Liquid-in-liquid printing of 3D and mechanically tunable conductive hydrogels

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作者
Xinjian Xie
Zhonggang Xu
Xin Yu
Hong Jiang
Hongjiao Li
Wenqian Feng
机构
[1] Sichuan University,College of Polymer Science and Engineering
[2] Sichuan University,Department of Pancreatic Surgery, Department of Biotherapy, West China Hospital
[3] Sichuan University,College of Chemical Engineering
[4] Sichuan University,State Key Laboratory of Polymer Materials Engineering
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Nature Communications | / 14卷
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摘要
Conductive hydrogels require tunable mechanical properties, high conductivity and complicated 3D structures for advanced functionality in (bio)applications. Here, we report a straightforward strategy to construct 3D conductive hydrogels by programable printing of aqueous inks rich in poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) inside of oil. In this liquid-in-liquid printing method, assemblies of PEDOT:PSS colloidal particles originating from the aqueous phase and polydimethylsiloxane surfactants from the other form an elastic film at the liquid-liquid interface, allowing trapping of the hydrogel precursor inks in the designed 3D nonequilibrium shapes for subsequent gelation and/or chemical cross-linking. Conductivities up to 301 S m−1 are achieved for a low PEDOT:PSS content of 9 mg mL−1 in two interpenetrating hydrogel networks. The effortless printability enables us to tune the hydrogels’ components and mechanical properties, thus facilitating the use of these conductive hydrogels as electromicrofluidic devices and to customize near-field communication (NFC) implantable biochips in the future.
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  • [1] Jiang Y(2022)Topological supramolecular network enabled high-conductivity, stretchable organic bioelectronics Science 375 1411-1417
  • [2] Benabid AL(2019)An exoskeleton controlled by an epidural wireless brain–machine interface in a tetraplegic patient: a proof-of-concept demonstration Lancet Neurol. 18 1112-1122
  • [3] Yang X(2019)Bioinspired neuron-like electronics Nat. Mater. 18 510-517
  • [4] Daly JJ(2008)Brain–computer interfaces in neurological rehabilitation Lancet Neurol. 7 1032-1043
  • [5] Wolpaw JR(2020)Materials for flexible bioelectronic systems as chronic neural interfaces Nat. Mater. 19 590-603
  • [6] Song E(2019)Second Skin Enabled by Advanced Electronics Adv. Sci. 6 1900186-397
  • [7] Li J(2022)Spelling interface using intracortical signals in a completely locked-in patient enabled via auditory neurofeedback training Nat. Commun. 13 1902062-422
  • [8] Won SM(2020)Flexible Hybrid Electronics for Digital Healthcare Adv. Mater. 32 386-1376
  • [9] Bai W(2018)Organic electronics for neuromorphic computing Nat. Electron. 1 412-689
  • [10] Rogers JA(2019)Fully portable and wireless universal brain–machine interfaces enabled by flexible scalp electronics and deep learning algorithm Nat. Mach. Intell. 1 1368-94