Publications by authors named "Helena Brinkmann"
Deep learning for low-data drug discovery: Hurdles and opportunities.
Curr Opin Struct Biol
June 2024
Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such scenarios, deep learning approaches-which are notoriously 'data-hungry'-might fail to live up to their promise.
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- Noble metal oxide additives significantly alter the sensing characteristics of metal oxide sensors, particularly through a mechanism called Fermi level pinning, as observed with platinum-loaded WO using operando infrared spectroscopy.
- Traditional methods like microscopy provide structural insights but don't reflect real operational conditions since they're often done in a vacuum and on unheated samples.
- The use of advanced in situ scanning transmission electron microscopy allows for detailed observations of heated samples in various gas environments, linking structural changes in noble metal nanoclusters to their infrared spectra and sensing responses, ultimately validating the Fermi level pinning mechanism.
In order to increase their stability and tune-sensing characteristics, metal oxides are often surface-loaded with noble metals. Although a great deal of empirical work shows that surface-loading with noble metals drastically changes sensing characteristics, little information exists on the mechanism. Here, a systematic study of sensors based on rhodium-loaded WO₃, SnO₂, and In₂O₃-examined using X-ray diffraction, high-resolution scanning transmission electron microscopy, direct current (DC) resistance measurements, operando diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy, and operando X-ray absorption spectroscopy-is presented.
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