Ionics最新文献

The electrochemical properties of lithium-rich layered transition metal oxide cathodes are profoundly affected by different kinds of material modifications, including elemental doping and compositing. This research endeavors to elucidate the synergistic impact of Fe doping along with graphene compositing on the structural and electrochemical characteristics of Li[Li_0.20Mn_0.54Ni_0.13Co_0.13]O₂ (LMNC) cathodes. Four distinct cathodic materials were synthesized utilizing the sol–gel method, which are pristine LMNC and the ones doped with the Fe dopant (0.075%) composited with graphene, and included with both the Fe dopant and graphene. Structural characterization tests substantiated the presence of the layered α-NaFeO₂ structure and revealed the maintenance of structural stability of the cathode consequent to doping and compositing processes. Electrochemical analyses exhibited that the incorporation of the Fe dopant and graphene significantly enhanced electronic conductivity, diminished polarization effects, and facilitated lithium-ion diffusion. Among all the characterized samples, the best electrochemical performances appeared for the LMNC cathode doped with Fe and composited with graphene. The discharge capacity and coulombic efficiency of the aforementioned sample reached 379.1 mAh g⁻¹ and > 94.1%, respectively. The excellent electrochemical performance of the LMNC cathode with both Fe and graphene was due to the doping and compositing processes, which could make it a potential candidate for high-performance lithium-ion batteries.

Silicon dioxide (SiO₂), an anode material for lithium-ion batteries (LIBs), faces critical challenges such as irreversible phase transitions, severe volume expansion, and interfacial side reactions, leading to low initial Coulombic efficiency and poor cycling stability. To address these issues, this study proposes a novel design of SnO₂-based SiO₂ nanotubes composites (SiO₂/SnO₂) synthesized using ammonium tartrate as a water-soluble template. The hollow tubular structure effectively accommodates volume changes of SiO₂ during lithiation/delithiation, while SnO₂ component can contribute lithium storage capacity and improve the electrochemical reaction kinetics. The optimized SiO₂/SnO₂ composite exhibits a high discharge capacity of 778.07 mAh g⁻¹ with a Coulombic efficiency exceeding 98% after 200 cycles at 1 A g⁻¹. Additionally, it demonstrates excellent rate capability, achieving 715.6 mAh g⁻¹ at 1 A g⁻¹ and recovering to 927.9 mAh g⁻¹ when the current density returns to 0.1 A g⁻¹. The synergistic combination of structural engineering and SnO₂ functionality not only mitigates mechanical degradation but also enhances reaction kinetics. This work provides a scalable strategy for developing high-performance anode materials with improved energy density and long-term cyclability for advanced LIBs.

Hybrid water electrolysis system was designed by using Ruthenium-Tin Oxide (RuSn_12.4O₂) electrocatalyst as anode material for efficient hydrogen production enhancing energy conversion efficiency. The RuSn_12.4O₂ Electrocatalyst was synthesized by hydrothermal method and exhibited exceptional activity, making it an optimal choice for Iodide oxidation reaction (IOR) and enabling energy-saving hydrogen production. The two-electrode acidic electrolyzer reduced voltage consumption by 0.51 V at 10 mA cm-² compared to oxygen evolution reaction (OER) at the same current density. This hybrid electrolysis system achieved a remarkable reduction in energy consumption of over 40 % compared to OER process. The Chrono-potentiometric test demonstrated that the RuSn_12.4O₂ electro-catalyst’s superior stability and low overpotential increase of 70 mV at 10 mAcm-². The RuSn_12.4O₂ electro-catalyst Tafel slope is also a crucial metric for understanding kinetic characteristics in both IOR and OER processes. Thus, RuSn_12.4O₂ electro-catalyst in IOR has a lower Tafel slope (61 mV dec^-1) than that in OER, according to the Tafel slopes determined from linear sweep voltammetry (LSV) curves. Additionally, at various potentials, the electro-catalyst's activity toward IOR to produce hydrogen demonstrated exceptional performance in this electrolysis system without causing any catalyst degradation.

Proton exchange membrane fuel cell (PEMFC) is a key technology to improve the utilization of renewable energy. To improve the performance and durability of PEMFCs, it is vital to enhance the gas mass transport capacity. To reveal the gas transport characteristics inside gas diffusion layers (GDL) of the PEMFC with gradient porosity distribution under different wetting conditions, the pore-scale model of coupled two-phase flow and gas transport processes based on the lattice Boltzmann method is developed to evaluate the performance of GDL under different gradient porosity distributions comprehensively. It is found that both the effective diffusion coefficient and permeability within GDLs with gradient porosity are affected by pore distribution, pore gradient, and liquid water saturation. With the increase of water saturation, the permeability in GDL with medium gradient porosity is relatively high. When the water saturation is 0.28, compared with the GDL under dry state, the permeability of GDLs with two-step, three-step, and linear porosity distributions decreased by 55.77%, 60.73%, and 58.69%, respectively. Besides, the flow resistance in GDL under linear porosity distribution is the lowest, followed by the one under two-step porosity distribution, and finally the one under three-step porosity distribution. Moreover, compared with the step gradient porosity distribution, the linear porosity distribution can effectively limit the droplet size and is less affected by the liquid water saturation. The linear structure under a medium porosity gradient can effectively improve the gas-liquid transport process inside the GDL.

