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September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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Insights into the electrochemical properties of germanium-cobalt-indium nanostructures in a wide temperature range

Electrochimica Acta. 2025. Vol. 512. Article 145441.
Gavrilin I. M., Emets V. V., Marinkin I. S., Kovtushenko E. V., Skundin A. M., T.L. Kulova, Volkov R. L., Borgardt N. I., Gavrilov S. A.

Germanium-cobalt-indium (Ge-Co-In) nanostructures are a promising material for negative electrodes of lithium- ion batteries aimed for arctic exploitation. Electrochemical impedance spectroscopy was used for a detailed study of the interaction of Ge-Co-In nanostructures with lithium in a temperature range from ‒35 to +20◦C. The discharge capacity at temperatures of 20, 0, ‒10, ‒20, and ‒35◦C amounted to 1400, 1228, 1040, 907, and 793 mAh g‒1, respectively. The impedance spectra measured at various lithiation degrees were found to differ but insignificantly whereas temperature variation resulted in notable changes in the spectra. A normalized charge transfer resistance for Ge-Co-In nanostructures was significantly (more than an order of magnitude) less than for Ge-In nanowires (obtained by the same method, but without the addition of cobalt salt into the electrolysis solution). It is this difference in charge transfer resistance that can explain the difference in the shapes of the impedance spectra for both objects. Also, in contrast to data for Ge-In nanowires, the dependences of the lithium diffusion coefficient in Ge-Co-In nanostructures on potential had a clearly defined minimum. The lithium diffusion coefficient in Ge-Co-In nanostructures slightly exceeded that in Ge-In nanowires, and the activation energy of lithium diffusion in Ge-Co-In nanostructures was marginally less than in Ge-In nanowires.

Research target: Chemistry
Language: English
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Keywords: temperature effectslithium insertion Ge nanowiresGe-Co-In nanostructures Electrochemical impedance spectroscopy Equivalent circuit Lithium-ion batteries
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