School of Science Department of Chemistry 2 Department of Chemistry Lead-free Perovskite Nanocrystals for Photo-induced Water Splitting Supervisor: HALPERT Jonathan Eugene / CHEM Student: OUYANG Boyu / CHEM-IRE Course: UROP1100, Spring UROP2100, Summer Colloidal photocatalytic hydrogen generation is a field that is gaining popularity in recent years. It allows conversion of solar energy into hydrogen gas to be stored as a fuel very easily. As a strong candidate in achieving large scale solar to hydrogen production, different photocatalysts have been synthesized. However, none of them achieved high enough efficiency or life spam long enough for commercial use. In this UROP project my work is to synthesize and study Cobalt(II) oxide (CoO) nanocrystal, a famous material for photo induced water splitting, and try my best to improve the system. Construction and Application of Surface Enhanced Raman Spectrometer in Biomolecules Characterization Supervisor: HUANG Jinqing / CHEM Student: CHAU Cheuk Yui Sherman / BICH Course: UROP1100, Fall With its highly distinct, non-destructive characterization of chemical samples, Raman spectroscopy is utilized in a plethora of fields, including pharmaceuticals, cosmetics, and geology. Its rapid identification of samples could therefore prove useful in differentiating between cancerous and non-cancerous breast tissue from the same patient. This report presents the application and viability of surface enhanced Raman spectroscopy in the characterization of biomolecules. The results provide evidence that the biochemical composition of tumor, papilloma, and fibroadenoma are notably different but contain a similar spectra pattern. To analyse the spatial configuration of the tissue sample, Raman mapping is also utilized to visually identify the discrepancy between cancerous and noncancerous breast tissue and its influence on Raman scattering. Construction and Application of Surface Enhanced Raman Spectrometer in Biomolecules Characterization Supervisor: HUANG Jinqing / CHEM Student: ZHOU Daoyu / SSCI Course: UROP1100, Summer In this research, we utilized Raman imaging combined with unsupervised machine learning (UMAP-DBSCAN) as an assistance to achieve the goal of determining the margin of the breast ductal carcinoma sample. So far, we have finished the Raman peak assignment and performed analysis based on the clusters obtained from DBSCAN, Raman imaging, and mean spectra of each cluster thereby some preliminary conclusions have been drawn. The clusters generated from machine learning show a well transitional pattern from collagens to malignant cells in many cases. We found malignant cells have higher lipids and protein contents and altered configurations in specific bands of Raman spectra compared with cells adjacent to ECM in the same region. Several possible reasons for these have been interpreted.