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Though sunlight is by far the most abundant renewable energy source available to humanity, its dilute and variable nature has kept efficient ways to collect, store, and distribute this energy tantalisingly out of reach. Turning the incoherent energy supply of sunlight into a coherent laser beam would overcome several practical limitations inherent in using sunlight as a source of clean energy: laser beams travel nearly losslessly over large distances, and they are effective at driving chemical reactions which convert sunlight into chemical energy. Here we propose a bio-inspired blueprint for a novel type of laser with the aim of upgrading unconcentrated natural sunlight into a coherent laser beam. Our proposed design constitutes an improvement of several orders of magnitude over existing comparable technologies: state-of-the-art solar pumped lasers operate above 1000 suns (corresponding to 1000 times the natural sunlight power). In order to achieve lasing with the extremely dilute power provided by sunlight, we here propose a laser medium comprised of molecular aggregates inspired by the architecture of photosynthetic complexes. Such complexes, by exploiting a highly symmetric arrangement of molecules organized in a hierarchy of energy scales, exhibit a very large internal efficiency in harvesting photons from a power source as dilute as natural sunlight. Specifically, we consider substituting the reaction center of photosynthetic complexes in purple bacteria with a suitably engineered molecular dimer composed of two strongly coupled chromophores. We show that if pumped by the surrounding photosynthetic complex, which efficiently collects and concentrates solar energy, the core dimer structure can reach population inversion, and reach the lasing threshold under natural sunlight. The design principles proposed here will also pave the way for developing other bio-inspired quantum devices.
Deep convolutional neural networks (DCNNs) have revolutionized computer vision and are often advocated as good models of the human visual system. However, there are currently many shortcomings of DCNNs, which preclude them as a model of human vision.
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Word embeddings are a popular way to improve downstream performances in contemporary language modeling. However, the underlying geometric structure of the embedding space is not well understood. We present a series of explorations using bio-inspired
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