Icing on fiber-reinforced composites poses a critical challenge in cold environments, leading to performance degradation and reduced operational reliability. To address this problem, approaches involving metallic wire stitching and microwave-assisted heating are investigated as a material-level deicing strategy. An optimal stitching geometry for concentrated electromagnetic wave absorption was initially designed using COMSOL Multiphysics simulations. The preliminary stitching design was experimentally validated using a microwave antenna system. The glass fiber-reinforced plastic (GFRP) specimen with a stitching length of 3 cm reached 74.8 degrees C within 300 s under 20 W of microwave heating. For the 1-line stitched GFRP, localized ice removal along the SUS/aramid stitching path was achieved within 15 min, followed by complete deicing within 30 min. The 8line stitched GFRP further reduced the complete deicing time to 25 min. The 1-line stitched GFRP exhibited improved flexural properties while maintaining tensile performance. After deicing, the stitched GFRP retained 91.0% of its original flexural strength, while the flexural modulus was nearly unchanged. Under low-velocity impact (15 J), the impact energy absorption increased slightly, consistent with a comparable impact response after repeated deicing. This research provides material-level insights into microwave-assisted deicing of composite structures.
Keeping track of Global Mean Sea Level (GMSL) over long periods is important for assessing coastal risk. However, deep learning models may produce predictions that exhibit short-term increases and decreases, potentially conflicting with expected long-term physical behavior. In this study, we propose a combined Convolutional Neural Network and Isotonic Regression framework, termed CNN-ISO, for modeling and forecasting annual GMSL changes over the period 1880–2024. The framework uses 11 predictors representing atmospheric, oceanic, and cryospheric conditions. To provide longer temporal sequences, the original 144 annual observations are linearly interpolated into 1728 monthly observations. Model evaluation follows a strict chronological split, with training data from 1880–1979, validation data from 1980–1999, and test data from 2000–2024. All preprocessing transformations and the isotonic mapping are fitted exclusively on the training set to prevent information leakage from later periods. On the unseen test period, CNN-ISO achieves R2=0.997 with a 95% confidence interval of [0.995,0.999], together with an MAE of 0.56 mm and an RMSE of 0.72 mm. For comparison, linear regression achieves R2=0.982. Although the predictive improvement over linear regression is relatively modest, CNN-ISO provides an additional constraint by producing monotonic out-of-sample projections, which is consistent with the assumed long-term behavior of GMSL. Under statistical trend extrapolation to 2050, CNN-ISO estimates a GMSL increase of 184.10 mm relative to the baseline. However, estimates vary substantially across models, with linear regression and LSTM producing 116.60 mm and 299.19 mm, respectively. These results should therefore be interpreted as trend-based statistical extrapolations rather than comprehensive climate projections, as the framework does not explicitly model ice-sheet dynamics, changes in ocean circulation, or future emission pathways. Nevertheless, the proposed approach provides a transparent framework for examining model uncertainty and may support early-stage coastal adaptation planning.
Background: The development of sustainable photocatalytic systems for simultaneous hydrogen production and wastewater remediation is crucial for addressing global energy and environmental challenges. Red mud analogue, an abundant industrial waste from alumina processing, poses serious ecological risks, yet its high-value reutilization remains limited. Method: A multifunctional TiO2-RMAFe-g-C3N4 heterostructured composite was rationally synthesized and further immobilized into a polymeric membrane matrix. Comprehensive structural, morphological, and optical characterizations were conducted, and the photocatalytic performance was evaluated through visible-lightdriven hydrogen evolution and membrane-assisted degradation of methylene blue (MB) and rhodamine B (RhB). Significant findings: The optimized TiO2-RMAFe-g-C3N4 photocatalyst exhibits a high hydrogen evolution rate of 1623 & micro;mol g-1 h-1, significantly surpassing that of the individual components. The corresponding photocatalytic membrane achieves near-complete degradation efficiencies of 99.7% for MB and 99.6% for RhB, together with excellent reusability and operational stability. The superior performance is attributed to enhanced visible-light absorption, accelerated charge separation, and efficient interfacial charge transfer induced by the synergistic heterojunction architecture. This study provides a viable waste-to-wealth strategy for red mud valorization and offers a promising platform for integrated clean hydrogen generation and wastewater treatment.
Background Sustainable biomass-to-multifunctional fluorescent carbon dots (CDs) with biocompatibility, photostability, and security-related optical properties are attractive for bioimaging and anti-counterfeiting technologies. However, developing such materials from renewable biomass through a simple and sustainable process remains challenging. Methods Fluorescent CDs were prepared from Bergera koenigii (curry) leaves through a simple hydrothermal carbonization approach, utilizing the naturally occurring phytoconstituents in the leaves as carbon and heteroatom precursors. The resulting curry leaf-derived carbon dots (CCDs) were systematically characterized using UV–Vis spectroscopy, photoluminescence spectroscopy, ATR-FTIR, X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM). Their stability and cytocompatibility were subsequently assessed, along with their potential for cellular imaging and anti-counterfeiting applications. Significant findings The resulting CCDs exhibited excitation-dependent cyan-blue fluorescence, high photostability, and prolonged storage stability. Spectroscopic analyses revealed abundant oxygen- and nitrogen-containing surface functionalities, while TEM showed quasi-spherical nanoparticles with an average diameter of approximately 5 nm and a graphitic lattice spacing of 0.21 nm. The CCDs displayed low cytotoxicity toward HCT-116 human colon cancer cells and enabled effective fluorescence-based cellular imaging. In addition, their strong fluorescence under ultraviolet excitation allowed their application as invisible fluorescent ink for anti-counterfeiting. These findings demonstrate that curry leaves can serve as a renewable precursor for producing multifunctional fluorescent CCDs with potential in biomedical imaging and information-security applications.
