Search Results (33)

Crop diseases cause significant losses in agricultural production; early capture and identification of disease spores enable disease monitoring and prevention. This study experimentally optimized the preparation of Au@Ag NRS (Gold core@Silver shell Nanorods) sol as a Surface-Enhanced Raman Scattering (SERS) enhancement reagent via a modified seed-mediated growth method. Using an existing microfluidic chip developed by the research group, disease spores were separated and enriched, followed by combining Au@Ag NRS with Crop Disease Spores through electrostatic adsorption. Raman spectroscopy was employed to collect SERS fingerprint spectra of Crop Disease Spores. The spectra underwent baseline correction using Adaptive Least Squares (ALS) and standardization via Standard Normal Variate (SNV). Dimensionality reduction preprocessing was performed using Principal Component Analysis (PCA) and Successive Projections Algorithm combined with Competitive Adaptive Reweighted Sampling (SCARS). Classification was then executed using Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The SCARS-MLP model achieved the highest accuracy at 97.92% on the test set, while SCARS-SVM, PCA-SVM, and SCARS-MLP models attained test set accuracy of 95.83%, 95.24%, and 96.55%, respectively. Thus, the proposed Au@Ag NRS-based SERS technology can be applied to detect airborne disease spores, establishing an early and precise method for Crop Disease detection. Full article

Per- and polyfluoroalkyl substances (PFASs) are a broad group of synthetic chemicals characterized by strong carbon–fluorine bonds, making them highly persistent and widely distributed in the environment. Their chain length and functional head groups, such as sulfonate and carboxylate groups, determine key molecular properties like hydrophobicity, acidity, and sorption behavior. These properties significantly impact the effectiveness of PFAS removal from water systems. This review provides a structural classification of PFASs and explores removal strategies, with a particular emphasis on adsorption. It examines sustainable adsorbents, including both natural materials (e.g., cellulose, chitosan, lignin, and cyclodextrins) and engineered synthetic materials (e.g., covalent organic frameworks, metal–organic frameworks, and molecularly imprinted polymers). The discussion highlights important parameters such as chain length and functional chemistry, as these can greatly influence removal efficiency. Furthermore, the discussion addresses the adsorption mechanisms-such as electrostatic attraction, hydrophobic interaction, and fluorophilic interactions-to show how they contribute in different systems. By combining structural insights with adsorption performance data, this review aims to help design and select sustainable, high-performance adsorbents for efficiently reducing PFASs in contaminated water. Full article

As an innovative plant protection method in precision agriculture, electrostatic spray technology can increase the droplet coverage area by over 30% coMpared to conventional spraying. This technology not only achieves higher droplet deposition density and coverage but also enables water and pesticide savings while reducing environmental pollution. This study, combining theoretical analysis with experimental validation, reveals the critical role of electrode material selection in induction-based electrostatic spray systems. Theoretical analysis indicates that the Fermi level and work function of electrode materials fundamentally determine charge transfer efficiency, while corrosion resistance emerges as a key parameter affecting system durability. To elucidate the effects of different electrode materials on droplet charging, a coMparative study was conducted on nickel, copper, and brass electrodes in both pristine and moderately corroded states based on the corrosion classification standard, using a targeted mesh-based charge-to-mass measurement device. The results demonstrated that the nickel electrode achieved a peak charge-to-mass ratio of 1.92 mC/kg at 10 kV, which was 8.5% and 11.6% higher than copper (1.77 mC/kg) and brass (1.72 mC/kg), respectively. After corrosion, nickel exhibited the smallest reduction in the charge-to-mass ratio (19.2%), significantly outperforming copper (40.2%) and brass (21.6%). Droplet size analysis using a Malvern Panalytical Spraytec spray particle analyzer (measurement range: 0.1–2000 µm) further confirmed the atomization advantages of nickel electrodes. The volume median diameter (Dv50) of droplets produced by nickel was 4.2–8 μm and 6.8–12.3 um smaller than those from copper and brass electrodes, respectively. After corrosion, nickel showed a smaller increase in droplet size spectrum inhomogeneity (24.5%), which was lower than copper (30.4%) and brass (25.8%), indicating superior droplet uniformity. By establishing a multi-factor predictive model for spray droplet size after electrode corrosion, this study quantifies the correlation between electrode characteristics and spray performance metrics. It provides a theoretical basis for designing weather-resistant electrostatic spray systems suitable for agricultural pesticide application scenarios involving prolonged exposure to corrosive chemicals. This work offers significant technical support for sustainable crop protection strategies. Full article

