Search Results (317)

The transition from conventional silica nanoparticle (SiNP) production based on purified alkoxysilanes and high-temperature flame hydrolysis of silicon tetrachloride to renewable and waste-derived silicon resources requires more than precursor substitution. It requires a mechanistic understanding of how feedstock mineralogy, silicon speciation, impurity chemistry, and processing history propagate through dissolution, nucleation, condensation, gelation, aging, drying, and pore evolution to determine material performance, environmental burden, and manufacturing feasibility. Although previous reviews have established the technical feasibility of producing silica from secondary resources, their predominant organization by feedstock, synthesis route, or application provides limited ability to explain why nominally similar processes generate materials with markedly different structural and functional properties. This review addresses these through a resource-pull, feedstock-to-function framework that links resource chemistry and process design to critical material attributes, application-specific specifications, sustainability, and scale-up requirements. Agricultural residues, industrial by-products, geothermal resources, waste glass, and fluorosilicate streams are critically compared according to silicon form and phase, reactivity, impurity profile, compositional variability, purification demand, and attainable product quality. Particular attention is given to waste-derived alkaline silicate systems, in which molecular, oligomeric, and colloidal silica coexist and therefore require characterization beyond bulk SiO₂ concentration. Established and emerging processing strategies, including controlled combustion and alkaline extraction, alkali fusion, ambient-pressure drying, microwave and mechanochemical activation, biogenic and biomimetic templating, and continuous processing, are evaluated according to their mechanistic effects, technological maturity, structural control, and demands for energy, reagents, water, solvents, effluent treatment, and capital. Across these routes, gelation and aging emerge as critical transfer stages through which feedstock composition is translated into network connectivity, pore architecture, shrinkage behavior, and ultimately functional performance. Evidence from secondary-source aerogels further shows that properly controlled waste-derived systems can attain BET surface areas of approximately 350–500 m² g⁻¹, within the textural range of many alkoxide-derived materials, indicating that feedstock variability, impurity management, and process control are more important constraints than an inherently lower performance ceiling. On this basis, this review proposes a minimum evidence framework comprising feedstock traceability, intermediate-speciation and colloidal characterization, silicon mass balance, gelation and aging metrics, application-specific qualification criteria, performance-normalized life cycle and techno-economic assessment, process analytical control, and staged pilot validation. Collectively, these principles provide a mechanistically grounded basis for moving sustainable silica synthesis beyond isolated proof-of-concept demonstrations toward reproducible, scalable, application-matched, and commercially credible manufacturing platforms. Full article

In the era of global energy transition, the development of third-generation photovoltaic technologies, such as dye-sensitized solar cells, has emerged as a paramount challenge in materials engineering. This study is dedicated to the synthesis and implementation of graphene quantum dots as eco-friendly sensitizers within DSSC architectures. The GQDs were synthesized via a microwave-assisted hydrothermal route using biodegradable organic precursors, providing a “green” alternative to conventional, toxic heavy-metal-based materials. The nanocrystalline structure and optoelectronic properties of the sensitizer were verified through UV-Vis and visual photoluminescence assessment. A focal point of this research was the optimization of the GQD concentration on the mesoporous surface of the titanium dioxide photoanode. Measurements were conducted utilizing a custom-designed experimental setup integrated with 3D-printed (FDM) components and an Arduino microcontroller, ensuring precise data acquisition under controlled illumination conditions (405–625 nm). The results indicated an optimal operational point at a fivefold dilution of the stock solution (0.4 g/dm³), which yielded the highest open-circuit voltage ($V_{\mathrm{oc}}$) of 545.4 mV under UV irradiation. The decline in photovoltaic performance observed at higher concentrations was attributed to excessive nanostructure agglomeration, which effectively blocked the mesopores of the semiconductor. Furthermore, the demonstrated high chemical capacitance of the system imparts electrochemical capacitor-like characteristics to the cell, enabling energy stabilization under fluctuating illumination. To elucidate the underlying sensitization mechanisms at the atomic level, computational simulations were conducted utilizing the MACE machine-learning potential and the GFN2-xTB semi-empirical method. The theoretical models revealed that the formation of stable covalent Ti–O–C bridges (chemisorption) is imperative for establishing strong interfacial electronic coupling. Solvation models and molecular dynamics (MD) at 300 K confirmed the thermodynamic and operational robustness of the hybrid system in an aqueous electrolyte. Ultimately, this combined experimental and theoretical work conclusively demonstrates that graphene quantum dots represent an efficient, highly stable, and non-toxic alternative to classic molecular dye sensitizers. Full article

