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The chemical hardness of adsorbents is an important physicochemical property in the process of adsorption based on the hard and soft acids and bases (HSAB) theory. Tuning chemical hardness of adsorbents modulated by their concomitants is a promising approach to enhance the adsorptive capacity in principle. In the present work, we report an efficient strategy that the adsorption capacity for aromatic sulfocompounds can be enhanced by tuning the chemical hardness. This strategy is first theoretically explored by introducing C element into the network of hexagonal boron nitride (h-BN) based on a series of model materials (model_xC, x = 1–5). Computational results show that the chemical hardness is reduced after gradually C-doping, which may lead to an enhancement of adsorption capacity according to the HSAB theory. Then, a series of C-doped h-BN materials (BCN-x, x = 10–50) were controlled synthesized. All of the as-prepared materials show better adsorption capacities (e.g., 27.43 mg g–1 for BCN-50) than pure h-BN. Experiment results show that the adsorption capacity correlates well with the C content in the BCN-x, which is consistent with the results predicted by theoretical calculation. This strategy may be helpful to rationally design highly efficient adsorbents in separation engineering and may be expanded to similar two-dimensional materials, where the π–π interaction is the dominant driven force.

Deep eutectic solvents (DESs), as ionic liquid analogues for green solvents, have gained increasing attentions in chemistry. In this work, three typical kinds of DESs (ChCl/Gly, ChCl/AcOH and ChCl/Urea) were successfully synthesized and characterized by Fourier transform infrared spectroscopy (FTIR) and Raman. Then comprehensive and systematical analyses were performed by the methods of density functional theory (DFT). Two methods (B3LYP/6-311++G(2d,p) and dispersion-corrected B3LYP-D3/6-311++G(2d,p)) were employed to investigate the structures, vibrational frequencies and assign their ownership of vibrational modes for the DESs, respectively. Nearly all the experimental characteristic peaks of IR and Raman were identified according to the calculated results. By linear fitting of the combined calculated vs experimental vibration frequencies, it can be found that both of the two methods are excellent to reproduce the experimental results. Besides, hydrogen bonds were proved to exist in DESs by IR spectrum, structure analysis and RDG analysis. This work was aimed at predicting and understanding the vibrational spectra of the three typical DESs based on DFT methods. Moreover, by comparing experimental and theoretical results, it provides us a deep understanding of the formation mechanisms of DESs.

The principle of kernel Extreme Learning Machine (ELM) is demonstrated. On this basis, a multi - scale wavelet kernel extreme learning machine is proposed. The multi-scale wavelet kernel is used as the kernel function of the extreme learning machine. The test shows that it is an achievable extreme learning machine. Experiments show that, using the multi-scale wavelet kernel extreme learning machine in the wind turbine bearing fault diagnosis has higher classification accuracy and speed than the support vector machine classification algorithm, and has excellent application value.