A physico-chemical characterisation of particulate emissions from a compression ignition engine : the influence of biodiesel feedstock

This study undertook a physico-chemical characterisation of particle emissions from a single compression ignition engine operated at one test mode with 3 biodiesel fuels made from 3 different feedstocks (i.e. soy, tallow and canola) at 4 different blend percentages (20%, 40%, 60% and 80%) to gain insights into their particle-related health effects. Particle physical properties were inferred by measuring particle number size distributions both with and without heating within a thermodenuder (TD) and also by measuring particulate matter (PM) emission factors with an aerodynamic diameter less than 10 μm (PM10). The chemical properties of particulates were investigated by measuring particle and vapour phase Polycyclic Aromatic Hydrocarbons (PAHs) and also Reactive Oxygen Species (ROS) concentrations. The particle number size distributions showed strong dependency on feedstock and blend percentage with some fuel types showing increased particle number emissions, whilst others showed particle number reductions. In addition, the median particle diameter decreased as the blend percentage was increased. Particle and vapour phase PAHs were generally reduced with biodiesel, with the results being relatively independent of the blend percentage. The ROS concentrations increased monotonically with biodiesel blend percentage, but did not exhibit strong feedstock variability. Furthermore, the ROS concentrations correlated quite well with the organic volume percentage of particles – a quantity which increased with increasing blend percentage. At higher blend percentages, the particle surface area was significantly reduced, but the particles were internally mixed with a greater organic volume percentage (containing ROS) which has implications for using surface area as a regulatory metric for diesel particulate matter (DPM) emissions.

Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks

Rule extraction from neural network algorithms have been investigated for two decades and there have been significant applications. Despite this level of success, rule extraction from neural network methods are generally not part of data mining tools, and a significant commercial breakthrough may still be some time away. This paper briefly reviews the state-of-the-art and points to some of the obstacles, namely a lack of evaluation techniques in experiments and larger benchmark data sets. A significant new development is the view that rule extraction from neural networks is an interactive process which actively involves the user. This leads to the application of assessment and evaluation techniques from information retrieval which may lead to a range of new methods.

A phenomenological model for the structure-composition relationship of the high Tc cuprates based on simple chemical principles

A simple phenomenological model for the relationship between structure and composition of the high Tc cuprates is presented. The model is based on two simple crystal chemistry principles: unit cell doping and charge balance within unit cells. These principles are inspired by key experimental observations of how the materials accommodate large deviations from stoichiometry. Consistent explanations for significant HTSC properties can be explained without any additional assumptions while retaining valuable insight for geometric interpretation. Combining these two chemical principles with a review of Crystal Field Theory (CFT) or Ligand Field Theory (LFT), it becomes clear that the two oxidation states in the conduction planes (typically d8 and d9) belong to the most strongly divergent d-levels as a function of deformation from regular octahedral coordination. This observation offers a link to a range of coupling effects relating vibrations and spin waves through application of Hund’s rules. An indication of this model’s capacity to predict physical properties for HTSC is provided and will be elaborated in subsequent publications. Simple criteria for the relationship between structure and composition in HTSC systems may guide chemical syntheses within new material systems.

Combined time- and space-resolved Raman spectrometer for the non-invasive depth profiling of chemical hazards

A time-resolved inverse spatially offset Raman spectrometer was constructed for depth profiling of Raman-active substances under both the lab and the field environments. The system operating principles and performance are discussed along with its advantages relative to traditional continuous wave spatially offset Raman spectrometer. The developed spectrometer uses a combination of space- and time-resolved detection in order to obtain high-quality Raman spectra from substances hidden behind coloured opaque surface layers, such as plastic and garments, with a single measurement. The time-gated spatially offset Raman spectrometer was successfully used to detect concealed explosives and drug precursors under incandescent and fluorescent background light as well as under daylight. The average screening time was 50 s per measurement. The excitation energy requirements were relatively low (20 mW) which makes the probe safe for screening hazardous substances. The unit has been designed with nanosecond laser excitation and gated detection, making it of lower cost and complexity than previous picosecond-based systems, to provide a functional platform for in-line or in-field sensing of chemical substances.

