Until now, organic electrode materials have been extensively researched and are expected to emerge as a promising rechargeable battery material, with advantages of adjustable structure, and stable electrochemical performance. Herein, we describe the synthesis of tannic acid metal salt [lithium tannic acid (LiTA) and sodium tannic acid (NaTA)] through a facile and mild acid-base reaction methodology. The LiTA anode delivers a reversible capacity of 151.7 mAh g À 1 at 100 mA g À 1 and maintains 100.5 mAh g À 1 after 100 cycles. Correspondingly, the NaTA anode presents a capacity of 147.7 mAh g À 1 at 80 mA g À 1 and remains at 79.9 mAh g À 1 after 100 cycles. Both materials show good electrochemical behavior as demonstrated by EIS and GITT measured transport dynamics. Furthermore, the ion storage mechanism investigated by ex-situ FTIR and ex-situ XPS indicated that the redox pair changes between the C=O and CÀ O bonds. Therefore, the feasibility of tannic acid metal salt as a rechargeable battery anode provides a strategy to search for other natural organic compounds as promising energy storage materials.
The electrocatalysts for oxygen evolution reaction (OER) with efficiency and low-cost have become a critical subject for renewable energy technology. Owing to excellent catalytic activity, the inexpensive M–N–C (M = metal) catalysts are generally considered as the promising electroactive materials for the OER. Recently, research has led to great progress in the performance of M–N–C electrocatalysts. However, the large-scale commercial application of M–N–C catalysts is still limited by their complicated preparation process. Herein, we introduce a facile and efficient Ni-N-C electrocatalyst derived from superabsorbent resin. Benefiting from the strong coupling between the Ni and highly electronegative O and N elements for more available active sites, as well as from a large increase in defects that improve the electronic transmission, the optimized Ni–N–C catalyst exhibits excellent OER catalytic performance, including current densities of 10 mA cm−2 at an ultra-small overpotential of 214 mV and 20 mA cm−2 at 245 mV, a low Tafel slope (48 mV dec−1), and good stability. Compared with the commercial RuO2 catalyst, the Ni–N–C catalyst developed in this work exhibits good catalytic performance with greatly reduced cost. This work may provide a potential solution for Ni–N–C as an excellent OER catalyst in large-scale industrial water-splitting.
Intelligent manufacturing for the fabric dyeing industry requires high-performance dyeing recipe recommendation systems. Nowadays, recommending dyeing recipes by mining dyeing manufacturing data has become a new direction for the development of recipe recommendation systems. As one of the indispensable parts in the system development, data pre-processing needs more than routine steps such as the removal of missing data and outliers. Considering that dyes can have very different coloration properties on different fabrics, dyeing manufacturing records for a given dye combination to different fabric types should be properly categorized before they are used for training regression models for dyeing recipe prediction. In this paper, we propose a simple but effective method for this categorization work. Our method uses conventional K-means clustering analysis to find fabric types that have similar coloration properties for a given dye combination. We have applied the method on a dye combination formed by Colvaceton reactive dye-navy blue CF (CRD-navy blue), Colvaceton reactive dye-bright red 3BSN150% (CRD-red) and Colvaceton reactive dye-yellow 3RS150% (CRD-yellow) on 28 different types of fabrics. We show that these 28 types of fabrics can be well categorized into 8 groups based on the coloration properties. Our proposed method can be listed as one of the standard data pre-processing steps in the development of data-mining based recipe recommendation systems.
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