While Thermal Infrared (TIR) sensors are standard for monitoring volcanic activity, their efficacy is severely compromised by meteorological clouds and dense volcanic ash. To overcome these optical limitations, we present the first ground-based application of a passive microwave radiometer for continuous volcano monitoring. Operating in the 10–12 GHz band, our Total Power Microwave Receiver is stationed 12 km from Mount Etna’s active craters to measure thermal emissions from eruptive hotspots. Unlike traditional TIR imaging, this low-cost, automated system exploits the atmospheric transparency of microwave wavelengths, enabling uninterrupted observation regardless of weather or solar illumination. We detail the system’s design and report its successful detection of volcanic phenomena during the 2023–2025 eruptive cycles, including the transit of a high-temperature ash cloud that triggered a significant radiometric peak. Our findings demonstrate that fixed-point microwave radiometry provides a reliable thermal signature of eruptive activity, offering a pioneering and highly accessible tool for the next generation of global volcanic early warning systems. Full article

This study evaluated potentially toxic element (PTEs) contamination (As, Cd, Co, Cr, Cu, Ni, Pb, and Zn) in soil and grapevine leaves across 36 sites in a historical vineyard near Iași, Romania, transitioning toward residential development. Soil samples were analyzed using energy-dispersive X-ray fluorescence (ED-XRF), while leaf tissues collected during the véraison stage (mid-August) underwent microwave-assisted acid digestion followed by inductively coupled plasma mass spectrometry (ICP-MS). Soil analyses revealed that average concentrations of Cr, Ni, and Pb exceeded national normal legal thresholds, while Cu and As surpassed alert permissible limits. Contamination factor ($CF$) and Geoaccumulation Index ($I_{geo}$) metrics confirmed significant anthropogenic Cu enrichment from historical viticultural treatments. Soil-to-leaf transfer factors ($TF$) approached or exceeded 1 for Cu and Zn, though grapevine was not identified as a hyperaccumulator. Spearman correlation modeling demonstrated a statistical decoupling between soil concentrations and foliar tissue levels for Cu ($r_s=-0.21$) and Zn ($r_s=0.075$), confirming that canopy accumulation is governed by historical agrochemical spraying and atmospheric deposition rather than root-driven uptake. Conversely, Pb ($r_s=0.43$) exhibited a moderate direct soil-to-leaf pathway. The potential ecological risk ($PER$) index indicated moderate-to-considerable cumulative risk, primarily driven by Cu ($E_{Cu}=68.64$) and Cd ($E_{Cd}=59.17$). Human health risk assessments established direct ingestion as the primary exposure pathway, with current non-carcinogenic hazards remaining within tolerable limits ($HI<1$). However, the proposed residential conversion increases exposure risks for vulnerable cohorts, underscoring the necessity of systematic soil screening and remediation prior to land redevelopment. Full article

Background: Incomplete microwave ablation (iMWA) of lung cancer often leads to rapid recurrence and metastasis, yet the underlying mechanisms remain unclear. This study explored whether iMWA promotes tumor progression by activating the cyclic GMP–AMP synthase–stimulator of interferon genes (cGAS–STING) signaling pathway and its downstream effector ZEB1 in tumor cells. Materials and Methods: An in vivo iMWA model was established in nude mice bearing H1650 lung tumors, and an in vitro sublethal heat treatment model was used to mimic incomplete ablation. Transcriptomic profiling, molecular assays and functional analyses assessed cellular behavior and signaling activity changes post-iMWA; genetic and pharmacologic interventions modulated STING signaling and autophagy. Results: Post-iMWA residual cells exhibited enhanced proliferation and invasion. Thermal injury induced necrosis and inhibited mitophagy, causing cytosolic mtDNA accumulation that activated the intrinsic cGAS–STING pathway. This upregulation of ZEB1 drove epithelial–mesenchymal transition and dissemination. Notably, silencing STING or ZEB1, or pharmacologically restoring autophagy, significantly suppressed tumor growth and metastasis. Conclusions: iMWA drives malignant progression of lung cancer through an mtDNA–cGAS–STING–ZEB1 signaling axis. Targeting this pathway—by inhibiting STING or enhancing autophagy—may represent a promising therapeutic strategy to mitigate recurrence and metastasis following microwave ablation. Full article

