Ionics最新文献

Mesoporous cubic SnS/reduced graphene oxide (rGO) composites were synthesized via a modified wet chemical method with varying rGO concentrations (0, 5, 10, and 15 wt%). The synthesized composites were systematically investigated for their dual functionality in electrochemical heavy metal ions detection and photocatalytic degradation of organic dyes. X-ray diffraction (XRD) and Raman spectra analysis verified the purity of cubic phase SnS and significantly improved crystallinity of nanoparticles upon rGO incorporation. Field-emission scanning electron microscopy (FE-SEM) revealed that rGO sheets facilitated the nucleation and growth of SnS nanoparticles, leading to grain growth and reducing aggregation. Among the composites, the SnS/rGO with 10 wt% rGO exhibited optimal properties, including a large BET surface area (78.2 m²/g), reduced charge transfer resistance (16.8 Ω), strong visible light absorption, and a high diffusion coefficient (8.7 × 10⁻⁹ mm²/s). Thermogravimetric analysis (TGA) and differential thermal analysis (DTA) were employed to assess the thermal stability and compositional behavior of pristine cubic SnS and SnS/rGO (10 wt%) composites. Electrochemical studies demonstrated that the optimal SnS/rGO composite exhibited the highest sensitivity (3.03 × 10⁻⁴ A/mM) and the lowest limit of detection (0.094 mM) for Pb²⁺ ions, which is attributed to efficient electron transport and abundant active sites. Photocatalytic experiments further demonstrated 96% degradation of crystal violet dye under visible light, following pseudo-first-order kinetics. The composite also showed excellent reusability and stability. These findings establish cubic SnS/rGO (10 wt%) as a promising material for environmental applications involving pollutant detection and remediation.

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Accurately perceiving the state of health (SoH) is beneficial for the lithium-ion batteries’ security and steady operation. However, the actual usage environment of batteries is complex, resulting in a host of noise and outliers in the raw data, which poses great adversity to SoH estimation. Therefore, a wolf pack algorithm (WPA) optimized decision tree (DT) method based on indirect Health features is introduced in this paper to estimate the SoH of batteries. Firstly, the multiple indirect health features extracted in this paper can uncover deeper health information and effectively decrease the interference of noise and adverse data, which could be measured directly by sensors. Secondly, considering factors such as algorithm convergence time and computational cost, the WPA demonstrates its excellence performance and it is used to optimize the hyperparameters of the decision tree. Compared with deep learning, the combination of WPA and DT does not require a large amount of data, so the computational cost is lower. In addition, the inherent robustness of WPA, together with the interpretability of decision trees, Helps improve the generalization and accuracy of the estimation. Finally, contrast experiments are utilized to prove the validity of the introduced indirect health features and SoH estimation methods. The results illustrate that using the introduced indirect health features and SoH estimation method can significantly enhance the SoH estimation accuracy. The experiments used the first 900 and first 130 cycles from MIT and NASA dataset, and the mean absolute error (MAE) and maximum absolute error (MaxAE) on two datasets are both within 2% and 5%, respectively.

High-voltage LiCoO₂ (LCO) cathodes based lithium-ion batteries (LIBs) provide high energy density, but their long-cycle stability is compromised by detrimental side reactions at high voltages and elevated temperatures. Herein, we demonstrate that the performance limitations of high-voltage LCO cathodes can be substantially alleviated by incorporating cyanobenzene (CB) as an electrolyte additive. The addition of 1 wt.% CB to a commercial electrolyte enables 4.2 Ah LCO||graphite pouch cells operating at 4.53 V to retain 80% of their initial capacity after 550 cycles at a current density of 1.0 C under an elevated temperature of 45 °C. It outperforms the cell that achieves 80% of their initial capacity after 450 cycles with 1 wt.% 1,3,6-hexanetricarbonitrile (HTCN), a well-known commercial nitrile additive. Experimental results and theoretical analyses reveal that this significant improvement is attributed to the formation of a more robust cathode electrolyte interphase (CEI) film induced by the CB additive. CB is more readily oxidized than HTCN, effectively suppressing the oxidative decomposition of electrolyte components and preserving the structural integrity of LCO under high-voltage conditions. Furthermore, CB demonstrates superior compatibility with graphite anodes relative to HTCN. This work highlights the critical role of nitrile molecular structure in forming a stable CEI on high-voltage cathodes, providing valuable guidance for improving the energy density of LIBs from facile interface engineering tactics.

