Superior detection and classification of ethanol and acetone using 3D ultra-porous γ-Fe2O3 nanocubes-based sensor

The assembly of primary nanoparticles to form hierarchical ultra-porous architectures is of great interest in various fields because of their extremely large surface area and porosity. In this work, the 3D ultra-porous γ-Fe2O3 nanocubes were synthesized by a simple method, which was derived from perfect Prussian Blue nanocubes by the oxidative decomposition process. The as-synthesized 3D γ-Fe2O3 nanocubes possess a large specific surface area and high porosity, which arise from the self-assembly of ultrafine nanoparticles. The 3D ultra-porous γ-Fe2O3 nanocubes-based sensors showed superior detection of acetone and ethanol with excellent sensitivity and rapid response time. The fantastic gas-sensing platform of 3D γ-Fe2O3 nanocubes could originate from their unique structures and interesting gas-sensing mechanisms. The linear discriminant analysis (LDA) algorithm was effectively used to discriminate between acetone and ethanol. •The perfect Prussian Blue nanocubes was synthesized by a simple hydrothermal method.•The 3D ultra-porous γ-Fe2O3 nanocubes was derived from Prussian Blue template.•3D ultra-porous γ-Fe2O3 nanocubes sensor shows outstanding performance toward Acetone and Ethanol with rapid response time.•The machine learning algorithms was used as effective approach for calcification of Acetone and Ethanol.