Surface Area and Local Curvature: Why Roughness Improves the Bioactivity of Neural Implants

• Published in: Langmuir 2022-06, Vol.38 (24), p.7512-7521
• Main Authors: Ding, Ruikang, Miller, Nathaniel C., Woeppel, Kevin M., Cui, Xinyan T., Jacobs, Tevis D. B.
• Format: Article
• Language: English
• Subjects: Coated Materials, Biocompatible - chemistry; Nanoparticles - chemistry; Surface Properties; Titanium - chemistry
• ISSN: 0743-7463; 1520-5827
• DOI: 10.1021/acs.langmuir.2c00473

While roughening the surface of neural implants has been shown to significantly improve their performance, the mechanism for this improvement is not understood, preventing systematic optimization of surfaces. Specifically, prior work has shown that the cellular response to a surface can be significantly enhanced by coating the implant surface with inorganic nanoparticles and neuroadhesion protein L1, and this improvement occurs even when the surface chemistry is identical between the nanoparticle-coated and uncoated electrodes, suggesting the critical importance of surface topography. Here, we use transmission electron microscopy to characterize the topography of bare and nanoparticle-coated implants across 7 orders of magnitude in size, from the device scale to the atomic scale. The results reveal multiscale roughness, which cannot be adequately described using conventional roughness parameters. Indeed, the topography is nearly identical between the two samples at the smallest scales and also at the largest scales but vastly different in the intermediate scales, especially in the range of 5–100 nm. Using a multiscale topography analysis, we show that the coating causes a 76% increase in the available surface area for contact and an order-of-magnitude increase in local surface curvature at characteristic sizes corresponding to specific biological structures. These are correlated with a 75% increase in bound proteins on the surface and a 134% increase in neurite outgrowth. The present investigation presents a framework for analyzing the scale-dependent topography of medical device-relevant surfaces, and suggests the most critical size scales that determine the biological response to implanted materials.