Water on hexagonal boron nitride from diffusion Monte Carlo

Despite a recent flurry of experimental and simulation studies, an accurate estimate of the interaction strength of water molecules with hexagonal boron nitride is lacking. Here we report quantum Monte Carlo results for the adsorption of a water monomer on a periodic hexagonal boron nitride sheet, which yield a water monomer interaction energy of -84 +/- 5 meV. We use the results to evaluate the performance of several widely used density functional theory (DFT) exchange correlation functionals, and find that they all deviate substantially. Differences in interaction energies between different adsorption sites are however better reproduced by DFT

Exploring water adsorption on isoelectronically doped graphene using alchemical derivatives

The design and production of novel 2-dimensional materials has seen great progress in the last decade, prompting further exploration of the chemistry of such materials. Doping and hydrogenating graphene is an experimentally realised method of changing its surface chemistry, but there is still a great deal to be understood on how doping impacts on the adsorption of molecules. Developing this understanding is key to unlocking the potential applications of these materials. High throughput screening methods can provide particularly effective ways to explore vast chemical compositions of materials. Here, alchemical derivatives are used as a method to screen the dissociative adsorption energy of water molecules on various BN doped topologies of hydrogenated graphene. The predictions from alchemical derivatives are assessed by comparison to density functional theory. This screening method is found to predict dissociative adsorption energies that span a range of more than 2 eV, with a mean absolute error $<0.1$ eV. In addition, we show that the quality of such predictions can be readily assessed by examination of the Kohn-Sham highest occupied molecular orbital in the initial states. In this way, the root mean square error in the dissociative adsorption energies of water is reduced by almost an order of magnitude (down to $\sim0.02$ eV) after filtering out poor predictions. The findings point the way towards a reliable use of first order alchemical derivatives for efficient screening procedures

Ordering of small particles in one-dimensional coherent structures by time-periodic flows

Small particles transported by a fluid medium do not necessarily have to follow the flow. We show that for a wide class of time-periodic incompressible flows inertial particles have a tendency to spontaneously align in one-dimensional dynamic coherent structures. This effect may take place for particles so small that often they would be expected to behave as passive tracers and be used in PIV measurement technique. We link the particle tendency to form one-dimensional structures to the nonlinear phenomenon of phase locking. We propose that this general mechanism is, in particular, responsible for the enigmatic formation of the `particle accumulation structures' discovered experimentally in thermocapillary flows more than a decade ago and unexplained until now

Machine learning potentials for complex aqueous made

Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex systems such as solid–liquid interfaces. Here we present a machine learning framework that enables the efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally optimal machine learning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-driven active learning protocol. Such models can afterward be applied in exhaustive simulations to provide reliable answers for the scientific question at hand or to systematically explore the thermal performance of ab initio methods. We showcase this methodology on a diverse set of aqueous systems comprising bulk water with different ions in solution, water on a titanium dioxide surface, and water confined in nanotubes and between molybdenum disulfide sheets. Highlighting the accuracy of our approach with respect to the underlying ab initio reference, the resulting models are evaluated in detail with an automated validation protocol that includes structural and dynamical properties and the precision of the force prediction of the models. Finally, we demonstrate the capabilities of our approach for the description of water on the rutile titanium dioxide (110) surface to analyze the structure and mobility of water on this surface. Such machine learning models provide a straightforward and uncomplicated but accurate extension of simulation time and length scales for complex systems

Structure of a model TiO2 photocatalytic interface

The interaction of water with TiO2 is crucial to many of its practical applications, including photocatalytic water splitting. Following the first demonstration of this phenomenon 40 years ago there have been numerous studies of the rutile single-crystal TiO2(110) interface with water. This has provided an atomic-level understanding of the water-TiO2 interaction. However, nearly all of the previous studies of water/TiO2 interfaces involve water in the vapour phase. Here, we explore the interfacial structure between liquid water and a rutile TiO2(110) surface pre-characterized at the atomic level. Scanning tunnelling microscopy and surface X-ray diffraction are used to determine the structure, which is comprised of an ordered array of hydroxyl molecules with molecular water in the second layer. Static and dynamic density functional theory calculations suggest that a possible mechanism for formation of the hydroxyl overlayer involves the mixed adsorption of O2 and H2O on a partially defected surface. The quantitative structural properties derived here provide a basis with which to explore the atomistic properties and hence mechanisms involved in TiO2 photocatalysis

Tuning dissociation using isoelectronically doped graphene and hexagonal boron nitride: water and other small molecules

