Spontaneous Liquefaction of Solid Metal-Liquid Metal Interfaces in Colloidal Binary Alloys.

Crystallization of alloys from a molten state is a fundamental process underpinning metallurgy. Here the direct imaging of an intermetallic precipitation reaction at equilibrium in a liquid-metal environment is demonstrated. It is shown that the outer layers of a solidified intermetallic are surprisingly unstable to the depths of several nanometers, fluctuating between a crystalline and a liquid state. This effect, referred to herein as crystal interface liquefaction, is observed at remarkably low temperatures and results in highly unstable crystal interfaces at temperatures exceeding 200 K below the bulk melting point of the solid. In general, any liquefaction process would occur at or close to the formal melting point of a solid, thus differentiating the observed liquefaction phenomenon from other processes such as surface pre-melting or conventional bulk melting. Crystal interface liquefaction is observed in a variety of binary alloy systems and as such, the findings may impact the understanding of crystallization and solidification processes in metallic systems and alloys more generally.

Liquid Metal Doping Induced Asymmetry in Two-Dimensional Metal Oxides.

The emergence of ferroelectricity in two-dimensional (2D) metal oxides is a topic of significant technological interest; however, many 2D metal oxides lack intrinsic ferroelectric properties. Therefore, introducing asymmetry provides access to a broader range of 2D materials within the ferroelectric family. Here, the generation of asymmetry in 2D SnO by doping the material with Hf0.5 Zr0.5 O2 (HZO) is demonstrated. A liquid metal process as a doping strategy for the preparation of 2D HZO-doped SnO with robust ferroelectric characteristics is implemented. This technology takes advantage of the selective interface enrichment of molten Sn with HZO crystallites. Molecular dynamics simulations indicate a strong tendency of Hf and Zr atoms to migrate toward the surface of liquid metal and embed themselves within the growing oxide layer in the form of HZO. Thus, the liquid metal-based harvesting/doping technique is a feasible approach devised for producing novel 2D metal oxides with induced ferroelectric properties, represents a significant development for the prospects of random-access memories.

Dynamic configurations of metallic atoms in the liquid state for selective propylene synthesis.

The use of liquid gallium as a solvent for catalytic reactions has enabled access to well-dispersed metal atoms configurations, leading to unique catalytic phenomena, including activation of neighbouring liquid atoms and mobility-induced activity enhancement. To gain mechanistic insights into liquid metal catalysts, here we introduce a GaSn0.029Ni0.023 liquid alloy for selective propylene synthesis from decane. Owing to their mobility, dispersed atoms in a Ga matrix generate configurations where interfacial Sn and Ni atoms allow for critical alignments of reactants and intermediates. Computational modelling, corroborated by experimental analyses, suggests a particular reaction mechanism by which Sn protrudes from the interface and an adjacent Ni, below the interfacial layer, aligns precisely with a decane molecule, facilitating propylene production. We then apply this reaction pathway to canola oil, attaining a propylene selectivity of ~94.5%. Our results offer a mechanistic interpretation of liquid metal catalysts with an eye to potential practical applications of this technology.

Behavior of Citrate-Capped Ultrasmall Gold Nanoparticles on a Supported Lipid Bilayer Interface at Atomic Resolution.

Nanomaterials have the potential to transform biological and biomedical research, with applications ranging from drug delivery and diagnostics to targeted interference of specific biological processes. Most existing research is aimed at developing nanomaterials for specific tasks such as enhanced biocellular internalization. However, fundamental aspects of the interactions between nanomaterials and biological systems, in particular, membranes, remain poorly understood. In this study, we provide detailed insights into the molecular mechanisms governing the interaction and evolution of one of the most common synthetic nanomaterials in contact with model phospholipid membranes. Using a combination of atomic force microscopy (AFM) and molecular dynamics (MD) simulations, we elucidate the precise mechanisms by which citrate-capped 5 nm gold nanoparticles (AuNPs) interact with supported lipid bilayers (SLBs) of pure fluid (DOPC) and pure gel-phase (DPPC) phospholipids. On fluid-phase DOPC membranes, the AuNPs adsorb and are progressively internalized as the citrate capping of the NPs is displaced by the surrounding lipids. AuNPs also interact with gel-phase DPPC membranes where they partially embed into the outer leaflet, locally disturbing the lipid organization. In both systems, the AuNPs cause holistic perturbations throughout the bilayers. AFM shows that the lateral diffusion of the particles is several orders of magnitude smaller than that of the lipid molecules, which creates some temporary scarring of the membrane surface. Our results reveal how functionalized AuNPs interact with differing biological membranes with mechanisms that could also have implications for cooperative membrane effects with other molecules.

