Ionics最新文献

The single-channel-serpentine proton exchange membrane water electrolyzer (PEMWE) has the potential to be a crucial element in the linking of power, transport, heating, and chemical industries in the foreseeable future of sustainable energy systems. Its performance is influenced by a number of factors. Within the many factors affecting their performance, the external operating parameters have a significant effect on the temperature distribution of the internal components as well as the moisture distribution. In this paper, a single-channel-serpentine flow field electrolyzer (SFFE) is built to address the simulation deficiencies of a conventional single-channel proton exchange membrane electrolyzer, and to more intuitively represent the effects of single-channel-serpentine external environmental parameters on the internal performance of the electrolyzer, in order to assess the effects of different voltages, operating temperatures, and inlet flow rates on the distribution of heat, the distribution of liquid water and gases, and the susceptibility to cross-penetration sites. The results show that when the operating voltage drops below 2.2 V, the liquid-saturated water undergoes a greater impact. Specifically, the voltage increase from 1.6 to 2.2 V results in a significant decrease from 0.97 to 0.79, corresponding to an 18.6% drop. That is, when the voltage is 2.2 V, the liquid water content ensures the adequacy of the reaction, while the inhibitory effect of bubble formation occurs at the beginning. Therefore, 338.15 K and 2.2 V are the most recommended experimental conditions.

In this work, rutile-phase Ti_0.95Nb_0.95O₄/C (TNO/C) composites with different carbon contents were obtained through solvothermal method and subsequent calcination. The effect of carbon component on the microstructure and electrochemical performances of TNO/C composites were investigated. The results indicate more oxygen vacancies, and higher contents of Nb⁴⁺ and Ti³⁺ can be obtained under higher carbon content, leading to enhanced conductivity. Besides serving as conductive agent, carbon component in TNO/C composites also acts as an active component for Li⁺ storage, and pseudocapacitance provided by carbon component increases with the increasing of its relative content. Therefore, TNO/C-27.0 composites with the highest carbon content in the as-prepared composites deliver the highest reversible capacities at different current densities and excellent cycling capability of 488 mAh·g⁻¹ at 0.3 A·g⁻¹ after 300 cycles and 331 mAh·g⁻¹ at 1.0 A·g⁻¹ after 500 cycles.

State of energy (SOE) estimation of lithium-ion batteries is the basis of electric vehicle driving range prediction. To improve the estimation accuracy of SOE under complex dynamic working conditions, this paper takes the ternary lithium-ion battery as the research object, chooses the second-order RC-PNGV model to model the polarization reaction inside the battery, and adopts the improved adaptive forgetting factor recursive least squares (MAFFRLS) method to identify the model parameters. For battery SOE estimation, an improved unscented particle filtering algorithm for particle swarm optimization is proposed, which introduces quantum theory into particle swarm optimization to solve the sub-depletion problem of unscented particle filtering and improves the accuracy and adaptability of real-time estimation of SOE in complex environments. Experimental validation is carried out by constructing different working conditions at multiple temperatures, and the results show that the maximum error of parameter identification using recursive least squares based on improved adaptive-forgotten factor is stabilized within 2%. Under the HPPC, BBDST, and DST working conditions, the MAE and RMSE are limited to within 1% when the quantum particle swarm optimized-unscented particle filtering (QPSO-UPF) algorithm is applied to estimate the SOE estimation, which indicates that the proposed algorithm has strong tracking ability and robustness to the SOE of lithium batteries.

The scientific community and energy stakeholders have placed a great deal of emphasis on electrochemical energy storage in the current era of increased energy demand. Using the unique BaS:NiS:Gd₂S₃ semiconductor chalcogenide, which is produced using diethyldithiocarbamate ligand as a chelating agent, the current work aims to improve the functionality of charge storage devices and water splitting for energy production. The light absorption of the sustainably produced BaS:NiS:Gd₂S₃ semiconductor rendered it an excellent photoactive material with the 2.93 eV of the energy band gap. The produced chalcogenide’s average crystallite size, with the mixed crystalline phases, was 17.61 nm, demonstrating exceptional crystallinity. Moreover, metallic sulfide linkages were explored using infrared spectroscopy, and they were located between 400 and 1000 cm⁻¹. Particles with variable shapes and a roughly rod-shaped fusion indicated a higher volume-surface area ratio and the presence of several sites. The BaS:NiS:Gd₂S₃ electrochemical performance was evaluated using a standard three-electrode setup with a background electrolyte of 1 M KOH. BaS:NiS:Gd₂S₃, with a specific power density of 5674.08 W kg⁻¹ and a specific capacitance of up to 745.55 F g⁻¹, is a great electrode material for energy storage applications. The comparable series resistance (R_s) of 1.14 Ω further supported this significant electrochemical performance. In terms of the electrochemical water splitting, the OER Tafel slope value is 87 mV/dec, respectively, suggested by the electrochemical data for the OER activity. This electrode produced a Tafel slope of 281 mV/dec and a HER overpotential value of 276 mV.

