In2TiO5, containing both early transition metal (d(0)) and p-block metal (d(10)), is a very promising candidate for possible application in H2 production because of its suitable edges of conduction and valence bands and the crystal structure, which is considered to favor mobility of charge carriers. Herein we report, for the first time, the synthesis of novel oxygen vacancies (OV), N-doped In2TiO5 (OV,N-In2TiO5) with controllable band gap. The resultant OV,N-In2TiO5 sample was prepared by a multistep sol-gel calcination process and studied as a near-infrared (NIR) light-driven photocatalyst for H2 production. OV and N-doping can effectively extend the photoresponse of In2TiO5 to the NIR region due to an interband springboard and the reduced band gap, thus leading to efficient NIR light photocatalytic H2 production activity with Pt as a cocatalyst.

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Precise reconstruction of the morphological structure of the soybean canopy and acquisition of plant traits have great theoretical significance and practical value for soybean variety selection, scientific cultivation, and fine management. Since it is difficult to obtain all-around information on living plants with traditional single or binocular machine vision, this paper proposes a three-dimensional (3D) method of reconstructing the soybean canopy for calculation of phenotypic traits based on multivision. First, a multivision acquisition system based on the Kinect sensor was constructed to obtain all-around point cloud data of soybean in three viewpoints, with different fertility stages of soybean as the research object. Second, conditional filtering and K-nearest neighbor filtering (KNN) algorithms were used to preprocess the raw 3D point cloud. The point clouds were matched and fused by the random sample consensus (RANSAC) and iterative closest point (ICP) algorithms to accomplish the 3D reconstruction of the soybean canopy. Finally, the plant height, leafstalk angle and crown width of soybean were calculated based on the 3D reconstruction of soybean canopy. The experimental results showed that the average deviations of the method was 2.84 cm, 4.0866° and 0.0213 m, respectively. The determination coefficients between the calculated values and measured values were 0.984, 0.9195 and 0.9235. The average deviation of the RANSAC + ICP was 0.0323, which was 0.0214 lower thanthe value calculated by the ICP algorithm. The results enable the precise 3D reconstruction of living soybean plants and quantitative detection for phenotypic traits.

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