Recently, the research group led by Prof. Han Lin reported their research progress on Raman technology and its application. Two research papers were published in the top journals (Food Chemistry (IF=9.231, Top) and Materials Today Nano (IF=13.364, Top).

Progress one: Surface-enhanced Raman spectroscopy (SERS) is an important technique for molecular detection and characterization due to its merits of non-damage, ultra-high sensitivity, fingerprint selectivity and being label-free. However, constructing high-performance SERS substrates is a challenge, associated with nanomaterials. In this work, a simple, ultra-sensitive and ultra-stable SERS substrate was constructed using the ultra-hydrophobic property of the C/Ag nanocomposites for the sensitive detection of melamine in milk. The SERS substrate was constructed by a one-step procedure through CPC and AgNO₃ reaction on the glass. The enhancement mechanism was verified by FDTD simulation. The work provided a simple, rapid construction approach of the ultra-sensitive, large-area SERS substrate, and a detection method of the melamine in milk, which would have significant applications in food safety, environmental monitoring, biomedicine and other fields. The research paper was published with the title "A self-assembly hydrophobic oCDs/Ag nanoparticles SERS sensor for ultrasensitive melamine detection in milk" on Food Chemistry (IF=9.231, top). PhD candidate Qiu Jiaoyan is the first author. Prof. Han Lin and Zhang Yu are the corresponding authors. Shandong University is the sole Author affiliation.

Progress two: In this work, an ultra-stable SERS substrate constructed on nanoflower GaN combined with machine learning technology to solve issues of the long-term stability and multiple analytes' rapid identification of the SERS sensors. A super stable, single-molecule level SERS sensor via one-step synthesizing Ag@ organic carbon nanodots nanoparticles on the nano-flower Gallium Nitride surface. The SERS sensor not only achieves single molecule detection with the enhancement factor of 2.483×109, but also provides excellent long-term stability in the air for up to eight months. Integrating with machine learning technology, the back-propagation neural algorithm networks classifiers, three similar analytes could be correctly recognized with an accuracy rate of 92.64%. The results of this work could promote practical applications in the fields of environmental pollution, food safety and biomedicine. The research paper was published with the title "Ag@C decorated GaN nanoflower enabled super-stable, single molecule level SERS substrate integrated with machine learning for multiple analytes identification" on Materials Today Nano (IF=13.364，Top). He Qihang (2019 Master student) and Qiu Jiaoyan (2020 PhD candidate) are the co-first authors. Prof. Han Lin and Zhang Yu are the corresponding authors. Shandong University is the sole Author affiliation.

The works were supported by the National Natural Science Foundation of China, the Major Scientific and Technological Innovation Project of Shandong Province, and the Fundamental Research Funds of Shandong University.

Links to the papers:

https://doi.org/10.1016/j.foodchem.2022.134241

https://doi.org/10.1016/j.mtnano.2023.100305