Transition Metal Dichalcogenides Nanoscrolls: Preparation and Applications

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) nanosheets have shown extensive applications due to their excellent physical and chemical properties. However, the low light absorption efficiency limits their application in optoelectronics. By rolling up 2D TMDCs nanosheets, the one-dimensional (1D) TMDCs nanoscrolls are formed with spiral tubular structure, tunable interlayer spacing, and opening ends. Due to the increased thickness of the scroll structure, the light absorption is enhanced. Meanwhile, the rapid electron transportation is confined along the 1D structure. Therefore, the TMDCs nanoscrolls show improved optoelectronic performance compared to 2D nanosheets. In addition, the high specific surface area and active edge site from the bending strain of the basal plane make them promising materials for catalytic reaction. Thus, the TMDCs nanoscrolls have attracted intensive attention in recent years. In this review, the structure of TMDCs nanoscrolls is first demonstrated and followed by various preparation methods of the TMDCs nanoscrolls. Afterwards, the applications of TMDCs nanoscrolls in the fields of photodetection, hydrogen evolution reaction, and gas sensing are discussed.

A Review of the Design and Performance of Catalysts for Hydrothermal Gasification of Biomass to Produce Hydrogen-Rich Gas Fuel

Supercritical water gasification has emerged as a promising technology to sustainably convert waste residues into clean gaseous fuels rich in combustible gases such as hydrogen and methane. The composition and yield of gases from hydrothermal gasification depend on process conditions such as temperature, pressure, reaction time, feedstock concentration, and reactor geometry. However, catalysts also play a vital role in enhancing the gasification reactions and selectively altering the composition of gas products. Catalysts can also enhance hydrothermal reforming and cracking of biomass to achieve desired gas yields at moderate temperatures, thereby reducing the energy input of the hydrothermal gasification process. However, due to the complex hydrodynamics of supercritical water, the literature is limited regarding the synthesis, application, and performance of catalysts used in hydrothermal gasification. Hence, this review provides a detailed discussion of different heterogeneous catalysts (e.g., metal oxides and transition metals), homogeneous catalysts (e.g., hydroxides and carbonates), and novel carbonaceous catalysts deployed in hydrothermal gasification. The article also summarizes the advantages, disadvantages, and performance of these catalysts in accelerating specific reactions during hydrothermal gasification of biomass, such as water-gas shift, methanation, hydrogenation, reforming, hydrolysis, cracking, bond cleavage, and depolymerization. Different reaction mechanisms involving a variety of catalysts during the hydrothermal gasification of biomass are outlined. The article also highlights recent advancements with recommendations for catalytic supercritical water gasification of biomass and its model compounds, and it evaluates process viability and feasibility for commercialization.

Spectroscopy from Machine Learning by Accurately Representing the Atomic Polar Tensor

Vibrational spectroscopy is a key technique to elucidate microscopic structure and dynamics. Without the aid of theoretical approaches, it is, however, often difficult to understand such spectra at a microscopic level. Ab initio molecular dynamics has repeatedly proved to be suitable for this purpose; however, the computational cost can be daunting. Here, the E(3)-equivariant neural network e3nn is used to fit the atomic polar tensor of liquid water a posteriori on top of existing molecular dynamics simulations. Notably, the introduced methodology is general and thus transferable to any other system as well. The target property is most fundamental and gives access to the IR spectrum, and more importantly, it is a highly powerful tool to directly assign IR spectral features to nuclear motion─a connection which has been pursued in the past but only using severe approximations due to the prohibitive computational cost. The herein introduced methodology overcomes this bottleneck. To benchmark the machine learning model, the IR spectrum of liquid water is calculated, indeed showing excellent agreement with the explicit reference calculation. In conclusion, the presented methodology gives a new route to calculate accurate IR spectra from molecular dynamics simulations and will facilitate the understanding of such spectra on a microscopic level.

Accurate diffusion coefficients of the excess proton and hydroxide in water via extensive ab initio simulations with different schemes

Despite its simple molecular formula, obtaining an accurate in silico description of water is far from straightforward. Many of its very peculiar properties are quite elusive, and in particular, obtaining good estimations of the diffusion coefficients of the solvated proton and hydroxide at a reasonable computational cost has been an unsolved challenge until now. Here, I present extensive results of several unusually long ab initio molecular dynamics (MD) simulations employing different combinations of the Born-Oppenheimer and second-generation Car-Parrinello MD propagation methods with different ensembles (NVE and NVT) and thermostats, which show that these methods together with the RPBE-D3 functional provide a very accurate estimation of the diffusion coefficients of the solvated H₃O⁺ and OH^- ions, together with an extremely accurate description of several properties of neutral water (such as the structure of the liquid and its diffusion and shear viscosity coefficients). In addition, I show that the estimations of D_H3O⁺ and D_OH^- depend dramatically on the simulation length, being necessary to reach timescales in the order of hundreds of picoseconds to obtain reliable results.

