What Makes a Good Descriptor for Heterogeneous Ice Nucleation on OH-Patterned Surfaces

Freezing of water is arguably one of the most common phase transitions on Earth and almost always happens heterogeneously. Despite its importance, we lack a fundamental understanding of what makes substrates efficient ice nucleators. Here we address this by computing the ice nucleation (IN) ability of numerous model hydroxylated substrates with diverse surface hydroxyl (OH) group arrangements. Overall, for the substrates considered, we find that neither the symmetry of the OH patterns nor the similarity between a substrate and ice correlate well with the IN ability. Instead, we find that the OH density and the substrate-water interaction strength are useful descriptors of a material's IN ability. This insight allows the rationalization of ice nucleation ability across a wide range of materials, and can aid the search and design of novel potent ice nucleators in the future.Comment: main text + S

Merging Data-Driven And Computational Methods to Understand Ice Nucleation

Heterogeneous ice nucleation (IN) is one of the most ubiquitous phase transitions on earth and impacts a plethora of fields in industry (e.g. air transport, food freezing and harsh-weather operations) and science (e.g. freeze avoidance of animals, cryobiology, cloud research). Still to date, we are lacking reliable answers to the question: What is it at the molecular scale that causes an impurity to facilitate the freezing process of supercooled liquid water? In this thesis we make headway towards identifying such microscopic principles by performing computational studies combined with data-driven approaches. In chapter 3 we screen a range of model substrates to disentangle the contributions of lattice match and hydrophobicity and find that there is a complex interplay and an enormous sensitivity to the atomistic details of the interface. In chapter 4 we show that the heterogeneous setting can alter the polymorph of ice that forms and introduce the concept of pre-critical fluctuations, yielding new ideas to design polymorph-targeting substrates. Chapter 5 deals with the liquid dynamics before and during the nucleation event, an aspect of nucleation that mostly goes unrecognized. We show that the homogeneous nucleation event happens in relatively immobile regions of the supercooled liquid, a finding that opens new avenues to understand and influence heterogeneous nucleation by targeting dynamics rather than structure. Finally, Chapter 6 builds on the large amount of data created during this project in that we combine all previously simulated systems and devise a machine-learning approach to find the most important descriptors for their ice nucleation activity (INA). With this we identify new microscopic guidelines and demonstrate that the quantitative prediction of heterogeneous INA is in reach. The unveiling of a computational artifact that potentially affects many computational interface studies is also part of this thesis

The Many Faces of Heterogeneous Ice Nucleation: Interplay Between Surface Morphology and Hydrophobicity

What makes a material a good ice nucleating agent? Despite the importance of heterogeneous ice nucleation to a variety of fields, from cloud science to microbiology, major gaps in our understanding of this ubiquitous process still prevent us from answering this question. In this work, we have examined the ability of generic crystalline substrates to promote ice nucleation as a function of the hydrophobicity and the morphology of the surface. Nucleation rates have been obtained by brute-force molecular dynamics simulations of coarse-grained water on top of different surfaces of a model fcc crystal, varying the water-surface interaction and the surface lattice parameter. It turns out that the lattice mismatch of the surface with respect to ice, customarily regarded as the most important requirement for a good ice nucleating agent, is at most desirable but not a requirement. On the other hand, the balance between the morphology of the surface and its hydrophobicity can significantly alter the ice nucleation rate and can also lead to the formation of up to three different faces of ice on the same substrate. We have pinpointed three circumstances where heterogeneous ice nucleation can be promoted by the crystalline surface: (i) the formation of a water overlayer that acts as an in-plane template; (ii) the emergence of a contact layer buckled in an ice-like manner; and (iii) nucleation on compact surfaces with very high interaction strength. We hope that this extensive systematic study will foster future experimental work aimed at testing the physiochemical understanding presented herein.Comment: Main + S

Crystal Nucleation in Liquids: Open Questions and Future Challenges in Molecular Dynamics Simulations

