EWCPS 2025 - 20th European Winter Conference on Plasma Spectrochemistry
Lecture
04.03.2025 (CET)
Unlocking Nanoparticle Composition: High Throughput, Size-Resolved Analysis with SMPS-ICP-MS
AA

Ayush Agarwal (M.Sc.)

Swiss Federal Institute of Technology Lausanne (École Polytechnique Fédérale de Lausanne)

Agarwal, A. (Speaker)¹; Torrent, L.²; Koolen, C.³; Crossley, K.⁴; Züttel, A.³; Ludwig, C.¹
¹Paul Scherrer Institute (PSI) & École Polytechnique Fédérale De Lausanne (EPFL), Villigen PSI (Switzerland); ²University of Girona; ³École polytechnique fédérale de Lausanne (EPFL), Sion (Switzerland); ⁴Paul Scherrer Institute (PSI), Villigen PSI (Switzerland)
Vorschau
26 Min. Untertitel (CC)

In material chemistry, precise characterization of nanoparticle size and composition is essential for optimizing the performance of catalysts and functional materials. This work introduces a high-throughput approach by integrating a Scanning Mobility Particle Sizer (SMPS) with Inductively Coupled Plasma Mass Spectrometry (ICP-MS), enabling simultaneous size distribution and elemental composition analysis. Unlike single-particle ICP-MS (sp-ICP-MS), this method is not significantly affected by the presence of dissolved species, allowing for more robust analysis in complex samples. Additionally, since size distribution is handled by SMPS, the ICP-MS can focus on its strength: determining the elemental composition. This means that even a single-quadrupole ICP-MS is sufficient to accurately analyze multimetallic nanoparticles, without the need for more advanced instruments like ICP-TOF-MS. The technique is demonstrated using copper-based nanoparticles for CO₂ reduction and perovskite-based catalysts for oxygen evolution reactions (OER). Validation with complementary methods, such as single-particle ICP-MS and transmission electron microscopy (TEM), confirmed the reliability of this setup. This approach offers an efficient alternative to traditional microscopy, enabling material chemists to streamline nanoparticle characterization and improve synthesis optimization.

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