Search Results (122)

Brewers’ spent grain (BSG) is the dominant solid side stream from wort separation, generating about 20 kg wet BSG per 100 L of beer and contributing hundreds of millions of tons annually worldwide, and thus a strategic feedstock for circular solutions in the brewing sector. This study situates BSG within that sustainability context and assesses its performance for removing metal ions and organic contaminants. A critical literature review with selected techniques (SEM, NIR/MIR, TGA) has been combined. SEM reveals a rough, fibrous–lamellar microtexture with high pore density, large pore-area fractions, submicron median equivalent diameters, and elevated edge density, consistent with accessible surface and mass-transfer pathways. Compiled adsorption evidence shows that raw and engineered BSG effectively capture diverse cations, including Cu(II), Cr(III/VI), Pb(II), Mn(II), U(VI) and selected rare-earth elements (REEs), demonstrable reusability, and fixed-bed breakthrough on the order of tens to hundreds of hours. Preservation options (drying, cooling/freezing, thermal inactivation, oxygen control) that enable safe storage and logistics for deployment have also been outlined. Overall, BSG emerges as a reliable, scalable biosorbent, with SEM-derived descriptors providing practical tools for performance prediction, while spectroscopic and thermal methods support material monitoring and process integration within a brewery’s circular economy. Full article

Accurate prediction of the state of charge (SOC) of a battery pack is essential to improve the operational efficiency and safety of energy storage systems. In this paper, we propose a novel lithium-ion battery (Lib) pack SOC prediction framework that combines redundant control correlation downscaling with Adaptive Error Variation Weighting Mechanism (AVM) fusion mechanisms. By integrating redundancy feature selection based on correlation analysis with global sensitivity analysis, the dimensionality of the input features was reduced by 81.25%. The AVM merges BiGRU’s ability to model short-term dynamics with Informer’s ability to capture long-term dependencies. This approach allows for complementary information exchange between multiple models. Experimental results indicate that on both monthly and quarterly slice datasets, the RMSE and MAE of the fusion model are significantly lower than those of the single model. In particular, the proposed model shows higher robustness and generalization ability in seasonal generalization tests. Its performance is significantly better than the traditional linear and classical filtering methods. The method provides reliable technical support for accurate estimation of SOC in battery management systems under complex environmental conditions. Full article

Iron oxide nanoparticles (IONPs) have emerged as key materials in magnetic hyperthermia (MH), a minimally invasive cancer therapy capable of selectively inducing apoptosis, ferroptosis, and other cell death pathways while sparing surrounding healthy tissue. This review synthesizes advances in the design, functionalization, and biomedical application of magnetic nanoparticles (MNPs) for MH, highlighting strategies to optimize heating efficiency, biocompatibility, and tumor targeting. Key developments include tailoring particle size, shape, and composition; doping with metallic ions; engineering multicore nanostructures; and employing diverse surface coatings to improve colloidal stability, immune evasion, and multifunctionality. We discuss preclinical and clinical evidence for MH, its integration with chemotherapy, radiotherapy, and immunotherapy, and emerging theranostic applications enabling simultaneous imaging and therapy. Special attention is given to the role of MNPs in immunogenic cell death induction and metastasis prevention, as well as novel concepts for circulating tumor cell capture. Despite promising results in vitro and in vivo, clinical translation remains limited by insufficient tumor accumulation after systemic delivery, safety concerns, and a lack of standardized treatment protocols. Future progress will require interdisciplinary innovations in nanomaterial engineering, active targeting technologies, and real-time treatment monitoring to fully integrate MH into multimodal cancer therapy and improve patient outcomes. Full article

Single electron capture in collisions involving neutral hydrogen atoms impacted by highly charged dressed projectiles is theoretically investigated using the distorted wave formalism. A series of continuum distorted wave approximations is employed to investigate the electron capture from neutral hydrogen atom impact by boron and carbon projectiles. The projectile potential is described using a two-parameter analytical Green–Sellin–Zachor (GSZ) model potential. The theoretical prediction of total cross sections are compared against other theories and experiments. We looked at a very broad range of collision energies, from 10 keV/u up to 10 MeV/u. In addition, the state-selective cross sections for boron ions are presented. Full article

