Perovskite solar cells (PSCs) are regarded as the most promising new generation of green energy technology due to their outstanding device performance and simple processing technology. Traditional processing methods, such as thermal annealing and thermal evaporation, face significant challenges in further enhancing device performance and stability. In recent years, laser processing has garnered extensive attention from researchers due to its notable advantages in terms of speed, high efficiency, and controllability. In this review, we systematically summarize the role of laser in the active layer, transport layer, and electrode of perovskite photovoltaic cells. First, we systematically elucidate the mechanism governing the nucleation and crystallization of laser-processed perovskite films, along with its influence on the micro-nano structures of these films. Concurrently, a thorough explication of the micro-nano structures pertaining to the laser-processed transport layer, the interconnection between transport layers, the electrode, and their respective impacts on carrier transport and collection efficiency within the device will be provided. Most importantly, we believe that these approaches will provide scientists with new ways of thinking and system schemes for improving the performance and stability of perovskite solar cells.

Ensuring the accurate estimation of the state of health (SOH) of lithium-ion batteries (LIBs) is essential for the reliability and safe operation of battery management systems. The prediction of SOH has witnessed significant advancements recently, largely propelled by the powerful nonlinear modeling capabilities of deep learning. Despite these advancements, the intricate nature of the battery degradation process poses a challenge in accurately simulating it using measurement data. In this paper, we introduce a novel approach by focusing on the charging voltage deviation, which is defined as the discrepancy between the charging voltage and its average value over each charge/discharge cycle. This deviation is rooted in the electrochemical reactions that lead to capacity decay and voltage fluctuations. We propose a convolutional neural network-long short-term memory (CNN-LSTM) hybrid framework aimed at estimating the SOH of the battery. For each charge/discharge cycle, a conventional CNN is employed to extract key capacity features from sequential charging data, encompassing voltage deviation, current, and charging duration. Following this, an LSTM network is leveraged to build the long-term dependencies of battery capacities, facilitating the SOH prediction process. The experimental results indicate that our model not only simplifies the computational complexity but also significantly enhances the precision of SOH predictions. This innovative approach holds promise for the advancement of battery management systems, ensuring their continued reliability and safety.

In recent decades, lithium-ion batteries (LIBs) have been widely adopted for large-scale energy storage due to their long cycle life and high energy density. However, the high cost and limited natural abundance of lithium highlight the urgent need to develop alternative devices, such as sodium-ion batteries (SIBs), which utilize abundant and readily available resources. Among SIB cathode materials, P2-phase Ni-Mn materials have emerged as commercially viable candidates because of their high operating voltage, good specific capacity, excellent sodium-ion conductivity, and robust stability under environmental conditions. Nevertheless, the Jahn-Teller effect triggered by high-voltage phase transitions, Na⁺/vacancy ordering, and the presence of Mn³⁺ at low voltages collectively lead to structural degradation and performance decline during cycling. By varying the macroscopic structural design and surface coating, elemental doping introduces one or more ions at the atomic scale, adjusting the valence states and reducing the band gap. This effectively alters the electronic structure and the intrinsic lattice of the cathode material, thereby accelerating reaction kinetics and yielding high-performance material characteristics. This review delves into the research advancements pertaining to tailored structural engineering strategies to address these challenges for P2-phase Ni-Mn layered oxides.

Marine biomass presents a promising and sustainable pathway for advancing electrochemical energy storage (EES) technologies. This review provides a comprehensive, state-of-the-art examination of marine biomass-derived carbon as a high-performance electrode material for EES devices. The global abundance and distribution of marine biomass are discussed, followed by a detailed investigation into the chemical composition of various aquatic organisms. Key conventional synthesis methods for converting marine biomass into carbon are critically analyzed, emphasizing strategies to enhance electrochemical performance. Diverse applications of marine biomass-derived carbon in EES are explored, offering an in-depth evaluation of its electrochemical activity and mechanical properties in relation to structural variations. A dedicated section addresses the “Technology to Market” transition, presenting a strategic overview of the commercial potential of this material. Lastly, the review identifies current challenges and future opportunities, emphasizing the need for continued research into both structural innovations and scalable solutions to advance sustainable energy storage systems, addressing critical environmental and economic issues.

