Search Results (198)

The preparation and use of iron oxide magnetic nanoparticles for water remediation is a widely investigated research field. To improve the efficacy of such nanomaterials, different synthetic processes and functionalization methods have been developed in the framework of green chemistry to exploit their magnetic properties and adsorption capacity in a sustainable way. In this work, iron oxide magnetic nanoparticles embedded in cross-linked sodium alginate beads designed to clean water from metal ions were magnetically characterized. In particular, the effect of copper adsorption on their magnetic properties was investigated. The magnetic characterization in a DC field of the beads before adsorption showed the presence of a superparamagnetic state at 300 K—a state that was also preserved after copper adsorption. The main differences in terms of magnetic properties before and after Cu²⁺ adsorption were the reduction of the magnetic signal (observed by comparing the saturation magnetization) and a different shape of the blocking temperature distribution obtained by magnetization versus temperature measurements. The evaluation of the reduction in magnetization can be important from the application perspective since it can affect the efficiency of the beads’ removal from the water medium after treatment. Full article

In the rapidly evolving field of biomedical engineering, hydrogels have emerged as highly versatile biomaterials that bridge biology and technology through their high water content, exceptional biocompatibility, and tunable mechanical properties. This review provides an integrated overview of both natural and synthetic hydrogels, examining their structural properties, fabrication methods, and broad biomedical applications, including drug delivery systems, tissue engineering, wound healing, and regenerative medicine. Natural hydrogels derived from sources such as alginate, gelatin, and chitosan are highlighted for their biodegradability and biocompatibility, though often limited by poor mechanical strength and batch variability. Conversely, synthetic hydrogels offer precise control over physical and chemical characteristics via advanced polymer chemistry, enabling customization for specific biomedical functions, yet may present challenges related to bioactivity and degradability. The review also explores intelligent hydrogel systems with stimuli-responsive and bioactive functionalities, emphasizing their role in next-generation healthcare solutions. In modern medicine, temperature-, pH-, enzyme-, light-, electric field-, magnetic field-, and glucose-responsive hydrogels are among the most promising “smart materials”. Their ability to respond to biological signals makes them uniquely suited for next-generation therapeutics, from responsive drug systems to adaptive tissue scaffolds. Key challenges such as scalability, clinical translation, and regulatory approval are discussed, underscoring the need for interdisciplinary collaboration and continued innovation. Overall, this review fosters a comprehensive understanding of hydrogel technologies and their transformative potential in enhancing patient care through advanced, adaptable, and responsive biomaterial systems. Full article

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field ¹H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H₂O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by ¹H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the ¹H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. ¹H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the ¹H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The ¹H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article

Magnetic anomaly data rapidly decay with distance and are susceptible to environmental magnetic noise, which leads to reduced accuracy and robustness in estimating magnetic source parameters. This shows significant differences between estimated and true values. Therefore, this study proposes a method for estimating magnetic source parameters based on the LM-OBF algorithm. This method transforms magnetic anomaly data into a two-dimensional orthogonal basis function space using the Gram–Schmidt orthogonalization process, establishing a new forward modeling relationship. It then constructs an objective function within a least squares framework and optimizes it using the Levenberg–Marquardt (LM) algorithm to achieve a stable estimation of magnetic source parameters. The experimental section tests this method using synthetic and field data, comparing it to traditional detection methods. The results demonstrated that the method maintains stable and accurate estimation of magnetic source parameters even at a signal-to-noise ratio (SNR) of −10 dB, outperforming traditional methods in terms of performance under strong noise interference conditions. Full article

In this paper, we present a novel passive dual-tuned magnetic metasurface, which can enhance the field distribution produced by a closely placed radio-frequency coil for both ¹H and ²³Na 1.5 T MRI imaging. In particular, the proposed solution comprises a 5 × 5 capacitively loaded array, in which each unit-cell is composed of two concentric spiral coils. Specifically, the unit-cell internal spiral coil operates at the proton Larmor frequency (64 MHz), whereas the external is at the sodium one (17 MHz). Therefore, the paper aims to demonstrate the possibility of enhancing the magnetic field distribution in transmission and reception for 1.5 T MRI scanners by using the same metasurface configuration for imaging both nuclei, thus drastically simplifying the required instrumentation. We first describe the theoretical model used to design and synthetize the dual-tuned magnetic metasurface. Next, full-wave simulations are carried out to validate the approach. Finally, we report the experimental results acquired by testing the fabricated prototype at the workbench, observing a good agreement with the theoretical design and the numerical simulations. In particular, the metasurface increases the transmission efficiency T_x in presence of a biological phantom by a factor 3.5 at 17 MHz and by a factor 5 at 64 MHz, respectively. The proposed solution can pave the way for MRI multi-nuclei diagnostic technique with better images quality, simultaneously reducing the scanning time, the invasiveness on the patient and the overall costs. Full article

