ABSTRACT
Highly-crystallized carbon nitride (HCCN) nanosheets exhibit significant potential for advancements in the field of photoelectric conversion. However, to fully exploit their potential, a thorough understanding of the fundamental excitonic photophysical processes is crucial. Here, the temperature-dependent excitonic photoluminescence (PL) of HCCN nanosheets and amorphous polymeric carbon nitride (PCN) is investigated using steady-state and time-resolved PL spectroscopy. The exciton binding energy of HCCN is determined to be 109.26 meV, lower than that of PCN (207.39 meV), which is attributed to the ordered stacking structure of HCCN with a weaker Coulomb interaction between electrons and holes. As the temperature increases, a noticeable reduction in PL lifetime is observed on both the HCCN and PCN, which is ascribed to the thermal activation of carrier trapping by the enhanced electron-phonon coupling effect. The thermal activation energy of HCCN is determined to be 102.9 meV, close to the value of PCN, due to their same band structures. Through wavelength-dependent PL dynamics analysis, we have identified the PL emission of HCCN as deriving from the transitions:σ*-LP,π*-π, andπ*-LP, where theπ*-LP transition dominants the emission because of the high excited state density of the LP state. These results demonstrate the impact of high-crystallinity on the excitonic emission of HCCN materials, thereby expanding their potential applications in the field of photoelectric conversion.
ABSTRACT
The adverse drug reaction (ADR) of traditional Chinese medicine injection (TCMI) has become one of the major concerns of public health in China. There are significant advantages for developing methods to improve the use of TCMI in routine clinical practice. The method of predicting TCMI-induced ADR was illustrated using a nested case-control study in 123 cases and 123 controls. The partial least squares regression (PLSR) models, which mapped the influence of basic characteristics and routine examinations to ADR, were established to predict the risk of ADR. The software was devised to provide an easy-to-use tool for clinic application. The effectiveness of the method was evaluated through its application to new patients with 95.7% accuracy of cases and 91.3% accuracy of controls. By using the method, the patients at high-risk could be conveniently, efficiently and economically recognized without any extra financial burden for additional examination. This study provides a novel insight into individualized management of the patients who will use TCMI.
