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August 13, 2026
‘Working with AI Solves a Wide Range of Engineering Problems
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
August 12, 2026
‘I Would Like My Research to Help Make the World a Calmer and Better Place
Whatever task Saraa Ali, Junior Research Fellow at the Laboratory of Methods for Big Data Analysis (LAMBDA) of the AI and Digital Science Institute (HSE Faculty of Computer Science), is working on, she thinks about how it can benefit people. She told the Young Scientists of HSE University project about her large family, diagnosing three-phase motors, and her dream of building a children’s home in her native country.
August 11, 2026
‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.

 

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(CoFeMnCuNiCr)3O4 High-Entropy Oxide Nanoparticles Immobilized on Reduced Graphene Oxide as Heterogeneous Catalysts for Solvent-Free Aerobic Oxidation of Benzyl Alcohol

ACS Applied Nano Materials. 2024. Vol. 7. No. 5. P. 5513–5524.
Seyedsaeed M., Ahmad Ostovari Moghaddam, Hadavimoghaddam F., Salari R., Varfolomeev M., Зиннатуллин А., Zinnatullin A.

The development of noble metal-free heterogeneous catalysts holds promise for the solvent-free and selective aerobic oxidation of organic compounds. However, the moderate activity of these catalysts under atmospheric conditions limits their industrial use. In this work, the synthesis of noble metal-free (CoFeMnCuNiCr)3O4 high-entropy oxide (HEO) nanoparticles and their grafting on reduced graphene oxide (rGO) to produce a HEO–rGO nanocomposite is detailed. X-ray diffraction (XRD), scanning electron microscopy (SEM), and Raman and Mössbauer spectroscopy analyses confirm the formation of the spinel HEO phase. HEO–rGO nanocomposites are used for aerobic and solvent-free oxidation of benzyl alcohol, displaying excellent catalytic performance. Up to 10.36% conversion and 78.5% selectivity of benzaldehyde can be achieved in only 4 h. An extensive analytical study shows that the excellent performance of HEO–rGO nanocomposites is attributed to the synergistic effect between the rGO active sites and the abundant oxygen vacancies within the HEO nanoparticles. Moreover, four robust machine learning models including Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) are applied to predict the selectivity of the oxidation reactions. The XGBoost is demonstrated as the best-fitting model for all data with an error of less than 2.5%. Overall, both experimental and numerical data suggest the potential application of the HEO–rGO nanocomposites in chemical industries for the selective oxidation of alcohols to added-value products.

Research target: Chemistry Materials Technologies
Language: English
Full text
DOI
Keywords: машинное обучениеHigh-entropy oxidesMachine learningВысокоэнтропийный оксидHEO NanoparticlesOxidation ReactionBenzyl AlcoholНаночастицы HEOРеакция окисленияБензиловый спирт
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