Ajeet Kaushik
Also known as: Ajeet Kaushik, Ph.D.
Florida Polytechnic University, Florida International University Herbert Wertheim College of Medicine, National Physical Laboratory CSIR, Florida International University
Research areas: Chemistry · Analytical Chemistry · Electrochemical sensors and biosensors · Advanced biosensing and bioanalysis techniques · Nanoparticle-Based Drug Delivery
0000-0003-4206-1541
About
Ajeet Kumar Kaushik, Ph.D. (http://akaushik3.wixsite.com/nanocare), is an Associate Professor of Chemistry at the Department of Chemistry of Florida Polytechnic University, Lakeland, USA. He is a recipient of various reputed awards and is exploring smart electrochemical sensing systems for rapid diagnostics and nanocarriers for on-demand site-specific delivery/release of therapeutics to prevent CNS diseases. His main research interest is developing nanomedicine along with exploring novel methods of brain delivery and wearable sensors for personalized health care. Dr. Kaushik received his Ph.D. (Chemistry and Biosensors) in collaboration with the National Physical Laboratory and Jamia Milia Islamia, New Delhi, India.
Employment
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Florida Polytechnic University Associate Professor2025 - Present
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Florida Polytechnic University Assistant Professor of Chemistry2021 - Present
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Florida Polytechnic University Assistant Professor of Chemistry2019 - 2021
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Florida International University Herbert Wertheim College of Medicine Assistant Professor2015 - 2019
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Florida International University Post Doctoral Associate2011 - 2015
Education
- 2004 - 2010
Projects & Funding
Projects & funding information is unavailable.
Publications (510)
- Characterization and evaluation of the ability of graphene quantum dots to affect α-synuclein aggregation in synucleinopathy models Save
- Advanced piezoelectric nanogenerators for next-generation self-powered nano-bio sensors Save
- Biogenic (fungus-driven) silver nanoparticles for efficient catalytic, antibacterial, and antioxidant performance Save
- Standing of Electrochemical Sensors on the 250th Anniversary of the USA Save
- Interfacial V5+/V4+ Redox Kinetics Enabling Fe-free Photo-Fenton Catalysis in α-V2O5-ACNT Nanohybrids for Efficient Removal of Mixed CBZ and DFN Save
- Selenium-Integrated MOFs and Their Derived Materials for Advanced Design, Functionality, and Applications Save
- Biomimetic Nanoparticles for Targeted and Efficient Cancer Therapy: Progress, Challenges and Perspectives Save
- On‐Skin Tattoo‐Embedded Sensors (TES) for Next‐Generation Sensing and Future Directions for Healthcare Monitoring Save
- Enhancing Machine Learning Models for Mental Health Classification Through Iterative Training and Text‐Based Augmentation Save
- 2D hybrid and biodegradable piezoelectric nanogenerators for self-powered systems: Next generation sustainable energy Save
- Advanced sensing and machine learning technologies for economically motivated adulterations (EMA) management: Trends toward sustainability Save
- Advanced tools and methodologies for identification, characterization, and quantification of micro/nano plastics in environmental matrices Save
- Corrigendum to “Microwave-assisted assembly of Ag2O-ZnO composite nanocones for electrochemical detection of 4-Nitrophenol and assessment of their photocatalytic activity towards degradation of 4-nitrophenol and methylene blue dye” [J Hazard Mater 416 (2… Save
- Electronic Humidity Sensors for Biomedical Applications: Trends, Machine Learning Support, and Emerging Nanomaterial Strategies Save
- Emerging palladium nanoparticles: Bridging sustainable synthesis, catalytic innovation, and biomedical translation Save
- Enhancing point-of-care diagnosis through brain-computer interaction and IoT Save
- Insights to cellular and molecular impacts of micro/nanoplastics and their additives for carcinogenic risks Save
- Integration of sustainable nanotechnology with artificial intelligence of things (AIoT) for environmental applications Save
- MIP-assisted electrochemical sensing of micro/nano-microplastic (polystyrene) for environmental surveillance Save
- Machine learning (ML)-assisted development of 2D green catalysts to support sustainability Save