Novel uses for 2-dimensional materials like graphene and hexagonal boron nitride (h-BN) are being frequently discovered especially for membrane and catalysis applications. Still however, a great deal remains to be understood about the interaction of environmentally and industrially elevant molecules such as water with these materials. Taking inspiration from advances in hybridising graphene and h-BN, we explore using density functional theory, the dissociation of water, hydrogen, methane, and methanol on graphene, h-BN, and their isoelectronic doped counterparts: BN doped graphene and C doped h-BN. We find that doped surfaces are considerably more reactive than their pristine counterparts and by comparing the reactivity of several small molecules we develop a general framework for dissociative adsorption. From this a particularly attractive consequence of isoelectronic doping emerges: substrates can be doped to enhance their reactivity specifically towards either polar or non-polar adsorbates. As such, these substrates are potentially viable candidates for selective catalysts and membranes, with the implication that a range of tuneable materials can be designed

The Impact of Inherited Retinal Diseases in the Republic of Ireland (ROI) and the United Kingdom (UK) from a Cost-of-Illness Perspective

To date, there has been a global lack of data regarding the prevalence of conditions falling under the Inherited Retinal Diseases (IRD) classification, the impact on the individuals and families affected, and the cost burden to economies. The absence of an international patient registry, and equitable access to genetic testing, compounds this matter. The resulting incomplete knowledge of the impact of IRDs hinders the development and commissioning of clinical services, provision of treatments, and planning and implementation of clinical trials. Thus, there is a need for stronger evidence to support value for money to regulatory bodies for treatments approved, and progressing through clinical trials. To ensure a strategic approach to future research and service provision, it is necessary to learn more about the IRD landscape. This review highlights two recent cost-of-illness reports on the socio-economic impact of 10 IRDs in the Republic of Ireland (ROI) and the United Kingdom (UK), which demonstrate the comprehensive impact of IRDs on individuals affected, their families, friends and society. Total costs attributable to IRDs in the ROI were estimated to be £42.6 million in 2019, comprising economic (£28.8 million) and wellbeing costs (£13.8 million). Wellbeing costs were estimated using the World Health Organization (WHO) burden of disease methodology, a non-financial approach, where pain, suffering and premature mortality are measured in terms of disability-adjusted-life-years (DALYs). In the UK, wellbeing costs attributable to IRDs were £196.1 million, and economic costs were £327.2 million amounting to £523.3 million total costs in 2019. Accounting for over one-third of total costs, the wellbeing burden of persons affected by IRDs should be emphasized and factored into reimbursement processes for therapies and care pathways. This targeted review presents the most current and relevant data on IRD prevalence in the ROI and the UK, and the impacts (financial and non-financial) of IRDs in terms of diagnosis, wellbeing, employment, formal and informal care, health system costs, deadweight losses and issues surrounding payers and reimbursement. This review demonstrates IRD patients and their families have common issues including, the need for timely equitable access to genetic testing and counselling, equality in accessing employment, and a revision of the assessment process for reimbursement of therapies currently focused on the cost-of-illness to the healthcare system. This review reveals that IRD patients do not frequently engage the healthcare system and as such suggests a cost-of-illness model from a societal perspective may be a better format

DRYP 1.0: a parsimonious hydrological model of DRYland Partitioning of the water balance

Dryland regions are characterized by water scarcity and are facing major challenges under climate change. One difficulty is anticipating how rainfall will be partitioned into evaporative losses, groundwater, soil moisture and runoff (the water balance) in the future, which has important implications for water resources and dryland ecosystems. However, in order to effectively estimate the water balance, hydrological models in drylands need to capture the key processes at the appropriate spatiotemporal scales including spatially restricted and temporally brief rainfall, high evaporation rates, transmission losses and focused groundwater recharge. Lack of available data and the high computational costs of explicit representation of ephemeral surface-groundwater interactions restrict the usefulness of most hydrological models in these environments. Therefore, here we have developed a parsimonious hydrological model (DRYP) that incorporates the key processes of water partitioning in dryland regions, and we tested it in the data-rich Walnut Gulch Experimental Watershed against measurements of streamflow, soil moisture and evapotranspiration. Overall, DRYP showed skill in quantifying the main components of the dryland water balance including monthly observations of streamflow (Nash efficiency (NSE) ~0.7), evapotranspiration (NSE > 0.6) and soil moisture (NSE ~0.7). The model showed that evapotranspiration consumes > 90 % of the total precipitation input to the catchment, and that < 1 % leaves the catchment as streamflow. Greater than 90 % of the overland flow generated in the catchment is lost through ephemeral channels as transmission losses. However, only ~35 % of the total transmission losses percolate to the groundwater aquifer as focused groundwater recharge, whereas the rest is lost to the atmosphere as riparian evapotranspiration. Overall, DRYP is a modular, versatile and parsimonious Python-based model which can be used to anticipate and plan for climatic and anthropogenic changes to water fluxes and storage in dryland region