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Bio-based compostable starch aerogels have significant potential as a sustainable alternative to traditional polymer aerogels across various applications. However, they suffer from very significant shrinkage, shown in published work as 40–50% using existing processes. We hypothesized that the shrinkage is largely caused by pore collapse through the solvent exchange process, during which the water used to fabricate the starch matrix is replaced with ethanol. To mitigate this issue, this work introduces two strategies: (1) implementing a deep-freezing protocol (DFP) prior to the solvent exchange, followed by pure ethanol solvent exchanges instead of water/ethanol mixtures, and (2) incorporating chitin as a structural additive. As a baseline, we fabricated potato starch aerogels (PSAs) using conventional processes of mixing, heating, and retrogradation. By applying a DFP before pure ethanol exchanges, shrinkage was reduced from 44% to 10% in pure PSA samples. Furthermore, the addition of chitin reduced shrinkage to 8% in potato starch-chitin aerogels. Porosity, density, surface area, pore size distribution, thermal decomposition temperature, thermal conductivities, and scanning electron microscopy images demonstrate a correlation between reduced shrinkage and desired thermal material properties. 

The development of thermoplastic starch (TPS) films is crucial for fabricating sustainable and compostable plastics with desirable mechanical properties. However, traditional design of experiments (DOE) methods used in TPS development are often inefficient. They require extensive time and resources while frequently failing to identify optimal material formulations. As an alternative, adaptive experimental design methods based on Bayesian optimization (BO) principles have been recently proposed to streamline material development by iteratively refining experiments based on prior results. However, most implementations are not suited to manage the heteroscedastic noise inherently present in physical experiments. This work introduces a heteroscedastic Gaussian process (HGP) model within the BO framework to account for varying levels of uncertainty in the data, improve the accuracy of the predictions, and increase the overall experimental efficiency. The aim is to find the optimal TPS film composition that maximizes its elongation at break and tensile strength. To demonstrate the effectiveness of this approach, TPS films were prepared by mixing potato starch, distilled water, glycerol as a plasticizer, and acetic acid as a catalyst. After gelation, the mixture was degassed via centrifugation and molded into films, which were dried at room temperature. Tensile tests were conducted according to ASTM D638 standards. After five iterations and 30 experiments, the films containing 4.5 wt% plasticizer and 2.0 wt% starch exhibited the highest elongation at break (M = 96.7%, SD = 5.6%), while the films with 0.5 wt% plasticizer and 7.0 wt% starch demonstrated the highest tensile strength (M = 2.77 MPa, SD = 1.54 MPa). These results demonstrate the potential of the HGP model within a BO framework to improve material development efficiency and performance in TPS film and other potential material formulations.