Klein Moberg Henrik, Abbondanza Giuseppe, Nedrygailov Ievgen, Albinsson David, Fritzsche Joachim, Langhammer Christoph
Department of Physics, Chalmers University of Technology, Göteborg, Sweden.
Nat Commun. 2025 Aug 5;16(1):7203. doi: 10.1038/s41467-025-62602-3.
Extracting weak signals from noise is a generic challenge in experimental science. In catalysis, it manifests itself as the need to quantify chemical reactions on nanoscopic surface areas, such as single nanoparticles or even single atoms. Here, we address this challenge by combining the ability of nanofluidic reactors to focus reaction product from tiny catalyst surfaces towards online mass spectrometric analysis with the high capacity of a constrained denoising auto-encoder to discern weak signals from noise. Using CO oxidation and CH hydrogenation on Pd as model reactions, we demonstrate that the catalyst surface area required for online mass spectrometry can be reduced by ≈ 3 orders of magnitude compared to state of the art, down to a single nanoparticle with 0.0072 ± 0.00086 μm surface area. These results advocate deep learning to improve resolution in mass spectrometry in general and for online reaction analysis in single-particle catalysis in particular.
从噪声中提取微弱信号是实验科学中的一个普遍挑战。在催化领域，这表现为需要对纳米级表面积上的化学反应进行量化，例如单个纳米颗粒甚至单个原子。在此，我们通过将纳米流体反应器将微小催化剂表面的反应产物聚焦用于在线质谱分析的能力与受限去噪自动编码器从噪声中辨别微弱信号的高能力相结合，来应对这一挑战。以钯上的一氧化碳氧化和甲烷加氢作为模型反应，我们证明，与现有技术相比，在线质谱所需的催化剂表面积可减少约3个数量级，低至表面积为0.0072±0.00086μm的单个纳米颗粒。这些结果提倡使用深度学习来提高一般质谱分析的分辨率，特别是单颗粒催化在线反应分析的分辨率。
