Ionics最新文献

This study explores the aggregation properties and interfacial behavior of imidazolium-based ionic liquids [C₁₄mim][Br] and [C₁₅mim][Br], in the presence of the dipeptide glycyl-L-alanine at varying concentrations (0.20, 0.40, 0.60 mol/kg) in aqueous media. Conductivity and surface tension measurements were conducted to determine the critical micelle concentration (CMC) and associated interfacial parameters. The results indicate that the addition of the dipeptide significantly reduces the CMC of both ILs, suggesting enhanced micellization. This behavior is attributed to electrostatic interactions and ion-pair formation between IL head groups and dipeptide molecules. The CMC of ionic liquids increases with temperature. This is because of weakening or disruption of hydrogen bonding between ionic liquid head groups and dipeptide molecule, which diminishes the stabilizing interactions, required for aggregation, thereby hindering micellization. Thermodynamic analysis confirms that micellization is a spontaneous and exothermic process. The Gibbs free energy of micellization (ΔG°_m) becomes increasingly negative with higher dipeptide concentrations, further supporting the enhancement of micellization. Furthermore, surface excess concentration (Γ_max) values were found to be positive, demonstrating reduced surface tension upon IL adsorption. Simultaneously, minimum area per molecule (A_min) increased, suggesting looser molecular packing at the interface. The more negative Gibbs free energy of adsorption (ΔG°_ad) compared to ΔG°_m suggests that adsorption at the air–water interface is thermodynamically favored over micellization. This study provides a systematic evaluation of the influence of a dipeptide on the micellization thermodynamics of imidazolium-based ILs, offering valuable insights into peptide–surfactant interactions in aqueous systems.

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The growth of industrial and commercial fuel cell applications as a clean energy source is one of the focal points for energy sector researchers, leading to a constant search for cost-effective and accurate modeling techniques. This study meets this need by proposing a systematic method of determining solid oxide fuel cell (SOFC) stack models by appropriately choosing unknown parameters, where the main objective is to minimize the sum of squared errors between model output voltage and experimental data. The proposed walrus optimization (WO) algorithm is used to obtain an improved efficiency and better results. The reliability of the system is thoroughly tested in two cases, in which the temperature and pressure are varied (from 1073 to 1273 K and from 1 to 9 atm). A detailed comparative analysis with nine other metaheuristic algorithms proves the superior effectiveness of the proposed technique. The results show that the WO algorithm has significantly better performance and reaches the lowest mean squared error (MSE) values of 2.57E−07. Statistical analysis of 100 independent runs further confirms its outstanding stability as indicated by the smallest standard deviations (0.191 at 1073 K) and the shortest computational times, in the range of 0.20 to 0.41 s. The robustness of the algorithm is clearly proven by its top rank in the Friedman ranking test with a score between 1.125 and 1.5625 in all the test cases. These statistical results, along with the convergence and boxplot analyses, clearly demonstrate the excellent efficiency, precision, and reliability of the proposed WO-based technique for SOFC parameter identification.

Accurate and rapid estimation of the State of Health (SOH) is crucial for battery management systems (BMS) in real-world vehicles. In this study, a novel FAP-TCN deep learning network is proposed for estimating the SOH of electric vehicles. This method innovatively proposes the Frequency-Dominant Adaption Periodic Network (FAP) module, which is capable of learning the periodic and frequency-domain characteristic information of battery degradation. At the same time, it incorporates the Temporal Convolutional Network (TCN) to enhance the network’s ability to infer the causal relationships in long time series. To address the issue of insufficient sample data, which leads to a decline in SOH estimation accuracy in real-world vehicles, this study integrates the Model-Agnostic Meta-Learning (MAML) approach and introduces the MAML-FAP-TCN framework to enhance SOH estimation accuracy under small sample conditions. Experimental results demonstrate that the FAP-TCN network achieves good performance on the DVC dataset, with a mean absolute error (MAE) of 1.68% and a root mean square error (RMSE) of 2.08%. Additionally, in small-sample experiments conducted on the NDVNEV dataset, the MAE and RMSE of the MAML-FAP-TCN network are 2.18% and 2.79%, respectively, showing an improvement of approximately 15.8% compared to the FAP-TCN network.