Low-temperature liquid platinum catalyst.

Insights into metal-matrix interactions in atomically dispersed catalytic systems are necessary to exploit the true catalytic activity of isolated metal atoms. Distinct from catalytic atoms spatially separated but immobile in a solid matrix, here we demonstrate that a trace amount of platinum naturally dissolved in liquid gallium can drive a range of catalytic reactions with enhanced kinetics at low temperature (318 to 343 K). Molecular simulations provide evidence that the platinum atoms remain in a liquid state in the gallium matrix without atomic segregation and activate the surrounding gallium atoms for catalysis. When used for electrochemical methanol oxidation, the surface platinum atoms in the gallium-platinum system exhibit an activity of [Formula: see text] three orders of magnitude higher than existing solid platinum catalysts. Such a liquid catalyst system, with a dynamic interface, sets a foundation for future exploration of high-throughput catalysis.

Deep eutectic solvents as cryoprotective agents for mammalian cells.

Cryopreservation has facilitated numerous breakthroughs including assisted reproductive technology, stem cell therapies, and species preservation. Successful cryopreservation requires the addition of cryoprotective agents to protect against freezing damage and dehydration. For decades, cryopreservation has largely relied on the same two primary agents: dimethylsulfoxide and glycerol. However, both of these are toxic which limits their use for cells destined for clinical applications. Furthermore, these two agents are ineffective for hundreds of cell types, and organ and tissue preservation has not been achieved. The research presented here shows that deep eutectic solvents can be used as cryoprotectants. Six deep eutectic solvents were explored for their cryoprotective capacity towards mammalian cells. The solvents were tested for their thermal properties, including glass transitions, toxicity, and permeability into mammalian cells. A deep eutectic solvent made from proline and glycerol was an effective cryoprotective agent for all four cell types tested, even with extended incubation prior to freezing. This deep eutectic solvent was more effective and less toxic than its individual components, highlighting the importance of multi-component systems. Cells were characterised post-thawing using atomic force microscopy and confocal microscopy. Molecular dynamics simulations support the biophysical parameters obtained by experimentation. This is one of the first times that this class of solvents has been systematically tested for cryopreservation of mammalian cells and as such this research opens the way for the development of potentially thousands of new cryoprotective agents that can be tailored to specific cell types. The demonstrated capacity of cells to be incubated with the deep eutectic solvent at 37 °C for hours prior to freezing without significant loss of viability is a major step toward the storage of organs and tissues.

Machine learning investigation of viscosity and ionic conductivity of protic ionic liquids in water mixtures.

Ionic liquids (ILs) are well classified as designer solvents based on the ease of tailoring their properties through modifying the chemical structure of the cation and anion. However, while many structure-property relationships have been developed, these generally only identify the most dominant trends. Here, we have used machine learning on existing experimental data to construct robust models to produce meaningful predictions across a broad range of cation and anion chemical structures. Specifically, we used previously collated experimental data for the viscosity and conductivity of protic ILs [T. L. Greaves and C. J. Drummond, Chem. Rev. 115, 11379-11448 (2015)] as the inputs for multiple linear regression and neural network models. These were then used to predict the properties of all 1827 possible cation-anion combinations (excluding the input combinations). These models included the effect of water content of up to 5 wt. %. A selection of ten new protic ILs was then prepared, which validated the usefulness of the models. Overall, this work shows that relatively sparse data can be used productively to predict physicochemical properties of vast arrays of ILs.

Engineered assembly of water-dispersible nanocatalysts enables low-cost and green CO2 capture.

