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3D spatiotemporally scalable in vivo neural probes based on fluorinated elastomers

Nature Nanotechnology volume 19, pages 319–329 (2024)Cite this article

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Abstract

Electronic devices for recording neural activity in the nervous system need to be scalable across large spatial and temporal scales while also providing millisecond and single-cell spatiotemporal resolution. However, existing high-resolution neural recording devices cannot achieve simultaneous scalability on both spatial and temporal levels due to a trade-off between sensor density and mechanical flexibility. Here we introduce a three-dimensional (3D) stacking implantable electronic platform, based on perfluorinated dielectric elastomers and tissue-level soft multilayer electrodes, that enables spatiotemporally scalable single-cell neural electrophysiology in the nervous system. Our elastomers exhibit stable dielectric performance for over a year in physiological solutions and are 10,000 times softer than conventional plastic dielectrics. By leveraging these unique characteristics we develop the packaging of lithographed nanometre-thick electrode arrays in a 3D configuration with a cross-sectional density of 7.6 electrodes per 100 µm2. The resulting 3D integrated multilayer soft electrode array retains tissue-level flexibility, reducing chronic immune responses in mouse neural tissues, and demonstrates the ability to reliably track electrical activity in the mouse brain or spinal cord over months without disrupting animal behaviour.

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Fig. 1: Implantable neural probes for spatiotemporally scalable in vivo electrophysiology.
Fig. 2: Fluorinated elastomers as soft and long-lived dielectrics.
Fig. 3: 3D integration of high-density soft microelectrode arrays for scalable neural probes.
Fig. 4: Long-term stable brain electrophysiology and implantation with reduced immune responses by scalable soft neural probes.
Fig. 5: Long-term stable spinal cord recording.

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Data availability

All data and materials that supporting the findings of this study are available within the paper and its Supplementary Information. Source data are provided with this paper.

Code availability

The authors declare that all the code supporting the findings of this study are available within the paper and its Supplementary Information. DeepLabCut is available at https://github.com/DeepLabCut/DeepLabCut. Mountainsort 4 is available at https://github.com/flatironinstitute/mountainsort. DropSnake is available at https://bigwww.epfl.ch/demo/dropanalysis/.

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Acknowledgements

We acknowledge the discussion and assistance from all Liu Group members. We acknowledge the support from NSF through the Harvard University Materials Research Science and Engineering Center Grant No. DMR-2011754; the Harvard University Center for Nanoscale Systems supported by the NSF; and the Aramont Fund for Emerging Science Research.

Author information

Author notes

  1. These authors contributed equally: Paul Le Floch, Siyuan Zhao, Ren Liu.

Authors and Affiliations

  1. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA

    Paul Le Floch, Siyuan Zhao, Ren Liu, Nicola Molinari, Eder Medina, Hao Shen, Junsoo Kim, Hao Sheng, Sebastian Partarrieu, Wenbo Wang, Guogao Zhang, Xin Tang, Katia Bertoldi, Boris Kozinsky, Zhigang Suo & Jia Liu

  2. Axoft, Inc., Cambridge, MA, USA

    Paul Le Floch, Hyunsu Park, Xian Gong, Andrew Spencer, Jongha Lee & Tianyang Ye

  3. Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX, USA

    Zheliang Wang & Nanshu Lu

  4. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Chanan Sessler & Xiao Wang

  5. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

    Chanan Sessler & Xiao Wang

  6. Robert Bosch LLC Research and Technology Center, Watertown, MA, USA

    Boris Kozinsky

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  1. Paul Le Floch
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Contributions

