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Abstract Lignin is considered as a renewable and sustainable resource for producing value-added aromatic chemicals and functional carbon materials. Herein, we develop a one-step catalyst-free depolymerization strategy to convert lignin into aryl monomers and carbon nanospheres simultaneously. Importantly, microwave-assisted depolymerization (MAD) in conjunction with dichloromethane (CH2Cl2) vapors is developed. The total mass yield of guaiacols reached the highest amount of 225.1 mg/g at 600 °C, and the highest yields of phenols (49.0 mg/g) and aromatic hydrocarbons (155.1 mg/g) were obtained at 700 °C. Hydrogen radicals and hydrogen chloride (HCl) are in-situ formed from CH2Cl2, significantly decreasing the activation barrier and reforming pyrolysis vapors to promote the formation of aryl monomers. Interestingly, uniform carbon nanospheres with an average size of 140 nm were produced as co-products at 700 °C. The microwave “hot-spots”, allied with the continuous surface erosion and the decrease in surface energy of lignin-derived carbon precursors by CH2Cl2 vapor, can be considered the driving force for the ultimate formation of carbon nanospheres. The CH2Cl2/MAD system produces aryl monomers (26.8 wt% yield) and carbon nanospheres (36.6 wt% yield) at 700 °C. We provide a facile, intriguing and scalable approach to convert lignin to valuable aryl monomers and sustainable carbon materials that can be applied in the chemistry, energy and environmental fields.

Lower-cost alternatives to platinum electrocatalysts are being explored for the sustainable production of hydrogen, but often trial-and-error approaches are used for their development. Now, principles are elucidated that suggest pathways to rationally design efficient metal-free electrocatalysts based on doped graphene.

Abstract Data from many real-world applications can be high dimensional and features of such data are usually highly redundant. Identifying informative features has become an important step for data mining to not only circumvent the curse of dimensionality but to reduce the amount of data for processing. In this paper, we propose a novel feature selection method based on bee colony and gradient boosting decision tree aiming at addressing problems such as efficiency and informative quality of the selected features. Our method achieves global optimization of the inputs of the decision tree using the bee colony algorithm to identify the informative features. The method initializes the feature space spanned by the dataset. Less relevant features are suppressed according to the information they contribute to the decision making using an artificial bee colony algorithm. Experiments are conducted with two breast cancer datasets and six datasets from the public data repository. Experimental results demonstrate that the proposed method effectively reduces the dimensions of the dataset and achieves superior classification accuracy using the selected features.

Abstract The potential of long short-term memory network on ultra-short term wind speed forecast attracted attentions of researchers in recent years. Extending a probabilistic long short-term memory network model to provide an uncertainty estimation than to make a point forecast is more valuable in practice. However, due to complex recurrent structure and feedback algorithm, large scale ensemble forecast based on resampling faces great challenges in reality. Instead, a reliable forecast method needs to be devised. Gaussian process regression is a probabilistic regression model based on Gaussian Process prior. It is reasonable to integrate Gaussian process regression with long short-term memory network for probabilistic wind speed forecast to leverage the superior fitting ability of the deep learning methods and to maintain the probability characteristics of Gaussian process regression. Hence, avoid the repeated training and heavy parameter optimization. The method is evaluated for wind speed forecast using the monitoring dataset provided by the National Wind Energy Technology Center. The results indicated that the proposed method improves the point forecast accuracy by up to 17.2%, and improves the interval forecast accuracy by up to 18.5% compared to state-of-the-art models. This study is of great significance for improving the accuracy and reliability of wind speed prediction and the sustainable development of new energy sources.

Abstract Urban renewal has become a key issue all through China's Transitional Period. The social, economic, political factors, and their correlations have been seriously considered. However, the effects of outdoor pedestrian thermal comfort and fluid ventilation effects as well as the residence living conditions in old city districts under urban reconstruction should, to some extent, be paid closer attention to. As an example, take an old city district in Wuhan where a comprehensive mathematical model describing the fluid flow and heat transfer characteristics has been presented. Considering the influences of ambient crosswind, solar radiation, and natural convection and radiation heat transfer, numerical analysis based on the original layout has been executed. By analyzing the temperature and velocity distributions of the old city district, a new planned layout has been presented in this research. By comparison, some basic rules have been advanced to achieve favorable thermal comfort and flow ventilation effects.