Catalytic solvent regeneration has attracted broad interest owing to its potential to reduce energy consumption in CO2 separation, enabling industry to achieve emission reduction targets of the Paris Climate Accord. Despite recent advances, the development of engineered acidic nanocatalysts with unique characteristics remains a challenge. Herein, we establish a strategy to tailor the physicochemical properties of metal-organic frameworks (MOFs) for the synthesis of water-dispersible core-shell nanocatalysts with ease of use. We demonstrate that functionalized nanoclusters (Fe3O4-COOH) effectively induce missing-linker deficiencies and fabricate mesoporosity during the self-assembly of MOFs. Superacid sites are created by introducing chelating sulfates on the uncoordinated metal clusters, providing high proton donation capability. The obtained nanomaterials drastically reduce the energy consumption of CO2 capture by 44.7% using only 0.1 wt.% nanocatalyst, which is a ∽10-fold improvement in efficiency compared to heterogeneous catalysts. This research represents a new avenue for the next generation of advanced nanomaterials in catalytic solvent regeneration.

Doping Process of 2D Materials Based on the Selective Migration of Dopants to the Interface of Liquid Metals.

The introduction of trace impurities within the doping processes of semiconductors is still a technological challenge for the electronics industries. By taking advantage of the selective enrichment of liquid metal interfaces, and harvesting the doped metal oxide semiconductor layers, the complexity of the process can be mitigated and a high degree of control over the outcomes can be achieved. Here, a mechanism of natural filtering for the preparation of doped 2D semiconducting sheets based on the different migration tendencies of metallic elements in the bulk competing for enriching the interfaces is proposed. As a model, liquid metal alloys with different weight ratios of Sn and Bi in the bulk are employed for harvesting Bi2 O3 -doped SnO nanosheets. In this model, Sn shows a much stronger tendency than Bi to occupy surface sites of the Bi-Sn alloys, even at the very high concentrations of Bi in the bulk. This provides the opportunity for creating SnO 2D sheets with tightly controlled Bi2 O3 dopants. By way of example, it is demonstrated how such nanosheets could be made selective to both reducing and oxidizing environmental gases. The process demonstrated here offers significant opportunities for future synthesis and fabrication processes in the electronics industries.

Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations.

Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water models have been developed, and it remains a challenge to find a single water model that accurately reproduces all experimental properties of water simultaneously. Here, we report a comprehensive comparison of structural and dynamic properties of 30 commonly used 3-point, 4-point, 5-point, and polarizable water models simulated using consistent settings and analysis methods. For the properties of density, coordination number, surface tension, dielectric constant, self-diffusion coefficient, and solvation free energy of methane, models published within the past two decades consistently show better agreement with experimental values compared to models published earlier, albeit with some notable exceptions. However, no single model reproduced all experimental values exactly, highlighting the need to carefully choose a water model for a particular study, depending on the phenomena of interest. Finally, machine learning algorithms quantified the relationship between the water model force field parameters and the resulting bulk properties, providing insight into the parameter-property relationship and illustrating the challenges of developing a water model that can accurately reproduce all properties of water simultaneously.

Predicting aqueous solubility by QSPR modeling.

The aqueous solubility is predicted here using quantitative structure property relationship (QSPR) models. In this study, we examine whether descriptors that individually yield favorable models for the prediction of the Gibbs energy of solvation and sublimation can be used in combination with octanol-water partition coefficient to produce QSPR models for the prediction of aqueous solubility. Based on this strategy, applied to seven distinct datasets, all models exhibited an R2 greater than 0.7 and Q2 greater than 0.6 for the estimation of aqueous solubility. We also determined how uncoupling the descriptors used to create QSPR models in the prediction of Gibbs energy of sublimation yielded an improved model. Model refinement using an artificial neural network applying the same descriptors generated significantly better models with improved R2 and standard deviation.

Nanostructure of a deep eutectic solvent at solid interfaces.