Pyrolysis-induced metal aggregation and pore collapse remain critical challenges for Fe–N-C oxygen reduction reaction (ORR) catalysts. This study develops an F127 surfactant-assisted approach to synthesize hierarchical core–shell Fe/N-doped carbon electrocatalysts from iron-modified ZIF-8 precursors. The F127 template performs three key functions: it reduces ZIF-8 particle size from 360 to 200 nm, forms protective graphitic carbon shells during pyrolysis, and generates hierarchical mesopores (4 nm, 17.8 nm, and 28 nm) through micelle carbonization. This triple action preserves structural integrity, yielding high surface area (946.6 m²·g⁻¹) and pore volume (1.174 cm³·g⁻²). Material analysis confirms F127 enhances defect density (I_D/I_G = 1.04) and optimizes nitrogen configuration (72.22% pyridinic/graphitic N), boosting Fe-N₄ site formation while preventing iron migration. The resulting catalyst exhibits exceptional ORR performance in 0.1 M KOH: 0.87 V half-wave potential, 5.14 mA cm⁻² limiting current density, near-ideal 4e⁻ transfer (< 2% H₂O₂ yield), and 97.93% stability retention after 6 h, surpassing Pt/C benchmarks. This work establishes a paradigm-shifting strategy for balancing active site density and structural integrity in non-precious metal ORR catalysts through rational surfactant templating.

This paper investigates the synthesis of iron phosphate (FePO₄) for lithium-ion battery cathodes from various raw materials as iron sources. FeSO₄·7H₂O offers a simple and cost-effective precursor for the FePO₄ precipitation method, though pH sensitivity can lead to uneven particle growth. FeCl₃ as an Fe source for FePO₄ synthesis through the hydrothermal method provides precise control over the morphology of the synthesized particle but requires longer reaction times. Fe₂O₃, commonly used in solid-state synthesis methods, ensures uniform particle distribution but reacts slowly. Fe(NO₃)₃ easily dissolves in water, allowing for controlled reactivity, though it is hygroscopic and produces toxic nitrogen oxide gas. Fe(NH₄)₂(SO₄)₂·6H₂O enables low-temperature synthesis but results in amorphous materials that lead to lower cell performance. Fe₃O₄ offers high stability and uniform particle size of the synthesized FePO₄ but requires energy-intensive reduction. FeC₂O₄·2H₂O facilitates the production of high-quality LiFePO₄, though its sintering process demands high temperatures. FeCO₃ exhibits a slow synthesis process and gives impurity challenges. Fe(OH)₃ is cost-effective but often yields amorphous and thermally unstable products. Beyond these conventional precursors, sustainable alternatives such as FeNi alloys from nickel laterite ore processing and Fe–P industrial waste are also considered. The transformation of FeNi alloys into FePO₄ and NiSO₄·6H₂O provides a dual benefit by supplying precursors for both LFP and NMC cathode materials, while valorizing industrial byproducts. This approach highlights a promising and sustainable route for future cathode material development.

Nickel selenides’ structural and composition diversity offers promising avenues for advanced battery anodes, yet their practical implementation faces persistent challenges of structural instability and sluggish kinetics. Addressing these limitations, We developed a spatially confined architecture using a template-directed hydrothermal method. This approach readily produced uniform Ni₃Se₄ nanoparticles (size ~ 110 nm) encapsulated within conductive carbon nanoplates. Time-dependent XRD analysis confirms continuous selenization-driven phase evolution, while XPS/TGA verify the composite (26.8 wt% carbon) comprises Ni²⁺, Se₂²⁻, and Se²⁻ species. This dual-role design—where carbon simultaneously serves as electron highway and volume-change buffer—enables breakthrough lithium storage performance: 586.2 mAh g⁻¹ after 100 cycles (0.2 A g⁻¹) with exceptional 375.5 mAh g⁻¹ retention at ultrahigh 5 A g⁻¹. Electrochemical analysis demonstrates carbon spacers reduce charge-transfer resistance by > 40% versus bare counterparts. The strategically engineered interface provides critical insights for stabilizing conversion-type anodes. This is significant because, although the sodium storage performance remains challenging (167.9 mAh g⁻¹ after 100 cycles), our study reveals fundamental differences when nickel selenide/carbon composites are employed as its anode materials.

