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A precursor‐engineering strategy coupled with a microwave molten‐salt process (PE‐MWMS) is developed to synthesize graphitic carbon nitride (g‐C3N4) with an isotype triazine/heptazine‐based g‐C3N4 heterojunction as a photocatalyst for the hydrogen evolution reaction (HER) under visible light illumination. Four hybrid precursor combinations—thiourea/melamine, thiourea/dicyandiamide, urea/melamine, and urea/dicyandiamide—are used to synthesize g‐C3N4 heterojunctions by the PE‐MWMS process. Control experiments indicate that the precursor components and microwave treatment have a great effect on the HER performance of the g‐C3N4 samples. Samples synthesized with the optimal molar ratios of thiourea/melamine (2:1), thiourea/dicyandiamide (2:1), urea/melamine (3:1), and urea/dicyandiamide (3:1), exhibit the highest HER rates of 3135, 2519, 2844, and 2565 μmol g−1 h−1, respectively. The amounts of heptazine and triazine units in the g‐C3N4 samples can be easily adjusted by changing the ratios of the hybrid precursors and play a decisive role in improving the photocatalytic HER activity. Because of the unique composition and microstructure, the efficient separation of electron–hole pairs, the broadened photo‐absorption edges, and the narrowed band gaps, the as‐obtained triazine/heptazine‐based g‐C3N4 nanostructures exhibit promising activity for HER application.

The purpose of this paper was to explore the utility of an electronic nose to detect the quality of litchi fruit stored in different environments. In this study, a PEN3 electronic nose was adopted to test the storage time and hardness of litchi that were stored in three different types of environment (room temperature, refrigerator and controlled-atmosphere). After acquiring data about the hardness of the sample and from the electronic nose, linear discriminant analysis (LDA), canonical correlation analysis (CCA), BP neural network (BPNN) and BP neural network-partial least squares regression (BPNN-PLSR), were employed for data processing. The experimental results showed that the hardness of litchi fruits stored in all three environments decreased during storage. The litchi stored at room temperature had the fastest rate of decrease in hardness, followed by those stored in a refrigerator environment and under a controlled-atmosphere. LDA has a poor ability to classify the storage time of the three environments in which litchi was stored. BPNN can effectively recognize the storage time of litchi stored in a refrigerator and a controlled-atmosphere environment. However, the BPNN classification of the effect of room temperature storage on litchi was poor. CCA results show a significant correlation between electronic nose data and hardness data under the room temperature, and the correlation is more obvious for those under the refrigerator environment and controlled-atmosphere environment. The BPNN-PLSR can effectively predict the hardness of litchi under refrigerator storage conditions and a controlled-atmosphere environment. However, the BPNN-PLSR prediction of the effect of room temperature storage on litchi and global environment storage on litchi were poor. Thus, this experiment proved that an electronic nose can detect the quality of litchi under refrigeratored storage and a controlled-atmosphere environment. These results provide a useful reference for future studies on nondestructive and intelligent monitoring of fruit quality.

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