Reprocessable Polymer Networks Containing Sulfur-Based, Percolated Dynamic Covalent Cross-Links and Percolated or Non-Percolated, Static Cross-Links.

One method to improve the properties of covalent adaptable networks (CANs) is to reinforce them with a fraction of permanent cross-links without sacrificing their (re)processability. Here, a simple method to synthesize poly(n-hexyl methacrylate) (PHMA) and poly(n-lauryl methacrylate) (PLMA) networks containing static dialkyl disulfide cross-links (utilizing bis(2-methacryloyl)oxyethyl disulfide, or DSDMA, as a permanent cross-linker) and dynamic dialkylamino sulfur-sulfur cross-links (utilizing BiTEMPS methacrylate as a dissociative dynamic covalent cross-linker) is presented. The robustness and (re)processability of the CANs are demonstrated, including the full recovery of cross-link density after recycling. The authors also investigate the effect of static cross-link content on the stress relaxation responses of the CANs with and without percolated, static cross-links. As PHMA and PLMA have very different activation energies of their respective cooperative segmental mobilities, it is shown that the dissociative CANs without percolated, static cross-links have activation energies of stress relaxation that are dominated by the dissociation of BiTEMPS methacrylate cross-links rather than by the cooperative relaxations of backbone segments, i.e., the alpha relaxation. In CANs with percolated, static cross-links, the segmental relaxation of side chains, i.e., the beta relaxation, is critical in allowing for large-scale stress relaxation and governs their activation energies of stress relaxation.

Sensitivity volume as figure-of-merit for maximizing data importance in electrical impedance tomography.

Objective.Electrical impedance tomography (EIT) is a noninvasive imaging method whereby electrical measurements on the periphery of a heterogeneous conductor are inverted to map its internal conductivity. The EIT method proposed here aims to improve computational speed and noise tolerance by introducing sensitivity volume as a figure-of-merit for comparing EIT measurement protocols.Approach.Each measurement is shown to correspond to a sensitivity vector in model space, such that the set of measurements, in turn, corresponds to a set of vectors that subtend a sensitivity volume in model space. A maximal sensitivity volume identifies the measurement protocol with the greatest sensitivity and greatest mutual orthogonality. A distinguishability criterion is generalized to quantify the increased noise tolerance of high sensitivity measurements.Main result.The sensitivity volume method allows the model space dimension to be minimized to match that of the data space, and the data importance to be increased within an expanded space of measurements defined by an increased number of contacts.Significance.The reduction in model space dimension is shown to increasecomputational efficiency, accelerating tomographic inversion by several orders of magnitude, while the enhanced sensitivitytolerates higher noiselevels up to several orders of magnitude larger than standard methods.