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Stretchable, Implantable, Nanostructured Flow-Diverter System for Quantification of Intra-Aneurysmal Hemodynamics Connor Howe, Saswat Mishra, Yun-Soung Kim, Yanfei Chen, Sang-Ho Ye, William R Wagner, Jaewoong Jeong, Hun-Soo Byun, Jong-Hoon Kim, Youngjae Chun, and Woon-Hong Yeo ACS Nano, Just Accepted Manuscript • DOI: 10.1021/acsnano.8b04689 • Publication Date (Web): 18 Jul 2018 Downloaded from http://pubs.acs.org on July 20, 2018

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Stretchable, Implantable, Nanostructured FlowDiverter System for Quantification of IntraAneurysmal Hemodynamics Connor Howe†⏊ , Saswat Mishra‡⏊ , Yun-Soung Kim‡, Yanfei Chen§, Sang-Ho Ye∥, William R. Wagner∥, Jae-Woong Jeong¶, Hun-Soo Byun┼, Jong-Hoon Kim┬, Youngjae Chun§∥, Woon-Hong Yeo†‡┤* † Department of Mechanical and Nuclear Engineering, Institute for Engineering and Medicine, Center for Rehabilitation Science and Engineering, Virginia Commonwealth University, Richmond, Virginia 23284. ‡ George W. Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332. § Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261. ∥ Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261. ¶ School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea 34141. ┼ Department of Chemical and Biomolecular Engineering, Chonnam National University, Yeosu, Jeonnam 59626, South Korea.

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┬ School of Engineering and Computer Science, Washington State University, Vancouver, Washington 98686. ┤Institute for Electronics and Nanotechnology, Bioengineering Interdisciplinary Program, Petit Institute for Bioengineering & Bioscience, and Center for Flexible Electronics, Georgia Institute of Technology, Atlanta, Georgia 30332. ⏊Equal contributions. *Address correspondence to [email protected]

Random weakening of an intracranial blood vessel results in abnormal blood flow into an aneurysmal sac. Recent advancements show that an implantable flow-diverter, integrated with a medical stent, enables a highly effective treatment of cerebral aneurysms by guiding blood flow into the normal vessel path. None of such treatment systems, however, offers post-treatment monitoring to assess the progress of sac occlusion. Therefore, physicians rely heavily on either angiography or magnetic resonance imaging. Both methods require a dedicated facility with sophisticated equipment settings and time-consuming, cumbersome procedures. In this paper, we introduce an implantable, stretchable, nanostructured flow-sensor system for quantification of intra-aneurysmal hemodynamics. The open-mesh membrane device is capable of effective implantation in complex neurovascular vessels with extreme stretchability (500% radial stretching) and bendability (180 degrees with 0.75 mm radius of curvature) for monitoring of the treatment progress. A collection of quantitative mechanics, fluid dynamics, and experimental studies establish the fundamental aspects of design criteria for a highly compliant, implantable device. Hemocompatibility study using fresh ovine blood captures the device feasibility for longterm insertion in a blood vessel, showing less platelet deposition compared to existing implantable materials. In vitro demonstrations of three types of flow-sensors show quantification of intra2 ACS Paragon Plus Environment

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aneurysmal blood flow in a pig aorta and the capability of observation of aneurysm treatment with a great sensitivity (detection limit: as small as 0.032 m/s). Overall, this work describes a mechanically soft flow-diverter system that offers an effective treatment of aneurysms with an active monitoring of intra-aneurysmal hemodynamics.

KEYWORDS: intracranial aneurysm, nanostructured sensor, flow-diverter, implantable, hemocompatible, and bioresorbable

An aneurysm occurs due to weakening of a vessel wall over time. The aneurysmal sac is the localized enlargement of the weakened vessel wall and can take on many forms, depending on geometry and location. There are a few attributes such as vortices, velocities, and impinging regions within the sac,1 but any untreated aneurysm that carries a high risk of rupture will often lead to death.2 Treatment of intracranial or cerebral aneurysms pose additional challenges due to the intricate neurovascular anatomy within the skull.3 Typical treatment methods seek to divert, impede, or reduce blood flow for preventing insertion to the aneurysmal sac. Recent advancements in treating such cerebral aneurysm include a flow-diverter system, made of a hyper-elastic thin film nitinol (TFN).4-6 This device is far less invasive than the standard treatment of surgical clipping and can also be used for wider array of aneurysms than a mechanical coiling alone. Another benefit of the TFN flow-diverter over the coiling method is the mitigation of the risk of aneurysmal wall perforation7 during intervention. However, direct implantation of a flow-diverter is not enough to conclude the successful treatment of the aneurysm due to the lack of monitoring capability.


