Recently, erbium-doped nanomaterials have been demonstrated to achieve multiband upconversion luminescence (UCL) via high excitation power and material alteration. In such a scenario, a large number of energy levels of...

In Yb-Er co-doped upconversion (UC) nanomaterials, upconversion luminescence (UCL) can be modulated to generate multiband UCL emissions by changing the concentration of activator Er3+. Nonetheless, the effect of the Er3+ concentrations on the kinetics of these emissions is still unknown. We here study the single β-NaYF4:Yb3+/Er3+ microcrystal (MC) doped with different Er3+ concentrations by nanosecond time-resolved spectroscopy. Interestingly, different Er3+ doping concentrations exhibit different UCL emission bands and UCL response rates. At low Er3+ doping concentrations (1 mol%), multiband emission in β-NaYF4:Yb3+/Er3+ (20/1 mol%) MCs could not be observed and the response rate of UCL was slow (5–10 μs) in β-NaYF4:Yb3+/Er3+. Increasing the Er3+ doping concentration to 10 mol% can shorten the distance between Yb3+ ions and Er3+ ions, which promotes the energy transfer between them. β-NaYF4:Yb3+/Er3+ (20/10 mol%) can achieve obvious multiband UCL and a quick response rate (0.3 µs). However, a further increase in the Er doping concentration (80 mol%) makes MCs limited by the CR process and cannot achieve the four-photon UC process (4F5/2 → 2K13/2 and 2H9/2 → 2D5/2). Therefore, the result shows that changing the Er3+ doping concentration could control the energy flow between the different energy levels in Er3+, which could affect the response time and UCL emission of the Yb/Er doped rare earth materials. Our work can facilitate the development of fast-response optoelectronics, optical-sensing, and display industries.

Chat applications using Artificial Intelligence (AI) based on Natural Language Processing (NLP) platforms have been reported to be gradually accepted by people. This research aims to investigate differences between human language processing and Natural Language Processing (NLP) system, which is the core technology of most chat applications, using the synchronized language model. To achieve this objective, this research first distribute and collect questionnaires with questions such as the frequency and motivation of using AI chatbots among university students. The study then evaluate the selected chatbot with linguistic method and knowledge through semantics and pragmatics. Practically, this study proposes valid approaches to perfect existing chatbots. This study suggests that AI chatbots based on NLP can be applied to complete tasks but differ apparently from the human language processing system. The conclusion drawn from this study is that if the AI chatbot is developed to recognize misspelled words and their vocabulary is expanded, it will enhance the applicability of AI chatbots and fit them into people's lives.