The global oil-processing industry generates substantial quantities of by-products and secondary streams, including oilseed cakes, pomaces, hulls, and wastewaters, which remain largely underutilized despite being rich sources of high-value bioactive compounds. The development of sustainable strategies for the valorization of these residues is increasingly recognized as a key component of circular bioeconomy and biorefinery frameworks. In this context, deep eutectic solvents (DESs) have attracted considerable attention as a new generation of designer solvents owing to their tunable physicochemical properties, low vapor pressure, ease of synthesis, and potential environmental compatibility. This review critically discusses the current state of knowledge regarding the application of DESs in the processing and valorization of oil industry by-products. Particular emphasis is placed on the relationship between DES composition, physicochemical characteristics, and extraction performance. Recent advances in the recovery of phenolic compounds, proteins, saccharides, and tocopherols from oilseed-derived residues are comprehensively examined, including the integration of DESs with intensified extraction techniques such as microwave-, ultrasound-, and ohmic-assisted extraction. Furthermore, the role of DESs in oil purification processes and the treatment of technological waste stream is evaluated. Emerging evidence indicates that DES-based systems not only enhance extraction efficiency and selectivity but may also improve the stability, bioaccessibility, and purity of the recovered compounds. Finally, the opportunities and challenges associated with the implementation of DES-based technologies within integrated biorefinery schemes are discussed, including solvent recovery, product scalability, sensory acceptability, and regulatory considerations. The available literature demonstrates that DESs constitute a versatile platform for the sustainable valorization of oil-processing residues, supporting the transition from conventional waste management approaches toward resource-efficient and circular production systems. Full article

Despite the aim of meeting the demand for long-term online monitoring of structural cracks in fields such as infrastructure, rail transit, aerospace and others, traditional detection methods fail to realize passive wireless, flexible conformal and non-contact measurement. This paper proposes a flexible wireless passive crack sensor based on resonant rings. Taking polyimide (PI) as the substrate, the sensor integrates a sensitive interdigital resonant ring structure. Variations in crack width disturb the electromagnetic field, which further leads to a resonant frequency shift to realize crack width detection. The sensing mechanism is elaborated based on microwave resonance and equivalent circuit theories. Structural optimization and crack width sensitivity analysis are carried out via electromagnetic simulation. Samples are fabricated by flexible printing technology, and a test platform is established. Experiments reveal that the sensor achieves excellent linearity within the crack width range of 0~2.5 mm, with the resonant frequency decreasing monotonically as crack width increases, and a sensitivity of 67.02 MHz/mm. It can operate stably under varying distances, installation angles and bending conditions, demonstrating outstanding flexible conformability. Featuring no power supply requirement, a chip-free design, a simple structure and strong anti-interference capability, the sensor is suitable for long-term crack monitoring of metal structures. Compared with existing studies, the proposed sensor exhibits prominent advantages in flexible adaptability, wireless passive performance and engineering practicability and can provide a novel wireless passive solution for structural health monitoring. Full article