Three-dimensional (3-D) magnetic inversion is an essential technique for revealing the distribution of subsurface magnetization structures. Conventional methods are often time-consuming and suffer from ambiguity due to limited observations and non-uniqueness. To address these limitations, we propose a novel inversion method under the machine learning framework. First, we design a training sample generation space by extracting the horizontal positions of magnetic sources from the analytic signal amplitude and the reduced-to-the-pole anomalies of magnetic field data. We then employ coordinate transformation to achieve data augmentation within the designed space. Subsequently, we utilize a broad learning network to map the magnetic anomalies to 3-D magnetization structures, reducing the magnetic inversion time. The efficiency of the proposed method is validated through both synthetic and field data. Synthetic examples indicate that compared to the traditional inversion method, the proposed method approximates the true model more closely. It also outperforms traditional and deep learning methods in terms of computational efficiency. In the field example of the Danzhukeng Pb-Zn-Ag deposit in South China, the inversion result is consistent with drilling and controlled-source audio frequency magnetotelluric survey data, providing valuable insights for subsequent exploration. This study provides a new practical tool for processing and interpreting magnetic anomaly data. Full article

The present study developed a novel approach for transforming red mud (RM) into soft magnetic materials (SMMs) for applications in advanced electrical devices in the form of Fe-Si and Fe-Si-Al alloys. A total of ten blends were prepared based on two RMs, three iron oxide additives (Fe₂O₃, black and red mill scales), alumina and carbonaceous reductants in a range of proportions. Carbothermic reduction of the blends was carried out in a vertical Tamman resistance furnace at 1600–1650 °C for 30 min in an argon atmosphere; synthetic graphite was used as a reductant. Reaction products were characterized using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray fluorescence (XRF) and X-ray diffraction (XRD). Significant amounts of Fe-rich metallic droplets/regions of different grain sizes (0.5 to 500 μm) were produced in these studies. The formation of Fe-Si alloys with Si contents from 3.9 to 6.7 wt.% was achieved in 8 out of 10 blends; the optimal levels of Si for SMMs ranged from 3.2 to 6.5 wt.%. There was clear evidence for the formation of Fe-Si-Al (up to 1.8 wt.% Al) alloys in 4 out of 10 blends. In addition to lowering operating challenges associated with RM processing, blending of RMs with iron oxide additives and alumina presents a novel recycling approach for converting RMs into valuable SMMs for possible emerging applications in renewable energy, storage, electrical vehicles and other fields. Along with reducing RM stockpiles across the globe, this approach is expected to improve resource efficiency, mitigating environmental impacts while generating economic benefits. Full article

The identification of areas that are susceptible to damage due to earthquakes is of utmost importance in tectonically active regions like Northeast India. This may provide valuable inputs for seismic hazard analysis; however, it poses significant challenges. The present study emphasized the integration of Interferometric Synthetic Aperture Radar (InSAR) deformation rates with conventional geological and geophysical data to investigate earthquake damage susceptibility in the Barapani Shear Zone (BSZ) region of Northeast India. We used MintPy v1.5.1 (Miami INsar Timeseries software in PYthon) on the OpenSARLab platform to derive time series deformation using the Small Baseline Subset (SBAS) technique. We integrated geology, geomorphology, gravity, magnetic field, lineament density, slope, and historical earthquake records with InSAR deformation rates to derive earthquake damage susceptibility using the weighted overlay analysis technique. InSAR time series analysis revealed distinct patterns of ground deformation across the Barapani Shear Zone, with higher rates in the northern part and lower rates in the southern part. The deformation values ranged from 6 mm/yr to about 18 mm/yr in BSZ. Earthquake damage susceptibility mapping identified areas that are prone to damage in the event of earthquakes. The analysis indicated that about 46.4%, 51.2%, and 2.4% of the area were low, medium, and high-susceptibility zones for earthquake damage zone. The InSAR velocity rates were validated with Global Positioning System (GPS) velocity in the region, which indicated a good correlation (R² = 0.921; ANOVA p-value = 0.515). Additionally, a field survey in the region suggested evidence of intense deformation in the highly susceptible earthquake damage zone. This integrated approach enhances our scientific understanding of regional tectonic dynamics, mitigating earthquake risks and enhancing community resilience. Full article

