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The promise of any small improvement in the performance of light-harvesting devices, is sufficient to drive enormous experimental efforts. However these efforts are almost exclusively focused on enhancing the power conversion efficiency with specific material properties and harvesting layers thickness, without exploiting the correlations present in sunlight -- in part because such correlations are assumed to have negligible effect. Here we show, by contrast, that these spatio-temporal correlations are sufficiently relevant that the use of specific detector geometries would significantly improve the performance of harvesting devices. The resulting increase in the absorption efficiency, as the primary step of energy conversion, may also act as a potential driving mechanism for artificial photosynthetic systems. Our analysis presents design guidelines for optimal detector geometries with realistic incident intensities based on current technological capabilities
The space-time correlations of streams of photons can provide fundamentally new channels of information about the Universe. Todays astronomical observations essentially measure certain amplitude coherence functions produced by a source. The spatial c
Long-range speckle correlations play an essential role in wave transport through disordered media, but have rarely been studied in other complex systems. Here we discover spatio-temporal intensity correlations for an optical pulse propagating through
Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k-space and time. In order to accelerate the dynamic MR imaging and to exploit k-t correlations from highly undersampled data, here we propose a novel deep learning based approach
Photosynthetic systems utilize adaptability to respond efficiently to fluctuations in their light environment. As a result, large photosynthetic yields can be achieved in conditions of low light intensity, while photoprotection mechanisms are activat
This paper investigates trajectory prediction for robotics, to improve the interaction of robots with moving targets, such as catching a bouncing ball. Unexpected, highly-non-linear trajectories cannot easily be predicted with regression-based fittin