Search Results (613)

Poor aqueous solubility still disqualifies many promising drug candidates at late stages of development. Amorphous solid dispersion (ASD) technology solves this limitation by trapping the active pharmaceutical ingredient (API) in a high-energy, non-crystalline form, yet most marketed ASDs rely on synthetic carriers such as polyvinylpyrrolidone (PVP) and hydroxypropyl methylcellulose (HPMC), which raise concerns about long-term biocompatibility, residual solvent load, and sustainability. This study summarizes the emergence of natural polymer-based ASDs (NP-ASDs), along with the bond mechanism reactions through which these natural polymers enhance drug performance. As a result, NP-ASDs exhibit improved physical stability and significantly enhance the dissolution rate of poorly soluble drugs. The structural features of natural polymers play a critical role in stabilizing the amorphous state and modulating drug release profiles. These findings support the growing potential of NP-ASDs as sustainable and biocompatible alternatives to synthetic carriers in pharmaceutical development. Full article

Soil-transmitted helminths (STHs) present a significant global health challenge, particularly in tropical and subtropical regions. The current diagnostic standard involves the microscopic examination of a stool smear but it lacks sensitivity to detect infections of low intensity. Innovative solutions like lab-on-a-disk (LoD) technologies are emerging, showing promise in detecting low-intensity infections. Field tests conducted using our SIMPAQ (single-image parasite quantification) LoD device have demonstrated its potential as a diagnostic tool, especially for such low-intensity infections. Nevertheless, the device’s efficiency has been limited by significant egg loss during sample preparation, low capture efficiency of eggs within the Field of View (FOV), and the presence of larger fecal debris that obstructs effective egg trapping and imaging. In this study, we conducted a set of laboratory experiments using model polystyrene particles and purified STH eggs to improve the sample preparation protocol. These experiments include the entire SIMPAQ procedure starting from sample preparation, infusing it into the LoD device, centrifugation, delivering the (model) eggs to the FOV, capturing an image, and analyzing it. We analyzed egg losses at each step of the procedure following the “standard” protocol, then elaborated and tested alternative, more efficient procedures. The resulting modified protocol significantly minimized particle and egg loss and reduced the amount of debris in the disk, thus enabling effective egg capture and clear images in the FOV, increasing the reliability of the diagnostic results. Full article

Blood is a widely used sample for diagnosing diseases such as malaria and diabetes. While diagnostic techniques have advanced, sample preparation remains labor-intensive, requiring steps like mixing and centrifugation. Microfluidic technologies have automated parts of this process, including cell lysis, yet challenges persist. Electrical lysis offers a chemical-free, continuous approach, but lysing small cells like red blood cells requires high electric fields, which can damage electrodes and cause system failures. Here, we present a microfluidic device utilizing ion concentration polarization (ICP) for rapid blood cell lysis at 75 V. Fluorescence imaging confirmed the formation of an ion depletion region near the Nafion^® nanochannel membrane, where the electric field was concentrated across the entire microchannel width. This phenomenon enabled the efficient trapping and lysis of blood cells under these conditions. Continuous blood injection achieved a lysis time of 0.3 s with an efficiency exceeding 99.4%. Moreover, lysed cell contents accumulated near the Nafion membrane, forming a concentrated lysate. This approach eliminates the need for high-voltage circuits or chemical reagents, offering a simple yet effective method for blood cell lysis. The proposed device is expected to advance lab-on-a-chip and point-of-care diagnostics by enabling rapid and continuous sample processing. Full article

Insect pests remain a major threat to agricultural productivity, particularly in open-field cropping systems where conventional monitoring methods are labor-intensive and lack scalability. This study presents the design, implementation, and field evaluation of a neural network-guided smart trap specifically developed to monitor and selectively capture nocturnal insect pests under real agricultural conditions. The proposed trap integrates light and rain sensors, servo-controlled mechanical gates, and a single-layer perceptron neural network deployed on an ATmega-2560 microcontroller by Microchip Technology Inc. (Chandler, AZ, USA). The perceptron processes normalized sensor inputs to autonomously decide, in real time, whether to open or close the gate, thereby enhancing the selectivity of insect capture. The system features a removable tray containing a food-based attractant and yellow and green LEDs designed to lure target species such as moths and flies from the orders Lepidoptera and Diptera. Field trials were conducted between June and August 2023 in La Barca, Jalisco, Mexico, under diverse environmental conditions. Captured insects were analyzed and classified using the iNaturalist platform, with the successful identification of key pest species including Tetanolita floridiana, Synchlora spp., Estigmene acrea, Sphingomorpha chlorea, Gymnoscelis rufifasciata, and Musca domestica, while minimizing the capture of non-target organisms such as Carpophilus spp., Hexagenia limbata, and Chrysoperla spp. Statistical analysis using the Kruskal–Wallis test confirmed significant differences in capture rates across environmental conditions. The results highlight the potential of this low-cost device to improve pest monitoring accuracy, and lay the groundwork for the future integration of more advanced AI-based classification and species recognition systems targeting nocturnal Lepidoptera and other pest insects. Full article

