Recent Submissions

  • Gaussian Blue Noise

    Ahmed, Abdalla G.M.; Ren, Jing; Wonka, Peter (ACM Transactions on Graphics, Association for Computing Machinery (ACM), 2022-11-30) [Article]
    Among the various approaches for producing point distributions with blue noise spectrum, we argue for an optimization framework using Gaussian kernels. We show that with a wise selection of optimization parameters, this approach attains unprecedented quality, provably surpassing the current state of the art attained by the optimal transport (BNOT) approach. Further, we show that our algorithm scales smoothly and feasibly to high dimensions while maintaining the same quality, realizing unprecedented high-quality high-dimensional blue noise sets. Finally, we show an extension to adaptive sampling.
  • Improving deep learning performance for predicting large-scale geological CO2 sequestration modeling through feature coarsening

    Yan, Bicheng; Harp, Dylan Robert; Chen, Bailian; Pawar, Rajesh J. (Scientific Reports, Springer Science and Business Media LLC, 2022-11-30) [Article]
    Physics-based reservoir simulation for fluid flow in porous media is a numerical simulation method to predict the temporal-spatial patterns of state variables (e.g. pressure p) in porous media, and usually requires prohibitively high computational expense due to its non-linearity and the large number of degrees of freedom (DoF). This work describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3-dimensional(3D) heterogeneous porous media. In particular, we develop an efficient feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by spatial interpolation. We validate the DL approach to predict pressure field against physics-based simulation data for a field-scale 3D geologic CO2 sequestration reservoir model. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening not only decreases the training time by >74% and reduces the memory consumption by >75%, but also maintains temporal error 0.63% on average. Besides, the DL workflow provides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation. The key findings from this research significantly improve the training and prediction efficiency of deep learning model to deal with large-scale heterogeneous reservoir models, and thus it can also be further applied to accelerate workflows of history matching and reservoir optimization for close-loop reservoir management.
  • Enhanced Selectivity in the Electroproduction of H2O2 via F/S Dual-Doping in Metal-Free Nanofibers

    Xiang, Fei; Zhao, Xuhong; Yang, Jian; Li, Ning; Gong, Wenxiao; Liu, Yizhen; Burguete-Lopez, A.; Li, Yulan; Niu, Xiaobin; Fratalocchi, Andrea (Advanced Materials, Wiley, 2022-11-30) [Article]
    Electrocatalytic two-electron oxygen reduction (2e- ORR) to hydrogen peroxide (H2 O2 ) is attracting broad interest in diversified areas including paper manufacturing, wastewater treatment, production of liquid fuels, and public sanitation. Current efforts focus on researching low-cost, large-scale, and sustainable electrocatalysts with high activity and selectivity. Here we engineer large-scale H2 O2 electrocatalysts based on metal-free carbon fibers with a fluorine and sulfur dual-doping strategy. Optimized samples yield with a high onset potential of 0.814 V versus reversible hydrogen electrode (RHE), an almost an ideal 2e- pathway selectivity of 99.1%, outperforming most of the recent reported carbon-based or metal-based electrocatalysts. First principle theoretical computations and experiments demonstrate that the intermolecular charge transfer coupled with electron spin redistribution from fluorine and sulfur dual-doping is the crucial factor contributing to the enhanced performances in 2e- ORR. This work opens the door to the design and implementation of scalable, earth-abundant, highly selective electrocatalysts for H2 O2 production and other catalytic fields of industrial interest.
  • Factors Limiting the Operational Stability of Tin–Lead Perovskite Solar Cells

