Exploring novel p-n core/shell structure in single α-Fe2O3 nanorods of hierarchical hollow microspheres for ultrasensitive acetone gas sensor

Fabricating metal oxide sensors with super-sensitivity and good distinguishing properties for gases is a challenge due to the difficulties in controlling vacancy-type structural defects of nanomaterials and the data processing. In this paper, we reported the fabrication of hierarchical α-Fe2O3 hollow microsphere (HFHM)-based sensor using a facile and scalable method with uniform-micro spherical carbon templates, as well as the data analysis using artificial intelligence (AI). The shell of the unique hollow microspheres was built by connecting many α-Fe2O3 nanorods, so the superstructures have 0D, 1D, and 3D structural features. In these α-Fe2O3 nanorods, positron annihilation measurements revealed abundant oxygen-vacancy clusters (11 atoms), nanopores (0.53 nm) and p-n core/shell structure. The HFHM-based sensors, hence, exhibited an extremely high sensitivity toward acetone (Response = 320 (200 ppm), limited detection (DL) ∼ 250 ppt) and ethanol (Response = 300 (200 ppm), DL ∼ 500 ppt), as well as a super-fast response time (1–2 s). In particular, by using the Principal Component Analysis (PCA), an applied AI tool, we were able to significantly improve the distinguishing and selective abilities of acetone and ethanol (high-response gas groups) as well as H2, CO and NH3 (low-response gas groups).