Field-effect transistor (FET) chemical sensors are essential for enabling sophisticated lifestyles and ensuring safe working environments. They can detect a wide range of analytes, including gaseous species (NO₂, NH₃, VOCs), ionic compounds, and biological molecules. Among the structural components of FETs, the gate configuration plays a vital role in controlling the semiconductor channel’s electrostatic environment, thereby strongly influencing sensing performance. Two-dimensional (2D) materials offer additional advantages in these sensors due to their rich surface chemistry and high sensitivity to external interactions. This review offers a comprehensive classification of 2D channel FET chemical sensors based on their gate configurations. Their working principles, fabrication strategies, and sensing performance are discussed in detail. A critical analysis of the advantages and challenges associated with each gate configuration is performed. This review aims to guide future research on the selection of appropriate device configurations for the development of excellent FET chemical sensors. Full article

Dipeptidyl peptidase-4 (DPP-4) inhibitors play a critical role in the management of type 2 diabetes; however, some synthetic drugs may cause adverse effects. Natural peptides derived from rice offer a promising alternative due to their favorable biocompatibility and development potential. In this study, an AI-assisted virtual screening pipeline integrating machine learning, molecular docking, and molecular dynamics (MD) simulations was established to identify and evaluate rice-derived DPP-4 inhibitory peptides. A random forest classification model achieved 85.37% accuracy in predicting inhibitory activity. Peptides generated by simulated enzymatic hydrolysis were screened based on machine learning and docking scores, and four proline-rich peptides (PPPPPPPPA, PPPSPPPV, PPPPPY, and CPPPPAAY) were selected for MD analysis. The simulation results showed that PPPSPPPV formed a stable complex with the DPP-4 catalytic triad (Ser592–Asp670–His702) through electrostatic and hydrophobic interactions, with low structural fluctuation (RMSF < 1.75 Å). In vitro assays revealed that PPPPPY exhibited the strongest DPP-4 inhibitory activity (IC₅₀ = 153.2 ± 5.7 μM), followed by PPPPPPPPA (177.0 ± 6.0 μM) and PPPSPPPV (216.3 ± 4.5 μM). This study presents an efficient approach combining virtual screening and experimental validation, offering a structural and mechanistic foundation for the development of natural DPP-4 inhibitory peptides as candidates for functional foods or adjunct diabetes therapies. Full article

Conventional drug delivery approaches, including tablets and capsules, often suffer from reduced therapeutic effectiveness, largely attributed to inadequate bioavailability and difficulties in ensuring patient adherence. These challenges have driven the development of advanced drug delivery systems (DDS), with hydrogels and especially nanogels emerging as promising materials to overcome these limitations. Hydrogels, with their biocompatibility, high water content, and stimuli-responsive properties, provide controlled and targeted drug release. This review explores the evolution, properties, and classifications of hydrogels versus nanogels and their applications in drug delivery, detailing synthesis methods, including chemical crosslinking, physical self-assembly, and advanced techniques such as microfluidics and 3D printing. It also examines drug-loading mechanisms (e.g., physical encapsulation and electrostatic interactions) and release strategies (e.g., diffusion, stimuli-responsive, and enzyme-triggered). These gels demonstrate significant advantages in addressing the limitations of traditional DDS, offering improved drug stability, sustained release, and high specificity. Their adaptability extends to various routes of administration, including topical, oral, and injectable forms, while emerging nanogels further enhance therapeutic targeting through nanoscale precision and stimuli responsiveness. Although hydrogels and nanogels have transformative potential in personalized medicine, challenges remain in scalable manufacturing, regulatory approval, and targeted delivery. Future strategies include integrating biosensors for real-time monitoring, developing dual-stimuli-responsive systems, and optimizing surface functionalization for specificity. These advancements aim to establish hydrogels and nanogels as cornerstones of next-generation therapeutic solutions, revolutionizing drug delivery, and paving the way for innovative, patient-centered treatments. Full article

Cell-penetrating peptides (CPPs) are a diverse group of peptides, typically composed of 4 to 40 amino acids, known for their unique ability to transport a wide range of substances—such as small molecules, plasmid DNA, small interfering RNA, proteins, viruses, and nanoparticles—across cellular membranes while preserving the integrity of the cargo. CPPs exhibit passive and non-selective behavior, often requiring functionalization or chemical modification to enhance their specificity and efficacy. The precise mechanisms governing the cellular uptake of CPPs remain ambiguous; however, electrostatic interactions between positively charged amino acids and negatively charged glycosaminoglycans on the membrane, particularly heparan sulfate proteoglycans, are considered the initial crucial step for CPP uptake. Clinical trials have highlighted the potential of CPPs in diagnosing and treating various diseases, including cancer, central nervous system disorders, eye disorders, and diabetes. This review provides a comprehensive overview of CPP classifications, potential applications, transduction mechanisms, and the most relevant algorithms to improve the accuracy and reliability of predictions in CPP development. Full article