P.L.F., S.Z., R.L. and J. Liu designed the experiments. P.L.F., H. Sheng, G.Z., C.S., W.W., X.W. and A.S. synthesized the fluorinated polymers. P.L.F. characterized the dielectric stability of polymer thin films. N.M. and B.K. conducted the MD simulations. P.L.F., R.L., H. Sheng, H.P. and X.G. developed the nanofabrication process for the neural probes. P.L.F., T.Y., J. Lee, X.G. and R.L. characterized the electrical properties of the neural probes. R.L. performed the SEM and FIB characterization. P.L.F. and S.Z. performed the animal experiments. P.L.F., S.P., H. Shen and X.T. analysed the electrophysiological recordings from behaving animals. J.K., Z.W., N.L. and Z.S. simulated and characterized the mechanical properties of the materials and devices. E.M. and K.B. conducted the finite element analysis. P.L.F. and J. Liu wrote the manuscript. All authors discussed and commented on the manuscript. J. Liu supervised the project.

Corresponding author

Correspondence to Jia Liu.

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Competing interests

P.L.F., H. Sheng and J. Liu are on a patent application filed by Harvard University related to this work. P.L.F., H.P., X.G., A.S., J. Lee, T.Y. and J. Liu declare financial interests in Axoft, Inc. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Modeling of electrical insulation performances of thin-film dielectrics in electrodes.

a, Schematic showing the transmission line model with conductive losses through the thin film dielectric encapsulation in a representative microelectrode structure. Vin, Vout, Zelec, Zamp, rc, cd, and rd denote the input voltage at the electrode tip, the output voltage, the electrode tip electrochemical impedance, the amplifier’s input impedance, the sheet resistance of the conductive line, the capacitance per unit length of the dielectric, and the resistance per unit length of the dielectric, respectively. b, Stripline geometry for the electrode interconnect line insulated by a dielectric. L, hc, and hd denote the length of the electrode, the thickness of the conductor, and the thickness of dielectric encapsulation on each side, respectively. c, Signal attenuation at 10 kHz plotted as a function of hd (for a dielectric constant εd = 3.2) for various thicknesses hc of the metal line (with a conductivity σc = 4.5×107 S/m). d, Signal attenuation as a function of frequency for various values of σc at the lossless (σd = 0) condition or lossy condition (σd = 5.18×10−8 S/m) due to ionic conduction within the dielectric encapsulation37. L = 0.05 m in (c) and (d), hc = 50 nm and hd = 1 µm in (c).

Extended Data Fig. 2 Composite beam model of scalable neural probes.

a, Composite beam model of a neural probe with 2N-1 layers of metal interconnects. hd and hm respectively denote the dielectric encapsulation and metal layer thickness. b, Flexural rigidity in the composite beam model of (a) as a function of the number of metal layers. This is the same simulation as used in Fig. 1g. c, Elastomer-to-plastic neural probes’ flexural rigidity ratio as a function of the number of metal layers. We choose Eplastic = 4 GPa, νplastic = 0.33, Eelastomer = 0.5 MPa, νelastomer = 0.5, Emetal = 79 GPa, νmetal = 0.22, hd = 2 µm and hm = 40 nm. (d, e) X-dependent flexural rigidity considering the split of neutral axis due to extreme modulus mismatch between metal and elastomer layer. d, Schematic for the trilayer beam bending problem. e, Flexural rigidity in the majority of the probe away from the free ends approaches the Euler-Bernoulli prediction. (f-h) Comparison of the mechanical behavior of soft PFPE-DMA and stiff SU-8 neural probes. f, Bending of 9-µm-thick, 500-µm-wide PFPE-DMA (left) and SU-8 (right) neural probes with a 140-nm-thick central Au layer, around a 1-mm-diameter glass capillary. (g, h) Finite element analysis of the bending of PFPE-DMA (left) and SU-8 (right) neural probes with the same geometry as in (f). The maximum principal strain is represented in the central metal layer (g) and the upper dielectric layer (h).

Extended Data Fig. 3 MD simulations.

