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.

Authors

Institutions

Publication Details

Journal
Journal of Visualized Experiments
Published
2025-09-19
DOI
https://doi.org/10.3791/68767
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Ravula Rajasekhar, Tapas Kumar Mandal, Shrikant Kashyap, Neha Majee
Journal of Visualized Experiments
Advanced Chemical Sensor Technologies
article

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

Ravula Rajasekhar, Tapas Kumar Mandal, Shrikant Kashyap, Neha Majee
article en

Abstract

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.

Journal of Visualized Experiments(223)
Indian Institute of Technology Guwahati (IN), Korean Council for University Education (KR)
Openalex Percentile: Top 76%
Advanced Chemical Sensor Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.