Real Applications Research Articles

A Multivariate Mixture Regression Model for Constrained Responses

Compositional data are vectors typically representing proportions of a whole, that is, those whose elements are strictly positive and subject to a unit-sum constraint. The increasing number of fields where this type of data arises makes the development of proper statistical tools an important issue. From a regression perspective, whenever the multivariate response is a compositional vector, a proper model that accounts for the unit-sum constraint is the well-established Dirichlet regression model. However, there are significant drawbacks mainly due to the limited flexibility of the Dirichlet distribution. The aim of this contribution is to introduce a new multivariate regression model for constrained responses, that is based on the extended flexible Dirichlet distribution (which is a structured mixture with Dirichlet distributed components). The new model is obtained by adopting a novel reparameterization which allows for, among other things, the presence of suitably designed cluster-specific regression patterns. It is shown to provide considerably greater flexibility and better performance than the standard Dirichlet regression model. In particular, from theoretical analysis, intensive simulation studies in many challenging scenarios, as well as from a real data application, it emerges that the new regression model can handle several issues affecting the Dirichlet regression, such as the presence of outliers, latent groups, multi-modality, and positive correlations.

A new method to perform lithium-ion battery pack fault diagnostics – Part 2: the algorithm performance in real-world scenarios and cell-to-cell transferability

The last decade has witnessed significant progress towards zero-emission electric aviation. Lithium-ion batteries are at the centre of this technological transformation. However, electric flight requires suitable considerations for safety concerns associated with Lithium-ion batteries. Fault diagnostic approaches aimed at validating the safety of batteries before every flight have emerged as an attractive prospect. In this pursuit, a three-paper series proposing a novel fault diagnosis algorithm is presented. The algorithm is capable of diagnosing faults in an aircraft battery using the data collected during charging and was previously validated for a particular cell type under steady charging conditions. In this paper, two extensional aspects of the algorithm are investigated: cell-to-cell transferability and disparate charging conditions. While crucial for practical implementation, these aspects were overlooked in previous literature. Through experiments conducted at module-level, it was revealed that the algorithm could diagnose faults for a different cell type with minimal preliminary cell-level characterisation, thus demonstrating its ease of transferability. Moreover, modifications introduced in the algorithm enabled it to perform fault diagnosis under unsteady charging conditions for an aerospace module despite significant internal temperature gradients. Thus, the successful working of the algorithm in the considered aspects, which are unprecedented in literature, prove its feasibility for real-life application.

Innovative Hierarchical Control of Multiple Microgrids: Cheetah Meets PSO

The expansion of microgrids (MGs) comes from the increasing integration of renewable energy resources (RES) and energy storage systems (ESs) into distribution networks. However, effective integration, coordination, and control of Multiple Microgrids (MMGs) during energy transition from microgrid to grid, and vice versa presents a formidable challenge. This arises from the necessity to ensure smooth operation, coordination, and control of MMGs for stability and efficiency during transitions. The dynamic operation of MMGs due to intermittent renewable energy in microgrids and load variations presents an additional challenge for hierarchical control techniques.This paper provides an Artificial Intelligent (AI)-based control technique and introduces an innovative hybrid optimization technique, denoted as HYCHOPSO, derived from the fusion of Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques, and the quantitative findings of extensive benchmark testing, constitute key aspects of novelty. The strategic choice of this novel optimization technique, coupled with a Proportional-Integral (PI) controller, provides key insights.HYCHOPSO is tested within benchmark functions and reaches its optimal score before 50 iterations as compared with optimization algorithms namely CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO which reaches after approximately 200 iterations. HYCHOPSO consistently exhibits the lowest mean values, indicating its robust convergence capabilities.The proposed control technique results in a significant reduction of the microgrid's Current Total Demand Distortion to 1.1%, meeting acceptable limits for a 600V-bus According to IEEE519-2014 standard, even under nonlinear loads. Additionally, precise regulation of voltage and frequency is achieved with0.19%−+ deviations and a frequency overshoot of 1.9%. Furthermore, optimization contributes to seamless energy transition from microgrid to grid, achieving synchronization in less than 5 milliseconds. This enhances the real application's reliability, flexibility, scalability, and robustness. Furthermore, HYCHOPSO is introduced as an AI-based technique to facilitate control parameter design under dynamic conditions, with substantial simulation cases demonstrating control feasibility

