Stabilization and Mineralization Mechanism of Cd with Cu-Loaded Attapulgite Stabilizer Assisted with Microwave Irradiation.

Cadmium (Cd) in soil was stabilized using copper loaded attapulgite (Cu/ATP) in a microwave (MW) system. Excellent Cd stability in soil was achieved with Cu/ATP addition due to higher adsorption energy (1.38 eV) of Cu/ATP for Cd than that of ATP (∼1 eV), confirmed by density functional theory calculations. The strong hybridization of the s, p-orbitals of Cd with the s, p, d-orbitals of Cu on ATP contributed to the strong interactions between Cd and Cu/ATP. The stability performance of Cd in Cu/ATP-treated soil was further enhanced after MW irradiation through a series of phase transformation to more stable Cd-bearing crystalline minerals. The transformation was initiated by MW-induced "hot spots", which created cationic vacancy on Cu/ATP surface and enhanced the solid-state reactions between Cd and Cu/ATP framework. The total bond orders of Cd in the formed CdAl4O7 crystalline mineral elevated to 3.38, which was 5-fold higher than that for Cd on Cu/ATP, ensuring the long-term stability of Cd even after 360 curing days. Cd contaminated soil from a former industrial electroplating site was successfully stabilized with the proposed strategy. The research provides an effective stabilization strategy as well as a comprehensive understanding of the mechanism of long-term Cd stabilization.

An adaptive group sparse feature decomposition method in frequency domain for rolling bearing fault diagnosis.

Group-sparse mode decomposition (GSMD) is a decomposition method designed based on the group sparse property of signals in frequency domain. It is proved to be highly efficient and robust against noise, showing bright prospects for bearing fault diagnosis. However, the following adverse factors may impede its application for incipient bearing fault feature extraction: Initially, the GSMD method did not consider the impulsiveness and periodicity of the bearing fault feature. As a result, the ideal filter bank generated by GSMD may not accurately cover the fault frequency band because it may produce over-coarse or over-narrow filter bank under the condition of strong interference harmonics, large random shocks and heavy noise. Moreover, the location of informative frequency band was obstructed since the bearing fault signal shows complicated distribution in frequency domain. To overcome the abovementioned limitations, an adaptive group sparse feature decomposition (AGSFD) method is proposed. Firstly, the harmonics, large-amplitude random shocks and periodic transient feature are modeled as limited bandwidth signals in the frequency domain. On this basis, an autocorrection of envelope derivation operator harmonic to noise ratio (AEDOHNR) indicator is proposed to guild the construction and optimization of the filter bank of AGSFD. Also, the regularization parameters of AGSFD are adaptively determined. With the optimized filter bank, the original bearing fault is decomposed into a serial of components with AGSFD method, where the sensitive fault-induced periodic transient component is maintained using the AEDOHNR indicator. Finally, the studies of the simulation and two experimental items are carried out to evaluate the feasibility and the superiority of AGSFD method. The results indicate the AGSFD method can identify the early failure in the presence of heavy noise, strong harmonics or random shocks and has better decomposition efficiency.