Severe structural collapse along with fast capacity fading is one of the key challenges to meet the needs for commercial O3-type layered cathodes. Here, Cu/Cr are utilized as robust dopants for O3-NaNi_0.2Fe_0.4-xMn_0.3Cu_0.1Cr_xO₂ (NFMCC) to achieve the purpose of reconstructing the crystal lattice and regulating the interlayer structure. It is found that the synergistic effect between Cu and Cr facilitates suppressing the oxygen vacancies and transition metals (TMs) migration in the TMs layer, bringing forth the release of the internal stress, and eventually preventing the rupture in the NFMCC polycrystals upon repeated cycling. The Na⁺/vacancy arrangement and phase transitions are also greatly suppressed, which is further verified by single voltage plateaus upon Na⁺ extraction/insertion. The sufficient sodium in the O3-type cathodes easily induces the good structural stability at deep desodiation states and adequate reversible capacity during Na⁺ desodiation. Consequently, Cu-substituted NFMCC exhibits a high specific capacity of 120 mAh g⁻¹ and remarkable cycling performance with a capacity retention of 87.71% after 100 cycles. This work provides a fundamental insight for paving the way to extend the lifespan of cathodes for SIBs.

LiFePO₄ (LFP) is widely used as cathode material in Li-ion batteries in electric vehicles (EV’s). The theoretical capacity of LFP is 170 mAhg⁻¹. It is difficult to achieve the theoretical capacity value, especially at high C-rates, mainly because of its poor ionic as well as electronic conductivity. Several doping strategies have been adopted of which Mn as well as V doping individually, show beneficial effect in improving the electrochemical performance. However, co-doping of these two ions and the synergistic effect, if any, on the electrochemical performance of LFP has not been explored hitherto. In the present study, Mn and V co-doped LFP cathode materials were synthesized by solvothermal method. Phase formation was confirmed by X-ray diffraction studies, while ⁷Li MAS NMR spectra revealed changes in isomeric shift (-18.03 ppm for pristine LFP, -1.01 ppm for Mn-doped, and -0.65 ppm for Mn, V co-doped LFP), confirming Mn and V are incorporated into the olivine lattice. The co-doped LFP exhibited a unique two-dimensional morphology with uniform, fluffy particles (~ 3 µm × 2 µm). X-ray photoelectron spectra confirmed the presence of Fe²⁺, Mn²⁺, and V⁴⁺ oxidation states. The Li-ion diffusion coefficient (D_Li+) of Mn and V co-doped LFP (6.93 × 10⁻¹⁵ cm²s⁻¹) was higher than that of pristine LFP (2.97 × 10⁻¹⁵ cm²s⁻¹), indicating enhanced Li-ion diffusion in the co-doped sample. Electrochemical tests in half-cell mode showed that co-doped LFP achieved a 167, 153 and 145 mAhg⁻¹ capacity at 0.1, 1.0, and 2.0 C-rates, respectively. Inaddition, the co-doped composition showed excellent capacity retention, even at high C-rates i.e., 135 mAhg⁻¹ with 90% retention after 500 cycles at 1C and 101.3 mAhg-1 with 70% retention after 1000 cycles at 2C. Also, the co-doped phase exhibited lower polarization and charge transfer resistance, highlighting its potential for high-performance lithium-ion batteries.

Graphical Abstract

The graphical image illustrates the discharge capacity and coulombic efficiency over 1000 cycles at 2C-rate. The discharge capacity reaching 101.3 mAhg⁻¹ (70% retention) after 1000 cycles, indicating decent long-term performance. Meanwhile, the coulombic efficiency remains consistent, close to 100%, demonstrating stable charge–discharge efficiency.

Highlighting the optimal performance of the Co-doped LFP cathode at a 2C rate over 1000 cycles with consistent coulombic efficiency.

