Publications by authors named "A A Bakulin"
Article Synopsis
- Metallic MXenes, like TiCT, show great potential in energy storage and electronics due to high electrical conductivity and light interaction.
- Research utilized ultrafast laser spectroscopy to examine how quickly electrons and lattices cool down in TiCT thin films compared to other similar materials.
- Findings reveal TiCT's fast cooling time (∼2.6 ps) without hot-phonon bottlenecks and slower heat dissipation over hundreds of nanoseconds, indicating challenges with heat transfer between flakes that could impact energy conversion applications.
Defect tolerance is a critical enabling factor for efficient lead-halide perovskite materials, but the current understanding is primarily on band-edge (cold) carriers, with significant debate over whether hot carriers can also exhibit defect tolerance. Here, this important gap in the field is addressed by investigating how intentionally-introduced traps affect hot carrier relaxation in CsPbX nanocrystals (X = Br, I, or mixture). Using femtosecond interband and intraband spectroscopy, along with energy-dependent photoluminescence measurements and kinetic modelling, it is found that hot carriers are not universally defect tolerant in CsPbX, but are strongly correlated to the defect tolerance of cold carriers, requiring shallow traps to be present (as in CsPbI).
View Article and Find Full Text PDFInferring the driving regulatory programs from comparative analysis of gene expression data is a cornerstone of systems biology. Many computational frameworks were developed to address this problem, including our iPAGE (information-theoretic Pathway Analysis of Gene Expression) toolset that uses information theory to detect non-random patterns of expression associated with given pathways or regulons. Our recent observations, however, indicate that existing approaches are susceptible to the technical biases that are inherent to most real world annotations.
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- The prediction of RNA structure from its sequence is challenging due to a lack of experimental data, which has slowed advancement in the field.
- Researchers have developed a dataset called Ribonanza, consisting of chemical mapping data from two million RNA sequences, collected through crowdsourcing platforms like Eterna.
- Utilizing this dataset, they created a deep learning model named RibonanzaNet, which, when fine-tuned, demonstrates superior performance in predicting various RNA behaviors, potentially improving understanding of RNA structures.
Conventional spectroscopies are not sufficiently selective to comprehensively understand the behaviour of trapped carriers in perovskite solar cells, particularly under their working conditions. Here we use infrared optical activation spectroscopy (i.e.
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