Acoustic coupling agents serve as critical interfacial materials connecting piezoelectric transducers with microfluidic chips in acoustofluidic systems. Their performance directly impacts acoustic wave transmission efficiency, device reusability, and reliability in biomedical applications. Considering the rapidly growing body of research in the field of acoustic microfluidics, this review aims to serve as an all-in-one reference on the role of acoustic coupling agents and relevant considerations pertinent to acoustofluidic devices for anyone working in or seeking to enter the field of disposable acoustofluidic devices. To this end, this review seeks to summarize and categorize key aspects of acoustic couplants in the implementation of acoustofluidic devices by examining their underlying physical mechanisms, material classifications, and core applications of coupling agents in acoustofluidics. Gel-based coupling agents are particularly favored for their long-term stability, high coupling efficiency, and ease of preparation, making them integral to acoustic flow control applications. In practice, coupling agents facilitate microparticle trapping, droplet manipulation, and biosample sorting through acoustic impedance matching and wave mode conversion (e.g., Rayleigh-to-Lamb waves). Their thickness and acoustic properties (sound velocity, attenuation coefficient) further modulate sound field distribution to optimize acoustic radiation forces and thermal effects. However, challenges remain regarding stability (evaporation, thermal degradation) and chip compatibility. Further aspects of research into gel-based agents requiring attention include multilayer coupled designs, dynamic thickness control, and enhancing biocompatibility to advance acoustofluidic technologies in point-of-care diagnostics and high-throughput analysis. Full article

We present a corrosion-resistant quadrupole ion trap mass spectrometer (QIT-MS) designed for trace detection of volatiles in sulfuric acid aerosols, with a specific focus on phosphine (PH₃). Here, we detail the gas calibration methodology using permeation tube technology for generating certified ppb-level PH₃/H₂S/CO₂ mixtures, and report results from mass spectra with sufficient resolution to distinguish isotopic envelopes that validate the detection of PH₃ at a concentration of 62 ppb. Fragmentation patterns for PH₃ and H₂S agree with NIST data, and signal-to-noise performance confirms ppb sensitivity over 2.6 h acquisition periods. We further assess spectral interferences from oxygen isotopes and propose a detection scheme based on isolated phosphorus ions (P⁺) to enable specific and interference-resistant identification of PH₃ and other reduced phosphorus species of astrobiological interest in Venus-like environments. This work extends the capabilities of QIT-MS for trace gas analysis in chemically aggressive atmospheric conditions. Full article

Insect pests are a major threat to agricultural production, causing significant crop yield reductions annually. Integrated pest management (IPM) is well-studied, but its precise application in farmlands is still challenging due to variable weather, diverse insect behaviors, crop variability, and soil heterogeneity. Recent advancements in Artificial Intelligence (AI) have shown the potential to revolutionize pest management by implementing 4R pest stewardship: right pest identification, right method selection, right control timing, and right action taken. This review explores the roles of AI technologies within the 4R framework, highlighting AI models for accurate pest identification, computer vision systems for real-time monitoring, predictive analytics for optimizing control timing, and tools for selecting and applying pest control measures. Innovations in remote sensing, UAV surveillance, and IoT-enabled smart traps further strengthen pest monitoring and intervention strategies. By integrating AI into 4R pest management, this study underscores the potential of precision agriculture to develop sustainable, adaptive, and highly efficient pest control systems. Despite these advancements, challenges persist in data availability, model generalization, and economic feasibility for widespread adoption. The lack of interpretability in AI models also makes some agronomists hesitant to adopt these technologies. Future research should focus on scalable AI solutions, interdisciplinary collaborations, and real-world validation to enhance AI-driven pest management in field crops. Full article

High-Bandwidth Memory (HBM) enables the bandwidth required by modern AI and high-performance computing, yet its three dimensional stack traps heat and amplifies thermo mechanical stress. We first review how conventional solutions such as heat spreaders, microchannels, high density Through-Silicon Vias (TSVs), and Mass Reflow Molded Underfill (MR MUF) underfills lower but do not eliminate the internal thermal resistance that rises sharply beyond 12layer stacks. We then synthesize recent hybrid bonding studies, showing that an optimized Cu pad density, interface characteristic, and mechanical treatments can cut junction-to-junction thermal resistance by between 22.8% and 47%, raise vertical thermal conductivity by up to three times, and shrink the stack height by more than 15%. A meta-analysis identifies design thresholds such as at least 20% Cu coverage that balances heat flow, interfacial stress, and reliability. The review next traces the chain from Coefficient of Thermal Expansion (CTE) mismatch to Cu protrusion, delamination, and warpage and classifies mitigation strategies into (i) material selection including SiCN dielectrics, nano twinned Cu, and polymer composites, (ii) process technologies such as sub-200 °C plasma-activated bonding and Chemical Mechanical Polishing (CMP) anneal co-optimization, and (iii) the structural design, including staggered stack and filleted corners. Integrating these levers suppresses stress hotspots and extends fatigue life in more than 16layer stacks. Finally, we outline a research roadmap combining a multiscale simulation with high layer prototyping to co-optimize thermal, mechanical, and electrical metrics for next-generation 20-layer HBM. Full article

