Silanized Graphene Oxide/Glass Fiber-Modified Epoxy Composite with Excellent Anti-Corrosion and Mechanical Properties as Offshore Oil Platform Safety Signs

Materials, Год журнала: 2025, Номер 18(9), С. 1920 - 1920

Опубликована: Апрель 24, 2025

Epoxy resin (EP) is a candidate material for offshore oil platform safety signs due to its excellent corrosion resistance property. However, fabricating EP with good anti-corrosion as well mechanical properties remains significant challenge. Here, we report new modification strategy simultaneously improve the and performance of by coupling it KH550 silanized graphene oxide (KGO) glass fiber (KGF). KGO KGF were grafted onto obtain modified material, i.e., KGO/KGF/EP composites characterized FITR, XRD, SEM, TGA confirm successful synthesis composites. It shown that tensile strength adhesion 85.5 MPa 16.0 MPa, which are 10.3% 23.1% higher than KGO/GF/EP. Compared KGF/EP, potential increased 9.9% rate decreased 98.8%. Moreover, fluid–structure simulation indicated maximum stress was within criteria under extreme wind speeds, demonstrating great sign applications.

Язык: Английский

Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics

Computers, Год журнала: 2025, Номер 14(5), С. 175 - 175

Опубликована: Май 4, 2025

This research explores the improvement of tsunami occurrence forecasting with machine learning predictive models using earthquake-related data analytics. The primary goal is to develop a framework that integrates wide range sources, including seismic, geospatial, and ecological data, toward improving accuracy lead times predictions. study employs methods, Random Forest Logistic Regression, for binary classification events. Data collection performed Kaggle dataset spanning 1995–2023, preprocessing exploratory analysis identify critical patterns. model achieved superior performance an 0.90 precision 0.88 compared Regression (accuracy: 0.89, precision: 0.87). These results underscore Forest’s effectiveness in handling imbalanced data. Challenges such as quality interpretability are discussed, recommendations future improvements real-time warning systems.

Язык: Английский