Synthetic peptides are commonly obtained by means of solid-phase peptide synthesis (SPPS), in which separation of the peptide from the solid support requires treatment with 92.5% v/v trifluoroacetic acid (TFA); therefore, peptides are obtained as trifluoroacetate salts. For promising anticancer/antibacterial peptides it is essential to exchange the counterion from trifluoroacetate to hydrochloride or acetate, since the former are more widely studied in biological activity assays. In this research, RP-SPE-based methodologies were designed, developed, and implemented for simultaneous counterion exchange and peptide purification. Critical process steps were identified and parameters such as mobile phase composition, elution, and program were optimized. Analysis of the counterion exchange reaction and characterization of the final products was performed using high-performance liquid chromatography, attenuated total reflectance, nuclear magnetic resonance, and mass spectrometry. Peptides with purities between 82–97% and a trifluoroacetate ion content less than 0.36% were obtained. This novel counterion exchange proved efficient for peptides with different characteristics such as length, polarity, polyvalency, and presence of non-natural amino acids or non-protein molecules, therefore showing a wide range of applications in the field of therapeutic peptides. The methods developed are fast, efficient, low-cost, and do not require robust instrumentation and can be routinely implemented in SPPS. Full article

Unmanned aerial vehicles (UAVs) are vulnerable to global positioning system (GPS) spoofing attacks, which can mislead their navigation systems and result in unpredictable catastrophic consequences. To address this issue, we propose a detection method based on stacked ensemble learning that combines convolutional neural network (CNN) and extreme gradient boosting (XGBoost) to detect spoofing signals in the GPS data received by UAVs. First, we applied the synthetic minority oversampling (SMOTE) technique to the dataset to address the issue of class imbalance. Then, we used a CNN model to extract high-level features, combined with the original features as input for the stacked model. The stacked model employs XGBoost as the base learner, which is optimized through five-fold cross-validation, and utilizes logistic regression for the final prediction. Furthermore, we incorporated magnetic field data to enhance the system’s robustness, thereby further improving the accuracy and reliability of GPS spoofing attack detection. Experimental results indicate that the proposed model achieved a high accuracy of 99.79% in detecting GPS spoofing attacks, demonstrating its potential effectiveness in enhancing UAV security. Full article

Background: A generative adversarial network (GAN) has gained popularity as a data augmentation technique in the medical field due to its efficiency in creating synthetic data for different machine learning models. In particular, the earlier literature suggests that the classification accuracy of a convolutional neural network (CNN) used for detecting brain tumors in magnetic resonance imaging (MRI) images increases when GAN-generated images are included in the training data together with the original images. However, there is little research about how the exact number of GAN-generated images and their ratio to the original images affects the results obtained. Materials and methods: Here, by using 1000 original images from a public repository with MRI images of patients with or without brain tumors, we built a GAN model to create synthetic brain MRI images. A modified U-Net CNN is trained multiple times with different training datasets and its classification accuracy is evaluated from a separate test set of another 1000 images. The Mann–Whitney U test is used to estimate whether the differences in the accuracy caused by different choices of training data are statistically significant. Results: According to our results, the use of GAN augmentation only sometimes produces a significant improvement. For instance, the classification accuracy significantly increases when 250–750 GAN-generated images are added to 1000 original images (p-values ≤ 0.0025) but decreases when 10 GAN-generated images are added to 500 original images (p-value: 0.03). Conclusions: Whenever GAN-based augmentation is used, the number of GAN-generated images should be carefully considered while accounting for the number of original images. Full article

