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In this study, structurally novel silicone\u2010based conductive adhesive from comb\u2010shaped supramolecular elastomers is reported, featuring self\u2010monitoring of adhesion state in real time manner via machine learning\u2010assisted traffic light color\u2010coding approach. Comb\u2010shaped supramolecular ion\u2010conducting polysiloxane P(Ba\u2010\n                    <jats:italic>co<\/jats:italic>\n                    \u2010Apy\u2010\n                    <jats:italic>co<\/jats:italic>\n                    \u2010DMS) are first constructed via effective hydrosilylation, yielding pendant H\u2010bonding barbiturate (Ba) and ionic liquids (Apy) moieties. Adhesion\u2010sensitive electrical properties and experimental database are therefore constructed from P(Ba\u2010\n                    <jats:italic>co<\/jats:italic>\n                    \u2010Apy\u2010\n                    <jats:italic>co<\/jats:italic>\n                    \u2010DMS) via digitally transforming of adhesion strength into capacitance signals. Such database is further used to train a hybrid deep learning architecture that integrates 1D convolutional neural networks (1D\u2010CNN) with long short\u2010term memory (LSTM) units. This model learns the capacitance\u2010adhesion mapping and translates the encoded signal features into States 1, 2, and 3. Moreover, a traffic light color\u2010coding approach on the perceptions of adhesion strength is developed, green, orange, and red LED light symbols are respectively associated with States 1, 2, and 3 leveraged from deep learning architecture, reflecting tight, stretched, and fractured adhesion. Our conductive adhesives illustrate the Internet of Things\u2010empowered adhesion state and structural health monitoring, offering promising opportunity to boost intelligentization and informatization of classical materials.\n                  <\/jats:p>","DOI":"10.1002\/adma.74834","type":"journal-article","created":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T03:45:51Z","timestamp":1787975151000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real Time Self\u2010Monitoring of Adhesion State via Machine Learning\u2010Assisted Traffic Light Color\u2010Coding in Silicone\u2010Based Smart Superglue"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8434-9539","authenticated-orcid":false,"given":"Qizhe","family":"Deng","sequence":"first","affiliation":[{"name":"Key Laboratory of Materials Chemistry for Energy Conversion and Storage Ministry of Education (HUST) School of Chemistry and Chemical Engineering Huazhong University of Science and Technology (HUST)  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6289-3261","authenticated-orcid":false,"given":"Yanfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Materials Science and Opto\u2010electronic Technology University of Chinese Academy of Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yantao","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Materials Chemistry for Energy Conversion and Storage Ministry of Education (HUST) School of Chemistry and Chemical Engineering Huazhong University of Science and Technology (HUST)  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Da","family":"Zheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Materials Chemistry for Energy Conversion and Storage Ministry of Education (HUST) School of Chemistry and Chemical Engineering Huazhong University of Science and Technology (HUST)  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5599-6819","authenticated-orcid":false,"given":"Senbin","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Materials Chemistry for Energy Conversion and Storage Ministry of Education (HUST) School of Chemistry and Chemical Engineering Huazhong University of Science and Technology (HUST)  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jintao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Materials Chemistry for Energy Conversion and Storage Ministry of Education (HUST) School of Chemistry and Chemical Engineering Huazhong University of Science and Technology (HUST)  Wuhan 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