Real Estate Website

Abstract: The motivation to develop Real Estate Web Application comes from my urge to learn Visual Studio .NET 2005 for building the business logic of the application, SQL server 2005 for database designing and using new web technologies like AJAX, Java Script for website designing. The most influential factor for selecting this application is to add some innovative features to the search engine of a Real Estate Website which can make the task of a property buyer easy to search for property listings. The most significant feature in these websites is the interactive search criterion which lets the buyer specify their requirements to get the correct set of records from the database. The search tool should be strong enough to include all the required features which a buyer may desire. Also, each search requires a post back call to the database to retrieve some set of records but making the website AJAX enabled prevents the annoying post backs. Thus, each call refreshes just the data grid to display the listings rather the whole web page. Some other new features like drag and drop tool to save the listings, retrieving related results for each search, etc make this application more feature rich.

Multi-functional and actively tunable terahertz metamaterial absorber based on graphene and vanadium dioxide composite structure

In this paper, a fabrication friendly terahertz (THz) metamaterial hybrid absorber comprised of square-shaped graphene and U-shaped vanadium dioxide is proposed and numerically analyzed. This device can be tuned dynamically to function as a wide-broadband, reduced-broadband, multi-band, or perfect single-band absorber. Switchable qualities are achieved through simultaneous tuning of the electrical properties of graphene and the phase transition properties of vanadium dioxide. The theory behind the absorption is explored using the distribution of the electric field and the theory of impedance matching. It is clear from the simulation results that the suggested structure can perform as a wide-broadband absorber for the transverse electric (TE) wave during the insulating phase of vanadium dioxide. In this case, graphene’s Fermi energy is configured to 0.7 eV, and it has a 0.1 ps relaxation time. Moreover, only changing the relaxation time to 0.5 ps enables the identical structure to perform as a multi-band absorber. On the contrary, the designed structure exhibits a reduced broadband absorption for the TE wave when the vanadium dioxide is thermally tuned to a metallic state and graphene’s Fermi energy is configured to 0 eV with a 0.1 ps relaxation time. Moreover, by inducing the metallic state in vanadium dioxide and adjusting the Fermi energy of graphene to 0.3 eV with a 0.1 ps relaxation time, the structure performs as a single-band perfect absorber. The main advantage of this work is that the proposed absorber device can be operated as a single-band, multi-band, or broadband absorber with two different bandwidths by applying external voltage and changing temperature. Additionally, for both the TE and transverse magnetic (TM) waves, the structure retains a high level of absorption until 40° of incident angle. Finally, due to its multi-functional property, simple geometric structure, and actively adjustable function, the presented structure has possible real-life applications in the terahertz frequency range.

Taillefer—A Tool for Sensitivity Analysis and Uncertainty Propagation Studies for Steady-State Thermal-Hydraulic Simulations of Involute Fuel Element Research Reactors

Taillefer is a versatile Python tool for carrying out Sensitivity Analysis (SA) and uncertainty propagation (UP) studies based on Monte Carlo sampling. Developed with the primary goal of investigating sensitivities and uncertainties of steady-state thermal-hydraulic (SSTH) safety parameters of the high-performance research reactors Forschungs Neutronenquelle Heinz Maier-Leibnitz (FRM II) in Garching, Germany, and the Réacteur à Haut Flux (RHF) in Grenoble, France, it can also be used for a large variety of other modeling problems. The work presented here aims to explain the underlying mathematical background of SA and UP studies with Taillefer and to show some steps to verify these routines. Furthermore, a real-life application example is provided that demonstrates Taillefer’s use in SSTH analysis of the RHF. For this purpose, Taillefer is coupled to the external thermal-hydraulic software PLTEMP/ANL, which is one of the codes used at FRM II and RHF to access SSTH performance and safety parameters. Determining these crucial quantities is part of identifying possible low-enriched uranium (LEU) core designs that are suitable to replace the currently used highly enriched uranium fuels of the two reactors, supporting global nonproliferation efforts. Taillefer is a powerful tool in these conversion studies, as it increases the reliability of the LEU safety parameters by providing information about sensitivities and uncertainties in addition to the nominal values predicted by the thermal-hydraulic software.

Computational Strategies in Drug Discovery: Leveraging Subtractive Genomic Analysis for Target Identification.

