Shrikant Kashyap, Rajasekhar Ravula, Neha Majee, Tapas K Mandal
{"title":"TD-DFT引导的先进电子眼传感技术,用于现场定量环境、生物和食品样品中的铁、铬、氟和砷。","authors":"Shrikant Kashyap, Rajasekhar Ravula, Neha Majee, Tapas K Mandal","doi":"10.3791/68767","DOIUrl":null,"url":null,"abstract":"<p><p>This paper demonstrates the step-by-step protocol of designing and developing an E-Eye-enabled multiplexed point-of-care testing (POCT) kit. We use time-dependent density function theory (TD-DFT) to explore the basic working principle of the sensors before going into the details of the device's hardware. The TD-DFT analysis gives the λmax of the targeted element, which helps find the most accurate route for detecting the pollutant in the analytes. The TD-DFT analysis is performed in Gaussian 09 and Gauss View 5.0 software. An optical sensor called the electronic eye (E-Eye) has been developed based on the λmax value and the Light Emitting Diode/Light Dependent Resistor (LED/LDR) principle. The E-Eye device has been fabricated with an LED, an LDR placed opposite each other, and other hardware, including a liquid crystal display (LCD) interfaced with a microprocessor across a voltage divider, acting as a central microprocessor. Initially, all the samples (environmental, biological, and food and beverages) were pre-processed to extract all the targeted elements in the aqueous phase. The specific (lock and key) reaction has been carried out in the specified reactor, and readings observed in the display unit have been recorded. An indigenously multiplexed device has also been fabricated to detect Fe, Cr, As, and F simultaneously. The efficacy of the sensors has been tested against more than 2000 samples and compared with the gold standard methods. A good precision has been confirmed with the accuracy of 95.3%, 94.7% and 95.4% with respect to the samples of environmental, biological, and food and beverages. It is important to note that the performance of the arsenic sensor has been compared with Atomic Absorption Spectrometry (AAS) results, and all other results, i.e., sensors for iron, chromium, and fluoride, have been compared with the results obtained from UV-Vis analysis.</p>","PeriodicalId":48787,"journal":{"name":"Jove-Journal of Visualized Experiments","volume":" 223","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"TD-DFT Guided Advanced E-Eye Sensing Technique for On-site Quantification of Fe, Cr, F, and As in the Environmental, Biological, and Food Samples.\",\"authors\":\"Shrikant Kashyap, Rajasekhar Ravula, Neha Majee, Tapas K Mandal\",\"doi\":\"10.3791/68767\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This paper demonstrates the step-by-step protocol of designing and developing an E-Eye-enabled multiplexed point-of-care testing (POCT) kit. We use time-dependent density function theory (TD-DFT) to explore the basic working principle of the sensors before going into the details of the device's hardware. The TD-DFT analysis gives the λmax of the targeted element, which helps find the most accurate route for detecting the pollutant in the analytes. The TD-DFT analysis is performed in Gaussian 09 and Gauss View 5.0 software. An optical sensor called the electronic eye (E-Eye) has been developed based on the λmax value and the Light Emitting Diode/Light Dependent Resistor (LED/LDR) principle. The E-Eye device has been fabricated with an LED, an LDR placed opposite each other, and other hardware, including a liquid crystal display (LCD) interfaced with a microprocessor across a voltage divider, acting as a central microprocessor. Initially, all the samples (environmental, biological, and food and beverages) were pre-processed to extract all the targeted elements in the aqueous phase. The specific (lock and key) reaction has been carried out in the specified reactor, and readings observed in the display unit have been recorded. An indigenously multiplexed device has also been fabricated to detect Fe, Cr, As, and F simultaneously. The efficacy of the sensors has been tested against more than 2000 samples and compared with the gold standard methods. A good precision has been confirmed with the accuracy of 95.3%, 94.7% and 95.4% with respect to the samples of environmental, biological, and food and beverages. It is important to note that the performance of the arsenic sensor has been compared with Atomic Absorption Spectrometry (AAS) results, and all other results, i.e., sensors for iron, chromium, and fluoride, have been compared with the results obtained from UV-Vis analysis.</p>\",\"PeriodicalId\":48787,\"journal\":{\"name\":\"Jove-Journal of Visualized Experiments\",\"volume\":\" 223\",\"pages\":\"\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2025-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jove-Journal of Visualized Experiments\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.3791/68767\",\"RegionNum\":4,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jove-Journal of Visualized Experiments","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.3791/68767","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
TD-DFT Guided Advanced E-Eye Sensing Technique for On-site Quantification of Fe, Cr, F, and As in the Environmental, Biological, and Food Samples.
This paper demonstrates the step-by-step protocol of designing and developing an E-Eye-enabled multiplexed point-of-care testing (POCT) kit. We use time-dependent density function theory (TD-DFT) to explore the basic working principle of the sensors before going into the details of the device's hardware. The TD-DFT analysis gives the λmax of the targeted element, which helps find the most accurate route for detecting the pollutant in the analytes. The TD-DFT analysis is performed in Gaussian 09 and Gauss View 5.0 software. An optical sensor called the electronic eye (E-Eye) has been developed based on the λmax value and the Light Emitting Diode/Light Dependent Resistor (LED/LDR) principle. The E-Eye device has been fabricated with an LED, an LDR placed opposite each other, and other hardware, including a liquid crystal display (LCD) interfaced with a microprocessor across a voltage divider, acting as a central microprocessor. Initially, all the samples (environmental, biological, and food and beverages) were pre-processed to extract all the targeted elements in the aqueous phase. The specific (lock and key) reaction has been carried out in the specified reactor, and readings observed in the display unit have been recorded. An indigenously multiplexed device has also been fabricated to detect Fe, Cr, As, and F simultaneously. The efficacy of the sensors has been tested against more than 2000 samples and compared with the gold standard methods. A good precision has been confirmed with the accuracy of 95.3%, 94.7% and 95.4% with respect to the samples of environmental, biological, and food and beverages. It is important to note that the performance of the arsenic sensor has been compared with Atomic Absorption Spectrometry (AAS) results, and all other results, i.e., sensors for iron, chromium, and fluoride, have been compared with the results obtained from UV-Vis analysis.
期刊介绍:
JoVE, the Journal of Visualized Experiments, is the world''s first peer reviewed scientific video journal. Established in 2006, JoVE is devoted to publishing scientific research in a visual format to help researchers overcome two of the biggest challenges facing the scientific research community today; poor reproducibility and the time and labor intensive nature of learning new experimental techniques.