Lung cancer, a global concern with high mortality rates, has multiple causative factors, including exposure to air pollution. This risk extends to microplastics, now recognized as constituents of particulate matter, a well-known carcinogen for lung cancer. Numerous studies have consistently shown the prevalence of microplastics originating from various sources, accumulating in the lungs upon inhalation. However, their direct influence on lung carcinogenesis remains less explored, highlighting the urgent need for high-priority research. This comprehensive review aims to analyze the effects of microplastics on lung carcinogenesis, drawing insights from a wide range of research studies. It addresses the sources and transport of microplastics infiltrating humans from indoor environments, leading to inflammation, oxidative stress, DNA damage, genomic instability, and immune modulation—factors closely associated with lung cancer development. The review also emphasizes pertinent in vitro and in vivo studies on microplastic exposure and their potential implications for lung cancer. Furthermore, it delves into the least explored and complex interplay between microplastics and particulate matter in the context of lung cancer, based on global cohort studies and meta-analyses. This review provides critical insights into microplastic and particulate matter exposure, emphasizing the need for extensive, long-term investigations across diverse human populations and regions to inform public health policies. Such research endeavors are essential for developing effective intervention strategies to mitigate the risks associated with these environmental contaminants.
Food packaging has evolved from passive containment to dynamic systems that actively monitor and communicate product quality. Traditional intelligent packaging technologies such as sensors, indicators, and RFID tags have established significant potential in extending shelf-life and ensuring food safety. However, the reliance on synthetic dyes and nonbiodegradable materials has raised concerns regarding sustainability and consumer health. Recent advances in natural pigments, particularly anthocyanins and other plant-derived compounds, offer eco-friendly alternatives for pH-sensitive freshness indicators. Moreover, digital sensing technologies such as RFID, NFC, and smartphone-based applications enable real-time data transmission and supply chain transparency. This review proposes a hybrid approach that integrates natural pigment-based indicators with digital sensing platforms to create multifunctional, sustainable packaging solutions. These systems provide dual benefits: visible colorimetric cues for consumers and wireless data for industry stakeholders. By bridging analog and digital monitoring, hybrid intelligent packaging can reduce food waste, enhance consumer trust, and align with global sustainability goals. Future research should focus on pigment stability, biopolymer compatibility, cost-effective scaling, and regulatory frameworks to accelerate the adoption of these next-generation packaging systems.
The review highlights the nutritional and phytochemical differences in Trigonella, Ocimum, and Brassica microgreens grown under various cultivation conditions. Microgreens, known for their concentrated nutrients and health-promoting compounds, have garnered attention as functional foods. The findings from the literature suggested that factors such as light intensity, temperature, soil composition, and hydroponic systems significantly influence the nutritional profiles and phytochemical content of these species. The summarized findings revealed significant variability in amino acids, sugar profile, and bioactive compounds, suggesting that optimizing growth conditions can enhance their nutritional value. This review emphasizes the potential of microgreens as nutrient-dense additions to the diet, highlighting the importance of controlled cultivation practices to maximize health benefits, while also providing a comprehensive exploration of specific genera, growing conditions, and their potential as functional foods.
Abstract Vibration and noise are critical factors in numerous engineering systems to mitigate their adverse effects. This document presents a comprehensive summary of vibration analysis research pertaining to beams, plates, and shells from 2000 to 2025. Consequently, these buildings have garnered significant attention. Moreover, the aspects of flexural behavior, damage progression, health monitoring tools, quasi-static bending, dynamic bending, and deflections are thoroughly examined and analyzed. The review examines several beam theory models, analytical approaches, and numerical techniques (RCAS, CAMRAD, GEBT) pertaining to dynamic and vibrational characterization of natural fiber composite materials (functionally graded materials, composite materials, nanomaterials), as well as experimental methodologies. The review's conclusion outlines future challenges, developmental requirements, and prospective research topics.
The consistent detection of low-level carbon monoxide (CO) and methane (CH 4 ) is important for safety, environmental monitoring, and early leak identification. Semiconductor metal oxides are considered to be the most promising candidates for such applications. However, their performance is usually hindered by limited speed and less sensitivity. Herein, three heterojunction systems—ZnO/SnO 2 (Z/S) (n–n), NiO/ZnO (N/Z) (p–n), and SnO 2 /NiO (S/N) (p–n)—were synthesized through a controlled hydrothermal route. A systematic comparative study of these n–n and p–n interfaces is carried out to determine the most effective heterojunction structure for improved room-temperature CO and CH 4 sensing performance. Structural and morphological studies have proved a well-defined interface with homogenous surface features. In fact, the phase purity was well proved by X-ray diffraction, the homogeneous element distribution was identified by field emission scanning microscopy/energy dispersive X-ray spectroscopy, and the surface roughness was decreased, as determined by atomic force microscopy 2D/3D profiles with R a values of 81.547 nm (Z/S), 46.084 nm (N/Z), and 24.272 nm (S/N). Ultraviolet–visible spectroscopy confirmed the sequential bandgap narrowing: 3.2 eV for Z/S, 2.6 eV for N/Z, and 2.4 eV for S/N, indicative of enhanced electron interaction across the interfaces. J–V measurements exhibited diode-like behavior, showing a sequential decrease in cutoff voltage to 0.7, 0.6, and 0.4 V, respectively. Accordingly, electrochemical impedance spectroscopy analysis showed a decrease in charge-transfer resistance from 16.2 to 2.2 and 1.4 kΩ. Improved electron mobility has also been demonstrated from thickness measurements, which revealed that the S/N film exhibited the thinnest layer (0.4 μm) relative to Z/S (2.0 μm) and N/Z films (1.4 μm). This was further emphasized in the gas-sensing experiment, which demonstrated the clear omnipotence of the S/N heterostructure; hence, it enabled a fast response/recovery time of 15/24 s for CO and 18/42 s for CH 4 at low concentrations with high sensitivity and long-term stability in ambient conditions. The improved sensing ability could be ascribed to the strong p–n junction, smallest depletion width, and low interfacial resistance. Moreover, the sensing ability of the sensor was validated using machine learning algorithms, wherein consistently, the Random Forest (RF) algorithm appeared to have a stronger predictive power than Support Vector Machine (SVM). In fact, RF performed better than SVM by achieving higher R-Squared (R 2 ) values for each regression problem, which were generally in the range of 0.80–0.85 compared to the lower and more fluctuating performance of SVM. Likewise, a drift was noticed in the classification performance where RF achieved accuracies of 0.91/0.88 for CO and 0.93/0.89 for CH 4 under A1 and A2 scenarios, respectively, outperforming SVM in all cases. Finally, this study hereby confirms that the concept of heterojunction, specifically the S/N interface, combined with machine learning-assisted analysis, provides a promising route forward for the realization of next-generation room temperature gas sensors with high sensitivity, fast response, and accurate predictive capabilities.
