Publication date: 21st July 2025
This symposium is focused on the ultrafast nanoscale spatio-temporal transport of excitons in energy materials and photosynthetic systems [1-4]. The natural sunlight illumination of ~1kW/m2 corresponds to about 10 photons/sec on an organic molecule cross-section. Yet, typical micro-spectroscopy, pump-probe, experiments require many orders of magnitude higher light level, at which saturation, annihilation, non-linear response and dissociation play important roles. To address any relevant function of photosynthesis one to needs to operate at natural conditions, far below shut-down: the light level of One Sun.
Here we present two strategies to study energy transfer at One-Sun and, despite the scarcity of photons, preserve the nanometer resolution to track excitons in photosynthetic and photovoltaic architectures, together with the crucial fs-ps response:
1. Structured Excitation Energy Transfer (StrEET): We push the required light levels down by at least 4 orders of magnitude, while preserving the nanoscale diffusion resolution, by encoded periodic spatial excitation close to diffraction limit. We determine the effective diffusivity D over 6 orders of magnitude excitation fluence range, revealing apparent increase at too high fluences due to onset of non-linear exciton-exciton annihilation as confirmed by lifetime decrease at higher fluence, above ~10 sun levels. [5]
2. Wide field SPAD array detection: for the first time we will explore SPAD array cameras for spatio-temporal imaging. For the periodic excitation, the full periodic distribution is captured and resolved, and no mask is needed. A 2D Fourier transform of the time-resolved image allows to easily compute the DC and the excitation frequency amplitudes, whose ratio isolates the diffusivity contribution to the amplitude decay. The APD array provides free choice of super-resolution illumination strategies, simplification of the encoding, and to speed up 100-1000 times by the parallel detection. [6]
This work is part of the project ERC Advanced Grant 101054846 FastTrack.
This work is part of the project ERC Advanced Grant 101054846 FastTrack.
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