Single-crystalline Pd nanocrystals enclosed by {111} or {100} facets with controllable sizes were synthesized and originally employed as catalysts in the aerobic oxidation of 5-hydroxymethyl-2-furfural (HMF). The experimental results indicated that the particle size and exposed facet of Pd nanocrystals could obviously influence their catalytic performance. The size-dependent effect of Pd nanocrystals in this reaction could only be derived from the different Pd dispersions. Therefore, the facet effect of Pd nanocrystals was first investigated in this work through experimental and theoretical approaches. It was found that Pd-NOs enclosed by {111} facets were more efficient than Pd-NCs enclosed by {100} facets for the aerobic oxidation of HMF, especially for the oxidation step from 5-hydroxymethyl-2-furancarboxylic acid (HMFCA) toward 5-formyl-2-furancarboxylic acid (FFCA). The TOF value of Pd-NOs­(6 nm) was 2.6 times as high as that of Pd-NCs­(7 nm) and 5.2 times higher than that of commercial Pd/C catalyst for HMF oxidation. Through density functional theory (DFT) calculations, the notably enhanced catalytic performance of Pd-NOs could be mainly attributed to the lower energy barrier in the alcohol oxidation step (from HMFCA to FFCA) and higher selectivity for O2 hydrogenation to produce peroxide.

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Façade inspection is a regular but necessary maintenance task to ensure the safety, functioning, and aesthetics of a building. Traditional visual identification of façade defects is dangerous, time-consuming, and insufficient. Based on an image dataset and deep learning algorithms, an automatic façade defects classification technique is developed in this research. A layer-based categorization rule is proposed to categorize façade defects. To handle the problem of imbalanced data size among defect classes, a meta learning-based method is applied, which reassigns weights to the training data. Experiments demonstrated that the proposed method had a stronger capacity to deal with the imbalanced dataset problem comparing with previous methods by improving the classification accuracy from 71.43% of a basic convolutional neural network (CNN) model to 82.86% of a meta learning-based CNN model.

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