The utilization of In-silico subtractive genomic analysis has emerged as an important and essential method in modern drug discovery and development since it significantly improves the process of identifying and validating potential targets for discovering novel therapeutic compounds to treat severe infections caused by Antimicrobial-resistant (AMR) - pathogenic species. This review provides a complete overview of the methodology, advantages, disadvantages, and prospects, associated with subtractive genomic research in the context of drug discovery and development. The initial phase of analysis encompasses the retrieval of data, which serves as a foundation for the subsequent data mining process in Phase 1. After data mining, Phase 2 utilizes subtractive channels for the target's non-homology and essentiality analysis. Phase 3 of the study aims to provide a comprehensive understanding of prospective targets by their qualitative characterization. Further, Phase 4 of the study emphasizes on conducting structure-based analyses, which involves the determination, refinement, evaluation, and validation of three-dimensional structures of the target proteins, along with their active site prediction and selection of the novel therapeutic compounds against that active site on the obtained targets through virtual screening and docking studies by utilizing various databases and servers. The therapeutic compounds obtained can be then validated by in vitro and in vivo testing, thereby establishing a connection between the computational predictions and real applications.

Introduction of Hybrid Additive Manufacturing for Producing Multi-Material Artificial Organs for Education and In Vitro Testing

The evolution of 3D printing has ushered in accessibility and cost-effectiveness, spanning various industries including biomedical engineering, education, and microfluidics. In biomedical engineering, it encompasses bioprinting tissues, producing prosthetics, porous metal orthopedic implants, and facilitating educational models. Hybrid Additive Manufacturing approaches and, more specifically, the integration of Fused Deposition Modeling (FDM) with bio-inkjet printing offers the advantages of improved accuracy, structural support, and controlled geometry, yet challenges persist in cell survival, interaction, and nutrient delivery within printed structures. The goal of this study was to develop and present a low-cost way to produce physical phantoms of human organs that could be used for research and training, bridging the gap between the use of highly detailed computational phantoms and real-life clinical applications. To this purpose, this study utilized anonymized clinical Computed Tomography (CT) data to create a liver physical model using the Creality Ender-3 printer. Polylactic Acid (PLA), Polyvinyl Alcohol (PVA), and light-bodied silicone (Polysiloxane) materials were employed for printing the liver including its veins and arteries. In brief, PLA was used to create a mold of a liver to be filled with biocompatible light-bodied silicone. Molds of the veins and arteries were printed using PVA and then inserted in the liver model to create empty channel. In addition, the PVA was then washed out by the final product using warm water. Despite minor imperfections due to the printer’s limitations, the final product imitates the computational model accurately enough. Precision adjustments in the design phase compensated for this variation. The proposed novel low-cost 3D printing methodology successfully produced an anatomically accurate liver physical model, presenting promising applications in medical education, research, and surgical planning. Notably, its implications extend to medical training, personalized medicine, and organ transplantation. The technology’s potential includes injection training for medical professionals, personalized anthropomorphic phantoms for radiation therapy, and the future prospect of creating functional living organs for organ transplantation, albeit requiring significant interdisciplinary collaboration and financial investment. This technique, while showcasing immense potential in biomedical applications, requires further advancements and interdisciplinary cooperation for its optimal utilization in revolutionizing medical science and benefiting patient healthcare.

Cross-domain Fault Diagnosis Network Based on Attributes and Features Transfer with Dual Classiffer under Limited and Unbalanced Datasets

Abstract Deep learning-based methods have shown great success in fault diagnosis due to their powerful feature extraction and non-linear fftting capabilities. Meanwhile, their remarkable performance is accompanied by constant operating conditions and sufffcient monitoring data. However, in real engineering environments, variable working conditions and limited and unbalanced data are common, which can widen the gap between fault diagnosis methods and real industrial applications. In this paper, we proposed a cross-domain fault diagnosis network based on a dual classiffer (CFDNet) with input being limited and unbalanced data to learn attributes and features for unsupervised domain adaptation (UDA). We found that the diagnostic performance is commonly bounded by the underlying knowledge, especially feature extraction from original data. Therefore, we designed a new feature encoder with features and relations, i.e., using a convolutional neural network and graph convolutional network, which improves extraction efffciency while retaining valuable information. Then, we discovered that enforced feature transfer can lead to negative transfer. To address this, we present a feature and attribute transfer framework, which not only achieves features transfer but also enables attributes transfer. Moreover, we observed that the limited and unbalanced datasets can cause label bias and biased training in the model. Hence, we designed dual classiffers to improve the probability of high-conffdence ffnal prediction by synthesizing diagnostic results. Comprehensive experiments conducted on three case studies demonstrate the effectiveness and superiority of our method for cross-domain fault diagnosis under limited and unbalanced datasets, which outperforms state-of-the-art (SOTA) methods in this study.

