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Solution-Processed Organic UV-Sensitive Neuromorphic Sensors for Healthy Protection Application
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DOI：10.1021/acsami.6c05141.png)
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Abstract
En 中文
Ultraviolet (UV) radiation is a key environmental trigger for the flare-ups of systemic lupus erythematosus (SLE), making personalized UV monitoring and early warning an urgent unmet clinical need for SLE management. Conventional UV detectors, however, cannot adapt to the cumulative and delayed characteristics of UV-induced damage in SLE patients or the interindividual differences in UV sensitivity and thus fail to provide personalized protection strategies. To enable precise UV damage assessment and proactive early warning for SLE patients, we developed an organic optoelectronic synaptic device through poly(amic acid) (PAA)/HfO2 heterogeneous interface engineering. Notably, these devices bridge fundamental UV detection with clinical personalization, as their bioinspired integration of “sensing-storage-computation” allows for capturing the complex characteristics of UV damage in SLE. The optimized devices demonstrate outstanding performance, featuring high mobility (27.5 cm2V–1s–1), responsivity (5.9 × 105 AW–1), specific detectivity (4.4 × 1016 Jones), and excellent cycling stability (>5,000 cycles). Functionally, these devices mimic biological sensitization and threshold-triggered responses to reproduce UV damage accumulation, a function unavailable in conventional devices. Dynamic tuning of the optical response threshold through gate voltage further allows customized configuration of early warning levels. Furthermore, the device can generate timely warnings before UV exposure reaches levels linked to SLE flare-ups, thus enabling effective preventive protection. This work offers a new strategy for personalized UV protection in SLE patients and expands the application scope of neuromorphic devices in autoimmune disease management.
Keywords:
Electromagnetic radiation
Insulators
Layers
Thickness
Transistors
systemic lupus erythematosus
solution shearing
synaptic transistors
interface optimization
neuromorphic devices
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