    Huerta Hernandez, Luis; Lanzetta, Luis Alejandro; Jang, Soyeong; Troughton, Joel; Haque, Mohammed; Baran, Derya (ACS Energy Letters, American Chemical Society (ACS), 2022-11-30) [Article]
    Tin–lead perovskite solar cells (TLPSCs) have emerged as one of the most efficient photovoltaic technologies. However, their stability under operational conditions (ambient air, temperature, bias, and illumination) is lagging behind their sharp efficiency increase, restraining their further development. In this Focus Review, we provide insights into the degradation mechanisms of tin–lead perovskites and summarize the principal factors that currently limit the operational stability of TLPSCs. Specifically, perovskite composition and the device architecture stand out as critical aspects governing their sensitivity toward stressors such as temperature and light. We discuss several strategies to overcome these limitations and emphasize the adoption of standardized methods to quantify the lifetime of a device. We further propose using various characterization techniques to identify possible device failure mechanisms. We expect this Focus Review to assist in the progress toward the development of efficient and stable perovskite devices.
  • Detection and Separation of Faults in Permanent Magnet Synchronous Machines using Hybrid Fault-Signatures

    Ullah, Zia; Im, JunHyuk; Ahmed, Shehab (IEEE, 2022-11-30) [Conference Paper]
    As digitalization in electric motors accelerates, online condition monitoring systems are becoming more popular, allowing unplanned downtime to be detected at its initial stage. Individual faults in motors are effectively diagnosed. However, due to identical signatures, fault separation and/or identification remain a challenge. This study presents the detection and separation of inter-turn short, demagnetization, static eccentricity, bearing, and the combination of these faults in permanent magnet synchronous machines. Hybrid fault signatures are used by monitoring the frequency spectrum of stator current, vibration, and induced voltage in the airgap. A planer-shaped airgap search coil (PASC) is employed to extract the induced voltage of each stator tooth. Faults-related anomalies in the induced-voltage, vibration, and the stator current caused are monitored. Any deviation in either signal is considered as generic fault indicator. Furthermore, specific fault features in all signals are used to classify these faults with improved accuracy. The PASC used in this study can also identify the location of the inter-turn short fault and the severity of demagnetization fault. The proposed method is verified using the finite element method simulation and experiments.
  • Demagnetization Risk Assessment in a Dual Stator Permanent Magnet Vernier Machines

    Ullah, Zia; Siddiqi, Mudassir Raza; Ahmed, Shehab (IEEE, 2022-11-30) [Conference Paper]
    As the topologies of permanent magnet vernier machines (PMVM) is getting more complex such as dual rotor and its variants. The thermal, mechanical, and especially demagnetization concern increasing. In this paper, the demagnetization risk evaluation of three similar topologies of dual stator radial type PMVM is presented. Three recently published topologies: dual winding with rotor-yoke, dual winding without rotor-yoke, and single winding without yoke are selected. This design highly improved the torque density and reduced the overall volume. However, the permanent magnets (PMs) in these topologies are at huge risk of irreversible demagnetization. Furthermore, the overall performance of PM-type machines is incomprehensible without a detailed demagnetization analysis. Therefore, a comprehensive mechanical, thermal, and demagnetization analysis considering various operating points and temperatures is conducted to evaluate the risk of demagnetization in these topologies. Finally, some modification are made to optimize of these designs. All analyses are carried out using finite element analysis and co-simulation in ANSYS maxwell and mechanical.
  • Real-Time Biosensor Platform Based on Novel Sandwich Graphdiyne for Ultrasensitive Detection of Tumor Marker

    Xu, Jing; Liu, Yinbing; Huang, Ke-Jing; Hou, Yang-Yang; Sun, Xiaoxuan; Li, Jiaqiang (Analytical Chemistry, American Chemical Society (ACS), 2022-11-29) [Article]
    Realization of a highly sensitive analysis and sensing platform is important for early-stage tumor diagnosis. In this work, a self-powered biosensor with a novel sandwich graphdiyne (SGDY) combined with an aptamer-specific recognition function was developed to sensitively and accurately detect tumor markers. Results indicated that the detection limits of microRNA (miRNA)-21 and miRNA-141 were 0.15 and 0.30 fM (S/N = 3) in the linear range of 0.05–10000 and 1–10000 fM, respectively. The newly designed platform has great promise for early-stage tumor diagnosis.
  • Cu(II)-Catalyzed, Site Selective Sulfoximination to Indole and Indolines via Dual C–H/N–H Activation

