Synchrotron-based VUV excitation-induced ultrahigh quality cool white light luminescence from Sm-doped ZnO.

We report that the rare-earth (RE) ion, Sm-doped ZnO, acts as white light emitting vacuum ultraviolet (VUV) phosphors and possesses an ultrahigh color rendering index (CRI) and color quality scale (CQS). The VUV-excited emission spectra measured from the synchrotron source reveal the emergence of multi-color emission bands in the visible-IR region and substantially depend on the concentration of Sm3+ ions. A mechanism is proposed to elucidate the origin behind the high-energy bandgap excitation of the host charge carrier and subsequent energy transfer to the Sm3+ states leading to additional green-yellow-orange emission bands of Sm3+(4G5/2→6HJ(J=5/2,7/2,and9/2)). High-quality cool white light (correlated color temperature 5600 K) having CIE coordinates (0.33, 0.35) with a CRI as high as 95.89 and a CQS value of 94.49 is achieved for Zn0.985Sm0.015O under synchrotron VUV radiations. This Letter demonstrates that RE activated ZnO-based phosphors are expected to be a promising candidate in solid state lighting, as well as plasma display devices.

A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques.

Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine-learning (ML) and deep-learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non-invasive treatment to the patients.

CTDN (Convolutional Temporal Based Deep- Neural Network): An Improvised Stacked Hybrid Computational Approach for Anticancer Drug Response Prediction.

The characterization of drug - metabolizing enzymes is a significant problem for customized therapy. It is important to choose the right drugs for cancer victims, and the ability to forecast how those drugs will react is usually based on the available information, genetic sequence, and structural properties. To the finest of our knowledge, this is the first study to evaluate optimization algorithms for selection of features and pharmacogenetics categorization using classification methods based on a successful evolutionary algorithm using datasets from the Cancer Cell Line Encyclopaedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC). The study proposes the uses of Firefly and Grey Wolf Optimization techniques for feature extraction, while comparing the traditional Machine Learning (ML), ensemble ML and Stacking Algorithm with the proposed Convolutional Temporal Deep Neural Network or CTDN. With the potential to increase efficiency from the suggested intelligible classifier model for a suggestive chemotherapeutic drugs response prediction, our study is important in particular for selecting an acceptable feature selection method. The comparison analysis demonstrates that the proposed model not only surpasses the prior state-of-the-art methods, but also uses Grey Wolf and Fire Fly Optimization to lessen multicollinearity and overfitting.

Bimetallic Implanted Plasmonic Photoanodes for TiO2 Sensitized Third Generation Solar Cells.

An auspicious way to enhance the power conversion efficiency (PCE) of third generation sensitized solar cells is to improve the light harvesting ability of TiO2 sensitizer and inhibition of back recombination reactions. In the present work, we have simultaneously comprehended both the factors using stable bimetallic Au and Ag metal nanoparticles (Mnps) embedded in TiO2 with ion implantation technique at lower fluence range; and explored them in third generation dye sensitized solar cells (DSSCs). The best performing Au-Ag implanted DSSC (Fluence- 6 × 1015 ions cm-2) revealed 87.97% enhancement in its PCE relative to unimplanted DSSC; due to plasmon induced optical and electrical effects of Mnps. Here, optimized bimetallic Au-Ag Mnps embedded in TiO2 improves light harvesting of N719 dye; due to the well matched localized surface plasmon resonance (LSPR) absorption band of Au and Ag with low and high energy absorption bands of N719 dye molecules, respectively. Furthermore, Au and Ag acts as charge separation centers in TiO2 that inhibit the recombination reactions occurring at photoanode/electrolyte interface via prolonging photo-generated electron lifetime; resulting in efficient inter-facial charge transportation in DSSCs.

Fabrication of plasmonic dye-sensitized solar cells using ion-implanted photoanodes.

Plasmonic dye-sensitized solar cells containing metal nanoparticles suffer from stability issues due to their miscibility with liquid iodine-based electrolytes. To resolve the stability issue, herein, an ion implantation technique was explored to implant metal nanoparticles inside TiO2, which protected these nanoparticles with a thin coverage of TiO2 melt and maintained the localized surface plasmon resonance oscillations of the metal nanoparticles to efficiently enhance their light absorption and make them corrosion resistant. Herein, Au nanoparticles were implanted into the TiO2 matrix up to the penetration depth of 22 nm, and their influence on the structural and optical properties of TiO2 was studied. Moreover, plasmonic dye-sensitized solar cells were fabricated using N719 dye-loaded Au-implanted TiO2 photoanodes, and their power conversion efficiency was found to be 44.7% higher than that of the unimplanted TiO2-based dye-sensitized solar cells due to the enhanced light absorption of the dye molecules in the vicinity of the localized surface plasmon resonance of Au as well as the efficient electron charge transport at the TiO2@Au@N719/electrolyte interface.