A fundamental challenge in lithium-ion battery charging is the inherent trade–off between charging speed and battery health. Fast charging tends to accelerate battery degradation, while slow charging extends downtime and intensifies range anxiety, heightening concerns over inadequate driving range during operation. This contradiction has become a key bottleneck restricting the advancement of electric vehicles. In response to the limitations of conventional charging strategies and optimization methods, which typically intensify this trade–off, this study proposes a novel two–stage fast charging optimization strategy for lithium–ion batteries. The proposed method first introduces a hybrid clustering algorithm that combines the canopy algorithm with bisecting K–means to achieve adaptive SOC staging. This staging is guided by the nonlinear characteristics of the internal resistance with respect to the state of charge (SOC), allowing for a data–driven division of charging phases. Following staging, a closed–loop optimization framework is developed. A wavelet neural network (WNN) is employed to precisely capture and approximate the nonlinear characteristics of the charging process for performance prediction, upon which a multi–strategy enhanced multi–objective particle swarm optimization (MOPSO) algorithm is applied to efficiently search for Pareto–optimal solutions that balance charging time and ohmic loss. In addition, an active learning mechanism is incorporated to refine the WNN using selectively sampled data iteratively, thereby improving prediction accuracy and the robustness of the optimization process. Experimental results demonstrate that when the SOC reaches 70%, the proposed method shortens the charging time by 12.5% and reduces ohmic loss by 31% compared with the conventional constant current–constant voltage (CC–CV) strategy, effectively achieving a balance between charging efficiency and battery health. Full article

Accurate lithium-ion battery state of health estimation is critical for safety and range anxiety mitigation. Existing methods often lack interpretability in the extraction of feature fragments and fail to model spatial correlations between features. To address these gaps, this paper introduces a novel framework centered on interpretable feature engineering and synergistic spatial–temporal learning. The core novelty lies in using incremental capacity (IC) analysis on charging data, captured by onboard sensors, to dynamically select a 0.1 V voltage window based on IC peaks, ensuring the extracted voltage and capacity fragments are physically meaningful. These fragments are then transformed into graph-structured data, enabling a graph attention network and a bi-directional gated recurrent unit to effectively capture both spatial dependencies and temporal degradation trends, with a residual connection optimizing the network. Validation on two public benchmark datasets demonstrates the model’s superiority, achieving an average mean absolute error of 0.561% and a root mean square error of 0.783%. Furthermore, the model exhibits a low computational footprint, requiring only 1.68 MFLOPs per inference, and its fast inference time of 17.55 ms on an embedded platform confirms its feasibility for practical deployment. Full article

A thermodynamically consistent model framework to describe ion transport in nanopores is presented. The continuum model unifies electro-diffusion and selective ion transport and extends the classical Poisson–Nernst–Planck (PNP) system for an idealized incompressible mixture by including finite ion size and solvation effects. Special emphasis is placed on the consistent modeling of the selectivity filter within the pore. It is treated as an embedded domain in which the constituents can change their chemical properties and mobility. Using this framework, we achieve good agreement with an experimentally observed current–voltage (IV) characteristic for an L-type selective calcium ion channel for a range of ion concentrations. In particular, we show that the model captures the experimentally observed anomalous mole fraction effect (AMFE). As a result, we find that calcium and sodium currents depend on the surface charge in the selectivity filter, the mobility of ions and the available space in the channel. Our results show that negative charges within the pore have a decisive influence on the selectivity of divalent over monovalent ions, supporting the view that AMFE can emerge from competition and binding effects in a multi-ion environment. Furthermore, the flexibility of the model allows its application in a wide range of channel types and environmental conditions, including both biological ion channels and synthetic nanopores, such as engineered membrane systems with selective ion transport. Full article

Environmental contamination is a critical global concern, primarily due to detrimental greenhouse gas (GHG) emissions, especially carbon dioxide (CO₂), which significantly contribute to climate change. Moreover, the presence of harmful heavy metals like Ni, Cd, Cu, Hg, and Pb in soil and water ecosystems has led to poor water quality. Noble metal nanoparticles (MNPs), for instance, Pd, Ag, Pt, and Au, have emerged as promising solutions for addressing environmental pollution. However, the practical utilization of MNPs faces challenges as they tend to aggregate and lose stability. To overcome this issue, the reverse double-solvent method (RDSM) was utilized to synthesis melamine-based porous polyaminals (POPs) as a supportive material for the in situ growing of silver nanoparticles (Ag NPs). The porous structure of melamine-based porous polyaminals, featuring aminal-linked (-HN-C-NH-) and triazine groups, provides excellent binding sites for capturing Ag⁺ ions, thereby improving the dispersion and stability of the nanoparticles. The resulting material exhibited ultrafine particle sizes for Ag NPs, and the incorporation of Ag NPs within the porous polyaminals demonstrated a high surface area (~279 m²/g) and total pore volume (1.21 cm³/g), encompassing micropores and mesopores. Additionally, the Ag NPs@POPs showcased significant capacity for CO₂ capture (2.99 mmol/g at 273 K and 1 bar) and effectively removed Cu (II), with a remarkable removal efficiency of 99.04%. The nitrogen-rich porous polyaminals offer promising prospects for immobilizing and encapsulating Ag nanoparticles, making them outstanding adsorbents for selectively capturing carbon dioxide and removing metal ions. Pursuing this approach holds immense potential for various environmental applications. Full article