Metal–organic frameworks (MOFs) are popular due to their large surface area, tunable pore topologies, and unique structural properties. They are promising energy storage possibilities due to their traits. This study successfully synthesized a Zn-MOF using a room-temperature Co-precipitation method. X-ray diffraction confirmed that Zn-MOF exhibits a monoclinic crystal structure with a well-defined framework composed of Zn metal ions coordinated with BDC ligands, ensuring structural integrity. Thermal stability investigation employing TG/DTA showed endothermic breakdown at 485 °C, demonstrating the MOF’s resilience. FTIR was utilized for functional groups analysis studies. SEM analysis revealed particles predominantly exhibiting a Hexagonal shape with a sub-micron particle size. The Zn-MOF electrode accomplishes a supreme specific capacity of 122 mAh/g at 0.5 A/g. Furthermore, the Zn-MOF exhibited excellent cyclic stability, with a coulombic efficiency of 98.24% and capacity retention of 84.61% subsequent 3000 cycles at a higher current density. The symmetric supercapacitor device offered efficient charge transport, characterized by a low charge transfer resistance of approximately 2.3 Ω. Notably, the device delivered a specific power of 884.12 W kg⁻¹and a specific energy of 10.3 Wh kg⁻¹. Prolonged cycling stability was observed, with columbic efficiency and capacity retentions of 94.40% and 83.52% after 4000 cycles. These results show that Zn-MOF is a promising electrode for symmetric supercapacitors.

Na₃V₂(PO₄)₃ (NVP) is considered the most promising cathode material for sodium-ion batteries due to its unique ion transport channel, but the toxicity and price of elemental V is forcing the search for new cathode materials. Na₄VMn(PO₄)₃(NVMP) has the same sodium superionic conductor (NASICON) structure as NVP, and it has higher voltage and energy density, but the Jahn–Teller effect of Mn³⁺, the dissolution of transition metals, and the poor conductivity severely limit its application. In this work, a simple two-step method was used to prepare amorphous Al₂O₃ and Al³⁺ doped double-acting NVMP, which not only suppressed the Jahn–Teller effect but also effectively improved structural stability. Among them, our prepared Na₄VMn(PO₄)₃ (NVMP-0.5%Al) with 0.5% amorphous Al₂O₃ coating still has a specific capacity of 58.7 mAh g⁻¹ with 67.9% capacity retention after 1000 cycles at 5 C current density, and at current densities of 0.2, 0.5, 1, 2, 5, 10, 15, and 20 C showed reversible capacities of 97.7, 89.1, 84.2, 82.5, 76.1, 70.0, 66.9, and 62.9 mAh g⁻¹, respectively. This work significantly improves the cycle stability and rate performance of NVMP and effectively solves the problems of NVMP, which will be beneficial to the practical application of NVMP.

Herein, we synthesized V₂O₅ nanosheets combinedly with spherical ZnO particles considered as a high-performance supercapacitor electrode through a two-step hydrothermal and annealing process. Furthermore, the obtained V₂O₅/ZnO composite material was characterized via X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy and X-ray photoelectron spectroscopy, which was confirmed by various physicochemical techniques. In addition, the resultant electrodes were evaluated the charge storage performance through a three-electrode system in an aqueous electrolyte system. As a result, the resultant electrode exhibited the areal capacitance of 37.5 mF cm⁻² at the current density value of 1 mA cm⁻² with 95% stability retention after 3000 cycles. The delivery of the capacitance, which enhances electrochemical performance and is derived from the different composite structure and work functions that take advantage of the synergetic effect to offer more electrochemically active spots and pathways, hence accelerating electron and ion transport.

