InP quantum dots (QDs) have attracted researchers’ interest due to their applicability in quantum dot light-emitting displays (QLED) or biomarkers for detecting cancers or viruses. The surface or interface control of InP QD core/ shell has substantially increased quantum efficiency, with a quantum yield of 100% reached by introducing HF to inhibit oxide generation. In this study, we focused on the control of bandgap energy of quantum dots by changing the Zn/(In+Zn) ratio in the In(Zn)P core. Zinc incorporation can change the photoluminescent light colors of green, yellow, orange, and red. Diluting a solution of as-synthesized QDs by more than 100 times did not show any quenching effects by the Förster resonance energy transfer phenomenon between neighboring QDs.

The bandgap characteristics of semiconductor materials are an important factor when utilizing semiconductor materials for various applications. In this study, based on data provided by AFLOW (Automatic-FLOW for Materials Discovery), the bandgap of a semiconductor material was predicted using only the material’s compositional features. The compositional features were generated using the python module of ‘Pymatgen’ and ‘Matminer’. Pearson’s correlation coefficients (PCC) between the compositional features were calculated and those with a correlation coefficient value larger than 0.95 were removed in order to avoid overfitting. The bandgap prediction performance was compared using the metrics of R2 score and root-mean-squared error. By predicting the bandgap with randomforest and xgboost as representatives of the ensemble algorithm, it was found that xgboost gave better results after cross-validation and hyper-parameter tuning. To investigate the effect of compositional feature selection on the bandgap prediction of the machine learning model, the prediction performance was studied according to the number of features based on feature importance methods. It was found that there were no significant changes in prediction performance beyond the appropriate feature. Furthermore, artificial neural networks were employed to compare the prediction performance by adjusting the number of features guided by the PCC values, resulting in the best R2 score of 0.811. By comparing and analyzing the bandgap distribution and prediction performance according to the material group containing specific elements (F, N, Yb, Eu, Zn, B, Si, Ge, Fe Al), various information for material design was obtained.

To improve photocatalytic performance, CdS nanoparticle deposited TiO2 nanotubular photocatalysts are synthesized. The TiO2 nanotube is fabricated by electrochemical anodization at a constant voltage of 60 V, and annealed at 500 for crystallization. The CdS nanoparticles on TiO2 nanotubes are synthesized by successive ionic layer adsorption and reaction method. The surface characteristics and photocurrent responses of TNT/CdS photocatalysts are investigated by scanning electron microscopy (SEM), X-ray diffraction (XRD), UV-Vis spectrometer and LED light source installed potentiostat. The bandgaps of the CdS deposited TiO2 photocatalysts are gradually narrowed with increasing of amounts of deposited CdS nanoparticles, which enhances visible light absorption ability of composite photocatalysts. Enhanced photoelectrochemical performance is observed in the nanocomposite TiO2 photocatalyst. However, the maximum photocurrent response and dye degradation efficiency are observed for TNT/CdS30 photocatalyst. The excellent photocatalytic performance of TNT/CdS30 catalyst can be ascribed to the synergistic effects of its better absorption ability of visible light region and efficient charge transport process.

ZnMgBeGaO/Ag/ZnMgBeGaO multilayer structures were sputter grown and characterized in detail. Results indicated that the electrical properties of the ZnMgBeGaO films were significantly improved by inserting an Ag layer with proper thickness (~ 10 nm). Structures with thicker Ag films showed much lower optical transmission, although the electrical conductivity was further improved. It was also observed that the electrical properties of the multilayer structure were sizably improved by annealing in vacuum (~35% at 300 oC). The optimum ZnMgBeGaO(20nm)/Ag(10nm)/ZnMgBeGaO(20nm) structure exhibited an electrical resistivity of ~2.6 × 10−5 Ωcm (after annealing), energy bandgap of ~3.75 eV, and optical transmittance of 65%~ 95 % over the visible wavelength range, representing a significant improvement in characteristics versus previously reported transparent conductive materials.

Use of low bandgap polymers is the most suitable way to harvest a broader spectrum of solar radiations for solar cells. But, still there is lack of most efficient low bandgap polymer. In order to solve this problem, we have synthesised a new low bandgap polymer and investigated its interaction with the ILs to enhance its conductivity. ILs may undergo almost unlimited structural variations; these structural variations have attracted extensive attention in polymer studies. In addition to this, UV-Vis spectroscopy, confocal Raman spectroscopy and FT-IR spectroscopy results have revealed that all studied ILs (tributylmethylammonium methyl sulfate [N1444] MeSO4] from ammonium family) and 1-methylimidazolium chloride ([MIM]Cl, and 1-butyl-3-methylimidazolium chloride [Bmim]Cl from imidazolium family) has potential to interact with polymer. Further, protic ILs shows enhanced conductivity than aprotic ILs with low bandgap polymer. This study provides the combined effect of low bandgap polymer and ILs that may generate many theoretical and experimental opportunities.