Open microfluidic devices based on actuation techniques such as electrowetting, dielectrophoresis, or thermocapillary stresses require controlled motion of small liquid droplets on the surface of glass or silicon substrates. In this article we explore the physical mechanisms affecting thermocapillary migration of droplets generated by surface temperature gradients on the supporting substrate. Using a combination of experiment and modeling, we investigate the behavior of the threshold force required for droplet mobilization and the speed after depinning as a function of the droplet size, the applied thermal gradient and the liquid material parameters. The experimental results are well described by a hydrodynamic model based on earlier work by Ford and Nadim. The model describes the steady motion of a two-dimensional droplet driven by thermocapillary stresses including contact angle hysteresis. The results of this study highlight the critical role of chemical or mechanical hysteresis and the need to reduce this retentive force for minimizing power requirements in microfluidic devices.

The design and performance of a miniaturized coplanar capacitive sensor is presented whose electrode arrays can also function as resistive microheaters for thermocapillary actuation of liquid films and droplets. Optimal compromise between large capacitive signal and high spatial resolution is obtained for electrode widths comparable to the liquid film thickness measured, in agreement with supporting numerical simulations which include mutual capacitance effects. An interdigitated, variable width design, allowing for wider central electrodes, increases the capacitive signal for liquid structures with non-uniform height profiles. The capacitive resolution and time response of the current design is approximately 0.03 pF and 10 ms, respectively, which makes possible a number of sensing functions for nanoliter droplets. These include detection of droplet position, size, composition or percentage water uptake for hygroscopic liquids. Its rapid response time allows measurements of the rate of mass loss in evaporating droplets.

There has been much recent research on identifying global community structure in networks. However

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Many datasets can be described in the form of graphs or networks where nodes in the graph represent entities and edges represent relationships between pairs of entities. A common property of these networks is their community structure, considered as clusters of densely connected groups of vertices, with only sparser connections between groups. The identification of such communities relies on some notion of clustering or density measure, which defines the communities that can be found. However, previous community detection methods usually apply the same structural measure on all kinds of networks, despite their distinct dissimilar features. In this paper, we present a new community mining measure, Max-Min Modularity, which considers both connected pairs and criteria defined by domain experts in finding communities, and then specify a hierarchical clustering algorithm to detect communities in networks. When applied to real world networks for which the community structures are already known, our method shows improvement over previous algorithms. In addition, when applied to randomly generated networks for which we only have approximate information about communities, it gives promising results which shows the algorithm's robustness against noise.