Controlling electrochemical deposition of lithium sulfide (Li2S) is a major challenge in lithium–sulfur batteries as premature Li2S passivation leads to low sulfur utilization and low rate ...capability. In this work, the solvent's roles in controlling solid Li2S deposition are revealed, and quantitative solvent‐mediated Li2S growth models as guides to solvent selection are developed. It is shown that Li2S electrodeposition is controlled by electrode kinetics, Li2S solubility, and the diffusion of polysulfide/Li2S, which is dictated by solvent's donicity, polarity, and viscosity, respectively. These solvent‐controlled properties are essential factors pertaining to the sulfur utilization, energy efficiency and reversibility of lithium–sulfur batteries. It is further demonstrated that the solvent selection criteria developed in this study are effective in guiding the search for new and more effective electrolytes, providing effective screening and design criteria for computational and experimental electrolyte development for lithium–sulfur batteries.
Quantitative solvent‐mediated Li2S growth models and property–performance relationships of solvents are developed for solvent selection in Li–S batteries. An effective solvent, propionitrile, is identified as a promising alternative solvent based on guidelines developed in this study.
Redox flow batteries (RFBs) have been limited by low energy density and high cost. Here, we employ highly-soluble, inexpensive and reversible polysulfide and iodide species to demonstrate a ...high-energy and low-cost all-liquid polysulfide/iodide redox flow battery (PSIB). In contrast to metal-hybrid or semi-solid approaches that are usually adapted for high-energy RFBs, the all-liquid characteristic of the PSIB is crucial to enable practical scale-up development. Combining the achieved energy density (43.1WhL−1Catholyte+Anolyte) and the inherent low materials cost of sulfur and iodine compared to vanadium, the PSIB system demonstrates a significantly lower materials cost per kilowatt hour ($85.4kWh−1) compared to the state-of-the-art vanadium-based redox flow batteries ($152.0–154.6kWh−1). Operando UV-Visible spectroscopy reveals superior electrochemical reversibility of both electrolytes. With its demonstrated energy density, inherent low material cost and benign chemical natures, the all-liquid PSIB offers a promising solution for high-energy-density and low-cost energy storage applications.
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•The polysulfide/iodide redox flow battery (PSIB) achieved one of the highest energy densities for all-liquid aqueous RFBs (43.1WhL−1Catholyte+Anolyte) with high coulombic efficiency (93–95%) and stable cycle life.•Owing to the high achieved energy density and the inherent low materials cost of sulfur and iodine compared to vanadium, the PSIB system demonstrates a significantly lower materials cost per kilowatt hour ($85.4kWh−1) compared to the state-of-the-art and best reported VRBs ($152.0–154.6kWh−1).•This study provides mechanistic understanding of the newly proposed battery chemistry using operando UV-Visible spectroscopy and reveals superior electrochemical reversibility of polysulfide and iodide redox chemistries.
Redox flow batteries (RFBs) are critical enablers for next‐generation grid‐scale energy‐storage systems, due to their scalability and flexibility in decoupling power and energy. Aqueous RFBs (ARFBs) ...using nonflammable electrolytes are intrinsically safe. However, their development has been limited by their low energy density and high cost. Developing ARFBs with higher energy density, lower cost, and longer lifespan than the current standard is of significant interest to academic and industrial research communities. Here, a critical review of the latest progress on advanced electrolyte material designs of ARFBs is presented, including a fundamental overview of their physicochemical properties, major challenges, and design strategies. Assessment methodologies and metrics for the evaluation of RFB stability are discussed. Finally, future directions for material design to realize practical applications and achieve the commercialization of ARFB energy‐storage systems are highlighted.
Aqueous redox flow batteries (ARFBs) are promising large‐scale energy storage technologies. The latest progress on advanced electrolyte design of ARFBs is reviewed, including a fundamental overview of their physicochemical properties, major challenges, and design strategies. Future directions for material design to realize practical applications and achieve the commercialization of ARFB energy‐storage systems are highlighted.
•The τ-CPHD filter is proposed with accurate extraction of target states and correct distribution of the legacy PHD.•A post-processing step is used to maintain track continuity without affecting the ...filtering process or overburdening the computing resources.•An individual target model and a group model are used in combination to achieve better state estimations.•An efficient initialization process is proposed based on the group motion.•This flexible multitarget filter can seamlessly and efficiently track multiple targets in groups or as individual targets in practical applications.
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In many surveillance applications, target tracking algorithms have to deal with dense group targets in contrast to independently moving and well-separated targets as assumed in most scenarios. An effective strategy to handle such a group of targets is to first track the overall group and then attempt to extract the states of individual targets. Based on the Random Finite Set theory, the τ-Cardinalized Probability Hypothesis Density (τ-CPHD) filter is proposed in this paper as an effective method for group target tracking. This filter accurately extracts target states and represents the distribution of the legacy PHD. Association and track extraction are done in the post-processing step, without affecting the filtering process or overloading the computing resources. Furthermore, the group motion is modeled in conjunction with individual target motion models, with model proportion being updated by the proposed filter. Initialization process is carried out adaptively using the group motion trend through group state fitting. All these characteristics make the new filter flexible and seamlessly applicable to real-world tracking problems with group targets. Simulations demonstrate the superior performance of the proposed filter with individual target and group motion models being considered in combination or separately.
