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Synthesis and characterization of oleic acid stabilized CdTe quantum dots and their properties as luminescence quencher of a pyridine pendented rod-coil homopolymer

Kaur Manpreet, Sahu Duryodhan

Materials Science Poland

2020

Vol. 38, No. 4

577--583

We report a facile one-step non aqueous synthesis of oleic acid stabilized cadmium telluride (CdTe) quantum dots (QDs) with an average diameter of 3 nm to 4 nm by hot injection method. The synthesized oleic acid capped QDs observed by TEM were nearly spherical. The optical properties of QDs were characterized by UV-Vis absorption spectra and photoluminescence (PL) spectra. The structures of QDs and their surface passivation were further verified using transmission electron microscope (TEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR). The quenching effect of the CdTe QD was explored by addition of CdTe nanocrystals into a solution of rod-coil homopolymer (poly[10-(6-(9,9-diethyl-7-(pyridin-4-yl)-9H-fluoren-2-yl)naphthalen-2-yloxy) decyl methacrylate]) (PFNA) having pendent pyridine. The gradual addition of quantum dots to the solution of PFNA quenched the PL spectra of PFNA. This may be used to explore the coordination ability of pyridine containing homopolymer with CdTe quantum dots.

Simultaneous feature weighting and parameter determination of Neural Networks using Ant Lion Optimization for the classification of breast cancer

Singh Dalwinder, Singh Birmohan, Kaur Manpreet

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 1

337--351

In this paper, feature weighting is used to develop an effective computer-aided diagnosis system for breast cancer. Feature weighting is employed because it boosts the classification performance more as compared to feature subset selection. Specifically, a wrapper method utilizing the Ant Lion Optimization algorithm is presented that searches for best feature weights and parametric values of Multilayer Neural Network simultaneously. The selection of hidden neurons and backpropagation training algorithms are used as parameters of neural networks. The performance of the proposed approach is evaluated on three breast cancer datasets. The data is initially normalized using tanh method to remove the effects of dominant features and outliers. The results show that the proposed wrapper method has a better ability to attain higher accuracy as compared to the existing techniques. The obtained high classification performance validates the work which has the potential for becoming an alternative to the other well-known techniques.