UG Student papter accepted in ACS ANM!

🎉 Congratulations | Undergraduate Students Publish in ACS Applied Nano Materials with Cover Art Selection!

We are thrilled to announce that Jian-Kai Wang and Lei Rao, undergraduate students from the College of Integrated Circuits and Optoelectronic Chips (Class of 2021), have published a research article as co-first authors in the prestigious SCI journal ACS Applied Nano Materials (American Chemical Society). The paper, titled “A Search-and-Verification Framework for Efficient Annealing Optimization in PbS Quantum Dot Films”, was also selected as the cover art of the journal’s issue — a testament to the students’ talent in both scientific research and visual communication!

🔬 Research Background & Significance

Solution-processed semiconductors like quantum dots and perovskites are emerging as key materials for next-generation optoelectronic devices due to their low cost, low-temperature processing, and tunable bandgaps. Among them, PbS quantum dots (QDs) stand out for their strong response in the short-wave infrared (SWIR) region, offering promising applications in surveillance, biomedical imaging, and remote sensing.

However, optimizing the annealing process—a critical step that influences film crystallinity, defect density, and carrier mobility—remains a major challenge. Traditional parameter tuning methods, such as single-variable optimization or full factorial designs, are often inefficient and prone to missing global optima.

🌟 Research Highlights

This work introduces a novel “Search-and-Verification” annealing optimization framework (ACO-HC) tailored for low-dimensional parameter spaces. By combining Ant Colony Optimization with Hill Climbing and integrating real-time experimental feedback, the framework achieves high-resolution (1 °C, 1 min) adaptive tuning. Remarkably, it improves device responsivity by 265% while reducing the number of experiments by 99%.

Multimodal structural characterizations (AFM, SEM, XRD) confirmed that enhanced device performance is primarily due to reduced surface roughness, leading to more efficient carrier transport and photoelectric conversion.

Notably, the proposed framework is generalizable and model-independent, making it applicable to a wide range of materials (quantum dots, perovskites, polymer semiconductors) and devices (photodetectors, solar cells, TFTs). This offers a versatile solution for transitioning from “experience-driven” to “data-driven” process design in low-dimensional material systems.

🔗 Read the Paper 👨‍🎓 Co-first authors: Jian-Kai Wang, Lei Rao (Undergraduate students, ICOC) 📮 Corresponding author: Dr. Haodong Tang, Assistant Professor

This achievement showcases the high caliber of our undergraduate research training system. Supported by the Optoelectronic Chip Integration & Display Laboratory, the project exemplifies how research-driven education empowers students to innovate and contribute meaningfully to high-level academic and technological development.

We look forward to seeing more of our students shine on the global research stage!

— College of Integrated Circuits and Optoelectronic Chips, Shenzhen Technology University (ICOC)