A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0

The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. Since it was first proposed in 2016, the algorithm has undergone some minor changes and there is now a configurable, scalable and easy to use version available in two open source repositories. We present an overview of the latest stable HIVE-COTE, version 1.0, and describe how it differs to the original. We provide a walkthrough guide of how to use the classifier, and conduct extensive experimental evaluation of its predictive performance and resource usage. We compare the performance of HIVE-COTE to three recently proposed algorithms

Understanding solvent spreading for Langmuir deposition of nanomaterial films: a Hansen solubility parameter approach

In order to prepare high-quality Langmuir films of two-dimensional materials it is important to select a solvent optimized for both exfoliation and spreading at the air-water interface. Whilst it is generally accepted that exfoliation and stabilisation of two-dimensional materials is well-described using the Hansen solubility parameter theory, a complementary description of solvent spreading behaviour is lacking. To this end we develop an understanding of solvent spreading using a Hansen solubility parameter framework. Our model accurately predicts the behaviour of both water-immiscible and water-miscible solvents in Langmuir film formation experiments. We demonstrate that spreading behaviour can be modified by controlling the surface pressure of the subphase using an amphiphilic species and accordingly utilise this approach to determine the maximum spreading pressure for a selection of solvents. Ultimately, by building on this understanding we open up additional routes to optimize the preparation of Langmuir films of two-dimensional materials and other nanoparticles

The Canonical Interval Forest {(CIF)} Classifier for Time Series Classification

Time series classification (TSC) is home to a number of algorithm groups that utilise different kinds of discriminatory patterns. One of these groups describes classifiers that predict using phase dependant intervals. The time series forest (TSF) classifier is one of the most well known interval methods, and has demonstrated strong performance as well as relative speed in training and predictions. However, recent advances in other approaches have left TSF behind. TSF originally summarises intervals using three simple summary statistics. The `catch22' feature set of 22 time series features was recently proposed to aid time series analysis through a concise set of diverse and informative descriptive characteristics. We propose combining TSF and catch22 to form a new classifier, the Canonical Interval Forest (CIF). We outline additional enhancements to the training procedure, and extend the classifier to include multivariate classification capabilities. We demonstrate a large and significant improvement in accuracy over both TSF and catch22, and show it to be on par with top performers from other algorithmic classes. By upgrading the interval-based component from TSF to CIF, we also demonstrate a significant improvement in the hierarchical vote collective of transformation-based ensembles (HIVE-COTE) that combines different time series representations. HIVE-COTE using CIF is significantly more accurate on the UCR archive than any other classifier we are aware of and represents a new state of the art for TSC

Edge selective gas detection using Langmuir films of graphene platelets

Recent advances in large-scale production of graphene have led to the availability of solution processable platelets at the commercial scale. Langmuir-Schaefer (L-S) deposition is a scalable process for forming a percolating film of graphene platelets which can be used for electronic gas sensing. Here, we demonstrate the use of this deposition method to produce functional gas sensors, using a chemiresistor structure from commercially-available graphene dispersions. The sensitivity of the devices and repeatability of the electrical response upon gas exposure has been characterized. Raman spectroscopy and Kelvin probe force microscopy (KPFM) show doping of the basal plane using ammonia (n-dopant) and acetone (p-dopant). The resistive signal is increased upon exposure to both gases showing that sensing originates from the change in contact resistance between nanosheets. We demonstrate that Arrhenius fitting of the desorption response potentially allows measurements of the desorption process activation energies for gas molecules adsorbed onto the graphene nanosheets

[Letter] Size selection of liquid-exfoliated 2D nanosheets

Here we present a size selection model for liquid-exfoliated two-dimensional nanosheets. The ability to consistently select exfoliated nanosheets with desired properties is important for development of applications in all areas. The model presented facilitates determination of centrifugation parameters for production of dispersions with controlled size and thickness for different materials, solvents and exfoliation processes. Importantly, after accounting for the influence of viscosity on exfoliation, comparisons of different solvents are shown to be well described by the surface tension and Hansen parameter matching. This suggests that previous analyses may have overestimated the relative performance of more viscous solvents. This understanding can be extended to develop a model based on the force balance of nanosheets falling under viscous drag during centrifugation. By considering the microscopic aspect ratio relationships, this model can be both calibrated for size selection of nanosheets and compare the exfoliation processes themselves

Cold spray deposition of metallic-UHTC composites

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