Autonomous Synthesis of Nanoparticles with Target Scattering Patterns

UDC.coleccionInvestigación
UDC.departamentoQuímica
UDC.endPage6782
UDC.grupoInvNanochemistry and Self-Assembly for Biological Sciences (NANOSELF4BIO)
UDC.institutoCentroCICA - Centro Interdisciplinar de Química e Bioloxía
UDC.issue8
UDC.journalTitleACS Nano
UDC.startPage6767
UDC.volume20
dc.contributor.authorAnker, Andy S.
dc.contributor.authorQuinson, Jonathan
dc.date.accessioned2026-04-09T09:19:54Z
dc.date.available2026-04-09T09:19:54Z
dc.date.issued2026-02-18
dc.description.abstract[Abstract] Controlled synthesis of materials with specified atomic structures underpins technological advances yet remains reliant on iterative, trial-and-error approaches. Nanoparticles (NPs), whose atomic arrangement dictates their emergent properties,1–5 are particularly challenging to synthesize due to numerous tunable parameters. Here, we introduce an autonomous approach that explicitly targets atomic-scale structure through scattering patterns. Our method autonomously designs synthesis protocols by matching real-time experimental total scattering (TS) and pair distribution function (PDF) data to simulated target patterns, without requiring embedded synthesis knowledge. We demonstrate this capability at a synchrotron by targeting two structurally distinct gold NP scattering patterns: 5 nm decahedral and 10 nm face-centered cubic structures. Ultimately, specifying target scattering patterns and autonomously approaching synthesis protocols that reproduce them experimentally may enable on-demand, atomic structure-informed materials design. ScatterLab thus provides a generalizable blueprint for autonomous, atomic structure-targeted synthesis across diverse systems and applications.
dc.description.sponsorshipThe work presented in this article is supported by Novo Nordisk Foundation grant NNF23OC0081359 and NNF24OC0089800. The authors acknowledge support from the Novo Nordisk Foundation Data Science Research Infrastructure 2022 Grant: A high-performance computing infrastructure for data-driven research on sustainable energy materials, Grant no. NNF22OC0078009. ASA and TV acknowledge the Pioneer Center for Accelerating P2X Materials Discovery (CAPeX), DNRF grant number P3. MGD worked on this project while at the Centre for Basic Machine Learning Research in the Life Sciences (MLLS), which is funded by the Novo Nordisk Foundation, grant NNF20OC0062606. JQ is thankful to the Aarhus University Research Foundation, grant number AUFF-E-2022-9-40, JQ and AS are thankful to the Independent Research Fund Denmark for the DFF-Green grant 3164-00128B. JQ thanks Espen D. Bøjesen, iNano, Aarhus University, Denmark, for facilitating access to the Talos F200X. MJ is supported by the Carlsberg Foundation, grant CF22-0367. We thank the Danish Agency for Science, Technology, and Innovation for funding the instrument center DanScatt. We acknowledge the MAX IV Laboratory for beamtime on the DanMAX beamline under proposal 20240084. Research conducted at MAX IV, a Swedish national user facility, is supported by Vetenskapsrådet (Swedish Research Council, VR) under contract 2018-07152, Vinnova (Swedish Governmental Agency for Innovation Systems) under contract 2018-04969 and Formas under contract 2019-02496. DanMAX is funded by the NUFI grant no. 4059-00009B
dc.description.sponsorshipDinamarca. Novo Nordisk Foundation; NNF23OC0081359
dc.description.sponsorshipDinamarca. Novo Nordisk Foundation; NNF24OC0089800
dc.description.sponsorshipDinamarca. Novo Nordisk Foundation; NNF22OC0078009
dc.description.sponsorshipDinamarca. Danish National Research Foundation; P3
dc.description.sponsorshipDinamarca. Novo Nordisk Foundation; NNF20OC0062606
dc.description.sponsorshipDinamarca. Aarhus University Research Foundation; AUFF-E-2022-9-40
dc.description.sponsorshipDinamarca. Independent Research Fund Denmark; 3164-00128B
dc.description.sponsorshipDinamarca. Carlsberg Foundation; CF22-0367
dc.description.sponsorshipSuecia. Swedish Research Council; 2018-07152
dc.description.sponsorshipSuecia. Swedish Governmental Agency for Innovation Systems; 2018-04969
dc.description.sponsorshipSuecia. Formas; 2019-02496
dc.description.sponsorshipDinamarca. National Committee for Research Infrastructure; 4059-00009B
dc.identifier.citationAnker, A. S.; Jensen, J. H.; González-Duque, M.; Moreno, R.; Smolska, A.; Juelsholt, M.; Hardion, V.; Jørgensen, M. R. V.; Faíña, A.; Quinson, J.; Støy, K.; Vegge, T. Autonomous Synthesis of Nanoparticles with Target Scattering Patterns. ACS Nano 2026, 20 (8), 6767–6782. https://doi.org/10.1021/acsnano.5c15488.
dc.identifier.doi10.1021/acsnano.5c15488
dc.identifier.issn1936-0851
dc.identifier.issn1936-086X
dc.identifier.urihttps://hdl.handle.net/2183/47910
dc.language.isoeng
dc.publisherAmerican Chemical Society
dc.relation.urihttps://doi.org/10.1021/acsnano.5c15488
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSelf-driving laboratories
dc.subjectAutonomous laboratories
dc.subjectRobotic synthesis
dc.subjectNanomaterials
dc.subjectX-ray scattering
dc.subjectMachine learning
dc.subjectSynchrotrons
dc.titleAutonomous Synthesis of Nanoparticles with Target Scattering Patterns
dc.typejournal article
dc.type.hasVersionVoR
dspace.entity.typePublication
relation.isAuthorOfPublicationdb91d112-8961-4887-87bd-a5ee7eeae652
relation.isAuthorOfPublication.latestForDiscoverydb91d112-8961-4887-87bd-a5ee7eeae652

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