As the basic unit of life, cells are compartmentalized microreactors with molecularly crowded microenvironments. The quest to understand the cell origin inspires the design of synthetic analogs to mimic their functionality and structural complexity. In this work, we integrate membraneless coacervate microdroplets, a prototype of artificial organelles, into a proteinosome to build hierarchical protocells that may serve as a more realistic model of cellular organization. The protocell subcompartments can sense extracellular signals, take actions in response to these stimuli, and adapt their physicochemical behaviors. The tiered protocells are also capable of enriching biomolecular reactants within the confined organelles, thereby accelerating enzymatic reactions. The ability of signal processing inside protocells allows us to design the Boolean logic gates (NOR and NAND) using biochemical inputs. Our results highlight possible exploration of protocell-community signaling and render a flexible synthetic platform to study complex metabolic reaction networks and embodied chemical computation.

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Doping with heteroatoms such as nitrogen and oxygen has been widely practiced to improve the capacitance of carbon electrodes for supercapacitor. However, the role of different heteroatoms and their local atomic configurations on the supercapacitor performance remains elusive, which hampers the rational design of carbon electrodes to achieve high energy density and power density. In this work, machine-learning models are applied to interpret how the surface chemistry affects the inoperando behavior of various carbon electrodes with different structural features such as the specific surface areas of micro-and mesopores. Quantitative descriptions have been established to predict how the configurations of nitrogen-doping and oxygendoping influence the capacitance and retention rate. The machinelearning models provide insights into the design and possible routes to the synthesis of nitrogen and oxygen co-doped carbon electrodes that maximize the energy storage capacity.

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Nanoporous materials are promising as the next generation of absorbents for gas storage and separation with ultrahigh capacity and selectivity. The recent advent of data-driven approaches in materials modeling provides alternative routes to tailor nanoporous materials for customized applications. Typically, a data-driven model requires a large amount of training data that cannot be generated solely by experimental methods or molecular simulations. In this work, we propose an efficient implementation of classical density functional theory with a graphic processing unit (GPU) for the fast yet accurate prediction of gas adsorption isotherms in nanoporous materials. In comparison to serial computing with the central processing unit, the massively parallelized GPU implementation reduces the computational cost by more than two orders of magnitude. The proposed algorithm renders new opportunities not only for the efficient screening of a large materials database for gas adsorption but it may also serve as an important stepping stone toward the inverse design of nanoporous materials tailored to desired applications.