(a, b) Simulated PFPE-DMA polymer chains before (a) and after (b) equilibration. The structure contains a water molecule enlarged for visibility. Black arrow is a cartoon view depicting the diffusion of the molecule during the simulation. c, Comparison of polymer densities obtained by MD simulation to data from the literature. d, Free volume percentage versus density of polymers obtained by MD simulations. Value = mean ± S.D. and n = 5 for each material in panels (c) and (d).

Source data

Extended Data Fig. 4 Electrochemical impedance spectroscopy (EIS) of dielectric polymer thin film and crosstalk between encapsulated microelectrodes in physiological solution over time.

a, Schematic showing the equivalent circuits of the dielectric polymer film in the three electrodes setup during EIS. RS and Zi represent the electrolyte resistance and interfacial impedance between the electrolyte and dielectric polymer, respectively. Cd and Rd represent the capacitance and resistance (due to the built-in ionic conductivity) of the dielectric polymer film, respectively. b, Schematic of a typical Nyquist plot of the electrochemical impedance measured in (a) adapted from Bard et al.41 As the thickness of the dielectric layer defines Cd, Rd can be extracted by fitting the Nyquist plot to a semi-circle at high frequencies37. c, Schematic of a typical Bode plot of the electrochemical impedance in (a). At low frequencies, deviation from the purely capacitive behavior of the dielectric polymer can be observed due to the built-in ionic conductivity. d, Averaged Bode plots (value = mean ± S.D., n = 4, 4, 4, 8, 12, 4, 3, 4 samples for PFPE-DMA, PHFIPA, PPFHEA, PDMS, H-SEBS, PIB, PI, SU-8, respectively) of modulus (top) and phase (bottom) of the normalized electrochemical impedance of dielectric polymers in their pristine condition and after aging in 10x PBS at 70 °C (at t/H2 = 5 days/µm2 for PFPE-DMA, PHFIPA, PPFHEA, PDMS, H-SEBS, PI, SU-8 and t/H2 = 1.55 days/µm2 for PDMS*). (e, f), Modulus of the normalized electrochemical impedance of various dielectric elastomers at 1 Hz (e) and 1 kHz (f) as a function of the normalized soaking time by the square of the sample thickness in 10x PBS at 70 °C (value = mean ± s.e.m., n = 4, 4, 4, 8, 12, 4, 3, 4 samples for PFPE-DMA, PHFIPA, PPFHEA, PDMS, H-SEBS, PIB, PI, SU-8, respectively). In (f), t1/2/H2 corresponds to the time required to decrease the initial modulus of the normalized impedance (Z0) by 50%. *PDMS samples degradation is very fast and debonding from the working electrode occurred around t/H2 = 2 days/µm2. Therefore, experiments for PDMS characterization were stopped before reaching t/H2 = 5 days/µm2. The vertical scale break is used to plot the lowest point in the PDMS curve. g, Schematics of the device used for the crosstalk test. The device consists of an array of 16 conductive metal interconnects regularly spaced over two layers and encapsulated by 1.2-µm-thick PFPE-DMA layers. The test devices are immersed in a grounded 1x PBS solution during measurement. h, Crosstalk measured as a function of frequency between channel 1 and the 15 other interconnects of the array (n = 15 interconnects). i, Crosstalk measured as a function of frequency for four interconnects located at the same position for three different samples (n = 4 for each device). j, Crosstalk at 1 kHz for a given sample (n = 15 interconnects) plotted as a function of soaking time in 1x PBS.

Source data

Extended Data Fig. 5 External Electrolyte Conductimetry (EEC) of dielectric polymer thin film.

a, Schematics of the experimental design. b, Schematics of equivalent boundary value problem and results for the diffusion of ions during EEC measurement. The schematics show ionic concentration inside the ion-filled polymer thin film over time of soaking in DI water. C0 represents the initial concentration of ions in the polymer thin film, and C is the concentration of ions in the DI water. c, Fitting of the experimental results to obtain ionic diffusivity D. d, Estimation of ionic diffusivity, D, and solubility, S, obtained by EEC for dielectric polymer thin films. (e-g) Temperature dependence of ionic diffusivity, solubility, and permeability of polymer thin films characterized by EEC measurement. e, Logarithm of the ionic diffusivity, D, measured by the EEC measurement, as a function of 1000/T (K−1). f, Logarithm of the ionic solubility, S, measured by EEC measurement, as a function of 1000/T (K−1). g, Logarithm of the ionic permeability, P, defined as the product S*D as a function of 1000/T (K−1). The linear fits in (e-g) were used to obtain energy parameters in Supplementary Table 7. T is the temperature for (e-g).

