One approach for closed-loop plastics recycling relies on reverting polymers back into monomers because one can then make new plastics without loss of properties. This depolymerization requirement restricts the molecular design to making polymers with high mechanical performance. We report a three-dimensional (3D) printing chemistry through stepwise photopolymerization by forming dithioacetal bonds. The polymerized network can be transformed back into a photoreactive oligomer by dissociation of the dithioacetal bonds. This network-oligomer transformation is reversible, therefore allowing circular 3D printing using the same material. Our approach offers the flexibility of making modular adjustments in the design of the network backbone of a polymer. This allows access to fully recyclable elastomers, crystalline polymers, and rigid glassy polymers with high mechanical toughness, making them potentially suitable for diverse applications.

Inspired by biological homeostatic processes, we present a soft lithography method that offers high efficiency and versatility.

The position information is usually needed when wireless sensor networks are applied for surveillance within certain region. Thus the localization of nodes is critical for WSNs. However, the present localization schemes mostly focus on static sensor networks, and in mobile WSNs, these schemes will not work well. This paper proposes a range-free Monte Carlo localization algorithm TSBMCL, which is extended from the Monte Carlo Boxed (MCB) scheme^[1]. In this scheme, the well localized nodes are applied to aid other nodes for localization based on the sequential Monte Carlo method. The scheme could further improve the localization accuracy of the WSNs. We evaluate the performance of the scheme with comparison to MCB using MATLAB. The simulation results demonstrate that the localization scheme outperforms the MCB scheme.