Capsaicin is a bioactive substance with diverse health-promoting properties, but its intense pungency and irritant side effects limit oral application. This study developed a Pueraria lobata particle-based Pickering emulsion (PE) as an oral delivery platform for capsaicin. Modified Pueraria lobata particles (MP-ps) were prepared using a microwave-assisted enzymolysis technique (15% hydration, 252 J/g microwave energy and 8 h pullulanase hydrolysis). MP-ps exhibited surface cracks and pores, altered starch crystallinity, and enhanced water-holding capacity and swelling power. Compared with original Pueraria lobata particles, MP-ps avoided the burst release of puerarin in the upper gastrointestinal tract, dropping from approximately 54% to 20%. An optimized capsaicin-loading PE with excellent physical stability was obtained from 4% MP-ps and a 9:1 (v/v) water-to-corn oil ratio, exhibiting complete core–shell architecture and oil-phase sequestration of capsaicin in TEM images. Gastrointestinal transit analysis in male KM mice indicated that MP-p-based PE reduced the exposure of the encapsulated lipophilic substance in the stomach and small intestine and promoted its accumulation in the colon. Irritation assays in male KM mice and Sprague-Dawley rats further demonstrated that this effective encapsulation alleviated the gastrointestinal irritation of capsaicin and improved its palatability. This study provided a food-derived material and an easy-to-build PE platform for oral lipophilic/irritant substance delivery, with potential for future health-regulating applications. Full article

Microwave scatterometers are capable of acquiring land surface backscattering coefficients day and night under all-weather conditions, offering advantages for snow cover monitoring. However, the relatively low spatial resolution of traditional scatterometer data limits their application in the fine-scale monitoring of snow cover distribution. To improve the spatial representation of snow cover and mitigate mixed-pixel effects in complex spring snowmelt scenarios, this study proposes an adaptive bilateral filtering scatterometer image reconstruction (SIR-ABF) algorithm based on the rotating fan-beam scanning characteristics of the FengYun-3E Wind Radar (FY-3E WindRAD). The Ku-band data of FY-3E WindRAD were reconstructed from the original 10 km resolution to an enhanced resolution of 3.125 km. Furthermore, by integrating the reconstructed scatterometer backscatter with multi-source auxiliary data, an optimal feature subset was determined through a feature selection strategy that considers both feature-label correlation and inter-feature multicollinearity. Finally, the best feature subset was combined with four machine learning (ML) models for snow cover classification. The results indicate that the Support Vector Machine (SVM) achieved the best performance, yielding an Overall Accuracy (OA), Macro-F1, and Kappa coefficient (Kc) of 91.64%, 86.47%, and 0.730, respectively. Compared with the snow cover classification results derived from the original 10 km Ku-band data, the 3.125 km reconstructed data provided more detailed spatial information and better characterized fragmented snow patches and snow transition boundaries. Further comparison with existing snow cover products demonstrated the spatial consistency and continuity of the proposed classification results, highlighting the potential of high-resolution scatterometer data for fine-scale snow cover monitoring during the spring snowmelt period in Northeast China. Full article

The rapid global transition toward sustainable food systems has intensified interest in alternative protein ingredients derived from both terrestrial plants and blue foods. However, a critical bottleneck in the commercialization of these proteins is the stabilization of flavor profiles during dehydration. Drying technologies ranging from conventional hot-air and heat pump drying to microwave and vacuum freeze-drying inevitably induce structural reorganization and oxidative modifications. These transformations fundamentally modulate how volatile flavor compounds are bound, retained, and released within the food matrix. This review proposes a comprehensive structure–process–function framework that mechanistically connects intrinsic protein architectures, drying-induced denaturation, and flavor-binding behavior. The review first contrasts globular plant proteins (e.g., soy, pea, and emerging tropical crops) with fibrous marine myofibrillar and collagenous proteins, emphasizing their distinct hierarchies, amino acid compositions, and oxidative vulnerabilities. It then critically evaluates how varying drying modalities drive protein unfolding, aggregation, and carbonylation, and how these transformations alter binding pocket accessibility, surface hydrophobicity, and lipid–protein–flavor crosstalk. Furthermore, it highlights the emerging role of hybrid plant–marine protein matrices as a strategy to optimize techno-functionality. By integrating structural biophysics with computational approaches such as molecular docking and structure-based modeling, this review provides a predictive conceptual map for designing flavor–protein interactions under specific dehydration histories. Ultimately, the proposed framework offers practical design principles for selecting protein sources and tailoring drying strategies to produce high-quality, sensorially superior, and sustainable next-generation food products. Full article

