PROGRESS AND POTENTIAL

Metabolic fingerprinting offers a rapid, minimally invasive route to in vitro diagnostics because small-molecule metabolites respond quickly to disease and therapy. Laser desorption/ionization mass spectrometry (LDI-MS) is attractive for this purpose due to simple workflows and high  throughput, yet conventional organic matrices generate strong low-mass background and variable crystallization that can mask metabolites and undermine reproducibility. Semiconductor nanomaterials provide an alternative substrate class with low background and engineerable electronic/thermal properties. Recent progress shows that the LDI performance can be actively tuned by materials design, rather than trial- and-error matrix selection. By adjusting band structures, introducing defects, and building heterojunction/ metal-semiconductor interfaces, semiconductor substrates can couple laser energy into photothermal heating and interfacial charge transfer, improving desorption/ionization efficiency while maintaining clean spectra. These advances have enabled direct metabolic fingerprinting of complex biofluids with tiny sample volumes, short acquisition times, and growing compatibility with machine learning models for diseases including cancers, neuropsychiatric disorders, and metabolic/systemic conditions.

The next potential leap is clinical translation: scalable fabrication with rigorous batch-to-batch quality control, standardized operating protocols, and quantitative mechanistic framework that separates thermal and charge-driven contributions under realistic sample conditions. Integrating time-resolved/in situ characterization, computation, and data-driven screening could guide predictable substrate design. With automated, high-throughput workflows and systematic benchmarking against clinical gold standards in multi-center cohorts, semiconductor-based LDI-MS could evolve from a promising research tool into a practical platform for rapid screening, stratification, and therapy monitoring.

Figure 1. Schematic overview of semiconductor nanomaterials on enhancing LDI-MS performance for metabolic fingerprinting in various diseases.

Figure 2. Semiconductor-based LDI-MS for metabolic fingerprinting in clinical diagnosis. (a) MO/CMO heterostructures for serum metabolic fingerprinting in epithelial ovarian cancer diagnosis. Reprinted with permission from Pei et al. Copyright 2023, Wiley. (b) Multi-shelled hollow Cr₂O₃ spheres (MHCSs) for urine and serum metabolic fingerprinting in schizophrenia diagnosis. Reprinted with permission from Li et al. Copyright 2021, Wiley. (c) Au NPs-anchored porous CoTiO3 microrods (CTO@Au) for serum metabolic fingerprinting in Alzheimer's disease diagnosis. Reprinted with permission from Zhang et al. Copyright 2023, Wiley. (d) Two-dimensional graphitized carbon-doped mesoporous titania nanosheets (2D-GC-mTNS) for urine metabolic fingerprinting in inflammatory bowel disease diagnosis. Reprinted with permission from Hu et al. Copyright 2022, Springer.

https://doi.org/10.1016/j.matlit.2026.100086