Preparation and characterization of a novel Co(II) optode based on polymer inclusion membrane

Suah, Faiz Bukhari Mohd

doi:10.1016/j.ancr.2017.02.001

Cited by 29 publications

(8 citation statements)

References 51 publications

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“…U-Net is a well-known structure in segmenting medical images. This network was provided by Ronneberger et al [30] based on the deconvolution concept presented by [31]. FCN inspires the U-Net model.…”

Section: -Methodologymentioning

confidence: 99%

Deep Learning Approach for Cardiac MRI Images

Sandooghdar¹

2022

jist

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Deep Learning (DL) is the most widely used image-analysis process, especially in medical image processing. Though DL has entered image processing to solve Machine Learning (ML) problems, identifying the most suitable model based on evaluation of the epochs is still an open question for scholars in the field. There are so many types of function approximators like Decision Tree, Gaussian Processes and Deep Learning, used in multi-layered Neural Networks (NNs), which should be evaluated to determine their effectiveness. Therefore, this study aimed to assess an approach based on DL techniques for modern medical imaging methods according to Magnetic Resonance Imaging (MRI) segmentation. To do so, an experiment with a random sampling approach was conducted. One hundred patient cases were used in this study for training, validation, and testing. The method used in this study was based on full automatic processing of segmentation and disease classification based on MRI images. U-Net structure was used for the segmentation process, with the use of cardiac Right Ventricular Cavity (RVC), Left Ventricular Cavity (LVC), Left Ventricular Myocardium (LVM), and information extracted from the segmentation step. With train and using random forest classifier, and Multilayer Perceptron (MLP), the task of predicting the pathologic target class was conducted. Segmentation extracted information was in the form of comprehensive features handcrafted to reflect demonstrative clinical strategies. Our study suggests 92% test accuracy for cardiac MRI image segmentation and classification. As for the MLP ensemble, and for the random forest, test accuracy was equal to 91% and 90%, respectively. This study has implications for scholars in the field of medical image processing.

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Section: -Methodologymentioning

confidence: 99%

Deep Learning Approach for Cardiac MRI Images

Sandooghdar¹

2022

jist

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“…Dioctyl phthalate (DOP) and nitrophenyl octyl ether (NPOE) are some of the most frequently used plasticizers with almost similar efficiencies. 21 As a result of their several advantages, PIMs have been widely explored for the removal and monitoring of different heavy metals, 23,24 dyes, 25,26 nutrients, 27,28 and organic molecules. 29 Recently, an extensive literature of various advanced treatment methods for attenuating pharmaceutically active compounds has been conducted.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of ciprofloxacin removal by using polymer inclusion membrane and its multivariate analysis with half‐fractional factorial design

Olasupo

Sadiq

Suah

2022

J of Chemical Tech & Biotech

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BACKGROUND: In this study, the use of polymer inclusion membranes (PIMs) to attenuate the recalcitrant contaminant of ciprofloxacin (CIP) in an aquatic system is presented. Poly(vinyl)chloride (PVC) was used as the base polymer, bis−2-(Ethylhexyl) phosphate (B2EHP) as the carrier, and dioctyl phthalate (DOP) as the plasticizer. Optimisation of the experiment was conducted using multivariate analysis of half-fractional factorial design, focusing on parameters like pH of the source solution, the source solution's concentration, carrier composition, the concentration of the receiving solution, and extraction time.RESULTS: It was found that PIMs with the composition of PVC 45%: B2EHP 25%: DOP 30% were the best suited for this purpose. The optimal removal efficiency of CIP was >99% at pH 6, 90% at pH 12, and 85% removal at neutral pH (7). After various optimisation steps, the method was applied to actual environmental wastewater and river samples with an average percentage removal efficiency of >97%.CONCLUSION: This study has successfully demonstrated the feasibility of using PIMs for the removal of CIP antibiotics. Hence, it may be considered a practical treatment approach for other pharmaceutical residues in the aquatic system.

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“…PIMs are a type of membrane in which the chromophore agent is contained within the polymeric network of a non-porous support in the presence or absence of a plasticizer. They have been used for sensing Co(II) [ 17 , 18 ], Al(III) [ 14 , 19 , 20 ], Zn(II) [ 21 ], and Cu(II) [ 22 ], among other metal ions. A recent overview of PIM applications, including sensors, has been presented elsewhere [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Integration of Response Surface Methodology (RSM) and Principal Component Analysis (PCA) as an Optimization Tool for Polymer Inclusion Membrane Based-Optodes Designed for Hg(II), Cd(II), and Pb(II)

García-Beleño

Miguel

2021

Membranes

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An optimization of the composition of polymer inclusion membrane (PIM)-based optodes, and their exposure times to metal ion solutions (Hg(II), Cd(II), and Pb(II)) was performed using two different chromophores, diphenylthiocarbazone (dithizone) and 1-(2-pyridylazo)-2-naphthol (PAN). Four factors were evaluated (chromophore (0.06–1 mg), cellulose triacetate (25–100 mg) and plasticizer amounts (25–100 mg), and exposure time (20–80 min)). Derringer’s desirability functions values were employed as response variables to perform the optimization obtained from the results of three different processes of spectral data treatment: two full-spectrum methods (M1 and M3) and one band-based method (M2). The three different methods were compared using a heatmap of the coefficients and dendrograms of the Principal Component Analysis (PCA)reductions of their desirability functions. The final recommended M3 processing method, i.e., using the scores values of the first two principal components in PCA after subtraction of the normalized spectra of the membranes before and after complexation, gave more discernable differences between the PIMs in the Design of Experiments (DoE), as the nodes among samples appeared at longer distances and varyingly distributed in the dendrogram analysis. The optimal values were time of 35–65 min, 0.53 mg–1.0 mg of chromophores, plasticizers 34.4–71.9 of chromophores, and 62.5–100 mg of CTA, depending on the metal ion. In addition, the method yielded the best outcomes in terms of interpretability and an easily discernable color change so that it is recommended as a novel optimization method for this kind of PIM optode.

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Preparation and characterization of a novel Co(II) optode based on polymer inclusion membrane

Cited by 29 publications

References 51 publications

Deep Learning Approach for Cardiac MRI Images

Deep Learning Approach for Cardiac MRI Images

Optimisation of ciprofloxacin removal by using polymer inclusion membrane and its multivariate analysis with half‐fractional factorial design

Integration of Response Surface Methodology (RSM) and Principal Component Analysis (PCA) as an Optimization Tool for Polymer Inclusion Membrane Based-Optodes Designed for Hg(II), Cd(II), and Pb(II)

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