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Post-treatment monitoring is important, especially for certain cerebral aneurysms, in which posttreatment complications occur within 1 month, resulting in morbidity and mortality rates up to 10%.8 Multiple studies indicate that many flow-diverting devices require both short-term and longterm follow-ups after treatment.9-13 The most common follow-up method is to utilize angiography, which uses an intravenously injected dye and CT or MRI scans to create a contrast image of the aneurysm sac in a blood vessel. However, the required procedure14 can be considered invasive and expensive, and often requires multiple hospital visits to determine satisfactory treatment, or an occlusion rate of ~75%.15 A few studies report that the required time for branch16 and bifurcation17 occlusions is ranged from 3 months to over 36 months, which involves additional follow-ups with angiography. Angiography also involves risk of complications due to the contrast dye itself and requires dedicated facilities not found in most standard physician’s office. As an alternative to the angiographic study, a development of implantable sensors was conducted, targeting for thrombosis diagnosis.18-20 Several devices have been implemented in the studies for the benefit of angiography-free monitoring.21,22 Such devices have shown promise in aortic aneurysm followups without the angiographic imaging method, but they are too bulky for easy implantation in small cerebral aneurysms (diameter < 7mm) due to the electronic components. Even though recent developments show miniaturized23 and implantable sensors,24-26 the overall dimension and lack of mechanical compliance prohibit intravenous, conformal deployment to the highly contoured and tapered neurovascular vessels. In this paper, we explore a flow-sensing method using a nanostructured capacitive sensor to quantify intra-aneurysmal blood flow. Recent advancements in soft electronics27,28 and sensors29,30 offer capabilities31,32 that were limited by rigid systems. We introduce a soft, stretchable, flexible, low-profile sensor system to overcome the challenges of cerebral intravenous navigation and


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deployment. The low-profile, elastomer encased design of the sensor conforms to the flow-diverter and normal blood flow path, maintaining the wall perforation risk mitigation desired with flowdiversion. The mesh-patterned, highly compliant sensor is integrated with a flow-diverter based on our previous work33 and can be easily embedded in a catheter, enabling safe travel through the complex neurovascular anatomy to a targeted cerebral aneurysm. Computational mechanics modeling and experimental study of the sensor validates the structural safety for deployment. 3D fluid dynamic analysis supports the effectiveness and sensing capability of the nanostructured capacitive sensor inserted in a blood vessel. Fabricated devices based on bio- and hemocompatible materials demonstrate highly sensitive quantification of intra-aneurysmal blood flow rates for silicone models and implantation in a pig artery.


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RESULTS Device design, material properties, and integration as a flow-diverter system. Fig. 1 shows the overview of the stretchable, implantable flow-diverter system for the highly sensitive monitoring of intra-aneurysmal hemodynamics in a blood vessel. The illustration in Fig. 1A displays a schematic view of the entire structure of the presented system, including a commercial neurovascular stent backbone (Neuroform, Stryker Neurovascular). This multi-layered hybrid system includes a hyper-elastic thin film nitinol (TFN) membrane wrapping the backbone stent, and a nanostructured capacitive ring-type flow-sensor sandwiched by polyimide (PI; HD Microsystems), fully encapsulated by a soft hemocompatible elastomeric membrane (Ecoflex, Smooth-On). The microstructured, highly porous TFN is fabricated by the magnetic sputter and lift-off method,2, 34 which offers an extreme radial stretchability up to 500% and bendability of 180 degrees. A multi-point conformal joining, supported by mechanics study, successfully integrates the TFN membrane with the stent backbone.33 On top of the structures, we incorporate a lowprofile, stretchable sensor to create an active flow-diverting system, feasible for neurovascular deployment and concurrent monitoring of flow variation. The sensor is composed of multiple functional layers (Fig. 1B) of dielectric, metal, and a biocompatible polymer (PI), which is completely enclosed by 100 µm-thick silicone elastomer layers. The open-mesh, capacitive sensor, consisting of two metal layers (300 nm-thick NiTi or Mg or 100 nm-thick Au) in a parallel plate configuration, is placed at the center of the flow-diverter allowing for capacitive response upon varying intra-aneurysmal flow. In this work, we used an in vitro animal model (Figs. 1C – 1E) to characterize and demonstrate the device functionality. The biological tissue setup utilizes a section of porcine artery (Fig. 1C) to illustrate deployment of the flow-diverter system (details of the tissue preparation and device


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deployment in the model appear in Supporting Note 1 and Fig. S1). Fig. 1D displays the inside view of the inserted device, positioned at the neck of the aneurysm and a magnified optical microscope image (Fig. 1E) captures the flow-sensor, attached to the TFN flow-diverter. To prevent the device flow away from the neck of the aneurysm, we integrated the nanostructured sensor with the TFN mesh and stent backbone via a medical grade, cyanoacrylate instant adhesive (MG30, Adhesive Systems, Frankfort, IL), as studied in our prior work.33 The sensor was structured in an array of interconnected serpentine patterns to accommodate excessive radial stretching and multi-modal bending during catheter insertion and deployment in a target vessel. The capacitive sensor measures the change of incoming flow to the aneurysm sac from the parent blood vessel via capacitance (C) variation, governed by: 𝐶 = 𝑒&

'( ) *

(1)

where eo is permittivity of space (F/m), er is the dielectric constant 3.3 of PI,35 A is the area of the parallel metal layers (m2), and d is the distance between the two layers. The capacitor’s area is 3.72 mm2, calculated as a percent coverage of a unit cell and multiplied by the overall area of the serpentine pattern (details in Supporting Note 2 and Fig. S2). The distance between the two metal layers is 1.4 µm: thickness of the spin coated PI. The capacitance value of the undeformed flowsensor is 77.6 pF, calculated by the equation (1). Experimental values, measured by a LCR meter (BK Instruments), show a range of 70 – 80 pF (details in the Methods section), which is caused by slight variation in the microfabrication process such as the PI thickness during the coating process. Sequential photos in Figs. 1F - H capture a flow-diverter, undergoing a mock catheter deployment in three states including catheter insertion (Fig. 1F), delivering (Fig. 1G), and final deployment (Fig. 1H). A self-expanding deployment method was used to deliver the flow-diverter system that closely resembles the widely used, successful method of commercial flow diverters.36 Ballooning,