Accurate large-scale crop mapping is fundamental to agricultural management. However, in Liaoning Province, undulating terrain and fragmented fields make fine crop classification challenging. In particular, corn and soybean have overlapping phenologies, which can lead to spectral and structural confusion in conventional optical–SAR feature spaces and limit mapping accuracy. This study proposes a fine crop mapping framework integrating optical phenotypic, microwave structural, and meteorological time-series features. To overcome the curse of dimensionality caused by high-dimensional heterogeneous data, an adaptive feature truncation mechanism based on the transition pattern of the marginal-gain curve was designed. Additionally, a pyramid multi-scale sliding window algorithm was constructed to optimize meteorological features, achieving dimensionality reduction and precise identification of phenologically sensitive windows. The results indicate that: (1) The multi-scale feature selection strategy effectively eliminates redundant variables and maximizes the inter-class discriminability of core features, significantly improving computational efficiency and classification performance. (2) High-frequency meteorological features provide key physiological constraints. Specifically, mid-May shortwave radiation, early October precipitation, and early August growing degree days constitute the core environmental–physiological features for distinguishing confused crops, helping to mitigate the spectral confusion of dryland crops. (3) Driven by the multi-source features, the Support Vector Machine (SVM) exhibits the optimal generalization robustness for processing high-dimensional structured data, yielding an overall classification accuracy of 91.80% and a Kappa coefficient of 0.8905. This framework provides a reliable methodological reference for high-precision crop monitoring in large-scale complex planting areas. Full article

High-precision WTC is essential for satellite altimetry and ocean dynamic environment monitoring. Existing WTC approaches often rely on globally unified statistical frameworks, which inadequately represent wind-speed-dependent nonlinear sea-surface microwave radiative responses and are prone to systematic bias under uneven observation distributions. To address these limitations, this study proposes an adaptive WTC method integrating overlapping wind-regime modeling, multi-scale collaborative sample balancing, and a model soft-fusion strategy. Firstly, a modeling framework with overlapping transition zones for low-, moderate-, and high-wind-speed regimes is established according to wind-speed-driven variations in sea-surface radiative responses, and sub-models are trained independently. Subsequently, a multi-scale sample balancing, combining global and local weights, is designed to enhance learning from sparse samples. Finally, a soft-fusion strategy based on a trapezoidal membership function is applied to dynamically weight sub-model outputs, ensuring retrieval continuity across transition zones. Using HY-2C Calibration Microwave Radiometer (CMR) observations, the proposed method is developed, trained, and evaluated against model-derived WTC and collocated Jason-3 AMR-2 measurements. Results show that the proposed method improves overall WTC retrieval accuracy and stability while effectively reducing systematic biases under wind-speed regimes with sparse observations, providing an effective and robust approach for high-accuracy WTC retrieval under various wind-speed conditions. Full article

The growing volume of industrial waste and the need for sustainable material solutions drive the search for cost-effective fillers and energy-efficient processing methods for polymer composites. This study investigates the valorization of brick dust (BD), a fine ceramic waste, as a reinforcing filler for unsaturated polyester resin (UPR), combined with microwave (MW) treatment applied at different stages of composite fabrication. The brick dust was comprehensively characterized using laser diffraction, SEM, EDX, XRD, and FTIR, revealing an environmentally safe aluminosilicate powder with a mean particle size of 3–6 µm, plate-like morphology, and surface hydroxyl groups favorable for matrix interaction. The optimal filler content was found to be 50 phr, which increased flexural strength by 6.5%, flexural modulus by 134%, tensile strength by 11%, and impact strength by 40% compared to neat UPR. Among the MW strategies evaluated, post-curing of the fully polymerized composite for 120 s proved most effective, yielding further improvements in flexural strength (110 MPa, +34.1%), flexural modulus (8250 MPa, +49.7%), impact strength (13.8 kJ/m², +119%), and Shore D hardness (88). MW post-curing also increased the gel fraction from 95.0% to 97.8%, raised the thermal stability index (THRI) from 150.6 to 165.8, and reduced equilibrium water absorption from 0.62% to 0.47% with a reversibility index of 87.5%. Fracture surface analysis confirmed a transition from interfacial debonding to cohesive matrix failure, with ultra-thin polymeric veils replicating the scaly filler structure. These results demonstrate that microwave post-curing synergistically enhances the mechanical, thermal, and moisture-resistant properties of brick dust-filled polyester composites. Full article