HYPOTHESIS: Deep eutectic solvents (DESs) are an attractive class of tunable solvents. However, their uptake for relevant applications has been limited due to a lack of detailed information on their structure-property relationships, both in the bulk and at interfaces. The lateral nanostructure of the DES-solid interfaces is likely to be more complex than previously reported and requires detailed, high-resolution investigation. EXPERIMENTS: We employ a combination of high-resolution amplitude-modulated atomic force microscopy and molecular dynamics simulations to elucidate the lateral nanostructure of a DES at the solid-liquid interface. Specifically, the lateral and near-surface nanostructure of the DES choline chloride:glycerol is probed at the mica and highly-ordered pyrolytic graphite interfaces. FINDINGS: The lateral nanostructure of the DES-solid interface is heterogeneous and well-ordered in both systems. At the mica interface, the DES is strongly ordered via polar interactions. The adsorbed layer has a distinct rhomboidal symmetry with a repeat spacing of ~0.9 nm comprising all DES species. At the highly ordered pyrolytic graphite interface, the adsorbed layer appears distinctly different, forming an apolor-driven row-like structure with a repeat spacing of ~0.6 nm, which largely excludes the chloride ion. The interfacial nanostructure results from a delicate balance of substrate templating, liquid-liquid interactions, species surface affinity, and packing constraints of cations, anions, and molecular components within the DES. For both systems, distinct near-surface nanostructural layering is observed, which becomes more pronounced close to the substrate. The surface nanostructures elucidated here significantly expand our understanding of DES interfacial behavior and will enhance the optimization of DES systems for surface-based applications.

Unique surface patterns emerging during solidification of liquid metal alloys.

It is well-understood that during the liquid-to-solid phase transition of alloys, elements segregate in the bulk phase with the formation of microstructures. In contrast, we show here that in a Bi-Ga alloy system, highly ordered nanopatterns emerge preferentially at the alloy surfaces during solidification. We observed a variety of transition, hybrid and crystal-defect-like patterns, in addition to lamellar and rod-like structures. Combining experiments and molecular dynamics simulations, we investigated the influence of the superficial Bi and Ga2O3 layers during surface solidification and elucidated the pattern-formation mechanisms, which involve surface-catalysed heterogeneous nucleation. We further demonstrated the dynamic nature and robustness of the phenomenon under different solidification conditions and for various alloy systems. The surface patterns we observed enable high-spatial-resolution nanoscale-infrared and surface-enhanced Raman mapping, which reveal promising potential for surface- and nanoscale-based applications.

A computational exploration of aggregation-induced excitonic quenching mechanisms for perylene diimide chromophores.

Perylene diimide (PDI) derivatives are widely used materials for luminescent solar concentrator (LSC) applications due to their attractive optical and electronic properties. In this work, we study aggregation-induced exciton quenching pathways in four PDI derivatives with increasing steric bulk, which were previously synthesized. We combine molecular dynamics and quantum chemical methods to simulate the aggregation behavior of chromophores at low concentration and compute their excited state properties. We found that PDIs with small steric bulk are prone to aggregate in a solid state matrix, while those with large steric volume displayed greater tendencies to isolate themselves. We find that for the aggregation class of PDI dimers, the optically accessible excitations are in close energetic proximity to triplet charge transfer (CT) states, thus facilitating inter-system crossing and reducing overall LSC performance. While direct singlet fission pathways appear endothermic, evidence is found for the facilitation of a singlet fission pathway via intermediate CT states. Conversely, the insulation class of PDI does not suffer from aggregation-induced photoluminescence quenching at the concentrations studied here and therefore display high photon output. These findings should aid in the choice of PDI derivatives for various solar applications and suggest further avenues for functionalization and study.

Structural insights into BCL2 pro-survival protein interactions with the key autophagy regulator BECN1 following phosphorylation by STK4/MST1.

