RESUMEN

3D printing has been widely used for on-demand prototyping of complex three-dimensional structures. In biomedical applications, PEDOT:PSS has emerged as a promising material in versatile bioelectronics due to its tissue-like mechanical properties and suitable electrical properties. However, previously developed PEDOT:PSS inks have not been able to fully utilize the advantages of commercial 3D printing due to its long post treatment times, difficulty in high aspect ratio printing, and low conductivity. We propose a one-shot strategy for the fabrication of PEDOT:PSS ink that is able to simultaneously achieve on-demand biocompatibility (no post treatment), structural integrity during 3D printing for tall three-dimensional structures, and high conductivity for rapid-prototyping. By using ionic liquid-facilitated PEDOT:PSS colloidal stacking induced by a centrifugal protocol, a viscoplastic PEDOT:PSS-ionic liquid colloidal (PILC) ink was developed. PILC inks exhibit high-aspect ratio vertical stacking, omnidirectional printability for generating suspended architectures, high conductivity (~286 S/cm), and high-resolution printing (~50 µm). We demonstrate the on-demand and versatile applicability of PILC inks through the fabrication of 3D circuit boards, on-skin physiological signal monitoring e-tattoos, and implantable bioelectronics (opto-electrocorticography recording, low voltage sciatic nerve stimulation and recording from deeper brain layers via 3D vertical spike arrays).

Asunto(s)

Materiales Biocompatibles , Coloides , Conductividad Eléctrica , Líquidos Iónicos , Poliestirenos , Impresión Tridimensional , Líquidos Iónicos/química , Coloides/química , Materiales Biocompatibles/química , Animales , Poliestirenos/química , Ratas , Tinta , Polímeros/química , Tiofenos/química , Neuronas/fisiología , Compuestos Bicíclicos Heterocíclicos con Puentes/química

RESUMEN

The commercialization of lithium-sulfur (Li-S) batteries has been hampered by diverse challenges, including the shuttle phenomenon and low electrical/ionic conductivity of lithium sulfide and sulfur. To address these issues, extensive research has been devoted to developing multifunctional interlayers. However, interlayers capable of simultaneously suppressing the polysulfide (PS) shuttle and ensuring stable electrical and ionic conductivity are relatively uncommon. Moreover, the use of thick and heavy interlayers results in an unavoidable decline in the energy density of Li-S batteries. We developed an ultrathin (750 nm), lightweight (0.182 mg cm-2) interlayer that facilitates mixed ionic-electronic conduction using the solution shearing technique. The interlayer, composed of carbon nanotube (CNT)/Nafion/poly-3,4-ethylenedioxythiophene:tetracyanoborate (PEDOT:TCB), effectively suppresses the shuttle phenomenon through the synergistic segregation and adsorption effects on PSs by Nafion and CNT/PEDOT, respectively. Furthermore, the electrical/ionic conductivity of the interlayer can be improved via counterion exchange and homogeneous Li+ ion flux/good wettability from SO3- functional group of Nafion, respectively. Enhanced sulfur utilization and reaction kinetics through polysulfide shuttle inhibition and facilitated electron/ion transfer by interlayer enable a high discharge capacity of 1029 mA h g-1 in the Li-S pouch cell under a high sulfur loading of 5.3 mg cm-2 and low electrolyte/sulfur ratio of 5 µL mg-1.

RESUMEN

Solution-based thin-film processing is a widely utilized technique for the fabrication of various devices. In particular, the tunability of the ink composition and coating condition allows precise control of thin-film properties and device performance. Despite the advantage of having such tunability, the sheer number of possible combinations of experimental parameters render it infeasible to efficiently optimize device performance and analyze the correlation between experimental parameters and device performance. In this work, a microfluidic screening-embedded thin-film processing technique is developed, through which thin-films of varying ratios of small molecule semiconductor:polymer blend are simultaneously generated and screened in a time- and resource-efficient manner. Moreover, utilizing the thin-films of varying combinations of experimental parameters, machine learning models are trained to predict the transistor performance. Gaussian Process Regression (GPR) algorithms tuned by Bayesian optimization shows the best predictive accuracy amongst the trained models, which enables narrowing down of the combinations of experimental parameters and investigation of the degree of vertical phase separation under the predicted parameter space. The technique can serve as a guideline for elucidating the underlying complex parameter-property-performance correlations in solution-based thin-film processing, thereby accelerating the optimization of various thin-film devices in the future.