We used classical molecular dynamics (MD) simulations to study the role of ion adsorption on electrokinetic transport in nanotubes, showing the importance of surface mobility [1], and to predict the non-linear response of electrolytes to electric fields from electric current fluctuations, showing the role ion-ion and ion-solvent correlations [2]. We developed tools to predict the power spectral density (PSD) of the ionic current through nanotubes using Brownian Dynamics simulations to reach low frequencies, showing the role of short-range repulsion, electrostatic interactions and confinement on the PSD [3]. We studied ionic fluctuations in finite observation volumes in bulk electrolytes, extending results on hyperuniformity to dynamical correlations [4]. We developed methods to model the dynamics of observables on longer scales, building Generalized Langevin equations from MD simulations [5] and identifying relevant collective variables [6]. We revisited analytical theories for transport in concentrated electrolytes [7] and the coupling between ion and solvent dynamics [8].
We used constant-potential MD to investigate electrode-electrolyte interfaces on the molecular scale. We wrote two review articles [9,10], showed the link between electrode charge fluctuations and interfacial free energy, and how to compute the latter as a function of electrode metallicity [11], which can also be tuned with the width of the Gaussian charge distribution born by each electrode atom [12] or parametrized on DFT calculations [13]. We investigated the charge distribution induced by an ion in vacuum or in an explicit solvent, to assess the predictions of continuum electrostatics [14], also considering the effect of the metallicity [15], and demonstrated the relevance of Molecular DFT to describe the solvation of ions at metallic interfaces [16]. We related the dynamics of charge fluctuations to the frequency-dependent electrical impedance of the system, validated this new approach on water/gold nanocapacitors [17] and applied it to electrolyte solutions [18]. We examined the effect of metallicity on the potential of mean force between an ion and a graphite surface [19].
We combined electronic DFT calculations and classical MD simulations to predict the electric field gradient fluctuations of ions in aqueous solutions, obtaining accurate predictions of the NMR relaxation rate at infinite dilution with a simple force field [20]. We obtained remarkable agreement with experiments over a wide range of concentrations and temperatures and highlighted the limitations of the continuous models traditionally used to interpret the experiments [21]. We extended these results to a wide family of simple electrolytes [22].
For structural correlations in electrolytes, we focused on interfacial and confinement effects. We explored the thermodynamics of phase transitions under confinement, clarifying the derivation and approximations of the so-called Gibbs-Thomson equation [23]. In Surface Force Balance experiments, the confined liquid is connected to a larger volume of liquid acting as a reservoir setting the chemical potential of all species. We implemented a recently proposed hybrid MD / Grand-canonical Monte-Carlo method in a standard simulation package [24] and used it to investigate the Donnan equilibrium for confined electrolytes [25].
We highlighted the central role of the dynamical charge structure factor, which can be related to almost all aspects covered in the various WPs, and compared the predictions of MD and BD for aqueous electrolytes to assess the limitations of the implicit-solvent description [26]. We developed strategies to efficiently estimate physical properties in simulations [27,28] and contributed to the Metalwalls code [29].
References acknowledging the project
1) JCP 156, 044703, 2022
2) JCP 155, 014507, 2021
3) JCP 158, 104103, 2023
4) Faraday Discuss. 246, 198, 2023
5) PNAS 119, e2117586119, 2022
6) JCTC 20, 3069, 2024
7) JCP 159, 164105, 2023
8) PRL 133, 268002, 2024
9) Ann. Rev. Phys. Chem. 72, 189, 2021
10) Chem. Rev. 122, 10860, 2022
11) PNAS 118, e2108769118, 2021
12) JCP 155, 044703, 2021
13) Adv. Mater. 36, 24052350, 2024
14) JCP 155, 204705, 2021
15) Mol. Phys. e2365990, 2024
16)
https://arxiv.org/abs/2503.11361(si apre in una nuova finestra) 17) PRL 130, 098001, 2023
18) PNAS 121, e2318157121, 2024
19) JCP 157, 095707, 2022
20) JCTC 17, 6006, 2021
21) Nature Commun. 14, 84, 2023
22)
https://arxiv.org/abs/2502.06409(si apre in una nuova finestra) 23) JCP 154, 114711, 2021
24) JCP 159, 144802, 2023
25) JCP 161, 054107, 2024
26) Faraday Discuss. 246, 225, 2023
27) JCP 153, 150902, 2020
28) JCP 154, 191101, 2021
29) JCP 157, 184801, 2022
