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Abstract MOx (M = Zn, Cu, Mn, Fe, Ce) nanoparticles (NPs) embedded in porous C with uniform diameter and dispersion were synthesized, with potential application as S-absorbents to protect catalysts from S-poisoning in catalytic hydrothermal gasification (cHTG) of biomass. S-absorption performance of MOx/C was evaluated by reacting the materials with diethyl disulfide at HTG conditions (450 °C, 30 MPa, 15 min). Their S-absorption capacity followed the order CuOx/C > CeOx/C ≈ ZnO/C > MnOx/C > FeOx/C. S was absorbed in the first four through the formation of Cu1.8S, Ce2S3, ZnS, and MnS, respectively, with a capacity of 0.17, 0.12, 0.11, and 0.09 molS molM −1. The structure of MOx/C (M = Zn, Cu, Mn) evolved significantly during S-absorption reaction, with the formation of larger agglomerates and separation of MOx particles from porous C. The formation of ZnS NPs and their aggregation in place of hexagonal ZnO crystals indicate a dissolution/precipitation mechanism. Note that aggregated ZnS NPs barely sinter under these conditions. Cu(0) showed a preferential sulfidation over Cu2O, the sulfidation of the latter seemingly following the same mechanism as for ZnO. In contrast, FeOx/C and CeOx/C showed remarkable structural stability with their NPs well-dispersed within the C matrix after reaction. MOx dissolution in water (from liquid to supercritical state) was modeled and a correlation between solubility and particle growth was found, comforting the hypothesis of the importance of an Ostwald ripening mechanism. CeOx/C with high structural stability and promising S-absorption capacity was suggested as a promising bulk absorbent for sulfides in cHTG of biomass.

Abstract Time–frequency ridge not only exhibits the variable process of non-stationary signal with time changing but also provides the information of signal synchronous or non-synchronous components for subsequent detection research. Consequently, the key is to decrease the error between real and estimated ridge in the time–frequency domain for accurate detection. In this article, an adaptive weighted smooth model is presented as a post-processing tool to refine the time–frequency ridge which is based on the coarse estimated time–frequency ridge using newly emerging time–frequency methods. Firstly, the coarse ridge is estimated by using multi-synchrosqueezing transform for vibration signal under variable speed conditions. Secondly, an adaptive weighted method is applied to enhance the large time–frequency energy value location of the estimated ridge. Then, the reasonable smooth regularization parameter associated with the vibration signal is constructed. Thirdly, the majorization–minimization method is developed for solving the adaptive weighted smooth model. Finally, the refined time–frequency characteristic is obtained by utilizing the stop criterion of the optimization model. Simulation and experimental signals are given to validate the performance of the proposed method by average absolute errors. Compared with other methods, the proposed method has the highest performance in refinement accuracy.