Search Results (275)

With the advancement of global carbon reduction efforts and the rapid development of battery industries, the scale of spent lithium-ion batteries (LIBs) has increased dramatically. Extracting lithium from spent LIBs to synthesize Li₄SiO₄ sorbents not only addresses the challenge of battery recycling but also reduces the production cost of CO₂ sorbents, making it a research hotspot. However, the CO₂ adsorption behavior of these sorbents under the effect of impurities may differ from the traditional Li₄SiO₄, and there is a lack of systematic research on the adsorption kinetics. To address this issue, two Li₄SiO₄ sorbents are prepared from spent ternary LIBs, and their adsorption kinetics are comprehensively investigated using classical kinetic models. Results show that the reaction order of LSO and Na-LSO is 0.41 and 1.63, respectively, with activation energies of 72.93 kJ/mol and 99.23 kJ/mol in the initial kinetic-controlled stage, and 323.15 kJ/mol and 176.79 kJ/mol in the following diffusion-controlled stage. In the cyclic processes, loss-in-capacity is observed on LSO due to the simultaneous decrease in rate constants in both the kinetic and diffusion-controlled stages, while Na-LSO could almost maintain its capacity by having a much bigger rate constant during the kinetic-controlled stage. This study reveals the adsorption kinetics of Li₄SiO₄ prepared from spent LIBs and could provide theoretical support for the targeted design of efficient and low-cost CO₂ sorbents. Full article

Background: Bipolar disorder (BD) is a chronic and disabling psychiatric condition characterized by recurring episodes of mania, hypomania, and depression. Despite the availability of mood stabilizers, antipsychotics, and antidepressants, long-term management remains challenging due to incomplete symptom control, adverse effects, and high relapse rates. Methods: This paper is a narrative review aimed at synthesizing emerging trends and future directions in the pharmacological treatment of BD. Results: Future pharmacotherapy for BD is likely to shift toward precision medicine, leveraging advances in genetics, biomarkers, and neuroimaging to guide personalized treatment strategies. Novel drug development will also target previously underexplored mechanisms, such as inflammation, mitochondrial dysfunction, circadian rhythm disturbances, and glutamatergic dysregulation. Physiological endophenotypes, such as immune-metabolic profiles, circadian rhythms, and stress reactivity, are emerging as promising translational tools for tailoring treatment and reducing associated somatic comorbidity and mortality. Recognition of the heterogeneous longitudinal trajectories of BD, including chronic mixed states, long depressive episodes, or intermittent manic phases, has underscored the value of clinical staging models to inform both pharmacological strategies and biomarker research. Disrupted circadian rhythms and associated chronotypes further support the development of individualized chronotherapeutic interventions. Emerging chronotherapeutic approaches based on individual biological rhythms, along with innovative monitoring strategies such as saliva-based lithium sensors, are reshaping the future landscape. Anti-inflammatory agents, neurosteroids, and compounds modulating oxidative stress are emerging as promising candidates. Additionally, medications targeting specific biological pathways implicated in bipolar pathophysiology, such as N-methyl-D-aspartate (NMDA) receptor modulators, phosphodiesterase inhibitors, and neuropeptides, are under investigation. Conclusions: Advances in pharmacogenomics will enable clinicians to predict individual responses and tolerability, minimizing trial-and-error prescribing. The future landscape may also incorporate digital therapeutics, combining pharmacotherapy with remote monitoring and data-driven adjustments. Ultimately, integrating innovative drug therapies with personalized approaches has the potential to enhance efficacy, reduce adverse effects, and improve long-term outcomes for individuals with bipolar disorder, ushering in a new era of precision psychiatry. Full article

