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 Ga
O
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.
Abstract
Catalytic solvent regeneration has attracted broad interest owing to its potential to reduce energy consumption in CO
2
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 (Fe
3
O
4
-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 CO
2
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.
Abstract
Organic photovoltaic (OPV) materials are promising candidates for cheap, printable solar cells. However, there are a very large number of potential donors and acceptors, making selection of ...the best materials difficult. Here, we show that machine-learning approaches can leverage computationally expensive DFT calculations to estimate important OPV materials properties quickly and accurately. We generate quantitative relationships between simple and interpretable chemical signature and one-hot descriptors and OPV power conversion efficiency (PCE), open circuit potential (
V
oc
), short circuit density (
J
sc
), highest occupied molecular orbital (HOMO) energy, lowest unoccupied molecular orbital (LUMO) energy, and the HOMO–LUMO gap. The most robust and predictive models could predict PCE (computed by DFT) with a standard error of ±0.5 for percentage PCE for both the training and test set. This model is useful for pre-screening potential donor and acceptor materials for OPV applications, accelerating design of these devices for green energy applications.
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/.
Ionic liquids (ILs) are a widely investigated class of solvents for scientific and industrial applications due to their desirable and “tunable” properties. The IL–solid interface is a complex entity, ...and despite intensive investigation, its true nature remains elusive. The understanding of the IL–solid interface has evolved over the last decade from a simple 1D double layer, to a 2D ordered interface, and finally a liquid‐specific, complex 3D ordered liquid interface. However, most studies depend solely on one technique, which often only examine one aspect of the interfacial nanostructure. Here, a holistic study of the protic IL–solid interface is presented, which provides a more detailed picture of IL interfacial solvation. The 3D nanostructure of the ethylammonium nitrate (EAN)–mica interface is investigated using a combination of 1D, 2D, and 3D amplitude modulated‐atomic force microscopy and molecular dynamics simulations. Importantly, it is found that the EAN–mica interface is more complex than previously reported, possessing surface‐adsorbed, near‐surface, surface‐normal, and lateral heterogeneity, which propagates at relatively large distances from the solid substrate. The work presented in this study meaningfully enhances the understanding of the IL–solid interface.
The ionic liquid–solid interface is investigated using a combination of high‐resolution techniques. The 3D nanostructure of the ethylammonium nitrate (EAN)–mica interface is revealed using a combination of 1D, 2D, and 3D atomic force microscopy, molecular dynamics simulations, and theoretical calculations. Importantly, the EAN–mica interface is more complex than previously reported.
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.
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.
Display omitted
•QSPR models of aqueous solubility of seven experimental datasets were studied.•Three different strategies were explored, building from predictions of the octanol-water partition coefficient and Gibbs sublimation energy.•The coefficient of regression, R2, of the final models spanned 0.87 to 0.95 for models created using artificial neural networks.
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.
Display omitted
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.
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.
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.
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 Bi2O3‐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 Bi2O3 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.
The interface of liquid metals is used as natural filtering for doping and harvesting 2D doped metal oxide semiconductors. 2D Bi2O3‐doped SnO semiconducting sheets are produced based on the different migration tendencies of Sn and Bi metals within the bulk competing for the selective enrichment of the liquid metal interface.
PDF
