ABSTRACT
The interaction of water with TiO2 surfaces is of crucial importance in various scientific fields and applications, from photocatalysis for hydrogen production and the photooxidation of organic pollutants to self-cleaning surfaces and bio-medical devices. In particular, the equilibrium fraction of water dissociation at the TiO2-water interface has a critical role in the surface chemistry of TiO2, but is difficult to determine both experimentally and computationally. Among TiO2 surfaces, rutile TiO2(110) is of special interest as the most abundant surface of TiO2's stable rutile phase. While surface-science studies have provided detailed information on the interaction of rutile TiO2(110) with gas-phase water, much less is known about the TiO2(110)-water interface, which is more relevant to many applications. In this work, we characterize the structure of the aqueous TiO2(110) interface using nanosecond timescale molecular dynamics simulations with ab initio-based deep neural network potentials that accurately describe water/TiO2(110) interactions over a wide range of water coverages. Simulations on TiO2(110) slab models of increasing thickness provide insight into the dynamic equilibrium between molecular and dissociated adsorbed water at the interface and allow us to obtain a reliable estimate of the equilibrium fraction of water dissociation. We find a dissociation fraction of 22 ± 6% with an associated average hydroxyl lifetime of 7.6 ± 1.8 ns. These quantities are both much larger than corresponding estimates for the aqueous anatase TiO2(101) interface, consistent with the higher water photooxidation activity that is observed for rutile relative to anatase.
Subject(s)
Molecular Dynamics Simulation , Water , Water/chemistry , Titanium/chemistryABSTRACT
The structure and dynamics of water on solid surfaces critically affect the chemistry of materials in ambient and aqueous environments. Here, we investigate the hydrogen bonding network of water adsorbed on the majority (101) surface of anatase TiO2, a widely used photocatalyst, using polarization- and azimuth-resolved infrared spectroscopy combined with neural network potential molecular dynamics simulations. Our results show that one monolayer of water saturates the undercoordinated titanium (Ti5c) sites, forming one-dimensional chains of molecule hydrogen bonded to surface undercoordinated bridging oxygen (O2c) atoms. As the coverage increases, water adsorption on O2c sites leads to significant restructuring of the water monolayer and the formation of a two-dimensional hydrogen bond network characterized by tightly bound pairs of water molecules on adjacent Ti5c and O2c sites. This structural motif likely persists at ambient conditions, influencing the reactions occurring there. The results reported here provide critical details of the structure of the water-anatase (101) interface that were previously hypothesized but unconfirmed experimentally.
ABSTRACT
The atmospheric methane (CH4) burden is rising sharply, but the causes are still not well understood. One factor of uncertainty is the importance of tropical CH4 emissions into the global mix. Isotopic signatures of major sources remain poorly constrained, despite their usefulness in constraining the global methane budget. Here, a collection of new δ13CCH4 signatures is presented for a range of tropical wetlands and rice fields determined from air samples collected during campaigns from 2016 to 2020. Long-term monitoring of δ13CCH4 in ambient air has been conducted at the Chacaltaya observatory, Bolivia and Southern Botswana. Both long-term records are dominated by biogenic CH4 sources, with isotopic signatures expected from wetland sources. From the longer-term Bolivian record, a seasonal isotopic shift is observed corresponding to wetland extent suggesting that there is input of relatively isotopically light CH4 to the atmosphere during periods of reduced wetland extent. This new data expands the geographical extent and range of measurements of tropical wetland and rice δ13CCH4 sources and hints at significant seasonal variation in tropical wetland δ13CCH4 signatures which may be important to capture in future global and regional models. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 2)'.
