Ionics最新文献

In this paper, a novel hybrid electrode materials together with graphene oxide or reduced graphene oxide (GO/rGO), titanium dioxide (TiO₂), polythiophene (PTh), and carbon black (CB) were prepared an easy, low cost, and sustainable approach to synthesize GO/TiO₂/PTh and rGO/TiO₂/PTh/CB nanocomposites. In addition, GO/TiO₂/PTh by different weight amount of TiO₂ (0.05, 0.075, 0.1, and 0.125 g) were studied in two-electrode system for supercapacitor device applications. Nanocomposites were characterized by Fourier-transform infrared spectroscopy-attenuated total reflectance (FTIR-ATR), scanning electron microscopy-energy-dispersive X-ray (SEM–EDX), atomic force microscopy (AFM) analysis, thermogravimetric-differential thermal analysis (TGA-DTA), Brunauer–Emmett–Teller (BET) analysis, and four-point probe analysis. The highest specific capacitance was achieved as C_sp = 1292.63 F/g for rGO/TiO₂/PTh/CB nanocomposite at 2 mV/s by CV method. The highest electrical conductivity was observed as Ϭ = 2.24 × 10⁻³ S/cm for PTh. PTh increases the electron transfer movement together with TiO₂ and CB nanoparticles. The surface area (58.62 m²/g for rGO) and pore volume of rGO (0.027 cm³/g for rGO) support the better electrochemical performance of rGO/TiO₂/PTh/CB nanocomposite. Moreover, the long-term stability experiments show that the highest initial specific capacitance preservation was obtained as 110% for rGO/TiO₂/PTh/CB nanocomposite at 100 mV/s for 1000 charge–discharge cycles. Moreover, it exhibits high coulombic efficiency. Electrochemical impedance spectroscopic (EIS) results were evaluated to interpret equivalent circuit models of [R_S(C₁(R₁(R₂C₂)))] obtained from ZSimpWin 3.22 simulation programme.

In this study, guar gum and polyvinyl alcohol(PVA) polymer electrolytes are prepared using solution casting method. The films are characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), electrochemical impedance spectroscopy (EIS), and UV–visible spectroscopy. XRD analysis indicates that the films have amorphous structure, and FTIR spectroscopy is used to identify the functional groups and interactions between the polymers and magnesium chloride (MgCl₂) salt. The FTIR spectra show clear absorption bands indicating successful integration and interaction within the blend. EIS measurements reveal that the ionic conductivity of the electrolyte film is reached upto 3.3 × 10⁻⁴ S/cm at a concentration of 0.4 g MgCl₂ in the blend. This significant ionic conductivity suggests that Guar gum:PVA blend has promising potential as a polymer electrolyte in electrochemical applications. UV–visible spectroscopy reveals the band gap of 2.58 eV, refractive index, and Urbach energy value of 0.32 eV for the prepared electrolyte. These results suggest that Guar gum:PVA:MgCl₂ polymer electrolytes offer promising prospects as efficient material for advancing sustainable energy solutions.

Manganese dioxide (MnO₂) are extremely promising materials for zinc-ion batteries because of their high specific capacity, high capacity for operation, affordability, and non-toxicity. However, the low conductivity and capacity degradation issues of MnO₂ limit its application. In this study, composite cathode materials of MnO_x@C are designed using a strategy that combines stirring synthesis with redox reactions. This method allows for the modification of the crystal structure while simultaneously controlling the thickness of the C layer, resulting in the enhancement of both cycle stability and conductivity in MnO_x@C. The MnOx@C composite shows remarkable performance in terms of current density (0.1 A g⁻¹) and capacity (320.3 mAh g⁻¹). Additionally, it exhibits excellent cycling stability, as evidenced by a capacity retention rate of 92% even after 1000 cycles at a current density of 1.0 A g⁻¹. These results surpass the multiplication capability and cycling stability of MnO₂, with a capacity of 254.1 mAh g⁻¹ when a current density of 0.1 A g⁻¹ is used. However, it only retains 70% after 1000 cycles of a current density of 1.0 A g⁻¹. This study offers a workable strategy for creating sophisticated cathodes that will improve zinc-ion battery performance.