    Kumar, Mohit; Rastogi, Anushka; Raziullah; Ahmad, Ashfaq; Gangwar, Manoj Kumar; Koley, Dipankar (Organic Letters, American Chemical Society (ACS), 2022-11-29) [Article]
    A copper-catalyzed protocol furnishing N-arylated sulfoximines has been developed via dual N-H/C-H activation. Arylalkyl- and less reactive diarylsulfoximines were efficiently coupled with privileged scaffolds like indolines, indoles, and N-Ar-7-azaindoles. Sulfoximines based on medicinally relevant scaffolds (phenothiazine, dibenzothiophene, thioxanthenone) were also well tolerated. Detailed mechanistic studies indicate that the deprotometalation and protodemetalation step is the reversible step.
  • CDAnet: A Physics-Informed Deep Neural Network for Downscaling Fluid Flows

    Hammoud, Mohamad Abed ElRahman; Titi, Edriss S.; Hoteit, Ibrahim; Knio, Omar (Journal of Advances in Modeling Earth Systems, American Geophysical Union (AGU), 2022-11-29) [Article]
    Generating high-resolution flow fields is of paramount importance for various applications in engineering and climate sciences. This is typically achieved by solving the governing dynamical equations on high-resolution meshes, suitably nudged towards available coarse-scale data. To alleviate the computational cost of such downscaling process, we develop a physics-informed deep neural network (PI-DNN) that mimics the mapping of coarse-scale information into their fine-scale counterparts of continuous data assimilation (CDA). Specifically, the PI-DNN is trained within the theoretical framework described by Foias et al. (2014) to generate a surrogate of the theorized determining form map from the coarse-resolution data to the fine-resolution solution. We demonstrate the PI-DNN methodology through application to 2D Rayleigh-Bénard convection, and assess its performance by contrasting its predictions against those obtained by dynamical downscaling using CDA. The analysis suggests that the surrogate is constrained by similar conditions, in terms of spatio-temporal resolution of the input, as the ones required by the theoretical determining form map. The numerical results also suggest that the surrogate’s downscaled fields are of comparable accuracy to those obtained by dynamically downscaling using CDA. Consistent with the analysis of Farhat, Jolly, and Titi (2015), temperature observations are not needed for the PI-DNN to predict the fine-scale velocity, pressure and temperature fields.
  • mpi4py.futures: MPI-based asynchronous task execution for Python

    Rogowski, Marcin; Aseeri, Samar A.; Keyes, David E.; Dalcin, Lisandro (IEEE Transactions on Parallel and Distributed Systems, IEEE, 2022-11-29) [Article]
    We present mpi4py.futures, a lightweight, asynchronous task execution framework targeting the Python programming language and using the Message Passing Interface (MPI) for interprocess communication. mpi4py.futures follows the interface of the concurrent.futures package from the Python standard library and can be used as its drop-in replacement, while allowing applications to scale over multiple compute nodes. We discuss the design, implementation, and feature set of mpi4py.futures and compare its performance to other solutions on both shared and distributed memory architectures. On a shared-memory system, we show mpi4py.futures to consistently outperform Python's concurrent.futures with speedup ratios between 1.4X and 3.7X in throughput (tasks per second) and between 1.9X and 2.9X in bandwidth. On a Cray XC40 system, we compare mpi4py.futures to Dask – a well-known Python parallel computing package. Although we note more varied results, we show mpi4py.futures to outperform Dask in most scenarios.
  • Remote Monitoring of Skin Temperature through a Wristband Employing a Printed VO2 Sensor