This review analyses and logically structures modern intelligent sensor technologies in the context of animal husbandry, feed production, and veterinary medicine. The main research discussed in the article focuses on machine learning based on modern neural network models, computer vision, and sensor systems that are transforming the methods for assessing the health, behavior, and nutrition of farm animals. The first part examines modern approaches to quality control and optimization of mineral and vitamin premixes, including visual inspection using visual sensors and neural networks. Key roles are played by precise dosing, component stability (minerals, vitamins), and the transition to more bioefficient organic forms of micronutrients to reduce environmental impact. Improvements in feed and premix production are analyzed, including automation, energy management, and the use of machine learning for non-destructive quality control, defect detection, mixing homogeneity assessment, and vitamin stability prediction. The second part analyzes methods for animal location and behavior detection. This article presents computer vision-based systems, including modifications of YOLO, for automatically tracking and classifying key behavioral patterns (lying down, standing, feeding, and aggression) in cattle and pigs, even in crowded conditions. It also discusses the use of ultra-wideband (UWB) systems and accelerometers combined with machine learning for high-precision positioning and detection of specific behavioral anomalies, such as lameness and playfulness. The third section focuses on the application of machine learning in veterinary diagnostics, including the automated interpretation of medical images (X-ray, ultrasound, and MRI) as sensor data streams for the diagnosis of cardiovascular, oncological, and orthopedic diseases in farm and small animals. Furthermore, the article examines the use of machine learning models for proactive disease diagnosis in farm animals and poultry based on multimodal data and image analysis. Considerable attention is given to methods and tools for radiometric diagnosis of animal diseases at an early stage using microwave sensors, as well as laser therapy and surgery in veterinary medicine. The review concludes that the integration of intelligent systems enables a transition to data-driven livestock management, significantly improving animal welfare and, consequently, the efficiency and sustainability of agricultural production. Full article

The potential health hazards caused by microwave exposure have attracted increasing attention. Microwave radiation has been reported to induce oxidative stress in neural tissues, which is considered one of the primary mechanisms underlying its adverse effects on central nervous system function. The hippocampus is sensitive to microwave radiation, whereas underlying cellular and molecular mechanisms remain incompletely understood. In this study, microwave-exposed mice exhibited significantly impaired performance in the Go/No-go, Y-maze, and novel object recognition tests at 6 h and 7 days post-exposure, indicating deficits in hippocampus-dependent working memory. Single-cell RNA sequencing of hippocampal tissues from control and microwave-exposed mice yielded 94,088 high-quality cells across eight major cell types. Astrocyte sub-clustering identified five transcriptionally distinct subpopulations, with Astrocyte_S100a6 and Astrocyte_Son proportions increased and Astrocyte_Serpinf1 decreased in the radiation group. Analysis of astrocyte transcriptional state transitions showed microwave-exposed astrocytes were preferentially distributed toward terminal reactive states with depletion at early homeostatic nodes. Cell–cell communication analysis revealed increased total interactions and interaction strength following radiation. Astrocyte outgoing signaling was increased for pathways associated with vascular remodeling, phagocytic regulation, and neuroinflammation, while pathways related to trophic support were decreased. Incoming signaling showed increased activity in pathways linked to phagocytic recruitment and inflammatory mediation. Taken together, these findings indicate that microwave exposure is associated with hippocampus-dependent working memory deficits accompanied by transcriptional remodeling of astrocyte subpopulation composition, directional astrocyte state transitions toward reactive phenotypes, and broad alterations in astrocyte-centered intercellular communication, providing a cellular and molecular framework for understanding astrocyte involvement in microwave radiation-associated hippocampal dysfunction. Full article