Nanosecond solvation dynamics of the hematite/liquid water interface at hybrid DFT accuracy using committee neural network potentials

Metal oxide/water interfaces play an important role in biology, catalysis, energy storage and photocatalytic water splitting. The atomistic structure at these interfaces is often difficult to characterize by experimental techniques, whilst results from ab initio molecular dynamics simulations tend to be uncertain due to the limited length and time scales accessible. In this work, we train a committee neural network potential to simulate the hematite/water interface at the hybrid DFT level of theory to reach the nanosecond timescale and systems containing more than 3000 atoms. The NNP enables us to converge dynamical properties, not possible with brute-force ab initio molecular dynamics. Our simulations uncover a rich solvation dynamics at the hematite/water interface spanning three different time scales: picosecond H-bond dynamics between surface hydroxyls and the first water layer, in-plane/out-of-plane tilt motion of surface hydroxyls on the 10 ps time scale, and diffusion of water molecules from the oxide surface characterized by a mean residence lifetime of about 60 ps. Calculation of vibrational spectra confirm that H-bonds between surface hydroxyls and first layer water molecules are stronger than H-bonds in bulk water. Our study showcases how state of the art machine learning approaches can routinely be utilized to explore the structural dynamics at transition metal oxide interfaces with complex electronic structure. It foreshadows that c-NNPs are a promising tool to tackle the sampling problem in ab initio electrochemistry with explicit solvent molecules.

Automated Workflow for Computation of Redox Potentials, Acidity Constants and Solvation Free Energies Accelerated by Machine Learning

Fast evolution of modern society stimulates intense development of new materials with novel functionalities in energy and environmental applications. Due to rapid progress of computer science, computational design of materials with target properties has recently attracted lots of interests. Accurate and efficient calculation of fundamental thermodynamic properties, including redox potentials, acidity constants, and solvation free energies, is of great importance for selection and design of desirable materials. Free energy calculation based on ab initio molecular dynamics (AIMD) can predict these properties with high accuracy at complex environments, however being impeded by high computational costs. To address this issue, this work develops an automated scheme that combines iterative training of machine learning potentials (MLPs) and free energy calculation, and deomonstrates that these thermodynamic properties can be computed by ML accelerated MD with ab initio accuracy and much longer time scale at cheaper costs, improving poor statistics and convergence of numerical integration by AIMD. Our automated scheme lays the foundation for computational chemistry-assisted materials design.

Direct conversion of almond waste into value-added liquids using carbon-neutral catalysts: Hydrothermal hydrogenation of almond hulls over a Ru/CNF catalyst

The almond industry leaves behind substantial amounts of by-products, with almond hulls being the primary residue generated. Given that one way to improve food security is by decreasing waste to reduce environmental impacts, developing sustainable processes to manage this by-product is necessary. Herein, we report on the hydrothermal hydrogenation of almond hulls over a carbon-neutral Ru supported on carbon nanofibres (Ru/CNF) catalyst, addressing the temperature, H₂ pressure, time and catalyst loading. These variables controlled the distribution of the reaction products: gas (0-5%), liquid (49-82%) and solid (13-51%), and ruled the composition of the liquid effluent. This aqueous fraction comprised oligomers (46-81 wt%), saccharides (2-7 wt%), sugar alcohols (2-15 wt%), polyhydric alcohols (1-8 wt%) and carboxylic acids (7-31 wt%). The temperature and reaction time influenced the extension of hydrolysis, depolymerisation, deamination, hydrolysis, hydrogenation and dehydration reactions. Additionally, the initial H₂ pressure and catalyst loading kinetically promoted these transformations, whose extensions were ruled by the amount of H₂ effectively dissolved in the reaction medium and the prevalence of hydrogenations over dehydration/decarboxylation reactions or vice versa depending on the catalyst loading. Process optimisation revealed that it is feasible to convert up to 67% of almond hulls into merchantable oligomers at 230 °C, 35 bar initial H₂, using 1 g catalyst/g biomass (0.4 g Ru/g biomass) for 360 min. Additionally, decreasing the temperature to 187 °C without modifying the other parameters could convert this material into oligomers (31 wt%) and small oxygenates (17 wt% carboxylic acids, 11 wt% sugar alcohols and 6 wt% polyhydric alcohols) concurrently. The theoretical energy assessment revealed that the total and partial combustion of the spent solid material could provide the required energy for the process and allow catalyst recovery and reutilisation. This environmental friendliness and holistic features exemplify a landmark step-change to valorising unavoidable food waste.