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typical angioplasty, can be utilized as an alternative, but this method could add excessive stress on tortuous neurovessels.37 The entire device maintains the structural integrity under ~200% compression and expansion during the deployment process. The device architecture, presented in this work, can incorporate a wide variety of materials, dependent on expected implantation time and material’s characteristics. As an example, we investigated three different materials of nitinol (NiTi), magnesium (Mg), and gold (Au) to design flow-sensors. Each material has its own advantage; Au has been widely used for biomedical devices with a great biocompatibility,38,39 NiTi has properties of hyper-elasticity and shape memory characteristics,40,41 and Mg has an ability to be safely and naturally absorbed in the body for transient sensing.42,43 For example, a series of representative photos in Fig. 1I captures the advantage of an Mg-based transient flow-sensor without backing PI and elastomers. The 100 nm-thick mesh sensor naturally dissolves in saline at 37°C with gentle agitation. Fig. 1J shows the functional sensor that measures incoming flow and the functionality disappears after ~10 minutes as the sensor dissolves in saline. On the other hand, another Mg-sensor that is supported by fully encapsulated PI and elastomers shows continuous sensing capability for 2 weeks. Overall, this study captures the programmable capability of the Mg-sensor’s functional lifetime in implantation.44,45 The device operation time can be controlled from a few days to months by additional coating of polymers, material’s thickness, or material’s composition.46,47 In addition, we verified that the Mg-based transient sensor was also dissolvable in human blood (details in Supporting Fig. S3) where the sensor lost functionality after 2 minutes. The dissolution rate in blood was faster than that in saline due to the higher concentration of chlorine ions in blood (Lampire biological lab).48


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Characterization of stretchable and flexible mechanics. We used finite element analysis (FEA; ABAQUS CAE 2016, Dassault Systems) to design a highly compliant structure for a flow–sensor. Considering the estimated deformation during sensor integration, catheter insertion, and deployment, we focused on stretchable and bendable building blocks. The computational model (Fig. 2A) includes an array of serpentine-like mesh patterns, configured in a ring-type structure for assembly with a TFN and stent backbone. The model structure with seven membranes (bottom elastomer, PI, metal, dielectric, metal, PI, and top elastomer) are meshed with C3D8R elements (8-node linear brick, reduced integration, and hourglass control) and the mesh quality is monitored during calculation. In addition, we employed an explicit dynamic method to model the highly nonlinear deformation under radial loading and bending (details appear in Supporting Fig. S4). Considering the cyclic compression and expansion of the sensor on a flow-diverter, the maximum radial loading is applied to the structure for 500% stretching; typically, a flow-diverter, delivered by a catheter, experiences a radial expansion from 200 to 400%.49 Even with the excessive radial strain, the designed NiTi structure (Fig. 2B) shows consistent mechanical stability with only less than 1% of maximum principal strain (MPS), compared to the fracture limit (12%).2, 50 Table 1 summarizes material’s properties and associated MPS values upon applied loadings for three different materials. Stretching behaviors and calculated MPS of Mg and Au sensors appear in Supporting Fig. S5. For radial bending, the sensor pattern is wrapped into cylindrical shape and bending (up to 180 degrees) is applied to monitor the estimated behaviors of navigation through highly curved vessels (Fig. 2C). As the bending angle increases up to 180 degrees, the top (distal wall) of the sensor pattern tends to be elongated while the bottom (proximal wall) tends to be shortened. The MPS occurs on those curvature junctions of the top and the value is below 1%; other areas of the sensor patterns show negligible MPS values (< 0.5%).


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To validate the computational results, we conducted a set of experimental measurements. Optical microscope images in Figs. 2D – 2F show the mechanical testing setup with compressed sensor (Fig. 2D), 500% radial stretching (Fig. 2E), and 180 degrees bending (Fig. 2F). Quantitative electrical measurement allows for detection of structural integrity by monitoring a change of resistance due to decrease of cross-sectional area (necking or fracturing) or mean free path of electrons (dislocations). The designed experimental study mimics mechanical expansion and bending, initialized from catheter insertion to deployment in a contoured vessel (optical photos in Figs. 2D – 2F). The initial setup (Fig. 2D) displays a compressed sensor package. For radial expansion, a hollow elastomer tube supports a sensor and electrical resistance is measured between both ends of the fixture during cyclic mechanical stretching. Flexible microwires make electrical connection to each contact pad at opposing ends of the sensor using a silver adhesive paste (Ted Pella) and a digital multi-meter (Keithley) measures the signal change (Fig. 2E; details appear in Supporting Note 3 and Fig. S6). The elastomer tube has an initial diameter of 1.5 mm, which expands to 9 mm after full inflation, creating a 500% increase in radial strain. Results of resistance measurements in Figs. 2G – 2I summarize the results of cyclic radial stretching for materials of NiTi, Mg, and Au. The consistent linear behavior of the signals with negligible change indicates structural integrity29, 51 even with the extreme loading conditions. Another area of concern for deployment of this sensor via catheter is the navigation through a neurovascular section. The highly intricate and twisting nature of the anatomy52 creates the additional requirement for flexibility and bendability of a sensing structure. The experimental setup for radial bending (Fig. 2F) has a sensor wrapped around a small elastomer tube with two supporting slides, which create multi-modal bending up to 180 degrees (details in Supporting Fig. S6). Figs. 2J – 2L summarize the result of resistance measurement during cyclic radial bending for three types of sensors. No