The nucleation of crystals in liquids is one of nature's most ubiquitous phenomena, playing an important role in areas such as climate change and the production of drugs. As the early stages of nucleation involve exceedingly small time and length scales, atomistic computer simulations can provide unique insight into the microscopic aspects of crystallization. In this review, we take stock of the numerous molecular dynamics simulations that in the last few decades have unraveled crucial aspects of crystal nucleation in liquids. We put into context the theoretical framework of classical nucleation theory and the state of the art computational methods, by reviewing simulations of e.g. ice nucleation or crystallization of molecules in solutions. We shall see that molecular dynamics simulations have provided key insight into diverse nucleation scenarios, ranging from colloidal particles to natural gas hydrates, and that in doing so the general applicability of classical nucleation theory has been repeatedly called into question. We have attempted to identify the most pressing open questions in the field. We believe that by improving (i.) existing interatomic potentials; and (ii.) currently available enhanced sampling methods, the community can move towards accurate investigations of realistic systems of practical interest, thus bringing simulations a step closer to experiments

Communication: Truncated non-bonded potentials can yield unphysical behavior in molecular dynamics simulations of interfaces

Non-bonded potentials are included in most force fields and therefore widely used in classical molecular dynamics simulations of materials and interfacial phenomena. It is commonplace to truncate these potentials for computational efficiency based on the assumption that errors are negligible for reasonable cutoffs or compensated for by adjusting other interaction parameters. Arising from a metadynamics study of the wetting transition of water on a solid substrate, we find that the influence of the cutoff is unexpectedly strong and can change the character of the wetting transition from continuous to first order by creating artificial metastable wetting states. Common cutoff corrections such as the use of a force switching function, a shifted potential, or a shifted force do not avoid this. Such a qualitative difference urges caution and suggests that using truncated non-bonded potentials can induce unphysical behavior that cannot be fully accounted for by adjusting other interaction parameters

On the physisorption of water on graphene: Sub-chemical accuracy from many-body electronic structure methods

Molecular adsorption on surfaces plays a central role in catalysis, corrosion, desalination, and many other processes of relevance to industry and the natural world. Few adsorption systems are more ubiquitous or of more widespread importance than those involving water and carbon, and for a molecular level understanding of such interfaces water monomer adsorption on graphene is a fundamental and representative system. This system is particularly interesting as it calls for an accurate treatment of electron correlation effects, as well as posing a practical challenge to experiments. Here, we employ many-body electronic structure methodologies that can be rigorously converged and thus provide faithful references for the molecule-surface interaction. In particular, we use diffusion Monte-Carlo (DMC), coupled cluster (CCSD(T)), as well as the random phase approximation (RPA) to calculate the strength of the interaction between water and an extended graphene surface. We establish excellent, sub-chemical, agreement between the complementary high-level methodologies, and an adsorption energy estimate in the most stable configuration of approximately -100\,meV is obtained. We also find that the adsorption energy is rather insensitive to the orientation of the water molecule on the surface, despite different binding motifs involving qualitatively different interfacial charge reorganisation. In producing the first demonstrably accurate adsorption energies for water on graphene this work also resolves discrepancies amongst previously reported values for this widely studied system. It also paves the way for more accurate and reliable studies of liquid water at carbon interfaces with cheaper computational methods, such as density functional theory and classical potentials

Predicting heterogeneous ice nucleation with a data-driven approach

Funder: EC | EC Seventh Framework Programm | FP7 Ideas: European Research Council (FP7-IDEAS-ERC - Specific Programme: "Ideas" Implementing the Seventh Framework Programme of the European Community for Research, Technological Development and Demonstration Activities (2007 to 2013)); doi: https://doi.org/10.13039/100011199; Grant(s): 616121Abstract: Water in nature predominantly freezes with the help of foreign materials through a process known as heterogeneous ice nucleation. Although this effect was exploited more than seven decades ago in Vonnegut’s pioneering cloud seeding experiments, it remains unclear what makes a material a good ice former. Here, we show through a machine learning analysis of nucleation simulations on a database of diverse model substrates that a set of physical descriptors for heterogeneous ice nucleation can be identified. Our results reveal that, beyond Vonnegut’s connection with the lattice match to ice, three new microscopic factors help to predict the ice nucleating ability. These are: local ordering induced in liquid water, density reduction of liquid water near the surface and corrugation of the adsorption energy landscape felt by water. With this we take a step towards quantitative understanding of heterogeneous ice nucleation and the in silico design of materials to control ice formation

Ice is born in low-mobility regions of supercooled liquid water.

