For newly developed semiconductors, obtaining high‐performance transistors and identifying carrier mobility have been hot and important issues. Here, large‐area fabrications and thorough analysis of InGaZnO transistors with enhanced current by simple encapsulations are reported. The enhancement in the drain current and on–off ratio is remarkable in the long‐channel devices (e.g., 40 times in 200 µm long transistors) but becomes much less pronounced in short‐channel devices (e.g., 2 times in 5 µm long transistors), which limits its application to the display industry. Combining gated four‐probe measurements, scanning Kelvin‐probe microscopy, secondary ion mass spectrometry, X‐ray photoelectron spectroscopy, and device simulations, it is revealed that the enhanced apparent mobility up to several tens of times is attributed to the stabilized hydrogens in the middle area forming a degenerated channel area while that near the source‐drain contacts are merely doped, which causes artifact in mobility extraction. The studies demonstrate the use of hydrogens to remarkably enhance performance of oxide transistors by inducing a new mode of device operation. Also, this study shows clearly that a thorough analysis is necessary to understand the origin of very high apparent mobilities in thin‐film transistors or field‐effect transistors with advanced semiconductors.
Traditional educational giants and natural language processing companies have launched several artificial intelligence (AI)‐enabled digital learning applications to facilitate language learning. One typical application of AI in digital language education is the automatic scoring application that provides feedback on pronunciation repeat outcomes. This research is motivated by the usage of automatic scoring‐empowered digital learning tools by language learners, and set out to uncover the influencing mechanisms of AI‐enabled automatic scoring application affordances on learners’ continuous learning intention. Specifically, based on affordance theory, we found several automatic scoring application affordances through in‐depth interviews. Considering the current lack of investigations on the mechanisms underlying automatic scoring application and its implications for learners’ learning behaviors, we built a model to examine the role of automatic scoring application affordances on cognitive/emotional engagement and following continuous learning intention. We further examined the moderation role of in‐job learners and student learners on the above relationships. The model was tested using a survey of 260 Chinese foreign language learners who used AI‐empowered learning tools to facilitate their language learning practices. This study explores why learners continuously use AI‐enabled automatic scoring applications by identifying the affordances that differentiate it from traditional educational technologies. Practitioners could take the identified affordances into account when designing AI‐enabled language learning applications.
Purpose
Because of the COVID-19, the digital transformation of global hospitality and tourism speeds up. This paper aims to provide comprehensive frame of the digital transformation for further hospitality and tourism research.
Design/methodology/approach
Through conducting a critical review of the impact of COVID-19, the current situation about the application of digital technology and digital transformation in hospitality and travel, this study used a qualitative approach to present the viewpoints.
Findings
This research presents a theoretical research framework for the hospitality and tourism about digital transformation, including possible directions, contexts and methods. It highlights the importance of digital transformation, and further proposing specific research topics.
Research limitations/implications
This research brings valuable implications and guidance for future research from the aspects of key research streams, research context and methodological approaches in hospitality and tourism about digital transformation.
Originality/value
This paper supplies existing critical reviewed research through paying attention to the digital transformation approach in hospitality and tourism, providing research guidance technically to the industry of hotels and travel.
Key indicatorsSingle-crystal X-ray study T = 293 K Mean (C-C) = 0.003 Å R factor = 0.049 wR factor = 0.120 Data-to-parameter ratio = 13.5For details of how these key indicators were automatically derived from the article, see
Current evidence has suggested that diabetes increases the risk of implanting failure, and therefore, appropriate surface modification of dental implants in patients with diabetes is crucial. TiO2 nanotube (TNT) has an osteogenic nanotopography, and its osteogenic properties can be further improved by loading appropriate drugs. Cinnamaldehyde (CIN) has been proven to have osteogenic, anti-inflammatory, and anti-bacterial effects. We fabricated a pH-responsive cinnamaldehyde-TiO2 nanotube coating (TNT-CIN) and hypothesized that this coating will exert osteogenic, anti-inflammatory, and anti-bacterial functions in a simulated diabetes condition. TNT-CIN was constructed by anodic oxidation, hydroxylation, silylation, and Schiff base reaction to bind CIN, and its surface characteristics were determined. Conditions of diabetes and diabetes with a concurrent infection were simulated using 22-mM glucose without and with 1-μg/mL lipopolysaccharide, respectively. The viability and osteogenic differentiation of bone marrow mesenchymal stem cells, polarization and secretion of macrophages, and resistance to Porphyromonas gingivalis and Streptococcus mutans were evaluated. CIN was bound to the TNT surface successfully and released better in low pH condition. TNT-CIN showed better osteogenic and anti-inflammatory effects and superior bacterial resistance than TNT in a simulated diabetes condition. These findings indicated that TNT-CIN is a promising, multifunctional surface coating for patients with diabetes needing dental implants.
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