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Double-walled polyurethane/poly(urea-formaldehyde) microcapsules (PU/UF) are prepared for use in self-healing materials. This modified encapsulation procedure combines two chemistries to form more robust capsule shell walls in a single operation. Robust capsules are formed by this procedure as long as the aromatic polyisocyanate prepolymer is soluble in the core liquid and the core liquid is compatible with isocyanates. Compared to a standard UF encapsulation, the modified procedure results in capsules with an increase in shell wall thickness from 200 to 675 nm as a function of the amount of PU added to the core liquid. Thermal stability of PU/UF microcapsules prepared with varying amounts of PU is compared to UF microcapsules. Mechanical properties of the PU/UF microcapsules are assessed from single-capsule compression testing.

Self-healing is achieved with a dual-microcapsule system utilizing epoxy-amine chemistry in a high temperature cured thermosetting epoxy polymer. One capsule contains a modified aliphatic polyamine prepared by vacuum infiltration of polyoxypropylenetriamine into hollow polymeric microcapsules. The second capsule contains a difunctional epoxide and reactive diluent. Healing efficiency is accessed through recovery of fracture toughness and excellent long-term stability at ambient conditions is demonstrated.

Recent years have witnessed the emergence of mobile crowd sensing (MCS) systems, which leverage the public crowd equipped with various mobile devices for large scale sensing tasks. In this paper, we study a critical problem in MCS systems, namely, incentivizing user participation. Different from existing work, we incorporate a crucial metric, called users' quality of information (QoI), into our incentive mechanisms for MCS systems. Due to various factors (e.g., sensor quality, noise, etc.) the quality of the sensory data contributed by individual users varies significantly. Obtaining high quality data with little expense is always the ideal of MCS platforms. Technically, we design incentive mechanisms based on reverse combinatorial auctions. We investigate both the singleminded and multi-minded combinatorial auction models. For the former, we design a truthful, individual rational and computationally efficient mechanism that approximately maximizes the social welfare with a guaranteed approximation ratio. For the latter, we design an iterative descending mechanism that achieves close-tooptimal social welfare while satisfying individual rationality and computational efficiency. Through extensive simulations, we validate our theoretical analysis about the close-to-optimal social welfare and fast running time of our mechanisms.

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