fig2

From: Defect engineering and machine learning enhance n-butanol sensing of γ-Fe₂O₃

Defect engineering and machine learning enhance <i>n</i>-butanol sensing of γ-Fe<sub>2</sub>O<sub>3</sub>

Figure 2. (A) EDS spectrum of Fe400; (B) EDS spectrum of 15%-Fe400; (C) XPS survey spectra of Fe400 and 15%-Fe400; (D) High-resolution Fe 2p XPS spectrum of Fe400; (E) High-resolution Fe 2p XPS spectrum of 15%-Fe400; (F) High-resolution O 1s XPS spectrum of Fe400; (G) High-resolution O 1s XPS spectrum of 15%-Fe400; (H) The distribution of oxygen elements in the samples after heat treatment at different temperatures; (I) Oxygen species distribution in samples with different additive ratios; (J) Raman spectra of 15%-Fe400 and Fe400; (K) Magnetic field-swept EPR spectra of Fe400 and 15%-Fe400; (L) Corresponding g-factor spectra of Fe400 and 15%-Fe400. All subfigures are original. Panels (A and B) were directly exported from the EDS characterization system; panels (C-L) were plotted using Origin 2024. EDS: Energy-dispersive X-ray spectroscopy; XPS: X-ray photoelectron spectroscopy; EPR: electron paramagnetic resonance; RH: relative humidity.