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.