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WIREs Comput Mol Sci
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Sensing and sensitivity: Computational chemistry of graphene‐based sensors

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Abstract 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. This article is categorized under: Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Density Functional Theory Electronic Structure Theory > Ab Initio Electronic Structure Methods
Classification of chemical sensors
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Various properties that can be computed to gain insights into the detection process
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(a) Decomposition of the attractive contributions to the adsorption energy of various organic molecules on graphene from symmetry‐adapted perturbation theory (SAPT). (Reprinted with permission from Reference 79. Copyright 2013 American Chemical Society). (b) Nonequilibrium Green's function (NEGF)‐density functional theory (DFT) simulated current–voltage curves of pristine graphene and its complexes with different amino acids. (Reprinted with permission from Reference 82. Copyright 2017 Elsevier). (c) NEGF‐DFT band structure and density of states (DOS) of an armchair graphene nanoribbon with NO2 and NH3 gas molecule adsorption. The local density of states of gas molecules is also plotted (red filled area under DOS curve). The Fermi level is set to zero. (Reprinted with permission from Reference 89. Copyright 2008 American Chemical Society)
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(a) Adsorption complexes formed by glycine (Gly), melamine (Mel), and porphine (PP, only the most stable) with graphene sheet together with the corresponding molecule‐surface averaged distance. (Reprinted with permission from 74. Copyright 2016 PCCP Owner Societies). (b) Snapshots from molecular dynamics (MD) simulations show behavior of ethanol (a') on graphene, (b') on graphene with trimer of methyl methacrylate (MMA), and (c') on graphene with octamer of MMA. The concentration of poly(methyl methacrylate) (PMMA) residues raises from top down and that of ethanol from left to right (corresponding to 10%, 50%, and 100% of monolayer coverage of the graphene surface). Ethanol molecules that are not adsorbed (i.e., further than 15 Å from the graphene surface) are not shown for clarity. Graphene is shown as gray sticks, ethanol as green/red/white sticks, and PMMA fragments as blue/red/white spheres. (Reprinted with permission from Reference 107. Copyright 2020 American Chemical Society)
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Schematic illustration of a prototypical conductometric electrochemical sensor
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(a) Optimized structures of the NO2 molecule adsorbed on (a') pristine graphene, (b') sp3‐type defect (epoxy group)‐containing graphene, (c') sp3‐type defect (carbonyl group)‐containing graphene, (d') sp3‐type defect (ether group)‐containing graphene, and (e') single vacancy graphene. The first and second rows show side and top views, respectively. The red, brown, and gray colors represent O, C, and N atoms, respectively. (Reprinted with permission from Reference 15. Copyright 2020 PCCP Owner Societies). (b) The linear relationship between [exp2(−Ead/kBT)/Eg4.5)] and 1/limit of detection (LOD) shows the influence of band gap and adsorption energy on NO2 gas sensing performance of materials. (Reprinted with permission from Reference 99. Copyright 2020 Elsevier)
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Examples of graphene models: (a) circumcoronene molecule, used as a nanoflake model for pristine graphene; (b) a periodic model of pristine graphene with a supercell of 32 carbon atoms (in red) obtained from a primitive unit cell containing two carbon atoms (in blue)
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Electronic Structure Theory > Ab Initio Electronic Structure Methods
Electronic Structure Theory > Density Functional Theory
Structure and Mechanism > Computational Materials Science

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