Liquid marbles,formation and locomotion using external fields and forces

Liquid marbles may be traditionally formed by rolling a droplet on a bed of non-wetting particles resulting in encapsulation and stabilisation. Particles used in this process may range from nanometre to millimetre if handled with sufficient care. This method, however, runs the risk of droplet coalescence and is limited to non-wetting particles. Currently there exist some alternative methods of formulation including using electrostatics to either deliver a particle bed to the droplet or pull the droplet to the particles. The former has shown some promise in potential batch processes but is hindered by interparticle forces. Additional production methods include a form of blender, but this has shown to be unable to produce marbles of a narrow size distribution. Once formed, liquid marbles have demonstrated value as potential blood typing devices, as micro-reaction vessels due to the inherent barrier between the internal phase and the substrate whilst maintaining gas permeability, and as contaminant sensors. Liquid marbles also demonstrate a remarkable level of elasticity under compressive force and reduced evaporation rates when compared to bare water droplets, a function of the size and composition of the stabilising particles. In addition to this, liquid marbles have been proposed as actuators. Locomotion may easily be induced in these structures, using electrostatics, sound, magnetism or light depending on the particle/liquid combinations used in formation, and the environment of deployment. This review seeks to present and summarise recent advances in the field of liquid marble manufacture and methods for actuation. We also aim to highlight potential future avenues of further study within this arena.     

TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions

Data Mining and Knowledge Discovery - The identification of relevant features, i.e., the driving variables that determine a process or the properties of a system, is an essential part of the...     

A fuzzy optimization model for planning integrated terrestrial carbon management networks

Clean Technologies and Environmental Policy - Biochar application and enhanced weathering are negative emission technologies (NETs) with the potential for large-scale deployment for the removal of...     

Vaporization of Protective Oxide Films into Different Gas Atmospheres

Oxidation of Metals - Vaporization often accompanies high-temperature oxidation and corrosion. In this review, vaporization under a vacuum as well as static gas and flowing gas conditions is...     

Using NMR Spectroscopy in the Fragment-Based Drug Discovery of Small-Molecule Anticancer Targeted Therapies

Against the challenge of providing personalized cancer care, the development of targeted therapies stands as a promising approach. The discovery of these agents can benefit from fragment-based drug discovery (FBDD) methods that help guide ligand design and provide key structural information on the targets of interest. In particular, nuclear magnetic resonance spectroscopy is a promising biophysical tool in fragment discovery due to its detection capabilities and versatility. This review provides an overview of FBDD, describes the basis of NMR-based fragment screening, summarizes some exciting technical advances reported over the past decades, and closes with a discussion of selected case studies where this technique has been used as part of drug discovery campaigns to produce lead compounds towards the design of anti-cancer targeted therapies.     

Growing a list

It is easy to find expert knowledge on the Internet on almost any topic, but obtaining a complete overview of a given topic is not always easy: information can be scattered across many sources and must be aggregated to be useful. We introduce a method for intelligently growing a list of relevant items, starting from a small seed of examples. Our algorithm takes advantage of the wisdom of the crowd, in the sense that there are many experts who post lists of things on the Internet. We use a collection of simple machine learning components to find these experts and aggregate their lists to produce a single complete and meaningful list. We use experiments with gold standards and open-ended experiments without gold standards to show that our method significantly outperforms the state of the art. Our method uses the ranking algorithm Bayesian Sets even when its underlying independence assumption is violated, and we provide a theoretical generalization bound to motivate its use.