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adverse effect from the mechanical bending is observed. Collectively, the mechanics study demonstrates the major advantage of the low-profile, open mesh sensor, which enables possible deployment in highly contoured neurovascular locations, not accessible by existing solid sensors. A set of experiments proves the advantage of the mesh sensor over a solid film-type sensor (details in Supporting Fig. S7). The solid sensor shows out-of-plane extrusion due to extreme deformation when embedded in a small, contoured vessel, while the mesh sensor shows a conformal lamination by following the vessel geometry. The mesh flow sensor also shows a good sensitivity to measure various flow velocities, even though the large areal coverage in the solid sensor has better sensitivity, which cannot overcome the limitation of mechanical flexibility and stretchability. Fig. 3 displays additional sets of high-resolution images from scanning electron microscopy (SEM) that investigates the material’s behaviors and mechanical stability with sequential radial stretching up to 500%. The collection of SEM images with a NiTi sensor clearly captures localized deformation, stretching, and out-of-plane buckling to accommodate the excessive strains on the structure. Images in Figs. 3A – 3C show a small radius (0.75 mm) of a semi-circle in the undeformed sensor, which continuously increase to 2.63 and 4.50 mm for 250 and 500% radial stretching, respectively. During the mechanical deformation, the buckling behavior of the nanostructured sensor (highlighted in Figs. 3F and 3I) is the key feature to minimize the maximum principal strain, while avoiding mechanical fracture with the extreme stretching. Overall, the results of 3D mechanics modeling and quantitative experimental studies demonstrate the mechanical safety and integrity of the sensors under loading conditions of radial stretching up to 500% and radial bending up to 180 degrees, which provides the applicability of the sensor for safe catheter deployment and navigation through the complex neurovascular anatomy.


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In vitro study of hemocompatibility. Hemocompatibility of the developed system is very important for a follow-up study including in vitro and in vivo feasibility demonstrations. We analyzed surface thrombotic deposition on the sensor system by using a fresh blood (Figs. 4A and 4B). The overall procedure followed an in vitro rocking test, described in our prior work.39 Whole fresh ovine blood was collected by jugular venipuncture with mixing citrated solution (0.0106 M/L as the final concentration; details in the Methods section). The deposited platelet on the sensor (encapsulated by a silicone elastomer; Fig. 4A) and four control samples are quantified by a lactate dehydrogenase assay53 with a cytotoxicity detection kit (Clontech Laboratories). A comparison plot in Fig. 4C summarizes the number of deposited platelets on samples including a sensor, tissue culture polystyrene (TCPS; NuncTM Cell-culture Treated Multidishes, Thermo Fisher Scientific), polydimethylsiloxane (PDMS; Dow Corning), thin film nitinol (NiTi; fabricated in this work), expanded polytetrafluoroethylene graft (ePTFE; BRAD Peripheral Vascular), and Polyethylene terephthalate fabric (Dacron; Cook Medical). Among the samples, sensor and NiTi make the flow-diverter system, used in this work, while TCPS and PDMS are well-known biocompatible materials, widely used for biomedical devices,54,55 and ePTFE and Dacron are very popular materials for implantable devices (stent grafts).56,57 The result shows the lowest values of platelet deposition (i.e., great hemocompatibility) for the sensor and NiTi. A set of demonstrative SEM images (Figs. 4D – 4I) captures clearly different surface morphologies and deposited platelets on various samples, after 3 hours of contact with the fresh ovine blood. TCPS sample displays a large number of platelets with the spread morphology and the PDMS has consistent number of platelets over the whole surface. Graft materials (ePTFE and Dacron) also present high numbers of platelet deposition, compared to the sensor and NiTi.


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Interestingly, PDMS shows higher platelet deposition than the Ecoflex-enclosed sensor that is made of the same chemical structure, silicone elastomer. The major difference between two materials is the mechanical rigidity (Young’s modulus: 69 kPa58 for Ecoflex and 1.8 Mpa59 for PDMS), related to the surface softness. We also speculate that the Ecoflex for the sensor would possess different surface roughness and surface chemistry, compared to the PDMS; Ecoflex58 is more hydrophilic than the PDMS sample.60 Smoother surface textures from sensor and NiTi present better hemocompatibility than rough geometries from ePTFE and Dacron. Collectively, even though the experimental study focuses on the acute thrombotic deposition, the quantitative comparison between materials supports the feasibility of our device before moving forward to in vivo study.

3D fluid dynamic analysis of flow sensing. Fig. 5 represents the result from fluid dynamic modeling, providing design guidelines for a 3D structured sensor system implanted in a blood vessel. The ring-shape mesh sensor, embedded in a stent backbone, is placed at the center of the aneurysmal neck (Fig. 5A). The nanostructure-based soft sensor makes the direct contact to the blood flow and monitors the incoming blood flow to the sac via mechanical deflection. The compliant membrane in the sensor experiences the capacitance change according to the amount of deflection induced by the incoming flow rate to the neck. The 3D fluid-structure interaction (FSI; modeled in COMSOL Multiphysics) includes a stent-embedded sensor system, cylindrical blood vessel (5 mm in diameter), and aneurysmal sac (7 mm in diameter) with an offset (4 mm) above the vessel centroid. The FSI modeling considers a blood flow with a laminar inlet velocity profile with no slip at the wall and zero pressure at the outlet. The blood model has a density of 1050 kg/m3 and dynamic


