Binding of histidine and proline with graphene: DFT study

Daggag, Dalia; Dorlus, Taylor; Dinadayalane, T. C.

doi:10.1016/j.cplett.2019.05.043

Cited by 10 publications

(9 citation statements)

References 48 publications

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“…Unraveling the nature and strength of an analyte's adsorption on the sensor's surface is vital for predicting device performance. Quantitative relationships between adsorption (or interaction) energy and the LOD of the sensor have been established, 99 and the dependence of a sensor's selectivity on the adsorption strength is well recognized 75,118,119 . As in the case of determining adsorption geometries, the availability of computational tools for quantifying and analyzing analyte‐sensor interactions is limited by the system size and thus the realism of the model.…”

Section: Sensor‐analyte Interactionmentioning

confidence: 99%

“…In addition to geometry, adsorption or interaction energy, and electronic transport, computational chemistry allows computing other properties of the analyte‐sensor complexes (Figure 7), ultimately aiding further insights into the sensor performance. Properties such as the density of states (DOS), energy gap between the HOMO and the LUMO, and the charge transfer characteristics can be employed to tune the sensing features 99 or to assess the quality of an in silico sensor model 118 …”

Section: Other Propertiesmentioning

confidence: 99%

“…For example, there is no difference in the HOMO–LUMO gap of histidine and proline adsorbed on graphene (at the M06‐2X/6‐31G(d) level), suggesting that the conductivity of the two complexes is the same. It is therefore impossible to use graphene as a conductivity sensor able to selectively detect the two biomolecules 118 . If the HOMO–LUMO gap computed for a nanoflake model for pristine graphene is almost zero, it is evidence of the suitability of the model to simulate the periodic system 118 …”

Section: Other Propertiesmentioning

confidence: 99%

“…It is therefore impossible to use graphene as a conductivity sensor able to selectively detect the two biomolecules 118 . If the HOMO–LUMO gap computed for a nanoflake model for pristine graphene is almost zero, it is evidence of the suitability of the model to simulate the periodic system 118 …”

Section: Other Propertiesmentioning

confidence: 99%

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Sensing and sensitivity: Computational chemistry of graphene‐based sensors

Piras

Ehlert

Gryn’ova

2021

WIREs Comput Mol Sci

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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

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Section: Sensor‐analyte Interactionmentioning

confidence: 99%

Section: Other Propertiesmentioning

confidence: 99%

Section: Other Propertiesmentioning

confidence: 99%

Section: Other Propertiesmentioning

confidence: 99%

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Sensing and sensitivity: Computational chemistry of graphene‐based sensors

Piras

Ehlert

Gryn’ova

2021

WIREs Comput Mol Sci

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“…Molecules in the crystal tend to arrange themselves in order to maximize the intermolecular interactions among them [18][19][20][21]. This molecular packing is controlled by many directional forces such as coordination interactions, hydrogen bonding, π-π stacking, C-H...π interactions and others [22][23][24][25][26][27][28]. Hirshfeld topology analysis is considered very important tool used to determine and quantify the intermolecular interactions in the crystal [29].…”

Section: Introductionmentioning

confidence: 99%

Intramolecular Hydrogen Bond, Hirshfeld Analysis, AIM; DFT Studies of Pyran-2,4-dione Derivatives

et al. 2021

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Intra and intermolecular interactions found in the developed crystals of the synthesized py-ron-2,4-dione derivatives play crucial rules in the molecular conformations and crystal stabili-ties, respectively. In this regard, Hirshfeld calculations were used to quantitatively analyze the different intermolecular interactions in the crystal structures of some functionalized py-ran-2,4-dione derivatives. The X-ray structure of pyran-2,4-dione derivative namely (3E,3'E)-3,3'-((ethane-1,2-diylbis(azanediyl))bis(phenylmethanylylidene))bis(6-phenyl-2H-pyran-2,4(3H)-dione) was determined. It crystallized in the monoclinic crystal system and C2/c space group with unit cell parameters: a = 14.0869(4) Å, b = 20.9041(5) Å, c = 10.1444(2) Å and β = 99.687(2)°. Generally, the H…H, H…C, O…H and C…C contacts are the most important interactions in the molecular packing of the studied pyran-2,4-diones. The molecular structure of these compounds is stabilized by intramolecular O…H hydrogen bond. The nature and strength of the O…H hy-drogen bonds were analyzed using atoms in molecules calculations. In all compounds, the O…H hydrogen bond belongs to closed-shell interactions where the interaction energies are higher at the optimized geometry than the X-ray one due to the shortening in the A…H distance as a con-sequence of the geometry optimization. These compounds have polar characters with different charged regions which explored using molecular electrostatic potential map. Their natural charges, reactivity descriptors and NMR chemical shifts were computed, discussed and com-pared.

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π–π Catalysis in Carbon Flatland—Flipping [8]Annulene on Graphene

Kroeger

Karton

2021

Chemistry A European J

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Noncovalent interactions are an integral part of the modern catalysis toolbox. Although stronger noncovalent interactions such as hydrogen bonding are commonly the main driving force of catalysis, π–π interactions typically provide smaller additional stabilizations, for example, to afford selectivity enhancements. Here, it is shown computationally that pristine graphene flakes may efficiently catalyze the skeletal inversions of various benzannulated cyclooctatetraene derivatives, providing an example of a catalytic process driven solely by π–π stacking interactions. Hereby, the catalytic effect results from disproportionate shape complementarity between catalyst and transition structure compared with catalyst and reactant. An energy decomposition analysis reveals electrostatic and, especially with increasing system size, to a larger extent, dispersion interactions as the origin of stabilization.

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Binding of histidine and proline with graphene: DFT study

Cited by 10 publications

References 48 publications

Sensing and sensitivity: Computational chemistry of graphene‐based sensors

Sensing and sensitivity: Computational chemistry of graphene‐based sensors

Intramolecular Hydrogen Bond, Hirshfeld Analysis, AIM; DFT Studies of Pyran-2,4-dione Derivatives

π–π Catalysis in Carbon Flatland—Flipping [8]Annulene on Graphene

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