Search Results (649)

The accelerating global demand for sustainable energy, driven by population growth, industrialization, and environmental concerns, has intensified the search for renewable alternatives to fossil fuels. Biofuels, including bioethanol, biodiesel, biogas, and biohydrogen, offer a viable and practical pathway to reducing net carbon dioxide (CO₂) emissions. Yet, their large-scale production remains constrained by biomass recalcitrance, high pretreatment costs, and the enzyme-intensive nature of conversion processes. Recent advances in enzyme immobilization using magnetic nanoparticles (MNPs), covalent organic frameworks, metal–organic frameworks, and biochar have significantly improved enzyme stability, recyclability, and catalytic efficiency. Complementary strategies such as cross-linked enzyme aggregates, carrier-free immobilization, and site-specific attachment further reduce enzyme leaching and operational costs, particularly in lipase-mediated biodiesel synthesis. In addition to biocatalysis, nanozymes—nanomaterials exhibiting enzyme-like activity—are emerging as robust co-catalysts for biomass degradation and upgrading, although challenges in selectivity and environmental safety persist. Green synthesis approaches employing plant extracts, microbes, and agro-industrial wastes are increasingly adopted to produce eco-friendly nanomaterials and bio-derived supports aligned with circular economy principles. These functionalized materials have demonstrated promising performance in esterification, transesterification, and catalytic routes for biohydrogen generation. Technoeconomic and lifecycle assessments emphasize the need to balance catalyst complexity with environmental and economic sustainability. Multifunctional catalysts, process intensification strategies, and engineered thermostable enzymes are improving productivity. Looking forward, pilot-scale validation of green-synthesized nano- and biomaterials, coupled with appropriate regulatory frameworks, will be critical for real-world deployment. Full article

Independent vector analysis (IVA) has emerged as a powerful tool for fusing and analyzing functional magnetic resonance imaging (fMRI) data. Applying IVA to multi-task fMRI data enhances analytical power by capturing the relationships across different tasks in order to discover their underlying multivariate relationship to one another. Incorporation of prior information into IVA enhances the separability and interpretability of estimated components. In this paper, we demonstrate successful fusion of multi-task fMRI feature data under two settings: constrained IVA and constrained transposed IVA (tIVA). We show that using these methods for fusing multi-task fMRI feature data offers novel ways to improve the quality and interpretability of the analysis. While constrained IVA extracts components linked to distinct brain networks, tIVA reverses the roles of spatial components and subject profiles, enabling flexible analysis of behavioral effects. We apply both methods to a multi-task fMRI dataset of 247 subjects. We demonstrate that for task-based fMRI, structural MRI (sMRI) references provide a better match for task data than resting-state fMRI (rs-fMRI) references, and using sMRI priors improves identification of group differences in task-related networks, such as the sensory-motor network during the Auditory Oddball (AOD) task. Additionally, constrained tIVA allows for targeted investigation of the effects of behavioral variables by applying them individually during the analysis. For instance, by using the letter number sequence subtest, a measure of working memory, as a behavioral constraint in tIVA, we observed significant group differences in the auditory and sensory-motor networks during the AOD task. Results show that the use of two constrained approaches, guided by well-aligned structural and behavioral references, enables a more comprehensive analysis of underlying brain function as modulated by task. Full article

Experimental and numerical investigations of the alternating current (AC) susceptibility, $\mathsf{\chi }\left(H\right) ~ \mathrm{d}M/\mathrm{d}H,$ examined multigrain La_0.66Sr_0.34MnO₃ (LSMO) thin films (thickness d = 250 nm) grown by radio-frequency (RF) magnetron sputtering on lattice-mismatched yttria-stabilized zirconia YSZ(001) substrates. The films exhibit a columnar structure comprising two types of grains, with (001)- and (011)-oriented planes of a pseudocubic lattice aligned parallel to the film surface. Field- and angle-dependent AC susceptibility measurements at 78 K reveal characteristic peak- and tip-like anomalies, attributed to contributions from grains with three distinct directions of easy magnetization axes within the film plane. Numerical modeling based on the transverse susceptibility theory for single-domain ferromagnetic grains, incorporating first- and second-order anisotropy constants, corroborates the experimental findings and elucidates the role of different grain types in magnetization switching and AC susceptibility response. This study provides a quantitative determination of the three in-plane easy magnetization axes in LSMO/YSZ(001) films and clarifies their influence on the magnetization dynamics of multigrain thin films. The demonstrated control over multigrain LSMO/YSZ(001) thin films with distinct in-plane easy magnetization axes and well-characterized AC susceptibility suggests potential applications in magnetic memory, spintronic devices, and precision magnetic sensing. Full article