With the rapid increase in global lithium demand, the exploration of newly discovered lithium in the bauxite of the Wenshan area in southeastern Yunnan has become increasingly important. However, the current research on clay-type lithium in the Wenshan area has primarily focused on local exploration, and large-scale predictive metallogenic studies remain limited. To address this, this study utilized multi-source remote sensing data from ZY1-02D and ASTER, combined with ALOS 12.5 m DEM and Sentinel-2 imagery, to carry out remote sensing mineral identification, structural interpretation, and prospectivity mapping for clay-type lithium in the Wenshan area. This study indicates that clay-type lithium in the Wenshan area is controlled by NW, EW, and NE linear structures and are mainly distributed in the region from north of the Wenshan–Malipo fault to south of the Guangnan–Funing fault. High-value areas of iron-rich silicates and iron–magnesium minerals revealed by ASTER data indicate lithium enrichment, while montmorillonite and cookeite identification by ZY1-02D have strong indicative significance for lithium. Field verification samples show the highest Li₂O content reaching 11,150 μg/g, with six samples meeting the comprehensive utilization criteria for lithium in bauxite (Li₂O ≥ 500 μg/g) and also showing an enrichment of rare earth elements (REEs) and gallium (Ga). By integrating stratigraphic, structural, mineral identification, geochemical characteristics, and field verification data, ten mineral exploration target areas were delineated. This study validates the effectiveness of remote sensing technology in the exploration of clay-type lithium and provides an applicable workflow for similar environments worldwide. Full article

As global climate change intensifies and the transition to clean energy accelerates, lithium-ion batteries—critical components of electric vehicles—are becoming increasingly vital in international trade networks. This study investigates the structural evolution and determinants of the electric vehicle lithium-ion battery trade network among European Union (EU) member states from 2012 to 2023, employing social network analysis and the multiple regression quadratic assignment procedure method. The findings demonstrate the transformation of the network from a centralized and loosely connected structure, with Germany as the dominant hub, to a more interconnected and decentralized system in which Poland and Hungary emerge as the leading players. Key network metrics, such as the density, clustering coefficients, and average path lengths, reveal increased regional trade connectivity and enhanced supply chain efficiency. The analysis identifies geographic and economic proximity, logistics performance, labor cost differentials, energy resource availability, and venture capital investment as significant drivers of trade flows, highlighting the interaction among spatial, economic, and infrastructural factors in shaping the network. Based on these findings, this study underscores the need for targeted policy measures to support Central and Eastern European countries, including investment in logistics infrastructure, technological innovation, and regional cooperation initiatives, to strengthen their integration into the supply chain and bolster their export capacity. Furthermore, fostering balanced inter-regional collaborations is essential in building a resilient trade network. Continued investment in transportation infrastructure and innovation is recommended to sustain the EU’s competitive advantage in the global electric vehicle lithium-ion battery supply chain. Full article

As an important component of current power and energy storage systems, lithium-ion batteries have essential scientific significance and application value in terms of accurately and reliably diagnosing their aging to determine system performance, identify potential faults in modules, and prolong their service life. For this purpose, this paper first briefly describes the working principle of lithium-ion batteries and illustrates the possible impacts of various aging mechanisms on the state of battery capacity. Secondly, starting from both implementable and laboratory perspectives, it sorts out and summarizes the diagnostic mechanisms and applicable scenarios of current typical battery aging state assessment and diagnosis methods. Then, targeting the specific aging mechanisms involved in batteries, it elaborates on the targeted diagnosis processes for each aging mechanism. Finally, combined with implementable and laboratory diagnosis methods, it systematically summarizes a highly standardized and universal routine diagnosis process for battery aging. In addition, in combination with the latest development of aging diagnosis and related technologies, this paper reflects on and discusses the possible future development directions of battery diagnosis technologies. Full article

