Ensemble-learning-assisted exhaled gas disease analysis based on in-situ construction of MOF-derived MO_x/GaN heterojunction sensor arrays.

ABSTRACT: Human exhaled gas is rich in biomarker information that could be used for early diagnosis of disease. With the development of nanotechnology and the Internet of Medical Things (IoMT), AI-assisted nano gas sensor arrays as a non-invasive exhaled gas detection platform brings fascinating technological solutions to the field of breath detection. Herein, we designed a new heterojunction sensing array by anchoring n-GaN nanoparticles on MOF-derived p-MO_x porous nanosheets. The gas sensor arrays demonstrated remarkable response speed (10 s), excellent repeatability, and extreme anti-humidity with a lower detection limit of 1 ppb at room temperature. Energy band structure combined with density functional theory (DFT) calculations were used to analyze the entire gas sensing process. Fur

SUBMITTER: Li D 

PROVIDER: S-EPMC12832609 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature