We report highly bendable and efficient perovskite solar cells (PSCs) that use thermally oxidized layer of Ti metal plate as an electron transport layer (ETL). The power conversion efficiency (PCE) of flexible PSCs reaches 14.9% with a short-circuit current density (J) of 17.9 mA/cm, open-circuit voltage (V) of 1.09, and fill factor (ff) of 0.74. Moreover, the Ti metal-based PSCs exhibit a superior fatigue resistance over indium tin oxide/poly(ethylene terephthalate) substrate. Flexible PSCs maintain 100% of their initial PCE even after PSCs are bent 1000 times at a bending radius of 4 mm. This excellent performance of flexible PSCs is due to high crystalline quality and low oxygen vacancy concentration of TiO layer. The concentration of oxygen vacancies in the oxidized Ti metal surface controls the electric function of TiO as ETL of PSCs. A decrease in the oxygen vacancy concentration of the TiO layer is critical to improving the electron collection efficiency of the ETL. Our results suggest that Ti metal-based PSCs possess excellent mechanical properties, which can be applied to the renewable energy source for flexible electronics.

Artificial Intelligence (AI) has penetrated the field of education. Trust has long been regarded as a driver for the acceptance of technology. Netnography and interviews were used to investigate trust in AI‐based educational systems from the perspective of users. We identified the factors influencing trust in AI‐based educational systems and categorized them as being related to technology, context and individual. Technology‐related factors encompass functionality, helpfulness, interpretability, dependability and interaction interface. Context‐related factors encompass benevolence of educational organizations, data management, teachers’ competencies, official norms and knowledge characteristics. Individual‐related factors encompass perception of the nature of learning, propensity to interact with teachers, perception of AI and autonomy orientation. The results from this paper will contribute to the literature on trust in technology and AI ethics in education.

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To investigate the effects of multiple environmental conditions on greenhouse gas (CO2 , N2 O, CH4 ) fluxes, we transferred three soil monoliths from Masson pine forest (PF) or coniferous and broadleaved mixed forest (MF) at Jigongshan to corresponding forest type at Dinghushan. Greenhouse gas fluxes at the in situ (Jigongshan), transported and ambient (Dinghushan) soil monoliths were measured using static chambers. When the transported soil monoliths experienced the external environmental factors (temperature, precipitation and nitrogen deposition) at Dinghushan, its annual soil CO2 emissions were 54% in PF and 60% in MF higher than those from the respective in situ treatment. Annual soil N2 O emissions were 45% in PF and 44% in MF higher than those from the respective in situ treatment. There were no significant differences in annual soil CO2 or N2 O emissions between the transported and ambient treatments. However, annual CH4 uptake by the transported soil monoliths in PF or MF was not significantly different from that at the respective in situ treatment, and was significantly lower than that at the respective ambient treatment. Therefore, external environmental factors were the major drivers of soil CO2 and N2 O emissions, while soil was the dominant controller of soil CH4 uptake. We further tested the results by developing simple empirical models using the observed fluxes of CO2 and N2 O from the in situ treatment and found that the empirical models can explain about 90% for CO2 and 40% for N2 O of the observed variations at the transported treatment. Results from this study suggest that the different responses of soil CO2 , N2 O, CH4 fluxes to changes in multiple environmental conditions need to be considered in global change study.