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Extended Data Fig. 6 Mechanical testing of PFPE-DMA films under various conditions.

(a-e) Tensile testing of PFPE-DMA films. a, Stress-stretch curve in uniaxial tension to rupture. The data are from Fig. 2h. b, Stress-stretch curve in uniaxial tension, with a maximum stretch of 1.1 to determine the elastic modulus (strain rate of 0.2 s−1). c, Cyclic testing with a progressive increase in the maximum stretch. d, Cyclic testing with a progressive increase in strain rate to show the rate-dependency of the hysteresis. e, Pure shear test of two specimens with a pre-crack to determine fracture toughness. We estimate a fracture toughness of 128 and 261 J/m2 for each specimen. (f-k) Adhesion energy measurements between PFPE-DMA layers. (f, g) PFPE-DMA – PFPE-DMA adhesion energies measured with the 90° peel test for two samples (using a stainless-steel microwire mesh in (f) and a Nylon mesh in (g)) as a function of displacement. As the displacement first increases, the crack blunts but does not grow, and the force increases (thus the energy measured) until the crack starts to propagate. The plateau characterizes the adhesion energy. Higher peeling rates correspond to higher adhesion energy values, due to the viscoelasticity of PFPE-DMA. h, PFPE-DMA – glass adhesion energy is lower than PFPE-DMA - PFPE-DMA adhesion energy in (f, g). i, Schematic of the 90° peel test. A microwire mesh (stainless steel or Nylon) lays on top of the first layer of PFPE-DMA solvent cast and hard-baked on a glass slide. The second layer of PFPE-DMA, thicker than the microwire mesh, is solvent casted and hard-baked on top of the mesh. Polyimide tape prevents the adhesion of the microwire mesh to the bottom layer of PFPE-DMA on one side, to initiate the pre-crack. j, Photograph of a sample, tweezers show the side with the polyimide tape for the pre-crack. k, Photograph taken during the 90° peel test. l, Delamination-free large deformation of multilayer PFPE-DMA neural probes. Photograph of a 4-metal layer PFPE-DMA neural probe on a thick, stretchable H-SEBS substrate before, during, and after uniaxial stretching. m, Resistance of metal interconnects in PFPE-DMA neural probes before and after uniaxial stretch. Relative changes in the resistance of the metal interconnects before (n = 3 interconnects) and after (n = 3 interconnects) releasing the device from the fabrication substrate and applied 2% (n = 1 sample) and 5% (n = 1 sample) uniaxial stretch (value = mean ± S.D. when n > 1 samples). (n-t) SEM images of a multilayer soft neural probe after undergoing an accelerated aging test in 1x PBS at 65 °C for 10 days. SEM overview of the probe (n) with three FIB-cut positions labeled in green, orange, and red, corresponding to cross-sections along the interconnect direction at the interface of electrode and interconnect (o, r), (p, s) parallel to the interconnects, (q, t) perpendicular to the interconnects.

