RESUMO
CsCu2I3 mixed with Cs3Cu2I5 has shown potential applications as white-light-emitting materials, while their growth, structural evolution behaviors, and their impact on photoluminescence of CsCu2I3 nanocrystals (NCs) are still not known. In this work, we investigated the growth and structural evolution of CsCu2I3 nanocrystals with increasing reaction temperature. At low temperature and in the presence of a high dosage of oleic acid and oleylamine, Cs3Cu2I5 nanoparticles, rather than CsCu2I3 NCs, preferred to form in the hot-injection reaction system. Increasing the reaction temperature promoted the formation of CsCu2I3 nanorods. Phase-pure CsCu2I3 nanorods were steadily obtained at 180 °C. Structural evolution from less copper-containing NCs to copper-rich ones in the low-temperature reaction condition is highly related to the coordination of copper ions with OAm. More importantly, accompanying the growth of nanorods and structural evolution from Cu3Cs2I5 to CsCu2I3, the color of photoluminescence emission of NCs changed from blue to nearly white and to yellow, but their photoluminescence quantum yield decreased from 36.00 to 9.86%. The finding in this work would give a view to the structural evolution of copper-containing perovskite-like halides, being helpful for adjusting their photoluminescence in white LEDs.
RESUMO
Background: Body constitution (BC) is the abstract concept indicating the state of a person's health in Traditional Chinese Medicine (TCM). The doctor identifies the body constitution of the patient through inspection and inquiry. Previous research simulates doctors to identify BC types according to a patient's objective physical indicators. However, the lack of subjective feeling information can reduce the accuracy of the machine to imitate the doctor's diagnosis. The Constitution in Chinese Medicine Questionnaire (CCMQ) is used to collect subjective information but suffers from low acquisition efficiency. Methods: This paper presents a personalized body constitution inquiry method based on a machine learning technique. It employs a random generator, a feature extractor, and a classifier to simulate the doctor inquiry and generate a personalized questionnaire. Specifically, the feature extractor evaluates and sorts the question of the constitution in the CCMQ based on the recognition results of the tongue coating image of patients. The sorted questions and relevant BC label are inputted into the classifier; the best questions are screened out for patients. Results: The experimental results show that our method can select personalized questions from the CCMQ for the patients, significantly reducing the time and the number of questions to answer. It also improves the accuracy of recognizing BC. Compared with the CCMQ, patients had 68.3% fewer questions to answer and the time occupied by answering is reduced by 80.3%. Conclusions: The proposed method can simulate the doctor's inquiry and pick out personalized questions for patients. It can act as auxiliary diagnosis tools to collect subjective patient feelings and help make further judgments on the patient's BC types.