Discovering the best feature extraction and selection algorithms for spontaneous facial expression recognition

Feature extraction and selection are critical processes in developing facial expression recognition (FER) systems. While many algorithms have been proposed for these processes, direct comparison between texture, geometry and their fusion, as well as between multiple selection algorithms has not been found for spontaneous FER. This paper addresses this issue by proposing a unified framework for a comparative study on the widely used texture (LBP, Gabor and SIFT) and geometric (FAP) features, using Adaboost, mRMR and SVM feature selection algorithms. Our experiments on the Feedtum and NVIE databases demonstrate the benefits of fusing geometric and texture features, where SIFT+FAP shows the best performance, while mRMR outperforms Adaboost and SVM. In terms of computational time, LBP and Gabor perform better than SIFT. The optimal combination of SIFT+FAP+mRMR also exhibits a state-of-the-art performance.

Source characterisation of road dust based on chemical and mineralogical composition

Road dust contain potentially toxic pollutants originating from a range of anthropogenic sources common to urban land uses and soil inputs from surrounding areas. The research study analysed the mineralogy and morphology of dust samples from road surfaces from different land uses and background soil samples to characterise the relative source contributions to road dust. The road dust consist primarily of soil derived minerals (60%) with quartz averaging 40-50% and remainder being clay forming minerals of albite, microcline, chlorite and muscovite originating from surrounding soils. About 2% was organic matter primarily originating from plant matter. Potentially toxic pollutants represented about 30% of the build-up. These pollutants consist of brake and tire wear, combustion emissions and fly ash from asphalt. Heavy metals such as Zn, Cu, Pb, Ni, Cr and Cd primarily originate from vehicular traffic while Fe, Al and Mn primarily originate from surrounding soils. The research study confirmed the significant contribution of vehicular traffic to dust deposited on urban road surfaces.

Deep Raman spectroscopy for the non-invasive standoff detection of concealed chemical threat agents

Deep Raman spectroscopy has been utilized for the standoff detection of concealed chemical threat agents from a distance of 15 meters under real life background illumination conditions. By using combined time and space resolved measurements, various explosive precursors hidden in opaque plastic containers were identified non-invasively. Our results confirm that combined time and space resolved Raman spectroscopy leads to higher selectivity towards the sub-layer over the surface layer as well as enhanced rejection of fluorescence from the container surface when compared to standoff spatially offset Raman spectroscopy. Raman spectra that have minimal interference from the packaging material and good signal-to-noise ratio were acquired within 5 seconds of measurement time. A new combined time and space resolved Raman spectrometer has been designed with nanosecond laser excitation and gated detection, making it of lower cost and complexity than picosecond-based laboratory systems.

Functionalised zinc oxide nanowire gas sensors : enhanced NO2 gas sensor response by chemical modification of nanowire surfaces

Surface coating with an organic self-assembled monolayer (SAM) can enhance surface reactions or the absorption of specific gases and hence improve the response of a metal oxide (MOx) sensor toward particular target gases in the environment. In this study the effect of an adsorbed organic layer on the dynamic response of zinc oxide nanowire gas sensors was investigated. The effect of ZnO surface functionalisation by two different organic molecules, tris(hydroxymethyl)aminomethane (THMA) and dodecanethiol (DT), was studied. The response towards ammonia, nitrous oxide and nitrogen dioxide was investigated for three sensor configurations, namely pure ZnO nanowires, organic-coated ZnO nanowires and ZnO nanowires covered with a sparse layer of organic-coated ZnO nanoparticles. Exposure of the nanowire sensors to the oxidising gas NO2 produced a significant and reproducible response. ZnO and THMA-coated ZnO nanowire sensors both readily detected NO2 down to a concentration in the very low ppm range. Notably, the THMA-coated nanowires consistently displayed a small, enhanced response to NO2 compared to uncoated ZnO nanowire sensors. At the lower concentration levels tested, ZnO nanowire sensors that were coated with THMA-capped ZnO nanoparticles were found to exhibit the greatest enhanced response. ΔR/R was two times greater than that for the as-prepared ZnO nanowire sensors. It is proposed that the ΔR/R enhancement in this case originates from the changes induced in the depletion-layer width of the ZnO nanoparticles that bridge ZnO nanowires resulting from THMA ligand binding to the surface of the particle coating. The heightened response and selectivity to the NO2 target are positive results arising from the coating of these ZnO nanowire sensors with organic-SAM-functionalised ZnO nanoparticles.

