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Chain Reaction winners to present research at young scientists' congress

Chain Reaction winners to present research at young scientists' congress

Rosatom announced the winners of a scientific and technical presentation competition at the Chain Reaction event in Nizhny Novgorod on 9 October. Their projects will be presented at the sixth Congress of Young Scientists.

The event took place on 5–7 October with support from the science and higher education ministry. Organisers said it brought together 200 young researchers from 40 nuclear-industry organisations and 25 universities. Participants discussed technological challenges for 2050 and AI applications in research and education.

Student winners included Sirius University’s Alyona Marchuk for a laser-beam control algorithm for welding and metal 3D printing. Darya Strelina of MISIS presented a non-destructive nuclear-fuel inspection method checking each pellet in 10–30 seconds. Anna Kamerilova, also from MISIS, studied the properties and safety of a magnesium alloy for medical implants.

Andrey Mezhnov of MIPT presented a machine-learning system that organisers said cut tungsten cracking analysis from 20 minutes to one. Andrey Plotnikov of MEPhI developed an autonomous groundwater-monitoring probe for uranium mining, delivering results in 15 minutes rather than a three-day laboratory wait.

Rosatom researcher winners included Nikita Antonov for fibre-optic sensors monitoring an RBMK-1000 reactor without shutdown and Maxim Mishin for speeding electromagnetic-wave calculations twentyfold. Irina Nikitina was recognised for a lightweight fire-resistant composite, Elena Buryakova for caesium-coolant heat pipes, Kamilla Alkenova for implant osseointegration research and Alexey Tretyakov for krypton capture technology that the corporation said could remove up to 90% of gaseous emissions.

Tatyana Terentyeva proposed making AI a full participant in research teams by 2035. Yevgeny Abakumov highlighted energy, computing capacity and materials needs. He also said transferring control to AI in safety-critical systems required an appropriate methodology.

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