Fire safe,sustainable loose furnishing

The aim of this exploratory study has been to investigate the fire properties and environmental aspects of different upholstery material combinations, mainly for domestic applications. An analysis of the sustainability and circularity of selected textiles, along with lifecycle assessment, is used to qualitatively evaluate materials from an environmental perspective. The cone calorimeter was the primary tool used to screen 20 different material combinations from a fire performance perspective. It was found that textile covers of conventional fibres such as wool, cotton and polyester, can be improved by blending them with fire resistant speciality fibres. A new three‐dimensional web structure has been examined as an alternative padding material, showing preliminary promising fire properties with regard to ignition time, heat release rates and smoke production.     

Unraveling the Lagged Effect of Hydro-meteorological Conditions On the Trophic State of a Reservoir By Applying Dynamic Regression

In this study we develop a novel approach to quantify the relative importance of hydro-meteorological (HM) conditions on the trophic state index (TSI) of a water reservoir (San Roque, Córdoba, Argentina). Seven HM variables measured at four reservoir sites and different depths over a time period of near 2 decades are used. We propose a dynamic regression model to predict the TSI from these variables aggregated over a range of time lags, which has not been applied in such a complex setting so far. By performing coefficient analysis, we quantify the relative importance of these variables on the TSI, as well as the time duration over which they have significant impact (lagged effect). Additionally, the analysis of the autoregressive and moving average (ARIMA) terms reveals the impact of the residual effects of previous trophic states on the current trophic state. We find that surface temperature and precipitation have the largest direct relationship to the TSI in the short-term, while the reservoir water level is inversely related to the TSI in the short- to mid-term. Also, the residual effects of the trophic state impact from 1 month (generally) up to 2 years (exceptionally). This approach can be applied to other water bodies affected by similar eutrophication phenomena.

Graphical abstract

     

Optimization methods in inverse problems and applications to science and engineering

Optimization and Engineering -     

Nanogrooved carbon microtubes for wet three‐dimensional printing of conductive composite structures

Recent advances in three‐dimensional (3D) printing have enabled the fabrication of interesting structures which are not achievable using traditional fabrication approaches. The 3D printing of carbon microtube composite inks allows fabrication of conductive structures for practical applications in soft robotics and tissue engineering. However, it is challenging to achieve 3D printed structures from solution‐based composite inks, which requires an additional process to solidify the ink. Here, we introduce a wet 3D printing technique which uses a coagulation bath to fabricate carbon microtube composite structures. We show that through a facile nanogrooving approach which introduces cavitation and channels on carbon microtubes, enhanced interfacial interactions with a chitosan polymer matrix are achieved. Consequently, the mechanical properties of the 3D printed composites improve when nanogrooved carbon microtubes are used, compared to untreated microtubes. We show that by carefully controlling the coagulation bath, extrusion pressure, printing distance and printed line distance, we can 3D print composite lattices which are composed of well‐defined and separated printed lines. The conductive composite 3D structures with highly customised design presented in this work provide a suitable platform for applications ranging from soft robotics to smart tissue engineering scaffolds. © 2019 Society of Chemical Industry     

Real-time organic aerosol chemical speciation in the indoor environment using extractive electrospray ionization mass spectrometry

Understanding the sources and composition of organic aerosol (OA) in indoor environments requires rapid measurements, since many emissions and processes have short timescales. However, real-time molecular-level OA measurements have not been reported indoors. Here, we present quantitative measurements, at a time resolution of five seconds, of molecular ions corresponding to diverse aerosol-phase species, by applying extractive electrospray ionization mass spectrometry (EESI-MS) to indoor air analysis for the first time, as part of the highly instrumented HOMEChem field study. We demonstrate how the complex spectra of EESI-MS are screened in order to extract chemical information and investigate the possibility of interference from gas-phase semivolatile species. During experiments that simulated the Thanksgiving US holiday meal preparation, EESI-MS quantified multiple species, including fatty acids, carbohydrates, siloxanes, and phthalates. Intercomparisons with Aerosol Mass Spectrometer (AMS) and Scanning Mobility Particle Sizer suggest that EESI-MS quantified a large fraction of OA. Comparisons with FIGAERO-CIMS shows similar signal levels and good correlation, with a range of 100 for the relative sensitivities. Comparisons with SV-TAG for phthalates and with SV-TAG and AMS for total siloxanes also show strong correlation. EESI-MS observations can be used with gas-phase measurements to identify co-emitted gas- and aerosol-phase species, and this is demonstrated using complementary gas-phase PTR-MS observations.     

Modification of narrow‐spectrum peptidomimetic polyurethanes with fatty acid chains confers broad‐spectrum antibacterial activity

Each year, thousands of patients die from antimicrobial‐resistant bacterial infections that fail to respond to conventional antibiotic treatment. Antimicrobial polymers are a promising new method of combating antibiotic‐resistant bacterial infections. We have previously reported the synthesis of a series of narrow‐spectrum peptidomimetic antimicrobial polyurethanes that are effective against Gram‐negative bacteria, such as Escherichia coli; however, these polymers are not effective against Gram‐positive bacteria, such as Staphylococcus aureus. With the aim of understanding the correlation between chemical structure and antibacterial activity, we have subsequently developed three structural variants of these antimicrobial polyurethanes using post‐polymerization modification with decanoic acid and oleic acid. Our results show that such modifications converted the narrow‐spectrum antibacterial activity of these polymers into broad‐spectrum activity against Gram‐positive species such as S. aureus, however, also increasing their toxicity to mammalian cells. Mechanistic studies of bacterial membrane disruption illustrate the differences in antibacterial action between the various polymers. The results demonstrate the challenge of balancing antimicrobial activity and mammalian cell compatibility in the design of antimicrobial polymer compositions. © 2019 Society of Chemical Industry     

Bayesian Inference for ARFIMA Models

This article develops practical methods for Bayesian inference in the autoregressive fractionally integrated moving average (ARFIMA) model using the exact likelihood function, any proper prior distribution, and time series that may have thousands of observations. These methods utilize sequentially adaptive Bayesian learning, a sequential Monte Carlo algorithm that can exploit massively parallel desktop computing with graphics processing units (GPUs). The article identifies and solves several problems in the computation of the likelihood function that apparently have not been addressed in the literature. Four applications illustrate the utility of the approach. The most ambitious is an ARFIMA(2,d,2) model for the Campito tree ring time series (length 5405), for which the methods developed in the article provide an essentially uncorrelated sample of size 16,384 from the exact posterior distribution in under four hours. Less ambitious applications take as little as 4 minutes without exploiting GPUs.     

Assessing students' conceptual understanding using an online three‐tier diagnostic test

When solving a mathematical problem, students who do not have sufficient conceptual understanding may perform poorly and exhibit misconceptions. This study was aimed to examine students' conceptual understanding and significant misconceptions when solving number sense‐related problems. An online three‐tier diagnostic test was administered to 125 fifth‐grade students with varied socio‐economic backgrounds in Hong Kong. Only 14.40% of the students exhibited high performance with high confidence, indicating that these students had a profound conceptual understanding of number sense. In addition, the majority of the students (66.40%) did not demonstrate number sense; these students exhibited several significant misconceptions and could solve the questions only by using a rule‐based method or guessing. Accordingly, most students performed unsatisfactorily on number sense‐related problems. This study is imperative in identifying early predictors and provides information for further compatible interventions in the teaching and learning of number sense in Hong Kong in particular and worldwide in general.