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Poly(lactic-co-glycolic) acid (PLGA) has been widely implemented in tissue engineering and drug delivery systems, stemming from its biocompatibility, controllable biodegradation, non-toxicity, non-immunogenicity, and tunable mechanical properties. PLGA exhibits a broad range of degradation times and modes, which can be finely tuned by adjusting various parameters, namely by altering the ratio of lactide and glycolide units, molecular weight, end group functionality, specimen geometry, processing temperature, and chemistry of the surrounding medium. To tailor the degradation profile, the in vitro profile should closely reflect the in vivo profile; however, the effects of mechanical loading coupled with hydrolysis on PLGA biodegradation are typically overlooked. To this end, this study investigates the combined effects of mechanical loading and hydrolysis at 37ºC on the changes in the chemical and physical properties of PLGA as it degrades with time. We found that after several days of combined loading and hydrolysis at 37ºC PLGA significantly creeps, whereas non-loaded (but hydrolyzed) specimens only slightly elongated after relatively long-term hydrolysis (~60 days). Despite this observation and perhaps counterintuitively, the hydrolyzed non-loaded samples exhibited faster degradation than hydrolyzed loaded samples. Additionally, our studies indicated the presence of bulk erosion in hydrolyzed non-loaded samples and surface erosion in hydrolyzed loaded samples. We also observed (only) physical ageing in control samples (loaded and non-loaded samples that were not immersed in PBS but exposed to 37 °C). Based on these observations, we discuss potential underlying mechanisms for the observed differences in the biodegradation behavior of PLGA specimens with and without mechanical loading. 

Abstract We present NoodlePrint, a generalized computational framework for maximally concurrent layer-wise cooperative 3D printing (C3DP) of arbitrary part geometries with multiple robots. NoodlePrint is inspired by a recently discovered set of helically interlocked space-filling shapes called VoroNoodles. Leveraging this unique geometric relationship, we introduce an algorithmic pipeline for generating helically interlocked cellular segmentation of arbitrary parts followed by layer-wise cell sequencing and path planning for cooperative 3D printing. Furthermore, we introduce a novel concurrence measure that quantifies the amount of printing parallelization across multiple robots. Consequently, we integrate this measure to optimize the location and orientation of a part for maximally parallel printing. We systematically study the relationship between the helix parameters (i.e., cellular interlocking), the cell size, the amount of concurrent printing, and the total printing time. Our study revealed that both concurrence and time to print primarily depend on the cell size, thereby allowing the determination of interlocking independent of time to print. To demonstrate the generality of our approach with respect to part geometry and the number of robots, we implemented two cooperative 3D printing systems with two and three printing robots and printed a variety of part geometries. Through comparative bending and tensile tests, we show that helically interlocked part segmentation is robust to gaps between segments.