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viscosity of 0.0035 Pa·s. The mean velocity varies from 0.1 to 0.5 m/s with an increment of 0.1 m/s to monitor the corresponding response of a sensor deflection. The sensor flow-diverter is enclosed by an elastomeric membrane (Ecoflex; thickness: 0.05 mm, Young’s modulus: 69 kPa, Poisson’s ratio: 0.48, and density: 1070 kg/m3). In the modeling, fine meshes are applied to the aneurysm neck area, focusing on the sensor system. We apply a steady state FSI for investigation of nominal deflection of the sensor, caused by incoming aneurysmal flow. The model compares the effectiveness of a flow-diverter system in the blood vessel by tracking the blood flow velocity (color gradient) and direction (white arrows in Figs. 5B – 5D). Under a normal blood flow (mean velocity: 0.5 m/s), the flow-diverter system proves the device capability by significantly reducing the incoming flow to the sac, preventing a possible rupture. Captured images in Fig. 5E shows the magnified views of deflected sensor at the aneurysm neck with different mean velocities (represented by different flows); high flow (0.5 m/s) causes a lot higher deflection (18 µm) than the low flow case (0.1 m/s) (details appear in Supporting Note 4 and Fig. S8). A comparison plot in Fig. 5F summarizes calculated sensor deflections according to variable flow rates (blue dots in the graph). The computational result is then compared with the experimental data, measured as capacitance values (red dots in the graph; details in Supporting Note 4 and Fig. S9). The capacitance fluctuation, caused by deformed dielectric layer, is the main mechanism behind the sensor response, in that varying degrees of physical deflection of the sensor leads to a respective capacitance change. Based on Figs. 5E - 5F and equation (1), it can be inferred that the variable factor responsible for the changes in the capacitance values is the dielectric thickness. Consequently, fluid with a certain flow rate exerts a specific compressive force against the dielectric layer, reducing the gap between the two metallic layers. FEA results in Supporting Fig. S10 visualize the mode of deflection that occur in the sensor system. A compressive force from


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fluid flow against the sensor results in a compressed PI dielectric layer for a capacitance change. Overall, the computational and experimental data agree very well in terms of the trend and amount of sensitivity that responds to the incoming flow.

In vitro demonstration of the device functionality in a porcine artery. Fig. 6 presents an in vitro study of the device functionality with an animal (porcine) model and human donor blood (Lampire biological lab). A porcine artery was cut along its length, wrapped around a 5 mm spacer tube, and sutured with an absorbable braided surgical set (AD Surgical; additional photos in Supporting Fig. S1). An artificial aneurysm sac was shaped from excess arterial vessel tissue and attached to the vessel via sutures. For an in vitro study, a flow-diverter system was placed inside the vessel centered on the neck of the aneurysm, with flexible microwires exiting the vessel for data acquisition. After the sensor deployment, the vessel and the tissue around each microwire were tightly sutured to prevent leaking. Fig. 6A shows the finalized tissue model with inserted human blood. Exploded photo in Fig. 6B displays a flow-sensor, inserted at the neck of the sac. During the experiment, a programmable water bath (StableTemp, Cole-Parmer) keeps the blood temperature steady at 37°C. To create a realistic biological condition, a pulsatile pump (55-1838, Harvard Apparatus) generates a driving flow of a blood through the model at a rate of 60 strokes per minute. A blood flow that passes through the aneurysmal neck where the sensor is present is measured by an LCR meter (891, BK Instruments) for 30 second intervals, which is correlated to the capacitance change (details of experimental setup and raw signals appear in Supporting Fig. S9A). To compensate the baseline noise and signal drift, a stagnant flow without pumping is recorded by the sensor. A signal processing algorithm, designed in MATLAB, examines the mean of the absolute values of the


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differential signals over each interval via filtering. This method allows the simplification of the capacitive fluctuation for each data section and the subtraction of the baseline values normalizes the device sensitivity according to the different flow rates (details of the signal analysis appear in Supporting Note 5 and Fig. S9B). A possible use of the flow-diverter system for a narrow and highly contoured neurovascular vessel is supported an in vitro experiment (details of the experimental setup and results in Supporting Fig. S11). The silicone model has a very complex geometry, along with a narrow vessel (3 mm in diameter), initiated by bifurcated vessels (Supporting Figs. S11a-b). We successfully deployed the open-mesh flow sensor at the neck of the sac and measured the capacitance value with flow insertion in the vessel (Supporting Figs. S11cd). The mesh sensor in the narrow vessel was still functional without mechanical fracture, while enabling the measurement of pulsatile flow. This in vitro experiment demonstrates the potential of the low-profile sensor’s use in neurovascular vessels. For the tissue model with human blood, Figs. 6C – 6E present the measured sensitivity of the flow–sensors, made of NiTi, Mg, and Au. Optical microscopic images capture the deployed, stretched flow-sensor at the aneurysm neck (top row in Figs. 6C – 6E). The result demonstrates that the nanostructured, soft sensors can detect mean vessel velocities as small as 0.032 m/s, which shows a very high sensitivity to monitor the alteration of the intra-aneurysmal flow.61 To compare the relative sensitivity, a non-stretchable Mg sensor with a solid film was prepared and tested in the same experimental setup (Supporting Fig. S12). While the sensitivity of the non-stretchable sensor was higher due to the larger areal deformation than the mesh sensor, sensor placement into a small, contoured vessel was extremely prohibitive and resulted in the sensor film buckling with out-of-plane deformations (Supporting Fig. S7). In addition, large differences in the response between the different sensor materials were observed (Figs. 6C – 6E). Our study of each material’s rigidity, measured via nanoindentation (Supporting


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Fig. S13), shows that three materials have different modulus values in the order of NiTi > Au > Mg. In other words, if the same external loading (e.g., fluid flow) is given to the materials, then the amount of deflection will be dependent on the modulus, resulting in the biggest deflection in Mg and the smallest in NiTi with the order of Mg > Au > NiTi. Overall, this result has a good qualitative agreement with the amount of capacitance differences between three materials in Figs. 6C - 6E.

