Specific ion effects (SIEs) play a pivotal role in governing particle suspension behavior and manipulation across diverse scientific and industrial fields. This review summarizes the underlying mechanisms of SIEs, focusing on ion accumulation at particle interfaces, Hofmeister series implications, electrokinetic properties, and their impact on coagulation, dispersion, and rheological characteristics. It examines the interplay among ion identity, concentration, and surface properties— including hydrophilicity, hydrophobicity, and surface-charge density— offering critical insights into particle–particle interactions. Particular emphasis is placed on the structural and electrostatic roles of the electrical double layer and the Stern layer. Recent advances in understanding the microscopic origins of SIEs, facilitated by innovative experimental approaches and computational modeling, are also highlighted. This review explores the influence of SIEs on aggregate formation and stability, gelation behavior, and suspension viscoelastic properties. These insights underscore the significance of accounting for SIEs across disciplines such as chemistry, industry, agriculture, environmental science, biology, and medical science, and emphasize the need for continued advances to realize innovative applications and transformative solutions.
Silicon-nitride ceramic substrates exhibit high thermal conductivity, high electrical insulation, high mechanical strength, low expansion. The Cu-clad ceramic substrate is the key material for the encapsulation of insulated gate bipolar transistor modules. In this review, we divide the research of high thermal conductivity silicon nitride ceramics into four stages: 1) exploration of thermal conductivity with different sintering additives; 2) using the anisotropy of silicon nitride grains to improve the thermal conductivity, through the addition of silicon nitride grain seeds combined with tape casting technology, sintered to obtain a thermal conductivity of 155 W/(m·K) silicon nitride, and with the development of the strong magnetic field alignment technology in the later stage, the thermal conductivity can be increased to 176 W/(m·K); 3) the use of sintered reaction-bonded silicon nitride technology to improve thermal conductivity, thermal conductivity can be increased to about 180 W/(m·K); 4) the application of high thermal conductivity silicon nitride ceramic substrate. Finally, future preparation methods and applications of high thermal conductivity silicon nitride ceramics are envisioned.
Particle characterization underpins a wide range of fields, including mineral processing, materials science, nanotechnology, pharmaceuticals, food processing, agriculture, and environmental applications, by providing essential information on particle size, shape, surface properties, and size distribution within material systems. Particle characterization techniques have undergone a significant transformation over the last 70 years, moving from simple instruments like sieving and sedimentation to AI-driven analytics and novel imaging technologies that examine structures at the nanoscale and even atomic scale. Because they relied on fundamental physical principles and diffraction limitations, early characterization techniques in the 1950s, like gravity sedimentation and optical microscopy, provided limited results. The introduction of light scattering techniques in the 1970s, such as dynamic light scattering (DLS) and laser diffraction, significantly improved accuracy and efficiency of particle size analysis. The development of atomic force microscopy (AFM), electron microscopy techniques including scanning electron microscopy (SEM) and transmission electron microscopy (TEM), X-ray diffraction (XRD), and nanoparticle tracking analysis (NTA) increased the capacity to analyze complex and ultra-small particles precisely. These innovations are driving groundbreaking applications across various domains, including drug delivery systems, sustainable materials development, and environmental monitoring. Despite these technological advancements, challenges such as scalability in industrial plants, environmental interference, and computational accuracy still pose significant hurdles that must be overcome to fully unlock the potential of next-generation characterization techniques. The ongoing convergence of nanotechnology, AI-driven analytics, and cutting-edge imaging technologies holds immense promise for the future of particle characterization and plant-scale applications. This review traces the historical evolution of particle characterization, highlights key technological milestones over different periods, and explores its future as a driving force for innovation in science and technology.
The valorization of Sargassum into valuable carbon materials is not only driven by the need to manage its massive accumulation on beaches every year, posing ecological, economic, and health challenges, but also by its unique composition rich in carbohydrates and bioactive materials, making it an ideal precursor for carbon-rich materials. Among various thermal transformation methods, hydrothermal carbonization, slow pyrolysis, and microwave-assisted methods (microwave-assisted hydrothermal carbonization and microwave-assisted pyrolysis) have been employed to convert Sargassum into solid carbon-based materials, such as hydrochar, biochar, carbon quantum dots (CQDs), and activated carbon. These materials have demonstrated a significant role in diverse applications, including water purification, soil enhancement, energy storage, catalysis, and biomedical fields. This review discusses these synthesis methods, essential pretreatment processes to prepare Sargassum before the synthesis processes, and posttreatment processes aimed at enhancing the carbon material properties. Finally, the work also discusses the challenges of Sargassum valorization and presents future directions. Given the scale of Sargassum overgrowth affecting coastlines in the Caribbean, Gulf of Mexico, and Atlantic Ocean, this review is significant in highlighting strategies for transforming marine biomass into high-value carbon materials and provides a comprehensive analysis of the existing studies on the conversion of Sargassum into carbon materials.
Metal oxide nanoparticles (NPs) are extensively employed in the biomedical, environmental, and industrial domains due to their unique physicochemical properties. However, concerns regarding their potential cytotoxicity require the development of accurate predictive models to assess nanoparticle safety. In this study, we present a machine learning-based framework for predicting the toxicity of metal oxide NPs using curated physicochemical descriptors. Data were systematically extracted and structured from 140 peer-reviewed publications, focusing on four representative metal oxide nanoparticles (ZnO, AgO, CuO, SiO2). To ensure accessibility and consistency, the dataset was structured using a Large Language Model (LLM) API and designed to be well-balanced and minimally correlated. The maintenance of a low correlation between features (average Pearson correlation = 0.19) was prioritized to reduce redundancy and improve the interpretability of the model results. Feature selection and Principal Component Analysis (PCA) confirmed that a subset of physical descriptors effectively captured toxicityrelated trends. The optimized Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) models achieved predictive accuracies of 77 % and 81 %, respectively, without overfitting. In addition, a synthetic dataset was generated to investigate the joint effects of core size and exposure dosage on toxicity probability. Overall, this study aims to provide a predictive approach framework for nanotoxicity assessment that might offer guidance for the rational design of safer nanoparticles.