Subject(s)
Oryza , Wetlands , Atmosphere , Methane , SeasonsABSTRACT
We report methane isotopologue data from aircraft and ground measurements in Africa and South America. Aircraft campaigns sampled strong methane fluxes over tropical papyrus wetlands in the Nile, Congo and Zambezi basins, herbaceous wetlands in Bolivian southern Amazonia, and over fires in African woodland, cropland and savannah grassland. Measured methane δ13CCH4 isotopic signatures were in the range -55 to -49 for emissions from equatorial Nile wetlands and agricultural areas, but widely -60 ± 1 from Upper Congo and Zambezi wetlands. Very similar δ13CCH4 signatures were measured over the Amazonian wetlands of NE Bolivia (around -59) and the overall δ13CCH4 signature from outer tropical wetlands in the southern Upper Congo and Upper Amazon drainage plotted together was -59 ± 2. These results were more negative than expected. For African cattle, δ13CCH4 values were around -60 to -50. Isotopic ratios in methane emitted by tropical fires depended on the C3 : C4 ratio of the biomass fuel. In smoke from tropical C3 dry forest fires in Senegal, δ13CCH4 values were around -28. By contrast, African C4 tropical grass fire δ13CCH4 values were -16 to -12. Methane from urban landfills in Zambia and Zimbabwe, which have frequent waste fires, had δ13CCH4 around -37 to -36. These new isotopic values help improve isotopic constraints on global methane budget models because atmospheric δ13CCH4 values predicted by global atmospheric models are highly sensitive to the δ13CCH4 isotopic signatures applied to tropical wetland emissions. Field and aircraft campaigns also observed widespread regional smoke pollution over Africa, in both the wet and dry seasons, and large urban pollution plumes. The work highlights the need to understand tropical greenhouse gas emissions in order to meet the goals of the UNFCCC Paris Agreement, and to help reduce air pollution over wide regions of Africa. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 2)'.
Subject(s)
Air Pollution , Wetlands , Agriculture , Animals , Cattle , Methane/analysis , SeasonsABSTRACT
We explore the role of long-range interactions in atomistic machine-learning models by analyzing the effects on fitting accuracy, isolated cluster properties, and bulk thermodynamic properties. Such models have become increasingly popular in molecular simulations given their ability to learn highly complex and multi-dimensional interactions within a local environment; however, many of them fundamentally lack a description of explicit long-range interactions. In order to provide a well-defined benchmark system with precisely known pairwise interactions, we chose as the reference model a flexible version of the Extended Simple Point Charge (SPC/E) water model. Our analysis shows that while local representations are sufficient for predictions of the condensed liquid phase, the short-range nature of machine-learning models falls short in representing cluster and vapor phase properties. These findings provide an improved understanding of the role of long-range interactions in machine learning models and the regimes where they are necessary.
ABSTRACT
We introduce a scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials. Application to liquid water shows that training the network with a relatively small number of molecular configurations is sufficient to predict the polarizability of arbitrary liquid configurations in close agreement with ab initio density functional theory calculations. In combination with a neural network representation of the interatomic potential energy surface, the scheme allows us to calculate the Raman spectra along 2-nanosecond classical trajectories at different temperatures for H2O and D2O. The vast gains in efficiency provided by the machine learning approach enable longer trajectories and larger system sizes relative to ab initio methods, reducing the statistical error and improving the resolution of the low-frequency Raman spectra. Decomposing the spectra into intramolecular and intermolecular contributions elucidates the mechanisms behind the temperature dependence of the low-frequency and stretch modes.
ABSTRACT
Water is of the utmost importance for life and technology. However, a genuinely predictive ab initio model of water has eluded scientists. We demonstrate that a fully ab initio approach, relying on the strongly constrained and appropriately normed (SCAN) density functional, provides such a description of water. SCAN accurately describes the balance among covalent bonds, hydrogen bonds, and van der Waals interactions that dictates the structure and dynamics of liquid water. Notably, SCAN captures the density difference between water and ice Ih at ambient conditions, as well as many important structural, electronic, and dynamic properties of liquid water. These successful predictions of the versatile SCAN functional open the gates to study complex processes in aqueous phase chemistry and the interactions of water with other materials in an efficient, accurate, and predictive, ab initio manner.
ABSTRACT
The properties of ionic micelles depend on the nature of the counterion, and these effects become more evident as the ion adsorption at the interface increases. Prediction of the relative extent of ion adsorption is required for rational design of ionic micellar aggregates. Unlike the well understood adsorption of monatomic ions, the adsorption of polyatomic ions is not easily predicted. We combined experimental and computational methods to evaluate the affinity of hydrotropic ions, i.e., ions with polar and apolar regions, to the surface of positively charged micelles. We analyzed cationic micelles of dodecyltrimethylammonium and six hydrotropic counterions: methanesulfonate, trifluoromethanesulfonate, benzenesulfonate, acetate, trifluoroacetate and benzoate. Our results demonstrated that the apolar region of hydrotropic ions had the largest influence on micellar properties. The dehydration of the apolar region of hydrotropic ions upon their adsorption at the micellar interface determined the ion adsorption extension, differently to what was expected based on Collins' law of matching affinities. These results may lead to more general models to describe the adsorption of ions, including polyatomic ions, at the micellar interface.