Structures of liquid and aqueous water isotopologues at ambient temperature from ab initio path integral simulations

The heavy hydrogen isotopes D and T are found in trace amounts in water, and when their concentration increases they can play an intricate role in modulating the physical properties of the liquid. We present an analysis of the microscopic structures of ambient light water (H₂O(l)), heavy water (D₂O(l)), T₂O(l), HDO(aq) and HTO(aq) studied by ab initio path integral molecular dynamics (PIMD). Unlike previous ab initio PIMD investigations of H₂O(l) and D₂O(l) [Chen et al., Phys. Rev. Lett., 2003, 91, 215503] [Machida et al., J. Chem. Phys., 2017, 148, 102324] we find that D₂O(l) is more structured than H₂O(l), as is predicted by the experiment. The agreement between the experiment and our simulation for H₂O(l) and D₂O(l) allows us to accurately predict the intra- and intermolecular structures of T₂O(l) HDO(aq) and HTO(aq). T₂O(l) is found to have a similar intermolecular structure to that of D₂O(l), while the intramolecular structure is more compact, giving rise to a smaller dipole moment than those of H₂O(l) and D₂O(l). For the mixed isotope species, HDO(aq) and HTO(aq), we find smaller dipole moments and fewer hydrogen bonds when compared with the pure species H₂O and D₂O. We can attribute this effect to the relative compactness of the mixed isotope species, which results in a lower dipole moment than that of the pure species.

Ab Initio Simulations of Water/Metal Interfaces

Structures and processes at water/metal interfaces play an important technological role in electrochemical energy conversion and storage, photoconversion, sensors, and corrosion, just to name a few. However, they are also of fundamental significance as a model system for the study of solid-liquid interfaces, which requires combining concepts from the chemistry and physics of crystalline materials and liquids. Particularly interesting is the fact that the water-water and water-metal interactions are of similar strength so that the structures at water/metal interfaces result from a competition between these comparable interactions. Because water is a polar molecule and water and metal surfaces are both polarizable, explicit consideration of the electronic degrees of freedom at water/metal interfaces is mandatory. In principle, ab initio molecular dynamics simulations are thus the method of choice to model water/metal interfaces, but they are computationally still rather demanding. Here, ab initio simulations of water/metal interfaces will be reviewed, starting from static systems such as the adsorption of single water molecules, water clusters, and icelike layers, followed by the properties of liquid water layers at metal surfaces. Technical issues such as the appropriate first-principles description of the water-water and water-metal interactions will be discussed, and electrochemical aspects will be addressed. Finally, more approximate but numerically less demanding approaches to treat water at metal surfaces from first-principles will be briefly discussed.

Ab initio study of nuclear quantum effects on sub- and supercritical water

The structures of water in the ambient, subcritical, and supercritical conditions at various densities were studied systematically by ab initio path integral molecular dynamics simulations. It was found that the nuclear quantum effects (NQEs) have a significant impact on the structure of hydrogen bonds in close contact, not only in the ambient condition but also in the sub- and supercritical conditions. The NQEs on the structure beyond the hydrogen bond contact are important in ambient water, but not much for water in the sub- and supercritical conditions. The NQEs are furthermore important for determining the number of hydrogen bonds in the ambient conditions, and this role is, however, diminished in the sub- and supercritical conditions. The NQEs do, nevertheless, show their importance in determining the intramolecular structure of water and the close contact structures of the hydrogen bonds, even at sub- and supercritical conditions. Using the RPBE-D3 functional, the computed radial distribution functions for ambient water are in excellent agreement with experimental data, upgrading our previous results using the BLYP-D2 functional [Machida et al., J. Chem. Phys. 148, 102324 (2018)]. The computed radial distribution functions for water in the sub- and supercritical conditions were carefully compared with experiment. In particular, we found that the first peak in hydrogen pair distribution functions matches only when the NQEs are taken into account.

