Band gap modified Al-doped Zn1−x Mg x O and Zn1−y Cd y O transparent conducting thin films

Abstract

Al-doped Zn 1-x Mg x O and Zn 1-y Cd y O thin films were prepared on glass substrates by sol-gel method. The codoping thin films showed preferential c-axis orientation, and the lattice constant c evaluated from the shift of the position of (002) peak displayed an increasing evolution from x = 8 at.% to y = 8 at.%, indicating a roughly statistical substitution of Mg 2? and Cd 2? for Zn 2? in their solid solution. The effects of narrowing and widening band gap (E g ) on conductivity of (Cd, Al) and (Mg, Al) codoped ZnO thin films were simultaneously investigated using transmission spectra and electrical measurements. The transmittances of these films are obviously decreased by vacuum annealing to 50-60%. However, the carrier concentration and Hall mobility both increase, and resistivity decreases with narrowing band gap in 1 at.% Al-doped Zn 1-x Mg x O and Zn 1-y Cd y O thin films from x = 8 at.% to y = 8 at.%. It is revealed that the conductivity of Al-doped ZnO thin films could be enhanced by this simple band gap modification.

Organic Light‐Emitting Transistors: Materials, Device Configurations, and Operations

“…Similar to OFET counterparts, [1][2][3][4][5][6][7][8][9][10] OLEDs [11][12][13][14][15][16][17][18][19][20] and OPVs, [21][22][23][24][25][26][27][28][29][30] the quality of OLETs is also intimately associated with intricate issues such as the type and assembly state of the involved active OSC materials, the confi guration of the devices, and the operation conditions of devices. While these complex factors make it a challenge to construct highperformance OLETs, at the same time they confer a variety of opportunities to manufacture sophisticated devices.…”

“…Organic electronic devices, such as organic fi eld-effect transistors (OFETs), [1][2][3][4][5][6][7][8][9][10] organic light-emitting diodes (OLEDs), [11][12][13][14][15][16][17][18][19][20] and organic photovoltaic cells (OPVs), [21][22][23][24][25][26][27][28][29][30] wherein various π-conjugated organic species, including small molecules, oligomers and polymers, work as electrically and/ or optically active semiconducting materials, have attracted general and broad interest from the interdisciplinary communities of micro/nano-electronics, chemistry, physics, and advanced materials science. From a technological point of view, the inherent characteristics of organic semiconductors (OSCs), such as their generally good solution processability, as well as good compatibility with large-area and fl exible solid supports, render organic electronic devices amenable to diverse solution processible methods, including interfacial assembly, casting, spin coating, layer-by-layer assembly, evaporation, or roll-to-roll protocols such as inkjet printing and screen printing, amongst others.…”

“…[ 1,[31][32][33]35,36 ] Besides the aforementioned prevalent merits of organic electronic devices, for instance, their inherent amenability to low-cost and large-scale manufacture on arbitrary solid supports, one of the most attractive advantages of OLETs over their inorganic counterparts is that the emission color can effectively be tuned in terms of molecular and/or supramolecular engineering. [ 33,34 ] In fact, in addition to three-terminal OLETs, light-generation functionalities can also be absolutely realized via conventional two-terminal OLEDs, [11][12][13][14][15][16][17][18][19][20] where most of the merits of OLETs can be harnessed in nearly the same way. Nevertheless, for the technological application of OLEDs, especially when they work as the component of active-matrix EL display panels, fi eld-effect transistors are always essentially required to control their EL behavior and to switch each individual OLED pixel ON or OFF in terms of controlling the current density.…”

Architecture Prediction of 3D Composites Using Machine Learning and No‐Destructive Technique

“…Traditional nondestructive techniques face significant challenges in predicting the architecture of composite materials due to their complex microstructures and the difficulty in modeling interactions between various components. [ 14‐18 ] Accurately predicting the architecture of unknown composite materials is crucial for explaining their properties and performance, optimizing new materials for specific applications, and understanding the behavior of existing materials. [ 19,20 ] Therefore, this study aims to investigate the feasibility of accurately predicting the 3D architecture of composites using ML and nondestructive techniques.…”