Recent nanofluidic measurements revealed strongly different surface charge measurements for boron-nitride and graphitic nanotubes when in contact with saline and alkaline water. 1,2 These observations contrast with the similar reactivity of a graphene layer and its boron nitride counterpart, using Density Functional Theory (DFT) framework, for intact and dissociative adsorption of gaseous water molecules. Here, we investigate, by DFT in implicit water, single and multiple adsorption of anionic hydroxide on single layers. A differential adsorption strength is found in vacuum for the first ionic adsorption on the two materials -chemisorbed on BN while physisorbed on graphene. The effect of implicit solvation reduces all adsorption values resulting in a favorable (non-favorable) adsorption on BN (graphene). We also calculate a pK a ≃ 6 for BN in water, in good agreement with experiments. Comparatively, the unfavorable results for graphene in water echoes the weaker surface charge measurements, but points to an alternative scenario.The successful isolation of single-layer graphene sheet3 has led to tremendous progress in the discovery of new 2D materials including boron nitride, silicene and transition metal dichalcogenides to cite a few. In particular hexagonal boron nitride monolayers share the planar honeycomb structure of graphene, with boron and nitrogen atoms alternating in the vortices of the honeycomb structure. Despite of their similar crystallographic structure, the electronic structure of BN and graphene nano sheets are drastically different : semimetallic for graphene and insulating for BN. Until recently both pristine layered materials were assumed to present inherently low chemical reactivity. However recent experimental works showed that covalent chemical fonctionalization could be achieved under drastic reactive conditions, either via reduction4,5 or reaction with oxidative reagents (H 2 O 2 ,6 oxygen radical7) or via fluorination8 Furthermore recent nanofluidic experiments have altered the long-standing picture of the chemical inertness of BN nanotubes by showing a large negative surface charge when the BN materials is in contact with aqueous saline solutions. In contrast, similar experimental investigations based on nanofluidic investigations showed that carbon nanotubes2 and planar graphitic surfaces9 exhibit smaller and even minute surface charge. The magnitude of the maximum charging -ranging from 0.1 C / m 2 for C to 1 C/m 2 for BN depending on the solution pH -rules out point defects as the sole origin of the measured surface charging. These experimental results therefore call for two challenging questions: (i) what are the potential chemical species present in basic and saline solutions that could adhere on the surface and strongly charge the pristine nanotubes ? (ii) how can we explain the different charging behavior of graphene and BN ideal nanotubes ? Europe PMC Funders GroupA recent DFT study has investigated the dissociation barrier of a water molecule in contact with pristine ...

The recent emergence of nanofluidics has highlighted the exceptional properties of graphene and its boron-nitride counterpart as confining nanomaterials for water and ion transport. Surprisingly, ionic transport experiments have unveiled a consequent electrification of the water/carbon surfaces, with a contrasting response for its water/boron-nitride homologue. In this paper, we report free energy calculations based on ab initio molecular dynamics simulations of hydroxide OH − ions in water near graphene and hexagonal boron nitride (h-BN) layers. Our results disclose that both surfaces get charged through hydroxide adsorption, but two strongly different mechanisms are evidenced. The hydroxide species shows weak physisorption on the graphene surface while it exhibits also strong chemisorption on the h-BN surface. Interestingly OH − is shown to keep very fast lateral dynamics and interfacial mobility within the physisorbed layer on graphene. Taking into account the large ionic surface conductivity, an analytic transport model allows to reproduce quantitatively the experimental data.