Speech Emotion Recognition Using a Multi-Time-Scale Approach to Feature Aggregation and an Ensemble of SVM Classifiers

Due to its relevant real-life applications, the recognition of emotions from speech signals constitutes a popular research topic. In the traditional methods applied for speech emotion recognition, audio features are typically aggregated using a fixed-duration time window, potentially discarding information conveyed by speech at various signal durations. By contrast, in the proposed method, audio features are aggregated simultaneously using time windows of different lengths (a multi-time-scale approach), hence, potentially better utilizing information carried at phonemic, syllabic, and prosodic levels compared to the traditional approach. A genetic algorithm is employed to optimize the feature extraction procedure. The features aggregated at different time windows are subsequently classified by an ensemble of support vector machine (SVM) classifiers. To enhance the generalization property of the method, a data augmentation technique based on pitch shifting and time stretching is applied. According to the obtained results, the developed method outperforms the traditional one for the selected datasets, demonstrating the benefits of using a multi-time-scale approach to feature aggregation.

Phase Engineering and Dispersion Stabilization of Cobalt toward Enhanced Hydrogen Evolution.

Phase engineering is promising to increase the intrinsic activity of the catalyst toward hydrogen evolution reaction (HER). However, the polymorphism interface is unstable due to the presence of metastable phases. Herein, phase engineering and dispersion stabilization are applied simultaneously to boost the HER activity of cobalt without sacrificing the stability. A fast and facile approach (plasma cathodic electro deposition) is developed to prepare cobalt film with a hetero-phase structure. The polymorphs of cobalt are realized through reduced stacking fault energy due to the doping of Mo, and the high temperature treatment resulted from the plasma discharge. Meanwhile, homogeneously dispersed oxide/carbide nanoparticles are produced from the reaction of plasma-induced oxygen/carbon atoms with electro-deposited metal. The existence of rich polymorphism interface and oxide/carbide help to facilitate H2 production by the tuning of electronic structure and the increase of active sites. Furthermore, oxide/carbide dispersoid effectively prevents the phase transition through a pinning effect on the grain boundary. As-prepared Co-hybrid/CoO_MoC exhibits both high HER activity and robust stability (44mV at 10mA cm-2, Tafel slope of 53.2mV dec-1, no degradation after 100 h test). The work reported here provides an alternate approach to the design of advanced HER catalysts for real application.

Graph neural network based abnormal perception information reconstruction and robust autonomous navigation

Purpose This paper aims to develop a robust navigation enhancement framework to handle one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents. Design/methodology/approach In this paper, the main idea is to fully exploit the consistent features among spatio-temporal data and thus detect the anomalies and build residual channels to reconstruct the abnormal information. The authors first develop an anomaly detection algorithm, then followed by a corresponding disturbed information reconstruction network which has strong robustness to address both the nature disturbances and external attacks. Finally, the authors introduce a fully end-to-end resilient navigation performance enhancement framework to improve the driving performance of existing self-driving models under attacks and disturbances. Findings Comparison results on CARLA platform and real experiments demonstrate strong resilience of the authors’ approach which enhances the navigation performance under disturbances and attacks. Originality/value Reliable and resilient navigation performance under various nature disturbances and even external attacks is one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents. The information reconstruction approach provides a resilient navigation performance enhancement method for existing self-driving models.

Perceptions, awareness, and implementation surrounding the assessment of frailty amongst vascular surgeons in Italy: Results from a nationwide cross-sectional survey.

We conducted this survey to gain insight into the real-life application and perceptions regarding the importance of peri-operative frailty assessment amongst vascular surgeons in Italy. Italian vascular surgeons were invited to participate in the survey using the list provided by the Italian Society for Vascular and Endovascular Surgery (1050 invited participants). A dedicated link to the survey was emailed through Google Forms, and reminders were automatically sent on a bi-weekly basis for a total of 8weeks before stopping data collection. The survey was completed by 225 respondents, thereby yielding an overall 21.5% response rate. While the vast majority of respondents stated they were aware of the meaning of frailty (93%) and agreed that its assessment was clinically relevant for patients undergoing vascular surgery (99%), only 44% of surveyed surgeons reported that they used a specific tool for peri-operative frailty assessment. However, most respondents indicated that routine evaluation of frailty was not performed at their institution (87%). The main limitations were identified as being the lack of confidence in choosing the best tool, followed by lack of awareness, lack of skilled operators, and lack of time. Our study showed that whilst most vascular surgeons in Italy are aware of the importance of frailty in affecting surgical outcomes across various interventions in the elective and non-elective settings, there is poor implementation of formal frailty assessment.

