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This paper reports the fabrication of β-Ga 2 O 3 nanomembrane (NM) based flexible photodetectors (PDs) and the investigation of their optoelectrical properties under bending conditions. Flexible β-Ga 2 O 3 NM PDs exhibited reliable solar-blind photo-detection under bending conditions. Interestingly, a slight shifting in wavelength of the maximum solar-blind photo-current was observed under the bending condition. To investigate the reason for this peak shifting, the optical properties of β-Ga 2 O 3 NMs under different strain conditions were measured, which revealed changes in the refractive index, extinction coefficient and bandgap of strained β-Ga 2 O 3 NMs due to the presence of nano-sized cracks in the β-Ga 2 O 3 NMs. The results of a multiphysics simulation and a density-functional theory calculation for strained β-Ga 2 O 3 NMs showed that the conduction band minimum and the valence band maximum states were shifted nearly linearly with the applied uniaxial strain, which caused changes in the optical properties of the β-Ga 2 O 3 NM. We also found that nano-gaps in the β-Ga 2 O 3 NM play a crucial role in enhancing the photoresponsivity of the β-Ga 2 O 3 NM PD under bending conditions due to the secondary light absorption caused by reflected light from the nano-gap surfaces. Therefore, this research provides a viable route to realize high-performance flexible photodetectors, which are one of the indispensable components in future flexible sensor systems. 

Flight delays occur in the air transportation system when disruptive events such as weather, equipment outage, or congestion create an imbalance between system capacity and demand. These cycles of disruptions and subsequent recoveries can be viewed from a dynamical systems perspective: exogenous inputs (convective weather, airspace restrictions, etc.)disrupt the system, inducing delays and inefficiencies from which the system eventually recovers. We study these disruption and recovery cycles through a state-space representation that captures the severity and spatial impact of airport delays. In particular, using US airport delay data from 2008-2017, we first identify representative disruption and recovery cycles. These representative cycles provide insights into the common operational patterns of disruptions and recoveries in the system. We also relate these representative cycles to specific off-nominal events such as airport outages, and elucidate the differing disruption-recovery pathways for various off-nominal events. Finally, we explore temporal trends in terms of when and how the system tends to be disrupted, and the subsequent recovery.