Particle-modified Surface Plasmon Resonance Biosensor

Surface plasmon resonance (SPR) biosensors have attracted great attention in scientific research in the past three decades. Extensive studies on the immobilisation of biorecognition elements have been conducted in pursuit of higher sensitivity, but trialled formats have focussed on a thin layer modification next to the plasmon film, which usually requires in situ derivatization. This thesis investigates an ‘off-chip’ immobilisation strategy for SPR biosensing using silica particles and considers the implications of a particle-modified evanescent field on the signal amplitude and kinetics, for an exemplar affinity binding between immobilised IgG and its anti-IgG complement. Submicron silica particles were synthesized as carriers for the bio-recognition elements. They were then immobilised to form a sub-monolayer on the gold film of an SPR biosensor using two methods: thiolsilane coupling and physical adsorption aided by mechanical pressure. The bio-sensitivity towards an antigen/antibody interaction was lower than an SPR biosensor with an alkanethiolate SAM due to the difference in ligand capacity and position in the evanescent field. The binding kinetics of antigen/antibody pair was found to follow the Langmuir model closely in a continuous flow configuration but was heavily limited by the mass transport from the bulk to the sensor surface in a stop-flow configuration. A packed channel configuration was designed with larger gel particles as ligand carriers, packed on top of a gold film to create a column-modified SPR biosensor. This sensor has comparable bio-sensitivity to the previous sub-monolayer particle-modified systems, but the binding and dissociation of the analyte was heavily dependent on mass transport and binding equilibria across the column. A bi-directional diffusion mechanism was proposed based on a two-compartment mass transport model and the expanded model fitted well with the experimental data. The column-modified sensor was also studied by SPR imaging and analyte band formation was observed and analysed. Using the lateral resolution, a multiplexing particle column configuration was explored, and its potential in distinguishing a multicomponent analyte.Agency for Science Technology and Research, Singapor

Particle size measurement using electrostatic sensor through spatial filtering method

Particle size measurement is important in powder and particle industries in which the particle size affects the productivity and efficiency of the machine, for example, in coal-fired power plants. An electrostatic sensor detects the electric charge from dry particles moving in a pipeline. Analysis of the detected signal can provide useful information about the particle velocity, mass flow rate, concentration and size. Using electrostatic sensors, previous researches studied particle sizing using magnitude dependent analysis which is a highly conditional method where the results can be affected by other parameters such as particle mass flow rate, velocity and concentration. This research proposes a magnitude independent analysis for particle sizing in the frequency domain called spatial filtering method. The solution was started by modeling and analysis of the charge induced to the ring electrode using finite-element analysis to find the sensitivity of electrode. A mathematical model was provided to compute particle position on the radial axis of the electrode and then a new technique was proposed to extract a single particle size from the calculated particle radial position. To validate the proposed method experimentally, a sensor was designed and five test particles ranging from 4 mm to 14 mm were selected for measurement. The results show a 0.44 mm estimation error between the estimated and expected results. The results also show that the method is promising for the establishment of a reliable and cost-effective solid particle sizing system

Review: The Use of Real-Time Fluorescence Instrumentation to Monitor Ambient Primary Biological Aerosol Particles (PBAP)

Primary biological aerosol particles (PBAP) encompass many particle types that are derived from several biological kingdoms. These aerosol particles can be composed of both whole living units such as pollen, bacteria, and fungi, as well as from mechanically formed particles, such as plant debris. They constitute a significant proportion of the overall atmospheric particle load and have been linked with adverse health issues and climatic effects on the environment. Traditional methods for their analysis have focused on the direct capture of PBAP before subsequent laboratory analysis. These analysis types have generally relied on direct optical microscopy or incubation on agar plates, followed by time-consuming microbiological investigation. In an effort to address some of these deficits, real-time fluorescence monitors have come to prominence in the analysis of PBAP. These instruments offer significant advantages over traditional methods, including the measurement of concentrations, as well as the potential to simultaneously identify individual analyte particles in real-time. Due to the automated nature of these measurements, large data sets can be collected and analyzed with relative ease. This review seeks to highlight and discuss the extensive literature pertaining to the most commonly used commercially available real-time fluorescence monitors (WIBS, UV-APS and BioScout). It discusses the instruments operating principles, their limitations and advantages, and the various environments in which they have been deployed. The review provides a detailed examination of the ambient fluorescent aerosol particle concentration profiles that are obtained by these studies, along with the various strategies adopted by researchers to analyze the substantial data sets the instruments generate. Finally, a brief reflection is presented on the role that future instrumentation may provide in revolutionizing this area of atmospheric research. Keywords: PBAP; WIBS; UV-APS; BioScout; fluorescence; real-time; bioaerosols

Manned GEO Satellite Servicing Mission Environmental Effects Measurements Study

A trade study was conducted to evaluate options for collecting space environment data in geosynchronous earth orbit to support a future manned satellite servicing mission

Review: the use of real-time fluorescence instrumentation to monitor ambient primary biological aerosol particles (PBAP)