When an ice crystal is born from liquid water, two key changes occur: (i) The molecules order and (ii) the mobility of the molecules drops as they adopt their lattice positions. Most research on ice nucleation (and crystallization in general) has focused on understanding the former with less attention paid to the latter. However, supercooled water exhibits fascinating and complex dynamical behavior, most notably dynamical heterogeneity (DH), a phenomenon where spatially separated domains of relatively mobile and immobile particles coexist. Strikingly, the microscopic connection between the DH of water and the nucleation of ice has yet to be unraveled directly at the molecular level. Here we tackle this issue via computer simulations which reveal that (i) ice nucleation occurs in low-mobility regions of the liquid, (ii) there is a dynamical incubation period in which the mobility of the molecules drops before any ice-like ordering, and (iii) ice-like clusters cause arrested dynamics in surrounding water molecules. With this we establish a clear connection between dynamics and nucleation. We anticipate that our findings will pave the way for the examination of the role of dynamical heterogeneities in heterogeneous and solution-based nucleation

Systemic Effects by Intrathecal Administration of Triamcinolone Acetonide in Patients With Multiple Sclerosis

In patients suffering from multiple sclerosis (MS), intrathecal injection of triamcinolone acetonide (TCA) has been shown to improve symptoms of spasticity. Although repeated intrathecal injection of TCA has been used in a number of studies in late-stage MS patients with spinal cord involvement, no clinical-chemical data are available on the distribution of TCA in cerebrospinal fluid (CSF) or serum. Moreover, the effects of intrathecal TCA administration on the concentrations of endogenous steroids remain poorly understood. Therefore, we have quantified TCA and selected endogenous steroids in CSF and serum of TCA-treated MS patients suffering from spasticity. Concentrations of steroids were quantified by LC-MS, ELISA, or ECLIA and compared with the blood-brain barrier status, diagnosed with the Reibergram. The concentration of TCA in CSF significantly increased during each treatment cycle up to >5 mu g/ml both in male and female patients (p30 ng/ml (p< 0.001) and severely depressed serum levels of cortisol and corticosterone (p< 0.001). In addition, concentrations of circulating estrogen were significantly suppressed (p< 0.001). Due to the potent suppressive effects of TCA on steroid hormone concentrations both in the brain and in the periphery, we recommend careful surveillance of adrenal function following repeated intrathecal TCA injections in MS patients

Implementation and Outcomes of Commercial Disease Management Programs in the United States: The Disease Management Outcomes Consolidation Survey

Despite widespread adoption of disease management (DM) programs by US health plans, gaps remain in the evidence for their benefit. The Disease Management Outcomes Consolidation Survey was designed to gather data on DM programs for commercial health plans, to assess program success and DM effectiveness. The questionnaire was mailed to 292 appropriate health plan contacts; 26 plans covering more than 14 million commercial members completed and returned the survey. Respondents reported that DM plays a significant and increasing role in their organizations. Key reasons for adopting DM were improving clinical outcomes, reducing medical costs and utilization, and improving member satisfaction. More respondents were highly satisfied with clinical results than with utilization or cost outcomes of their programs (46%, 17%, and 13%, respectively). Detailed results were analyzed for 57 DM programs with over 230,000 enrollees. Most responding plans offered DM programs for diabetes and asthma, with return on investment (ROI) ranging from 0.16:1 to 4:1. Weighted by number of enrollees per DM program, average ROI was 2.56:1 for asthma (n = 1,136 enrollees) and 1.98:1 for diabetes (n = 25,364). Most (but not all) respondents reported reduced hospital admissions, increasing rates of preventive care, and improved clinical measures. Few respondents provided detailed information about DM programs for other medical conditions, but most that did reported positive outcomes. Lack of standardized methodology was identified as a major barrier to in-house program evaluation. Although low response rate precluded drawing many general conclusions, a clear need emerged for more rigorous evaluation methods and greater standardization of outcomes measurement. (Disease Management 2005;8:253–264)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63399/1/dis.2005.8.253.pd