Zinc oxide and nickel oxide nanocomposite was successfully synthesized using the co-precipitation technique and evaluated for their photocatalytic efficiency in the degradation of hexavalent chromium. The synthesized nanocomposite was characterized using various techniques such as X-ray diffraction, Fourier transform infrared spectroscopy, ultraviolet–visible spectroscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy to analyze its structure, morphology, chemical bonding, photoabsorption properties, and elemental composition. X-ray diffraction exhibits the presence of zinc oxide, nickel oxide, and zinc oxide and nickel oxide nanocomposite, with average crystallite sizes of 37.92 nm, 56.23 nm, and 41.77 nm, respectively. Fourier transform infrared spectroscopy was used to analyze the chemical composition of zinc oxide and nickel oxide nanocomposite. The ultraviolet–visible spectroscopy exhibited an absorption peak at 447 nm, indicating a bandgap of 2.77 eV. A novel industrial-oriented photoreactor was designed for the reduction of hexavalent chromium under ultraviolet–visible illumination with varying operational parameters. The effectiveness of the experiment was assessed using response surface methodology based on central composite design, which demonstrated a remarkable 95% reduction of hexavalent chromium under optimized conditions. This study validates the accuracy of central composite design in predicting levels of hexavalent chromium reduction. The rate constant for the degradation kinetics of hexavalent chromium was studied to be 0.00450 min⁻¹, indicating adherence to pseudo-second-order kinetics. Additionally, the prepared nanocomposite exhibited significant antibacterial activity against Bacillus subtilis, Escherichia coli, and Pseudomonas putida at different concentrations.

State of energy (SOE) estimation of lithium-ion batteries is the basis for electric vehicle range prediction. To improve the estimation accuracy of SOE under complex dynamic operating conditions. In this paper, ternary lithium-ion batteries are used as the object of study and propose a hybrid approach that combines a particle swarm optimization-based forgetting factor recursive least squares method with an improved curve-increasing particle swarm optimization-extended particle filter algorithm for accurate estimation of the state of energy of lithium-ion batteries. Firstly, for the accuracy defects of the FFRLS method, the particle swarm optimization algorithm is used to optimize the initial value of the optimal parameters and the value of the forgetting factor. Secondly, the curve-increasing strategy is introduced into particle swarm optimization to solve the sub-poor problem of extended particle filtering. Experimental validation through different working conditions at multiple temperatures. The results show that the maximum error of parameter identification using the PSO-FFRLS algorithm is stabilized within 1.5%, and the SOE estimation error is within 1.5% for both BBDST and DST conditions at both temperatures. Therefore, the algorithm has high accuracy and robustness under different complex working conditions. The estimation results prove the effectiveness of the energy state estimation.

Accurate prediction of the state of health (SOH) and remaining useful life (RUL) of batteries is essential for ensuring their safe and stable operation. Given the strong correlation between SOH and RUL, it is imperative to employ a joint prediction approach for a comprehensive evaluation of the actual battery aging condition. Therefore, this paper proposes a novel approach for jointly predicting SOH and RUL of lithium-ion batteries, which is based on the optimization of health features and fusion of multiple models. Multiple health features (HFs) are extracted from the charging voltage curve, and the fusion HF is optimized using kernel principal component analysis (KPCA) to leverage the complementary advantages of multiple features. Deep Gaussian process regression (DGPR) is employed to address the issue of lacking confidence interval expression in the prediction process. Combined with particle swarm optimization (PSO) and bidirectional long short-term memory (Bi-LSTM) network, fusion models of PSO-DGPR and Bi-LSTM-PSO-DGPR are developed for the prediction of SOH and RUL, respectively. In order to achieve the joint prediction of SOH and RUL, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is utilized to decompose the SOH prediction results, obtaining reliable capacity data for RUL prediction and eliminating local regeneration fluctuations caused by capacity regeneration effects. Furthermore, CALCE and NASA data sets are employed for validation. The experimental results demonstrate a significant reduction in the prediction error, compared to other prediction methods, thereby enhancing the accuracy and reliability of SOH and RUL prediction.

