Electronic Supplementary Information ( ESI ) Ambient Rutile VO 2 ( R ) Hollow Hierarchitectures with Rich Grain Boundaries from New-State Nsutite-Type VO 2 , Displaying Enhanced Hydrogen Adsorption Behavior
S1. Shape evolution of the new-state VO 2 hollow spherical nanoarchitectures S2. Compositional characterization of the new-state nsutite-type VO 2 precursor S3. Summary XRD patterns of the nsutite-type VO 2 prepared by different chemical reactions S4. Structural determination of new-state VO 2 S5. Calculation details and phase conversion process from the new-state nsutite-type VO 2 to rutile VO 2 (R) S6. Characterization of the GBR-VO 2 (R) hollow hierarchitectures S7. Gas adsorption experiments of different vanadium oxides
Electronic Supplementary Information
Giuseppe Mattioli, 2, ∗ Marcel Risch, Aldo Amore Bonapasta, Holger Dau, and Leonardo Guidoni 2, † Istituto di Struttura della Materia (ISM) del Consiglio Nazionale delle Ricerche, Via Salaria Km 29.5, CP 10, 00016 Monterotondo Stazione, Italy Dept. of Physics, “Sapienza” Università di Roma, P.le A. Moro 2, 00185 Roma, Italy Dept. of Physics, Freie Universität Berlin, Arnimallee 14, 14195 Berlin, Germany Dipartimento di Chimica, Ingegneria Chimica e Materiali, Università de L’Aquila, Località Campo di Pile, 67100 L’Aquila, Italy
The Analysis on Fire Smoke Exhausting Robot Vibration Based on Wavelet Transform
Belt relaxation, which affects a lot on operation reliability of the fire smoke exhausting robot, is hard to detect. In this paper, a new method for belt relaxation feature recognition based on continuous wavelet transform (CWT) is proposed, and a vibration model which simplified the robot as a 2-dof forced vibration system under harmonic excitation is established. The vibration acceleration signal has been collected using the IEPE sensor and data acquisition card, experimental results verified the accuracy of the vibration model, and weak impact signal caused by the belt relaxation was distinguished, that testify the practicability of the CWT method in belt relaxation feature recognition.