Aqueous sodium-ion batteries (ASIBs) hold great potential for large-scale stationary energy storage thanks to their high safety, cost-effectiveness, and environmental sustainability. Nevertheless, the limited electrochemical stability window (ESW) of water-based electrolytes, combined with the restricted performance of electrode materials, hinders their practical application. In this study, a synergistic strategy combining electrolyte modification and electrode optimization was proposed. Urea, an inexpensive and non-flammable additive, was introduced into the Na₂SO₄ aqueous electrolyte to reconstruct the hydrogen-bond network, enhancing the stability of water molecules and expanding the ESW. Simultaneously, a carbon composite was used to enhance the electronic conductivity of NaTi₂(PO₄)₃ (NTP), a promising anode material for ASIBs. The synergistic effect of the urea additive and carbon composite effectively improved the cycling performance and rate capability of the NTP anode. The modified half-cell exhibited notably improved initial specific capacity (111.1 mAh g⁻¹) and capacity retention (54.8% after 100 cycles at 1 C), compared to the unmodified system (90.9 mAh g⁻¹ and 48.8%). Furthermore, a pouch-type full battery (Na_0.44MnO₂ cathode||NTP/C anode) employing the 2 wt% urea-added electrolyte showed excellent cycling performance, demonstrating promising potential for practical applications. This dual-modification approach provides an effective and economical pathway toward high-performance ASIBs.

In this study, a high-performance hard carbon composite was synthesized for sodium-ion battery anodes using pitch-modified corn starch. Through a co-carbonization approach involving pre-oxidized pitch and corn starch, the expansion and foaming typically observed during starch pyrolysis were effectively mitigated, leading to a notable decrease in specific surface area to 4.33 m² g^⁻1 at 1400 °C. The molten pitch served as a filler to establish a pre-sodiated interface, resulting in a high initial Coulombic efficiency of 95.47%. Optimization of the pyrolysis temperature to 1400 °C yielded a composite with increased interlayer spacing (3.811 Å), a partially graphitized structure, and a high abundance of oxygen-containing functional groups. These characteristics collectively enhanced sodium storage performance, achieving a remarkable reversible capacity of 391.6 mAh g^⁻1. Furthermore, the composite exhibited excellent cycling stability (89.4% capacity retention after 500 cycles at 300 mA g^⁻1) and exceptional rate capability (289.8 mAh g^⁻1 at 1500 mA g^⁻1). These improvements can be attributed to the mechanical reinforcement from the pitch coating, the optimized porous structure, and the enhanced electronic conductivity facilitated by the developed graphitic microdomains. This research presents a scalable and sustainable method for fabricating cost-effective biomass-derived carbon anodes, offering valuable insights for advancing next-generation high-performance sodium-ion batteries.

In this study, binder-free cobalt–nickel phosphate (CNP) battery-type electrodes were prepared using a sonochemical-assisted chemical bath deposition (S-CBD) approach, with key parameters optimized through Design of Experiments (DoE). By comparing it with the conventional CBD process, ultrasonic treatment improved precursor dispersion and nucleation efficiency, leading to the formation of uniform nanosheet structures. A series of CNP electrodes with varying sonication times, amplitudes, and Co:Ni ratios (1:0, 3:1, 1:1, 1:3, and 0:1) were synthesized and systematically evaluated for their electrochemical performance. Among them, the Co3Ni1P (Co:Ni = 3:1) electrode under optimized conditions (sonication time of 50 min and amplitude of 51.6%) demonstrated the most outstanding electrochemical performance, delivering a high specific capacity of 877.8 C/g at 3 A/g, and exhibiting the lowest charge transfer resistance of 0.3 Ω. Furthermore, it showed high electrochemical stability with 87.1% retention after 10,000 cycles at 10 A/g. Accordingly, an asymmetric device, Co3Ni1P//AC supercapattery, was assembled and sustained 83.5% of its initial capacity at 10 A/g after 5000 cycles. The energy density (E_S) reached 74.1 Wh/kg. The enhanced performance was attributed to its interconnected nanosheet morphology and the synergistic redox activity of Co and Ni species, which together promote efficient ion diffusion and increase the number of accessible active sites. These findings underscore the potential of S-CBD synthesis combined with statistical optimization for developing high-performance energy storage devices.

The cooling system of energy storage battery cabinets is critical to battery performance and safety. This study addresses the optimization of heat dissipation performance in energy storage battery cabinets by employing a combined liquid-cooled plate and tube heat exchange method for battery pack cooling, thereby enhancing operational safety and efficiency. The study first constructs a mesh model coupling contact interactions, material properties, and load-bearing structural effects, followed by multi-condition rigid-body simulations. Results indicate that the battery module and cooling system operate normally under all conditions when the horizontal and vertical beam thicknesses, side panel thickness, internal frame thickness, and four connector dimensions are 5 mm, 5 mm, 3 mm, and 13.5 mm, respectively. The study also identified optimal cooling performance by adjusting the tee valve diameter and cold plate channel width: the best cooling effect was achieved with a cold plate width of 30 mm and a tee valve diameter of 6 mm. Following optimization, the battery box temperature decreased from 45.2 to 36.3 °C.