One-parametric class of smoothing functions for (WSOCCP) and its Jacobian consistency

In this paper, we are interested in the study of weighted second-order cone complementarity problem (WSOCCP) and the smoothing function associated with it. A weighted second-order cone complementarity problem is to find a pair of vectors ( x , y ) that belong to the intersection of a manifold and a second-order cone such that the product of the vectors in a certain algebra, x ∘ y equals a given weight vector w. It is obvious to see that if w is zero, we get a second-order cone complementarity problem (SOCCP). Thus, (WSOCCP) provides the essential framework for a broader class of problems arising from real applications (science and engineering) more conveniently. This paper demonstrates the Jacobian consistency of the one-parametric class of smoothing functions. Furthermore, we provide an estimation of the distance between the subgradient of the one-parametric class of the weighted SOC complementarity functions and the gradient of its associated smoothing function. This estimation facilitates to adjust an appropriate parameter in smoothing methods.

Synthesis of polyaniline-doped magnetic iron foam photo catalyst (PANI@mIF) for integrated adsorptive photocatalytic degradation of nicosulfuron

ABSTRACT Nicosulfuron have low volatility and slow biodegradation rate, leading to its long persistency in soil and crops, which further upraised the risk of contamination of nearby water and severe damage to the crop rotation as may be its precursors are more harmful than nicosulfuron itself. In the present work, we performed a degradation study for the efficient removal of nicosulfuron from soil and water using Polyaniline-doped magnetic iron foam (PANI@mIF). The Photo catalyst was synthesised by doping of polyaniline on magnetic iron foam at 70°C and applied for the efficient photocatalytic degradation of nicosulfuron. PANI@mIF was then characterised through FTIR, SEM, EDX and XRD. The degradation of nicosulfuron was investigated under UV radiations and the resulted data were examined by fitting isotherms models. The Langmair model is well fitted to the adsorption data with R2 of 0.925, which demonstrates monolayer adsorption of nicosulfuron on the surface of PANI@mIF. The maximum adsorption capacity and percent degradation of 87% was achieved with PANI@mIF. Comparatively, good adsorption capacity of PANI@mIF is attributed to a large number of H bonding and electrostatic interactions. Kinetic study indicates the adsorption of nicosulfuron by PANI@mIF followed 2nd order kinetics with R2 of 0.958.The negative values of Gibbs free energy (−18294.3, 19543.1,-7530.4, −7307.7 and −7969.8), enthalpy (−1.97 × 10−4) and entropy (−3.54 × 10−3) indicate spontaneous and exothermic nature of the process. The reusable nature of PANI@mIF with 5 batches reflected its inventive photo catalytic behaviour for real sample applications and waste water treatment.

Rechargeable Zn‐H2O hydrolysis battery for hydrogen storage and production

AbstractReactive metals hydrolysis offers significant advantages for hydrogen storage and production. However, the regeneration of common reactive metals (e.g., Mg, Al, etc.) is energy‐intensive and produces unwanted byproducts such as CO2 and Cl2. Herein, we employ Zn as a reactive mediator that can be easily regenerated by electrolysis of ZnO in an alkaline solution with a Faradaic efficiency of >99.9 %. H2 is produced in the same electrolyte by constructing a Zn‐H2O hydrolysis battery consisting of a Zn anode and a Raney‐Ni cathode to unlock the Zn‐H2O reaction. The entire two‐step water splitting reaction with a net energy efficiency of 70.4 % at 80 °C and 50 mA cm−2. Additionally, the Zn‐H2O system can be charged using renewable energy to produce H2 on demand and runs for 600 cycles only sacrificing 3.76 % energy efficiency. DFT calculations reveal that the desorption of H* on Raney‐Ni (−0.30 eV) is closer to zero compared with that on Zn (−0.87 eV), indicating a faster desorption of H* at low overpotential. Further, a 24 Ah electrolyzer is demonstrated to produce H2 with a net energy efficiency of 65.5 %, which holds promise for its real application.