    Fatani, Firas; Vaseem, M.; Akhter, Zubair; Bilal, Rana Muhammad; Shamim, Atif (IEEE Sensors Journal, IEEE, 2022-11-29) [Article]
    The need for highly sensitive, environmentally stable, mechanically flexible, and low-cost temperature sensors for on-body measurements has been increasing with the wide adoption of personal Healthcare-Internet-of-Things (H-IoT) devices. Printed electronics (PE) is a good platform for such sensors because it enables the realization of flexible devices through simple and rapid methods at a relatively low cost. However, previously reported printed temperature sensors suffer from poor sensitivity and/or environmental instability. In this paper, we report a custom Tungsten (W)-doped Vanadium Dioxide (VO2) ink-based screen-printed temperature sensor having the highest Temperature-Coefficient-of-Resistance (TCR) of 2.78%∙°C-1 with a resolution of 0.1°C between 30°C and 40°C. To protect it from environmental effects, a fluoropolymer-based passivation layer is added for accurate temperature readings even in 90% relative humidity. The sensor is printed on a flexible substrate and shows minimal deterioration in performance over 1000 bending cycles. For wearability and remote monitoring, the sensor is integrated with a custom Bluetooth Low Energy (BLE) wireless readout in the form of a wristband. The BLE readout comprises an ultra-thin and flexible patch antenna optimized for both BLE bandwidth and human wearability. It demonstrates a minimal SAR value of only 0.068W/kg, making it safe to wear. Despite the antenna’s thin structure (0.004λ), it has a gain of 1.65dBi, enabling an excellent communication range. The proposed wristband is tested on ten volunteers and under daily activities, which shows promising results with a maximum error of 0.16°C with reference to those of a commercial thermometer.
  • Pretreatment of membrane dye wastewater by CoFe-LDH-activated peroxymonosulfate: Performance, degradation pathway, and mechanism.

    Wang, Ziwei; Tan, Yannan; Duan, Xiaoguang; Xie, Yongbing; Jin, Haibo; Liu, Xiaowei; Ma, Lei; Gu, Qiangyang; Wei, Huangzhao (Chemosphere, Elsevier BV, 2022-11-29) [Article]
    When a membrane is used to treat dye wastewater, dye molecules are continually concentrated at the membrane surface over time, resulting in a dramatic decrease in membrane flux. Aside from routine membrane cleaning, the pretreatment of dye wastewater to degrade organic pollutants into tiny molecules is a facile solution to the problem. In this study, the use of layered double hydroxide (LDH) to activate peroxymonosulfate (PMS) for efficient degradation of organic pollutant has been thoroughly investigated. We utilized a simple two-drop co-precipitation process to prepare CoFe-LDH. The transition metal components in CoFe-LDH effectively activate PMS to create oxidative free radicals, and the layered structure of LDH increases the number of active sites, and thereby considerably enhancing the reaction rate. It was found that the reaction process produced non-free and free radicals, including singlet oxygen (1O2), sulfate radicals (SO4•-), and hydroxyl radicals (•OH), with 1O2 being the dominant reactive species. Under the optimal conditions (pH 6.7, PMS dosage 0.2 g/L, catalyst loading 0.1 g/L), the degradation of Acid Red 27 dye in the CoFe-LDH/PMS system reached 96.7% within 15 min at an initial concentration of 200 mg/L. The CoFe-LDH/PMS system also exhibited strong resistance to inorganic ions and pH during the degradation of organic pollutants. This study presents a novel strategy for the synergistic treatment of dye wastewater with free and non-free radicals produced by LDH-activated PMS in a natural environment.
  • An Overview of the Oil+Brine Two-Phase System in the Presence of Carbon Dioxide, Methane, and Their Mixture

