Yunju Xiao | Biosensors | Best Researcher Award

Published on by Sciencefather

Mrs. Yunju Xiao | Biosensors | Best Researcher Award

Mrs. Yunju Xiao | Biosensors | Junior Physician at Guangdong Provincial People’s Hospital | China

Mrs. Yunju Xiao is an accomplished researcher specializing in biomedical sensing, nanomaterials-based analytical systems, and molecular diagnostics, recognized for her contributions to advancing innovative detection platforms for clinical and translational applications. She completed her education through rigorous training in medical sciences and research methodology, culminating in a doctoral qualification from a leading medical university where she focused on the development of nano-enabled biosensing technologies for sensitive biomarker identification and disease monitoring. Building on her academic foundation, she has gained significant professional experience at Guangdong Provincial People’s Hospital of Southern Medical University, where she contributes to multidisciplinary projects aimed at improving diagnostic precision and healthcare outcomes. Her research interests span catalytic hairpin assembly mechanisms, gold nanoparticle engineering, signal-enhanced detection systems, surface-enhanced Raman spectroscopy, and microRNA-based disease diagnostics, reflecting her dedication to bridging basic nanoscience with practical clinical applications. Mrs. Yunju Xiao’s research skills include expertise in nanoscale material synthesis, molecular probe design, advanced spectroscopic techniques, microfluidic diagnostics, data interpretation, and laboratory protocol optimization, allowing her to make meaningful contributions to emerging diagnostic research. She has authored multiple peer-reviewed publications indexed in Scopus and other leading databases, accumulating notable citations that reflect the visibility and academic value of her work. Her recognized publication in Sensors and Actuators B: Chemical demonstrates her ability to produce impactful research within competitive scientific fields. Throughout her academic and professional career, she has earned recognition for her scholarly output and has received honors for her contribution to collaborative research efforts and high-quality scientific dissemination. Her involvement in scientific communities further supports her commitment to continuous learning and professional advancement. Mrs. Yunju Xiao’s work reflects analytical rigor, innovation, and a strong commitment to addressing scientific challenges related to early disease detection and diagnostic sensitivity. She continues to expand her research through interdisciplinary collaborations, aiming to contribute to the development of next-generation biosensing systems. In conclusion, Mrs. Yunju Xiao exemplifies a promising and dedicated researcher whose background in biomedical diagnostics, strong methodological skills, and expanding publication record position her as an influential contributor to the future of diagnostic technology and biomedical research.

Academic Profile: ORCID | Scopus

Featured Publications:

  1. Xiao, Y. (2026). Catalytic hairpin assembly (CHA)-driven AuNP tetramer assembly-based SERS platform for sensitive detection of EV-miRNAs. Sensors and Actuators B: Chemical.

 

 

Galina Malykhina | Sensors | Best Researcher Award

Published on by Sciencefather

Prof. Dr. Galina Malykhina | Sensors | Best Researcher Award

Prof. Dr. Galina Malykhina | Sensors | professor at Peter the Great St.Petersburg Polytechnic University | Russia

Prof. Dr. Galina Malykhina is a distinguished researcher and professor at Peter the Great St. Petersburg Polytechnic University, Russia, with extensive expertise in measurement information technologies and computational modeling. She completed her Ph.D. at the same university, establishing a strong foundation in applied engineering and computational sciences. Over the years, Prof. Dr. Malykhina has led and contributed to numerous international research projects, particularly in areas such as cerebral autoregulation assessment, two-phase flow control systems in oil production, and physics-informed neural network methods for modeling chemical reactors. Her professional experience encompasses academic leadership, mentoring graduate and doctoral students, and supervising high-impact research collaborations across interdisciplinary fields. Prof. Dr. Malykhina’s research interests include signal processing, computational engineering, neural network modeling, and the application of physics-based methods in complex engineering systems. She possesses advanced research skills in real-time data analysis, modeling of parameterized singular perturbation problems, wavelet cross-correlation techniques, and software-hardware integration for physiological assessments. Throughout her career, she has published multiple influential papers in reputable journals such as Sensors, Processes, and Computation, garnering significant citations that reflect the impact and recognition of her work in the global scientific community. She is an active member of IEEE and ACM, contributing to peer review activities, technical committees, and international workshops. Her dedication to advancing research excellence has been recognized with numerous awards and honors, celebrating her contributions to computational technologies, neural network modeling, and applied measurement systems. Prof. Dr. Malykhina’s ongoing work demonstrates remarkable potential for future contributions, particularly in integrating AI-driven methodologies with engineering measurement systems. Her leadership, innovative research, mentorship, and commitment to scientific advancement position her as an influential figure in her field, poised to continue shaping the development of cutting-edge computational and engineering technologies, while fostering collaboration and knowledge transfer across international research communities.

Academic Profile: ORCID | Scopus

Featured Publications:

  1. Semenyutin, V., Antonov, V., Malykhina, G., Nikiforova, A., Panuntsev, G., Salnikov, V., & Vesnina, A. (2025). Software and hardware complex for assessment of cerebral autoregulation in real time. Sensors. Citation: 12

  2. Arseniev, D., Malykhina, G., & Kratirov, D. (2024). Wavelet cross-correlation signal processing for two-phase flow control system in oil well production. Processes. Citation: 18

  3. Tarkhov, D., Lazovskaya, T., & Malykhina, G. (2023). Constructing physics-informed neural networks with architecture based on analytical modification of numerical methods by solving the problem of modelling processes in a chemical reactor. Sensors. Citation: 22

  4. Lazovskaya, T., Malykhina, G., & Tarkhov, D. (2021). Physics-based neural network methods for solving parameterized singular perturbation problem. Computation. Citation: 30