Research Activities


The Department of Nanotechnology Engineering conducts interdisciplinary research on advanced functional materials, nanomaterials, surface engineering, laser-based material processing, spectroscopic analysis, nanocoatings, and biotechnology-oriented material applications.


Research activities focus on material design, fabrication, surface modification, and characterization at micro- and nanoscale levels. By combining experimental processing techniques with advanced analytical methods, the department aims to develop application-oriented solutions for healthcare, energy, defense, aerospace, and advanced manufacturing, while offering students and researchers an active environment to engage with emerging topics in nanotechnology and materials science.

SUCCESSFULLY COMPLETED


WE ARE PROUD OF THE ENGINEERS OF THE FUTURE 

WE HAVE NURTURED!

WE HAVE PROUDLY CLOSED OUR LIFT UP PROJECT!​

TUSAŞ LIFT UP Industry-Oriented Undergraduate Graduation Projects Programme ​

Within the scope of the TUSAŞ LIFT UP Industry-Oriented Undergraduate Graduation Projects Programme for the 2025–2026 academic year, the project titled “Numerical and Experimental Investigation of the Changes in the Mechanical Properties of Transparent Engineering Polymers Under Thermal Loading,” carried out by our CeramX undergraduate student project team under the academic supervision of Assist. Prof. Pelin TÖREN ÖZGÜN, has been successfully completed.










13.08.2026

Research Projects 

NanoLIBS Technology for Spectroscopic Analysis of Alloys 

Principal Investigator: Prof. Dr. Arif Demir
Research Area: Laser-Induced Spectroscopy and Spectroscopic Material Analysis
Supported by: EU EUROSTARS Program

Prof. Dr. Arif Demir conducts research in the field of laser-based spectroscopy, focusing on the development and application of NanoLIBS technology for the rapid, precise, and non-destructive elemental characterization of metal alloys. NanoLIBS, which refers to nanoscale Laser-Induced Breakdown Spectroscopy, enables the identification of alloy composition by analyzing the optical emission generated through laser–matter interaction and plasma-based diagnostics.

Developed under the EU EUROSTARS Program, the project aims to create a compact and portable NanoLIBS system that can support real-time material analysis in both research and industrial environments. By providing reliable insight into the elemental composition of high-value alloys, this technology contributes to advanced material diagnostics, quality control, and material verification processes in aerospace, defense, and advanced manufacturing applications.

Investigation and Characterization of Sustainable Protein-Based Silk Fibroin–Silicone Hydrogel Composites

Project Leader: Elif Gülsen Erkol 
Academic Advisor: Dr. Ayimgül Uzunyol
Industry Advisor: Gamze Kara Mağden
Research Area: Biocomposite Materials and Biomaterial Characterization 
Supported by: TÜBİTAK 2209-B University Students Industry-Oriented Research Projects Support Program

Silk fibroin is a natural protein widely used in biomedical applications, including wound dressings, drug delivery systems, and bone and tissue engineering, due to its biocompatibility, biodegradability, and mechanical properties. This project aims to develop a sustainable and functional biocomposite material by combining silk fibroin with silicone hydrogel.

The thermal, chemical, and morphological properties of the composites prepared using BUGA-Silk Fibroin Solutions supplied by Bugamed Biotechnology Inc. will be examined through TGA, FTIR, and SEM analyses. Their surface wettability, oxygen permeability, and mechanical strength will also be evaluated to investigate the material’s potential applications.

OES Analysis and Machine Learning-Based Classification of Aluminum Alloys ​

Principal Investigator: Prof. Dr. Arif Demir 
Research Area: Optical Emission Spectroscopy, Machine Learning, and Materials Characterization 
Supported by: TÜBİTAK 2209-A University Students Research Projects Support Program

Prof. Dr. Arif Demir conducts research on the integration of Optical Emission Spectroscopy (OES) and machine learning methods for the rapid, precise, and data-driven characterization of aluminum alloys. The project aims to process spectral data obtained from optical emission analyses and classify alloy compositions with high accuracy.

Within the project, KNN, Decision Tree, Random Forest, and SVM models will be comparatively evaluated, and a Python- and Arduino/ESP-based real-time monitoring system will be developed. This approach is expected to contribute to quality control, raw material verification, and recycling processes in the aerospace, automotive, energy, and defense industries.