The current study explores the synthesis and electrochemical performance of potassium birnessite as a cathode material for sodium-ion batteries (SIBs), achieved through partial ion exchange resulting from partial potassium deintercalation followed by sodium intercalation during the first electrochemical cycle. Three samples of potassium birnessite (KB400, KB500, and KB600) are synthesized using a sol-gel method and subsequently calcined at different temperatures to evaluate the influence of crystal water and K⁺ ions on structural stability and their electrochemical performance. X-ray diffraction analysis confirms the formation of samples with high crystallinity. Additionally, X-ray fluorescence, X-ray photoelectron spectroscopy, and thermogravimetric analysis are employed to verify their chemical composition and oxidation states. Among the samples, KB500 exhibits the most favorable electrochemical performance, achieving a specific capacity of 175 mAh g^-1 at C/10 when cycled within a voltage range of 1.6-4.2 V. Long-term cycling tests at a narrower potential range of 2-3.6 V demonstrate promising values of 110 mAh g^-1 in capacity for KB500, with a retention of 90% over 80 cycles. The presence of potassium and interlayer water is crucial for enhancing structural stability and ion diffusion. These findings suggest that KB500 could serve as a promising cathode material for SIBs, providing a structurally stable option for energy storage applications.

In this study, we explore the electronic and optical properties of Cu₂ZnSn_1−xGe_xS₄ using density functional theory combined with hybrid functional calculations. Alloying Cu₂ZnSnS₄ with Ge and the formation of a band gap gradient are investigated as strategies to improve the efficiency of single-junction photovoltaic (PV) devices and as top cells in tandem solar cells. Our findings reveal that increasing Ge concentration leads to a rise in the band gap, with a small bowing constant (b ≈ 0.02 eV) indicating good miscibility of Ge in the host lattice. The electronic properties suggest that lower Ge incorporation may be optimal for PV applications. Additionally, device simulations were conducted to evaluate the impact of Cu₂ZnSn_1−xGe_xS₄ layer thickness on device performance, with and without a back surface field. The integration of first-principles calculations with SCAPS-1D simulations offers a comprehensive framework for predicting the performance of Cu₂ZnSn_1−xGe_xS₄ solar cells, highlighting the potential of Ge alloying for enhancing PV efficiency.

Transition metal molybdates (AMoO₄ where A = Ni, Co, Mn, Fe, and Zn) have attracted much attention as promising electrode materials for energy storage devices due to their multi-electron redox capability, higher electrical conductivity, good chemical and thermal stability, and stable crystal structure to get superior electrochemical performance. Transition metal molybdates and their graphene-based composites possess multidimensional morphology for supercapacitors. The morphology-dependent supercapacitor behavior has been reviewed in the present article. The formation mechanism of AMoO₄ nanostructures in the form of 1D, 2D, and 3D has been identified and respective supercapacitor behavior is outlined. The density functional theory based on the calculated electronic properties of AMoO₄ has been discussed. Additionally, the application of machine learning techniques in predicting and analyzing the relationships of AMoO₄ has been discussed for the first time. By leveraging ML algorithms, we identify key parameters influencing their energy storage capabilities, providing insights into the rational design of molybdate-based composites. Integrating experimental results with ML-driven optimization offers a novel pathway for accelerating the development of next-generation energy storage devices. In conclusion, future perspectives and challenges have been discussed.

Aqueous electrolytes, with their inherent safety, low cost, and eco-friendliness, provide a promising alternative for energy storage devices, but their application is limited due to the narrow electrochemical stability window of water. Using super-concentrated electrolytes has been demonstrated effectives in expanding the electrochemical window of aqueous electrolytes. However, this approach also brings in several challenges, including decreased ionic conductivity, poor wettability, and increased temperature sensitivity due to the near-saturated salt concentrations. In this study, we employed a water-miscible ionic liquid (i.e., 1-butyl-3-methylimidazolium trifluoromethanesulfonate) to break the solubility limitations faced in super-concentrated electrolytes and created a new “water in ionic liquid” electrolyte that simultaneously featured with broad electrochemical window, decent ionic conductivity, and wide temperature compatibility. Moreover, a prototype of electrochemical double-layer supercapacitor utilizing the “water in ionic liquid” electrolyte demonstrates outstanding performance characteristics, including a high operating voltage (2.6 V), excellent rate capability with 81% capacitance retention from 0.5 to 30 A g^-1, remarkable cyclic stability with 75% capacitance retention after 120,000 cycles, along with broad temperature compatibility from -20°C to 60°C. These findings not only provide new insights into electrolyte engineering but also offer a pathway for designing innovative aqueous electrolytes for energy storage devices with balanced electrochemical performance.