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Extended Data Fig. 7 Nanofabrication of soft neural probes.

a, Schematics showing the stepwise nanofabrication process of 3D stacked soft electrode arrays encapsulated by PFPE-DMA elastomers. (b, c) Schematic design (b) and photographic image (c) showing the photomask aligner-compatible, 3D-printed Nitrogen diffuser that provides the Nitrogen atmosphere for an oxygen-free photolithography. The Nitrogen diffuser is compatible with the standard 3-inch chuck of a Karl Suss MA6 mask aligner. (d, e) Schematics show the side views of the fabrication substrate inside a photomask aligner without (d) or with (e) a photolithographically defined SU-8 spacer. Without the SU-8 spacer, the direct contact to the photomask damages the soft fluorinated elastomer precursor spin-coated onto the substrate (a). In contrast, with the spacer, the few micrometers gap between the PFPE-DMA surface and the photomask preserves the integrity and smooth surface of PFPE-DMA film, allowing for high-resolution patterning required for the soft brain probes fabrication. (f-h) Enabling photolithographic patterning of metals by plasma treatment of PFPE-DMA films. f, Photograph of DI water (top) and LOR 3 A (bottom) drops on the PFPE-DMA film surfaces in their pristine state, immediately after 6 min plasma treatment, and 1 hr in ambient condition after the plasma treatment. (g, h), DI water (g) and LOR3A (h) drops contact angles as a function of plasma parameters. n = 4 drops for each sample (except ‘DI Water - 2 min, t = 0’, ‘LOR3A - Pristine’ and ‘LOR3A - 6 min, t = 0’ for which n = 3, 5 and 5 drops respectively), value = mean ± S.D. *** p < 0.001, two-tailed, unpaired t-test. The results suggest a significant decrease in LOR 3 A contact angle after 6 min plasma treatment, which, however, can only last for less than 30 min. (i-k) Bonding input/output (I/O) metal pads to flexible cables. i, Schematic of the side view of a brain probe showing that I/O pads are defined on the silica substrate for bonding and connected to interconnects through the smooth edge of the slightly overexposed PFPE-DMA dielectric layer. Flexible cables are bonded through the anisotropic conductive film to I/O pads. j, BF microscopic image of a 250-µm-wide I/O pad. Scale bar, 50 µm. k, Zoom-in view of the red box highlighted region in (j). The focal plane in the images has been adjusted stepwise (indicated by black arrows) to show that the sputtered Al/Au layer deposited on the smooth edge of the PFPE-DMA layer connected with the Cr/Au I/O pads. l, Schematic showing that smooth edges of the overexposed PFPE-DMA dielectric layers are necessary for ensuring the metal interconnects from the PFPE-DMA layers can connect to the I/O regions on the silicon oxide substrate. m, Pseudo-colored SEM images showing the metal interconnects patterned from the PFPE-DMA layers to the I/O pads on the substrate. Pseudo-colors were used to highlight different metal and PFPE-DMA layers.

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Extended Data Fig. 8 Imaging and characterization of soft neural probes.

(a-c) Array of PFPE-DMA neural probes containing four layers of electrode arrays with 64 channels. The array is positioned next to a ten-cent dime in (a), floating on the surface of a water droplet in (b), and bent on a thick H-SEBS substrate in (c). d, Stitched BF optical images showing an array of PFPE-DMA neural probes, each containing four layers of electrode arrays with 64 channels, on a 100-nm-thick nickel layer pattern on a silicon wafer. e, Zoom-in BF image showing the sensor region of a representative neural probe. f, Height profile along the blue arrow in (e) measured by profilometer, showing the smooth PFPE-DMA and metal layers. g, Zoom-in view of the region highlighted by the red dashed box in (e). (h, i), SEM images of the neural probe design seen in (d, e, g). j-k, SEM images of curved edge PFPE-DMA features on a silicon oxide wafer. l, Optical images of high-density Au interconnects with a width of 2.4 µm and pitch of 5.0 µm. m, FIB combined with SEM showing the cross-section of the red dashed box highlighted region in the inset. (n, o) FIB combined with SEM images showing the cross-section before and after stretching the neural probe with four layers of electrode arrays to 20% and releasing them. p, Resistance measurement of Al/Au interconnects on PFPE-DMA with different length-to-width ratios. The Al/Au interconnects have a thickness of 40/100 nm. q, Wafer-scale fabrication of PFPE-DMA neural probes on two 3-inch silicon oxide wafers. Each wafer contains 9 probes. (r, s) Electroplating of Pt black on the metal electrodes in PFPE-DMA neural probes. r, Sputtered Al/Au electrodes after PEDOT:PSS electrodeposition. s, Sputtered Al/Au electrodes after Pt Black electrodeposition by cyclic voltammetry. t, Chronic impedance measurements of PFPE-DMA neural probes. Impedance modulus of 40 µm-diameter electrodes with sputtered Pt immediately after device release and after soaking in 1× PBS at 37 °C, n = 14 electrodes, value = mean ± S.D.

