A5064922304- Gas Sensing Nanomaterials and Sensors
- Cell Image Analysis Techniques
- Advanced Fiber Optic Sensors
- Advanced Fluorescence Microscopy Techniques
- Analytical Chemistry and Sensors
- Advanced Biosensing Techniques and Applications
- Machine Learning in Materials Science
- Spectroscopy and Laser Applications
- Dark Matter and Cosmic Phenomena
- Particle physics theoretical and experimental studies
- Image Processing Techniques and Applications
- Cosmology and Gravitation Theories
- Advanced Electron Microscopy Techniques and Applications
- Advanced Chemical Sensor Technologies
- Microfluidic and Bio-sensing Technologies
- Neural Networks and Applications
- Extracellular vesicles in disease
Chalmers University of Technology
2020-2025
University of Technology
2024
Rapidly detecting hydrogen leaks is critical for the safe large-scale implementation of technologies. However, to date, no technically viable sensor solution exists that meets corresponding response time targets under relevant conditions. Here, we demonstrate how a tailored long short-term transformer ensemble model accelerated sensing (LEMAS) speeds up an optical plasmonic by factor 40 and eliminates its intrinsic pressure dependence in environment emulating inert gas encapsulation...
Abstract Label-free characterization of single biomolecules aims to complement fluorescence microscopy in situations where labeling compromises data interpretation, is technically challenging or even impossible. However, existing methods require the investigated species bind a surface be visible, thereby leaving large fraction analytes undetected. Here, we present nanofluidic scattering (NSM), which overcomes these limitations by enabling label-free, real-time imaging diffusing inside...
Abstract Environmental humidity variations are ubiquitous and high characterizes fuel cell electrolyzer operation conditions. Since hydrogen-air mixtures highly flammable, tolerant H 2 sensors important from safety process monitoring perspectives. Here, we report an optical nanoplasmonic hydrogen sensor operated at elevated temperature that combined with Deep Dense Neural Network or Transformer data treatment involving the entire spectral response of enables a 100 ppm limit detection in...
We compute the projected sensitivity to dark matter (DM) particles in sub-GeV mass range of future direct detection experiments using germanium and silicon semiconductor targets. perform this calculation within photon model for DM-electron interactions likelihood ratio as a test statistic, Monte Carlo simulations, background models that we extract from recent experimental data. present our results terms scattering cross section values required reject only hypothesis favour plus DM signal...
Recent advancements in deep learning (DL) have propelled the virtual transformation of microscopy images across optical modalities, enabling unprecedented multimodal imaging analysis hitherto impossible. Despite these strides, integration such algorithms into scientists' daily routines and clinical trials remains limited, largely due to a lack recognition within their respective fields plethora available methods. To address this, we present structured overview cross-modality transformations,...
Abstract Extracting tiny signals from noise is a generic challenge in experimental science. In catalysis, this manifests itself as the need to quantify chemical reactions on nanoscopic surface areas, such single nanoparticles or even atoms. Here, we address by combining ability of nanofluidic reactors focus reaction product catalyst surfaces towards online mass spectrometric analysis with unrivalled constrained denoising autoencoder discern noise. Using CO oxidation Pd model reaction,...
We show that a custom ResNet-inspired CNN architecture trained on simulated biomolecule trajectories surpasses the performance of standard algorithms in terms tracking and determining molecular weight hydrodynamic radius biomolecules low-kDa regime optical microscopy. high accuracy precision is retained even below 10-kDa regime, constituting approximately an order magnitude improvement limit detection compared to current state-of-the-art, enabling analysis hitherto elusive species such as...
The ability to rapidly detect hydrogen gas upon occurrence of a leak is critical for the safe large-scale implementation (energy) technologies. However, date, no technically viable sensor solution exists that meets corresponding response time targets set by stakeholders at relevant conditions. Here, we demonstrate how tailored Long Short-term Transformer Ensemble Model Accelerated Sensing (LEMAS) accelerates state-of-the-art optical plasmonic up factor 40 in an oxygen-free inert environment,...
We show that a custom ResNet-inspired CNN architecture trained on simulated biomolecule trajectories surpasses the performance of standard algorithms in terms tracking and determining molecular weight hydrodynamic radius biomolecules low-kDa regime NSM optical microscopy. high accuracy precision is retained even below 10-kDa regime, constituting approximately an order magnitude improvement limit detection compared to current state-of-the-art, enabling analysis hitherto elusive species such...