Pushing the boundaries of single molecule microscopy, optical sensing and mass spectrometry through deep learning
Doctoral thesis, 2024

Deep learning has transformed sensing and characterization technologies, enabling significant advancements across various scientific domains. This thesis investigates the application of deep learning techniques to enhance the applicability of microscopy, spectrometry, and sensing, particularly under high noise conditions. The central hypothesis of this research is that deep learning can substantially improve the sensitivity and specificity of these technologies, allowing for the detection and analysis of minute signals that were previously obscured by the noise.

Key contributions include the development of novel deep learning methods that enhance nanofluidic scattering microscopy, nanoplasmonic sensing, and mass spectrometry. These methods enable precise quantification of chemical reactions on nanoscale surfaces, detailed detection of cellular structures and molecular interactions, and accurate identification of low-concentration substances amidst strong background signals. Collectively, these advancements push the boundaries of what can be measured and observed at microscopic and molecular scales, offering groundbreaking applications in environmental monitoring and healthcare diagnostics.

Mass Spectrometry

Nanoplasmonic Sensing

Environmental Monitoring

Virtual Staining

Cross-modality Transformation

Nanofluidic Scattering Microscopy

Deep Learning

Biomolecule Detection

Author

Henrik Klein Moberg

Chalmers, Physics, Chemical Physics

Included papers

Henrik Klein Moberg, Joachim Fritzsche, Giovanni Volpe, Christoph Langhammer, Deep-learning enabled mass spectrometry of the reaction product from a single catalyst nanoparticle

Manuscript

Viktor Martvall, Henrik Klein Moberg, Athanasios Theodiridis, David Tomecek, Pernilla Tanner, Paul Erhart, Christoph Langhammer, Accelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning

Manuscript

Henrik Klein Moberg, Bohdan Yeroshenko, David Albinsson, Joachim Fritzsche, Daniel Midtvedt, Barbora Spackova, Giovanni Volpe, Christoph Langhammer, Deep Learning Microscopy for Label-free Biomolecule Weight-and-Size Characterization in the Single-kDa Regime

Manuscript

Popular science description

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Research Project(s)

NACAREI: Nanofluidic Catalytic Reaction Imaging

European Commission (EC) (101043480), 2023-01-01 -- 2027-12-31.

The Sub-10 nm Challenge in Single Particle Catalysis

Swedish Research Council (VR) (2018-00329), 2019-01-01 -- 2024-12-31.

Nanoplasmonisk Sensing's Heliga Graal

Swedish Foundation for Strategic Research (SSF) (FFL15-0087), 2017-01-01 -- 2021-12-31.

Categorizing

Areas of Advance

Nanoscience and Nanotechnology

Infrastructure

C3SE (-2020, Chalmers Centre for Computational Science and Engineering)

Chalmers Materials Analysis Laboratory

Myfab (incl. Nanofabrication Laboratory)

Subject Categories (SSIF 2011)

Other Physics Topics

Nano Technology

Computer Science

Identifiers

ISBN

978-91-8103-118-8

Other

Series

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5576

Publisher

Chalmers

Public defence

2024-11-13 09:30 -- 12:00

PJ Fysik Origo, Fysikgården 4

Online

Opponent: Prof. Dr. Ir. Fons J Verbeek, Inst. of Advanced Computer Science, Leiden University

More information

Latest update

1/10/2025