The intrinsic characteristic features of metal–organic frameworks offer unique, great opportunities to develop novel materials with applications in very diverse fields. Aiming to take advantage of these, the application of post-synthetic methodologies has revealed itself to be a powerful approach to the isolation and structuration of metal ions, molecules, or more complex species, either within MOF channels or reticulated at their network, rendering novel and exciting MOFs with new or improved functionalities. Herein, we report the partial post-synthetic metal exchange of Zn(II) metal ions by Co(II) ones in water-stable three-dimensional CaZn₆-MOF 1, derived from the amino acid S-methyl-L-cysteine, allowing us to obtain two novel MOFs with increasing contents of the Co(II) ions Co_4%@1 and Co_8%@1. Remarkably, the presented post-synthetic metal exchange methodology has two relevant implications for us: (i) it allowed us to obtain two novel MOFs, which were not accessible by direct synthesis, and (ii) enabled us to transform physical properties within this family of isoreticular MOFs from the diamagnetic pristine MOF 1 to MOFs Co_4%@1 and Co_8%@1, exhibiting field-induced, frequency-dependent, alternating current magnetic susceptibility signals, which are characteristic features of single-molecule magnets. Full article

Defects in the free layer are considered to be the main cause of the balloon effect, but there is little insight into the synthetic antiferromagnetic (SAF) layer. To address this shortcoming, in this work, an optimized SAF layer was introduced in the perpendicular magnetic tunneling junction (pMTJ) stack to eliminate the low-probability bifurcated-switching phenomenon. The results indicated that the Hf field in the film stack improved significantly from ~5700 Oe to ~7500 Oe. A magnetoresistive random access memory (MRAM) test chip was also fabricated with a 300 mm process, resulting in a significantly improved ballooning effect. The results also indicated that the switching voltage decreased by 18.6% and the writing energy decreased by 33.7%. In addition, the low-probability stray field along the x-axis was thought to be the main cause of the ballooning effect, and was experimentally optimized for the first time by enhancing the SAF layer. This work provides a new perspective on spin-flipping dynamics, facilitating a deeper comprehension of the internal mechanism and helping to secure improvements in MRAM performance. Full article

Micro- and nanorobots (MNRs) have attracted significant interest owing to their promising applications in various fields, including environmental monitoring, biomedicine, and microengineering. This review explores advances in the synthetic routes used for the preparation of MNRs, focusing on both top-down and bottom-up approaches. Although the top-down approach dominates the field because of its versatility in design and functionality, bottom-up strategies that utilize template-assisted electrochemical deposition and bioconjugation present unique advantages in terms of biocompatibility. This review investigates the diverse propulsion mechanisms employed in MNRs, including magnetic, electric, light, and biological forces, which enable efficient navigation in various fluidic environments. The interplay between the synthesis and propulsion mechanisms of MNRs in the development of colorimetric and fluorescence detection platforms is emphasized. Additionally, we summarize the recent advancements in MNRs as sensing and biosensing platforms, particularly focusing on colorimetric and fluorescence-based detection systems. By utilizing the controlled motion of MNRs, dynamic changes in the fluorescent signals and colorimetric responses can be achieved, thereby enhancing the sensitivity and selectivity of biomolecular detection. This review highlights the transformative potential of MNRs in sensing applications and emphasizes their role in advancing diagnostic technologies through innovative motion-driven signal transduction mechanisms. Subsequently, we provide an overview of the primary challenges currently faced in MNR research, along with our perspective on the future applications of MNR-assisted colorimetric and fluorescence biosensing in chemical and biological sensing. Moreover, issues related to enhanced stability, biocompatibility, and integration with existing detection systems are discussed. Full article

Iron oxide nanoparticles (IONs) are extensively used in biomedical applications due to their unique magnetic properties. This study optimized ION synthesis via the co-precipitation method, exploring the impact of the reactant concentrations (Fe(II) and Fe(III)), NaOH concentration, temperature (30 °C–80 °C), stirring speed (0–1000 rpm), and dosing rate (10–600 s) on particle size and growth. Using small-angle X-ray scattering (SAXS), we observed, for example, that higher temperatures (e.g., 67 °C compared with 53 °C) led to a 50% increase in particle size, while the stirring speed and NaOH concentration also influenced nucleation and aggregation. These results provide comprehensive insights into optimizing synthetic conditions for targeted applications in biomedical fields, such as drug delivery and magnetic resonance imaging (MRI), where precise control over nanoparticle size and properties is crucial. Full article