Heortia vitessoides Moore (Lepidoptera: Pyralidae), the dominant outbreak defoliator of Aquilaria sinensis (Myrtales: Thymelaeaceae, the agarwood-producing tree), poses a severe threat to the sustainable development of the agarwood industry. Current research has preliminarily revealed its biological traits and gene functions. However, significant gaps persist in integrating climate adaptation mechanisms, control technologies, and host interaction networks across disciplines. This review systematically synthesizes the multidimensional mechanisms underlying H. vitessoides outbreaks through the logical framework of “Fundamental Biology of Outbreaks—Environmental Drivers—Control Strategies—Molecular Regulation—Host Defense.” First, we integrate the biological characteristics of H. vitessoides with its climatic response patterns, elucidating the ecological pathways through which temperature and humidity drive population outbreaks by regulating development duration and host resource availability. Subsequently, we assess the efficacy and limitations of existing control techniques (e.g., pheromone trapping, Beauveria bassiana application), highlighting the critical bottleneck of insufficient mechanistic understanding at the molecular level. Building on this, we delve into the molecular adaptation mechanisms of H. vitessoides. Specifically, detoxification genes (e.g., HvGSTs1) and temperature stress-responsive genes (e.g., HvCAT, HvGP) synergistically enhance stress tolerance, while chemosensory genes mediate mating and host location behaviors. Concurrently, we reveal the host defense strategy of A. sinensis, involving activation of secondary metabolite defenses via the jasmonic acid signaling pathway and emission of volatile organic compounds that attract natural enemies—an “induced resistance–natural enemy collaboration” mechanism. Finally, we propose future research directions: deep integration of gene editing to validate key targets, multi-omics analysis to decipher the host–pest–natural enemy interaction network, and development of climate–gene–population dynamics models. These approaches aim to achieve precision control by bridging molecular mechanisms with environmental regulation. This review not only provides innovative pathways for managing H. vitessoides but also establishes a paradigm for cross-scale research on pests affecting high-value economic forests. Full article

The removal of partially hydrogenated fats, as well as the substitution of saturated fats with healthier alternatives, has become increasingly common due to their well-established association with adverse health effects. As a result, the demand for alternative formulations in the food industry has driven the development of a promising emerging technology: oleogels. Oleogels are a semi-solid material made by trapping liquid oil within a three-dimensional network formed by structuring agents. Within this context, this study aimed to develop and characterize margarines prepared with oleogels formulated from extra virgin olive oil, coconut oil, starch, and beeswax at varying concentrations. The proposed oleogel-based formulations exhibited a high melting temperature range and lower enthalpy. Although lipid oxidation levels differed between the commercial and oleogel-based margarines, they remained within acceptable limits. A significant difference in color was observed, with the oleogel formulations imparting a slight greenish hue compared to the commercial margarine. In terms of microstructure, the commercial margarine presented smaller and more uniformly distributed water droplets. Oleogel-based margarines demonstrated technological feasibility. Considering consumers’ growing interest in food innovation and health-conscious products, olive oil-based oleogel margarines represent a promising alternative, particularly due to the nutritional benefits associated with olive oil. Full article

This study addresses the critical challenges of hydrogen permeation and embrittlement in metallic pipelines for hydrogen storage and transportation by developing an epoxy resin-based composite coating with enhanced hydrogen barrier properties. Using cold spray technology, the fabricated coatings with controlled 250–320 μm thicknesses incorporating graphene/ceramic composite particles uniformly dispersed in the epoxy matrix. Microstructural characterization revealed dense morphology and excellent interfacial bonding. Electrochemical hydrogen charging tests demonstrated remarkable hydrogen permeation reduction, showing a strong positive correlation between coating thickness and barrier performance. The optimal 320 μm-thick coating achieved a hydrogen content of only 0.28 ± 0.09 ppm, representing an 89% reduction compared to that in uncoated substrates. The superior performance originates from the Al₂O₃/SiO₂ networks providing physical barriers, graphene offering high-surface-area adsorption sites, and MgO chemically trapping hydrogen atoms. Post-charging analysis identified interfacial stress concentration and hydrogen-induced plasticization as primary causes of ceramic particle delamination. This work provides both fundamental insights and practical solutions for designing high-performance protective coatings in long-distance hydrogen pipelines. Full article