Photoelectrochemical (PEC) water splitting offers an eco-friendly approach to generate hydrogen (H₂) fuel using solar energy, enabling renewable energy production without carbon emissions. Herein, an efficient photoanode for the conversion of H₂O into H₂ is prepared by implanting Cu nanoparticles on vacancy-rich TiO₂ nanorod arrays (NRAs). The unique structure of TiO₂ NRAs provides a high surface area and direct charge transport pathways, which are critical for enhancing PEC efficiency. The TiO₂ with rich oxygen vacancies facilitates charge carrier diffusion and reveals a significant rise of photo-generated carriers due to the reduced bandgap. Besides, the localized surface plasmon resonance (LSPR) effect of Cu greatly improved the visible-light harvesting capacity. At 1.23 V vs. RHE under 300 W Xe lamp illumination, Cu/def-TiO₂ achieves a maximum photocurrent density of 1.90 mA cm⁻² in 0.1 M Na₂SO₄ solution, representing a 2.3-fold enhancement compared to pristine TiO₂. This work explains the mechanism of the synergistic effect of Cu and oxygen vacancies to improve PEC performance, offering novel insights for designing high-performance photoanodes.

Accurate state-of-health (SOH) estimation of lithium-ion batteries under partial charging conditions remains challenging due to the inflexibility of existing methods requiring fixed voltage intervals. To address this challenge, this work proposes a novel ensemble learning framework capable of handling arbitrary voltage segments (as short as 0.01 V) by integrating fractional-order differential features, mutual information theory, and adaptive feature reuse. First, multiple base learners are independently trained on distinct voltage sub-intervals extracted from the constant-current charging phase. Their outputs are then concatenated and processed through a meta-model to obtain the final result. To enhance feature diversity, both integer-order and fractional-order differential curves are introduced, while mutual information theory is employed to identify the most discriminative feature set. The optimal base and meta-models are selected via cross-validation. Experiments on batteries with divergent aging patterns demonstrate excellent performance, achieving MAE and RMSE below 0.02 across all voltage intervals. This outperforms conventional approaches and maintains computational efficiency for real-world deployment.

Proton exchange membrane fuel cells (PEMFCs) are vital for sustainable energy applications due to their efficiency, low emissions, and quiet operation. Accurate optimization of design variables is essential for their performance enhancement, but existing algorithms like WR-BBO, TB-BBO, and HBBOS struggle with issues like slow convergence, local optima entrapment, and parameter sensitivity. The hybrid grouping biogeography-based optimization (HG-BBO) algorithm uses migration and mutation operators for local and global search, respectively. HG-BBO was applied to optimize the design variables of 12 PEMFC stacks: BCS 500 W, Nedstack 600 W PS6, SR-12 W, Horizon H-12, Ballard Mark V, and STD 250 W. Comparative analysis with algorithms such as BBO-M, DE-BBO, Lx-BBO, BHCS, BLPSO, and HBBOS revealed that HG-BBO outperforms them in terms of accuracy, convergence speed, and robustness. The objective function sum of squared error (SSE) for stack voltage is minimized using different algorithms for comparative analysis. Simulation results for I-V and V-P characteristics aligned closely with experimental data under varying temperature and pressure conditions. These findings highlight HG-BBO’s theoretical significance in solving nonlinear optimization problems and its practical utility in enhancing PEMFC design and operational reliability. Future work will focus on real-time optimization, algorithm hybridization, and scaling to larger energy systems, offering a robust tool for advancing PEMFC technology.

The rapid advancement of lithium-ion batteries highlights the critical importance of mechanical behavior in determining battery life and safety. This study presents an electrochemical-thermal–mechanical (ETM) coupling model based on layer-wise (LW) theory to investigate the effect of spatial stress non-uniformity on the electrochemical performance of lithium-ion batteries. The proposed model extends the existing framework by applying layer-wise (LW) theory at the cell level. It provides a detailed description of the battery structure while efficiently capturing the interactions between the expansion of multiple electrode layers. The model analyzes strain and stress distributions across different layers, focusing on how these factors influence the electrochemical properties of multilayer electrodes. The results reveal that the absolute strain of each component follows the order: negative electrode > separator > positive electrode > collector, with strain increasing from the inner to the outer layers. This strain characteristic significantly impacts the solid and liquid phase volume fractions within the electrodes. Furthermore, different component arrangements lead to variations in the stress distribution inside the battery. These findings provide valuable insights for optimizing battery structural design and addressing load-related challenges.

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