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Direct-Print 3D Electrodes for Large-Scale, High-Density, and Customizable Neural Interfaces
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Advanced Science - 2024 - Wang - Direct‐Print 3D Electrodes for Large‐Scale High‐Density and Customizable Neural (1).pdf
Date
2025-01-20
Author
Wang, Pingyu
Wu, Eric G.
Uluşan, Hasan
Zhao, Eric Tianjiao
Phillips, A. J.
Kling, Alexandra
Hays, Madeline Rose
Vasireddy, Praful Krishna
Madugula, Sasidhar
Vilkhu, Ramandeep
Hierlemann, Andreas
Hong, Guosong
Chichilnisky, E. J.
Melosh, Nicholas A.
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Silicon-based microelectronics can scalably record and modulate neural activity at high spatiotemporal resolution, but their planar form factor poses challenges in targeting 3D neural structures. A method for fabricating tissue-penetrating 3D microelectrodes directly onto planar microelectronics using high-resolution 3D printing via 2-photon polymerization and scalable microfabrication technologies are presented. This approach enables customizable electrode shape, height, and positioning for precise targeting of neuron populations distributed in 3D. The effectiveness of this approach is demonstrated in tackling the critical challenge of interfacing with the retina-specifically, selectively targeting retinal ganglion cell (RGC) somas while avoiding the axon bundle layer. 6,600-microelectrode, 35 mu m pitch, tissue-penetrating arrays are fabricated to obtain high-fidelity, high-resolution, and large-scale retinal recording that reveals little axonal interference, a capability previously undemonstrated. Confocal microscopy further confirms the precise placement of the microelectrodes. This technology can be a versatile solution for interfacing silicon microelectronics with neural structures at a large scale and cellular resolution.
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Ecological Notes (EN)
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New Deep-Sea Scorpaenidae (NDSS)
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Western Pacific Ocean (WPO)
URI
https://hdl.handle.net/11511/113697
Journal
ADVANCED SCIENCE
DOI
https://doi.org/10.1002/advs.202408602
Collections
Department of Electrical and Electronics Engineering, Article
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BibTeX
P. Wang et al., “Direct-Print 3D Electrodes for Large-Scale, High-Density, and Customizable Neural Interfaces,”
ADVANCED SCIENCE
, vol. 12, no. 3, pp. 0–0, 2025, Accessed: 00, 2025. [Online]. Available: https://hdl.handle.net/11511/113697.