Abstract As a type of solid waste, the used cigarette filters (UCF) were utilized to produce ester-rich bio-oil via a cleaner production process, namely, microwave-assisted pyrolysis (MAP). The pyrolysis efficiency was significantly enhanced owing to the high heating rate under MAP conditions with assistance of microwave absorber silicon carbide (SiC) in reactor and adding methanol into N2 carrier gas. Compared with the traditional tubular muffle furnace heating method yielding 0% of bio-oil, the MAP heating method obtained 29.17% of bio-oil from UCF. The bio-oil yield from UCF increased from 29.17% to 46.71% due to the introduction of methanol. Results of the gas chromatography/mass spectrometry showed that esters were the main components in the bio-oils (over 40%), especially of methyl acetate (over 12%). The aromatic compounds of phenols and polycyclic aromatic hydrocarbons (PAHs) were also produced from the MAP of UCF. The bio-char from MAP of UCF exhibited the mesoporous property (e.g., over 500 m2/g of specific surface area). It is expected to produce over 350000 metric tons ester-rich bio-oil if the proposed technology can be scaled up globally. It will be a preeminent contribution for the conversion of UCF to the cleaner production and our environments.

Abstract This work focused on a novel form-stable phase change material (FSPCM) for building energy saving. Capric acid (CA) and palmitic acid (PA) were selected to prepare binary eutectic. The CA-PA/wood flour (WF) composites with different mass ratios were prepared by the vacuum impregnation method. After the shape-stabilized properties were investigated, the results indicated that there were the good encapsulate capacity of WF to CA-PA eutectic and the slight effect of WF on the crystallization. Phase change wood plastic composites (WPCs) using CA-PA /WF composites as the latent heat storage medium and PVC as the matrix were prepared. The relations between mass ratios of CA-PA /WF composites and mechanical properties of WPCs were discussed. The results revealed that the WPCs with appropriate mass fraction of CA-PA eutectic had desired mechanical properties for practical applications. The prepared composites were examined by the scanning electron microscope (SEM), differential scanning calorimetry (DSC), fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TG) and accelerated thermal cycling test. SEM images showed that the WPC prepared by the CA-PA /WF composite with the mass ratio of 0.4:1(WPC0.4) was more compact and uniform, but as the amount of CA-PA eutectic increased, more gaps and cracks appeared. DSC results showed that the WPC0.8 melted at 22.03 °C with a latent heat of 28.16 J/g and froze at 20.06 °C with a latent heat of 29.77 J/g, which was more applicable to the thermal energy storage systems for buildings. FTIR results demonstrated that there was no chemical reaction between CA-PA eutectic and components in WPC. The prepared FSPCM exhibited excellent thermal stability for the building materials from the TG analysis. Furthermore, the results of accelerated thermal cycling test showed that the composite had a good thermal reliability after 500 thermal cycles.

Abstract To investigate the effect of manufacturing method on thermal and flammable performance of CaCO3 impregnation modification (IM) treated bamboo pulp fiber (BPF) composites. The composite was made from HDPE with BPF contents of 30 wt% and 50 wt% by hot press molding (HPMP), extrusion molding (EMP) and injection molding process (IMP). The results showed that the E′, crystallinity index (CrI) and heat distortion temperature (HDT) of the composites manufactured by EMP were highest. IM had a slight positive effect on E’ and CrI of BPF/HDPE composites. The HDT of composites increased when the fiber content increased, leading to an increase in the heatproof property correspondingly. Compared to the samples manufactured by HPMP and EMP, the samples manufactured by IMP were extremely ignitable and flammable. Nano CaCO3 could improve the flame retardancy performance of samples manufactured by EMP effectively and the fiber content had an effect on the flame retardancy performance of samples, especially the samples treated by nano CaCO3. The average mass loss rate demonstrated a similar trend to the peak heat release rate for all composites. IM was more effective for EMP to improve the thermal stability and flame retardancy of composites compared with HPMP and IMP.