3D printing of conducting polymers

Abstract

Conducting polymers are promising material candidates in diverse applications including energy storage, flexible electronics, and bioelectronics. However, the fabrication of conducting polymers has mostly relied on conventional approaches such as ink-jet printing, screen printing, and electron-beam lithography, whose limitations have hampered rapid innovations and broad applications of conducting polymers. Here we introduce a high-performance 3D printable conducting polymer ink based on poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) for 3D printing of conducting polymers. The resultant superior printability enables facile fabrication of conducting polymers into high resolution and high aspect ratio microstructures, which can be integrated with other materials such as insulating elastomers via multi-material 3D printing. The 3D-printed conducting polymers can also be converted into highly conductive and soft hydrogel microstructures. We further demonstrate fast and streamlined fabrications of various conducting polymer devices, such as a soft neural probe capable of in vivo single-unit recording.

Fig. 1. Design of 3D printable conducting polymer ink.

a, b, Pristine PEDOT:PSS solution (a) can be converted into a 3D printable conducting polymer ink (b) by lyophilization in cryogenic condition and re-dispersion with a solvent. c, 3D-printed conducting polymers can be converted into a pure PEDOT:PSS both in dry and hydrogel states by dry-annealing and subsequent swelling in wet environment, respectively. d CryoTEM image of a pristine PEDOT:PSS solution. e CryoTEM image of a 3D printable conducting polymer ink. f TEM image of a dry-annealed 3D-printed conducting polymer. g–j Images of re-dispersed suspensions with varying PEDOT:PSS nanofibril concentration. k SAXS characterization of conducting polymer inks with varying PEDOT:PSS nanofibril concentration. The d-spacing L is calculated by the Bragg expression L = 2π/q_max. l Apparent viscosity as a function of shear rate for conducting polymer inks of varying PEDOT:PSS nanofibril concentration. m Apparent viscosity of conducting polymer inks as a function of PEDOT:PSS nanofibril concentration. n Shear storage modulus as a function of shear stress for conducting polymer inks of varying PEDOT:PSS nanofibril concentration. o Shear yield stress of conducting polymer inks as a function of PEDOT:PSS nanofibril concentration. For TEM images in (d–f), the experiments were repeated (n = 5) based on independently prepared samples with reproducible results. Scale bars, 100 nm.

Fig. 2. 3D printing of conducting polymers.

a–d SEM images of 3D-printed conducting polymer meshes by 200-µm (a), 100-µm (b), 50-µm (c), and 30-µm (d) nozzles. e Sequential snapshots for 3D printing of a 20-layered meshed structure by the conducting polymer ink. f 3D-printed conducting polymer mesh after dry-annealing. g 3D-printed conducting polymer mesh in hydrogel state. h Sequential snapshots for 3D printing of overhanging features over high aspect ratio structures by the conducting polymer ink. i 3D-printed conducting polymer structure with overhanging features in hydrogel state. Scale bars, 500 µm (a); 200 µm (b–d); 1 mm (a–d, inset panels); 2 mm (e–i).

Fig. 3. Properties of 3D-printed conducting polymers.

a Conductivity as a function of nozzle diameter for 3D-printed conducting polymers in dry and hydrogel states. b Conductivity as a function of bending radius for 3D-printed conducting polymers in dry (17 µm, thickness) and hydrogel (78 µm, thickness) states. PI indicates polyimide. c Conductivity as a function of bending cycles for 3D-printed conducting polymers in dry (17 µm, thickness) and hydrogel (78 µm, thickness) states. d Nyquist plot obtained from the EIS characterization for a 3D-printed conducting polymer on Pt substrate (78 µm, thickness) overlaid with the plot predicted from the corresponding equivalent circuit model. In the equivalent circuit models, R_e represents electronic resistance, R_i represents ionic resistance, R_c represents the total ohmic resistance of the cell assembly, CPE_dl represents the double-layer constant phase element (CPE), whereas CPE_g represents the geometric CPE. CPE is used to account inhomogeneous or imperfect capacitance and are represented by the parameters Q and n where Q represents the peudocapacitance value and n represents the deviation from ideal capacitive behavior. The true capacitance C can be calculated from these parameters by using the relationship C = Qω_maxⁿ⁻¹, where ω_max is the frequency at which the imaginary component reaches a maximum. The fitted values for 3D-printed PEDOT:PSS are R_e = 107.1 Ω, R_i = 105.5 Ω, R_c = 14.07 Ω, Q_dl = 1.467 × 10⁻⁵ F sⁿ⁻¹, n_dl = 0.924, Q_g = 4.446 × 10⁻⁷ F sⁿ⁻¹, and n_dl = 0.647. e CV characterization for a 3D-printed conducting polymer on Pt substrate. f Nanoindentation characterizations for 3D-printed conducting polymers in dry and hydrogel states with the JKR model fits. Values in (a–c) represent the mean and the standard deviation (n = 5 per each testing conditions based on independently prepared samples and performed experiments).

Fig. 4. 3D printing of conducting polymer devices.

a Sequential snapshots for 3D printing of high-density flexible electronic circuit patterns by the conducting polymer ink. b Lighting up of LED on the 3D-printed conducting polymer circuit. PETE indicates polyethylene terephthalate. c Bending of the 3D-printed conducting polymer circuit without failure. d Image of the 3D-printed soft neural probe with 9-channels by the conducting polymer ink and the PDMS ink. e Image of the 3D-printed soft neural probe in magnified view. f Images of the implanted 3D-printed soft neural probe (top) and a freely moving mouse with the implanted probe (bottom). g, h Representative electrophysiological recordings in the mouse dHPC by the 3D-printed soft neural probe. Local field potential (LFP) traces (0.5 to 250 Hz) under freely moving conditions (g). Continuous extracellular action potential (AP) traces (300 to 40 kHz) recorded under freely moving conditions (h). i Principal component analysis of the recorded single-unit potentials from (h). j Average two units spike waveforms recorded over time corresponding to clusters in (i). Scale bars, 5 mm (a–c); 1 mm (d, e); 2 mm (f).