BaBiNb₅O_15- based oxide ion conductors have shown promise for solid fuel cell applications at medium temperatures. In this study, BaBi_0.98Sr_0.02Nb_5-xTi_xO_15-δ (x = 0.02, 0.04, 0.05, 0.06, 0.08) samples were prepared by the conventional solid-state synthesis with two-step sintering method. The electrical properties and oxygen relaxation behaviors of the BaBi_0.98Sr_0.02Nb_5-xTi_xO_15-δ samples were investigated. With increasing the Ti content, the bulk conductivity of the BaBi_0.98Sr_0.02Nb_5-xTi_xO_15-δ samples showed a trend of increasing first and then decreasing. When the doped Ti content reached 5 mol%, the highest bulk conductivity of BaBi_0.98Sr_0.02Nb_5-xTi_xO_15-δ samples can be got, 5.27 × 10⁻⁵ S/cm at 773 K. According to the dielectric relaxation spectroscopy measurement, the BaBi_0.98Sr_0.02Nb_4.95Ti_0.05O_15-δ sample exhibits the higher moveable oxygen vacancy concentration and mobility of oxygen vacancies. This could be the possible reason that there is the highest bulk conductivity in the BaBi_0.98Sr_0.02Nb_4.95Ti_0.05O_15-δ sample. Additionally, the influence of sintering temperature on the electrical properties of the BaBi_0.98Sr_0.02Nb_4.95Ti_0.05O_15-δ compound was investigated. When the BaBi_0.98Sr_0.02Nb_4.95Ti_0.05O_15-δ sample is sintered at a temperature of 1423 K, it exhibits the best bulk conductivity and total conductivity.

MXenes are a kind of novel and interesting new materials, and carbon dots (CDs) are also concerned because of their processability, versatility, environmental protection, and low cost. Both MXenes and CDs are chemically stable and have a large surface area and high electrical conductivity, which are promising alternative electrode materials for supercapacitors. Moreover, MnO₂ can also improve the energy density of the electrode materials. In this paper, Ti₃C₂T_x/CDs and Ti₃C₂T_x/CDs@MnO₂ were prepared by a hydrothermal method and their supercapacitor performance were also investigated by a series of electrochemical methods. From the CV profile in a three-electrode system, Ti₃C₂T_x/CDs@MnO₂ electrode exhibited a high specific capacitance of 281.3 F g⁻¹ at a scan rate of 5 mV s⁻¹, which was higher than that of Ti₃C₂T_x/CDs (160.3 F g⁻¹). Ti₃C₂T_x/CDs showed a good cycling stability with a capacitance retention of 82.38% after 10,000 cycles. Meanwhile, a symmetric supercapacitor was successfully assembled using Ti₃C₂T_x/CDs@MnO₂ as electrodes, with an energy density of 5.77 Wh kg⁻¹ at a corresponding power density of 120 W kg⁻¹. This work offers a theoretical foundation and a technological path for synthesizing highly effective ternary composite of MXene-based as energy storage materials.

Spinel cobalt ferrite (CoFe₂O₄) nanoparticles have garnered significant interest due to their versatile properties and potential applications. Among the various factors influencing their properties, annealing temperature stands out as a crucial parameter. In this comprehensive analysis, we studied the effects of various annealing temperatures (400, 500, 600, and 700 °C) on the structural, optical, morphological, magnetic, and photocatalytic characteristics of CoFe₂O₄ nanoparticles. X-ray powder diffraction (XRD) studies reveal the crystalline structure of CoFe₂O₄ nanoparticles. The crystallite size was increased from 13.544 nm to 20.312 nm with the increasing annealing temperature from 400 to 700 °C. The effect of annealing temperature on cationic-anionic interaction of the nanomaterials is studied through Fourier transform infrared spectroscopy (FTIR). Morphological and elemental composition of the nanomaterials is evaluated through Field Effect Scanning electron microscopy (FESEM) and energy dispersive spectroscopy (EDS). Changes in annealing temperature lead to variations in particle size, morphology, and surface area, influencing their catalytic activity. UV–visible spectroscopy (UV–Vis) and photoluminiscene spectroscopy (PL) were employed to investigate the optical properties of CoFe₂O₄ nanoparticles. Annealing temperature variation influences the band gap energy, affecting the absorption and emission properties of the nanoparticles. Tauc’s plot was employed to obtain the band gap of the nanomaterials. The band gap was decreased from 2.33 eV to 1.92 eV with rise in annealing temperature. An emission peak at 465.80 nm was observed through PL spectroscopy. The magnetic properties of CoFe₂O₄ nanoparticles, including saturation magnetization and coercivity, are examined using a vibrating sample magnetometer (VSM). The sample annealed at 400 °C has shown highest magnetic parameters with saturation magnetization, coercivity, and remanent magnetization of 51.53 emu/g, 1860 Oe, and 17.5 emu/g respectively. Annealing temperature variation alters the magnetic behavior, affecting the nanoparticles' utility in magnetic and photocatalytic applications. The photocatalytic performance of CoFe₂O₄ nanoparticles is examined through degradation of methylene blue dye under UV–Visible irradiation. The sample annealed at 400 °C exhibited the highest photocatalytic efficiency, achieving 74.46% discoloration. The involvement of hydroxyl ion (OH⁻) and singlet oxygen ion (O₂⁻) was verified through a Scavenger test using Isopropyl Alcohol (IPA) and Disodium Ethylenediaminetetraacetic Acid (EDTA 2NA), respectively. The kinetics of dye removal was analyzed using various rate models. Additionally, the material's stability was evaluated by calculating its recycling efficiency over four successive cycles.