Metal Oxide Nanostructure-Based Gas Sensor for Carbon Dioxide Detection

To increase the sensitivity and efficiency of a gas sensor, nanostructured ZnO and Co3O4 layers were obtained by hydrothermal synthesis directly on the electrode surface, eliminating the use of binders. Scanning electron microscope images showed that the resulting nanostructured coatings were characterised by good adhesion to the surface and high porosity, which opened up the possibility of their further use in the process of developing a gas sensor. The efficiency of the obtained nanostructured coatings and their sensitivity at room temperature to various concentrations of CO2 were determined. The resistance curves of the samples were obtained as a function of gas concentration in the chamber, for Co3O4 and ZnO nanostructures.

PEI-Functionalized Carbon Nanotube Thin Film Sensor for CO2 Gas Detection at Room Temperature

In this study, a polyethyleneimine (PEI)-functionalized carbon nanotube (CNT) sensor was fabricated for carbon dioxide detection at room temperature. Uniform CNT thin films prepared using a filtration method were used as resistive networks. PEI, which contains amino groups, can effectively react with CO2 gas by forming carbamates at room temperatures. The morphology of the sensor was observed, and the properties were analyzed by scanning electron microscope (SEM), Raman spectroscopy, and fourier transform infrared (FT-IR) spectroscopy. When exposed to CO2 gas, the fabricated sensor exhibited better sensitivity than the pristine CNT sensor at room temperature. Both the repeatability and selectivity of the sensor were studied.

Research on dark channel dehazing of single-image based on non-dispersive infrared (NDIR) detection technology

This paper presents an experimental study on the non-dispersive infrared (NDIR) detection technology and dark channel dehazing technology. Based on the analysis of Beer-Lambert Law and differential carbon dioxide detection principle, this paper proposes an atmospheric light value estimation algorithm based on NDIR detection technology. First, the change characteristics of the gas concentration in indoor smoky environment are collected and analyzed. Then appropriate weighting coefficients are chosen based on the gas characteristics to estimate the atmospheric light value. Finally, the digital image dehazing technology through dark channel prior is used for calculation to obtain a haze-free image with high quality and high resolution. The experiment in this paper proves the feasibility of combining NDIR detection technology with dehazing technology, and its ability to improve image quality and achieve better restoration effect.

Performance Improvement of Spaceborne Carbon Dioxide Detection IPDA LIDAR Using Optimization of Optical Detector and Amplifier Linearity

The spaceborne double-pulse integrated-path differential absorption (IPDA) light detection and ranging (LIDAR) system was found to be helpful in observing atmospheric CO2 and understanding the carbon cycle. The airborne experiments of a scale prototype of China’s planned spaceborne IPDA LIDAR was implemented in 2019. A problem with data inversion caused by the detector module nonlinearity was found. Through many experiments, the amplifier circuit board (ACB) of the detector module was proved to be the main factor causing the nonlinearity. Through amplifier circuit optimization, the original bandwidth of the ACB was changed to 1 MHz by using a fifth-order active filter. Compared with the original version, the linearity of optimized ACB is improved from 42.6% to 0.0747%. The optimized ACB was produced and its linearity was verified by experiments. In addition, the output waveform of the optimized ACB changes significantly, which will affect the random error (RE) of the optimized IPDA LIDAR system. Through the performance simulation, the RE of more than 90% of the global area is less than 0.728 ppm. Finally, the transfer model of the detector module was given, which will be helpful for the further optimization of the CO2 column-averaged dry-air mixing ratio (XCO2) inversion algorithm.