    Nair, Arun Kumar Narayanan; Che Ruslan, Mohd Fuad Anwari; Cui, Ronghao; Sun, Shuyu (Industrial & Engineering Chemistry Research, American Chemical Society (ACS), 2022-11-29) [Article]
    An overview of the molecular simulation studies of the oil+brine two-phase system in the presence of CO2, CH4, and their mixture at geological conditions is presented. The simulation results agreed well with the experimental results and the density gradient theory predictions on the basis of the cubic–plus–association equation of state (CPA EoS) (withDebye–Hückel electrostatic term) and the perturbed chain statistical associating fluid theory (PC-SAFT) EoS. The interfacial tension (IFT) of the alkane+H2O system showed almost a linear increase with an increasing number of carbon atoms in the alkane molecule. These IFTs are approximately equal for linear, branched, and cyclic alkanes. Here, the negative surface excess of the alkanes might explain the increase in the IFTs with an increase in the pressure. The surface excesses of the alkanes increased with decreasing temperature. This may explain the decrease of the slopes in the IFT versus pressure plot with a decrease in the temperature. The IFT behavior of the alkane+water+CH4/CO2 system was found to be similar to that observed for the alkane+water system. The addition of CO2 had a more significant influence on the IFT than the addition of CH4. Here, CH4 and CO2 exhibited a positive surface excess. The negative surface excess of the salt ions probably explains the increase in the IFTs of the alkane+brine system with increasing salt content. The solubilities of CH4 and/or CO2 in the H2O-rich phase of the alkane+brine+CH4/CO2 system increased with decreasing salt content (salting-out effect). The IFT of the aromatic hydrocarbon+H2O system is much lower than that of the alkane+H2O system. The surface excess followed the order o-xylene > ethylbenzene > toluene > benzene for the aromatic hydrocarbon+H2O system. This trend has a direct correlation with the aromatic–aromatic interaction.
  • Investigation of soot sensitivity to strain rate in ethylene counterflow soot formation oxidation flames

    Quadarella, Erica; Li, Zepeng; Guo, Junjun; Roberts, William L.; Im, Hong G. (Proceedings of the Combustion Institute, Elsevier BV, 2022-11-29) [Article]
    Soot sensitivity to strain rate is mainly responsible for soot formation intermittence in practical combustion devices. This work provides a fundamental study on soot formation in Soot Formation Oxidation (SFO) counterflow flames at varying strain rates. While the problem has been extensively studied in Soot Formation (SF) configurations, where the dominant process is nucleation, investigations remain scarce in the corresponding SFO cases. In the latter, the high temperatures and strong oxidative environments make the surface reactions prevail over nucleation. The work provides a new dataset for ethylene SFO flames in a wide range of strain rates and sheds light on the main processes concurring in determining soot strain rate sensitivity in such conditions. In particular, the peak of soot volume fraction (SVF) is primarily controlled by surface growth and oxidation. The latter becomes progressively more dominant on the side of the SVF distribution toward the oxidizer nozzle, where the presence of oxidizing agents is significant. The soot mechanism adopted predicts a SVF distribution and sensitivity to strain rate in agreement with experimental data. The latter is found similar to corresponding SF cases, although soot loads in the two configurations differ by almost an order magnitude, and the SVF sensitivity is known to be more accentuated for lower soot loads. A deeper investigation revealed that the nucleation process through dimerizations primarily controls the SVF sensitivity, providing the onset of soot necessary for further growth. Then, the latter tends to reduce SVF sensitivity depending on its impact. PAH sensitivities mostly agree with theoretical observation even though further validations on the kinetic mechanism are needed to improve its predictions in lean conditions. The simplistic yet effective model based on the hybrid method of moments and the employment of a reduced kinetic mechanism makes the approach amenable for turbulent computational fluid dynamic (CFD) simulations.
  • Bismuthene Arrays Harvesting Reversible Plating-Alloying Electrochemistry Toward Robust Lithium Metal Batteries