ABSTRACT
Fundamental studies of the dielectrics of confined water are critical to understand the ion transport across biological and synthetic nanochannels. The relevance of these fundamental studies, however, surmounts the difficulty of probing water's dielectric constant as a function of a fine variation in confinement. In this work, we explore the computational efficiency of machine learning potentials to derive the confinement effects on the dielectric constant, polarization, and dipole moment of water. Our simulations predict an enhancement of the axial dielectric constant of water under extreme confinement, arising from either the formation of ferroelectric structures of ordered water or larger dipole fluctuations facilitated by the disruption of water's H-bond network. Our study highlights the impact of hydrophobic nanoconfinement on the dielectric constant and on the ionic and electronic structure of water molecules, pointing to the importance of geometric flexibility and electronic polarizability to properly model confinement effects on water.
ABSTRACT
Direct air capture of CO2 using supported amines provides a promising means to achieve the net-zero greenhouse gas emissions goal; however, many mechanistic details regarding the CO2 adsorption process in condensed phase amines remain poorly understood. This work combines machine learning potentials, enhanced sampling and grand-canonical Monte Carlo simulations to directly compute experimentally relevant quantities to elucidate the mechanism of CO2 chemisorption in liquid ammonia as a model system. Our simulations suggest that CO2 capture in the liquid occurs in a sequential fashion, with the formation of a metastable zwitterion intermediate. Furthermore, we identified the importance of solvent-mediated proton transfer and solvent dynamics, not only in the reaction pathway but also in the efficiency of CO2 chemisorption. Beyond liquid ammonia, the methodology presented here can be readily extended to simulate amines with more complex chemical structures under experimental conditions, paving the way to elucidate the structure-performance of amines for CO2 capture.
ABSTRACT
The electronic properties and optical response of ice and water are intricately shaped by their molecular structure, including the quantum mechanical nature of the hydrogen atoms. Despite numerous previous studies, a comprehensive understanding of the nuclear quantum effects (NQEs) on the electronic structure of water and ice at finite temperatures remains elusive. Here, we utilize molecular simulations that harness efficient machine-learning potentials and many-body perturbation theory to assess how NQEs impact the electronic bands of water and hexagonal ice. By comparing path-integral and classical simulations, we find that NQEs lead to a larger renormalization of the fundamental gap of ice, compared to that of water, ultimately yielding similar bandgaps in the two systems, consistent with experimental estimates. Our calculations suggest that the increased quantum mechanical delocalization of protons in ice, relative to water, is a key factor leading to the enhancement of NQEs on the electronic structure of ice.
ABSTRACT
Improved understanding of proton transfer in nanopores is critical for a wide range of emerging applications, yet experimentally probing mechanisms and energetics of this process remains a significant challenge. To help reveal details of this process, we developed and applied a machine learning potential derived from first-principles calculations to examine water reactivity and proton transfer in TiO2 slit-pores. We find that confinement of water within pores smaller than 0.5 nm imposes strong and complex effects on water reactivity and proton transfer. Although the proton transfer mechanism is similar to that at a TiO2 interface with bulk water, confinement reduces the activation energy of this process, leading to more frequent proton transfer events. This enhanced proton transfer stems from the contraction of oxygen-oxygen distances dictated by the interplay between confinement and hydrophilic interactions. Our simulations also highlight the importance of the surface topology, where faster proton transport is found in the direction where a unique arrangement of surface oxygens enables the formation of an ordered water chain. In a broader context, our study demonstrates that proton transfer in hydrophilic nanopores can be enhanced by controlling pore size, surface chemistry, and topology.