Accurate 3D reconstruction of guidewires is crucial in minimally invasive surgery and interventional procedures. Traditional biplanar X-ray–based reconstruction methods can achieve reasonable accuracy but involve high radiation doses, limiting their clinical applicability; meanwhile, single-view images inherently lack reliable depth cues. To address these issues, this paper proposes a multimodal guidewire 3D reconstruction approach that integrates magnetic field information. The method first employs the MiDaS v3 network to estimate an initial depth map from a single image and then incorporates tri-axial magnetic field measurements to enrich and refine the spatial information. To effectively fuse the two modalities, we design a multi-stage strategy combining nearest-neighbor matching (KNN) with a cross-modal attention mechanism (Cross-Attention), enabling accurate alignment and fusion of image and magnetic features. The fused representation is subsequently fed into a PointNet-based regressor to generate the final 3D coordinates of the guidewire. Experimental results demonstrate that our method achieves a root-mean-square error of 2.045 mm, a mean absolute error of 1.738 mm, and a z-axis MAE of 0.285 mm on the test set. These findings indicate that the proposed multimodal framework improves 3D reconstruction accuracy under single-view imaging and offers enhanced visualization support for interventional procedures. Full article

Low-back pain (LBP) is a leading cause of disability worldwide. When symptoms persist beyond 4–6 weeks, when red flags are suspected, or when precise patient selection for procedures is needed, imaging—primarily MRI (Magnetic Resonance Imaging)—becomes pivotal. The purpose is to provide a pragmatic, radiology-first roadmap that aligns an imaging phenotype with anatomical targets and appropriate image-guided interventions, integrating MRI-based phenotyping with image-guided interventions for subacute and chronic LBP. In this narrative review, we define operational MRI criteria to distinguish radicular from non-radicular phenotypes and to contextualize endplate/Modic and facet/sacroiliac degenerative changes. We then summarize selection and technique for major procedures: epidural and periradicular injections (including selective nerve root blocks), facet interventions with medial branch radiofrequency ablation (RFA), sacroiliac joint injections and lateral branch RFA, basivertebral nerve ablation (BVNA) for vertebrogenic pain, percutaneous disc decompression, minimally invasive lumbar decompression (MILD), and vertebral augmentation for painful fractures. For each target, we outline preferred and alternative guidance modalities (fluoroscopy, CT, or ultrasound), key safety checks, and realistic effect sizes and durability, emphasizing when to avoid low-value or poorly indicated procedures. This review proposes a phenotype-driven reporting template and a care-pathway table linking MRI patterns to diagnostic blocks and definitive image-guided treatments, with the aim of reducing cascade testing and therapeutic ambiguity. A standardized phenotype → target → tool approach can make MRI reports more actionable and help clinicians choose the right image-guided intervention for the right patient, improving outcomes while prioritizing safety and value. Full article

Wireless power transfer (WPT) technologies are advancing rapidly, yet their development trajectories within specific regional contexts remain underexplored. This review synthesizes India’s contributions to both near-field and far-field WPT research. We conducted a systematic literature survey spanning 2018–2024 to identify dominant technological themes, benchmark performance against global standards, and analyze innovation patterns within India’s research ecosystem. The review reveals a consistent focus on robust, cost-effective, and context-appropriate designs across both domains. In near-field WPT, Indian research emphasizes misalignment-tolerant magnetic coupling and high-frequency power converters for applications including electric vehicle charging and biomedical implants. In far-field WPT, progress is evident in rectenna architectures that enhance angular coverage and efficiency, particularly for IoT networks. We consolidate quantitative performance metrics from the literature to establish reference benchmarks and delineate persistent research gaps. We propose a forward-looking research agenda aimed at aligning WPT innovation with India’s sustainable development goals and energy accessibility challenges. This analysis provides a foundation for understanding how regional ecosystems shape technological priorities and offers insights for global WPT development. Full article

