Pub Date : 2018-09-01DOI: 10.29088/SAMI/AJCA.2018.2.711
M. Heravi, H. Oskooie, Z. Latifi, Hoda Hamidi
Herein a direct method for synthesis of tetracyanocyclopropanes by the treatment of carbonyl compounds, malononitrile and hexamethylenetetramine-bromine (HMTAB) is described in the presence of catalytic amount of DABCO in ethanol media at room temperature.
{"title":"One-Pot Synthesis of Tetracyanocyclopropane Derivatives Using Hexamethylenetetramine-Bromine (HMTAB)","authors":"M. Heravi, H. Oskooie, Z. Latifi, Hoda Hamidi","doi":"10.29088/SAMI/AJCA.2018.2.711","DOIUrl":"https://doi.org/10.29088/SAMI/AJCA.2018.2.711","url":null,"abstract":"Herein a direct method for synthesis of tetracyanocyclopropanes by the treatment of carbonyl compounds, malononitrile and hexamethylenetetramine-bromine (HMTAB) is described in the presence of catalytic amount of DABCO in ethanol media at room temperature.","PeriodicalId":7207,"journal":{"name":"Advanced Journal of Chemistry-Section A","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79343259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.29088/SAMI/AJCA.2018.3.1231
I. Amini, K. Pal, S. Esmaeilpoor, A. Abdelkarim
A quantitative structure–retention relation (QSRR) study was conducted on the retention times of 160 pesticides and 25 environmental organic pollutants in wine and grape. The genetic algorithm was used as descriptor selection and model development method. Modeling of the relationship between selected molecular descriptors and retention time was achieved by linear (partial least square; PLS) and nonlinear (kernel PLS: KPLS and Levenberg-Marquardt artificial neural network; L-M ANN) methods. The QSRR models were validated by cross-validation as well as application of the models to predict the retention of external set compounds, which did not have contribution in model development steps. Linear and nonlinear methods resulted in accurate prediction whereas more accurate results were obtained by L-M ANN model. The best model obtained from L-M ANN showed a good R2 value (determination coefficient between observed and predicted values) for all compounds, which was superior to those of other statistical models. This is the first research on the QSRR of the compounds in wine and grape against the retention time using the GA-KPLS and L-M ANN.
{"title":"Prediction of two-dimensional gas chromatography time-of-flight mass spectrometry retention times of 160 pesticides and 25 environmental organic pollutants in grape by multivariate chemometrics methods","authors":"I. Amini, K. Pal, S. Esmaeilpoor, A. Abdelkarim","doi":"10.29088/SAMI/AJCA.2018.3.1231","DOIUrl":"https://doi.org/10.29088/SAMI/AJCA.2018.3.1231","url":null,"abstract":"A quantitative structure–retention relation (QSRR) study was conducted on the retention times of 160 pesticides and 25 environmental organic pollutants in wine and grape. The genetic algorithm was used as descriptor selection and model development method. Modeling of the relationship between selected molecular descriptors and retention time was achieved by linear (partial least square; PLS) and nonlinear (kernel PLS: KPLS and Levenberg-Marquardt artificial neural network; L-M ANN) methods. The QSRR models were validated by cross-validation as well as application of the models to predict the retention of external set compounds, which did not have contribution in model development steps. Linear and nonlinear methods resulted in accurate prediction whereas more accurate results were obtained by L-M ANN model. The best model obtained from L-M ANN showed a good R2 value (determination coefficient between observed and predicted values) for all compounds, which was superior to those of other statistical models. This is the first research on the QSRR of the compounds in wine and grape against the retention time using the GA-KPLS and L-M ANN.","PeriodicalId":7207,"journal":{"name":"Advanced Journal of Chemistry-Section A","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82204347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.29088/SAMI/AJCA.2018.5.3955
J. Ghodsi, A. Rafati, Yalda Shoja
A new and easy to fabricate voltammetric biosensor for acetaminophen determination was developed based on horseradish peroxidase (HRP) trapped between silica sol-gel film and multi-walled carbon nanotubes on glassy carbon electrode. Acetaminophen determinations were carried out in presence of H2O2 as enzyme activator. The modified electrode showed excellent electrocatalytic activity and rapid response to acetaminophen in the presence of H2O2 as enzyme activator. Various parameters influencing the biosensor performance such as amount of enzyme, H2O2 concentration, potential scan rate and pH have been investigated. Under the optimal conditions, a wide linear range of 1.85×10−6 to 2.7×10−3 M for acetaminophen determination was obtained. Limit of detection was calculated about 18 nM and sensitivity was about 220 nA/µM. Furthermore, the proposed biosensor was successfully examined for simultaneous determination of acetaminophen with uric acid (UA) and folic acid (FA) as prevalent interferes. The proposed biosensor showed satisfactory stability for 3 weeks and applicability of developed biosensor was confirmed with accurately evaluation of acetaminophen in real samples such as urine and tablet.
{"title":"Determination of acetaminophen using a glassy carbon electrode modified by horseradish peroxidase trapped in MWCNTs/silica sol-gel matrix","authors":"J. Ghodsi, A. Rafati, Yalda Shoja","doi":"10.29088/SAMI/AJCA.2018.5.3955","DOIUrl":"https://doi.org/10.29088/SAMI/AJCA.2018.5.3955","url":null,"abstract":"A new and easy to fabricate voltammetric biosensor for acetaminophen determination was developed based on horseradish peroxidase (HRP) trapped between silica sol-gel film and multi-walled carbon nanotubes on glassy carbon electrode. Acetaminophen determinations were carried out in presence of H2O2 as enzyme activator. The modified electrode showed excellent electrocatalytic activity and rapid response to acetaminophen in the presence of H2O2 as enzyme activator. Various parameters influencing the biosensor performance such as amount of enzyme, H2O2 concentration, potential scan rate and pH have been investigated. Under the optimal conditions, a wide linear range of 1.85×10−6 to 2.7×10−3 M for acetaminophen determination was obtained. Limit of detection was calculated about 18 nM and sensitivity was about 220 nA/µM. Furthermore, the proposed biosensor was successfully examined for simultaneous determination of acetaminophen with uric acid (UA) and folic acid (FA) as prevalent interferes. The proposed biosensor showed satisfactory stability for 3 weeks and applicability of developed biosensor was confirmed with accurately evaluation of acetaminophen in real samples such as urine and tablet.","PeriodicalId":7207,"journal":{"name":"Advanced Journal of Chemistry-Section A","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90918712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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