Leonas Valkunas’s research while affiliated with Center for Physical Sciences and Technology and other places

Light nanoscopy is attracting widespread interest for the visualization of fluorescent structures at the nanometre scale. Recently, a variety of methods have overcome the diffraction limit, yet in practice they are often constrained by the requirement of special fluorophores, nontrivial data processing, or a high price and complex implementation. Therefore, confocal microscopy, yielding a relatively low resolution, is still the dominant method in biosciences. It was shown that image scanning microscopy (ISM) with an array detector could improve the resolution of confocal microscopy. Here, we review the principles of the confocal microscopy and present a simple method based on ISM with a different image reconstruction approach, which can be easily implemented in any camera-based laser-scanning set-up to experimentally obtain the theoretical resolution limit of confocal microscopy. Our method, single pixel reconstruction imaging (SPiRI), enables high-resolution 3D imaging utilizing image formation only from a single pixel of each of the recorded frames. We achieve the experimental axial resolution of 330 nm, which was not shown before by basic confocal or ISM-based systems. The SPiRI method exhibits a low lateral-to-axial FWHM aspect ratio, which means a considerable improvement in 3D fluorescence imaging. As a demonstration of SPiRI, we present the 3D-structure of a bacterial chromosome with an excellent precision.

The problems of open classical systems usually correspond to a motion of a test particle that interacts with a large number of bath oscillators. Often, the test particle itself can be considered a harmonic oscillator. For such composite systems, exact numerical solutions are available, but they can become increasingly costly for a large number of bath oscillators. Here we take inspiration from the recent work on open quantum systems and investigate the applicability of the frozen-modes approximation to such classical systems. This approach assumes that some part of the low-frequency bath modes are frozen, thus only their initial values need to be considered. We show that by applying the frozen-modes approximation one can significantly increase the accuracy of the perturbative multiple-scales solution, especially for slow baths. This approach provides a good accuracy even for strong system–bath couplings, a regime that is not accessible to straightforward applications of the perturbation theory. We also suggest a rule for the splitting of spectral density to the fast and slow bath modes. We find that our approach gives excellent results for the ohmic spectral density, but it could be applied for other similar spectral densities as well.

Solid and liquid stilbene forms have been characterized by a range of tools: from AFM microscopy to CARS microspectroscopy and optical spectroscopy. Obtained experimental observations are analyzed by means of...

Photosystem I (PSI) light-harvesting antenna complexes LHCI contain spectral forms that absorb and emit photons of lower energy than that of its primary electron donor, P700. The most red-shifted fluorescence is associated with the Lhca4 complex. It has been suggested that this red emission is related to the inter-chlorophyll charge transfer (CT) states. In this work we present a systematic quantum-chemical study of the CT states in Lhca4, accounting for the influence of the protein environment by estimating the electrostatic interactions. We show that significant energy shifts result from these interactions and propose that the emission of the Lhca4 complex is related not only to the previously proposed a603⁺–a608⁻ state, but also to the a602⁺–a603⁻ state. We also investigate how different protonation patterns of protein amino acids affect the energetics of the CT states.

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.

Variability in the spectral properties of solid conformations of stilbene under various external conditions still remains obscure. The photophysical properties of trans-stilbene solution in solid polystyrene glass have been studied by absorption and time-resolved fluorescence. Concentration-induced quenching has been observed for small concentrations of stilbene. At large concentrations, the spectroscopic characteristics become split between the two phases of the sample: single-molecule properties are responsible for absorption, while the micro-crystalline phase dominates in fluorescence. Ab initio and molecular dynamics analyses suggest permanent twisting of the stilbene molecular structure upon crystallization, which supports spectroscopic phase separation.

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.