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Open microfluidics have emerged as a low-cost, pumpless alternative strategy to conventional microfluidics for delivery of fluid for a wide variety of applications including rapid biochemical analysis and medical diagnosis. However, creating open microfluidics by tuning the wettability of surfaces typically requires sophisticated cleanroom processes that are unamenable to scalable manufacturing. Herein, we present a simple approach to develop open microfluidic platforms by manipulating the surface wettability of spin-coated graphene ink films on flexible polyethylene terephthalate via laser-controlled patterning. Wedge-shaped hydrophilic tracks surrounded by superhydrophobic walls are created within the graphene films by scribing micron-sized grooves into the graphene with a CO 2 laser. This scribing process is used to make superhydrophobic walls (water contact angle ∼160°) that delineate hydrophilic tracks (created through an oxygen plasma pretreatment) on the graphene for fluid transport. These all-graphene open microfluidic tracks are capable of transporting liquid droplets with a velocity of 20 mm s −1 on a level surface and uphill at elevation angles of 7° as well as transporting fluid in bifurcating cross and tree branches. The all-graphene open microfluidic manufacturing technique is rapid and amenable to scalable manufacturing, and consequently offers an alternative pumpless strategy to conventional microfluidics and creates possibilities for diverse applications in fluid transport. 

Most cyber-physical human systems (CPHS) rely on users learning how to interact with the system. Rather, a collaborative CPHS should learn from the user and adapt to them in a way that improves holistic system performance. Accomplishing this requires collaboration between the human-robot/human-computer interaction and the cyber-physical system communities in order to feed back knowledge about users into the design of the CPHS. The requisite user studies, however, are difficult, time consuming, and must be carefully designed. Furthermore, as humans are complex in their interactions with autonomy it is difficult to know, a priori, how many users must participate to attain conclusive results. In this paper we elaborate on our work to infer intrinsic user qualities through human-robot interactions correlated with robot performance in order to adapt the autonomy and improve holistic CPHS performance. We first demonstrate through a study that this idea is feasible. Next, we demonstrate that significant differences between groups of users can impact conclusions particularly where different autonomies are involved. Finally, we also provide our rich, extensive corpus of user study data to the wider community to aid researchers in designing better CPHS. 

Users play an integral role in the performance of many robotic systems, and robotic systems must account for differences in users to improve collaborative performance. Much of the work in adapting to users has focused on designing teleoperation controllers that adjust to extrinsic user indicators such as force, or intent, but do not adjust to intrinsic user qualities. In contrast, the Human-Robot Interaction community has extensively studied intrinsic user qualities, but results may not rapidly be fed back into autonomy design. Here we provide foundational evidence for a new strategy that augments current shared control, and provide a mechanism to directly feed back results from the HRI community into autonomy design. Our evidence is based on a study examining the impact of the user quality “locus of control” on telepresence robot performance. Our results support our hypothesis that key user qualities can be inferred from human-robot interactions (such as through path deviation or time to completion) and that switching or adaptive autonomies might improve shared control performance.