Biodegradable and renewable poly(lactic acid) (PLA) fibrous membranes have emerged as a promising approach to replace traditional polymeric air filters, but are still challenged by relatively low electroactivity and poor electret properties. Herein, we disclose an effective electrospinning strategy to substantially promote the electroactivity by extreme orientation of stereocomplexed PLA (OSC-PLA), conferring significant alignment and refinement of the electrospun nanofibers (diameter of ∼370 nm), as well as facilitate the formation of electroactive phases (i.e., β-phase, electrospun phase, and stereocomplex crystals). Benefiting from the well-tailored morphology and remarkable electroactivity, the OSC-PLA nanofibrous membranes (NFMs) were characterized by largely improved dielectric properties, surface potential (over 3.64 kV), and PM0.3 filtration properties (93.33% at 85 L/min) compared to the pure PLLA membrane (2.01 kV and 80.21%). It enabled the assembly of triboelectric nanogenerator (TENG) air filters using the OSC-PLA NFMs as the active layer, yielding excellent charge regeneration mechanisms and an output current of nearly 80 nA, as driven by respiratory vibrations at 85 L/min, thus permitting high humidity resistance, as exemplified by a PM0.3 removal efficiency of 95.02% at a RH of 90%. Moreover, deep machine learning and man–machine interaction were integrated with the OSC-PLA-based TENGs, demonstrating long-term monitoring and a high-precision diagnosis (accuracy of 96.88%) of various respiratory patterns. The proposed ecofriendly and electroactive ultrafine PLA nanofibers are highly appealing for sustained respiratory healthcare and self-powered intelligent diagnosis.
Xiaobin Ren,Zhirong Liu,Song Wang,Zelin Cao,Kaikai Gao,Guangdong Zhou,Lu Ye,Bai Sun
Despite the progress in machine vision, existing image recognition systems still operate in isolation from the physical world, lacking dynamic temporal encoding, in-memory processing, and intrinsic interpretability. Here, a memristive vision architecture that integrates multimodal sensing, physical memory, and medical inference within a unified device-level framework is developed. Specifically, spatial gradients from retinal fundus images are encoded into Ag/Indigo/FTO memristor array via pulse modulation, generating conductance maps (“G maps”) that serve both as nonvolatile memory and as neuromorphic inputs for lesion segmentation via lightweight U-Net models. Beyond static vision, the same memristor exhibits strong temporal sensitivity, enabling biologically realistic nociceptive encoding and Morse code recognition. These results demonstrate a cross-task, cross-scale system that merges physical encoding with machine intelligence, bridging the gap between material dynamics and high-level perception. Therefore, this paradigm offers a scalable, interpretable hardware foundation for next-generation bio-perceptive computing and intelligent diagnostics.
危险品化学品经营许可证（带存储）