DISCUSSION The limitation of this study is in the inability to perform long-term in vivo investigation due to the current lack of the integrated wireless communication system as planned in our previous work.62 Thus, this work focused on in vitro study that offered more controllable and idealized settings than in vivo study to design and characterize the developed sensor system. Currently, we are working on the development of a wireless telemetry unit to incorporate with the flow-diverter device for real-time, continuous monitoring of hemodynamics. As an example, a schematic illustration in Supporting Fig. S14 captures one of the wireless systems that can be utilized; an inductive coupling method that uses a pair of wireless coils could be an ideal option for the implantable system since it does not require active circuits or batteries. It should be noted that the current device that utilizes flexible microwires to measure capacitance can also be usable in possible in vivo experiment, even without the wireless system, through the direct integration with a vessel-inserted guidewire, obviating the need for additional incisions for data acquisition. Another possible concern to be tested in future in vivo study is the chance of uneven sac occlusion, in which the center of the aneurysm neck may be occluded while the proximal or distal region may not. It will potentially lead to a false positive reading of a sensor for full sac occlusion. While this


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concern should be validated by future work, our CFD study in Fig. 5 estimates that the sensing area in the middle of the ostium will detect the majority of the flow change, not proximal or distal parts. The largest intra-saccular blood velocities were observed at the ostium where the sensor was located (Figs. 5C – 6E). The introduction of the sensor with the flow-diverter (Fig. 5D) modifies the velocity profile entering the sac, in which the largest magnitude is located at the sensor. Consequently, the incoming flow magnitude change (Fig. 5E) is directly detected by the sensor at the neck.

CONCLUSION The collective results presented here show that a nanostructured, stretchable, implantable sensor offers highly sensitive quantification of flow variations in a blood vessel. As an alternative to expensive and invasive angiography, a wireless sensor package offers a more practical way of frequent post-treatment monitoring of intra-aneurysmal hemodynamics. We anticipate the integration of the wireless component, based on passive inductive coupling without the use of a circuit and battery, will allow this flow-diverter system to be a much desired replacement for neurovascular angiographic follow-ups. Three types of sensor materials demonstrate the system versatility for tailored applications in a prolonged implantation (Au), shape memory configuration (NiTi), and transient, short-term use (Mg). The combination of computational and experimental studies of mechanics designs a highly deformable, bendable (180 degrees) and stretchable (500%) sensor, capable of a catheter-assisted deployment in a narrow and contoured neurovascular vessel. Hemocompatibility investigation provides evidence for a body implantable flow-diverter system with minimized platelet deposition. Fluid dynamic analysis and experimental validation reveals the sensor performance that detects flow velocity variations as small as 0.032 m/s via capacitance


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monitoring. Comparison of the intra-aneurysmal flow velocity detected by the sensor combined with an estimated occlusion rate will allow for determining of the flow-diverter treatment progress.


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EXPERIMENTAL SECTION Fabrication of a flow–sensor. The sensor fabrication utilizes conventional microfabrication techniques, combined with a transfer printing and hard-soft integration method.58, 63 Spin casting is used to deposit polymethylmethacrylate (PMMA; 100 nm in thickness) and polyimide (PI; 1.4 µm in thickness) layers on a prepared glass slide or Si wafer. The serpentine patterns of the bottom capacitive layer are created by photolithography and wet etching of sputter deposited metals. The dielectric layer (PI) was spin coated onto the bottom metal layer and the same process is used to create the top capacitive metal layer. After a top PI layer is deposited, a final photo resist mask is applied and reactive ion etching is used to remove all unwanted PI and finalize the mesh patterning for the sensor. The structure is released from the carrying substrate by submerging in acetone to dissolve the PMMA sacrificial layer. Full details of the fabrication process can be found in Supporting Note 6 and Fig. S15a. Water-soluble tape (3 M) allows for retrieval of the completed sensor and transfer to a silicone elastomer (Ecoflex 00-30, Smooth-On). After dissolving the tape in water, flexible microwires are connected to the sensor via silver adhesive at the contact pad location of the legs (contact pads highlighted in Supporting Fig. S15b).

Mechanics modeling. We use finite element analysis (ABAQUS, Dassault Systems) for the radial stretching and bending study (Supporting Fig. S4). In the radial stretching, a deformable cylindrical surface was added as an expander and meshed with SFM3D4R elements (4-node quadrilateral surface element). The contact between the expander and sensor was modeled as frictionless in the tangential direction and hard contact in the normal direction. The applied strains to the sensor were up to 500%, which was increased incrementally over time. For the bending model, a deformable cylinder was added and aligned with the sensor coaxially, And was meshed


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with C3D8R elements. A tie constraint was applied on the contact surface between the outer base support and inner sensor surface to ensure proper contact. A pair of rigid cylindrical surfaces were added to the top and base support, after which an upward displacement was applied to cause bending on the sensor. The contact between the rigid support faces and the base support surface was modeled as frictionless in the tangential direction and hard contact in the normal direction.