BECN1/Beclin 1 is a critical protein in the initiation of autophagosome formation. Recent studies have shown that phosphorylation of BECN1 by STK4/MST1 at threonine 108 (T108) within its BH3 domain blocks macroautophagy/autophagy by increasing BECN1 affinity for its negative regulators, the anti-apoptotic proteins BCL2/Bcl-2 and BCL2L1/Bcl-xL. It was proposed that this increased binding is due to formation of an electrostatic interaction with a conserved histidine residue on the anti-apoptotic molecules. Here, we performed biophysical studies which demonstrated that a peptide corresponding to the BECN1 BH3 domain in which T108 is phosphorylated (p-T108) does show increased affinity for anti-apoptotic proteins that is significant, though only minor (<2-fold). We also determined X-ray crystal structures of BCL2 and BCL2L1 with T108-modified BECN1 BH3 peptides, but only showed evidence of an interaction between the BH3 peptide and the conserved histidine residue when the histidine flexibility was restrained due to crystal contacts. These data, together with molecular dynamics studies, indicate that the histidine is highly flexible, even when complexed with BECN1 BH3. Binding studies also showed that detergent can increase the affinity of the interaction. Although this increase was similar for both the phosphorylated and non-phosphorylated peptides, it suggests factors such as membranes could impact on the interaction between BECN1 and BCL2 proteins, and therefore, on the regulation of autophagy. Hence, we propose that phosphorylation of BECN1 by STK4/MST1 can increase the affinity of the interaction between BECN1 and anti-apoptotic proteins and this interaction can be stabilized by local environmental factors. Abbreviations: asu: asymmetric unit; BH3: BCL2/Bcl-2 homology 3; DAPK: death associated protein kinase; MD: molecular dynamics; MST: microscale thermophoresis; NMR: nuclear magnetic resonance; PDB: protein data bank; p-T: phosphothreonine; SPR: surface plasmon resonance; STK4/MST1: serine/threonine kinase 4.

Predicting the Enthalpy and Gibbs Energy of Sublimation by QSPR Modeling.

The enthalpy and Gibbs energy of sublimation are predicted using quantitative structure property relationship (QSPR) models. In this study, we compare several approaches previously reported in the literature for predicting the enthalpy of sublimation. These models, which were reproduced successfully, exhibit high correlation coefficients, in the range 0.82 to 0.97. There are significantly fewer examples of QSPR models currently described in the literature that predict the Gibbs energy of sublimation; here we describe several models that build upon the previous models for predicting the enthalpy of sublimation. The most robust and predictive model constructed using multiple linear regression, with the fewest number of descriptors for estimating this property, was obtained with an R2 of the training set of 0.71, an R2 of the test set of 0.62, and a standard deviation of 9.1 kJ mol-1. This model could be improved by training using a neural network, yielding an R2 of the training and test sets of 0.80 and 0.63, respectively, and a standard deviation of 8.9 kJ mol-1.

BioPPSy: An Open-Source Platform for QSAR/QSPR Analysis.

The reliability of quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) models is often difficult to assess due to the problems of accessing the tools and data used to build the models. We present here BioPPSy, which aims to fill this gap by providing an easy-to-use open-source software platform. We demonstrate the program capabilities by calculating three key properties used in drug discovery, aqueous solubility, Caco-2 cell permeability and blood-brain barrier permeability. A comparison is made with a number of previously reported methods, taken from the literature, for each property. The software, including source code, current models and databases, is available from https://sourceforge.net/projects/bioppsy/.

Docking, CoMFA and CoMSIA studies of a series of sulfonamides derivatives as carbonic anhydrase I inhibitors.

3D-QSAR methods, CoMFA region focusing (CoMFA-RF) and CoMSIA along with docking studies carried out for investigating 32 carbonic anhydrase I inhibitors. These inhibitors have been studied for the development of antiglaucoma, antitumor, antiobesity or anticonvulsant drugs. Docking analysis by GOLD provide conformations which have been realigned in CoMFA and CoMSIA models. Training set for the CoMFA-RF and CoMSIA models using 24 docked conformations gives q(2)(Loo) values of 0.615 and 0.637 and r(2)(nv) values of 0.701 and 0.713, respectively. The results of CoMFA-RF and CoMSIA with and without docked conformations were compared. The ability of prediction and robustness of the models were evaluated by test set, cross validation (leave-one-out and leave-ten-out), bootstrapping, and progressive scrambling approaches. The all-orientation search (AOS) was used to achieve the best orientation to minimize the effect of initial orientation of the structures. The docking results confirmed CoMFA and CoMSIA contour maps.