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Ion transport in solid polymer electrolytes is crucial for applications like energy conversion and storage, as well as carbon dioxide capture. However, most of the materials studied in this area are petroleum-based. Natural materials (biopolymers) have the potential to act as alternatives to petroleum-based products and, when derived with ionic liquid (IL) functionalities, present a sustainable alternative for conductive materials by offering tunable morphological, thermal, and mechanical properties. In this study, a series of IL-functionalized cellulose derivatives with variations in pendant alkyl chain length, counteranions, and degrees of substitution were synthesized in order to explore structure-property relationships. Emphasis was placed on investigating morphological, thermal, and ionic conductivity changes, hypothesizing that materials synthesized with longer alkyl chains would exhibit increased backbone-to-backbone spacing, thereby lowering the glass transition temperature, and enhancing ionic conductivity. A variety of characterization techniques were used for this investigation, including nuclear magnetic resonance spectroscopy (NMR), elemental analysis, Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), X-ray scattering, and dielectric relaxation spectroscopy (DRS). The findings reveal a link between longer alkyl chain lengths, expanded backbone-backbone spacing, and side chain interdigitation. Within each set of samples, heightened ionic conductivity was observed with the introduction of bulkier, less coordinating anions, underscoring the significant influence of counteranion size. 

ABSTRACT Modern engineered systems, and learning‐based systems, in particular, provide unprecedented complexity that requires advancement in our methods to achieve confidence in mission success through test and evaluation (T&E). We define learning‐based systems as engineered systems that incorporate a learning algorithm (artificial intelligence) component of the overall system. A part of the unparalleled complexity is the rate at which learning‐based systems change over traditional engineered systems. Where traditional systems are expected to steadily decline (change) in performance due to time (aging), learning‐based systems undergo a constant change which must be better understood to achieve high confidence in mission success. To this end, we propose pairing Bayesian methods with systems theory to quantify changes in operational conditions, changes in adversarial actions, resultant changes in the learning‐based system structure, and resultant confidence measures in mission success. We provide insights, in this article, into our overall goal and progress toward developing a framework for evaluation through an understanding of equivalence of testing. 

Training deep learning models requires having the right data for the problem and understanding both your data and the models’ performance on that data. Training deep learning models is difficult when data are limited, so in this paper, we seek to answer the following question: how can we train a deep learning model to increase its performance on a targeted area with limited data? We do this by applying rotation data augmentations to a simulated synthetic aperture radar (SAR) image dataset. We use the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique to understand the effects of augmentations on the data in latent space. Using this latent space representation, we can understand the data and choose specific training samples aimed at boosting model performance in targeted under-performing regions without the need to increase training set sizes. Results show that using latent space to choose training data significantly improves model performance in some cases; however, there are other cases where no improvements are made. We show that linking patterns in latent space is a possible predictor of model performance, but results require some experimentation and domain knowledge to determine the best options.