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Valentine P. Ananikov

Publications and source records attributed to Valentine P. Ananikov.

3 recordsLinked to original sources

Mapping the strong-to-weak coupling crossover in polymer-film microcavity lasers

Organic semiconductors are particularly attractive for polaritonics due to their large exciton binding energies and oscillator strengths. Among them, the ladder-type conjugated polymer poly(paraphenylene) is distinguished by its rigid backbone, narrow exciton linewidth, high photoluminescence quantum yield, and enhanced photostability, making it an excellent candidate for organic polariton devices. While polariton lasing has been reported in various organic systems, systematic studies of the transition from polariton lasing to conventional photon lasing within a single, well-controlled material platform remain limited. Here, we present planar organic microcavities incorporating MeLPPP as the active medium, in which continuous tuning of the effective cavity length within a single device enables us to map the strong-to-weak coupling transition across five distinct cavity-mode orders. We demonstrate an approximately eighteen-fold increase in the lasing threshold when crossing from polariton to photon lasing. We further establish a quantitative framework in which the spectral dependence of the threshold governs a universal V-shaped blueshift of the emission energy across both coupling regimes. Finally, we show that vibron-mediated exciton relaxation, previously identified in the strong-coupling limit, persists across the crossover: lasing-threshold minima track the vibron resonances throughout the coupling transition.

physics.optics↗

Lost Data in Electron Microscopy

The goal of this study is to estimate the amount of lost data in electron microscopy and to analyze the extent to which experimentally acquired images are utilized in peer-reviewed scientific publications. Analysis of the number of images taken on electron microscopes at a core user facility and the number of images subsequently included in peer-reviewed scientific journals revealed low efficiency of data utilization. Up to around 90% of electron microscopy data generated during routine instrument operation remain unused. Of the more than 150 000 electron microscopy images evaluated in this study, only approximately 3 500 (just over 2%) were made available in publications. For the analyzed dataset, the amount of lost data in electron microscopy can be estimated as >90% (in terms of data being recorded but not being published in peer-reviewed literature). On the one hand, these results highlight a shortcoming in the optimal use of microscopy images; on the other hand, they indicate the existence of a large pool of electron microscopy data that can facilitate research in data science and the development of AI-based projects. The considerations important to unlock the potential of lost data are discussed in the present article.

cs.DB↗

Machine learning meets mass spectrometry: a focused perspective

Mass spectrometry is a widely used method to study molecules and processes in medicine, life sciences, chemistry, catalysis, and industrial product quality control, among many other applications. One of the main features of some mass spectrometry techniques is the extensive level of characterization (especially when coupled with chromatography and ion mobility methods, or a part of tandem mass spectrometry experiment) and a large amount of generated data per measurement. Terabyte scales can be easily reached with mass spectrometry studies. Consequently, mass spectrometry has faced the challenge of a high level of data disappearance. Researchers often neglect and then altogether lose access to the rich information mass spectrometry experiments could provide. With the development of machine learning methods, the opportunity arises to unlock the potential of these data, enabling previously inaccessible discoveries. The present perspective highlights reevaluation of mass spectrometry data analysis in the new generation of methods and describes significant challenges in the field, particularly related to problems involving the use of electrospray ionization. We argue that further applications of machine learning raise new requirements for instrumentation (increasing throughput and information density, decreasing pricing, and making more automation-friendly software), and once met, the field may experience significant transformation.

physics.chem-ph↗