Hydrogen (H2) has gradually become a substitute for traditional energy, but its potential danger cannot be ignored. In this study, litchi-like g-C3N4/In2O3composites were synthesized by a hydrothermal method and used to develop H2sensors. The morphology characteristics and chemical composition of the samples were characterized to analyze the gas-sensing properties. Meanwhile, a series of sensors were tested to evaluate the gas-sensing performance. Among these sensors, the sensor based on the 3 wt% g-C3N4/In2O3(the mass ratio of g-C3N4to In2O3is 3:100) showeds good response properties to H2, exhibiting fast response/recovery time and excellent selectivity to H2. The improvement in the gas-sensing performance may be related to the special morphology, the oxygen state and the g-C3N4/In2O3heterojunction. To sum up, a sensor based on 3 wt% g-C3N4/In2O3exhibits preeminent performance for H2with high sensitivity, fast response, and excellent selectivity.Keywords:g-C3N4/In2O3;litchi-like;H2sensor;fast response;high selectivity
Xiaohang Yang,Weilun Yu,Wensi Wang,Da Wang,Qiyao Wang,Xuyang Huo
A strategy of tuning the size of In 2 O 3 nanocube was employed to change the surface property of g–C 3 N 4 –In 2 O 3 heterojunction to boost its photocatalytic H 2 production. In the type II g–C 3 N 4 –In 2 O 3 heterojunction, the change on the size of In 2 O 3 nanocube (30, 60 and 120 nm) could regulate visible light utilization ability and photoelectric conversion efficiency of photogenerated charge carriers. The directional transfer of charge carriers caused the accumulation of photoexcited electron in plasmonic In 2 O 3 and localized electric field enhancement. The intensity and spatial distribution of localized electric field could be tuned by the adjustable size of In 2 O 3 nanocube. Therefore, the obtained g–C 3 N 4 –In 2 O 3 heterojunction with an optimal size of In 2 O 3 nanocube (60 nm) exhibited supreme photocatalytic H 2 evolution rate of 3.1 mmol h −1 g −1 with a high apparent quantum efficiency of 5.27%, which was 7.5 times higher than that of bare g-C 3 N 4 nanosheet (0.4 mmol h −1 g −1 ).
The demand for gas sensors is experiencing rapid growth, driven by the increasing need for air quality detection in the face of environmental pollution and industrial emissions. However, the widely used metal oxide semiconductor (MOS) gas sensors suffer from drawbacks such as low ability to predict gas concentration and poor selectivity . In this work, a sensor array was fabricated consisting of 10 various gas sensors and combined with an advanced deep learning algorithm to achieve precise predictions of nitrogen dioxide (NO 2 ) at low concentrations. First, graphitic carbon nitride (g-C 3 N 4 ) and In 2 O 3 nanoparticles were composited with different mass ratios, resulting in the formation of distinct g-C 3 N 4 /In 2 O 3 composites as sensing materials. Afterward, SEM, TEM , and XRD were employed to characterize the morphology, structure, and elemental composition of the samples. Furthermore, sensing properties including response, selectivity , and repeatability, are studied for 10 gas sensors based on g-C 3 N 4 /In 2 O 3 composites. Finally, the convolutional neural network-efficient channel attention-gate recursive unit (CNN-AGRU) model was proposed to analyze the patterns of the signals collected from the sensor array when exposed to various NO 2 concentrations from 1 to 9 ppm. The results demonstrated the integration of high-selectivity sensing materials to NO 2 and an advanced deep learning algorithm CNN-AGRU enables to realize a high concentration prediction accuracy of about 97.04% and a low prediction error of 0.20527 ppm for NO 2 gas.
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