ABSTRACT
The hydrogen-bond network of confined water is expected to deviate from that of the bulk liquid, yet probing these deviations remains a significant challenge. In this work, we combine large-scale molecular dynamics simulations with machine learning potential derived from first-principles calculations to examine the hydrogen bonding of water confined in carbon nanotubes (CNTs). We computed and compared the infrared spectrum (IR) of confined water to existing experiments to elucidate confinement effects. For CNTs with diameters >1.2 nm, we find that confinement imposes a monotonic effect on the hydrogen-bond network and on the IR spectrum of water. In contrast, confinement below 1.2 nm CNT diameter affects the water structure in a complex fashion, leading to a strong directional dependence of hydrogen bonding that varies nonlinearly with the CNT diameter. When integrated with existing IR measurements, our simulations provide a new interpretation for the IR spectrum of water confined in CNTs, pointing to previously unreported aspects of hydrogen bonding in this system. This work also offers a general platform for simulating water in CNTs with quantum accuracy on time and length scales beyond the reach of conventional first-principles approaches.
ABSTRACT
High-throughput electronic structure calculations (often performed using density functional theory (DFT)) play a central role in screening existing and novel materials, sampling potential energy surfaces, and generating data for machine learning applications. By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semilocal DFT and furnish a more accurate description of the underlying electronic structure, albeit at a computational cost that often prohibits such high-throughput applications. To address this challenge, we have constructed a robust, accurate, and computationally efficient framework for high-throughput condensed-phase hybrid DFT and implemented this approach in the PWSCF module of Quantum ESPRESSO (QE). The resulting SeA approach (SeA = SCDM + exx + ACE) combines and seamlessly integrates: (i) the selected columns of the density matrix method (SCDM, a robust noniterative orbital localization scheme that sidesteps system-dependent optimization protocols), (ii) a recently extended version of exx (a black-box linear-scaling EXX algorithm that exploits sparsity between localized orbitals in real space when evaluating the action of the standard/full-rank V^xx operator), and (iii) adaptively compressed exchange (ACE, a low-rank V^xx approximation). In doing so, SeA harnesses three levels of computational savings: pair selection and domain truncation from SCDM + exx (which only considers spatially overlapping orbitals on orbital-pair-specific and system-size-independent domains) and low-rank V^xx approximation from ACE (which reduces the number of calls to SCDM + exx during the self-consistent field (SCF) procedure). Across a diverse set of 200 nonequilibrium (H2O)64 configurations (with densities spanning 0.4-1.7 g/cm3), SeA provides a 1-2 order-of-magnitude speedup in the overall time-to-solution, i.e., ≈8-26× compared to the convolution-based PWSCF(ACE) implementation in QE and ≈78-247× compared to the conventional PWSCF(Full) approach, and yields energies, ionic forces, and other properties with high fidelity. As a proof-of-principle high-throughput application, we trained a deep neural network (DNN) potential for ambient liquid water at the hybrid DFT level using SeA via an actively learned data set with ≈8,700 (H2O)64 configurations. Using an out-of-sample set of (H2O)512 configurations (at nonambient conditions), we confirmed the accuracy of this SeA-trained potential and showcased the capabilities of SeA by computing the ground-truth ionic forces in this challenging system containing >1,500 atoms.
ABSTRACT
Electrocatalysts encapsulated by an ultrathin and semipermeable oxide layer offer a promising avenue for efficient, selective, and cost-effective production of hydrogen through photoelectrochemical water splitting. This architecture is especially attractive for Z-scheme water splitting, for which a nanoporous oxide film can be leveraged to mitigate undesired, yet kinetically facile, reactions involving redox shuttles, such as aqueous iron cations, by limiting transport of these species to catalytically active sites. In this work, molecular dynamics simulations were combined with electrochemical measurements to provide a mechanistic understanding of permeation of water and Fe(III)/Fe(II) redox shuttles through nanoporous SiO2 films. It is shown that even for SiO2 pores with a width as small as 0.8 nm, water does not experience any energy barrier for permeating into the pores due to a favorable interaction with hydrophilic silanol groups on the oxide surface. In contrast, permeation of Fe(III) and Fe(II) into microporous SiO2 pores is limited due to high energy barriers, which stem from a combination of distortion and dehydration of the second and third ion solvation shells. Our simulations and experimental results show that SiO2 coatings can effectively mitigate undesired Fe(III)/Fe(II) redox reactions at underlying electrodes by attenuating permeation of iron cations, while allowing water to permeate and thus participate in water splitting reactions. In a broader context, our study demonstrates that selectivity of solvated cations can be manipulated by controlling the pore size and surface chemistry of oxide films.
