2026-10-11T12:37:14Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33945323 2026-09-20T14:04:11Z category_146 category_7 category_21 category_272 category_873 category_502 category_734 category_931 category_135 portal_63 item_type_2 month_year_09_2026

Multimodal Perception through Synergistic Interface Fusion and Machine-Learning Decoupling in Vertically Integrated Flexible Sensors Lu Wang (45927) Langyuan Cao (25077890) Jianhua Fan (115961) Jiaze Ding (25077893) Changchao Zhang (9924844) Kunyang Wang (2917784) Zhiwu Han (1575754) Luquan Ren (482357) Space Science Medicine Biotechnology Environmental Sciences not elsewhere classified Chemical Sciences not elsewhere classified Astronomical and Space Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Computational Biology synergistic interface fusion random forest algorithm precision multiparameter sensing combining structural isolation 80 – 95 70 – 80 suppresses multimodal interference 57 %/% rh pcrm composite enhances rh ), respectively 8 %/% rh multimodal perception ultrathin six tangential interfacial study establishes square pressure signal decoupling signal crosstalk planar layouts novel vertical material system material coupling learning decoupling layer architecture interfacial adhesion highly integrated 285 mpa Flexible integrated sensors have been constrained by signal crosstalk from planar layouts and material coupling, restricting the capability for high-precision multiparameter sensing. Here, we develop a biomimetic vertically integrated flexible sensor (BVIS) based on a poly(vinyl alcohol)-cellulose nanofiber-reduced graphene oxide multiwalled carbon nanotube (PVA-CNF-RGO-MCNT, PCRM) material system. Serpentine temperature-sensitive, humidity-sensitive, and square pressure-sensitive arrays are assembled into an ultrathin six-layer architecture through a water-induced interfacial fusion mechanism. The molecular-level interpenetrating network formed by the PCRM composite enhances the interfacial adhesion and suppresses multimodal interference. The humidity units exhibit sensitivities of 2.8%/%RH (70–80% RH) and 8.57%/%RH (80–95% RH), respectively, while the tangential interfacial and T-peel strengths reach 2.592 and 0.285 MPa. By combining structural isolation with a random forest algorithm for signal decoupling, the platform achieves recognition accuracies of 96.79% and 99.8% for temperature-position and humidity-position mapping, respectively. The study establishes a novel vertical-layered multimodal sensing paradigm for highly integrated and anticrosstalk flexible intelligent sensing systems. 2026-09-20T00:00:00Z Dataset Media 10.1021/acs.nanolett.6c01353.s003 https://figshare.com/articles/media/Multimodal_Perception_through_Synergistic_Interface_Fusion_and_Machine-Learning_Decoupling_in_Vertically_Integrated_Flexible_Sensors/33945323 CC BY-NC 4.0