Distinct molecular dynamics dividing liquid-like and gas-like supercritical hydrogens

Understanding how a supercritical fluid is related to normal liquid and gas and separating it into liquid-like and gas-like regions are of fundamental and practical importance. Despite the usefulness of hydrogen storage, molecular dynamics images on supercritical hydrogens exhibiting strong nuclear quantum effects are scarce. Taking advantage of the non-empirical ab initio molecular dynamics method for hydrogen molecules, we found that, while radial distribution functions and diffusion show a monotonic change along the density, van Hove time correlation functions and intramolecular properties such as bond length and vibrational frequency exhibit the anomalous order crossing the Widom line. By demonstrating that the anomalous order stemmed from the largest deviations between liquid-like and gas-like solvations formed around the Widom line, we concluded that this supercritical fluid is a mixture of liquid and gas possessing heterogeneity. The obtained physical insights can be an index to monitor the supercriticality and to identify distinct liquid-like and gas-like supercritical fluids.

Subcritical and supercritical water oxidation for dye decomposition

The total annual output of synthetic dyes exceeds 7 × 10⁵ tons. About 1,000 tons of non-biodegradable synthetic dyes are released every year into the natural streams and water sources from textile wastes. The release of these colored wastewater exerts negative impact on aquatic ecology and human beings because of the poisonous and carcinogenic repercussions of dyes involved in coloration production. Therefore, with a growing interest in the environment, efficient technologies need to be developed to eliminate dyes from local and industrial wastewater. Supercritical water oxidation as a promising wastewater treatment technology has many advantages, such as a rapid reaction and pollution-free products. However, due to corrosion, salt precipitation and operational problems, supercritical water oxidation process did not gain expected industrial development. These technical difficulties can be overcome by application of non-corrosive subcritical water as a reaction medium. This work summarizes the negative impacts of dyes and role of subcritical and supercritical water and their efficiencies in dye oxidation processes.

Dielectric Constant of Liquid Water Determined with Neural Network Quantum Molecular Dynamics

The static dielectric constant ϵ_{0} and its temperature dependence for liquid water is investigated using neural network quantum molecular dynamics (NNQMD). We compute the exact dielectric constant in canonical ensemble from NNQMD trajectories using fluctuations in macroscopic polarization computed from maximally localized Wannier functions (MLWF). Two deep neural networks are constructed. The first, NNQMD, is trained on QMD configurations for liquid water under a variety of temperature and density conditions to learn potential energy surface and forces and then perform molecular dynamics simulations. The second network, NNMLWF, is trained to predict locations of MLWF of individual molecules using the atomic configurations from NNQMD. Training data for both the neural networks is produced using a highly accurate quantum-mechanical method, DFT-SCAN that yields an excellent description of liquid water. We produce 280×10^{6} configurations of water at 7 temperatures using NNQMD and predict MLWF centers using NNMLWF to compute the polarization fluctuations. The length of trajectories needed for a converged value of the dielectric constant at 0°C is found to be 20 ns (40×10^{6} configurations with 0.5 fs time step). The computed dielectric constants for 0, 15, 30, 45, 60, 75, and 90°C are in good agreement with experiments. Our scalable scheme to compute dielectric constants with quantum accuracy is also applicable to other polar molecular liquids.

First-Principles Simulations of CuCl in High-Temperature Water Vapor

Experimental data suggest that the solubility of copper in high-temperature water vapor is controlled by the formation of hydrated clusters of the form CuCl(H₂O)_n, where the average number of water molecules in the cluster generally increases with increasing density [Migdisov, A. A.; et al. Geochim. Cosmochim. Acta 2014, 129, 33-53]. However, the precise nature of these clusters is difficult to probe experimentally. Moreover, there are some discrepancies between experimental estimates of average cluster size and prior simulation work [Mei, Y. Geofluids 2018, 2018, 4279124]. We have performed first-principles Monte Carlo (MC) and molecular dynamics (MD) simulations to explore these clusters in finer detail. We find that molecular dynamics is not the most appropriate technique for studying aggregation in vapor phases, even at relatively high temperatures. Specifically, our MD simulations exhibit substantial problems in adequately sampling the equilibrium cluster size distribution. In contrast, MC simulations with specialized cluster moves are able to accurately sample the phase space of hydrogen-bonding vapors. At all densities, we find a stable, slightly distorted linear H₂O-Cu-Cl structure, which is in agreement with the earlier simulations, surrounded by a variable number of water molecules. The surrounding water molecules do not form a well-defined second solvation shell but rather a loose network of hydrogen-bonded water with molecular CuCl on the outside edge of the water cluster. We also find a broad distribution of hydration numbers, especially at higher densities. In contrast to previous simulation work but in agreement with experimental data, we find that the average hydration number substantially increases with increasing density. Moreover, the value of the hydration number depends on the choice of cluster definition.