Porous carbon fibers possess the advantages of high conductivity, rich channels for mass transport and electron transfer, and abundant anchoring sites for loading highly active components. These favorable merits enable porous carbon fibers to be promising candidates for serving as advanced electrocatalysts. To gain a deeper understanding of the merits and potentials of porous carbon fibers for boosting electrocatalytic reactions, this review summarizes recent advances in modifying them to boost electrocatalytic reactions. The review is started by discussing the typical synthesis methods of porous carbon fibers, which include the electrospinning-carbonization, in-situ composite growth, and chemical etching methods. Subsequently, some effective strategies for the modification of porous carbon fibers are also discussed, such as heteroatom doping, defect engineering, single-metal and their compounds doping, and coupling with other compounds. Moreover, the applications of porous carbon fibers for boosting electrocatalytic reactions (e.g., ORR, OER, HER, CO₂ reduction, nitrate reduction) are also comprehensively discussed, highlighting the significance of porous carbon fibers for boosting electrocatalytic reactions. Finally, the review also lists some challenges of this interesting field and proposes the direction for guiding the synthesis of more efficient electrocatalysts.

Accurately forecasting the remaining useful life (RUL) of lithium-ion batteries (LIBs), a critical component in electric vehicles, is closely tied to essential factors such as system safety, maintenance costs, and resource utilization efficiency. However, achieving accurate RUL predictions remains challenging due to complex nonlinear degradation from variable operating conditions. This study presents a hybrid predictive framework that combines a dual-stream Mamba (DSMamba) and a dynamic filtering frequency mixing learner (DFM) in a synergistic manner. First, Fast Fourier Transform (FFT) decomposes the capacity sequence into low-frequency trends and high-frequency components, forming a dynamic frequency-domain separation framework. A multi-layer perceptron (MLP) generates adaptive time-varying filter coefficients that amplify salient frequency bands and suppress irrelevant noise. This approach solves the frequency-domain mismatch of traditional fixed filters in dynamic degradation scenarios. Second, we propose the DSMamba architecture to extract cross-cycle dynamic features via coupled dual-input cross-connection blocks and state-space models. These two components work together, with the DFM refining the data by filtering out noise and DSMamba extracting meaningful dynamic features from the filtered data. This design also expands the receptive field of battery data representations and significantly enhances long-sequence feature capture capability. On two public datasets, CALCE and NASA, the proposed model achieves the best MAE and RMSE values of 0.0103 and 0.0181. Compared with the MambaSimple model that does not integrate dynamic filtering and dual-stream structure optimization, the proposed optimization model in this paper reduces the error rate by 13.2% and the root mean square error rate by 14.2%.

This paper synthesizes multi-element metal sulfide anode materials for sodium-ion batteries through rational experimental design, component control, and composite modification to enhance cycle life and electrochemical performance. An orthogonal experiment is designed to systematically investigate and optimize the solvothermal synthesis conditions for multi-component ZnCoNi sulfide composites. The electrochemical sodium storage performance of the optimized material is further improved by constructing heterogeneous nanostructures and carbon composite. Results show that after carbon composite, the charge specific capacity increases from 492.6 mAh·g⁻¹ to 528.4 mAh·g⁻¹ at 0.1 A·g⁻¹ after 50 cycles, with an 87.3% capacity retention rate compared to the first cycle. Moreover, the material maintains a capacity of 437.7 mAh·g⁻¹ after 500 cycles at 1.0 A·g⁻¹, demonstrating enhanced cycling stability. The synergistic interactions at the heterostructure interfaces also significantly boost the overall electrochemical performance of the battery.

Electric vehicles (EVs) rely on lithium-ion batteries (LIBs) due to their high energy density, lightweight design, and long lifespan. Also, sudden dendrite formation in LIBs can lead to battery failure, reduced performance, safety hazards, and increased maintenance costs. But, none of the existing works provided timely alerts and focused on mitigation strategies during sudden dendrite formation in LIBs. To overcome this challenge, this paper introduces an integrated framework combining a Tsallis Sin-Swish ridge-based feed forward neural network (T2SR-FFNN), ZBellSin-fuzzy (ZBS-Fuzzy), and droop control system (DCS). Initially, the EV dataset is collected for the EV-power demand prediction. Then, the input data are preprocessed. From the preprocessed EV data, the features are gathered for predicting the power demand of EVs. On the LiB side, the dataset undergoes balancing, preprocessing, and feature extraction, thus enabling accurate state estimation using LM-EKF and effective cell balancing utilizing 2SCSB. LIB modeling with voltage, current, and temperature inputs supports real-time battery step-size prediction, whereas impedance measurement detects dendrite formation. A ZBS-Fuzzy system generates alerts, and if risks are detected, then a DCS is activated to mitigate hazards. Experimental validation demonstrates that the proposed framework enhances power demand prediction accuracy (i.e., 98.72%), ensures safe battery operation, and outperforms traditional methods in managing EV performance and safety.