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search.filters.applied.f.access: openAccess × End date: 2024 × Start date: 2024 × Start date: 2021 × DDC: 610 × Subject: article ×

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    Ultrafast vibrational control of organohalide perovskite optoelectronic devices using vibrationally promoted electronic resonance
    (Basingstoke : Nature Publishing Group, 2023) Gallop, Nathaniel. P.; Maslennikov, Dmitry R.; Mondal, Navendu; Goetz, Katelyn P.; Dai, Zhenbang; Schankler, Aaron M.; Sung, Woongmo; Nihonyanagi, Satoshi; Tahara, Tahei; Bodnarchuk, Maryna I.; Kovalenko, Maksym V.; Vaynzof, Yana; Rappe, Andrew M.; Bakulin, Artem A.
    Vibrational control (VC) of photochemistry through the optical stimulation of structural dynamics is a nascent concept only recently demonstrated for model molecules in solution. Extending VC to state-of-the-art materials may lead to new applications and improved performance for optoelectronic devices. Metal halide perovskites are promising targets for VC due to their mechanical softness and the rich array of vibrational motions of both their inorganic and organic sublattices. Here, we demonstrate the ultrafast VC of FAPbBr3 perovskite solar cells via intramolecular vibrations of the formamidinium cation using spectroscopic techniques based on vibrationally promoted electronic resonance. The observed short (~300 fs) time window of VC highlights the fast dynamics of coupling between the cation and inorganic sublattice. First-principles modelling reveals that this coupling is mediated by hydrogen bonds that modulate both lead halide lattice and electronic states. Cation dynamics modulating this coupling may suppress non-radiative recombination in perovskites, leading to photovoltaics with reduced voltage losses.
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    Correcting systematic errors by hybrid 2D correlation loss functions in nonlinear inverse modelling
    (San Francisco, California, US : PLOS, 2023) Mayerhöfer, Thomas G.; Noda, Isao; Pahlow, Susanne; Heintzmann, Rainer; Popp, Jürgen
    Recently a new family of loss functions called smart error sums has been suggested. These loss functions account for correlations within experimental data and force modeled data to obey these correlations. As a result, multiplicative systematic errors of experimental data can be revealed and corrected. The smart error sums are based on 2D correlation analysis which is a comparably recent methodology for analyzing spectroscopic data that has found broad application. In this contribution we mathematically generalize and break down this methodology and the smart error sums to uncover the mathematic roots and simplify it to craft a general tool beyond spectroscopic modelling. This reduction also allows a simplified discussion about limits and prospects of this new method including one of its potential future uses as a sophisticated loss function in deep learning. To support its deployment, the work includes computer code to allow reproduction of the basic results.