    Ding, Yifan; Sun, Yingjie; Shi, Zixiong; Yang, Xianzhong; Yu, Xiaoyu; Wang, Xiaojing; Sun, Jingyu (Small Structures, Wiley, 2022-11-29) [Article]
    3D lithiophilic skeletons have attracted enormous attention in homogenizing local current distribution and optimizing metal deposition in the pursuit of robust Li metal anodes. Nonetheless, their practicability is markedly plagued by the cumbersome production routes and mediocre Coulombic efficiency (CE) of Li plating/stripping. Herein, scalable in situ growth of uniform bismuthene arrays over commercial Cu foam via spontaneous galvanic replacement reaction is demonstrated. Exhaustive structural/electrochemical measurements in combination with theoretical calculations collectively disclose the reversible plating-alloying mechanism, wherein the formed Li3Bi alloy interphase aids to lower the Li nucleation overpotential and elevate the CE performance. The thus-designed Li metal electrode sustains a stable cyclic operation at 1 mA cm−2/1 mAh cm−2 for 1600 h. When paired with LiFePO4 and sulfur cathodes, the Li metal batteries enable gratifying rate capability and cycling durability. This straightforward maneuver opens a new frontier in the scalable manufacturing of pragmatic current collectors in an economic fashion.
  • EEG-Based Mental Tasks Recognition via a Deep Learning-Driven Anomaly Detector

    Dairi, Abdelkader; Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying (Diagnostics, MDPI AG, 2022-11-29) [Article]
    This paper introduces an unsupervised deep learning-driven scheme for mental tasks’ recognition using EEG signals. To this end, the Multichannel Wiener filter was first applied to EEG signals as an artifact removal algorithm to achieve robust recognition. Then, a quadratic time-frequency distribution (QTFD) was applied to extract effective time-frequency signal representation of the EEG signals and catch the EEG signals’ spectral variations over time to improve the recognition of mental tasks. The QTFD time-frequency features are employed as input for the proposed deep belief network (DBN)-driven Isolation Forest (iF) scheme to classify the EEG signals. Indeed, a single DBN-based iF detector is constructed based on each class’s training data, with the class’s samples as inliers and all other samples as anomalies (i.e., one-vs.-rest). The DBN is considered to learn pertinent information without assumptions on the data distribution, and the iF scheme is used for data discrimination. This approach is assessed using experimental data comprising five mental tasks from a publicly available database from the Graz University of Technology. Compared to the DBN-based Elliptical Envelope, Local Outlier Factor, and state-of-the-art EEG-based classification methods, the proposed DBN-based iF detector offers superior discrimination performance of mental tasks.
  • Computational Network Analysis of Host GeneticRisk Variants of Severe COVID-19

    Alsaedi, Sakhaa B.; Mineta, Katsuhiko; Gao, Xin; Gojobori, Takashi (Research Square Platform LLC, 2022-11-29) [Preprint]
    Background: Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis of previously identified risk variants and infer the hidden interactions between their molecular networks through disease mapping and the similarity of the molecular functions between constructed networks. Results: We designed a four-stage computational workflow for systematic genetic analysis of the risk variants. We integrated the molecular profiles of the risk factors with associated diseases, then constructed protein-protein interaction networks. We identified 24 protein-protein interaction networks with 939 interactions derived from 109 filtered risk variants in 60 risk genes and 56 proteins. The majority of molecular functions, interactions and pathways are involved in immune responses; several interactions and pathways are related to the metabolic and cardiovascular systems, which could lead to multi-organ complications and dysfunction. Conclusions: This study highlights the importance of analyzing molecular interactions and pathways to understand the heterogeneous susceptibility of the host immune response to SARS-CoV-2. We propose new insights into pathogenicity analysis of infections by including genetic risk information as essential factors to predict future complications during and after infection. This approach may assist more precise clinical decisions and accurate treatment plans to reduce COVID-19 complications.
  • Assessing the potential of solubility trapping in unconfined aquifers for subsurface carbon storage.