ABSTRACT
Free energy sampling, deep potential molecular dynamics, and characterizations provide insights into the impact of epoxide-functionalization on the hydrogen bonding and mobility of poly(ethylenimine), a promising CO2 sorbent. These findings rationalize the anti-degradation effects of epoxide functionalization and open up new avenues for designing more durable CO2 sorbents.
ABSTRACT
Electrode-water interfaces under voltage bias demonstrate anomalous electrostatic and structural properties that are influential in their catalytic and technological applications. Mean-field and empirical models of the electrical double layer (EDL) that forms in response to an applied potential do not capture the heterogeneity that polarizable, liquid-phase water molecules engender. To illustrate the inhomogeneous nature of the electrochemical interface, Born-Oppenheimer ab initio molecular dynamics calculations of electrified Au(111) slabs interfaced with liquid water were performed using a combined explicit-implicit solvent approach. The excess charges localized on the model electrode were held constant and the electrode potentials were computed at frequent simulation times. The electrode potential in each trajectory fluctuated with changes in the atomic structure, and the trajectory-averaged potentials converged and yielded a physically reasonable differential capacitance for the system. The effects of the average applied voltages, both positive and negative, on the structural, hydrogen bonding, dynamical, and vibrational properties of water were characterized and compared to literature where applicable. Controlled-potential simulations of the interfacial solvent dynamics provide a framework for further investigation of more complex or reactive species in the EDL and broadly for understanding electrochemical interfaces in situ.
ABSTRACT
TiO2 is a widely used photocatalyst in science and technology and its interface with water is important in fields ranging from geochemistry to biomedicine. Yet, it is still unclear whether water adsorbs in molecular or dissociated form on TiO2 even for the case of well-defined crystalline surfaces. To address this issue, we simulated the TiO2-water interface using molecular dynamics with an ab initio-based deep neural network potential. Our simulations show a dynamical equilibrium of molecular and dissociative adsorption of water on TiO2. Water dissociates through a solvent-assisted concerted proton transfer to form a pair of short-lived hydroxyl groups on the TiO2 surface. Molecular adsorption of water is ΔF = 8.0 ± 0.9 kJ mol-1 lower in free energy than the dissociative adsorption, giving rise to a 5.6 ± 0.5% equilibrium water dissociation fraction at room temperature. Due to the relevance of surface hydroxyl groups to the surface chemistry of TiO2, our model might be key to understanding phenomena ranging from surface functionalization to photocatalytic mechanisms.
ABSTRACT
In this work, an investigation of supercritical water is presented combining inelastic and deep inelastic neutron scattering experiments and molecular dynamics simulations based on a machine-learned potential of ab initio quality. The local hydrogen dynamics is investigated at 250 bar and in the temperature range of 553-823 K, covering the evolution from subcritical liquid to supercritical gas-like water. The evolution of libration, bending, and stretching motions in the vibrational density of states is studied, analyzing the spectral features by a mode decomposition. Moreover, the hydrogen nuclear momentum distribution is measured, and its anisotropy is probed experimentally. It is shown that hydrogen bonds survive up to the higher temperatures investigated, and we discuss our results in the framework of the coupling between intramolecular modes and intermolecular librations. Results show that the local potential affecting hydrogen becomes less anisotropic within the molecular plane in the supercritical phase, and we attribute this result to the presence of more distorted hydrogen bonds.
ABSTRACT
The photocatalytic activity of TiO2 for water splitting has been known for decades, yet the adsorption structure and hydrogen bonding of water at the interface with TiO2 have remained controversial. We investigate the prototypical aqueous interface with anatase TiO2 (101) using ab initio molecular dynamics (AIMD) with the strongly constrained and appropriately normed (SCAN) density functional, recently shown to provide an excellent description of the properties of bulk liquid water. We find that water forms a stable bilayer of intact molecules with ice-like dynamics and enhanced dipole moment and polarizability on the anatase surface. The orientational order and H-bond environment of interfacial water are reflected in the computed sum frequency generation (SFG) spectrum, which agrees well with recent measurements in the OH stretching frequency range (3000-3600 cm-1). Additional AIMD simulations for a model interface with 66% of dissociated water in the contact layer show that surface hydroxyls disrupt the order in the bilayer and lead to a much faster orientational dynamics of interfacial water. Nonetheless, the computed SFG spectrum for the hydroxylated surface also agrees with experiment, suggesting that SFG measurements in a wider frequency range would be necessary to unambiguously identify the character of interfacial water on anatase.