Analysis and optimisation of a novel 'almond-refinery' concept: Simultaneous production of biofuels and value-added chemicals by hydrothermal treatment of almond hulls

For the first time, this work investigates the achievability of developing a biorefinery concept around almond hulls by hydrothermal treatment (HTT), thoroughly scrutinising the influence of the temperature (200-300 °C), pressure (100-180 bar), time (20-180 min) and solid loading (5-25 wt%). This process allowed the conversion of almond hulls into four main products: gas (2-13%), bio-oil (2-12%), aqueous (4-69%) and hydro-char (17-89%). The gas consisted of a mix of H₂, CO₂, CO and CH₄ with a LHV fluctuating from 1 to 13 MJ/m³ STP. The bio-oil comprised a mixture of alkanes, aldehydes, ketones, phenols, furans, benzenes and nitrogen compounds with a HHV between 21 and 31 MJ/kg. The solid product resembled an energetic hydro-char material (HHV 21-31 MJ/kg), while the aqueous effluent comprised a mixture of value-added chemicals, including saccharides and small oxygenated compounds. The production of biofuels can be maximised at 256 °C and 100 bar, using a 5 wt% solid loading for 157 min, conditions at which 43% of the original feedstock can be converted into an elevated energy-filled bio-oil (11% yield, 30 MJ/kg), along with a high energetic hydro-char (32% yield, 29 MJ/kg). Regarding value-added chemicals, up to 10% of the almond hulls can be converted into a bio-oil with a high proportion (45%) of phenolic species at 250 °C and 144 bar with a solid loading of 5 wt% for 167 min. In comparison, a sugar-rich (81 C-wt%) solution can be produced in high yield (54%), by treating a 24 wt% suspension at 252 °C and 180 bar for 153 min. Therefore, the versatility, novelty and intrinsic green and holistic nature of this 'almond-refinery' concept exemplify a landmark achievement in future energy and chemicals production from biomass, which might help render the complete bio-refinery for almond hulls more cost-effectively and ecologically feasible.

NaCl aggregation in water at elevated temperatures and pressures: Comparison of classical force fields

The properties of water vary dramatically with temperature and density. This can be exploited to control its effectiveness as a solvent. Thus, supercritical water is of keen interest as solvent in many extraction processes. The low solubility of salts in lower density supercritical water has even been suggested as a means of desalination. The high temperatures and pressures required to reach supercritical conditions can present experimental challenges during collection of required physical property and phase equilibria data, especially in salt-containing systems. Molecular simulations have the potential to be a valuable tool for examining the behavior of solvated ions at these high temperatures and pressures. However, the accuracy of classical force fields under these conditions is unclear. We have, therefore, undertaken a parametric study of NaCl in water, comparing several salt and water models at 200 bar-600 bar and 450 K-750 K for a range of salt concentrations. We report a comparison of structural properties including ion aggregation, hydrogen bonding, density, and static dielectric constants. All of the force fields qualitatively reproduce the trends in the liquid phase density. An increase in ion aggregation with decreasing density holds true for all of the force fields. The propensity to aggregate is primarily determined by the salt force field rather than the water force field. This coincides with a decrease in the water static dielectric constant and reduced charge screening. While a decrease in the static dielectric constant with increasing NaCl concentration is consistent across all model combinations, the salt force fields that exhibit more ionic aggregation yield a slightly smaller dielectric decrement.

Supercritical Water is not Hydrogen Bonded

Thinking about water is inextricably linked to hydrogen bonds, which are highly directional in character and determine the unique structure of water, in particular its tetrahedral H-bond network. Here, we assess if this common connotation also holds for supercritical water. We employ extensive ab initio molecular dynamics simulations to systematically monitor the evolution of the H-bond network mode of water from room temperature, where it is the hallmark of its fluctuating three-dimensional network structure, to supercritical conditions. Our simulations reveal that the oscillation period required for H-bond vibrations to occur exceeds the lifetime of H-bonds in supercritical water by far. Instead, the corresponding low-frequency intermolecular vibrations of water pairs as seen in supercritical water are found to be well represented by isotropic van-der-Waals interactions only. Based on these findings, we conclude that water in its supercritical phase is not a H-bonded fluid.

Aqueous solvation of the chloride ion revisited with density functional theory: impact of correlation and exchange approximations

Aqueous chloride is simulated using PBE-D3, PBE0-D3, and SCAN to investigate the impact of exchange and correlation approximations; we find the exact exchange fraction strongly impacts the energetics and polarizability of solvated chloride.