The first electrochemical evaluation and voltammetric detection of the insecticide emamectin benzoate using an unmodified boron-doped diamond electrode

McCormick, Wesley; Robertson, Peter K. J.; Skillen, Nathan; McCrudden, Denis

doi:10.1016/j.rechem.2023.100865

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…22,34,35 3-and 4-hydroxycoumarin, if formed, are below the detection limit, which was also reported for photolysis and radiolysis of coumarin aqueous solutions, probably due to unfavorable electronic configuration on the lactone ring. 22,24,33 Based on the results in Fig. 2, it can be concluded that the same hydroxycoumarin isomers are formed and their quantity increases with the irradiation time, attesting for a growing production of HO with irradiation time and/or dose.…”

Section: Detection and Quantification Of Hydroxyl Radicalsmentioning

confidence: 83%

“…Among them, only 7-hydroxycoumarin (7-OHCou) possesses a high fluorescence yield, 22 allowing an indirect but sensitive detection of HO by a simple fluorescence assay. Considering the numerous advantages of coumarin as a probe of HO , it was applied for a large variety of systems such as exposition to ionizing radiation, 20,23 UV photolysis, 24,25 plasma irradiation, 7,26 sonication, 27 Fenton reaction 28 and also to biological hydroxylating systems. 29 In the case of LTP irradiation, coumarin was already used as a HO probe in only a few studies.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying hydroxyl radicals generated by a low-temperature plasma using coumarin: methodology and precautions

Ducrozet,

Sebastian,

Garcia Villavicencio

et al. 2024

Phys. Chem. Chem. Phys.

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Section: Detection and Quantification Of Hydroxyl Radicalsmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Quantifying hydroxyl radicals generated by a low-temperature plasma using coumarin: methodology and precautions

Ducrozet,

Sebastian,

Garcia Villavicencio

et al. 2024

Phys. Chem. Chem. Phys.

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“…Additionally, a number of experimental factors, including as the dosage of the photocatalyst, the medium's pH, the concentration of contaminants, the wavelength and intensity of the light, and others, have an important impact on the photocatalytic activity [24][25][26] . The conventional experimental methods for assessment of photocatalytic activity are UV-Vis spectroscopy [27][28][29] , gas chromatography analysis 30,31 , chemical oxygen demand 29,32 , fluorescence spectroscopy 33 , electrochemical methods 34 . These methods of experimentation require longer reaction times to get measurable contaminant degradation, which causes delays in the collection of results.…”

mentioning

confidence: 99%

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Javed,

Shahab,

Asif

et al. 2024

Sci Rep

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The escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO2) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO2 photocatalytic degradation rate of air contaminants. The models are developed using literature data and different methodical tools are used to evaluate the developed ML models. XGB, DT and LR2 models have high R2 values of 0.93, 0.926 and 0.926 in training and 0.936, 0.924 and 0.924 in test phase. While ANN, RR and LR models have lowest R2 values of 0.70, 0.56 and 0.40 in training and 0.62, 0.63 and 0.31 in test phase respectively. XGB, DT and LR2 have low MAE and RMSE values of 0.450 min-1/cm2, 0.494 min-1/cm2 and 0.49 min-1/cm2 for RMSE and 0.263 min-1/cm2, 0.285 min-1/cm2 and 0.29 min-1/cm2 for MAE in test stage. XGB, DT, and LR2 have 93% percent errors within 20% error range in training phase. XGB has 92% and DT, and LR2 have 94% errors with 20% range in test phase. XGB, DT, LR2 models remained the highest performing models and XGB is the most robust and effective in predictions. Feature importances reveal the role of input parameters in prediction made by developed ML models. Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively in providing efficient models to estimate photocatalytic degradation rate of air contaminants using TiO2.

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Nanodiamond-Based Materials for Biomedical Sensors and Drug Delivery

Jiwanti,

Dewi,

Wardhana

2024

Nanomaterials for Biomedical and Bioengineering Applications

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The first electrochemical evaluation and voltammetric detection of the insecticide emamectin benzoate using an unmodified boron-doped diamond electrode

Cited by 6 publications

References 39 publications

Quantifying hydroxyl radicals generated by a low-temperature plasma using coumarin: methodology and precautions

Quantifying hydroxyl radicals generated by a low-temperature plasma using coumarin: methodology and precautions

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Nanodiamond-Based Materials for Biomedical Sensors and Drug Delivery

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