A semiconductor interface triple-molecular gate-based and artificial intelligence raman spectrum analysis sensing platform and biological applications thereof
By constructing a ZrOx heterointerface on the ZrS2 semiconductor surface and introducing a dopamine linker layer and a hydrophobic alkyl brush, combined with machine learning algorithms, the selectivity and speed issues of LysoPC detection in complex biological systems were solved, achieving highly sensitive quantitative analysis suitable for the detection and management of neurological diseases.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANGHAI FOURTH PEOPLES HOSPITAL (SHANGHAI FOURTH PEOPLES HOSPITAL AFFILIATED TO TONGJI UNIV)
- Filing Date
- 2026-05-20
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies for detecting lipids and small molecule metabolites in complex biological systems suffer from problems such as cumbersome pretreatment, long detection cycles, insufficient selectivity, and difficulty in achieving rapid quantification. In particular, in the detection of lipid molecules such as LysoPC, traditional Raman spectroscopy signals are weak and lack chemical selectivity, making it difficult to meet the requirements for detecting low-abundance molecules.
A ZrS2@ZrOx heterostructure was constructed using ZrS2 semiconductor nanostructures, and a dopamine linker layer and C12-C20 hydrophobic alkyl brushes were introduced to form a triple molecular-gated hybrid interface. A concentration prediction model was constructed by combining machine learning algorithms to achieve highly selective identification and highly sensitive quantitative analysis of LysoPC.
It achieves highly sensitive and selective detection of LysoPC in complex biological systems, with a detection limit of 0.28 mg/mL, a signal-to-noise ratio greater than 18.5, and a single detection time of less than 1 minute. It has good sensitivity, stability and reproducibility, and is suitable for risk assessment of neurological diseases and perioperative neurological function management.
Smart Images
Figure CN122409620A_ABST