Sensing and sensitivity: Computational chemistry of graphene‐based sensors

Highly efficient, tunable, biocompatible, and environmentally friendly electrochemical sensors featuring graphene-based materials pose a formidable challenge for computational chemistry. In silico rationalization, optimization and, ultimately, prediction of their performance requires exploring a vast structural space of potential surface-analyte complexes, further complicated by the presence of various defects and functionalities within the infinite graphene lattice. This immense number of systems and their periodic nature greatly limit the choice of computational tools applicable at a reasonable cost. An alternative approach using finite nanoflake models opens the doors to many more advanced and accurate electronic structure methods, while sacrificing the realism of representation. Locating the surface-analyte complex is followed by an in-depth in silico analysis of its energetic and electronic properties using, for example, energy decomposition schemes, as well as simulation of the signal, for example, a zero-bias transmission spectra or a current–voltage curve, by means of the nonequilibrium Green's function method. These and other properties are examined in the context of a sensor's selectivity, sensitivity, and limit of detection with an aim to establish design principles for future devices. Herein, we analyze the advantages and limitations of diverse computational chemistry methods used at each of these steps in simulating graphene-based electrochemical sensors. We present outstanding challenges toward predictive models and sketch possible solutions involving such contemporary techniques as multiscale simulations and high-throughput screening.