Modeling and random search optimization for the polysilicon CVD reactor; Results in Control and Optimization; Vol. 13
| Parent link: | Results in Control and Optimization.— .— Amsterdam: Elsevier Science Publishing Company Inc. Vol. 13.— 2023.— Article number 100320, 10 p. |
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| Ente Autore: | |
| Altri autori: | , , , , , , , |
| Riassunto: | Title screen Growing concerns about pesticide residues in agriculture are pushing the scientific community to develop innovative and efficient methods for detecting these substances at low concentrations down to the molecular level. In this context, surface-enhanced Raman spectroscopy (SERS) is a powerful analytical method that has so far already undergone some validation for its effectiveness in pesticide detection. However, despite its great potential, SERS faces significant difficulties obtaining reproducible and accurate pesticide spectra, particularly for some of the most widely used pesticides, such as malathion, chlorpyrifos, and imidacloprid. Those inconsistencies can be attributed to several factors, such as interactions between pesticides and SERS substrates and the variety of substrates and solvents used. In addition, differences in the equipment used to obtain SERS spectra and the lack of standards for control experiments further complicate the reproducibility and reliability of SERS data. This review systematically discusses the problems mentioned above, including a comprehensive analysis of the challenges in precisely evaluating SERS spectra for pesticide detection. We not only point out the existing limitations of the method, which can be traced in previous review works, but also offer practical recommendations to improve the quality and comparability of SERS spectra, thereby expanding the potential applications of the method in such an essential field as pesticide detection. Текстовый файл AM_Agreement |
| Lingua: | inglese |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://doi.org/10.1016/j.rico.2023.100320 |
| Natura: | Elettronico Capitolo di libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=673562 |
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| 200 | 1 | |a Modeling and random search optimization for the polysilicon CVD reactor |f Bangwen Xi, Gang Xiong, Kirill A. Kozin [et al.] | |
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| 320 | |a References: 25 tit. | ||
| 330 | |a Growing concerns about pesticide residues in agriculture are pushing the scientific community to develop innovative and efficient methods for detecting these substances at low concentrations down to the molecular level. In this context, surface-enhanced Raman spectroscopy (SERS) is a powerful analytical method that has so far already undergone some validation for its effectiveness in pesticide detection. However, despite its great potential, SERS faces significant difficulties obtaining reproducible and accurate pesticide spectra, particularly for some of the most widely used pesticides, such as malathion, chlorpyrifos, and imidacloprid. Those inconsistencies can be attributed to several factors, such as interactions between pesticides and SERS substrates and the variety of substrates and solvents used. In addition, differences in the equipment used to obtain SERS spectra and the lack of standards for control experiments further complicate the reproducibility and reliability of SERS data. This review systematically discusses the problems mentioned above, including a comprehensive analysis of the challenges in precisely evaluating SERS spectra for pesticide detection. We not only point out the existing limitations of the method, which can be traced in previous review works, but also offer practical recommendations to improve the quality and comparability of SERS spectra, thereby expanding the potential applications of the method in such an essential field as pesticide detection. | ||
| 336 | |a Текстовый файл | ||
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| 461 | 1 | |t Results in Control and Optimization |c Amsterdam |n Elsevier Science Publishing Company Inc. | |
| 463 | 1 | |t Vol. 13 |v Article number 100320, 10 p. |d 2023 | |
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| 610 | 1 | |a CVD reactor | |
| 610 | 1 | |a Simulator | |
| 610 | 1 | |a Random search | |
| 610 | 1 | |a Nonlinear regression fitting | |
| 701 | 0 | |a Xi Bangwen | |
| 701 | 0 | |a Xiong Gang | |
| 701 | 1 | |a Kozin |b K. A. |c specialist in the field of automation and electronics |c Associate Professor of Tomsk Polytechnic University, Candidate of technical sciences |f 1980- |g Kirill Andreevich |9 17363 | |
| 701 | 0 | |a He Chang | |
| 701 | 1 | |a Tamir |b T. S. |g Tariku Sinshaw | |
| 701 | 0 | |a Song Yonggang | |
| 701 | 0 | |a Liu Xiong | |
| 701 | 0 | |a Shen Zhen | |
| 712 | 0 | 2 | |a Томский политехнический университет |c 1991- |9 26305 |4 570 |
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