Magnetochemistry

The tumor microenvironment, characterized by higher acidity, hypoxia, and dense cellular structures, plays a pivotal role in cancer progression, therapeutic resistance, and treatment response. Nanoparticle-based contrast agents enable the precise delineation of solid regions within heterogeneous tumors through advanced molecular imaging techniques. Since 1956, ultrasound (US) medical imaging has provided essential anatomical and functional insights about internal organs. More recently, magnetomotive ultrasound (MMUS) has emerged as a promising imaging modality, using a modulated magnetic field to exert force on superparamagnetic iron oxide nanoparticles (SPIONs), inducing motion in the surrounding tissues through mechanical coupling. In parallel, magnetic hyperthermia (MH), which employs localized heating by alternating magnetic fields, has demonstrated significant potential in selectively destroying cancer cells while sparing healthy tissues. This review summarizes the current state of IONP-based contrast agents, with particular emphasis on their use in MH for cancer treatment, as well as their potential in multimodal imaging, including MMUS, and photoacoustic (PA) imaging. The advantages and limitations of IONPs in tumor detection and characterization are discussed, examining the development of surface-functionalized MNPs, and analyzing how material properties and environmental factors affect their diagnostic and therapeutical performance. Finally, strategies for combining MMUS and PA modalities for pre-clinical cancer imaging are proposed.

Magnetic fluid sealing is a novel sealing technology wherein magnetic fluids play a pivotal role in the sealing process. The yield stress of the magnetic fluid directly affectsits sealing performance and is governed by multiple interdependent factors. Conventional approaches that evaluate the effect of a single parameter while keeping other parameters constant are insufficient to fully characterize the relative contributions of each parameter to the yield stress. In this study, we investigate the preparation factors affecting the yield stress of kerosene-based magnetic fluids and propose a parameter sensitivity analysis method based on orthogonal experimental design to determine the optimal combination of factor levels within the studied range. The sensitivity of key preparation factors affecting the yield stress of kerosene-based magnetic fluids was determined via range and variance analyses of the orthogonal experimental data. The factors, ranked in descending order of sensitivity, were surfactant (C₁₈H₃₄O₂) dosage, precipitant (NH₃·H₂O) dosage, and deionized water (H₂O) volume. Moreover, the effects of different levels of the same factor were analyzed using multiple approaches. These findings provide a theoretical foundation for optimizing the preparation of magnetic fluids and enhancing their sealing performance.

Lanthanide-based single-molecule magnets are promising candidates for potential applications. Their magnetism is governed by ligand-field splittings, which may require up to 27 ligand-field parameters for accurate modeling. Determining these parameters reliably from measured data is a major challenge, for which machine learning approaches offer promising solutions. We provide an overview of these approaches and present our perspective on addressing the inverse problem relating experimental data to ligand-field parameters. Previously, a machine learning architecture combining a variational autoencoder (VAE) and an invertible neural network (INN) showed promise for analyzing temperature-dependent magnetic susceptibility data. In this work, the VAE-INN model is extended through data augmentation to enhance its tolerance to common experimental inaccuracies. Focusing on second-order ligand-field parameters, diamagnetic and molar-mass errors are incorporated by augmenting the training dataset with experimentally motivated error distributions. Tests on simulated experimental susceptibility curves demonstrate substantially improved prediction accuracy and robustness when the distributions correspond to realistic error ranges. When applied to the experimental susceptibility curve of the complex ${\mathrm{Al}}_2^{\mathrm{III}}$${\mathrm{Er}}_2^{\mathrm{III}}$, the augmented VAE–INN recovers ligand-field solutions consistent with least-squares benchmarks. The proposed data augmentation thus overcomes a key limitation, bringing the ML approach closer to practical use for higher-order ligand-field parameters.