Organic dispersions of graphene oxide can be thermally reduced in polar organic solvents under reflux conditions to afford electrically conductive, chemically active reduced graphene oxide (CARGO) with tunable C/O ratios, dependent on the boiling point of the solvent. The reductions are achieved after only 1 h of reflux, and the corresponding C/O ratios do not change upon further thermal treatment. Hydroxyl and carboxyl groups can be removed when the reflux is carried out above 155 °C, while epoxides are removable only when the temperature is higher than 200 °C. The increasing hydrophobic nature of CARGO, as its C/O ratio increases, improves the dispersibility of the nanosheets in a polystyrene matrix, in contrast to the aggregates formed with CARGO having lower C/O ratios. The excellent processability of the obtained CARGO dispersions is demonstrated via free-standing CARGO papers that exhibit tunable electrical conductivity/chemical activity and can be used as lithium-ion battery anodes with enhanced Coulombic efficiency.
Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the in vivo behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of Mycoplasma genitalium, we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of wholecell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline. Synthetic biology aims to engineer synthetic genetic circuits to endow cells and organisms with the ability to address new applications, including the production of drugs and industrial products, the design of new therapeutics for human diseases, and the study of basic biological processes. Despite significant advances in the field over the last decade, the synthetic biology design cycle has been hampered by the lack of predictive models. In contrast, more established engineering disciplines, such as civil and electrical engineering can rely on rational design by using models that can capture the behavior of real-world systems with a high degree of accuracy. This is currently not the case for synthetic biology, as models are generally only able to make qualitative predictions about system behavior. As a result, producing a functional engineered biological system usually requires extensive manual tuning by trial-and-error, a timeconsuming process that significantly slows the biological design process. The lack of predictive power in current models is partly because these models account for only the synthetic circuits themselves, but not other ongoing processes in the cells in which they reside. However, the complex dynamics of synthetic circuits may affect the host cell and vice versa, leading to divergence between current models and experimental results. Recently, a whole-cell model of a small and simple bacteri...
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