Upconversion is a photon-management process especially suited to water-splitting cells that exploit wide-bandgap photocatalysts. Currently, such catalysts cannot utilize 95% of the available solar photons. We demonstrate here that the energy-conversion yield for a standard photocatalytic water-splitting device can be enhanced under solar irradiance by using a low-power upconversion system that recovers part of the unutilized incident sub-bandgap photons. The upconverter is based on a sensitized triplet-triplet annihilation mechanism (sTTA-UC) obtained in a dye-doped elastomer and boosted by a fluorescent nanocrystal/polymer composite that allows for broadband light harvesting. The complementary and tailored optical properties of these materials enable efficient upconversion at subsolar irradiance, allowing the realization of the first prototype water-splitting cell assisted by solid-state upconversion. In our proof-of concept device the increase of the performance is 3.5%, which grows to 6.3% if concentrated sunlight (10 sun) is used. Our experiments show how the sTTA-UC materials can be successfully implemented in technologically relevant devices while matching the strict requirements of clean-energy production.
In this letter, we propose to augment image-based place recognition with structural cues. Specifically, these structural cues are obtained using structure-from-motion, such that no additional sensors are needed for place recognition. This is achieved by augmenting the 2D convolutional neural network (CNN) typically used for image-based place recognition with a 3D CNN that takes as input a voxel grid derived from the structure-from-motion point cloud. We evaluate different methods for fusing the 2D and 3D features and obtain best performance with global average pooling and simple concatenation. On the Oxford RobotCar dataset, the resulting descriptor exhibits superior recognition performance compared to descriptors extracted from only one of the input modalities, including state-ofthe-art image-based descriptors. Especially at low descriptor dimensionalities, we outperform state-ofthe-art descriptors by up to 90%.
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