Research Area

Fundamental and applications

Research Motivation:

•  Improved Efficiency: Explore how integrating 2D materials with plasmonic or metamaterial systems can enhance the efficiency and tuning of light-matter interactions for applications in sensing and imaging.

• Scalability and Fabrication: Investigate more scalable fabrication methods for complex metasurface structures that maintain performance while reducing production costs.

• Broadband Response: Study the potential for developing metasurfaces that achieve broadband optical responses across different wavelengths to enable multifunctional devices in photonics.

Previous related work:

• https://doi.org/10.25916/sut.26271751.v1

• https://doi.org/10.1364/OE.22.013234

• https://doi.org/10.1021/acsami.7b08985 

• https://doi.org/10.2147/IJN.S44163 

• https://doi.org/10.1109/JPHOT.2018.2825435 

Development of 3rd generation solar cell (Thin Film/Plasmonic/QD/Perovskite/IB /STPV (Single/Tandem)

Research Motivation:

• Material Stability: Focus on the long-term stability and environmental effects of emerging materials (like perovskites and quantum dots) in solar cells to improve their practical application.

• Efficiency Limitations: Address the efficiency bottlenecks in tandem solar cells, particularly in combinations of different materials, to maximize energy conversion rates.

• Cost Reduction: Research cost-effective synthesis and deposition techniques for thin-film solar cells to reduce the overall production expenses while maintaining performance.

Previous related work:

• https://doi.org/10.1016/j.solener.2024.113064 

• https://doi.org/10.1016/j.egyr.2024.03.007 

•  https://doi.org/10.1007/s12596-020-00656-w 

• https://doi.org/10.1109/ACCESS.2024.3382713

• https://doi.org/10.1016/j.heliyon.2023.e16749

•   https://doi.org/10.1364/OE.510421

Plasmonic ad metamaterial/2D/metasurface based optical sensor

Research Motivation:

• Sensitivity Enhancement: Explore novel plasmonic designs in biosensors that significantly improve detection limits for biomolecules at low concentrations, enabling early diagnostics.

• Integration with Imaging Techniques: Investigate the fusion of metamaterials with conventional imaging systems for higher resolution and contrast in biological imaging applications.

• Flexibility and Biocompatibility: Research on the development of flexible and biocompatible metasurfaces for real-time monitoring of physiological changes in vivo.

Previous related work:

• https://doi.org/10.1088/1402-4896/ad735b 

• https://doi.org/10.1016/j.measurement.2024.114652

• https://doi.org/10.1109/JSEN.2024.3372692 

• https://doi.org/10.1016/j.heliyon.2024.e33445

• https://doi.org/10.1016/j.heliyon.2024.e33272

• https://doi.org/10.1109/JSEN.2024.3404479

• https://doi.org/10.1088/1402-4896/ad3513  

• https://doi.org/10.1007/s11468-024-02674-x 

NP-Cell Interaction, Uptake, Aggregation kinetics,  Signalling, drug delivery, tracking, Disease detection and diagnosis etc. 

Research Motivation:

• Targeted Drug Delivery: Investigate the mechanisms of nanoparticle-cell interactions specifically tailored to enhance targeted drug delivery systems, focusing on optimizing uptake and reducing off-target effects.

• Disease Detection Sensitivity: Research innovative nanostructures that improve the sensitivity and specificity of disease detection through optical methods, particularly in low-abundance biomarkers.

• Aggregation Kinetics: Analyze the aggregation kinetics of nanoparticles in biological systems to understand their implications for imaging and therapeutic efficacy.

Previous related work:

• https://doi.org/10.25916/sut.26271751.v1

• https://doi.org/10.1109/JSEN.2024.3404479

• https://doi.org/10.1021/acsomega.4c07914 

• https://doi.org/10.1039/D3AN01982A

Investigate Optical and electrical property using ML/Deep learning

Research Motivation:

• Material Characterization: Develop ML algorithms for real-time characterization of photonic materials and devices, focusing on rapid optimization of their optical properties.

• Design Efficiency: Explore inverse design methodologies using ML to discover novel photonic structures that achieve desired optical outputs without extensive trial-and-error processes.

• Integration with Simulation: Investigate synergy between machine learning models and traditional simulation techniques to enhance the accuracy and efficiency of optical property predictions.

Previous related work:

• https://doi.org/10.1021/acsomega.4c07914 

• https://doi.org/10.1039/D3AN01982A

Optical nanoantenna and photonic device

Research Motivation:

• Enhanced Coupling: Explore mechanisms to enhance light-matter coupling in nanodevices, potentially leading to applications in quantum optics and information processing.

• High-Temperature Stability: Research the stability of optical nanoantennas and photonic devices at elevated temperatures for applications in harsh environments.

• Waveguide Miniaturization: Investigate approaches to miniaturize waveguides while maintaining signal integrity, aiming to advance on-chip photonic circuits.

