Paper Title
AI-Assisted Hybrid Nanostructured ZnO Semiconductor Thin Films for High-Performance Flexible Optoelectronic and Smart Sensing Applications
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Registration ID: IJNRD_325600
Published ID: IJNRD2605680
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Keywords
ZnO Nanostructures, Flexible Electronics, Optoelectronic Devices, Smart Sensors, Semiconductor Thin Films, Artificial Intelligence, Machine Learning, Hybrid Nanocomposites.
Abstract
Nanostructured semiconductor thin films have become highly important materials for modern flexible optoelectronic and smart sensing applications because of their improved optical, electrical, and surface properties at the nanoscale. Among different semiconductor materials, Zinc Oxide (ZnO) has received significant attention due to its wide band gap, high exciton binding energy, good transparency, environmental stability, and low production cost. Even though earlier studies reported considerable progress in ZnO nanostructures, several practical challenges still remain, including poor carrier transport efficiency, electron-hole recombination losses, limited long-term stability, and lack of intelligent optimization methods for achieving better device performance.In this research work, AI-assisted hybrid nanostructured ZnO semiconductor thin films are developed for advanced flexible optoelectronic and smart sensing applications. The proposed study combines machine learning-based optimization techniques with hybrid semiconductor nanocomposite technology to improve structural uniformity, optical absorption, electrical conductivity, and photoresponse behavior. The ZnO nanostructured thin films are prepared using simple and cost-effective methods such as sol-gel spin coating and hydrothermal synthesis on flexible substrates. Detailed characterization studies are carried out using X-ray diffraction (XRD), scanning electron microscopy (SEM), UV–Visible spectroscopy, photoluminescence (PL), and electrical conductivity analysis to investigate crystallinity, surface morphology, band gap energy, and carrier transport properties.The AI-assisted optimization approach helps in predicting suitable synthesis conditions such as annealing temperature, precursor concentration, film thickness, and nanoparticle distribution to obtain enhanced optoelectronic performance. In addition, the incorporation of advanced materials such as graphene and metal oxide composites improves charge separation efficiency, sensitivity, flexibility, and environmental stability. The experimental results show enhanced optical transparency, reduced band gap energy, faster photoresponse, improved responsivity, and low-power operation compared to conventional semiconductor thin films.The developed semiconductor nanostructures show excellent potential for applications in flexible photodetectors, wearable electronics, intelligent gas sensors, biomedical monitoring devices, energy harvesting systems, and IoT-based smart sensing technologies. This research provides an effective approach for developing sustainable, energy-efficient, and intelligent nano-optoelectronic systems for future advanced electronic applications.
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How To Cite (APA)
Dr ANKAM RAGHAVENDRA RAO (May-2026). AI-Assisted Hybrid Nanostructured ZnO Semiconductor Thin Films for High-Performance Flexible Optoelectronic and Smart Sensing Applications. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(5), g651-g656. https://ijnrd.org/papers/IJNRD2605680.pdf
Issue
Volume 11 Issue 5, May-2026
Pages : g651-g656
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Paper Reg. ID: IJNRD_325600
Published Paper Id: IJNRD2605680
Research Area: Physics All
Author Type: Indian Author
Country: guntur, ANDHRA PRADESH, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2605680.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2605680
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