[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117915-en":3,"doc-seo-117915-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117915,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Facilitating Development of Special Clays by Incorporation of Machine Learning Techniques","Research focuses on advancing the development of special clay-based materials through incorporation of machine learning techniques. Data-driven models accelerate characterization and design by learning processing–property relationships and enabling multiobjective optimization. Machine learning is used to predict morphology and surface activity, identify effective nanoclay composite formulations for multi-mycotoxin removal, and forecast rheological viscosity of clay-polymer systems. Selected prototypes are validated experimentally, demonstrating improved acid character and high chlorophyll-a removal, detoxifier performance with targeted toxin bioavailability reduction, and stable gel strength for engineering operations.","Facilitating development of special clays by incorporation of machine learning techniques .  \nGiulia Lo Dico  \nA dissertation submitted by in partial fulfillment of the requirements for the degree of Doctor of Philosophy in  \nMaterials Science and Engineering  \nUniversidad Carlos III de Madrid  \nDirector:  \nMaciej Haranczyk  \nCo-director:  \nVeronica Carcelén  \nTutor:  \nPaula Alvaredo Olmos  \nEsta tesis se distribuye bajo licencia “Creative Commons Reconocimiento – No Comercial –  \nSin Obra Derivada”.  \nACKNOWLEDGEMENT  \nI would like to express my gratitude to IMDEA Materials, Tolsa, and UC3M for creating and supporting this bold project. I am also thankful to the Community of Madrid for providing funding and resources.  \nI am deeply grateful to my thesis director and co-director, Maciej and Veronica, and to Juanjo, for their faith in me and for providing me the opportunity to explore and solve problems. I would like to especially thank you, Maciej for your unwavering support and guidance during the most challenging moments.  \nI am also thankful to Veronica and Antonio for making the implementation of machine learning in all projects of the Research & Technological Innovation department at Tolsa a reality.  \nFinally, I would like to extend my heartfelt thanks to all the wonderful individuals at IMDEA and Tolsa who have made this experience memorable and filled it with enjoyable moments and exciting adventures. They have become my friends and this experience will always hold a special place in my heart.  \nGrazie.  \nPUBLISHED AND PRESENTED CONTENTS  \nLo Dico, G.; Nuñez, Á. P.; Carcelén, V.; Haranczyk, M. Machine-Learning-Accelerated Multimodal Characterization and Multiobjective Design Optimization of Natural Porous Materials. Chem. Sci. 2021, 12 (27), 9309–9317. [https://doi.org/10.1039/d1sc00816a](https://doi.org/10.1039/d1sc00816a).  \n• This work is included as the Chapter 3 of this manuscript.  \n• In this contribution I implemented data-driven techniques, i.e., machine learning, to accelerate the characterization and design of clay-based materials for catalytic applications. I used a historical dataset collected by Tolsa Company to train Extra Tree Regressor models to predict the morphology and the surface activity of processed nanoclays. The high throughput of the models enabled exploration of processing parameter–property correlations and multiobjective optimization of prototype materials. One of such identified prototypes was experimentally prepared and tested revealing appreciable acid character improvement and 79% removal of chlorophyll-a in a acid-catalyzed degradation.  \nLo Dico, G.; Croubles, S.; Carcelén, V.; Haranczyk, M. Machine learning-aided design of composite mycotoxin detoxifier material for animal feed. Sci. Rep. 2022, 12 (1), 1- 11. [https://doi.org/10.1038/s41598-022-08410-x](https://doi.org/10.1038/s41598-022-08410-x).  \n• This work is included as the Chapter 4 of this manuscript.  \n• In this contribution I applied statistical machine learning methods, trained on an in vitro mycotoxin adsorption dataset, to identify nanoclay composite formulations with high removal capacity and selectivity towards various mycotoxins. The findings were validated with an in vivo toxicokinetic study, based on the detection of biomarkers for mycotoxinexposure in broilers, realized in collaboration with the Faculty of Veterinary Medicine, Ghent University. The optimal detoxifier provided reduction of the oral toxin bioavailability after single bolus administration.  \nLo Dico, G.; Muñoz, B.; Carcelén, V.; Haranczyk, M. Data-driven experimental design of rheological clay-polymer composites. Ind. Eng. Chem. Res. 2022, 61 (31), 11455–11463. [https://doi.org/10.1021/acs.iecr.2c00936](https://doi.org/10.1021/acs.iecr.2c00936) .  \n• This work is included as the Chapter 5 of this manuscript.  \n• In this contribution I coupled advanced design of experiments with machine learning to accelerate the design of rheological clay-based additiv","cbCaihqx7Ysuvhuw","https://ap.wps.com/l/cbCaihqx7Ysuvhuw","pdf",5891274,1,210,"English","en",105,"# Acknowledgement\n# Published and Presented Contents\n## Machine-Learning-Accelerated Multimodal Characterization and Multiobjective Design Optimization of Natural Porous Materials\n## Machine learning-aided design of composite mycotoxin detoxifier material for animal feed\n## Data-driven experimental design of rheological clay-polymer composites\n## Toward the role of moisture in natural and thermally-treated clay materials","[{\"question\":\"How does machine learning support the characterization and design of clay-based materials?\",\"answer\":\"It uses data-driven models trained on historical and generated datasets to accelerate prediction of morphology, surface activity, and key material properties, enabling faster exploration of processing–property correlations.\"},{\"question\":\"What kinds of clay materials and application areas are addressed?\",\"answer\":\"The work targets clay-based materials for catalytic applications, adsorption-based detoxification for mycotoxins in animal feed, and rheological clay-polymer composites for engineering operational contexts.\"},{\"question\":\"How are proposed prototypes validated?\",\"answer\":\"Identified formulations and prototypes are prepared and tested experimentally, including performance evaluation for acid character improvement, chlorophyll-a removal, detoxifier effects assessed through toxicokinetic biomarkers, and characterization to confirm stable gel strength.\"}]","Facilitating Development of Special Clays by Incorporation of Machine Learning Techniques | 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does machine learning support the characterization and design of clay-based materials?","Question",{"text":76,"@type":77},"It uses data-driven models trained on historical and generated datasets to accelerate prediction of morphology, surface activity, and key material properties, enabling faster exploration of processing–property correlations.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What kinds of clay materials and application areas are addressed?",{"text":81,"@type":77},"The work targets clay-based materials for catalytic applications, adsorption-based detoxification for mycotoxins in animal feed, and rheological clay-polymer composites for engineering operational contexts.",{"name":83,"@type":74,"acceptedAnswer":84},"How are proposed prototypes validated?",{"text":85,"@type":77},"Identified formulations and prototypes are prepared and tested experimentally, including performance evaluation for acid character improvement, chlorophyll-a removal, detoxifier effects 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