We develop a hybrid computational approach to examine the mechanical properties and self-healing behavior of nanogel particles that are cross-linked by both stable and labile bonds. The individual nanogels are modeled via the lattice spring model (LSM), which is an effective method for probing the response of materials to mechanical deformation. The cross-links between the nanogels are simulated via the hierarchical Bell model (HBM), which allows us to capture the rupturing of multiple parallel bonds as the result of an applied force. Because the labile bonds are relatively reactive, they can reform after they have been ruptured. To incorporate the possibility of bonds reforming, we modify the HBM formalism and validate the modified HBM by considering a system of two surfaces, which are connected by multiple parallel bonds. We then use our hybrid HBM/LSM to simulate the behavior of the cross-linked nanogels under a tensile deformation. In these simulations, each labile linkage between the nanogels contains at most N parallel bonds. We vary the fraction of labile linkages and the value of N in these linkages to determine the optimal conditions for improving the robustness of the material. Although numerous parallel bonds within a linkage enhance the strength of the material, these bonds diminish the ductility and the ability of the material to undergo the structural rearrangements that are necessary for self-repair. For a relatively low fraction of labile bonds and N ≤ 4, however, we can significantly improve the strength of the material and preserve the self-healing properties. For instance, a sample with 30% labile linkages and N = 4 per linkage is roughly 200% stronger than a sample that is cross-linked solely by stable bonds and can still undergo self-repair in response to the tensile deformation. The results reveal how mechanical stress can lead not only to the appearance of cavities within the material but also to bond formation that "heals" these cavities and thus prevents the catastrophic failure of the material.

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A range of new technologies have the potential to help people, whether traditionally considered hearing impaired or not. These technologies include more sophisticated personal sound amplification products, as well as real-time speech enhancement and speech recognition. They can improve user’s communication abilities, but these new approaches require new ways to describe their success and allow engineers to optimize their properties. Speech recognition systems are often optimized using the word-error rate, but when the results are presented in real time, user interface issues become a lot more important than conventional measures of auditory performance. For example, there is a tradeoff between minimizing recognition time (latency) by quickly displaying results versus disturbing the user’s cognitive flow by rewriting the results on the screen when the recognizer later needs to change its decisions. This article describes current, new, and future directions for helping billions of people with their hearing. These new technologies bring auditory assistance to new users, especially to those in areas of the world without access to professional medical expertise. In the short term, audio enhancement technologies in inexpensive mobile forms, devices that are quickly becoming necessary to navigate all aspects of our lives, can bring better audio signals to many people. Alternatively, current speech recognition technology may obviate the need for audio amplification or enhancement at all and could be useful for listeners with normal hearing or with hearing loss. With new and dramatically better technology based on deep neural networks, speech enhancement improves the signal to noise ratio, and audio classifiers can recognize sounds in the user’s environment. Both use deep neural networks to improve a user’s experiences. Longer term, auditory attention decoding is expected to allow our devices to understand where a user is directing their attention and thus allow our devices to respond better to their needs. In all these cases, the technologies turn the hearing assistance problem on its head, and thus require new ways to measure their performance.

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