Graphene
oxide, decorated with surface oxygen functionalities,
has emerged as an alternative to precious-metal catalysts for many
reactions. Herein, we report that graphene oxide becomes superactive
for C–C coupling upon incorporation of a highly oxidized surface
associated with Brønsted acidic oxygen functionality and defect
sites along the surface and edges. The resulting improved graphene
oxide (IGO) demonstrates significantly higher activity over commonly
used framework zeolites for the upgrade of low-carbon biomass furanics
to high-carbon fuel precursors. A maximum 95% yield of C15 fuel precursor with high selectivity is obtained at low temperature
(60 °C) and neat conditions via hydroxyalkylation/alkylation
(HAA) of 2-methylfuran (2-MF) and furfural. Coupling of 2-MF with
carbonyl compounds ranging from C3 to C6 produces
precursors of carbon numbers 12 to 21 with a high yield. The catalyst
regains nearly full activity upon regeneration. Extensive microscopic
and spectroscopic characterization of the fresh and reused IGO carbocatalysts
indicates that defects and the enhanced oxygen content are strongly
correlated with the high activity of IGO. Density functional theory
calculations reveal defects at carbonyl sites as suitable Brønsted
acidic oxygen functional groups. A plausible reaction mechanism is
also hypothesized.
Kinetic Monte Carlo simulation is an integral tool in the study of complex physical phenomena present in applications ranging from heterogeneous catalysis to biological systems to crystal growth and atmospheric sciences. Sensitivity analysis is useful for identifying important parameters and rate-determining steps, but the finite-difference application of sensitivity analysis is computationally demanding. Techniques based on the likelihood ratio method reduce the computational cost of sensitivity analysis by obtaining all gradient information in a single run. However, we show that disparity in time scales of microscopic events, which is ubiquitous in real systems, introduces drastic statistical noise into derivative estimates for parameters affecting the fast events. In this work, the steady-state likelihood ratio sensitivity analysis is extended to singularly perturbed systems by invoking partial equilibration for fast reactions, that is, by working on the fast and slow manifolds of the chemistry. Derivatives on each time scale are computed independently and combined to the desired sensitivity coefficients to considerably reduce the noise in derivative estimates for stiff systems. The approach is demonstrated in an analytically solvable linear system.
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