An artificial synaptic device that can provide color discrimination, image storage, and image recognition is highly required to mimic the human vision for biological robots. All-inorganic halide perovskites have attracted extensive attention for the reason of their high stability and favorable photoelectric properties. In this study, a light-stimulated synaptic phototransistor based on a CsPbBr 3 /organic semiconductor hybrid film is reported. The fabricated CsPbBr 3 film exhibits an island structure, which reduces the hysteresis effectively and at the same time achieves a high specific detectivity of up to 2 × 10 15 Jones. The decay of the photocurrent can be delayed by changing the gate bias, which is essential for achieving high-performance light-stimulated synaptic devices. Due to the outstanding detectivity of the device, the obvious synaptic functions can be observed when triggered by a light signal with a power of 1.6 nW that is much weaker than previous most perovskite-based hybrid synaptic phototransistors under a low operating voltage of −1 V. The electrical power consumption of the device could be as low as 0.076 pJ when the power of light spike was 7.36 nW. Taking into account this characterization, with changing of light intensity or wavelength, the contrast of the image was enlarged, which can further promote the image recognition accuracy. More significantly, this CsPbBr 3 /TIPS hybrid film can be fabricated by facile and low-cost solution processes. This study indicates the great potential of solution-processed perovskite-based light-stimulated synapses for future artificial visual systems.

This review mainly focuses on the recent important work on stability-enhanced strategies of luminescent materials. Various strategies on the fabrications have been summarized and corresponding optoelectronic applications are presented.

Delayed hospital discharges for patients needing rehabilitation in a postacute setting can exacerbate hospital-acquired mobility loss, prolong functional recovery, and increase costs. Systematic measurement of patient mobility by nurses early during hospitalization has the potential to help identify which patients are likely to be discharged to a postacute care facility versus home. To test the predictive ability of this approach, a machine learning classification tree method was applied retrospectively to a diverse sample of hospitalized patients (N = 805) using training and validation sets. Compared with patients discharged to home, patients discharged to a postacute facility were older (median, 64 vs 56 years old) and had lower mobility scores at hospital admission (median, 32 vs 41). The final decision tree accurately classified the discharge location for 73% (95%CI:67%-78%) of patients. This study emphasizes the value of systematically measuring mobility in the hospital and provides a simple decision tree to facilitate early discharge planning.

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