Implementing a systematic process for rapidly embedding sustainability within chemical engineering education: a case study of James Cook University, Australia

Sustainability has emerged as a primary context for engineering education in the 21st Century, particularly the sub-discipline of chemical engineering. However, there is confusion over how to go about integrating sustainability knowledge and skills systemically within bachelor degrees. This paper addresses this challenge, using a case study of an Australian chemical engineering degree to highlight important practical considerations for embedding sustainability at the core of the curriculum. The paper begins with context for considering a systematic process for rapid curriculum renewal. The authors then summarise a 2-year federally funded project, which comprised piloting a model for rapid curriculum renewal led by the chemical engineering staff. Model elements contributing to the renewal of this engineering degree and described in this paper include: industry outreach; staff professional development; attribute identification and alignment; program mapping; and curriculum and teaching resource development. Personal reflections on the progress and process of rapid curriculum renewal in sustainability by the authors and participating engineering staff will be presented as a means to discuss and identify methodological improvements, as well as highlight barriers to project implementation. It is hoped that this paper will provide an example of a formalised methodology on which program reform and curriculum renewal for sustainability can be built upon in other higher education institutions.

Low temperature synthesis of carbon nanotubes on indium tin oxide electrodes for organic solar cells

The electrical performance of indium tin oxide (ITO) coated glass was improved by including a controlled layer of carbon nanotubes directly on top of the ITO film. Multi-wall carbon nanotubes (MWCNTs) were synthesized by chemical vapor deposition, using ultra-thin Fe layers as catalyst. The process parameters (temperature, gas flow and duration) were carefully refined to obtain the appropriate size and density of MWCNTs with a minimum decrease of the light harvesting in the cell. When used as anodes for organic solar cells based on poly(3-hexylthiophene) (P3HT) and phenyl-C61-butyric acid methyl ester (PCBM), the MWCNT-enhanced electrodes are found to improve the charge carrier extraction from the photoactive blend, thanks to the additional percolation paths provided by the CNTs. The work function of as-modified ITO surfaces was measured by the Kelvin probe method to be 4.95 eV, resulting in an improved matching to the highest occupied molecular orbital level of the P3HT. This is in turn expected to increase the hole transport and collection at the anode, contributing to the significant increase of current density and open circuit voltage observed in test cells created with such MWCNT-enhanced electrodes.

Real-time detection of concealed chemical hazards under ambient light conditions using Raman spectroscopy

Current concerns regarding terrorism and international crime highlight the need for new techniques for detecting unknown and hazardous substances. A novel Raman spectroscopy-based technique, spatially offset Raman spectroscopy (SORS), was recently devised for non-invasively probing the contents of diffusely scattering and opaque containers. Here, we demonstrate a modified portable SORS sensor for detecting concealed substances in-field under different background lighting conditions. Samples including explosive precursors, drugs and an organophosphate insecticide (chemical warfare agent surrogate) were concealed inside diffusely scattering packaging including plastic, paper and cloth. Measurements were carried out under incandescent and fluorescent light as well as under daylight to assess the suitability of the probe for different real-life conditions. In each case, it was possible to identify the substances against their reference Raman spectra in less than one minute. The developed sensor has potential for rapid detection of concealed hazardous substances in airports, mail distribution centers and customs checkpoints.

An enhanced mill extraction model

An enhanced mill extraction model has been developed to calculate mill performance parameters and to predict the extraction performance of a milling unit. The model takes into account the fibre suspended in juice streams and calculates filling ratio, reabsorption factor, imbibition coefficient, and separation efficiency using more complete definitions than those used in previous extraction models. A mass balance model is used to determine the fibre, brix and moisture mass flows between milling units so that a complete milling train, including the return stream from the juice screen, is modelled. Model solutions are presented to determine the effect of different levels of fibre in juice and efficiency of fibre separation in the juice screen on brix extraction. The model provides more accurate results than earlier models leading to better understanding and improvement of the milling process.

A mass spectrometric assay for the quantification of neuropeptide PYY in plasma

A multiple reaction monitoring mass spectrometric assay for the quantification of PYY in human plasma has been developed. A two stage sample preparation protocol was employed in which plasma containing the full length neuropeptide was first digested using trypsin, followed by solid-phase extraction to extract the digested peptide from the complex plasma matrix. The peptide extracts were analysed by LC-MS using multiple reaction monitoring to detect and quantify PYY. The method has been validated for plasma samples, yielding linear responses over the range 5–1,000 ng mL−1. The method is rapid, robust and specific for plasma PYY detection.