The inconsistent response curve of delicate micro/nanofiber (MNF) sensors during cycling measurement is one of the main factors which greatly limit their practical application. In this paper, we proposed a temperature sensor based on the copper rod-supported helical microfiber (HMF). The HMF sensors exhibited different light intensity-temperature response relationships in single-cycle measurements. Two neural networks, the deep belief network (DBN) and the backpropagation neural network (BPNN), were employed respectively to predict the temperature of the HMF sensor in different sensing processes. The input variables of the network were the sensor geometric parameters (the microfiber diameter, wrapped length, coiled turns, and helical angle) and the output optical intensity under different working processes. The root mean square error (RMSE) and Pearson correlation coefficient (R) were used to evaluate the predictive ability of the networks. The DBN with two restricted Boltzmann machines (RBMs) provided the best temperature prediction results (RMSE and R of the heating process are 0.9705 °C and 0.9969, while the values of RMSE and R of the cooling process are 0.786 6 °C and 0.997 7, respectively). The prediction results obtained by the optimal BPNN (five hidden layers, 10 neurons in each layer, RMSE=1.126 6 °C, R=0.995 7) were slightly inferior to those obtained by the DBN. The neural network could accurately and reliably predict the response of the HMF sensor in cycling operation, which provided the possibility for the flexible application of the complex MNF sensor in a wide sensing range.

We demonstrate visualized microwire sensors based on fluorescence indication for detecting the concentrations of the aqueous solutions. The single Rhodamine (RhB) doped polymer microwires (PMWs) which are excited by the waveguiding excitation method are used as the sensory area. According to the fluorescent microimages of the PMWs, stable periodic oscillations could be observed in the RhB-doped PMWs. The fluorescent period which is dependent on the concentration is further analyzed by image processing and information extraction algorithms. Corresponding to a 1.0% change, the period length change of the visualized sensor reaches ∼380 nm, ∼270 nm, and ∼300 nm in NaCl, KCl, and sucrose solutions, respectively. The dection limits of the three solutions are estimated to be around 1.5 × 10−4%. The dye-doped PMW sensors by fluorescence indication and image analysis proposed here realize the direct visualized detection in concentration sensing, making it possible to avoid the challenges of stability and weak signal detection and offer a potentially stable and cost-effective approach for micro/nanofiber sensor application.

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