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Nanosecond Nanothermometry in an Electron Microscope.

Nano Lett

January 2025

University Paris-Saclay, CNRS, Laboratoire de Physique des Solides, Orsay 91405, France.

Thermal transport in nanostructures plays a critical role in modern technologies. As devices shrink, techniques that can measure thermal properties at nanometer and nanosecond scales are increasingly needed to capture transient, out-of-equilibrium phenomena. We present a novel pump-probe photon-electron method within a scanning transmission electron microscope (STEM) to map temperature dynamics with unprecedented spatial and temporal resolutions.

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Introduction: Chronic low back pain (CLBP) is a global health issue, and its nonspecific causes make treatment challenging. Understanding the neural mechanisms of CLBP should contribute to developing effective therapies.

Objectives: To compare current source density (CSD) and functional connectivity (FC) extracted from resting electroencephalography (EEG) between patients with CLBP and healthy controls and to examine the correlations between EEG indices and symptoms.

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Convergent-beam attosecond x-ray crystallography.

Struct Dyn

January 2025

Center for Free-Electron Laser Science CFEL, Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany.

Sub-ångström spatial resolution of electron density coupled with sub-femtosecond to few-femtosecond temporal resolution is required to directly observe the dynamics of the electronic structure of a molecule after photoinitiation or some other ultrafast perturbation, such as by soft X-rays. Meeting this challenge, pushing the field of quantum crystallography to attosecond timescales, would bring insights into how the electronic and nuclear degrees of freedom couple, enable the study of quantum coherences involved in molecular dynamics, and ultimately enable these dynamics to be controlled. Here, we propose to reach this realm by employing convergent-beam x-ray crystallography with high-power attosecond pulses from a hard-x-ray free-electron laser.

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Radiation therapy (RT) is widely used for cancer treatment but is found with side effects of radiation dermatitis and fibrosis thereby calling for timely assessment. Nevertheless, current clinical assessment methods are found to be subjective, prone to bias, and accompanied by variability. There is, therefore, an unmet clinical need to explore a new assessment technique, ideally portable and affordable, making it accessible to less developed regions too.

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Conventional scanned optical coherence tomography (OCT) suffers from the frame rate/resolution tradeoff, whereby increasing image resolution leads to decreases in the maximum achievable frame rate. To overcome this limitation, we propose two variants of machine learning (ML)-based adaptive scanning approaches: one using a ConvLSTM-based sequential prediction model and another leveraging a temporal attention unit (TAU)-based parallel prediction model for scene dynamics prediction. These models are integrated with a kinodynamic path planner based on the clustered traveling salesperson problem to create two versions of ML-based adaptive scanning pipelines.

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