The electrochemical impedance spectrometry (EIS) measurement coupled with equivalent-circuit (EC) model is widely used to monitor the health status of proton exchange membrane fuel cell (PEMFC). ...However, limited studies are available to investigate the impact of measurement uncertainty on the evaluation of EC parameters. To fill this gap, this study aims at establishing a machine learning-based method to improve identification of EC parameters based on EIS measurement for PEMFC health monitoring. This computational method was employed to estimate the uncertainty of EC parameters with regard to oxygen stoichiometry that could be quantified by means of charge transfer resistance and limited current density. Gaussian process classification and combined sampling methods together with theoretical calculation were adopted to generate the synthetic data with an R2 of 0.991 to determine the measurement uncertainty of EC parameters. The effectiveness of this computational approach was validated against high-level noisy experimental data. The result shows that this approach can effectively reduce the variances of EC parameters from over 400% to less than 30% and satisfactorily quantify the measurement uncertainty within 35 s. The introduction of synthetic data allows Gaussian process regression to predict the most reliable EIS spectra in the absence of measured oxygen stoichiometry with the computational time optimized. This study expands our capacity in improving the PEMFC health management via machine learning-based computational methods.
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•Studied EIS measurement uncertainty via machine learning (ML) methods.•Adopted surrogate equivalent circuit model to synthesize EIS data.•Quantified measurement uncertainty with regard to oxygen stoichiometry.•Discussed effect of synthetic data as training data on the ML performance.
The quality of reservoir water is important to the health and wellbeing of human and animals. Eutrophication is one of the most serious problems threatening the safety of reservoir water resource. ...Machine learning (ML) approaches are effective tools to understand and evaluate various environmental processes of concern, such as eutrophication. However, limited studies have compared the performances of different ML models to reveal algal dynamics using time-series data of redundant variables. In this study, the water quality data from two reservoirs in Macao were analyzed by adopting various ML approaches, including stepwise multiple linear regression (LR), principal component (PC)-LR, PC-artificial neuron network (ANN) and genetic algorithm (GA)-ANN-connective weight (CW) models. The influence of water quality parameters on algal growth and proliferation in two reservoirs was systematically investigated. The GA-ANN-CW model demonstrated the best performance in reducing the size of data and interpreting the algal population dynamics data, which displayed higher R-squared, lower mean absolute percentage error and lower root mean squared error values. Moreover, the variable contribution based on ML approaches suggest that water quality parameters, such as silica, phosphorus, nitrogen, and suspended solid have a direct impact on algal metabolisms in two reservoirs’ water systems. This study can expand our capacity in adopting ML models in predicting algal population dynamics based on time-series data of redundant variables.
Graphic Abstract for the machine learning model development and the analysis of important water quality parameters in the study of “Assessing the algal population dynamics using multiple machine learning approaches: Application to Macao Reservoirs”. Display omitted
•Machine learning (ML) models simulate algal population dynamics in Macao reservoirs.•Compare ML predictive performances on the time-series data of redundant variables.•GA-ANN-CW model obtained the best result of R-squared, MAPE and RMSE.•Si, P, N, turbidity and SS directly influent algal metabolisms from ML models.
Here we report a novel lithium–selenium (Li–Se) battery with hybrid electrolytes that permit fast solution-phase Li–Se redox reactions, eliminate polyselenides-shuttling and suppress the formation of ...lithium dendrite. The hybrid electrolytes consist of NASICON-type Li1.5Al0.5Ge1.5 (PO4)3 (LAGP) ceramics sandwiched by tetraethylene glycol dimethyl ether (TEGDME) electrolyte, enabling the use of low-cost and highly-scalable carbon, demonstrating full utilization of the Se cathode (e.g., 677mAhg−1 at 0.1C (100% of theoretical capacity)), superior cycling stability (e.g., 613mAhg−1 over 500 cycles at 0.8C, fading rate=0.008% per cycle), and promising rate capabilities (e.g., 540mAhg−1 at 1.5C). With the hybrid electrolytes, we further demonstrate a high Se loading of 5.9mgcm−2 with a stable discharge capacity of 454mAhg−1 (2.68mAhcm−2). Our work demonstrates that combining solid state electrolyte with liquid glyme electrolyte is key to achieve stable and high-performance Li–Se batteries.
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•Solid/liquid hybrid electrolytes were applied to achieve stable Li–Se batteries.•Polyselenides-shuttling was eliminated and lithium dendrite was suppressed.•Li–Se batteries demonstrated superior electrochemical reversibility and stability.
Metal‐based aqueous redox flow batteries (ARFBs) such as zinc‐based ARFBs have attracted remarkable attention owing to their intrinsic high energy density. However, severe dendrite issues limit their ...efficiency and lifespan. Here an aqueous metal anode operating between Sn(OH)62− (stannate) and metal Sn is presented, providing a reversible four‐electron transfer at −0.921 V vs standard hydrogen electrode. In strong contrast to severe Zn dendrites, the Sn(OH)62−/Sn electrode shows smooth and dendrite‐free morphology, which can be attributed to its intrinsic low‐surface‐energy anisotropy which facilitates isotropic crystal growth of Sn metal. By coupling with iodide/tri‐iodide (I−/I3−), the static Sn–I cell demonstrates a stable cycling for 500 cycles (more than 2 months). In contrast, the state‐of‐the‐art Zn anode suffers from serious dendrites and lasts less than 45 cycles (190 h) in Zn–I cells. A stable continuous flow cycling of Sn–I cell achieves a Sn areal capacity of 73.07 mAh cm−2 at an average discharge voltage of 1.3 V for 350 h. The alkaline Sn electrode demonstrates dendrite‐free morphology and superior performance in cycle life and areal capacity compared to state‐of‐the‐art Zn metal anodes, offering a promising metal anode for high‐energy ARFBs and other metal‐based rechargeable aqueous batteries.
A metal anode based on the Sn(OH)62−/Sn redox couple at −0.921 V vs standard hydrogen electrode is reported. In contrast to severe Zn dendrites, Sn metal prefers isotropic crystal growth without dendrites, leading to superior performance in lifespan and areal capacity in Sn–I cells. The Sn anode represents a promising electrode candidate for metal‐based rechargeable aqueous batteries.
