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Nitrogen-doped carbon nanorods (N-CNRs) are prepared by a direct carbonization method using polyaniline nanorods as the carbon precursor. The electrochemical behavior of the N-CNRs-Nafion modified electrode is evaluated in connection with dopamine and ascorbic acid by cyclic voltammetry and differential pulse voltammetry. Experimental results indicate that the N-CNRs modified electrode has improved current response and fast electron transfer kinetics. The linear response for the selective determination of dopamine in the presence of ascorbic acid is obtained in the range of 0.008 mM to 15.0 mM with a detection limit of 8.9 6 10 29 M (S/N = 3) by differential pulse voltammetry under optimum conditions. The N-CNRs-Nafion modified electrode exhibits a wide linear range, very low detection limit and anti-interference ability. Meanwhile, a kinetic reaction process and a reaction mechanism for the N-CNRs are also proposed.

Machined surfaces are rough from a microscopic perspective no matter how finely they are finished. Surface roughness is an important factor to consider during production quality control. Using modern techniques, surface roughness measurements are beneficial for improving machining quality. With optical imaging of machined surfaces as input, a convolutional neural network (CNN) can be utilized as an effective way to characterize hierarchical features without prior knowledge. In this paper, a novel method based on CNN is proposed for making intelligent surface roughness identifications. The technical scheme incorporates there elements: texture skew correction, image filtering, and intelligent neural network learning. Firstly, a texture skew correction algorithm, based on an improved Sobel operator and Hough transform, is applied such that surface texture directions can be adjusted. Secondly, two-dimensional (2D) dual tree complex wavelet transform (DTCWT) is employed to retrieve surface topology information, which is more effective for feature classifications. In addition, residual network (ResNet) is utilized to ensure automatic recognition of the filtered texture features. The proposed method has verified its feasibility as well as its effectiveness in actual surface roughness estimation experiments using the material of spheroidal graphite cast iron 500-7 in an agricultural machinery manufacturing company. Testing results demonstrate the proposed method has achieved high-precision surface roughness estimation.