Analytical Chemistry Award
Henrik Klein Moberg
Chalmers University of Technology, Sweden.
The Analytical Chemistry Award profile examines the research contributions and scholarly record of Henrik Klein Moberg, affiliated with Chalmers University of Technology, Sweden. His listed publications encompass analytical measurement technologies, nanoplasmonic sensing, microscopy and deep-learning-assisted scientific analysis. The research record provides a basis for evaluating thematic relevance to analytical chemistry and its developing interdisciplinary applications. The bibliographic information presented here identifies five relevant publications and the associated researcher metrics. [1]
Abstract
This article presents an academic research profile of Henrik Klein Moberg in the context of the Analytical Chemistry Award and the International Analytical Chemistry Awards. His reported research output includes publications in Nature Communications, ACS Sensors, Advanced Photonics and Nature Methods. The selected studies address mass spectrometry, plasmonic hydrogen sensing, biological microscopy and nanofluidic measurement. A recurring feature of the identified research is the integration of computational approaches, particularly deep learning and neural networks, with experimental measurement techniques. The profile also documents the supplied Scopus indicators of nine documents, 225 citations and an h-index of six. These indicators provide bibliometric context, while the publications provide evidence of research activity relevant to analytical chemistry. [1]
Keywords
Analytical chemistry; Henrik Klein Moberg; Chalmers University of Technology; mass spectrometry; deep learning; artificial intelligence; neural networks; nanoplasmonics; hydrogen sensors; nanofluidics; biological microscopy; single-particle analysis; scientific instrumentation; research impact.
Introduction
Analytical chemistry is concerned with the development and application of methods for identifying, separating, quantifying and characterising chemical substances. Modern analytical research increasingly combines advanced instrumentation, nanotechnology, computational modelling and artificial intelligence to improve the sensitivity, selectivity and interpretation of experimental measurements
Research Profile
Henrik Klein Moberg is associated with Chalmers University of Technology in Sweden. His supplied Scopus author record identifies nine documents, 225 citations and an h-index of six. The listed publications indicate research involving analytical measurement, nanoplasmonic sensing, computational microscopy and deep-learning-assisted instrumentation. [1]
Research Contributions
The selected publications identify several interconnected research themes relevant to analytical chemistry and advanced scientific measurement. These contributions can be considered in terms of analytical instrumentation, nanoscale sensing, computational interpretation and imaging technologies.
Publications
The following selected publications are associated with the research profile of Henrik Klein Moberg. They cover deep-learning-assisted mass spectrometry, plasmonic hydrogen sensing, biological microscopy and nanofluidic scattering microscopy. The records include articles published in established scientific journals and provide identifiable DOI links for bibliographic verification. [2] [3] [4] [5] [6]
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Deep-learning-enabled online mass spectrometry of the reaction product of a single catalyst nanoparticle.
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Accelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning.
Research Impact
The research topics represented in the selected publications are relevant to several areas of scientific and technological development. Their potential significance can be considered through analytical performance, measurement sensitivity, computational interpretation and the investigation of chemical and biological systems at small scales.
Scientific and technological relevance
- Advanced analytical instrumentation: The mass spectrometry research addresses the investigation of reaction products associated with individual catalyst nanoparticles.
- Artificial intelligence integration: Transformer-based and neural-network approaches demonstrate the relevance of computational techniques to scientific measurement and sensor interpretation.
Award Suitability
The relevance of Henrik Klein Moberg’s listed research to the International Analytical Chemistry Awards can be considered through the connection between the selected publications and the central objectives of analytical chemistry. The publications address analytical measurement, chemical sensing, nanoscale characterisation and the application of artificial intelligence to experimental data.
Conclusion
Henrik Klein Moberg’s selected research publications demonstrate engagement with analytical measurement technologies and interdisciplinary scientific methods. The identified work covers deep-learning-enabled mass spectrometry, plasmonic hydrogen sensing, biological microscopy and nanofluidic scattering microscopy. Collectively, these publications illustrate the integration of artificial intelligence, optical technologies and nanoscale measurement in contemporary analytical research.
External Links
References
The following references provide the researcher profile and publication records used in this article. DOI links are supplied where available to facilitate access to the original scholarly publications.
- Elsevier. (n.d.). Scopus author details: Henrik Klein Moberg, Author ID 57217004950. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=57217004950
- Deep-learning-enabled online mass spectrometry of the reaction product of a single catalyst nanoparticle. (2025). Nature Communications. Published 5 August 2025.
https://doi.org/10.1038/s41467-025-62602-3
- Accelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning. (2025). ACS Sensors. Published 24 January 2025.
https://doi.org/10.1021/acssensors.4c02616
- Cross-modality transformations in biological microscopy enabled by deep learning. (2024). Advanced Photonics, 6(6), 064001. Published 27 November 2024.
https://doi.org/10.1117/1.ap.6.6.064001
- Neural network enabled nanoplasmonic hydrogen sensors with 100 ppm limit of detection in humid air. (2024). Nature Communications. Published 8 February 2024.
https://doi.org/10.1038/s41467-024-45484-9
- Label-free nanofluidic scattering microscopy of size and mass of single diffusing molecules and nanoparticles. (2022). Nature Methods. Scopus EID: 2-s2.0-85131101921.
https://doi.org/10.1038/s41592-022-01491-6