Hemocompatibility test. The overall procedure followed an in vitro rocking test, described in our prior work.64 Whole fresh ovine blood was collected by jugular venipuncture with mixing citrated solution. All animal procedures were approved by the Institutional Animal Care and Use Committee at the University of Pittsburgh. The blood was distributed into a vacutainer tube (BD Vacutainer) where each of the sterilized samples were placed, and then incubated for 3 hours at 37°C on a hematology mixer (Fisher Scientific). After the blood contact, the samples were gently rinsed with PBS solution 10 times. Deposited platelets on samples were quantified by a lactate dehydrogenase assay,53 with a Cytotoxicity Detection Kit (Clontech Laboratories).

Fluid structure interaction (FSI) analysis. The FSI simulation for a flow-sensor was conducted using COMSOL Multiphysics (Supporting Note 4 and Fig. S8). The mesh elements were tetrahedral (maximum size: 1 mm and minimum size: 0.01 mm), with the highest density meshing around the neck and sensor region. Investigation was conducted on the half plane symmetry of the model. The anterior and posterior boundary of the aneurysm sac had a node distribution of 35 elements per 25 degrees where the upper boundary of the sac had 60 elements per 180 degrees. Similar dimensions for physical experimental models and simulation modeling were used. The parent vessel diameter is 5 mm while the aneurysmal sac is modeled as a sphere of 7 mm diameter.


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The blood properties used include density (1050 kg/m^3) and viscosity (0.0035 Pa*s). Standard parabolic flow velocity profiles were used with selected mean velocities at the inlet.

Experimental measurement of flow rates. For the experimental study, mean vessel velocities were chosen as 0.032, 0.067, 0.097, 0.125, and 0.142 m/s (details in Supporting Note 4 and Fig. S8). We designed a testing model with the same design as in the computational model; PDMS was used to make an aneurysm replica from a tube (5 mm in diameter) with an aneurysm sac (7 mm in diameter). A pulsatile pump (55-1838, Harvard Apparatus) was used to drive the flow of a blood through the model at a rate of 60 strokes per minute. The stroke volume was adjusted to match with the modeled flow rates. For data acquisition, flexible microwires were attached to the sensor before encapsulation. An LCR meter (BK Instruments) was used to record capacitive change from a sensor through the CP-D function at a rate of 7.5 kHz and 0.5 V.


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Figure 1. Overview of an integrated flow-diverter system. (A) Illustration of an integrated flowdiverter including a nanostructured sensor on a stent backbone wrapped in a TFN mesh. (B) Schematic view of a capacitive flow-sensor with multi-layers. (C) Image of a pig aorta with an aneurysm sac. (D) Exploded view of the open model in (C), showing the device centered on the 23 ACS Paragon Plus Environment

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aneurysm neck. (E) Microscope image of the flow-sensor. (F–H) Sequential photos displaying the deployment of a catheter-integrated flow-diverter; (F) collapsed device in a catheter, (G) expanding device when pushed out, and (H) fully deployed device. (I) Time lapse images showing a single-layer Mg sensor, dissolved in a normal saline bath at 37°C. (J) Measurement of flow change from the Mg sensor in (I), losing the sensing capability after 10 minutes due to the sensor dissolution. (K) Normalized value (arbitrary unit) of flow capacitance from a multi-layer Mg sensor, encapsulated by PI and elastomer, showing consistent functionality up to 2 weeks without dissolution.


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Figure 2. Characterization of stretchable and flexible mechanics. (A) Finite element analysis (FEA) setup for radial stretching modeling of a sensor with a small representative segment wrapped concentrically around an expandable cylinder. (B) Flow-sensor under excessive radial strains of 250 and 500%. (C) Sensor with a radial bending at 0, 90, and 180 degrees. Scale bars in 25 ACS Paragon Plus Environment

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(B) and (C) present the maximum principal strain values. (D-F) Optical microscope images of an experimental setup to validate the modeling result; (D) undeformed sensor wrapped around an elastomer tube (1 mm in diameter), (E) sensor undergoing radial expansion with 500% strains, which is applied by the inflatable tube, and (F) sensor with multi-modal bending up to 180 degrees, applied by a 1 mm-diameter tube. (G-I) Experimental results of resistance measurements according to applied cyclic radial loadings up to 500%; there are three types of sensors materials including (G) NiTi, (H) Mg, and (I) Au. All sensors have negligible change of resistance, showing mechanical integrity without fracture. (J-L) Experimental results of resistance measurements according to applied cyclic bending up to 180 degrees; there are three types of sensors materials including (J) NiTi, (K) Mg, and (L) Au. All sensors show no adverse effect from the excessive cyclic bending tests.


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Figure 3. Scanning electron microscope (SEM) investigation of a sensor under radial stretching. (A-C) SEM images of a representative NiTi sensor, attached to an elastomeric tube (radius: 1 mm) for radial stretching; exploded views with increasing magnification from (A) to (B) and (C). (D-F) Images of the sensor undergoing 250% radial stretching with increasing magnification, showing the moderate buckling behavior. (G-I) Images of the sensor at 500% radial stretching with increasing magnification, displaying the extreme buckling behavior, which helps to avoid mechanical fracture.


