Constructing Built-In Electric Field in Hierarchical-Flower Heterostructure for High-Performance Serum Metabolic Assay

Laser desorption ionization mass spectrometry (LDI-MS) is a critical platform for high-throughput nontargeted metabolomics analysis in clinical diagnosis. However, traditional organic matrices inherently suffer from background interference in the low-mass range and exhibit low sensitivity for small molecule detections. Heterostructure has been regarded as an effective structure for high charge carrier mobility and tunable band gaps, which can enhance ion transfer efficiency and photothermal conversion during the LDI-MS process. In this work, Fe₃O₄/MoS₂ nanoparticles with hierarchical-flower heterostructure were facilely synthesized as a novel matrix of LDI-MS to enhance the detection of serum metabolic profilings (SMPs), which was further applied for the early diagnosis of lung cancer. The heterostructure of Fe₃O₄/MoS₂ can construct a built-in electric field to inhibit electron-hole recombination. Additionally, its abundant defect structures synergistically accelerate interfacial charge transfer, thereby promoting desorption and ionization processes. As a result, the newly developed Fe₃O₄/MoS₂ nanomatrix demonstrated exceptional performance in LDI-MS, significantly surpassing the conventional matrices by at least 1 order of magnitude. Subsequently, information-rich SMPs were successfully obtained from merely 1 μL of serum. More than 90% of the metabolic features exhibited RSDs below 30% in quality control samples, highlighting the high reproducibility of our method for clinical applications. Furthermore, hundreds of lung cancer patients and healthy controls can be clearly distinguished based on their SMPs by using appropriate machine learning models. Finally, two key metabolites associated with lung cancer were identified as potential biomarkers, which showed promising diagnostic capability with an AUC value of 0.824 in the validation set. Taken together, Fe₃O₄/MoS₂ nanoparticles emerge as a promising nanomatrix with superior LDI efficiency and the developed LDI-MS platform proves to be a powerful tool for serum metabolic profiling, offering significant potential for lung cancer diagnosis.