Innovative Approach to Improving Gas-to-Liquid Fuel Catalysis via Nanosensor Network Modulation

Fischer–Tropsch
synthesis, a major process for converting
natural gas to liquid hydrocarbons (GTL), suffers from selectivity
limitations that refer to the ratio of highly useful hydrocarbons
to the total product output. Existing strategies for selectivity improvement,
such as manipulation of reactor operating conditions (temperature,
pressure, etc.) and catalyst design variables may be classified as
top-down approaches. In this work, a bottom-up approach is proposed
in which surface processes can be controlled via a nanosensor network
(NSN) involving the turning on or off of elementary steps creating
undesired species and redirection of surface efforts to step(s) leading
to the desired products. The overall effect of these nanolevel communications
offers superior selectivity to that hitherto possible by reducing
the rate of hydrogenation of surface unsaturated species to paraffin
(HTP) reactions. Our numerical and simulation results confirm substantial
improvement of overall selectivity in a catalyst that is equipped
with a highly reliable NSN.