Laboratory evaluation of Drawtex Hydroconductive Dressing with LevaFiber technology

Good wound bed preparation is an essential aspect of wound care and effective wound healing. Removal of dead and necrotic tissue either through autolytic or interventional debridement, followed by good exudate management, inhibition of matrix metalloproteases and bacterial bioburden control should allow the chronic wound to process to closure. It is known, still, that wound healing in these circumstances is not a simple process and that maintaining a healthy wound bed is central to the process. Many practitioners rely on the TIME (Tissue, Infection/Inflammation, Moisture balance and wound Edge) framework to help them with wound bed preparation and there are a variety of dressings available to help with debridement, exudate management, reduction of bacterial bioburden and inhibit metalloproteases. The sequence of application of the various dressings will depend upon their function. This study describes the function of a dressing, Drawtex, a hydroconductive dressing, which can be used to assist with wound bed preparation through its absorption, sequestration and retention properties. The absorption over time, ability to sequester and retain bacteria were assessed in the laboratory using a variety of methods. Drawtex was shown to absorb eight times its own weight in fluid over time and it showed a 90% reduction in bacterial numbers over a 24hr period in sequestration experiments. Utilisation of direct observation by scanning electron microscopy demonstrated bacterial retention in the fibres

A big data approach to assess the influence of road pavement condition on truck fleet fuel consumption

In Europe, the road network is the most extensive and valuable infrastructure asset. In England, for example, its value has been estimated at around £344 billion and every year the government spends approximately £4 billion on highway maintenance (House of Commons, 2011). Fuel efficiency depends on a wide range of factors, including vehicle characteristics, road geometry, driving pattern and pavement condition. The latter has been addressed, in the past, by many studies showing that a smoother pavement improves vehicle fuel efficiency. A recent study estimated that road roughness affects around 5% of fuel consumption (Zaabar & Chatti, 2010). However, previous studies were based on experiments using few instrumented vehicles, tested under controlled conditions (e.g. steady speed, no gradient etc.) on selected test sections. For this reason, the impact of pavement condition on vehicle fleet fuel economy, under real driving conditions, at network level still remains to be verified. A 2% improvement in fuel efficiency would mean that up to about 720 million liters of fuel (~£1 billion) could be saved every year in the UK. It means that maintaining roads in better condition could lead to cost savings and reduction of greenhouse gas emissions. Modern trucks use many sensors, installed as standard, to measure data on a wide range of parameters including fuel consumption. This data is mostly used to inform fleet managers about maintenance and driver training requirements. In the present work, a ‘Big Data’ approach is used to estimate the impact of road surface conditions on truck fleet fuel economy for many trucks along a motorway in England. Assessing the impact of pavement conditions on fuel consumption at truck fleet and road network level would be useful for road authorities, helping them prioritize maintenance and design decisions

Route level analysis of road pavement surface condition and truck fleet fuel consumption

Experimental studies have estimated the impact of road surface conditions on vehicle fuel consumption to be up to 5% (Beuving et al., 2004). Similar results have been published by Zaabar and Chatti (2010). However, this was established testing a limited number of vehicles under carefully controlled conditions including, for example, steady speed or coast down and no gradient, amongst others. This paper describes a new “Big Data” approach to validate these estimates at truck fleet and route level, for a motorway in the UK. Modern trucks are fitted with many sensors, used to inform truck fleet managers about vehicle operation including fuel consumption. The same measurements together with data regarding pavement conditions can be used to assess the impact of road surface conditions on fuel economy. They are field data collected for thousands of trucks every day, year on year, across the entire network in the UK. This paper describes the data analysis developed and the initial results on the impact of road surface condition on fuel consumption for journeys of 157 trucks over 42.6km of motorway, over a time period of one year. Validation of the relationship between road pavement surface condition and vehicle fuel consumption will increase confidence in results of LCA analyses including the use phase

Low Power Embedded Processing of Scintillation Events with Silicon Photo Multipliers

The advancement and use of silicon photo multiplier (SiPM) technology has enabled portable devices for applications such as scintillation detection to be developed. The proposed analogue to digital converter (ADC) architecture and field programmable gate array (FPGA) system configuration advances on analogue signal processing methods, traditionally employed for gamma isotope identification applications. This is achieved by high speed sampling of SiPM output signals and real-time FPGA processing, whilst consuming low power, thus extending device operation times. Results demonstrate 7-bit peak capture accuracy of an 8 μs scintillation event, using a 25 MHz ADC sample rate

The construction of imaginaries of the public as a threat to synthetic biology

Scientific institutions and innovation-focused government bodies have identified public attitudes to synthetic biology as an obstruction to the field. This view is based on a perception that the public is (or will likely become) fearful of synthetic biology and that a ‘public scare’ would impede development of the field. Fear of the public's fear of synthetic biology, which I characterise as ‘synbiophobia-phobia’, has been the driving force behind the promotion of public engagement and other activities to address ‘ethical, legal and social issues' (ELSI). These activities have been problematic in two ways. Firstly, they are based on the discredited ‘deficit-model’ understanding of public responses to science, in which negative public attitudes towards science are thought to result from a lack of scientific knowledge. Secondly, they have taken for granted sociotechnical expectations put forward by scientific institutions. These promises of the field, and the tacit normative commitments embedded within them, have not been opened up to public appraisal. In these ways, synthetic biology exemplifies many phenomena described by Welsh and Wynne (2013). This article analyses the ontological stakes in the work conducted by scientific institutions to conjure up imaginaries of publics with respect to synthetic biology. As synthetic biology emerges as a field of hope under threat from publics, how have science, publics and the relations between them become defined

Dielectric barrier plasma discharge exsolution of nanoparticles at room temperature and atmospheric pressure Dataset

The dataset that corresponds to the results reported in the paper are included within this record as an Excel file and with tabs corresponding to each figure. Additional results and raw data underlying this work (full set of microscopy images and size analysis and statistics, high resolution deconvoluted x-ray photoelectron spectra and control magnetic measurements) are available in the Supporting Information (in PDF format) or on request following instructions provided here. This work has been supported by EPSRC through the UK Catalysis Hub (EP/R027129/1) and the Emergent Nanomaterials-Critical Mass Initiative (EP/R023638/1, EP/R023921/1, EP/R023522/1, EP/R008841/1) as well as the Royal Society (IES\R2\212049). F.F. gratefully acknowledges support from the National Research Council of Italy (2020 STM program). I.S.M. acknowledges funding from the Royal Academy of Engineering through a Chair in Emerging Technologies Award entitled “Engineering Chemical Reactor Technologies for a Low-Carbon Energy Future” (Grant CiET1819\2\57). KK acknowledges funding from the Henry Royce Institute (EP/X527257/1)