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Reconfigurable microrobots promise advancements in microsurgical tools, self‐healing materials, and environmental remediation by enabling precise, adaptive functionalities at small scales. However, predicting their behaviors a priori remains a significant challenge, limiting the pace of design and discovery. To address this, a Monte Carlo simulation framework is presented for predicting the folding behavior of self‐assembled, sequence‐encoded microrobot chains composed of magnetic particles, enabling efficient exploration of their large design space. This computational framework employs metrics of radius of gyration, tortuosity, and symmetry score to map the design space, identify functional sequences, and predict likely folding behaviors before fabrication. The framework through experiments to demonstrate accuracy in capturing folding behaviors is validated. Statistical analysis reveals adherence to self‐avoiding walk principles from polymer theory, providing a foundation for understanding how input sequences drive folding capabilities. Moreover, the simulation surpasses current experimental capabilities, enabling exploration of novel microrobot designs, such as sequences incorporating mixtures of cubes and triangular prism subunits, which exhibit distinct compressive behaviors. Beyond the sequence‐encoded microrobots investigated in this study, this framework offers broad utility for the design of reconfigurable microscale systems by reducing reliance on experimental prototyping and accelerating discovery of new functional microrobots for use in biomedicine, materials engineering, and sustainability. 

In the biopharmaceutical industry, virus filters are crucial for ensuring the removal of endogenous and adventitious viruses as part of the viral clearance strategy. Although traditionally described as a size-exclusion mechanism, virus retention has a pro-cess-dependent nature where challenging conditions, such as process disruptions, may compromise membrane retention and significantly increase virus filtrate concentrations. The detailed mechanisms underlying this loss of retention are challenging to determine using traditional breakthrough experiments. In this work, single particle tracking and kinetic simulations were employed to connect individual particle behavior to the observed macroscopic losses in virus retention. Our experiments, using fluorescently labeled ΦX174 bacteriophage as a model parvovirus, replicated conditions representative of process disruptions within the Pegasus SV4, a homogeneous polymeric virus filtration membrane. During flow, phage particles retained were trapped within relatively large cavity spaces that had downstream constrictions aligned with the flow direction; the trapped particles were dynamic and exhibited significant intra-cavity motion. Upon flow stoppage, particles escaped from these retention locations rapidly, with approximately 90% of previously trapped particles being remobilized for process dis-ruption time ranging from 2 to 10 minutes, suggesting that local cavity escape had reached saturation at these timescales. Diffusion experiments within the membrane revealed isotropic and Fickian motion, hindered by more than an order of mag-nitude compared to diffusion in unconfined liquid. Despite the reduced mobility within the membrane, the substantial diffusion coefficient of 4.19 ± 0.06 µm²/s indicated that virus particles could travel tortuous but non-retentive pathways through the membrane on length scales equal to or greater than the membrane thickness during a disruption event. A 1D kinetic Monte-Carlo simulation successfully connected single-particle behavior to macroscopically observed virus release, indicating that significant diffusive release into the filtrate can occur even without the resumption of flow. This work provides crucial insights into the retention behavior of homogeneous membranes during periods of disruption, enabling the design of more robust mitigation strategies and filter designs. 

Abstract Designing complex synthetic materials for enzyme immobilization could unlock the utility of biocatalysis in extreme environments. Inspired by biology, we investigate the use of random copolymer brushes as dynamic immobilization supports that enable supra-biological catalytic performance of immobilized enzymes. This is demonstrated by immobilizingBacillus subtilisLipase A on brushes doped with aromatic moieties, which can interact with the lipase through multiple non-covalent interactions. Incorporation of aromatic groups leads to a 50 °C increase in the optimal temperature of lipase, as well as a 50-fold enhancement in enzyme activity. Single-molecule FRET studies reveal that these supports act as biomimetic chaperones by promoting enzyme refolding and stabilizing the enzyme’s folded and catalytically active state. This effect is diminished when aromatic residues are mutated out, suggesting the importance of π-stacking and π-cation interactions for stabilization. Our results underscore how unexplored enzyme-support interactions may enable uncharted opportunities for using enzymes in industrial biotransformations.