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Nanomechanical silicon nitride (SiN) drum resonators are currently employed in various fields of applications that arise from their unprecedented frequency response to physical quantities. In the present study, we investigate the thermal transport in nanomechanical SiN drum resonators by analytical modelling, computational simulations, and experiments for a better understanding of the underlying heat transfer mechanism causing the thermal frequency response. Our analysis indicates that radiative heat loss is a non-negligible heat transfer mechanism in nanomechanical SiN resonators limiting their thermal responsivity and response time. This finding is important for optimal resonator designs for thermal sensing applications as well as cavity optomechanics., Comment: 8 pages, 4 figures in main text, 3 figures in supplementary

Single-molecule microscopy has become an indispensable tool for biochemical analysis. The capability of characterizing distinct properties of individual molecules without averaging has provided us with a different perspective for the existing scientific issues and phenomena. Recently, the super-resolution fluorescence microscopy techniques have overcome the optical diffraction limit by the localization of molecule positions. However, the labelling process can potentially modify the intermolecular dynamics. Based on the highly-sensitive nanomechanical photothermal microscopy reported previously, we propose optimizations on this label-free microscopy technique towards localization microscopy. A localization precision of 3 $\unicode{x212B}$ is achieved with gold nanoparticles, and the detection of polarization-dependent absorption is demonstrated, which opens the door for further improvement with polarization modulation imaging., Comment: 5 pages, 6 figures

Sub-nanometer displacement detection lays the solid foundation for critical applications in modern metrology. In-plane displacement sensing, however, is mainly dominated by the detection of differential photocurrent signals from photodiodes, with resolution in the nanometer range. Here, we present an integrated in-plane displacement sensor based on a nanoelectromechanical trampoline resonator. With a position resolution of 4 $pm/Hz^{1/2}$ for a low laser power of 85 $\mu$W and a repeatability of 2 nm after 5 cycles of operation as well as good long-term stability, this new detection principle provides a reliable alternative for overcoming the current position detection limit in a wide variety of research and application fields., Comment: 9 pages, 4 figures in main text, 6 figures in supplementary

Acetaldehyde (AA) isomerization (to vinylalcohol, VA) and decomposition (into either CO+CH$_4$ and H$_2$+H$_2$CCO) is studied using a fully dimensional, reactive potential energy surface represented as a neural network (NN). The NN, trained on 432'399 reference structures from MP2/aug-cc-pVTZ calculations has a MAE of 0.0453 kcal/mol and an RMSE of 1.186 kcal/mol for a test set of 27'399 structures. For the isomerization process AA $\rightarrow$ VA the minimum dynamical path implies that the C-H vibration, and the C-C-H (with H being the transferring H-atom) and the C-C-O angles are involved to surmount the 68.2 kcal/mol barrier. Using an excess energy of 93.6 kcal/mol - the energy available in the solar spectrum and sufficient to excite to the first electronically excited state - to initialize the molecular dynamics, no isomerization to VA is observed on the 500 ns time scale. Only with excess energies of $\sim$ 127.6 kcal/mol (including the zero point energy of the AA molecule), isomerization occurs on the nanosecond time scale. Given that collisional de-excitation at atmospheric conditions in the stratosphere occurs on the 100 ns time scale, it is concluded that formation of VA following photoexcitation of AA from actinic photons is unlikely. This also limits the relevance of this reaction pathway to be a source for formic acid.

Hydrogenated amorphous silicon (a-Si:H) has remarkable radiation resistance properties and can be deposited on a lot of different substrates. A-Si:H based particle detectors have been built since mid 1980s as planar p-i-n or Schottky diode structures; the thickness of these detectors ranged from 1 to 50 micron. However MIP detection using planar structures has always been problematic due to the poor S/N ratio related to the high leakage current at high depletion voltage and the low charge collection efficiency. The usage of 3D detector architecture can be beneficial for the possibility to reduce inter-electrode distance and increase the thickness of the detector for larger charge generation compared to planar structures. Such a detector can be used for future hadron colliders for its radiation resistance and also for X-ray imaging. Furthermore the possibility of a-Si:H deposition on flexible materials (like kapton) can be exploited to build flexible and thin beam flux measurement detectors and x-ray dosimeters., Comment: 10 pages 5 figures, 1 table

Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN). We report results on two winters ( 2016 - 17 and 2017 - 18 ) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters., Comment: Accepted for ISPRS Congress 2020, Nice, France