Leonas Valkunas

Diatoms are unicellular photosynthetic microalgae that contribute nearly a quarter of the global primary production. Knowledge of the mechanisms, networks, and time scales of the excitation transfer is crucial for a deeper understanding of their photosynthetic machinery. Unfortunately, the short excited-state lifetime of photosystem I at room temperature and research focus on isolated structures have limited the development of a complete excitation transfer scheme in intact cells. Here, we address this gap through picosecond time-resolved fluorescence measurements on intact Cyclotella meneghiniana cells obtained from 16 to 130 K and room temperature. Analysis of the data in terms of evolution-associated spectra reveals a complex interplay of excitation transfer, population of fluorescing trapping states, and excitation quenching at the whole-cell level. We show rapid depopulation of all antenna complexes, on the order of 30 ps even at 16 K, indicating a more efficient energy-transfer network in diatoms than in higher plants.

Photosynthetic light-harvesting complexes (LHCs) exhibit remarkable diversity in their electronic properties despite structural homology. In this study, we investigate the environment-dependent local excitations and chlorophyll–chlorophyll charge transfer (CT) states in the LHCI antenna of plant Photosystem I, focusing on the Lhca1–4 subcomplexes. Using high-resolution structural data from Mazor et al. [Nat. Plants, 3, 17014 (2017); PDB ID: 5L8R], we performed QM/MM calculations to quantify electrostatic energy shifts induced by the protein environment. We identified seven low energy states with non-negligible CT mixing that may contribute to the red-shifted fluorescence observed experimentally. Comparative analysis with previous results based on the slightly lower resolution structure by Qin et al. [Science, 348, 989 (2015); PDB ID: 4XK8] reveals significant sensitivity of CT state energetics to subtle structural variations and protonation patterns of the amino acids, particularly involving GLU153 in Lhca4. Our findings underscore the critical importance of accurate structural modeling and protonation state assignment in predicting pigment electronic properties and interpreting spectroscopic data. This work advances our understanding of the mechanistic basis of red-shifted fluorescence in LHCI and highlights the need for multi-structure validation in theoretical studies of photosynthetic complexes.

Concentration quenching (CQ) of fluorescence (FL) is a phenomenon in which an increase in the molecular concentration of a fluorophore leads to a reduction of its FL quantum yield. Although it has been recognized for decades, its microscopic origin remains not fully understood. CQ is commonly attributed to excitation migration from fluorescent species to non-emissive traps, although the physical nature of these traps is often unspecified. In this work, we study CQ in solutions of free-base and zinc-containing phthalocyanines (Pcs) by combining steady-state and time-resolved FL spectroscopy as well as quantum-chemical calculations. While steady-state FL spectra remain essentially unchanged across the 0.1–10 mM concentration range for both Pc molecules, the absorption spectra exhibit concentration-dependent changes, which are especially pronounced for free-base Pc. The FL decay kinetics for both Pcs with increasing concentration evolve monotonically from mono-exponential to distinctly non-exponential behavior, altogether indicating the formation of ground-state aggregates that are non-emissive. Density functional theory calculations show that both Pc molecules can form optically dark, thermodynamically stable H-dimers, suggesting that such dimers may serve as the statistical pair traps responsible for CQ. Together, these results provide a microscopic picture of CQ in phthalocyanine solutions and contribute to the understanding of the underlying photophysics and its application in the light-to-energy conversion molecular system, where efficient energy transfer is crucial.

Simulations of the absorption line shapes of various molecular systems provide insights into experimental data and can be used as tests for different models. The second-order complex-time dependent Redfield (ctR) theory has been shown to possess an excellent combination of accuracy and numerical efficiency. Nonetheless, there are some cases where improvements in its accuracy are desirable. In this study, we have developed a fourth-order extension of the ctR theory (ctR4). By assuming an exponential decomposition of the bath correlation function, we have derived analytical expressions, thus avoiding costly numerical integration in the time domain. Our results show that the ctR4 approach can provide higher quality results for the Debye spectral density, even in the case when the reorganization energies of different molecules are slightly different. On the other hand, for the Ohmic spectral density with exponential cutoff, the ctR4 line shapes are more accurate only for small reorganization energies, while for the super-Ohmic spectral density, the original second-order theory provides better results. Moreover, for some parameter values, the ctR4 line shapes can exhibit negative features. Therefore, the original ctR theory remains preferable in practical calculations.

Vibrational spectra of excitons in purple bacterial antennae indicate on how many bacteriochlorophylls they reside and suggest in which proportions, and reveal vibronic modes that could open additional energy transfer pathways.

Aggregates and crystallites of trans-stilbene are formed spontaneously on the surface of a polystyrene film with smooth transition from a mono-molecular form to a monocrystal form when the concentration of stilbene is increased.

Fluorescence concentration quenching occurs when increasing molecular concentration of fluorophores results in a decreasing fluorescence quantum yield. Even though this phenomenon has been studied for decades, its mechanisms and signatures are not yet fully understood. The complexity of the problem arises due to energy migration and trapping in huge networks of molecules. Most of the available theoretical work focuses on integral quantities like fluorescence quantum yield and mean excitation lifetime. In this work, we present a numerical study of the fluorescence decay kinetics of three-dimensional and two-dimensional molecular systems. We investigate the differences arising from the variations in models of trap formations. We also analyze the influence of the molecular orientations to the fluorescence decay kinetics. We compare our results to the well-known analytical models and discuss their ranges of validity. Our findings suggest that the analytical models can provide inspiration for different ways of approximating the fluorescence kinetics, yet more detailed analysis of the experimental data should be done by comparison with numerical simulations.

Spectral properties of solid trans-stilbene reflect diversification of molecular conformations in nanocrystals.

Machine learning (ML) approaches are attracting wide interest in the chemical physics community since a trained ML system can predict numerical properties of various molecular systems with a small computational cost. In this work, we analyze the applicability of deep, sequential, and fully connected neural networks (NNs) to predict the excitation decay kinetics of a simple two-dimensional lattice model, which can be adapted to describe numerous real-life systems, such as aggregates of photosynthetic molecular complexes. After choosing a suitable loss function for NN training, we have achieved excellent accuracy for a direct problem—predictions of lattice excitation decay kinetics from the model parameter values. For an inverse problem—prediction of the model parameter values from the kinetics—we found that even though the kinetics obtained from estimated values differ from the actual ones, the values themselves are predicted with a reasonable accuracy. Finally, we discuss possibilities for applications of NNs for solving global optimization problems that are related to the need to fit experimental data using similar models.