Zoos and aquariums house a wide range of species, yet research remains heavily skewed toward mammals and camera-based technologies. This systematic literature review examines the use of computing technologies to monitor or interact with animals in zoos, aquariums, or wildlife parks, with a focus on taxonomic representation and technological diversity. A total of 125 studies published between 2014 and 2024 met the inclusion criteria, encompassing 151 recorded instances of technology use. Cameras and video systems were the most frequently used tools, appearing alone in 40% of studies and in combination with other technologies in an additional 18.4%, accounting for 48.3% of all technology instances. Most studies focused on only mammals (73.5%), and behavioral monitoring was the most common research aim (40.9%). These findings suggest an uneven distribution of research shaped more by convenience and familiarity than by welfare need, highlighting a critical opportunity to diversify both species focus and technological application. Increased investment in underrepresented species and underutilized tools will help ensure that research better reflects the full spectrum of animal needs and experiences. Full article

Perovskite quantum dots (PVK QDs) are gaining significant attention as potential materials for next-generation memory devices leveraged by their ion dynamics, quantum confinement, optoelectronic synergy, bandgap tunability, and solution-processable fabrication. In this review paper, we explore the fundamental characteristics of organic/inorganic halide PVK QDs and their role in resistive switching memory architectures. We provide an overview of halide PVK QDs synthesis techniques, switching mechanisms, and recent advancements in memristive applications. Special emphasis is placed on the ionic migration and charge trapping phenomena governing resistive switching, along with the prospects of photonic memory devices that leverage the intrinsic photosensitivity of PVK QDs. Despite their advantages, challenges such as stability, scalability, and environmental concerns remain critical hurdles. We conclude this review with insights into potential strategies for enhancing the reliability and commercial viability of PVK QD-based memory technologies. Full article

The greenhouse effect, responsible for trapping heat in Earth’s atmosphere, has a parallel thermal phenomenon at the indoor scale known as the Room-Level Greenhouse Effect (RGHE), where solar radiation elevates room temperatures and increases energy consumption. The RGHE contributes to indoor temperature increases of 4–10 °C and elevates energy demands by 15–30% in high solar exposure zones, the effect being even worse in tropical zones. To address this problem, an innovative analog microarchitecture is proposed for real-time RGHE detection by sensing the sunlight intensity radiation factor (SIR). A compact analog system is introduced, comprising three stages: a Sensing Circuit Stage (SCS) that isolates the dynamic sunlight signal f (r) from static room condition factors (RCFs), an Amplification Stage (AS) that shifts and boosts the signal, and a Stabilized Peak Detection Stage (SPDS) that captures the peak solar intensity. The microsystem was tested across fixed f (m) levels of 0.75 V, 1.0 V, and 1.5 V, and varying f (r) values of 3 mV, 4 mV, and 5 mV. It successfully detects peak voltages ranging from 1.69 V to 1.92 V, with stabilization achieved within 60 µs, enabling accurate detection of the f (r) signal. The proposed microarchitecture offers a scalable approach to localized thermal monitoring in smart building environments using fully analog circuitry, designed and simulated in Cadence Virtuoso using the TSMC 180 nm technology library. Full article

Deep neural networks provide a powerful driving force for breakthroughs in semantic segmentation technology. However, the current mainstream architecture generally falls into the “parameter redundancy trap” in pursuit of accuracy improvement, which brings a large number of calculations and model parameters, forcing researchers to seek a new structural paradigm balance between pixel-level parsing accuracy and the limited computing power of embedded devices. We propose a lightweight semantic segmentation network with multi-level feature fusion and dual attention coordination. In view of the large number of parameters in the traditional backbone network and the fact that it only outputs semantic features at the end of the network but lacks shallow feature information, it will cause significant information loss in the decoder stage, which may lead to fuzzy segmentation results and the misclassification of categories. We design a lightweight backbone network with multi-level feature fusion capability. The detail recovery capability is enhanced in the reconstruction process layer by constructing a cross-stage feature aggregation module system; secondly, in view of the lack of effective feature attention in previous methods, we propose a new DCA module in the proposed network and introduce CBAM in the multi-level special fusion network at a shallow level, which improves the model’s category discrimination ability with minimal parameter overhead, thereby optimizing feature expression and improving segmentation performance. The results show that in the Cityscapes dataset, the mIoU reaches 75.29% with only 5.82 M parameters. In the Pascal VOC 2012 dataset experiment, the proposed model achieves an mIoU of 74.24% with only 5.869 M parameters. Compared with DCN-Deeplabv3+ network, the parameters comprise 48% of it, but the accuracy is improved by 1.66%. Compared with the UNet and PSPNet models, the parameters are reduced by 86.63% and 87.44%, respectively. Full article