QD optoelectronics leverages size-dependent quantum confinement effects within semiconductor nanocrystals to achieve precise bandgap tunability and exceptional light-matter interaction. By virtue of their solution-processability, colloidal QD enables scalable, low-temperature manufacturing across lightweight and flexible substrates beyond the limits of traditional bulk semiconductors. This versatile material platform holds immense potential for revolutionizing high-efficiency photovoltaics, narrow-band photodetection, and silicon-integrated near-infrared photonics. Ultimately, pioneering QD materials engineering and device integration establishes a critical foundation for next-generation, high-performance optoelectronic devices.
By harnessing the bandgap-tuning capabilities and low-temperature, scalable manufacturing potential of colloidal quantum dots (CQDs) on flexible substrates, our published research has significantly advanced next-generation optoelectronic devices:
• Solar Energy Innovation: Developed novel p-type PbS CQD homojunction solar cells [48] and engineered advanced CQD materials to enhance luminescent solar concentrator efficiency [53].
• On-Chip Sensing & Photodetectors: Advanced performance benchmarks for CQD photodetectors [47] and achieved monolithic integration of CQDs on silicon platforms, expanding capabilities for on-chip near-infrared detection [49, 55, 58, 60].
• Field Leadership & Reviews: Authored two authoritative review papers [57, 61] synthesizing state-of-the-art advances and guiding future developments in the field.
Micro- and nanophotonics governs the precise manipulation of light at sub-micron scales through the custom engineering of dielectric, metallic, and hybrid nanostructures. By controlling light-matter interactions at dimensions comparable to or smaller than optical wavelengths, this discipline unlocks extraordinary optical behaviors, such as customized light scattering and spatial tailoring, that are unachievable with conventional bulk optics. Crucially, leveraging functional nanostructures alongside cost-effective, sustainable materials enables the scalable integration of advanced light management directly into real-world device architectures. Ultimately, micro- and nanophotonic engineering drives transformative impacts across diverse disciplines, spanning energy-efficient solid-state lighting, optical biomedical therapeutics, and environmental remediation.
By leveraging metallic and dielectric micro-/nanostructures to govern light-matter interactions, our published research advances scalable, cost-effective light management across photonic and cross-disciplinary domains:
• Advanced Light Management and Solid-State Lighting: Engineered hybrid cellulose nanocrystal:polymer optical diffusers that achieve an exceptional combination of high haze and high transparency. This innovation demonstrates the integration of widely available, low-cost materials to boost white LED performance [51].
• Biomedical Photonic Membranes: Collaborated with Prof. Zhang (University of Alberta) to elucidate how nanofiber membrane architecture dictates optical properties, directly advancing functional capabilities in light-assisted wound healing [68, 69].
• Environmental Applications: Successfully extended micro-/nanophotonic structural design into environmental engineering, demonstrating high-performance membrane solutions for water treatment [56].
Advanced sensor technology serves as the critical interface bridging real-world physical, optical, and biological phenomena with digital data processing networks. By leveraging functional nanomaterials, such as graphene and colloidal quantum dots, alongside precision micro-/nanostructures, these devices achieve unprecedented sensitivity, miniaturization, and multi-modal responsiveness. This paradigm holds transformative potential for next-generation wearable healthcare, integrated optical sensing, and label-free single-cell biomedical diagnostics. Ultimately, high-performance micro- and nanostructured sensing platforms accelerate the deployment of intelligent, lab-on-a-chip systems for real-time environmental and physiological monitoring.
By engineering nanostructured materials and device architectures across multi-modal sensing domains, our published research has delivered high-impact achievements in physical, optical, and biological detection:
• Physical and Flexible Sensing: Applied graphene and advanced nanomaterials to engineer high-performance touch and pressure sensors for wearable and physical monitoring applications [50].
• High-Impact Optical and Infrared Detection: Pioneered CQD-based photodiodes, silicon-integrated near-infrared sensors, and filter-free narrow-band photodetectors, publishing landmark results in top-tier journals (ACS Nano, ACS Photonics, Optics Letters) as shown in Contribution 1.
• Bio-Sensing and Microfluidic Cell Manipulation: Co-supervised doctoral research with Prof. Jie Chen, leveraging micro-/nanostructured devices to advance single-cell manipulation and label-free biomolecular detection [62, 65, 66, 74].
AI for photonics represents a transformative paradigm shift that integrates machine learning and deep neural networks directly into optical physics to automate, accelerate, and refine device design. By replacing traditional, trial-and-error simulation methods with data-driven forward modeling and inverse design frameworks, AI can rapidly discover non-intuitive nanostructures capable of tailoring light with unprecedented precision. This synergy drastically reduces computational overhead and prototyping cycles for complex optical architectures, ranging from smart solid-state lighting to integrated photonic circuits. Ultimately, merging AI with photonic engineering bridges the gap between theoretical optical manipulation and the scalable manufacturing of next-generation optoelectronic systems.
By leveraging machine learning and advanced neural network topologies to bypass traditional design bottlenecks, our published and ongoing works accelerate the optimization and manufacturing of high-performance optical systems:
• High-Precision Forward and Inverse Modeling: Engineered a deep neural network predicting white LED angular color uniformity and luminous flux with sub-1% error, paired with an inverse design model that rapidly determines required optical film parameters [59].
• Breakthrough Spatial Light Tailoring: Published a first-in-class study in Advanced Optical Materials [70], deploying spherical convolutional neural networks (CNNs) and nanocrystal diffusers to achieve customizable LED brightness patterns, disproving long-standing assumptions regarding the directional limitations of light diffusers.
• 3D Environmental and Infrastructure Applications: Developed 3D spatial light control algorithms applied to high-efficiency street lighting architectures, currently submitted for peer review.