Steel fiber-reinforced concrete (SFRC) can exhibit markedly improved mechanical performance when the fibers are preferentially aligned along the principal tensile stress directions. One method of aligning steel fibers is using magnetic methods. However, most existing magnetic alignment techniques rely on solenoids and are restricted to one-dimensional alignment and relatively small specimen sizes. This paper presents a novel planar magnetic orientation device capable of producing arbitrary two-dimensional fiber layouts and demonstrates its applicability from laboratory-scale proof-of-concept tests to large high-performance fiber-reinforced concrete (HPFRC) structural elements. The concept is first verified on transparent ultrasound gel specimens, where image analysis confirms fiber orientation in the prescribed angles. The method is then applied to small prismatic HPFRC specimens (40 mm × 40 mm × 160 mm) with fiber contents of 0.5%, 1.0%, and 1.5%, exposed to different magnetic field intensities (80 mT–140 mT). Flexural tests show increases in average flexural strength compared to non-oriented reference specimens, with 100 mT providing the most efficient alignment for the investigated mixture. A non-destructive electromagnetic method based on the measurement of the quality factor Q of a coil correlates well with flexural strength. Finally, the device is integrated with an industrial robot and used to orient fibers in large HPFRC slabs (1000 mm × 410 mm), achieving an average increase in flexural tensile strength of about 64% relative to non-oriented slabs. The results demonstrate that planar magnetic orientation is a promising approach for tailoring fiber layouts in SFRC structural elements and for enabling automated, programmable manufacturing. Full article

Liquid Crystal Elastomers combine the elasticity of polymer networks with the anisotropic ordering of liquid crystals, thus enabling reversible shape modifications and stimulus responsive actuation. Unfortunately, manual LCE fabrication remains limited by operator-dependent variability, which can lead to inconsistent film thickness and manufacturing times inadequate for a mass production. This work presents a low-cost, automated manufacturing framework that redesigns the mechanical assembly steps of the traditional one-step LCE fabrication process. The design includes rubbing, slide alignment, spacer placement, and infiltration cell assembly to ensure consistent film quality and scalability. A customized Cartesian robot, built by adapting a modified X–Y core 3D printer, integrates specially designed manipulator systems, redesigned magnetic slide holders, automated rubbing tools, and supporting fixtures to assemble infiltration devices in an automated way. Validation tests demonstrate reproducible infiltration, improved mesogen alignment confirmed via polarized optical microscopy, and high geometric repeatability, although glass-slide thickness variability remains a significant contributor to deviations in final film thickness. By enabling parallelizable low-cost production, the designed hardware demonstrates its effectiveness in devising the scalable manufacturing of LCE films suited for advanced therapeutic and engineering applications. Full article

Background and objectives. White matter hyperintensities (WMHs) on brain magnetic resonance imaging (MRI) are linked to cognitive decline, but clinical assessment still relies mainly on visual grading (Fazekas), which is coarse and rater-dependent. We described the lesion volume of WMHs and the association of the anatomical distribution with the severity of cognitive impairment using automated lesion analysis. In addition, we evaluated whether automated volumetric quantification is more strongly associated with cognitive performance than visual grading. Materials and Methods. In a retrospective cross-sectional study, forty-one adults referred for cognitive concerns underwent standardised 3.0 tesla MRI. White matter hyperintensities were automatically segmented using Icometrix software to obtain total and regional volumes (periventricular, subcortical, brainstem, cerebellum). Visual grading used the Fazekas scale separately for periventricular and deep white matter, with a combined grade defined by the higher of the two. Cognitive performance was grouped based on the Montreal Cognitive Assessment (MoCA) into high (≥26), moderate (18–25), and low (≤17). Statistics included Spearman’s correlation and the Kruskal–Wallis test with Dunn’s post hoc test where applicable. Results. Higher total white matter hyperintensity volume was associated with lower Montreal Cognitive Assessment scores and showed significant differences across cognitive groups. The Fazekas combined grade correlated more weakly with the MoCA score. Regional volumetric differences showed trends, but were not statistically significant. Total volumetric burden increased stepwise across combined Fazekas categories, supporting convergent validity between methods. Conclusions. Our study found that automated volumetric quantification provides a more objective, sensitive, and scalable measure of white matter hyperintensity burden than visual grading, aligns more closely with cognitive status, and is better suited for longitudinal monitoring and research endpoints. Full article

