Surface Energy Engineering of Buried Interface for Highly Stable Perovskite Solar Cells with Efficiency Over 25.

The abundant oxygen-related defects (e.g., O vacancies, O-H) in the TiO2 electron transport layer results in high surface energy, which is detrimental to effective carrier extraction and seriously impairs the photovoltaic performance and stability of perovskite solar cells. Here, novel surface energy engineering (SEE) is developed by applying a surfactant of heptadecafluorooctanesulfonate tetraethylammonium (HFSTA) on the surface of the TiO2 . Theoretical calculations show that the HFSTA-TiO2 is less prone to form O vacancies, leading to lower surface energy, thus improving the carrier-extraction efficiency. The experimental results show that superior perovskite film is obtained due to the reduced heterogeneous nucleation sites and improved crystallization process on the modified TiO2 . Furthermore, the flexible long alkyl chains in HFSTA considerably relieve the compressive stresses at the buried interface. By combining the passivation of TiO2 , crystallization process modulation, and stress relief, a champion PCE up to 25.03% is achieved. The device without encapsulation sustains 92.2% of its initial PCE after more than 2500 h storage under air ambient with relative humidity of 25-30%. The SEE of a buried interface paves a new way toward high-efficiency, stable perovskite solar cells.

Comparison of the performance of hydrochar, raw biomass, and pyrochar as precursors to prepare porous biochar for the efficient sorption of phthalate esters.

In this study, three high-performance porous biochars were synthesized by the cocarbonization of Pistia stratiotes-derived precursors (raw biomass, hydrochar and pyrochar) with potassium hydroxide and utilized for the sorption of diethyl phthalate from aqueous solution. The developed pore structure, surface functional groups, high hydrophobicity characteristic and graphene structure of porous biochars contributed to the excellent sorption quantity of up to 813 mg g-1 (Ce, 25 mg L-1). Among the three precursors, hydrochar-derived porous biochar showed better properties in terms of its specific surface area and hydrophobicity, and it displayed the highest sorption capacity. The sorption kinetics and isotherm experiments confirmed that pore filling and partitioning dominated the sorption capacity while the mass transfer, hydrogen bonding and π-π stacking in the hydrochar limited the sorption rate. This finding helped to propose a feasible method for the efficient utilization of invasive aquatic plants and provided novel insight into the selection of precursors for preparing porous biochars.

Exploring and Characterizing Patient Multibehavior Engagement Trails and Patient Behavior Preference Patterns in Pathway-Based mHealth Hypertension Self-Management: Analysis of Use Data.

BACKGROUND: Hypertension is a long-term medical condition. Mobile health (mHealth) services can help out-of-hospital patients to self-manage. However, not all management is effective, possibly because the behavior mechanism and behavior preferences of patients with various characteristics in hypertension management were unclear. OBJECTIVE: The purpose of this study was to (1) explore patient multibehavior engagement trails in the pathway-based hypertension self-management, (2) discover patient behavior preference patterns, and (3) identify the characteristics of patients with different behavior preferences. METHODS: This study included 863 hypertensive patients who generated 295,855 use records in the mHealth app from December 28, 2016, to July 2, 2020. Markov chain was used to infer the patient multibehavior engagement trails, which contained the type, quantity, time spent, sequence, and transition probability value (TP value) of patient behavior. K-means algorithm was used to group patients by the normalized behavior preference features: the number of behavioral states that a patient performed in each trail. The pages in the app represented the behavior states. Chi-square tests, Z-test, analyses of variance, and Bonferroni multiple comparisons were conducted to characterize the patient behavior preference patterns. RESULTS: Markov chain analysis revealed 3 types of behavior transition (1-way transition, cycle transition, and self-transition) and 4 trails of patient multibehavior engagement. In perform task trail (PT-T), patients preferred to start self-management from the states of task blood pressure (BP), task drug, and task weight (TP value 0.29, 0.18, and 0.20, respectively), and spent more time on the task food state (35.87 s). Some patients entered the states of task BP and task drug (TP value 0.20, 0.25) from the reminder item state. In the result-oriented trail (RO-T), patients spent more energy on the ranking state (19.66 s) compared to the health report state (13.25 s). In the knowledge learning trail (KL-T), there was a high probability of cycle transition (TP value 0.47, 0.31) between the states of knowledge list and knowledge content. In the support acquisition trail (SA-T), there was a high probability of self-transition in the questionnaire (TP value 0.29) state. Cluster analysis discovered 3 patient behavior preference patterns: PT-T cluster, PT-T and KL-T cluster, and PT-T and SA-T cluster. There were statistically significant associations between the behavior preference pattern and gender, education level, and BP. CONCLUSIONS: This study identified the dynamic, longitudinal, and multidimensional characteristics of patient behavior. Patients preferred to focus on BP, medications, and weight conditions and paid attention to BP and medications using reminders. The diet management and questionnaires were complicated and difficult to implement and record. Competitive methods such as ranking were more likely to attract patients to pay attention to their own self-management states. Female patients with lower education level and poorly controlled BP were more likely to be highly involved in hypertension health education.