Type I Tobit Bayesian Additive Regression Trees for censored outcome regression

Censoring occurs when an outcome is unobserved beyond some threshold value. Methods that do not account for censoring produce biased predictions of the unobserved outcome. This paper introduces Type I Tobit Bayesian Additive Regression Tree (TOBART-1) models for censored outcomes. Simulation results and real data applications demonstrate that TOBART-1 produces accurate predictions of censored outcomes. TOBART-1 provides posterior intervals for the conditional expectation and other quantities of interest. The error term distribution can have a large impact on the expectation of the censored outcome. Therefore, the error is flexibly modeled as a Dirichlet process mixture of normal distributions. An R package is available at https://github.com/EoghanONeill/TobitBART.

Enhanced Oxygen Evolution and Zinc-Air Battery Performance via Electronic Spin Modulation in Heterostructured Catalysts.

Beyond optimizing electronic energy levels, the modulation of the electronic spin configuration is an effective strategy, often overlooked, to boost activity and selectivity in a range of catalytic reactions, including the oxygen evolution reaction (OER). This electronic spin modulation is frequently accomplished using external magnetic fields, which makes it impractical for real applications. Herein, spin modulation is achieved by engineering Ni/MnFe2O4 heterojunctions, whose surface is reconstructed into NiOOH/MnFeOOH during the OER. NiOOH/MnFeOOH shows a high spin state of Ni, which regulates the OH- and O2 adsorption energy and enables spin alignment of oxygen intermediates. As a result, NiOOH/MnFeOOH electrocatalysts provide excellent OER performance with an overpotential of 261 mV at 10 mA/cm2. Besides, rechargeable zinc-air batteries based on Ni/MnFe2O4 show a high open circuit potential of 1.56 V and excellent stability for more than 1000 cycles. This outstanding performance is rationalized using density functional theory calculations, which show that the optimal spin state of both Ni active sites and oxygen intermediates facilitates spin-selected charge transport, optimizes the reaction kinetics, and decreases the energy barrier to the evolution of oxygen. This study provides valuable insight into spin polarization modulation by heterojunctions enabling the design of next-generation OER catalysts with boosted performance. This article is protected by copyright. All rights reserved.

Utilizing rare earth titanates to improve performance of solid-state electrochromic device

Exploring new materials and synthesis recipes are required to enhance the electrochromic performance especially, when used in solid-state devices. Here, polycrystalline gadolinium titanate (Gd2TiO5 or GTO), synthesized using a simple solid-state reaction method, has been used for this purpose by combining it with polythiophene (P3HT). The electrochemical investigation of the Gd2TiO5 doped P3HT electrode has been carried out using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), which reveals the dominance of diffusion-controlled mechanism over charge storage on the electrode surface as compared to the P3HT electrode. The Gd2TiO5 doped P3HT solid-state electrochromic device shows color modulation at 515 nm and 670 nm wavelengths with a color contrast of as high as 79% and 42%, respectively, under an external bias of as low as ±1.4 V. The prepared device switches between maroon to a transparent state in less than a second under the external bias (±1.4 V) with a high coloration efficiency of 346 cm2/C. The device shows improved cycle life over 100 switching cycles at both the wavelengths, which makes it more suitable for real-life applications.

In Situ-Initiated Poly-1,3-dioxolane Gel Electrolyte for High-Voltage Lithium Metal Batteries

To realize high-energy-density Li metal batteries at low temperatures, a new electrolyte is needed to solve the high-voltage compatibility and fast lithium-ion de-solvation process. A gel polymer electrolyte with a small-molecular-weight polymer is widely investigated by combining the merits of a solid polymer electrolyte (SPE) and liquid electrolyte (LE). Herein, we present a new gel polymer electrolyte (P-DOL) by the lithium difluoro(oxalate)borate (LiDFOB)-initiated polymerization process using 1,3-dioxolane (DOL) as a monomer solvent. The P-DOL presents excellent ionic conductivity (1.12 × 10−4 S cm−1) at −20 °C, with an oxidation potential of 4.8 V. The Li‖LiCoO2 cell stably cycled at 4.3 V under room temperature, with a discharge capacity of 130 mAh g−1 at 0.5 C and a capacity retention rate of 86.4% after 50 cycles. Moreover, a high-Ni-content LiNi0.8Co0.1Mn0.1O2 (NCM811) cell can steadily run for 120 cycles at −20 °C, with a capacity retention of 88.4%. The underlying mechanism of high-voltage compatibility originates from the dense and robust B- and F-rich cathode interface layer (CEI) formed at the cathode interface. Our report will shed light on the real application of Li metal batteries under all-climate conditions in the future.