible oxygen redox. Moreover, structural integrity is greatly improved. These lead to enhanced electrochemical performances. With the optimized sample, a remarkable capacity retention of 92% in the half cell after 100 cycles and 95% in the full cell after 170 cycles can be achieved. Altogether, this work provides an alternative way to stabilize P2-type Mn-based layer oxide cathodes, which in turn, put forward the development of this material for the next-generation SIBs.
Design principles for NASICON super-ionic conductors
Na Super Ionic Conductor (NASICON) materials are an important class of solid-state electrolytes owing to their high ionic conductivity and superior chemical and electrochemical stability. In this paper, we combine first-principles calculations, experimental synthesis and testing, and natural language-driven text-mined historical data on NASICON ionic conductivity to achieve clear insights into how chemical composition influences the Na-ion conductivity. These insights, together with a high-throughput first-principles analysis of the compositional space over which NASICONs are expected to be stable, lead to the successful synthesis and electrochemical investigation of several new NASICONs solid-state conductors. Among these, a high ionic conductivity of 1.2 mS cm ₋1 could be achieved at 25 °C. We find that the ionic conductivity increases with average metal size up to a certain value and that the substitution of PO 4 polyanions by SiO 4 also enhances the ionic conductivity. While optimal ionic conductivity is found near a Na content of 3 per formula unit, the exact optimum depends on other compositional variables. Surprisingly, the Na content enhances the ionic conductivity mostly through its effect on the activation barrier, rather than through the carrier concentration. These deconvoluted design criteria may provide guidelines for the design of optimized NASICON conductors.
Sequencing the Genomes of the First Terrestrial Fungal Lineages: What Have We Learned?
The first genome sequenced of a eukaryotic organism was for Saccharomyces cerevisiae, as reported in 1996, but it was more than 10 years before any of the zygomycete fungi, which are the early-diverging terrestrial fungi currently placed in the phyla Mucoromycota and Zoopagomycota, were sequenced. The genome for Rhizopus delemar was completed in 2008; currently, more than 1000 zygomycete genomes have been sequenced. Genomic data from these early-diverging terrestrial fungi revealed deep phylogenetic separation of the two major clades—primarily plant—associated saprotrophic and mycorrhizal Mucoromycota versus the primarily mycoparasitic or animal-associated parasites and commensals in the Zoopagomycota. Genomic studies provide many valuable insights into how these fungi evolved in response to the challenges of living on land, including adaptations to sensing light and gravity, development of hyphal growth, and co-existence with the first terrestrial plants. Genome sequence data have facilitated studies of genome architecture, including a history of genome duplications and horizontal gene transfer events, distribution and organization of mating type loci, rDNA genes and transposable elements, methylation processes, and genes useful for various industrial applications. Pathogenicity genes and specialized secondary metabolites have also been detected in soil saprobes and pathogenic fungi. Novel endosymbiotic bacteria and viruses have been discovered during several zygomycete genome projects. Overall, genomic information has helped to resolve a plethora of research questions, from the placement of zygomycetes on the evolutionary tree of life and in natural ecosystems, to the applied biotechnological and medical questions.
Data-Driven Control: Theory and Applications
The ushering in of the big-data era, ably supported by exponential advances in computation, has provided new impetus to data-driven control in several engineering sectors. This topic's rapid and deep expansion has precipitated the need to showcase the highlights of data-driven approaches. There has been a rich history of contributions from the control systems community in data-driven control. At the same time, several new concepts and research directions have also been introduced in recent years. Many of these contributions and concepts have started to transition from theory to practical applications. This paper will overview the historical contributions and highlight recent concepts and research directions.
Hybrid Simulation of Proton Cyclotron Waves Upstream of Mars Generated by Pickup Ion Beam Distribution
Linear instability analysis as well as a corresponding two‐dimensional hybrid simulation are performed to examine the excitation of the proton cyclotron waves observed upstream of Mars. The waves are believed to be excited by the pickup ions produced from the ionization of the Martian hydrogen exosphere. And previous statistical analysis of wave observations suggested that the waves are mostly related to pickup ion beam velocity distributions. While earlier linear instability analysis of pickup ion beam distributions has mainly been focused on the parallel unstable modes, our analysis reveals that the maximum growth rate occurs at very oblique propagation. The corresponding hybrid simulation confirms the linear analysis results and further demonstrates that the pickup ions are scattered toward an isotropic shell velocity distribution by the waves excited. Interestingly, the waves at oblique propagation gradually damp out and the system is eventually dominated by waves of quasi‐parallel propagation.
Roll-to-roll solvent-free manufactured electrodes for fast-charging batteries
In response to the growing demand for lithium-ion batteries (LIBs), we demonstrate a solvent-free manufacturing technology that can avoid toxic organic solvents and form unique electrode structures to overcome the bottlenecks in low costs and fast charging. The lower tortuosity achieved by the open pores in the dry-printed (DP) electrode allows for a shorter Li+ diffusion pathway, which leads to better rate performance. The DP pouch cells exhibit higher capacity retention of 78% and 69% at 3C and 4C, respectively, compared with 67% and 52% for the slurry cast (SL) cells at the same rates. Moreover, the coating layer on the surface of active materials prevents the excess side reaction between active materials and electrolytes, which prolongs the cycle life of the DP cells. This manufacturing process is a roll-to-roll system with immense potential to be scaled up, providing a more efficient and economical way for battery manufacturing.