Previous related work:

•  https://doi.org10.1109/ACCESS.2024.3382713 

•  https://doi.org/10.1007/s12596-022-00837-9 

Smart City, Smart House, Smart Farming, Smart Health, Smart Grid, etc.

Research Motivation:

• Energy Optimization: Research smart algorithms for energy management in smart grids and smart farming to optimize resource usage and reduce waste.

• Interoperability Challenges: Investigate solutions to improve interoperability among various IoT devices and platforms to enhance functionality and user experience in smart environments.

• Data Privacy and Security: Examine strategies for ensuring data privacy and security within IoT applications in smart cities, especially concerning sensitive user information and autonomous systems.

Previous related work:

• https://doi.org/10.1016/j.treng.2025.100304 

• https://doi.org/10.1007/s43926-024-00073-6 

• https://doi.org/10.1007/s13762-024-05954-5 

• https://doi.org/10.1145/3429523.3429526 

• https://doi.org/10.1371/journal.pone.0304657

• https://doi.org/10.1016/j.heliyon.2024.e26348. 

Photonic inverse design, Disease detection, Cell segmentation, precision agriculture, and Energy Management

Research Motivation:

• Generalization of Models: Focus on enhancing the generalizability of machine learning models in photonics and biology to ensure reliable performance across varying conditions and datasets.

• Adaptive Algorithms: Explore adaptive machine learning algorithms that can continuously learn and optimize while being deployed in dynamic environments, such as precision agriculture.

• Hybrid AI Approaches: Research hybrid approaches combining traditional algorithms with deep learning for improved accuracy in tasks like disease detection and cell segmentation.

Previous related work:

• https://doi.org/10.1021/acsomega.4c07914 

• https://doi.org/10.1039/D3AN01982A

• https://doi.org/ 10.1109/GlobConHT56829.2023.10087679 

• https://doi.org/10.1109/ICEEICT53079.2022.9768614

Design of optical gate for quantum and photic applications

Research Motivation:

• Performance Under Different Conditions: Investigate the robustness and performance of optical logic gates under various operating conditions and noise levels commonly encountered in quantum applications.

• Integration with Other Technologies: Explore methods for integrating optical logic devices with existing electronic systems to promote hybrid computing architectures.

• Scalability: Address challenges in the scalability of optical logic devices for practical quantum computing applications, focusing on fabrication and system integration.

Previous related work:

• https://doi.org/10.1088/1402-4896/ad735b 

Renewable based CO2 mitigation, Digital Twin, Net Zero, EV, Battery management, Algae Based CO2 mitigation

Research Motivation:

• Data-Driven Optimization: Investigate the use of digital twins for real-time optimization and predictive maintenance in renewable energy systems, such as wind and solar farms.

• Behavioral Modeling: Study social behavior and technological adoption factors influencing renewable energy usage in communities, emphasizing strategies for achieving net-zero targets.

• Integration of CO2 Mitigation Strategies: Examine the effectiveness of combined renewable-based CO2 mitigation strategies, such as algae cultivation and biomaterials, within digital twin frameworks.

Previous related work:

• https://doi.org/10.1109/GlobConHT56829.2023.10087679 

• https://doi.org/10.1109/ICMEE.2010.5558476. 

Spintronics for Next-Generation Quantum Computing, Devices, Sensing, and Storage 

Research Motivation:

• Quantum Computing Advancement: Spin-based qubits in wideband materials enable robust quantum information processing with reduced decoherence.

• Energy-Efficient Devices: Spintronics offers ultra-low-power alternatives to conventional electronics by utilizing electron spin rather than charge, improving energy efficiency by orders of magnitude.

• High-Density Data Storage: Spintronic memory devices, such as MRAM, provide non-volatile, scalable, and energy-efficient storage solutions for future computing architectures.

• Next-Gen Sensing Platforms: Spin-based sensors exploit quantum properties for highly sensitive detection of magnetic, electric, and biological signals, unlocking breakthroughs in healthcare and environmental monitoring.

Harnessing Robotics, AI, and IoT for Smarter, Sustainable Precision Agriculture

Research Motivation:

• Optimize productivity and resource use: Integration of robots, drones, and IoT sensors enables site-specific management of water, fertilizer, and pesticides, reducing waste and maximizing crop yield.

• Enable intelligent decision-making: Machine learning and deep learning models process large-scale agricultural data to predict crop growth, detect diseases early, and support real-time interventions.

• Enhance sustainability and resilience: Smart farming systems reduce input costs, minimize environmental impacts, and improve adaptability to climate variability and resource scarcity.

• Advance food security and SDGs: Scalable AI- and IoT-enabled frameworks align precision agriculture with national food security priorities and global Sustainable Development Goals (SDGs).

Previous related work:

• https://doi.org/10.1371/journal.pone.0304657

• https://doi.org/10.1109/ICCIT60459.2023.10508530