Creating effective electrocatalysts has been seen as a potential solution for wastewater contaminated with nitrate. In this research, we employed copper foam (Cu foam) as a substrate to successfully fabricate a novel non-precious three-dimensional CoFe@Cu foam electrode, exhibiting outstanding catalytic activity and stability. The study found that this material shows significant potential as a cathode for the electrochemical reduction of nitrates. Notably, under a current density of 30 mA/cm², the CoFe@Cu foam cathode demonstrates excellent performance in removing nitrate from simulated wastewater, achieving an efficiency of up to 96.3% within 60 min. This result is significantly higher than the 20.3% removal efficiency observed with the copper foam electrode under the same conditions, highlighting the clear advantage of the CoFe@Cu foam cathode in the electrochemical reduction of nitrates and its superior capability for effective nitrate removal. The enhanced functionality can be attributed to the increased quantity of active sites and the synergistic relationship between iron (Fe) and cobalt (Co). Additionally, the impact of chloride ions (Cl⁻) on the reduction of ammonium nitrogen (NH₄⁺-N) formation and the enhancement of total nitrogen (TN) removal was evaluated. The results showed that the addition of 1.0 g/L Cl⁻ significantly reduced NH₄⁺-N formation and achieved a 100% TN removal rate after 90 min of treatment. Based on an analysis of the experimental results, a possible pathway for nitrate electroreduction was proposed.

This study investigates the electrocatalytic hydrogen production and supercapacitor effectiveness of hexagonal KCu₇S₄, a material abundant in nature, which serves as a promising dual-functional material for sustainable applications. Concerning the electrochemical reactions, the KCu₇S₄ possesses a unique layered hexagonal structure that minimizes internal resistance and facilitates effective ion transport. Comprehensive characterization techniques such as X-ray diffraction (XRD), analysis, Laser Raman spectroscopy, field-emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS) showcase its beneficial structural properties. Techniques such as cyclic voltammetry (CV), linear sweep voltammetry (LSV), electrochemical impedance spectroscopy (EIS), and chronoamperometry (CA) were utilized to evaluate the true efficacy of the electrocatalytic hydrogen evolution reaction (HER). Galvanostatic charge–discharge (GCD) analysis was used to assess the performances of supercapacitor electrodes. The HER experiments indicate that KCu₇S₄ serves as an efficient electrocatalyst since it exhibits the lowest HER overpotential of 90 mV at − 10 mA cm⁻² and the Tafel slope of 138 mV dec⁻¹ (using Ni-foam as substrate). The supercapacitor examinations revealed that the KCu₇S₄ materials exhibited a greater specific capacitance value of 416 F g⁻¹ (three-electrode mode with graphite sheet) at 3 A g⁻¹ and 54 F g⁻¹ (from the device) at a current density of 1 A g⁻¹. The constructed device delivers a power density value of 700 W kg⁻¹ and an energy density value of 14.78 Wh kg⁻¹ respectively. These findings illustrated the feasibility of producing and utilizing KCu₇S₄ on a pilot scale as a bifunctional material for electrocatalyst and supercapacitor uses in practical scenarios.

Lithium-ion batteries are pivotal in the automotive INDUSTRY for their high energy density. Accurate state of charge estimation is essential for optimizing battery performance and longevity. This study utilizes a third-order resistance–capacitance equivalent circuit model with parameters estimated via MATLAB/Simulink Simscape. Four state of charge estimation methods: Coulomb counting, Extended Kalman filter, Unscented Kalman filter, and Extended Kalman-Bucy filter are evaluated. Extended Kalman-Bucy filter demonstrated the highest accuracy (Mean Absolute Error = 0.008%, Root Mean Square Error = 0.01%) but required the longest computation time (32.938 s), whereas Coulomb counting was the fastest (6.237 s) but least accurate (Mean Absolute Error = 0.0445%, Root Mean Square Error = 0.0548%). To enhance state of charge estimation, a deep neural network is designed to predict equivalent circuit model parameters based on state of charge and temperature. The deep neural network predictions were integrated into the Extended Kalman-Bucy filter using two strategies: Direct Integration and Fusion Integration. The Fusion method demonstrated the best performance (Mean Absolute Error = 0.12%, Root Mean Square Error = 0.15%) but had a higher execution time (605 s) compared to Direct Integration (602 s) and lookup tables (19 s). These findings highlight the potential of deep neural network enhanced filtering techniques to significantly improve state of charge estimation accuracy.

