Autonomous Synthesis of Nanoparticles with Target Scattering Patterns
| UDC.coleccion | Investigación | |
| UDC.departamento | Química | |
| UDC.endPage | 6782 | |
| UDC.grupoInv | Nanochemistry and Self-Assembly for Biological Sciences (NANOSELF4BIO) | |
| UDC.institutoCentro | CICA - Centro Interdisciplinar de Química e Bioloxía | |
| UDC.issue | 8 | |
| UDC.journalTitle | ACS Nano | |
| UDC.startPage | 6767 | |
| UDC.volume | 20 | |
| dc.contributor.author | Anker, Andy S. | |
| dc.contributor.author | Quinson, Jonathan | |
| dc.date.accessioned | 2026-04-09T09:19:54Z | |
| dc.date.available | 2026-04-09T09:19:54Z | |
| dc.date.issued | 2026-02-18 | |
| 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.sponsorship | The 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.sponsorship | Dinamarca. Novo Nordisk Foundation; NNF23OC0081359 | |
| dc.description.sponsorship | Dinamarca. Novo Nordisk Foundation; NNF24OC0089800 | |
| dc.description.sponsorship | Dinamarca. Novo Nordisk Foundation; NNF22OC0078009 | |
| dc.description.sponsorship | Dinamarca. Danish National Research Foundation; P3 | |
| dc.description.sponsorship | Dinamarca. Novo Nordisk Foundation; NNF20OC0062606 | |
| dc.description.sponsorship | Dinamarca. Aarhus University Research Foundation; AUFF-E-2022-9-40 | |
| dc.description.sponsorship | Dinamarca. Independent Research Fund Denmark; 3164-00128B | |
| dc.description.sponsorship | Dinamarca. Carlsberg Foundation; CF22-0367 | |
| dc.description.sponsorship | Suecia. Swedish Research Council; 2018-07152 | |
| dc.description.sponsorship | Suecia. Swedish Governmental Agency for Innovation Systems; 2018-04969 | |
| dc.description.sponsorship | Suecia. Formas; 2019-02496 | |
| dc.description.sponsorship | Dinamarca. National Committee for Research Infrastructure; 4059-00009B | |
| dc.identifier.citation | Anker, 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.doi | 10.1021/acsnano.5c15488 | |
| dc.identifier.issn | 1936-0851 | |
| dc.identifier.issn | 1936-086X | |
| dc.identifier.uri | https://hdl.handle.net/2183/47910 | |
| dc.language.iso | eng | |
| dc.publisher | American Chemical Society | |
| dc.relation.uri | https://doi.org/10.1021/acsnano.5c15488 | |
| dc.rights | Attribution 4.0 International | en |
| dc.rights.accessRights | open access | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Self-driving laboratories | |
| dc.subject | Autonomous laboratories | |
| dc.subject | Robotic synthesis | |
| dc.subject | Nanomaterials | |
| dc.subject | X-ray scattering | |
| dc.subject | Machine learning | |
| dc.subject | Synchrotrons | |
| dc.title | Autonomous Synthesis of Nanoparticles with Target Scattering Patterns | |
| dc.type | journal article | |
| dc.type.hasVersion | VoR | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | db91d112-8961-4887-87bd-a5ee7eeae652 | |
| relation.isAuthorOfPublication.latestForDiscovery | db91d112-8961-4887-87bd-a5ee7eeae652 |
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