There is an increasing demand for the development of efficient and sustainable battery recycling processes. Currently, many recycling processes rely on toxic inorganic acids to recover materials from high-value battery chemistries such as lithium nickel manganese cobalt oxides (NMCs) and lithium cobalt oxide (LCOs). However, as cell manufacturers seek more cost-effective battery chemistries, the value of the spent battery value chain is increasingly diluted by chemistries such as lithium iron phosphate (LFPs). These cheaper alternatives present a difficulty when recycling, as current recycling processes are geared towards dealing with high-value chemistries; thus, the current processes become less economical. To date, much research is focused on treating a single battery chemistry; however, often, the feed material entering a battery recycling facility is contaminated with other battery chemistries, e.g., LFP feed contaminated with NMC, LCO, or LMOs. This research aims to selectively leach various battery chemistries out of a mixed feed material with the aid of a green organic acid, namely oxalic acid. When operating at the optimal conditions (2% solids, 0.25 M oxalic acid, natural pH around 1.15, 25 °C, 60 min), this research has proven that oxalic acid can be used to selectively dissolve 95.58% and 93.57% of Li and P, respectively, from a mixed LFP-NMC mixed feed, all while only extracting 12.83% of Fe and 8.43% of Mn, with no Co and Ni being detected in solution. Along with the high degree of selectivity, this research has also demonstrated, through varying the pH, that the selectivity of the leaching system can be altered. It was determined that at pH 0.5 the system dissolved both the NMC and LFP chemistries; at a pH of 1.15, the LFP chemistry (Li and P) was selectively targeted. Finally, at a pH of 4, the NMC chemistry (Ni, Co and Mn) was selectively dissolved. Full article

Microscopic image automatic recognition is a core technology for mineral composition analysis and plays a crucial role in advancing the intelligent development of smart mining systems. To overcome the limitations of traditional lithium ore analysis and meet the challenges of deployment on edge devices, we propose PS-YOLO-seg, a lightweight segmentation model specifically designed for lithium mineral analysis under visible light microscopy. The network is compressed by adjusting the width factor to reduce global channel redundancy. A PSConv-based downsampling strategy enhances the network’s ability to capture dim mineral textures under microscopic conditions. In addition, the improved C3k2-PS module strengthens feature extraction, while the streamlined Segment-Efficient head minimizes redundant computation, further reducing the overall model complexity. PS-YOLO-seg achieves a slightly improved segmentation performance compared to the baseline YOLOv12n model on a self-constructed lithium ore microscopic dataset, while reducing FLOPs by 20%, parameter count by 33%, and model size by 32%. Additionally, it achieves a faster inference speed, highlighting its potential for practical deployment. This work demonstrates how architectural optimization and targeted enhancements can significantly improve instance segmentation performance while maintaining speed and compactness, offering strong potential for real-time deployment in industrial settings and edge computing scenarios. Full article

Background: Emotional dysregulation (ED) is a transdiagnostic feature of multiple adolescent psychiatric disorders and a predictor of functional impairment and self-harming behaviors. Despite its clinical relevance, pharmacological treatments targeting ED in youth remain underexplored. This retrospective study investigated the clinical effectiveness and tolerability of adjunctive lithium therapy in adolescents with severe ED, independent of specific diagnostic categories. Methods: A total of 35 inpatients (13–17 years) with significant ED were divided into two groups based on pharmacological treatment: lithium add-on therapy (Li group, n = 17) and standard therapy without lithium (Control group, n = 18). Clinical severity (CGI-S) and global functioning (C-GAS) were assessed at baseline (T0), 6 months (T1), and 12 months (T2). A mixed-design ANOVA was performed to assess group × time interactions. Adverse events and treatment adherence were also examined. Results: At T1, the Li group showed a significantly greater reduction in symptom severity (CGI-S) compared to the Control group (p = 0.029). Global functioning (C-GAS) improved over time in both groups (p < 0.001), with no significant interaction effects. Adverse effects, primarily metabolic and endocrine, were more frequent in the Li group but did not reduce adherence. Conclusions: Adjunctive lithium therapy may reduce symptom severity in adolescents with severe ED without negatively affecting treatment tolerability or adherence. These findings support the potential utility of lithium in complex adolescent cases and warrant further prospective research. Full article

