Integration of response surface methodology (RSM), machine learning (ML), and artificial intelligence (AI) for enhancing properties of polymeric nanocomposites‐A review DOI Creative Commons
Yasir Raza, Hassan Raza, Arslan Ahmed

и другие.

Polymer Composites, Год журнала: 2025, Номер unknown

Опубликована: Май 12, 2025

Abstract This review elucidates the amalgamation of machine learning (ML), artificial intelligence (AI), and response surface methodology (RSM) for optimization fabrication enhancement properties polymeric nanocomposites. It analyzes recent accomplishments, methodologies, future possibilities in this interdisciplinary field. Polymers their nanocomposites are garnering attention because cost‐effectiveness, biodegradability, non‐toxicity. Polymeric have been employed several technical applications; nevertheless, restricted mechanical, electrical, thermal impeded extensive use. Numerous additives, including clay, fiber, two‐dimensional materials such as graphene or MoS 2 , were extensively nanofillers to enhance qualities. The effects filler concentration thoroughly examined by conventional approaches; however, via statistical techniques may be more suitable. method produces accurate results with a reduced number tests. Diverse techniques, Taguchi RSM, alongside ML algorithms, can ascertain optimal concentration, type, method, characterization, process parameters properties, manufacturing, efficiency polymers polymer‐based superior compared methods. Nonetheless, ML/AI also utilized attain additional improvements requisite thermal, electrochemical properties. Recent advancements emphasized, use is proposed progress. Highlights Summarized Presented process, production, additive various polymers. ML‐based efficiency. Future directions: AI improve nanocomposite

Язык: Английский

Rationally Designed High-Temperature Polymer Dielectrics for Capacitive Energy Storage: An Experimental and Computational Alliance DOI

Pritish S. Aklujkar,

Rishi Gurnani,

Pragati Rout

и другие.

Progress in Polymer Science, Год журнала: 2025, Номер unknown, С. 101931 - 101931

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1

Insights into Synthesis and Optimization Features of Reverse Osmosis Membrane Using Machine Learning DOI Open Access
Weimin Gao, Guang Wang, Junguo Li

и другие.

Materials, Год журнала: 2025, Номер 18(4), С. 840 - 840

Опубликована: Фев. 14, 2025

Reverse osmosis membranes have been predominantly made from aromatic polyamide composite thin-films, although significant research efforts dedicated to discovering new materials and synthesis technologies enhance the water-salt selectivity of in past decades. The lack breakthroughs is partly attributed limited comprehensive understanding relationships between membrane features their performance. Insights into intrinsic reverse (RO) based on metadata were obtained using explainable artificial intelligence understand unify efforts. related chemistry, structure, modification methods, performance RO derived dataset more than 1000 membranes. Seven machine learning (ML) models constructed evaluate performances, applicability for tasks was assessed metadata. contribution analyzed, ranking importance revealed. This work holds promise analysis, evaluating against state art developing an inverse design strategy discovery high-performance

Язык: Английский

Процитировано

0

Tuning membrane surface wetting behaviour via dual-nanomaterial functionalization for efficient water purification DOI
Shuai Liang, Zhibo Ma,

Zhonghua Fan

и другие.

Journal of Membrane Science, Год журнала: 2025, Номер unknown, С. 123970 - 123970

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Machine learning in membrane science: Bridging materials, structures, and performance for next-generation membrane design DOI
Lijun Liang, Dan Lu, Yuhuan Qin

и другие.

Separation and Purification Technology, Год журнала: 2025, Номер unknown, С. 133091 - 133091

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

PLGA-based long-acting injectable (LAI) formulations DOI
Kinam Park

Journal of Controlled Release, Год журнала: 2025, Номер unknown, С. 113758 - 113758

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Advances in Porous Materials for Transuranic Element Separation DOI

Li-Ying Wang,

Jipan Yu,

Zhirong Liu

и другие.

Acta Chimica Sinica, Год журнала: 2025, Номер 83(4), С. 401 - 401

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Explainable Artificial Intelligence (XAI) for Material Design and Engineering Applications: A Quantitative Computational Framework DOI Creative Commons
Bokai Liu, Pengju Liu, Weizhuo Lu

и другие.

International journal of mechanical system dynamics, Год журнала: 2025, Номер unknown

Опубликована: Май 20, 2025

ABSTRACT The advancement of artificial intelligence (AI) in material design and engineering has led to significant improvements predictive modeling properties. However, the lack interpretability machine learning (ML)‐based informatics presents a major barrier its practical adoption. This study proposes novel quantitative computational framework that integrates ML models with explainable (XAI) techniques enhance both accuracy property prediction. systematically incorporates structured pipeline, including data processing, feature selection, model training, performance evaluation, explainability analysis, real‐world deployment. It is validated through representative case on prediction high‐performance concrete (HPC) compressive strength, utilizing comparative analysis such as Random Forest, XGBoost, Support Vector Regression (SVR), Deep Neural Networks (DNNs). results demonstrate XGBoost achieves highest (), while SHAP (Shapley Additive Explanations) LIME (Local Interpretable Model‐Agnostic provide detailed insights into importance interactions. Additionally, deployment trained cloud‐based Flask‐Gunicorn API enables real‐time inference, ensuring scalability accessibility for industrial research applications. proposed addresses key limitations existing approaches by integrating advanced techniques, handling nonlinear interactions, providing scalable strategy. contributes development interpretable deployable AI‐driven informatics, bridging gap between data‐driven predictions fundamental science principles.

Язык: Английский

Процитировано

0

Integration of response surface methodology (RSM), machine learning (ML), and artificial intelligence (AI) for enhancing properties of polymeric nanocomposites‐A review DOI Creative Commons
Yasir Raza, Hassan Raza, Arslan Ahmed

и другие.

Polymer Composites, Год журнала: 2025, Номер unknown

Опубликована: Май 12, 2025

Abstract This review elucidates the amalgamation of machine learning (ML), artificial intelligence (AI), and response surface methodology (RSM) for optimization fabrication enhancement properties polymeric nanocomposites. It analyzes recent accomplishments, methodologies, future possibilities in this interdisciplinary field. Polymers their nanocomposites are garnering attention because cost‐effectiveness, biodegradability, non‐toxicity. Polymeric have been employed several technical applications; nevertheless, restricted mechanical, electrical, thermal impeded extensive use. Numerous additives, including clay, fiber, two‐dimensional materials such as graphene or MoS 2 , were extensively nanofillers to enhance qualities. The effects filler concentration thoroughly examined by conventional approaches; however, via statistical techniques may be more suitable. method produces accurate results with a reduced number tests. Diverse techniques, Taguchi RSM, alongside ML algorithms, can ascertain optimal concentration, type, method, characterization, process parameters properties, manufacturing, efficiency polymers polymer‐based superior compared methods. Nonetheless, ML/AI also utilized attain additional improvements requisite thermal, electrochemical properties. Recent advancements emphasized, use is proposed progress. Highlights Summarized Presented process, production, additive various polymers. ML‐based efficiency. Future directions: AI improve nanocomposite

Язык: Английский

Процитировано

0