Diffusion models have recently emerged as a new paradigm to solve inverse problems in magnetic resonance imaging (MRI). This paper systematically categorizes forty representative studies that use diffusion models for MRI reconstruction. The studies are systematically divided into three dimensional categories: (1) model type distribution that includes standard, score-based diffusion, hybrid and physics-informed diffusion, latent diffusion and cold diffusion; (2) application domain distribution that includes accelerated, motion-corrected, dynamic, quantitative and cross-modal MRI; and (3) domain of reconstruction distribution that includes image-space, k-space, latent-space and hybrid approaches. In this context, diffusion-driven methods represent a significant methodological shift, evolving from purely data-driven denoising toward unified, physics-aligned reconstruction frameworks. Hybrid and cold diffusion approaches further integrate probabilistic generative priors with explicit MRI acquisition physics, which enables improved fidelity, artifact suppression and enhanced generalization across sampling regimes and anatomical domains. This study reviews current advances in the field of MRI reconstruction methods and gives future research directions, emphasizing the need for interpretable, data-efficient and multi-domain generative models that are capable of robustly addressing the diverse challenges of MRI reconstruction. Full article

Accurate co-registration between on-scalp Optically Pumped Magnetometer (OPM)–Magnetoencephalography (MEG) sensors and anatomical Magnetic Resonance Imaging (MRI) remains a critical bottleneck restricting the spatial fidelity of source localization. Optical Scanning Image (OSI) methods can provide high spatial accuracy but depend on surface visibility and cannot directly determine the internal sensitive point of each OPM sensor. Coil-based magnetic dipole localization, in contrast, targets the sensor’s internal sensitive volume and is robust to occlusion, yet its accuracy is affected by coil fabrication imperfections and the validity of the dipole approximation. To integrate the complementary advantages of both approaches, we propose a hybrid co-registration framework that combines Rigid Coil Structures (RCS), magnetic dipole-based sensor localization, and optical orientation constraints. A complete multi-stage co-registration pipeline is established through a unified mathematical formulation, including MRI–OSI alignment, OSI–RCS transformation, and final RCS–sensor localization. Systematic simulations are conducted to evaluate the accuracy of the magnetic dipole approximation for both cylindrical helical coils and planar single-turn coils. The results quantify how wire diameter, coil radius, and turn number influence dipole model fidelity and offer practical guidelines for coil design. Experiments using 18 coils and 11 single-axis OPMs demonstrate positional accuracy of a few millimeters, and optical orientation priors suppress dipole-only orientation ambiguity in unstable channels. To improve the stability of sensor orientation estimation, optical scanning of surface markers is incorporated as a soft constraint, yielding substantial improvements for channels that exhibit unstable results under dipole-only optimization. Overall, the proposed hybrid framework demonstrates the feasibility of combining magnetic and optical information for robust OPM–MEG co-registration. Full article

Electric vehicles (EVs) have gained significant popularity globally during the past decade. This is mostly due to their reduced emissions of hydrocarbons and greenhouse gases. Electric vehicles acquire their electricity via wireless energy transmission, thereby circumventing the challenges associated with conventional techniques. The coils that transmit and receive signals deteriorate in performance and age as temperatures increase. Under extreme conditions, this may result in fire hazards and further safety issues. This article examined the electromagnetic and thermal dispersion of a magnetically coupled coil model for electric vehicles. This paper studied the electromagnetic and temperature distribution of the magnetically coupled coil model for electric vehicles. The coils were designed utilizing ANSYS software, with boundary conditions and pertinent parameters configured accordingly. The transmitter and receiver coils were identical in dimensions, with an inner diameter of 100 mm, an outer diameter of 295 mm, and an air gap of 60 mm between them. The magnetic coil was simulated and analyzed using copper as a material. In the aligned positions, the coupling coefficient between the transmitter and receiver coil was 0.168, its maximum temperature was 16.92 °C, and it was lower for the safety of the human body. An actual prototype was built to confirm the simulation results and to establish that the methodology employed in this research is applicable to the design of magnetic coils for a wireless charging system for electric vehicle models. Full article