To address the problem of low efficiency in estimating the state of health (SOH) of lithium-ion batteries, a method based on the maximal information coefficient (MIC) algorithm and the back propagation (BP) neural network optimized by the golden jack optimization (GJO) algorithm is proposed in this study. Firstly, six aging features of SOH were extracted from the University of Maryland’s lithium-ion battery aging test data, and three high-quality aging features were selected using the MIC algorithm; then, the GJO algorithm is selected to optimize the initial weights and thresholds of the BP neural network to eliminate the problem of overfitting in the BP neural network; finally, GJO-BP was compared with BP neural networks optimized by genetic algorithm (GA) and simulated annealing (SA) algorithm. The results showed that after optimization using the MIC algorithm, the average error (MAE) of the model decreased by 31.37% compared to before optimization for aging characteristics; the reduction in MAE for GJO-BP compared to BP is 18.57% and 22.85% higher than that for GA-BP and SA-BP, respectively, while the convergence speed of GJO-BP is 50% faster than that of SA-BP. High-efficiency lithium battery SOH estimation can be achieved.

Titanium lithium ion sieve (Ti-LIS) has attracted much attention due to their excellent adsorption properties and easy preparation process. In this study, the Li₂TiO₃ with different doping amounts of W (LTWO) was synthesized by the hydrothermal method. Then, the LTWO was washed with HCl to obtain the adsorbent of W-doped H₂TiO₃ (HTWO). The structure and adsorption properties of HTWO-4 were tested and the results showed that the introduction of W into HTWO leads to lattice defects, which increase oxygen vacancies and promote Li⁺ diffusion in the adsorbent. The effects of adsorption time, initial Li⁺ concentration, and pH on the adsorption properties of Li⁺ were systematically evaluated. The adsorption process of HTWO was confirmed as chemisorption and monolayer adsorption by simulating the pseudo-second-order kinetic model and Langmuir model. Among them, HTWO-4 exhibits a higher adsorption capacity than others, with a value of 34.68 mg/g in a LiCl solution (Li⁺  = 210 mg/L). Additionally, the HTWO-4 exhibits superior adsorption selectivity of Li⁺ over Mg²⁺, Ca²⁺, K⁺, and Na⁺, maintaining a high Li⁺ adsorption capacity after five regeneration cycles. This work provides an ideal candidate adsorbent in the field of lithium resource utilization.

LaNiO₃ and Zn-doped LaNiO₃ for supercapacitor application are reported here. Chemical precipitation was the method used to prepare the material. By varying doping concentrations, the impact of Zn substitution in LaNiO₃ was investigated. X-ray diffraction (XRD), scanning electron microscopy (SEM), Raman spectroscopy, Brunauer–Emmett–Teller (BET), and electrochemical experiments are used to evaluate the as-prepared nanomaterials. LaNiO₃’s orthorhombic phase formation is confirmed by X-ray diffraction. Surface morphology is analyzed by SEM and porosity was found out from BET characterization. Using different scan intervals and a constant potential window, cyclic voltammetry (CV) was carried out. The charge storage mechanism was analyzed using CV curves and data. Less charge transfer resistance was seen in the apparent Nyquist plot, and the pseudocapacitive characteristic was demonstrated by the oxidation/reduction peak appearances. The prepared electrode material shows a maximum capacitance value of 741.07 F/g and 732.86 F/g by GCD and CV curves respectively. The energy and power density of the prepared electrode was found to be 39.319 Wh/kg and 1.524 kW/kg respectively for 1 A/g current density. The obtained results expose the produced composition of 5 wt% Zn-doped LaNiO₃, a potential material for the electrodes.

In the context of the burgeoning new energy industry, lithium iron phosphate (LiFePO₄)-based batteries have gained extensive application in large-scale energy storage. Nevertheless, the inherent flammability of the traditional ester liquid electrolyte renders the thermal runaway of LiFePO₄ batteries a critical scientific issue under overcharge circumstances. This research centers on the gas-production analysis and corresponding failure mechanism of 5Ah LiFePO₄ pouch batteries subjected to diverse overcharge conditions. By employing in-situ differential electrochemical mass spectrometry (in-situ DEMS), gas chromatography, and in-situ thermocouple monitoring apparatus, an in-depth exploration was conducted into the evolution of characteristic gases and the concomitant temperature alterations during battery failure. The findings reveal that at relatively low overcharge voltages, the hydrogen content exhibits a significant variation; conversely, at higher overcharge voltages, the contents of carbon dioxide and carbon monoxide manifest a notable increase. These results hold substantial implications for the fabrication of relevant early warning devices and the prompt alert of potential hazards, thereby facilitating a more profound comprehension of the overcharge gas-production behavior of LiFePO₄ batteries and augmenting the safety and dependability of energy storage systems.