A combined experimental and theoretical study is carried out on the single-electron capture process in He⁺–He collisions at energies ranging from 0.5 keV/u to 5 keV/u. Using cold target recoil ion momentum spectroscopy, we obtain state-selective cross sections and angular differential cross sections. Within the entire studied energy range, the dominant channel is the electron captured into the ground-state, and the relative contribution of the dominant channel shows a decreasing trend with increasing energy. The angular differential cross sections of ground-state capture exhibit obvious oscillatory structures. To understand the oscillatory structures of the differential cross sections, we also performed theoretical calculations using the two-center atomic orbital close-coupling method, which well reproduced the oscillatory structures. The results indicate that these structures are strongly correlated to the oscillatory structures of the impact parameter dependence of electron probability. Full article

Polymer inclusion membranes (PIMs) have undergone substantial advancements in their selectivity and efficiency, driven by their increasing deployment in separation processes, environmental remediation, and sensing applications. This review presents recent progress in the development of PIMs, focusing on strategies to enhance ion and molecule selectivity through the incorporation of novel carriers, including ionic liquids and task-specific extractants, as well as through polymer functionalization techniques. Improvements in mechanical and chemical stability, achieved via the utilization of high-performance polymers such as polyvinylidene fluoride (PVDF) and polyether ether ketone (PEEK), as well as cross-linking approaches, are critically analyzed. The expanded application of PIMs in the removal of heavy metals, organic micropollutants, and gas separation, particularly for carbon dioxide capture, is discussed with an emphasis on efficiency and operational robustness. The integration of PIMs with electrochemical and optical transduction platforms for sensor development is also reviewed, highlighting enhancements in sensitivity, selectivity, and response time. Furthermore, emerging trends towards the fabrication of sustainable PIMs using biodegradable polymers and green solvents are evaluated. Advances in scalable manufacturing techniques, including phase inversion and electrospinning, are addressed, outlining pathways for the industrial translation of PIM technologies. The review concludes by identifying current limitations and proposing future research directions necessary to fully exploit the potential of PIMs in industrial and environmental sectors. Full article

Background Plasma membrane proteins (PMPs) play key roles in cell signalling, adhesion, and trafficking, and are attractive therapeutic targets in cancer due to their surface accessibility. However, their typically low abundance limits detection by conventional proteomic approaches. Methods: To improve PMP detection, we employed a surface proteomics workflow combining cell surface biotinylation and affinity purification prior to LC-MS/MS analysis in cervical (SiHa) and bladder (UMUC3) cancer cell lines cultured under normoxic (21% O₂) or hypoxic (0.1% O₂) conditions. Results: In SiHa cells, 43 hypoxia-upregulated proteins were identified exclusively in the biotin-enriched fraction, including ITGB2, ITGA7, AXL, MET, JAG2, and CAV1/CAV2. In UMUC3 cells, 32 unique upregulated PMPs were detected, including CD55, ADGRB1, SLC9A1, NECTIN3, and ACTG1. These proteins were not observed in corresponding whole-cell lysates and are associated with extracellular matrix remodelling, immune modulation, and ion transport. Biotinylation enhanced the detection of membrane-associated pathways such as ECM organisation, integrin signalling, and PI3K–Akt activation. Protein–protein interaction analysis revealed links between membrane receptors and intracellular stress regulators, including mitochondrial proteins. Conclusions: These findings demonstrate that surface biotinylation improves the sensitivity and selectivity of plasma membrane proteomics under hypoxia, revealing hypoxia-responsive proteins and pathways not captured by standard whole-cell analysis. Full article