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Figure 4. Hemocompatibility study of a sensor system. (A) Microscope image of a flow-sensor system, embedded in a biomimetic aneurysm model. The device is placed at the center of the


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aneurysm neck. (B) Photo of the device-inserted model in (A) after blood insertion into the vessel for hemocompatibility study. (C) Number of deposited platelets on samples including TCPS, PDMS, sensor, NiTi, ePTFE, and Dacron. Error bar shows standard deviation from 3 trials (n=3). (D-I) Collection of representative SEM images showing the surface morphology with deposited platelets on the samples of (D) TCPS, (E) PDMS, (F) Sensor, (G) NiTi, (H) ePTFE, and (I) Dacron. Scale bars in large images are 50 µm, while insets present 1 µm.


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Figure 5. Fluid dynamic analysis. (A) 3D computational model of a sensor on a stent backbone, together embedded in a parent blood vessel (5 mm in diameter) and aneurysm sac. (B-D) Images showing velocity amplitude in an entire model and expanded cross-sectional profile view of the aneurysm. Applied mean velocity in the parent vessel is 0.5 m/s with steady flow conditions; (B) Model without any intervention device (i.e., open untreated aneurysm). (C) Model with the flowdiverter deployed to the aneurysm site which displays a significant decrease of the incoming flow to the aneurysm sac. (D) Model with the implanted flow-diverter and sensor, showing effects of the added sensor to the system on intra-saccular flow. (E) Images capturing sensor deflections in 30 ACS Paragon Plus Environment

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the neck of the aneurysm, caused by low flow (left; 0.1 m/s vessel mean blood velocity) and high flow (right; 0.5 m/s vessel mean blood velocity). Scale bars present the maximum deflection of the sensor. (F) Simultaneous comparison between the computation results (maximum sensor deflection) and experimental measurements (normalized capacitance change in the sensor), caused by variations of flow rates. The experimental results validate the computational modeling with the well-agreed values.


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Figure 6. In vitro demonstration of the device functionality. (A) Image of an in vitro experimental setup with a porcine aneurysm model and inserted human blood. Highlighted rectangular section indicates the location of an implanted device. (B) Photo of a flow sensor attached to stent before implantation the model. (C-E) Optical microscope images of (C) NiTi, (D) Mg, and (E) Au sensors (above) and experimental results showing capacitance change according to the flow velocity. Overall, those sensors demonstrate highly sensitive detection of a small flow velocity change (down to 0.032 m/s), generated by a pulsatile blood pump to simulate the pumping action of the heart. Error bars represent standard deviation from 3 trials.


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Table 1. Material’s properties and calculated maximum principal strain (MPS) values. Young’s modulus Poisson’s ratio (GPa)

MPS from 180 MPS from 500% degrees bending radial stretching (%) (%)

NiTi65

6

0.33

0.91

0.84

Mg66

4.5

0.35

0.91

0.89

Au29

79

0.4

0.98

0.94

Ecoflex67 0.069

0.49

-

-

PI68

0.34

-

-

2.5

*The fracture limits of NiTi, Mg, and Au are 12%50, 10%69, and 5%63, respectively.


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ASSOCIATED CONTENT Supporting Information. The Supporting Information is available free of charge on the ACS Publications website. Note S1. Preparation of an in vitro animal model. Note S2. Flow-sensor dimension. Note S3. Mechanical test. Note S4. Fluid structure interaction (FSI) analysis. Note S5. Experimental validation of the sensor functionality. Note S6. Fabrication of a flow-sensor. Figure S1. Preparation of an animal model. Figure S2. Sensor design. Figure S3. Dissolution test in human blood. Figure S4. FEA geometric model. Figure S5. FEA results of mechanical stretching and bending. Figure S6. Mechanical integrity test. Figure S7. Comparison of sensor performance between a mesh sensor and solid sensor. Figure S8. Sensor performance simulation. Figure S9. Sensor data and analysis. Figure S10. Computational modeling for the effect of sensor deflection. Figure S11. Flow-diverter system in a narrow and highly contoured neurovascular vessel. Figure S12. Sensitivity of a non-stretchable Mg sensor. Figure S13. Measurement of a thin film modulus via nanoindentation. Figure S14. System designs for wireless or minimally invasive flow monitoring. Figure S15. Illustration of the sensor fabrication process and microscope images. 34 ACS Paragon Plus Environment

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AUTHOR INFORMATION Corresponding Author *Prof. Woon-Hong Yeo, Petit Microelectronics Center, 791 Atlantic Drive NW #204, Atlanta, GA 30332, 404-385-5710, [email protected]. ORCID Woon-Hong Yeo: 0000-0002-5526-3882 Author Contributions C.H., S.M., Y.C., and W.-H.Y. conceived and designed the research; C.H., S.M., Y.K., H.B., and J.K. performed the experiment; C.H. and Y.C. conducted the computational modeling; S.Y. and W.W. conducted the hemocompatibility study; J.J., H.B., J.K., Y.J., and W.-H.Y. analyzed the data; C.H., S.M., Y.K., Y.C., S.Y., and W.-H.Y. wrote the paper. Acknowledgment C.H., S.M., and W.-H.Y. acknowledge a seed grant from Institute for Electronics and Nanotechnology at Georgia Tech. W.-H.Y. acknowledge a research grant from the Fundamental Research Program (PNK5061) of Korea Institute of Materials Science, and startup funding from the Woodruff School of Mechanical Engineering at Georgia Institute of Technology. This work was performed in part at the Georgia Tech Institute for Electronics and Nanotechnology, a member of the National Nanotechnology Coordinated Infrastructure, which is supported by the National Science Foundation (Grant ECCS-1542174). Y.C. acknowledges a support from the University of Pittsburgh Central Research Development Fund. Conflict of Interest The authors declare no competing financial interest. 35 ACS Paragon Plus Environment

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