    Addassi, Mouadh; Omar, Abdirizak; Hoteit, Hussein; Alafifi, Abdulkader Musa; Arkadakskiy, Serguey; Ahmed, Zeyad T; Kunnummal, Noushad; Gislason, Sigurdur R; Oelkers, Eric H (Scientific reports, Springer Science and Business Media LLC, 2022-11-28) [Article]
    Carbon capture and storage projects need to be greatly accelerated to attenuate the rate and degree of global warming. Due to the large volume of carbon that will need to be stored, it is likely that the bulk of this storage will be in the subsurface via geologic storage. To be effective, subsurface carbon storage needs to limit the potential for CO2 leakage from the reservoir to a minimum. Water-dissolved CO2 injection can aid in this goal. Water-dissolved CO2 tends to be denser than CO2-free water, and its injection leads immediate solubility storage in the subsurface. To assess the feasibility and limits of water-dissolved CO2 injection coupled to subsurface solubility storage, a suite of geochemical modeling calculations based on the TOUGHREACT computer code were performed. The modelled system used in the calculations assumed the injection of 100,000 metric tons of water-dissolved CO2 annually for 100 years into a hydrostatically pressured unreactive porous rock, located at 800 to 2000 m below the surface without the presence of a caprock. This system is representative of an unconfined sedimentary aquifer. Most calculated scenarios suggest that the injection of CO2 charged water leads to the secure storage of injected CO2 so long as the water to CO2 ratio is no less than ~ 24 to 1. The identified exception is when the salinity of the original formation water substantially exceeds the salinity of the CO2-charged injection water. The results of this study indicate that unconfined aquifers, a generally overlooked potential carbon storage host, could provide for the subsurface storage of substantial quantities of CO2.
  • Global Depths for Irregularly Observed Multivariate Functional Data

    Qu, Zhuo; Dai, Wenlin; Genton, Marc G. (arXiv, 2022-11-28) [Preprint]
    Two frameworks for multivariate functional depth based on multivariate depths are introduced in this paper. The first framework is multivariate functional integrated depth, and the second framework involves multivariate functional extremal depth, which is an extension of the extremal depth for univariate functional data. In each framework, global and local multivariate functional depths are proposed. The properties of population multivariate functional depths and consistency of finite sample depths to their population versions are established. In addition, finite sample depths under irregularly observed time grids are estimated. As a by-product, the simplified sparse functional boxplot and simplified intensity sparse functional boxplot are proposed for visualization without data reconstruction. A simulation study demonstrates the advantages of global multivariate functional depths over local multivariate functional depths in outlier detection and running time for big functional data. An application of our frameworks to cyclone tracks data demonstrates the excellent performance of our global multivariate functional depths.
  • Porous Metal Current Collectors for Alkali Metal Batteries

    Chen, Jianyu; Wang, Yizhou; Li, Sijia; Chen, Huanran; Qiao, Xin; Zhao, Jin; Ma, Yanwen; Alshareef, Husam N. (Advanced Science, Wiley, 2022-11-27) [Article]
    Alkali metals (i.e., Li, Na, and K) are promising anode materials for next-generation high-energy-density batteries due to their superior theoretical specific capacities and low electrochemical potentials. However, the uneven current and ion distribution on the anode surface probably induces undesirable dendrite growth, which leads to significant safety hazards and severely hinders the commercialization of alkali metal anodes. A smart and versatile strategy that can accommodate alkali metals into porous metal current collectors (PMCCs) has been well established to resolve the issues as well as to promote the practical applications of alkali metal anodes. Moreover, the proposal of PMCCs can meet the requirement of the dendrite-free battery fabrication industry, while the electrode material loading exactly needs the metal current collector component as well. Here, a systematic survey on advanced PMCCs for Li, Na, and K alkali metal anodes is presented, including their development timeline, categories, fabrication methods, and working mechanism. On this basis, some significant methodology advances to control pore structure, surface area, surface wettability, and mechanical properties are systematically summarized. Further, the existing issues and the development prospects of PMCCs to improve anode performance in alkali metal batteries are discussed.

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