Accurate prediction of lithium-ion battery capacity degradation under dynamic loads is crucial yet challenging due to limited data availability and high cell-to-cell variability. This study proposes a Latent Gaussian Process (GP) model to forecast the full distribution of capacity fade in the form of high-dimensional histograms, rather than relying on point estimates. The model integrates Principal Component Analysis with GP regression to learn temporal degradation patterns from partial early-cycle data of a target cell, using a fully degraded reference cell. Experiments on the NASA dataset with randomized dynamic load profiles demonstrate that Latent GP enables full-lifecycle capacity distribution prediction using only early-cycle observations. Compared with standard GP, long short-term memory (LSTM), and Monte Carlo Dropout LSTM baselines, it achieves superior accuracy in terms of Kullback–Leibler divergence and mean squared error. Sensitivity analyses further confirm the model’s robustness to input noise and hyperparameter settings, highlighting its potential for practical deployment in real-world battery health prognostics. Full article

Fault diagnosis accuracy in lithium-ion battery-based energy storage systems is significantly constrained by the limited availability of fault-specific datasets. This study addresses this critical issue by proposing a diffusion-based data augmentation methodology tailored explicitly for battery fault diagnosis scenarios. The proposed conditional diffusion model leverages transfer learning and attention-enhanced fine-tuning strategies to generate high-quality synthetic fault data, ensuring targeted representation of rare fault conditions. By integrating condition-aware sampling strategies, the approach effectively mitigates mode collapse issues frequently encountered in adversarial generative methods, thus substantially enriching the diversity and quality of fault representations. Comprehensive evaluation using statistical similarity measures and downstream classification tasks demonstrates notable improvements. After the integration of attention mechanisms, the Pearson correlation coefficient between the synthetic and real samples increases from 0.29 to 0.91. In downstream diagnostic tasks, models trained on augmented datasets exhibit substantial gains in regards to the recall and F1-score, which increase from near-zero levels to values exceeding 0.91 for subtle overcharge and overdischarge faults. These results confirm the effectiveness and practical utility of the proposed augmentation approach in enhancing diagnostic performance under data-scarce conditions. Full article

Lithium, a critical element for clean energy and modern technologies, plays an indispensable role in advancing renewable energy storage, electric vehicles, and high-tech industries. The rapidly growing demand for lithium, along with its limited global production, has led to concerns about the sustainability of current extraction and processing technologies for efficient lithium recovery. This comprehensive review explores global trends in lithium processing, focusing on spodumene beneficiation and extraction techniques. While highlighting well-established conventional processes, such as dense media separation (DMS), flotation, high-temperature roasting, and acid or alkali treatment, it underscores the environmental and economic challenges of these processes, particularly when applied to low-grade lithium ores, which are increasingly being targeted to meet lithium demand. Innovative methods, such as microwave irradiation, are also explored for their potential to improve process efficiency, reduce energy consumption, and minimize environmental impact, offering promising pathways to overcome the limitations of traditional lithium recovery techniques. A significant contribution of this review is its focus on the largely untapped lithium resources of Kazakhstan, presenting geological insights and the potential for sustainable development. By addressing knowledge gaps and integrating technological, eco-friendly, and regional development perspectives, this study provides valuable insights for advancing lithium processing toward more sustainable and circular practices aligned with global climate and resource efficiency goals. Full article

The European Union (EU) has put forward a new regulatory framework for batteries through the EU Batteries Regulation (2023/1542), which sets a series of minimum thresholds of recycled materials for electric vehicle (EV) batteries sold on the EU market. Since the EU is the largest market for China’s EV export, compliance with the EU Batteries Regulation is a prerequisite for China’s EV export. To evaluate the feasibility of meeting these regulatory requirements, a future-oriented Chinese EV recycled materials use potential analysis model has been developed, forecasting the maximum proportion of recycled materials in China’s EV batteries from 2020 to 2035. To find out the risk factors, influencing aspects such as battery lifespan, demand, technology development, collection rate, and battery reshoring have been considered. The findings indicate that compared to other metals, the maximum proportion of recycled lithium is the lowest, forecast to be 21.2% in 2031, and increasing to 28.3% by 2035. Conversely, the maximum proportion of recycled graphite is the highest, at 28.9% in 2031 and reaching 41.3% in 2035. These results suggest that Chinese EV batteries could meet the targets set by the EU Batteries Regulation in most scenarios. Moreover, the analysis indicates that battery lifespan and collection rate constitute significant risk factors potentially influencing the recycled material content in Chinese EV batteries, which in turn impacts Chinese EV export to the EU. Finally, policy recommendations are proposed to enhance EV export and to bolster EV battery recycling industry development. Full article