Over the years, pulses and cereals have been identified as promising sources of plant proteins. The intensive production of these crops and concerns about food security and malnutrition worldwide have intensified research into their separation. While wet extraction remains the standard protein isolation method, the search for more sustainable extraction methods is still ongoing. Two dry fractionation techniques, air classification and tribo-electrostatic separation, have been discussed in this review. This review highlights the design aspects of air classifiers including the cut-off point and flow rate, and for electrostatic separators, factors such as charger materials, the nature of the flow in charger tubes, and the strength of the electric field potential have been discussed in detail. Our analysis revealed that cascading the two techniques should help enhance the concentration and purity of the separated fractions. While limitations such as low purity and low yield exist, current research studies are focused on overcoming such drawbacks. Dry fractionation exhibits potential as a sustainable processing method while also preserving the native functionality of the proteins, making it easier to incorporate the fractions in commercial scale processes. Full article

In this study, a two-dimensional separation of microparticles based on their settling velocity and triboelectric charge ability is achieved using an air classifier for size fractionation and simultaneous charging, followed by an electrostatic separator. In the first part, considerations for enhancing particle classification with high sharpness and low-pressure drops are discussed through improvements in blade design investigated with CFD simulations and validated experimentally. Blades with extended lengths towards the center of the classifier prevent the formation of high-velocity vortices, thereby minimizing the back-mixing of particles and enhancing separation sharpness. This approach also reduces pressure drops associated with these flow vortices. In the second part of the study, the modified blades within the classifier are utilized for two-dimensional separation. Powders from two different materials are fed into the classification system, where particles become triboelectrically charged, mainly through collisions with the walls of the classification system components. Coarse particles are rejected at the wheel and exit the classifier, while differently charged fine particles of the two materials are directed into an electrostatic separator for material sorting. An enrichment of approximately 25–35% for both materials has been achieved on the electrodes of the separator. Full article

The surge in demand for 3D MOSFETs, such as FinFETs, driven by recent technological advances, is explored in this review. FinFETs, positioned as promising alternatives to bulk CMOS, exhibit favorable electrostatic characteristics and offer power/performance benefits, scalability, and control over short-channel effects. Simulations provide insights into functionality and leakage, addressing off-current issues common in narrow band-gap materials within a CMOS-compatible process. Multiple structures have been introduced for FinFETs. Moreover, some studies on the fabrication of FinFETs using different materials have been discussed. Despite their potential, challenges like corner effects, quantum effects, width quantization, layout dependencies, and parasitics have been acknowledged. In the post-planar CMOS landscape, FinFETs show potential for scalability in nanoscale CMOS, which leads to novel structures for them. Finally, recent developments in FinFET-based sensors are discussed. In a general view, this comprehensive review delves into the intricacies of FinFET fabrication, exploring historical development, classifications, and cutting-edge ideas for the used materials and FinFET application, i.e., sensing. Full article

Space radiation particle detection plays a crucial role in scientific research and engineering practice, especially in particle species identification. Currently, commonly used in-orbit particle identification techniques include telescope methods, electrostatic analysis time of flight (ESA × TOF), time-of-flight energy (TOF × E), and pulse shape discrimination (PSD). However, these methods usually fail to utilize the full waveform information containing rich features, and their particle identification results may be affected by the random rise and fall of particle deposition and noise interference. In this study, a low-latency and lightweight onboard FPGA real-time particle identification platform based on full waveform information was developed utilizing the superior target classification, robustness, and generalization capabilities of convolutional neural networks (CNNs). The platform constructs diversified input datasets based on the physical features of waveforms and uses Optuna and Pytorch software architectures for model training. The hardware platform is responsible for the real-time inference of waveform data and the dynamic expansion of the dataset. The platform was utilized for deep learning training and the testing of the historical waveform data of neutron and gamma rays, and the inference time of a single waveform takes 4.9 microseconds, with an accuracy rate of over 97%. The classification expectation FOM (figure-of-merit) value of this CNN model is 133, which is better than the traditional pulse shape discrimination (PSD) algorithm’s FOM value of 0.8. The development of this platform not only improves the accuracy and efficiency of space particle discrimination but also provides an advanced tool for future space environment monitoring, which is of great value for engineering applications. Full article