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Extended Data Fig. 9 Implantation of soft neural probes in the mice brain.

(a, b) Schematics showing the shuttle-driven implantation technique for elastomer-based brain probes. a, The brain probe held by a SU-8 plastic frame was transferred to the brain surface. b, Schematics showing the implantation steps (side view). (I) After positioning the probe on the brain surface, a tungsten shuttle (core diameter of 70 µm, conically etched at the tip) pokes through a hole at the top of the device and penetrates the brain tissue. (II) Once the desired depth of implantation is reached, the tungsten shuttle is withdrawn from the brain tissue. (III) The SU-8 plastic frame, lying on the surface of the brain, is removed before (IV) sealing the craniotomy area with dental cement. c, Shuttle-driven implantation during craniotomy. A soft brain probe is guided inside brain tissues by a tungsten shuttle which pokes through a circular hole at the top of the device. d, Schematic of measurement setup for head-fixed mouse.

Extended Data Fig. 10 Long-term stable brain electrophysiology by a 2-µm-thick, single metal-layer soft neural probe.

a, (Top) Typical unit activities (300–3,000 Hz bandpass filtered) from a PFPE-DMA soft brain probe at 4-week post-implantation. (Bottom) Unit activities of the dashed red box highlighted region in the top panel. b, Average single-unit waveforms detected in (a). c, Representative average waveforms from 2- to 10-week post-implantation. d, Clusters of waveforms detected for each channel in (c) as a function of post-implantation weeks in the PCA space. PC1 and PC2 represent the first two axes in the PCA from the clustering algorithm. (e-g) Noise level per channel (e), peak-valley amplitude (f), and firing rate (g) of the units detected in (c) (n = 8 units, mean ± S.D.) vs. implantation time. h, Biweekly evolution of the ISI distribution for each unit shown in panel (c), bin size 2 ms. i, Waveform similarity of the units detected in (c-h) (n = 8 units, mean ± S.D.) as a function of post-implantation weeks compared.

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Supplementary information

Supplementary Information

Supplementary Discussions 1–6 and Tables 1–9.

Reporting Summary

Supplementary Video 1

SU-8 and PFPE-DMA neural probes with the same geometry and thickness are poked and stretched with tweezers. The SU-8 probe fractures easily whereas the PFPE-DMA probe withstands large deformations.

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Source Data Fig. 1

Source data and literature data.

Source Data Fig. 2

Source data for electrochemical measurements.

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Source data for impedance measurements and literature data.

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Source data for electrophysiological measurements.

Source Data Fig. 5

Source data for histological and behaviour experiments.

Source Data Extended Data Fig. 3

Source data for MD simulations.

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Source data for impedance and crosstalk measurements.

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Source data for ionic conductivity measurements.

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Source data for mechanical properties testing.

Source Data Extended Data Fig. 7

Source data for contact angle measurements.

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Source data for characterization of the devices.

Source Data Extended Data Fig. 10

Source data for electrophysiological measurements.

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Le Floch, P., Zhao, S., Liu, R. et al. 3D spatiotemporally scalable in vivo neural probes based on fluorinated elastomers. Nat. Nanotechnol. 19, 319–329 (2024). https://doi.org/10.1038/s41565-023-01545-6

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