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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Enhancement of Photocatalytic and Photoelectrochemical Performance of ZnO by Mg Doping: Experimental and Density Functional Theory Insights

The Journal of Physical Chemistry Letters. 2023. Vol. 14. No. 18. P. 4134–4141.
Das A., Liu D., Wary R. R., Andrey S. Vasenko, Prezhdo O. V., Nair R.

Doped ZnO nanostructures have shown great potential for solar energy applications. Considering the compatible ionic radius, Mg atoms can be doped into ZnO at different concentrations. The current work reports a combined experimental and DFT study on the influence of Mg dopant concentration on ZnO performance simultaneously for photocatalytic dye removal and photoelectrochemical water splitting. Among all the samples, Mg(3)-ZnO (3 at% Mg) exhibits superior sunlight-driven photocatalytic performance. The optimal Mg-ZnO shows an 8-fold increase in the photocatalytic activity compared to the pristine ZnO. Likewise, the most active photocatalyst shows high photoelectrochemical performance with a photocurrent response of 1.54 mA at the lowest onset potential, 11 times higher than the pristine ZnO. Tuning of the Mg content results in generation of extra charge carriers and a reduced recombination rate, which are the crucial factors responsible for enhanced photocatalytic and photoelectrochemical performance.

Research target: Chemistry
Language: English
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Keywords: DFTPhotocatalytic performancePhotoelectrochemical performanceZnO
Publication based on the results of:
Quantum phenomena in solid-state composite systems (2023)
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