Prediction of activation energy of lignocellulosic biomass pyrolysis through thermogravimetry-assisted machine learning

Biomass and Bioenergy, Journal Year: 2025, Volume and Issue: 194, P. 107644 - 107644

Published: Feb. 3, 2025

Language: Английский

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Advanced Low-Dimensional Carbon Nanomaterials for Oxygen Electrocatalysis

Nanomaterials, Journal Year: 2025, Volume and Issue: 15(4), P. 254 - 254

Published: Feb. 7, 2025

Amid rising global energy demand and worsening environmental pollution, there is an urgent need for efficient storage conversion technologies. Oxygen electrocatalytic reactions, specifically the oxygen reduction reaction (ORR) evolution (OER) are critical processes in these Low-dimensional carbon nanomaterials, including zero-dimensional dots, one-dimensional nanotubes, two-dimensional graphene, demonstrate substantial potential electrocatalysis due to their unique physical chemical properties. On one hand, low-dimensional materials feature distinct geometric structures that enable customization of highly active sites electrocatalysis. other sp2 hybridization present contributes existence π electrons, which enhances conductivity facilitates catalytic activity stability. This article reviews recent advancements development catalysts based on focusing characteristics, synthesis methods, performance, applications devices. Additionally, we address current challenges faced by nanomaterials outline future research directions expedite practical applications.

Language: Английский

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Prediction of ultimate strength and strain in FRP wrapped oval shaped concrete columns using machine learning

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 28, 2025

The accurate prediction of the strength enhancement ratio ([Formula: see text]) and strain (εcc/εco) in FRP-wrapped elliptical concrete columns is crucial for optimizing structural performance. This study employs machine learning (ML) techniques to enhance accuracy reliability. A dataset 181 samples, derived from experimental studies finite element modeling, was utilized, with a 70:30 train-test split (127 training samples 54 testing samples). Four ML models: Decision Tree (DT), Adaptive Boosting (ADB), Stochastic Gradient (SGB), Extreme (XGB) were trained optimized using Bayesian Optimization refine their hyperparameters improve performance.Results demonstrate that SGB achieved best performance predicting [Formula: text], an R2 0.850, lowest RMSE (0.190), highest generalization capability, making it most reliable model predictions. For (εcc/εco), XGB outperformed other models, achieving 0.779 (2.162), indicating better balance between accuracy, generalization, minimal overfitting. DT ADB exhibited lower predictive performance, higher residual errors capacity. Furthermore, Shapley Additive exPlanations analysis identified FRP thickness-elastic modulus product (tf × Ef) compressive as influential features impacting both ratios. To facilitate real-world applications, interactive graphical user interface developed, enabling engineers input ten parameters obtain real-time

Language: Английский

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Effect of plastic fibers on OPC-based concrete in circular economy

Innovative Infrastructure Solutions, Journal Year: 2025, Volume and Issue: 10(2)

Published: Jan. 19, 2025

Language: Английский

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Compressive strength prediction for sandcrete blocks with metakaolin: experiment and multiple linear regression analysis

Deleted Journal, Journal Year: 2025, Volume and Issue: 2(1)

Published: March 20, 2025

Language: Английский

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Metaheuristic-driven CatBoost model for accurate seepage loss prediction in lined canals

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2025, Volume and Issue: 8(5)

Published: March 25, 2025

Language: Английский

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