The integration of two-dimensional Ti₃C₂T_x nanosheets and other materials offers broader application options in the antibacterial field. Ti₃C₂T_x-based composites demonstrate synergistic physical, chemical, and photodynamic antibacterial activity. In this review, we aim to explore the potential of Ti₃C₂T_x-based composites in the fabrication of an antibiotic-free antibacterial agent with a focus on their systematic classification, manufacturing technology, and application potential. We investigate various components of Ti₃C₂T_x-based composites, such as metals, metal oxides, metal sulfides, organic frameworks, photosensitizers, etc. We also summarize the fabrication techniques used for preparing Ti₃C₂T_x-based composites, including solution mixing, chemical synthesis, layer-by-layer self-assembly, electrostatic assembly, and three-dimensional (3D) printing. The most recent developments in antibacterial application are also thoroughly discussed, with special attention to the medical, water treatment, food preservation, flexible textile, and industrial sectors. Ultimately, the future directions and opportunities are delineated, underscoring the focus of further research, such as elucidating microscopic mechanisms, achieving a balance between biocompatibility and antibacterial efficiency, and investigating effective, eco-friendly synthesis techniques combined with intelligent technology. A survey of the literature provides a comprehensive overview of the state-of-the-art developments in Ti₃C₂T_x-based composites and their potential applications in various fields. This comprehensive review covers the variety, preparation methods, and applications of Ti₃C₂T_x-based composites, drawing upon a total of 171 English-language references. Notably, 155 of these references are from the past five years, indicating significant recent progress and interest in this research area. Full article

Organic–inorganic hybrids represent a good solution to improve the solubility and dissolution rates of poorly soluble drugs whose number has been increasing in the last few years. One of the most diffused inorganic matrices is hydroxyapatite (HAP), which is a biocompatible and osteoconductive material. However, the understanding of the hybrids’ functioning mechanisms is in many cases limited; thus, thorough physicochemical characterizations are needed. In the present paper, we prepared hybrids of pure and Mg-doped hydroxyapatite with meloxicam, a drug pertaining to the Biopharmaceutical Classification System (BCS) class II, i.e., drugs with low solubility and high permeability. The hybrids’ formation was demonstrated by FT-IR, which suggested electrostatic interactions between HAP and drug. The substitution of Mg in the HAP structure mainly produced a structural disorder and a reduction in crystallite sizes. The surface area of HAP increased after Mg doping from 82 to 103 m²g⁻¹ as well as the pore volume, justifying the slightly high drug amount adsorbed by the Mg hybrid. Notwithstanding the low drug loading on the hybrids, the solubility, dissolution profiles and wettability markedly improved with respect to the drug alone, particularly for the Mg doped one, which was probably due to the main distribution of the drug on the HAP surface. Full article

This article presents a technique to carry out fault classification using an equal-angle integer-period array convolutional neural network (EAIP-CNN) to process the electrostatic signal of working roller bearings. Firstly, electrostatic signals were collected using uniform angle sampling to ensure the angle intervals between two adjacent data points stayed the same and the signal length was fixed to a pre-determined number of rotation cycles. Then, this one-dimensional signal was transformed into a two-dimensional matrix, where the component of each row was the signal in one period, and the ordinate value of each row represented the corresponding rotation period. Therefore, the row and column indexes of the matrix had a specific meaning instead of simply splitting and stacking the data. Finally, the matrixes were utilized to train the CNN network and test the classification performance. The results show that the classification rate using this technique reaches 95.6%, which is higher than that of 2D CNNs without equal-angle integer-period arrays. Full article

As a subclass of the biopharmaceutical classification system (BCS) class II, basic drugs (BCS IIB) exhibit pH-dependent solubility and tend to generate supersaturation in the gastrointestinal tract, leading to less qualified in vitro–in vivo correlation (IVIVC). This study aims to develop a physiologically based multi-cup dissolution approach to improve the evaluation of the supersaturation for a higher quality of IVIVC and preliminarily explores the molecular mechanism of supersaturation and precipitation of ketoconazole affected by Polyvinylpyrrolidone–vinyl acetate copolymer (PVPVA) and hydroxypropyl methyl-cellulose (HPMC). The concentration of ketoconazole in each cup of the dynamic gastrointestinal model (DGIM) was measured using fiber optical probes. Molecular interactions between ketoconazole and PVPVA or HPMC were simulated by Materials Studio. The results demonstrated that PVPVA and HPMC improved and maintained the supersaturation of ketoconazole. PVPVA exhibited superior precipitation inhibitory effect on ketoconazole molecule aggregation due to slightly stronger van der Waals forces as well as unique electrostatic forces, thereby further enhancing in vitro drug absorption, which correlated well with in vivo drug absorption. Compared with a conventional dissolution apparatus paddle method, the DGIM improved the mean prediction error through the IVIVC from 19.30% to 9.96%, reaching the qualification criteria. In conclusion, the physiologically based multi-cup dissolution approach enables improved evaluation of supersaturation in gastrointestinal transportation of BCS IIB drug ketoconazole, enabling screening screen precipitation inhibitors and achieving qualified IVIVC for drug formulation studies. Full article