{
  "abstract": "Long-afterglow light-emitting devices (LALEDs), which combine the capabilities of sensing, memory, processing, and display integration, allow for the simultaneous implementation of optical and electrical in-sensor computing. However, the inherently low conductivity of conventional deep-ultraviolet (DUV) responsive materials hinders their use in the DUV-responsive LALEDs (DUV-LALEDs). Herein, we demonstrate that sol-gel-fractured indium-magnesium oxide (InMgO) concurrently exhibits ideal nano-interface for DUV photon absorption and semiconductor crystal for efficient charge transport via hopping, enabling to reach high mobility (0.6 cm2 V−1 s−1), excellent memory dynamic range (70 dB), and responsivity (523.7 A/W). The InMgO-based DUV-LALEDs display an electrical and optical post-synaptic output when irradiated with DUV light. Moreover, hardware-level noise-filtering processes to the pre-synaptic weight are revealed in the DUV-LALEDs, emerging as the inhibition of light emission due to the insufficient post-synaptic charge injection from the channel layer. Consequently, an adjustable noise-filtering in-sensor computing is successfully achieved by modulating the channel length. By taking advantage of the DUV-LALED multifunctional nature, fusion-node reservoir computing networks are employed to accomplish multi-dimensional recognition tasks, displaying a high recognition accuracy of 99%. These findings demonstrate that the joint materials and devices optimization is a powerful strategy for fabricating cost-efficient DUV analytical chips.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "Zhongshi Ju"
    },
    {
      "name": "Peng Li"
    },
    {
      "name": "Jingsong Yuan"
    },
    {
      "name": "Boyuan Yu"
    },
    {
      "name": "Bin Han"
    },
    {
      "name": "Yusheng Chen"
    },
    {
      "name": "Jiangang Ma"
    },
    {
      "name": "Haiyang Xu"
    },
    {
      "name": "Yichun Liu"
    },
    {
      "name": "Paolo Samorì"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/06trkmeptw9m7.bib",
  "cited_by": 0,
  "doi": "10.1002/adma.74481",
  "fulltext_url": null,
  "id": "06trkmeptw9m7",
  "issns": [
    "0935-9648",
    "1521-4095"
  ],
  "landing_url": "https://univoak.hal.science/hal-05842257v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.chim"
    ],
    [
      "openalex",
      "2208|Advanced Memory and Neural Computing"
    ]
  ],
  "openalex": "W7197035382",
  "pdf_url": "https://univoak.hal.science/hal-05842257/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-06",
  "publisher": "Wiley-VCH Verlag",
  "ris": "https://research.draconitic.com/paper/06trkmeptw9m7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "hal-05842257",
      "source": "hal",
      "url": "https://univoak.hal.science/hal-05842257v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "hal:0.chim",
      "subfield": "general"
    }
  ],
  "title": "Metal Oxide Nano-Interface Boosting the Deep Ultraviolet Adjustable Noise-Filtering In-Sensor Computing",
  "title_text": "Metal Oxide Nano-Interface Boosting the Deep Ultraviolet Adjustable Noise-Filtering In-Sensor Computing",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/06trkmeptw9m7",
  "venue": "Advanced Materials",
  "versions": []
}