[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123345-en":3,"doc-seo-123345-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},123345,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","In silico exploration of graphene nanoflakes - From DFT simulations to machine learning-driven toxicity predictions","Theoretical multiscale research integrates Density Functional Theory (DFT) simulations with machine learning to predict graphene nanoflake toxicity. Size, shape, and symmetry effects on graphene nanoflakes are analyzed via DFT, while nanoflake interactions with human proteins and cell membranes are treated as Molecular Initiating Events for adverse outcome pathways. Generated interaction and impact data are used to train models that predict flake properties and biological interactions, assigning a single toxicity-relevant score per nanoflake. This structure-property-toxicity framework supports safer, more efficient design and application.","NanoImpact 38 (2025) 100563  \nContents lists available at ScienceDirect  \nNanoImpact  \njournal [homepage: www.elsevier.com/locate/nanoimpact](homepage: www.elsevier.com/locate/nanoimpact)  \n| Research paper\u003Cbr>In silico exploration of graphene nanoflakes: From DFT simulations to machine learning-driven toxicity predictions\u003Cbr>Nuria Aguilar a, Patricia de la Fuente b, Natalia Fern´andez-Pampínb, Sonia Martel b, Laura G´omez-Cuadradob, Pedro Angel Marcos b,c, Alfredo Bol b,c, Carlos Rumbob, Santiago Aparicio a,c,*\u003Cbr>a Department of Chemistry, University of Burgos, 09001 Burgos, Spain\u003Cbr>b International Research Center in Critical Raw Materials for Advanced Industrial Technologies (ICCRAM), University of Burgos, 09001 Burgos, Spain c Department of Physics, University of Burgos, 09001 Burgos, Spain |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Editor: Bernd Nowack |  | The present theoretical work provides a ground-breaking and comprehensive study of graphene nanoflakes integrating Density Functional Theory (DFT) simulations, toxicity predictions and a machine learning approach. The properties of graphene nanoflakes as a function of size, shape, and symmetry are systematically analysed using DFT calculations. The interaction of these nanoflakes with human proteins and cell membranes, considered as Molecular Initiating Events for diverse Adverse Outcome Pathways, is explored to infer potential toxicity effects. Leveraging the generated data, machine learning models were developed to predict flake properties and biological interactions. A single score representing the biological interaction or impact of graphene nanoflakes on both proteins and plasma membranes is assigned to each evaluated nanoflake to infer its potential toxicity. Our multiscale approach bring valuable insights into the structure-property-toxicity relationships of graphene nanoflakes, paving the way for their safe and efficient design and application. |\n| Keywords:\u003Cbr>Graphene nanoflakes\u003Cbr>Density Functional Theory (DFT) In silico toxicity\u003Cbr>Machine learning\u003Cbr>Nano-bio interactions |  |  |\n\n1. Introduction  \nGraphene, a two-dimensional carbon allotrope with a honeycomb lattice structure, has captivated the scientific community since its isolation in 2004 (Novoselov et al., 2004). Its exceptional properties, including high electrical and thermal conductivity, mechanical strength, and large surface area, have positioned graphene as a promising material for various applications ranging from electronics to biomedical devices (Avouris and Dimitrakopoulos, 2012; Seabra et al., 2014). Graphene nanoflakes (GNFs), finite-sized graphene sheets typically less than 100 nm in lateral dimensions, have emerged as particularly interesting structures due to their unique size-dependent properties and enhanced edge effects (Son et al., 2006; Yazyev, 2010).The properties of GNFs are heavily influenced by their size, shape, and edge structure, which can significantly affect their electronic, magnetic, and chemical characteristics (Güçlü et al., 2014). Understanding these structureproperty relationships is crucial for tailoring GNFs for specific applications. Density Functional Theory (DFT) simulations have proven to be a powerful tool in elucidating the fundamental properties of GNFs (Hod  \net al., 2008; Ezawa, 2007). Recent studies have employed DFT to investigate the electronic structure of GNFs with various edge configurations, revealing the emergence of localized edge states and magnetism in certain geometries (Zou et al., 2011; Lee et al., 2005). The sizedependent properties of GNFs have been explored through DFT calculations, demonstrating how the energy gap and magnetic moment evolve with increasing flake dimensions (Fern´andez-Rossier and Palacios, 2007). Triangular GNFs exhibit size-dependent magnetic properties, with the total spin increasing linearly with the edge length (Sharma et al., 2014). Similarly, hexagonal GNFs have been found to di","cbCail6FnE29Rfx3","https://ap.wps.com/l/cbCail6FnE29Rfx3","pdf",5932708,1,13,"English","en",105,"# Introduction\n## Graphene and graphene nanoflakes: structure and properties\n## Role of DFT in understanding size- and edge-dependent behavior\n## Motivation: potential toxicity and the need for prediction methods","[{\"question\":\"What workflow combines DFT simulations and machine learning in this study?\",\"answer\":\"DFT calculations quantify how nanoflake properties vary with size, shape, and symmetry. The generated interaction data with proteins and plasma membranes are then used to train machine learning models for toxicity predictions.\"},{\"question\":\"How is nanoflake toxicity inferred from interactions with proteins and cell membranes?\",\"answer\":\"Interactions with human proteins and cell membranes are modeled as Molecular Initiating Events within adverse outcome pathways, and a single biological-interaction score is assigned to each evaluated nanoflake to infer potential toxicity.\"},{\"question\":\"Why are size, shape, and edge structure central to predicting behavior and toxicity?\",\"answer\":\"Graphene nanoflake properties are strongly influenced by size, shape, and edge structure, which in turn affect electronic, magnetic, and chemical characteristics and can drive different levels of interaction with biological systems.\"}]","In silico exploration of graphene nanoflakes - From DFT simulations to machine learning-driven toxicity predictions | PDF",1785816050,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"in-silico-exploration-of-graphene-nanoflakes-from-dft-simulations-to-machine-learning-driven-toxicity-predictions","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/in-silico-exploration-of-graphene-nanoflakes-from-dft-simulations-to-machine-learning-driven-toxicity-predictions/123345/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What workflow combines DFT simulations and machine learning in this study?","Question",{"text":75,"@type":76},"DFT calculations quantify how nanoflake properties vary with size, shape, and symmetry. The generated interaction data with proteins and plasma membranes are then used to train machine learning models for toxicity predictions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is nanoflake toxicity inferred from interactions with proteins and cell membranes?",{"text":80,"@type":76},"Interactions with human proteins and cell membranes are modeled as Molecular Initiating Events within adverse outcome pathways, and a single biological-interaction score is assigned to each evaluated nanoflake to infer potential toxicity.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are size, shape, and edge structure central to predicting behavior and toxicity?",{"text":84,"@type":76},"Graphene nanoflake properties are strongly influenced by size, shape, and edge structure, which in turn affect electronic, magnetic, and chemical characteristics and can drive different levels of interaction with biological systems.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]