262da4df4b0a71bf0e36999d11bbc822 Chem_a2022v1012p504.pdf 3da8cb506a3216271649ff0b635e08c7e2340200 Chem_a2022v1012p504.pdf 6be1561b4b829d38802e7231efb4402d4399867531983d3a5fa27bad2af0ea8c Chem_a2022v1012p504.pdf Title: Quantitative Estimation of COD Values from an Array of Metal Nanoparticle Modified Electrodes and Artificial Neural Networks "2279 Subject: Water quality monitoring has become critical in modern societies in multiple areas and at different stages. In this regard, chemical oxygen demand (COD) has become a key index in water testing, as it readily allows the determination of its overall quality and the presence of organic contaminants. However, conventional COD determination presents several drawbacks in view of the use of toxic reagents and possible interferences. The electrochemical determination of COD can be an alternative with many advantages, especially if using an array of sensors. Herein, the use of an electronic tongue (ET) for the estimation of COD was explored. The proposed ET was formed by an array of five voltammetric electrodes modified with different metal nanoparticles. An artificial neural network (ANN) model was built based on the responses of the array towards glucose and glycine as standards. This model was then used with real and spiked water samples, and the results compared to the electrochemical calibration and the commercial COD colorimetric methods. While the COD values of the real samples were low and outside the range of the ANN model, a satisfactory prediction for the spiked samples was achieved, showing a good agreement with the reference colorimetric method, that was better than the performance of the conventional electrochemical calibration method. Keywords: chemical oxygen demand; wastewater; metal nanoparticles; voltammetric sensors; artificial neural networks; electronic tongue Author: Qing Wang, Xavier Cetó and Manel del Valle Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Fri Dec 2 13:47:16 2022 CET ModDate: Fri Dec 2 13:51:42 2022 CET Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 16 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 5041258 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- SOZKOC+VnURWPalladioL-Bold Type 1 Custom yes yes no 10 0 VJAUGW+VnURWPalladioL Type 1 Custom yes yes no 15 0 AJAVSB+URWPalladioL-Bold Type 1 Custom yes yes no 19 0 TUPKRJ+URWPalladioL-Roma Type 1 Custom yes yes no 24 0 AMQIIO+URWPalladioL-Ital Type 1 Custom yes yes no 28 0 CFBZBS+CMSY10 Type 1 Builtin yes yes no 32 0 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AFIHNG+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 304 0 AFIHNI+PalatinoLinotype TrueType WinAnsi yes yes no 307 0 AFIHNK+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 310 0 AFIHNG+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 327 0 AFIHNI+PalatinoLinotype TrueType WinAnsi yes yes no 330 0 AFIHNK+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 333 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2023-11-01 02:32:32 CET RepresentationInformation: Chem_a2022v1012p504.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2023-10-31 18:43:29 CET Size: 5041258 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 498 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4d3167f4 Item: Title: Experimental Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@ed9d034 Children: Item: Title: Chemicals and Reagents Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@6121c9d6 Item: Title: Apparatus Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@87f383f Item: Title: Electrode Fabrication Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4eb7f003 Item: Title: Data Analysis Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@eafc191 Item: Title: Samples under Study Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@612fc6eb Item: Title: Results and Discussion Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1060b431 Children: Item: Title: Preparation of the CuO/Cu Electrode Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@612679d6 Item: Title: Voltammetric Response of the Electrodes Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@11758f2a Item: Title: Repeatability Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@e720b71 Item: Title: Interference of Chloride Ions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1b26f7b2 Item: Title: Quantitative Analysis of Real Samples Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@491cc5c9 Children: Item: Title: Calibration Curve Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@74ad1f1f Item: Title: Artificial Neural Network Method Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@6a1aab78 Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@462d5aee Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@69b0fd6f Info: Title: Quantitative Estimation of COD Values from an Array of Metal Nanoparticle Modified Electrodes and Artificial Neural Networks "2279 Author: Qing Wang, Xavier Cetó and Manel del Valle Subject: Water quality monitoring has become critical in modern societies in multiple areas and at different stages. In this regard, chemical oxygen demand (COD) has become a key index in water testing, as it readily allows the determination of its overall quality and the presence of organic contaminants. However, conventional COD determination presents several drawbacks in view of the use of toxic reagents and possible interferences. The electrochemical determination of COD can be an alternative with many advantages, especially if using an array of sensors. Herein, the use of an electronic tongue (ET) for the estimation of COD was explored. The proposed ET was formed by an array of five voltammetric electrodes modified with different metal nanoparticles. An artificial neural network (ANN) model was built based on the responses of the array towards glucose and glycine as standards. This model was then used with real and spiked water samples, and the results compared to the electrochemical calibration and the commercial COD colorimetric methods. While the COD values of the real samples were low and outside the range of the ANN model, a satisfactory prediction for the spiked samples was achieved, showing a good agreement with the reference colorimetric method, that was better than the performance of the conventional electrochemical calibration method. 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