Current multimodal imaging–based source localization (SoL) methods often rely on synchronously recorded data, and many neural network–driven approaches require large training datasets, conditions rarely met in clinical neuroimaging. To address these limitations, we introduce MieSoL (Multimodal Mutual Information Extraction and Source Localization), a unified framework that fuses EEG and MRI, whether acquired synchronously or asynchronously, to achieve robust cross-modal information extraction and high-accuracy SoL. Targeting neuroimaging applications, MieSoL combines Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG), leveraging their complementary strengths—MRI’s high spatial resolution and EEG’s superior temporal resolution. MieSoL addresses key limitations of existing SoL methods, including poor localization accuracy and an unreliable estimation of the true source number. The framework combines two existing components—Unified Left Eigenvectors (ULeV) and Efficient High-Resolution sLORETA (EHR-sLORETA)—but integrates them in a novel way: ULeV is adapted to extract a noise-resistant shared latent representation across modalities, enabling cross-modal denoising and an improved estimation of the true source number (TSN), while EHR-sLORETA subsequently performs anatomically constrained high-resolution inverse mapping on the purified subspace. While EHR-sLORETA already demonstrates superior localization precision relative to sLORETA, replacing conventional PCA/ICA preprocessing with ULeV provides substantial advantages, particularly when data are scarce or asynchronously recorded. Unlike PCA/ICA approaches, which perform denoising and source selection separately and are limited in capturing shared information, ULeV jointly processes EEG and MRI to perform denoising, dimension reduction, and mutual-information-based feature extraction in a unified step. This coupling directly addresses longstanding challenges in multimodal SoL, including inconsistent noise levels, temporal misalignment, and the inefficiency of traditional PCA-based preprocessing. Consequently, on synthetic datasets, MieSoL achieves 40% improvement in Average Correlation Coefficient (ACC) and 56% reduction in Average Error Estimation (AEE) compared to conventional techniques. Clinical validation involving 26 epilepsy patients further demonstrates the method’s robustness, with automated results aligning closely with expert epileptologist assessments. Overall, MieSoL offers a principled and interpretable multimodal fusion paradigm that enhances the fidelity of EEG source localization, holding significant promise for both clinical and cognitive neuroscience applications. Full article

We report a fully biogenic route to ZnO, Fe₃O₄, and their hydrothermally coupled ZnO/Fe₃O₄ heterostructure and establish a synthesis–structure–function link. Phase-pure, quasi-spherical wurtzite ZnO and finer inverse-spinel Fe₃O₄ nanoparticles assemble into a biphasic interface without forming a solid solution; optical analysis yields E_g = 2.36 eV (ZnO), 1.46 eV (Fe₃O₄), and 1.45 eV (ZnO/Fe₃O₄), while PL shows near-band-edge quenching and green–yellow defect reweighting at 490–560 nm, consistent with interfacial band bending. Magnetically, ZnO/Fe₃O₄ is soft-ferrimagnetic with M_S/M_R/H_C = 226 emu g⁻¹/17 emu g⁻¹/0.010 T (at 300 K), enabling rapid magnetic recovery. Under natural sunlight (572.6 ± 32 W m⁻²), adsorption-corrected methylene blue removal (10 mg L⁻¹; 10 mg in 50 mL) gives real degradation rates RDR = 90% (ZnO), 65% (ZnO/Fe₃O₄), and 30% (Fe₃O₄) at 180 min, with pseudo–first-order constants k = 1.9 × 10⁻², 0.7 × 10⁻², and 0.4 × 10⁻² min⁻¹, respectively; dark adsorption baselines are 10%, 14%, and 49%. Reusability over four cycles preserves pseudo-first-order kinetics (ZnO/Fe₃O₄: 65% → 50%). Scavenger tests implicate ${}^{•}\mathrm{O}\mathrm{H}$ as the dominant oxidant in ZnO and ZnO/Fe₃O₄, and ${}^{•}\mathrm{O}_2^{-}$ in Fe₃O₄. Taken together, the band alignment, photoluminescence quenching, radical-scavenger profiles, and kinetic synergy are consistent with a defect-rich S/Z-scheme-like ZnO/Fe₃O₄ heterojunction, delivering a green, sunlight-operable, and recyclable platform for affordable wastewater remediation. Full article

The liquid crystal (LC) poly-γ-benzyl-L-glutamate (PBLG) is known to possess a narrow biphasic range at the phase transition from an isotropic liquid to an anisotropic liquid crystal. We have characterized the biphasic region via deuterium nuclear magnetic resonance (NMR) of the deuterated solvent CDCl₃, with which isotropic and anisotropic populations can unambiguously be identified and quantified due to the quadrupolar coupling induced by partial alignment. In addition to a dilution series, we measured the kinetics of the alignment inside the magnet for each dilution step and were able to follow the kinetic buildup of partial alignment. Beginning with the dynamic line broadening indicative of slow fluctuations, to microheterogeneous patches of isotropic and anisotropic islands, with increasing island size being consistent with sharpened spectra, ending in fully separated isotropic and anisotropic phases on top of each other after two weeks. In addition, we studied the influence of the two example guest molecules borneol and camphor—which essentially differ in their capability to act as hydrogen bond donors or acceptors—on the biphasic region of PBLG. Full article