School of Engineering Department of Chemical and Biological Engineering 86 Organic Molecules for Hydrogen Storage Supervisor: SHAO Minhua / CBE Student: CHO Sungbin / CEEV Course: UROP 1100, Fall With hydrogen’s promising potential as a new energy source, developing catalysts for the dehydrogenation of liquid organic hydrogen carriers has been a primary focus of research. This study concentrates on synthesizing a non-noble catalyst using cobalt as the metal and magnesium aluminate as the catalyst support and on measuring the catalytic activity in the dehydrogenation of decalin. The performance of dehydrogenation is qualitatively assessed using gas chromatography-mass spectrometry. The results suggest that further optimization is needed for the cobalt-based catalyst to achieve better performance. Future research will focus on synthesizing cobalt-based catalysts using different methodologies or catalysts with different metals and supports to identify an effective catalyst. Auto-Identification of Defects in Organic Semiconductors with Computer Vision Supervisor: Cindy TANG / CBE Student: ZISENGWE Vimbai Norraine / CENG Course: UROP 1100, Summer Defects in polymer films—ranging from pinholes to cracks—pose significant threats to material performance and manufacturing yield. This report investigates the application of Faster R-CNN for defect detection in microscopy images of polymer coatings. A custom data pipeline, domain-specific augmentation, and architectural refinements such as reflection-invariant convolutions and thickness-adaptive pooling were implemented. Results show detection of common defects but reduced performance on rare or subtle anomalies. Bounding box inflation and dataset limitations remain key challenges. While performance improved with extended training, alternative architectures such as YOLO may offer better real-time accuracy. Recommendations include tighter annotations and defect-specific augmentation strategies. Big Data: Bioinformatic Analysis of Single-Cell Genomic Data Supervisor: WU Angela Ruohao / CBE Student: CHEN Yanyu / BCB Course: UROP 1100, Summer Single-cell RNA sequencing is a cutting-edge technique that enables transcriptome profiling at the single-cell level (scRNA-seq). It illustrates the characteristics of a certain cell population, the interactions between various cell types, and the interactions between cells. Every aspect of scRNA-seq is covered in this review, including increasing methods, data running, hurdles, and challenges.
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