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CO 2 on Graphene: Benchmarking Computational Approaches to Noncovalent Interactions

Abstract

Not specified

Authors: Christopher Ehlert, Anna Piras, Ganna Gryn’ova

Date Published: 3rd Oct 2023

Publication Type: Journal

Metal-Free Molecular Catalysts for the Oxygen Reduction Reaction: Electron Affinity as an Activity Descriptor

Abstract (Expand)

Heteroatom-doped polyaromatic hydrocarbons (or nanographenes) are promising molecular electrocatalysts for the oxygen reduction reaction (ORR). Here, we use density functional theory to investigate the … first step of the ORR pathway (chemisorption) for a set of molecules with experimentally determined catalytic activities. Weak chemisorption is found for only negatively charged catalysts, and a strong correlation is observed between the computed electron affinities and experimental catalytic activities for a range of B- and B,N-doped polyaromatic hydrocarbons. The electron affinity is put forward as a simple activity descriptor of charged (activated) catalysts on an electrode.

Authors: Christopher Ehlert, Anna Piras, Juliette Schleicher, Ganna Gryn’ova

Date Published: 19th Jan 2023

Publication Type: Journal

The Law of Attraction: Relating Computed Energetics of Physisorption with Performance of Graphene-Based Sensors for Nitroaromatic Contaminants

Abstract (Expand)

The ability to detect persistent nitroaromatic contaminants, e.g. DNT and TNT, with high sensitivity and selectivity is central to environmental science and medicinal diagnostics. Graphene-based materials … rise to this challenge, offering supreme performance, biocompatibility, and low toxicity at a reasonable cost. In the first step of the electrochemical sensing process, these substrates establish non-covalent interactions with the analytes, which we show to be indicative of their respective detection limits. Employing a combination of semiempirical tight binding quantum chemistry, meta- dynamics, density functional theory, and symmetry-adapted perturbation theory in conjunction with curated data from experimental literature, we investigate the physisorption of DNT and TNT on a series of functionalised graphene derivatives. In agreement with experimental observations, systems with greater planarity and positively charged substrates afford stronger non-covalent interactions than their highly oxidised distorted counterparts. Despite the highly polar nature of the investigated species, their non-covalent interactions are largely driven by dispersion forces. To harness these design principles, we considered a series of boron and nitrogen (co)doped two-dimensional materials. One of these systems featuring a chain of B–N–C units was found to adsorb nitroaromatic molecules stronger than the pristine graphene itself. These findings form the basis for the design principles of sensing materials and illustrate the utility of relatively low cost in silico procedures for testing the viability of designed graphene-based sensors for a plethora of analytes.

Authors: Anna Piras, Ganna Gryn'ova

Date Published: 5th Apr 2021

Publication Type: Unpublished

Sensing and sensitivity: Computational chemistry of graphene‐based sensors

Abstract (Expand)

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.

Authors: Anna Piras, Christopher Ehlert, Ganna Gryn'ova

Date Published: 3rd Mar 2021

Publication Type: Journal

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