The exponential growth of lithium-ion battery consumption has amplified the urgency of identifying sustainable and economically viable recycling solutions. This study proposes an integrated decision-making framework based on the T-Spherical Fuzzy Einstein Interaction Aggregator DEMATEL-CoCoSo approach to comprehensively evaluate and rank battery recycling technologies under uncertainty. Ten key evaluation criteria—encompassing environmental, economic, and technological dimensions—were identified through expert consultation and literature synthesis. The T-Spherical Fuzzy DEMATEL method was first applied to analyze the causal interdependencies among criteria and determine their relative weights, revealing that environmental drivers such as energy consumption, greenhouse gas emissions, and waste generation exert the most systemic influence. Subsequently, six recycling alternatives were assessed and ranked using the CoCoSo method enhanced by Einstein-based aggregation, which captured the complex interactions present in the experts’ evaluations and assessments. Results indicate that Direct Recycling is the most favorable option, followed by the Hydrometallurgical and Bioleaching methods, while Pyrometallurgical Recycling ranked lowest due to its high energy demands and environmental burden. The proposed hybrid model effectively handles linguistic uncertainty, expert variability, and interdependent evaluation structures, offering a robust decision-support tool for sustainable technology selection in the circular battery economy. The framework is adaptable to other domains requiring structured expert-based evaluations under fuzzy environments. Full article

A State of Health (SOH) estimation of lithium-ion batteries is a core function of battery management systems, directly affecting the safe operation, lifetime prediction, and economic efficiency of batteries. However, existing methods still face challenges in balancing feature robustness and model generalization ability; for instance, some studies rely on features whose physical correlation with SOH lacks strict verification, or the models struggle to simultaneously capture the temporal dynamics of health factors and nonlinear mapping relationships. To address this, this paper proposes an SOH estimation method based on incremental capacity (IC) curves and a Temporal Convolutional Network—Relevance Vector Machine (TCN-RVM) model, with core innovations reflected in two aspects. Firstly, five health factors are extracted from IC curves, and the strong correlation between these features and SOH is verified using both Pearson and Spearman coefficients, ensuring the physical rationality and statistical significance of feature selection. Secondly, the TCN-RVM model is constructed to achieve complementary advantages. The dilated causal convolution of TCN is used to extract temporal local features of health factors, addressing the insufficient capture of long-range dependencies in traditional models; meanwhile, the Bayesian inference framework of RVM is integrated to enhance the nonlinear mapping capability and small-sample generalization, avoiding the overfitting tendency of complex models. Experimental validation is conducted using the lithium-ion battery dataset from the University of Maryland. The results show that the mean absolute error of the SOH estimation using the proposed method does not exceed 0.72%, which is significantly superior to comparative models such as CNN-GRU, KELM, and SVM, demonstrating higher accuracy and reliability compared with other models. Full article

Accurately monitoring coupled water–nitrogen stress is critical for wheat (Triticum aestivum L.) productivity under climate change. This study developed a machine learning framework utilizing multimodal leaf electrophysiological signals––intrinsic resistance, impedance, capacitive reactance, inductive reactance, and capacitance––to decouple water and nitrogen stress signatures in wheat. A parallel modelling strategy was implemented employing Gradient Boosting, Random Forest, and Ridge Regression, selecting the optimal algorithm per feature based on predictive performance. Controlled pot experiments revealed IZ as the paramount biomarker across leaf positions, indicating its sensitivity to ion flux perturbations under abiotic stress. Crucially, algorithm-feature specificity was identified: Ridge Regression excelled in modeling linear responses due to its superior noise suppression, while GB effectively captured nonlinear dynamics. Flag leaves during reproductive stages provided significantly more stable predictions compared to vegetative third leaves, aligning with their physiological primacy as source organs. This framework offers a robust, non-invasive approach for real-time water and nitrogen stress diagnostics in precision agriculture. Full article

Lithium-ion battery lifetime prediction traditionally relies on extensive full-cycle testing, which is costly and time-consuming. This paper proposes a novel framework for strategic sample optimization that significantly reduces testing requirements while preserving the ability to capture key degradation patterns. The approach combines combinatorial analysis with model-informed selection to identify a minimal yet representative subset of test conditions that span the primary stress axes, temperature, depth of discharge, and charge rate. A recent predictive modeling technique is then used to validate that the selected samples enable accurate lifetime estimation. Results demonstrate that as few as 3 samples (11% of the original 27-sample dataset) can achieve prediction errors below 2%, reducing cycling costs by approximately 90%. This framework offers a scalable solution for battery developers seeking to streamline accelerated aging protocols. Future work will extend the methodology to additional publicly available datasets to assess its generalizability across chemistries and use cases. Full article