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Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning. In the application of high-throughput thickness as a downstream process of the high-throughput optimization of optoelectronic thin films with autonomous workflows, data scarcity occurs especially for new materials. To achieve high-throughput thickness characterization, we propose a machine learning model called thicknessML that predicts thickness from UV-Vis spectrophotometry input and an overarching transfer learning workflow. We demonstrate the transfer learning workflow from generic source domain of generic band-gapped materials to specific target domain of perovskite materials, where the target domain data only come from limited number (18) of refractive indices from literature. The target domain can be easily extended to other material classes with a few literature data. Defining thickness prediction accuracy to be within-10% deviation, thicknessML achieves 92.2% (with a deviation of 3.6%) accuracy with transfer learning compared to 81.8% (with a deviation of 3.6%) 11.7% without (lower mean and larger standard deviation). Experimental validation on six deposited perovskite films also corroborates the efficacy of the proposed workflow by yielding a 10.5% mean absolute percentage error (MAPE).

A air quality monitoring system based on the NB-IoT was designed for wireless monitoring of special field environment. The STM32FI03RCT6 microprocessor unit was the core processor and the data of gas pollutants was collected by electrochemical gas sensor through serial port. The switching mechanism of wireless network and the data storage mechanism of disconnected network was designed. The network communication based on the TCP/IP protocol was realized through NB-IoT module or GPRS module. The experimental results show that the data transmission of NB-IoT network is stable. The system can automatically switch to GPRS network as the NB-IoT network is abnormal. In the absence of network, the data is stored in SPI Flash. The collected data can be reloaded as the disconnected network is recovered.