NH3 emissions were limited strictly because of the threat for human health and sustainable development. Pt/Al2O3 and Pt/CeZrO2 were prepared by the impregnation method. Differences in surface chemical states, reduction ability, acid properties, morphological properties, reaction mechanisms, and ammonia oxidation activity were studied. It indicated that Pt species states were affected by different metal–support interactions. The homogeneously dispersed Pt species over Pt/Al2O3 exposed Pt(111) because of weak metal–support interactions; there even existed an obvious interface between Pt and Al2O3. While obscure even an overlapped interface was observed over Pt/CeZrO2, resulting in the formation of PtO because of the oxygen migration from CeZrO2 to Pt species (confirmed by CO-FTIR, the cycled H2-TPR and transmission electron microscopy results). It was noteworthy that different reaction mechanisms were induced by different states of Pt species; NH was the key intermediate species for ammonia oxidation reaction over Pt/Al2O3, but two kinds of intermediates, N2H4 and HNO, were observed for Pt/CeZrO2. It consequently resulted in the obvious distinction of the NH3-SCO catalytic performance; the light-off temperatures of NH3 over Pt/Al2O3 and Pt/CeZrO2 were 231 and 275 °C, respectively, while the maximum N2 selectivity (65%) was obtained over Pt/CeZrO2, it was obviously better than that over Pt/Al2O3.

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In this paper, a novel state of health (SOH) estimation method based on partial charge voltage and current data is proposed. The extraction of feature variables, which are energy signal, the Ah-throughput, and the charge duration, is discussed and analyzed. The support vector machine (SVM) with radial basis function (RBF) as kernel function is applied for the SOH estimation. The predictive performance of the SOH by the SVM are performed with full and partial charging data. Experiment results show that the addressed approach enables estimating the SOH accurately for practical application.

Due to the lack of a general descriptor to predict the activity of nanomaterials, the current exploration of nanozymes mainly depended on trial-and-error strategies, which hindered the effective design of nanozymes. Here, with the help of a large number of Ni−O−Co bonds at the interface of heterostructures, a prediction descriptor was successfully determined to reveal the double enzyme-like activity mechanisms for Ni/CoMoO 4 . Additionally, DFT calculations revealed that interface engineering could accelerate the catalytic kinetics of the enzyme-like activity. Ni−O−Co bonds were the main active sites for enzyme-like activity. Finally, the colorimetric signal and intelligent biosensor of Ni/CoMoO 4 based on deep learning were used to detect organophosphorus and ziram sensitively. Meanwhile, the in situ FTIR results uncovered the detection mechanism: the target molecules could block Ni−O− Co active sites at the heterostructure interface leading to the signal peak decreasing. This study not only provided a well design strategy for the further development of nanozymes or other advanced catalysts, but it also designed a multifunctional intelligent biosensor platform. Furthermore, it also provided preferable ideas regarding the catalytic mechanism and detection mechanism of heterostructure nanozymes.

A morphology-controlled molten polymerization route was developed to synthesize SmMnO (SMO) perovskite catalysts with netlike (SMO-N), granular-like (SMO-G), and bulk (SMO-B) structures. The SMO perovskites were formed directly by a molten polymerization method, and their morphologies were controlled by using the derivative polymers as templates. Among all catalysts, the porous SMO-N exhibited the highest activity, over which the toluene, benzene, and o-xylene were completely oxidized to CO at 240, 270, and 300 °C, respectively, which was comparable to that of typical noble-metal catalysts. The apparent activation energies of toluene over SMO-N (56.4 kJ·mol) was much lower than that of SMO-G (70.8 kJ·mol) and SMO-B (90.1 kJ·mol). Based on the results of scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, and H temperature-programmed reduction characterization, we deduce that the excellent removal efficiency of volatile organic compounds (VOCs) over SMO-N catalyst was attributable to the special structure, high surface Mn/Mn and O/O molar ratios, and strong reducibility. Due to the high activity, low cost, and simple preparation strategy, the SMO catalyst is a promising catalyst for VOC removal.