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News
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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[B10H10]2– Nanoclusters Covalently-Immobilized to Hybrid SiO2 Aerogels for Slow Neutron Shielding Applications

ACS Applied Nano Materials. 2022. Vol. 5. No. 8. P. 11529–11538.
Yorov K., Zhdanov A., Kamilov R., Alexander E. Baranchikov, Kopitsa G., Pokrovskiy O., Vladimir K. Ivanov

An alkoxysilane derivative bearing 10-vertex closo-decaborate nanocluster (B10H102–) was successfully synthesized and immobilized in SiO2 aerogel matrix. closo-Decaborate-containing SiO2 aerogels showed high specific surface (~750 m2/g), open porosity (~95 %) and extremely low apparent density (80 mg/cm3) as well as increased thermal stability of closo-decaborate moiety. Despite the low boron content (~1.2 mol.% of boron nanoclusters comprising natural mixture of boron isotopes), the aerogel nanomaterial demonstrated excellent neutron capture properties exceeding lightweight polymer-based analogues. Cell viability assessment indicated the pronounced dose-dependent toxicity of borylated aerogel with respect to malignant cells (U251 MG glioblastoma line), and low toxicity with respect to normal cells (dental pump stem cells). The combination of the properties of closo-decaborate modified silica aerogels make them good candidate material for shielding of patients and healthcare personnel during boron neutron capture therapy procedures.

Research target: Chemistry Nanotechnologies
Language: English
DOI
Keywords: aerogel
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