The recycling of lithium-ion batteries is picking up rather slowly, although recent rapid growth in consumption and increasing prevalence of battery electric vehicles have increased the quantity of recoverable material from past years of production. Yet, the diversity of different product types i.e., chemistries and product life spans complicates the recovery of raw materials. At present, large-scale industrial recycling of lithium-ion batteries employs (1) pyrometallurgy, with downstream hydrometallurgy for recovery of refined metals/salts; and (2) hydrometallurgy, requiring upstream mechanical shredding of cells and/or modules. Regulatory requirements, especially in Europe, and the high industry concentration along the lithium-ion battery value chain drive recycling efforts forward. The present study aims to quantify the potential contribution of 2nd lithium from recycling to battery production on a global and European scale up to 2050. The overall recycling output of lithium in any given year depends on the interactions between several different factors, including past production, battery lifetime distributions, and recovery rates, all of which are uncertain. The simplest way to propagate input uncertainties to the final results is to use Monte Carlo-type simulations. Calculations were done separately for EVs and portable batteries. The overall supply of lithium from recycling is the sum of the contributions from EVs and portable electronics from both the EU and the RoW in each battery production scenario. Results show a total global supply of recycled lithium below 20% in each scenario until 2050. On the EU level, the contribution of recycled lithium may reach up to 50% due to the high collection and recovery rate targets. Full article

This paper presents a novel adaptive cell recombination strategy for balancing lithium-ion battery packs, targeting electric vehicle (EV) applications. The proposed method dynamically adjusts the series–parallel configuration of individual cells based on instantaneous state of charge (SoC) and load demand, without relying on conventional DC-DC converters or passive components. A hardware-efficient switching topology using SPDT (Single Pole Double Throw) switches enables flexible recombination and fault isolation with minimal complexity. The control algorithm, implemented in MATLAB/Simulink, evaluates multiple cell-grouping configurations to optimize balancing speed, energy retention, and operational safety. Simulation results under charging, discharging, and resting conditions demonstrate up to 80% faster balancing compared to sequential methods, with significantly lower component count and minimal energy loss. Validation using Panasonic NCR18650PF cells confirms the model’s real-world applicability. The method offers a scalable, high-speed, and energy-efficient solution for integration into next-generation battery management systems (BMS), achieving performance gains typically reserved for more complex converter-based architectures. Full article

The electrode materials from spent lithium-ion batteries consist of graphite and lithium cobalt oxides (LCO), which cannot be efficiently separated by the conventional flotation technique due to the fine size distributions of graphite and LCO. In this work, nanobubbles were introduced to the flotation system of electrode materials. Nanobubbles were produced with the method of temperature difference. Different degrees of gas oversaturation in the water/slurry were achieved by raising the temperature of cold water (stored at 4 °C for at least 72 h) to target values of 20 °C, 25 °C, and 30 °C. It was found that the height and lateral distance of nanobubbles increased with the degree of gas oversaturation of water. In addition, the larger graphite agglomerations were observed to form in the presence of nanobubbles. The D₅₀ (chord length) of graphite agglomerations increased by 8 μm, 11 μm, and 21 μm, respectively, compared with the D₅₀ of graphite in natural water. More graphite agglomerations adhered to a captive bubble with the aid of nanobubbles than in the case of no nanobubbles, which was indicated by increased wrapping angles of graphite (agglomerations) adhering to a captive bubble. Furthermore, the maximum adhesion force between a captive bubble and substrate increases to 220, 270, and 300 μN as cold water temperature increases to 20, 25, and 30 °C, respectively. The frost of nanobubbles on a graphite surface and the resulting graphite agglomerations through the bridging effect of nanobubbles are thought to be responsible for the improved flotation performance of electrode materials. The present results indicate that the flotation performance of fine minerals can be regulated by regulating the gas oversaturation degree of the slurry. Full article