Publications by authors named "K A Nikolaev"

Electronic waste (e-waste) contains substantial quantities of valuable precious metals, particularly gold (Au). However, inefficient metal recovery leads to these precious metals being discarded in landfills, causing significant water and environmental contamination. This study introduces a two-dimensional (2D) electrode with a layered graphene oxide membrane functionalized by chitosan (GO/CS).

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Physiological mechanisms underlying relationships between the trematode parthenitae and their molluscan hosts are poorly understood. In this study, we estimated the cardiac function of gastropods Littorina littorea L. infected with Himasthla elongata and Cryptocotyle lingua under laboratory conditions and in situ.

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The increasing structural complexity and downscaling of modern nanodevices require continuous development of structural characterization techniques that support R&D and manufacturing processes. This work explores the capability of laboratory characterization of periodic planar nanostructures using 3D X-ray standing waves as a promising method for reconstructing atomic profiles of planar nanostructures. The non-destructive nature of this metrology technique makes it highly versatile and particularly suitable for studying various types of samples.

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The extraction of gold (Au) from electronic waste (e-waste) has both environmental impact and inherent value. Improper e-waste disposal poses environmental and health risks, entailing substantial remediation and healthcare costs. Large efforts are applied for the recovery of Au from e-waste using complex processes which include the dissolution of Au, its adsorption in an ionic state and succeeding reduction to metallic Au.

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Article Synopsis
  • The study develops a machine learning model to predict major adverse cardiac events (MACEs) in high-risk myocardial infarction (MI) patients, incorporating clinical, imaging, laboratory, and genetic data.
  • It analyzes data from 218 MI patients over 9 years, focusing on the influence of the VEGFR-2 gene variant as part of the overall risk assessment.
  • The CatBoost algorithm performed best, with statin dosage and genetic factors identified as key predictors of adverse events, highlighting the potential for personalized treatment approaches based on genetic information.
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