| Autor: |
Zhang Z; State Key Laboratory of Bio-Fibers and Eco-Textiles, College of Materials Science and Engineering, Qingdao University, Qingdao 266071, P. R. China., Liang J; State Key Laboratory of Bio-Fibers and Eco-Textiles, College of Materials Science and Engineering, Qingdao University, Qingdao 266071, P. R. China., Liu K; State Key Laboratory of Bio-Fibers and Eco-Textiles, College of Materials Science and Engineering, Qingdao University, Qingdao 266071, P. R. China., Tian W; Key Laboratory of Chemical Engineering in South Xinjiang, College of Chemistry and Chemical Engineering, Tarim University, Alar 843300, P. R. China., Liang X; State Key Laboratory of Bio-Fibers and Eco-Textiles, College of Materials Science and Engineering, Qingdao University, Qingdao 266071, P. R. China., Zhao K; State Key Laboratory of Advanced Processing and Recycling of Nonferrous Metals, Lanzhou University of Technology, Lanzhou 730050, P. R. China., Zhang K; State Key Laboratory of Bio-Fibers and Eco-Textiles, College of Materials Science and Engineering, Qingdao University, Qingdao 266071, P. R. China.; Key Laboratory of Chemical Engineering in South Xinjiang, College of Chemistry and Chemical Engineering, Tarim University, Alar 843300, P. R. China. |
| Abstrakt: |
Reliable and real-time monitoring of seafood decay is attracting growing interest for food safety and human health, while it is still a great challenge to accurately identify the released triethylamine (TEA) from the complex volatilome. Herein, defect-engineered WO 3- x architectures are presented to design advanced TEA sensors for seafood quality assessment. Benefiting from abundant oxygen vacancies, the obtained WO 2.91 sensor exhibits remarkable TEA-sensing performance in terms of higher response (1.9 times), faster response time (2.1 times), lower detection limit (3.2 times), and higher TEA/NH 3 selectivity (2.8 times) compared with the air-annealed WO 2.96 sensor. Furthermore, the definite WO 2.91 sensor demonstrates long-term stability and anti-interference in complex gases, enabling the accurate recognition of TEA during halibut decay (0-48 h). Coupled with the random forest algorithm with 70 estimators, the WO 2.91 sensor enables accurate prediction of halibut storage with an accuracy of 95%. This work not only provides deep insights into improving gas-sensing performance by defect engineering but also offers a rational solution for reliably assessing seafood quality. |
