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July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.
July 20, 2026
Scientists Propose Method for More Efficient Resource Use in Machine Learning
An international group of researchers, including mathematicians from the AI and Digital Science Institute at the HSE Faculty of Computer Science, has provided a theoretical justification for a simple and computationally efficient method of estimating uncertainty in Stochastic Gradient Descent (SGD). The paper has been published on the scientific preprint server arXiv.org and presented at AISTATS 2026.

 

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A comparative study of medium-entropy oxide and metal oxide nanoparticles on graphene oxide for benzyl alcohol oxidation under solvent-free conditions

Materials Advances. 2026. Vol. 7. No. 2. P. 1127–1137.
Mehrabi-Kalajahi S., Vasigh S. A., Yousefi Bavili H., Ostovari Moghaddam A., Varfolomeev M.

A series of monometallic catalysts, such as Fe3O4, CoO, NiO, and CuO, along with their entropy-stabilized nanoparticles [(Fe,Co,Ni,Cu)3O4 medium-entropy oxide (MEO)], were synthesized and subsequently utilized for the solvent-free and selective aerobic oxidation of benzyl alcohol when supported on graphene oxide (GO). The resulting nanocomposites underwent characterization through various techniques including X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), transmission electron microscopy (TEM), Brunauer–Emmett–Teller (BET) analysis, thermogravimetric analysis (TGA), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS), which collectively confirmed the successful formation of monometallic and spinel (Fe,Co,Ni,Cu)3O4 MEO nanoparticles integrated into GO. Among the utilized catalysts, the (Fe,Co,Ni,Cu)3O4 MEO–GO nanoparticles exhibit remarkable catalytic efficiency. A conversion rate of up to 17.2% and 28.8%, along with selectivity rates of 71.4% and 65.7% for benzaldehyde, can be attained in just 5 h at air pressures of 1 and 10 atm, respectively. A comprehensive analytical investigation indicates that the remarkable performance of (Fe,Co,Ni,Cu)3O4 MEO–GO nanocomposites can be ascribed to the synergistic interaction between the active sites of GO and the plentiful oxygen vacancies present in the (Fe,Co,Ni,Cu)3O4 MEO nanoparticles. The exceptional catalytic behavior of (Fe,Co,Ni,Cu)3O4 MEO–GO nanocomposites is attributed, according to detailed analyses, to the synergy between GO active sites and the numerous oxygen vacancies within the MEO nanoparticles. In summary, the extensive experimental findings indicate the promising use of (Fe,Co,Ni,Cu)3O4 MEO–GO nanocomposites within the chemical sector for the targeted oxidation of alcohols into high-value products.

Research target: Materials Technologies Chemistry
Language: English
Full text
DOI
Keywords: medium-entropy oxide
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