Data assimilation for microstructure evolution: Grain growth in kinetic Monte Carlo
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Zika virus RNA structure controls its unique neurotropism by bipartite binding to Musashi-1
Human RNA binding protein Musashi-1 (MSI1) plays a critical role in neural progenitor cells (NPCs) by binding to various host RNA transcripts. The canonical MSI1 binding site (MBS), A/GU (1-3) AG single-strand motif, is present in many RNA virus genomes, but only Zika virus (ZIKV) genome has been demonstrated to bind MSI1. Herein, we identified the AUAG motif and the AGAA tetraloop in the Xrn1-resistant RNA 2 (xrRNA2) as the canonical and non-canonical MBS, respectively, and both are crucial for ZIKV neurotropism. More importantly, the unique AGNN-type tetraloop is evolutionally conserved, and distinguishes ZIKV from other known viruses with putative MBSs. Integrated structural analysis showed that MSI1 binds to the AUAG motif and AGAA tetraloop of ZIKV in a bipartite fashion. Thus, our results not only identified an unusual viral RNA structure responsible for MSI recognition, but also revealed a role for the highly structured xrRNA in controlling viral neurotropism.
Electrochemistry at Back-Gated, Ultrathin ZnO Electrodes: Field-Effect Modulation of Heterogeneous Electron Transfer Rate Constants by 30× with Enhanced Gate Capacitance
We report steady-state voltammetry of outer-sphere redox species at back-gated ultrathin ZnO working electrodes in order to determine electron transfer rate constants k ET as a function of independently-controlled gate bias, V G . We demonstrate that k ET can be modulated as much as 30-fold by application of V G ≤ 8 V. Key to this demonstration was integrating the ultrathin (5 nm) ZnO on a high dielectric constant (k) insulator, HfO 2 (30 nm), which was grown on a Pd metal gate. The high-k HfO 2 dramatically decreased the required V G values and increased the gate-induced charge in ZnO compared to previous studies. Importantly, the enhanced gating power of the Pd/HfO 2 /ZnO stack meant it was possible to observe a non-monotonic dependence of k ET on V G , which reflects the inherent density of redox acceptor states in solution. Furthermore, this work adds to the growing body of literature demonstrating that electrochemical kinetics (i.e., rate constants and overpotentials) at ultrathin working electrodes can be tuned by V G , independent of the conventional electrochemical working electrode potential.
Unraveling the nature of sulfide ions in hydrometallurgical recycling of NCM622 cathode material
The use of lithium-ion batteries (LIBs) has increased dramatically since its initial inception in the late 20th century. Such a surge in the LIB market and industry has resulted in a huge demand for mineral resources. Besides, the large scale of production will lead to massive amounts of waste batteries in the end. As such, recycling is seen as an end-of-pipe process to resolve sustainability and environmental concerns. The hydrometallurgical recycling is considered to be the most prominent method to recover cathode materials from spent LIBs owing to its high leaching efficiency and low energy costs. A series of procedures including pretreatment of the spent LIBs, acid leaching of the black mass, chemical co-precipitation of the hydroxide precursor, and sintering of the cathode active material need to be operated precisely. In this aspect, the impurities introduced during the process could pose a serious threat to the reaction stability as well as recycling consistency. Here, the impact of sulfide ions on recovered LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622) cathode material with the method of hydrometallurgy is first investigated in detail. This study shows that hydrometallurgical process is not obviously influenced by sulfide impurity. No signs of secondary phase or impurity inclusion are spotted in synthesized cathodes. Under 5 at % sulfide concentration conditions, the obtained NCM622 cathode exhibits a capacity of 159.4 mAh/g after 100 cycles at 1/3C, which is at the same level as virgin benchmark. The difference in rate performance between cathodes with and without additional sulfide is less than 2 % or even smaller. In short, the results indicate a neutral status of sulfide ions in hydrometallurgical recycling.
Co-Processing Agricultural Residues and Wet Organic Waste Can Produce Lower-Cost Carbon-Negative Fuels and Bioplastics
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Recognition of glycoside hydrolase 12 proteins by the immune receptor RXEG1 confers Fusarium head blight resistance in wheat
Summary Fusarium head blight (FHB), caused by Fusarium graminearum , is a devastating disease in wheat ( Triticum aestivum ) that results in substantial yield losses and mycotoxin contamination. Reliable genetic resources for FHB resistance in wheat are lacking. In this study, we characterized glycoside hydrolase 12 (GH12) family proteins secreted by F. graminearum . We established that two GH12 proteins, Fg05851 and Fg11037, have functionally redundant roles in F. graminearum colonization of wheat. Furthermore, we determined that the GH12 proteins Fg05851 and Fg11037 are recognized by the leucine‐rich‐repeat receptor‐like protein RXEG1 in the dicot Nicotiana benthamiana . Heterologous expression of RXEG1 conferred wheat responsiveness to Fg05851 and Fg11037, enhanced wheat resistance to F. graminearum and reduced levels of the mycotoxin deoxynivalenol in wheat grains in an Fg05851/Fg11037‐dependent manner. In the RXEG1 transgenic lines, genes related to pattern‐triggered plant immunity, salicylic acid, jasmonic acid, and anti‐oxidative homeostasis signalling pathways were upregulated during F. graminearum infection. However, the expression of these genes was not significantly changed during infection by the deletion mutant ΔFg05851/Fg11037 , suggesting that the recognition of Fg05851/Fg11037 by RXEG1 triggered plant resistance against FHB. Moreover, introducing RXEG1 into three other different wheat cultivars via crossing also conferred resistance to F. graminearum . Expression of RXEG1 did not have obvious deleterious effects on plant growth and development in wheat. Our study reveals that N. benthamiana RXEG1 remains effective when transferred into wheat, a monocot, which in turn suggests that engineering wheat with interfamily plant immune receptor transgenes is a viable strategy for increasing resistance to FHB.