Dual-Interface-Reinforced Flexible Perovskite Solar Cells for Enhanced Performance and Mechanical Reliability.

Two key interfaces in flexible perovskite solar cells (f-PSCs) are mechanically reinforced simultaneously: one between the electron-transport layer (ETL) and the 3D metal-halide perovskite (MHP) thin film using self-assembled monolayer (SAM), and the other between the 3D-MHP thin film and the hole-transport layer (HTL) using an in situ grown low-dimensional (LD) MHP capping layer. The interfacial mechanical properties are measured and modeled. This rational interface engineering results in the enhancement of not only the mechanical properties of both interfaces but also their optoelectronic properties holistically. As a result, the new class of dual-interface-reinforced f-PSCs has an unprecedented combination of the following three important performance parameters: high power-conversion efficiency (PCE) of 21.03% (with reduced hysteresis), improved operational stability of 1000 h T90 (duration at 90% initial PCE retained), and enhanced mechanical reliability of 10 000 cycles n88 (number of bending cycles at 88% initial PCE retained). The scientific underpinnings of these synergistic enhancements are elucidated.

Knowledge extraction and transfer in data-driven fracture mechanics.

Data-driven approaches promise to usher in a new phase of development in fracture mechanics, but very little is currently known about how data-driven knowledge extraction and transfer can be accomplished in this field. As in many other fields, data scarcity presents a major challenge for knowledge extraction, and knowledge transfer among different fracture problems remains largely unexplored. Here, a data-driven framework for knowledge extraction with rigorous metrics for accuracy assessments is proposed and demonstrated through a nontrivial linear elastic fracture mechanics problem encountered in small-scale toughness measurements. It is shown that a tailored active learning method enables accurate knowledge extraction even in a data-limited regime. The viability of knowledge transfer is demonstrated through mining the hidden connection between the selected three-dimensional benchmark problem and a well-established auxiliary two-dimensional problem. The combination of data-driven knowledge extraction and transfer is expected to have transformative impact in this field over the coming decades.