A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

In this review article, the authors discuss the use of machine learning algorithms as tools for the prediction of physical and chemical properties of ionic liquids.     

The Effect of the Spacer of Bis(biurea) Ligands on the Structure of A2L3‐type (A=anion) Phosphate Complexes

By tuning the length and rigidity of the spacer of bis(biurea) ligands L, three structural motifs of the A₂L₃ complexes (A represents anion, here orthophosphate PO₄^3?), namely helicate, mesocate, and mono‐bridged motif, have been assembled by coordination of the ligand to phosphate anion. Crystal structure analysis indicated that in the three complexes, each of the phosphate ions is coordinated by twelve hydrogen bonds from six surrounding urea groups. The anion coordination properties in solution have also been studied. The results further demonstrate the coordination behavior of phosphate ion, which shows strong tendency for coordination saturation and geometrical preference, thus allowing for the assembly of novel anion coordination‐based structures as in transition‐metal complexes.     

Curvature investigation in tapered fibers and its application to sensing and mode conversion

Non-adiabatic tapered fibers are basic photonic components used in a wide range of applications. Here we investigate a way to increase their utility through the controllable bending of one of their tapered sections. The experiments carried out explain, for the first time, the mechanics of this approach showing how these tapers can be used to build more sensitive sensors. Their use as highly efficient mode converters is also discussed.     

Local linear convergence analysis of Primal–Dual splitting methods

In this paper, we study the local linear convergence properties of a versatile class of Primal–Dual splitting methods for minimizing composite non-smooth convex optimization problems. Under the assumption that the non-smooth components of the problem are partly smooth relative to smooth manifolds, we present a unified local convergence analysis framework for these methods. More precisely, in our framework, we first show that (i) the sequences generated by Primal–Dual splitting methods identify a pair of primal and dual smooth manifolds in a finite number of iterations, and then (ii) enter a local linear convergence regime, which is characterized based on the structure of the underlying active smooth manifolds. We also show how our results for Primal–Dual splitting can be specialized to cover existing ones on Forward–Backward splitting and Douglas–Rachford splitting/ADMM (alternating direction methods of multipliers). Moreover, based on these obtained local convergence analysis result, several practical acceleration techniques are discussed. To exemplify the usefulness of the obtained result, we consider several concrete numerical experiments arising from fields including signal/image processing, inverse problems and machine learning. The demonstration not only verifies the local linear convergence behaviour of Primal–Dual splitting methods, but also the insights on how to accelerate them in practice.     

Radial breathing-mode frequency of elastically confined spherical nanoparticles subjected to circumferential magnetic field

Knowledge of the vibrational properties of nanoparticles is of fundamental interest since it is a signature of their morphology, and it can be utilized to characterize their physical properties. In addition, the vibration characteristics of the nanoparticles coupled with surrounding media and subjected to magnetic field are of recent interest. This paper develops an analytical approach to study the radial breathing-mode frequency of elastically confined spherical nanoparticles subjected to magnetic field. Based on Maxwell's equations, the nonlocal differential equation of radial motion is derived in terms of radial displacement and Lorentz's force. Bessel functions are used to obtain a frequency equation. The model is justified by a good agreement between the results given by the present model and available experimental and atomic simulation data. Furthermore, the model is used to elucidate the effect of nanoparticle size, the magnetic field and the stiffness of the elastic medium on the radial breathing-mode frequencies of several nanoparticles. Our results reveal that the effects of the magnetic field and the elastic medium are significant for nanoparticle with small size.     

Modelling pH-Dependent and Microstructure-Dependent Streaming Potential Coefficient and Zeta Potential of Porous Sandstones

In this paper, we have significantly modified an existing model for calculating the zeta potential and streaming potential coefficient of porous media and tested it with a large, recently published, high-quality experimental dataset. The newly modified model does not require the imposition of a zeta potential offset but derives its high salinity zeta potential behaviour from Stern plane saturation considerations. The newly modified model has been implemented as a function of temperature, salinity, pH, and rock microstructure both for facies-specific aggregations of the new data and for individual samples. Since the experimental data include measurements on samples of both detrital and authigenic overgrowth sandstones, it was possible to model and test the effect of widely varying microstructural properties while keeping lithology constant. The results show that the theoretical model represents the experimental data very well when applied to model data for a particular lithofacies over the whole salinity, from 10^?5 to 6.3 mol/dm³, and extremely well when modelling individual samples and taking individual sample microstructure into account. The new model reproduces and explains the extreme sensitivity of zeta and streaming potential coefficient to pore fluid pH. The low salinity control of streaming potential coefficient by rock microstructure is described well by the modified model. The model also behaves at high salinities, showing that the constant zeta potential observed at high salinities arises from the development of a maximum charge density in the diffuse layer as it is compressed to the thickness of one hydrated metal ion.     

Teaching the Chemistry of Platinum

Following colonisation of South America by the Spanish, many new naturally occurring substances were sent to Europe. One of these was the silvery, unreactive metal, platinum, discovered in New Grenada in the mid-eighteenth century. It was often found in granular form, associated with gold, and the challenge to chemists was to refine it, produce it as wire or sheet, and determine its chemical properties. This interested the professor of chemistry at the University of Edinburgh, Joseph Black, who was able to obtain samples from London-based Spanish contacts, particularly Ignacio Luzuriaga. This paper examines how Black transmitted his knowledge of the metal to large numbers of students attending his annual course.