Primary biological aerosol particles (PBAP) encompass many particle types that are derived from several biological kingdoms. These aerosol particles can be composed of both whole living units such as pollen, bacteria, and fungi, as well as from mechanically formed particles, such as plant debris. They constitute a significant proportion of the overall atmospheric particle load and have been linked with adverse health issues and climatic effects on the environment. Traditional methods for their analysis have focused on the direct capture of PBAP before subsequent laboratory analysis. These analysis types have generally relied on direct optical microscopy or incubation on agar plates, followed by time-consuming microbiological investigation. In an effort to address some of these deficits, real-time fluorescence monitors have come to prominence in the analysis of PBAP. These instruments offer significant advantages over traditional methods, including the measurement of concentrations, as well as the potential to simultaneously identify individual analyte particles in real-time. Due to the automated nature of these measurements, large data sets can be collected and analyzed with relative ease. This review seeks to highlight and discuss the extensive literature pertaining to the most commonly used commercially available real-time fluorescence monitors (WIBS, UV-APS and BioScout). It discusses the instruments operating principles, their limitations and advantages, and the various environments in which they have been deployed. The review provides a detailed examination of the ambient fluorescent aerosol particle concentration profiles that are obtained by these studies, along with the various strategies adopted by researchers to analyze the substantial data sets the instruments generate. Finally, a brief reflection is presented on the role that future instrumentation may provide in revolutionizing this area of atmospheric research

Controlo de temperatura de um gasificador de biomassa

In recent history, the growing environmental crisis and the unsustainable overuse of fossil fuels have become a catalyst for the development of environmentally friendly or carbon neutron energy sources. Such fact lead to the reemergence of gasification in the research and development community. This technology was prominent during World War II due to the unavailability of oil existent at that time, mostly using coal as fuel. With the end of the war, so came the end of its development. Initially, the literature will be reviewed in order to assess the instrumentation technologies needed to measure the gasification process’ operational parameters, and thus, allow its monitoring and control. In order to facilitate the analysis of the data from the developed instrumentation system, a visualization tool was developed. The literature was then reviewed again in order to find the most suitable model topology for the gasification process. This revealed neural networks as the most reliable model architecture for such endeavor. A gasification model was then devised using experimental data present in the literature. The devised model was then used to establish a simulation and controller design environment. This enabled the development of Model Predictive Controller to control the temperature inside the gasifier. The devised model showed great potential as a prediction model, in spite of the deterioration presented when used as a simulator. The developed controller was able to stabilize the model generated output for all tested set-points. The develop work constitutes a solid ground for future work.O desenfreado crescimento da crise ambiental e uso insustentável de combustíveis fósseis vivido nas últimas décadas tem vindo a tornar-se num catalisador na busca de soluções carbonicamente neutras de produção de energia. Este facto levou ao ressurgimento dos processos de gasificação, principalmente de biomassa, como um tema na comunidade de pesquisa e desenvolvimento. Esta tecnologia foi predominante durante a segunda guerra mundial, período no qual a dificuldade de obtenção de petróleo levou acréscimo da sua necessidade, sendo carvão o combustível utilizado. Com o fim da guerra, veio também o fim do seu desenvolvimento. Inicialmente, será realizada uma revisão de literatura que culminará na escolha dos instrumentos de medição e atuação necessários para proceder à monitorização e controlo dos parâmetros operacionais do processo de gasificação. De modo a facilitar a analise dos dados presentes nestes sensores foi desenvolvida uma aplicação de visualização de informação. Findada esta etapa procedeu-se a uma nova revisão da literatura focada na procura de um modelo para o processo de gasificação. Esta revisão revelou as redes neuronais como sendo a melhor topologia para descrever o processo. Utilizando dados disponíveis na literatura procedeu-se à identificação do sistema em causa. O modelo desenvolvido foi utilizado para estabelecer um ambiente de simulação e desenho de controladores e assim, desenvolver um controlador preditivo baseado em modelo para controlar a temperatura dentro do gasificador. O modelo desenvolvido apresenta um grande potencial como modelo de predição, apesar da deterioração do seu desempenho quando usado como simulador. O controlador desenvolvido foi capaz de estabilizar a saída gerada pelo modelo de simulação para todos os set-points testados. O trabalho desenvolvido constitui uma base de trabalho bastante completa que deverá facilitar desenvolvimentos futuros.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Particle size distribution estimation of a powder agglomeration process using acoustic emissions

Washing powder needs to undergo quality checks before it is sold, and according to a report by the partner company, these quality checks include an offline procedure where a reference sieve analysis is used to determine the size distributions of the powder. This method is reportedly slow, and cannot be used to measure large agglomerates of powders. A solution to this problem was proposed with the implementation of real time Acoustic Emissions (AE) which would provide the sufficient information to make an assessment of the nature of the particle sizes. From the literature reviewed for this thesis, it was observed that particle sizes can be monitored online with AE but there does not appear to be a system capable of monitoring particle sizes for processes where the final powder mixture ratio varies significantly. This has been identified as a knowledge gap in existing literature and the research carried out for this thesis contributes to closing that gap. To investigate this problem, a benchtop experimental rig was designed. The rig represented limited operating conditions of the mixer but retained the critical factors. The acquired data was analysed with a designed hybrid signal processing method based on a time domain analysis of impact peaks using an amplitude threshold approach. Glass beads, polyethylene and washing powder particles were considered for the experiments, and the results showed that within the tested conditions, the designed signal processing approach was capable of estimating the PSD of various powder mixture combinations comprising particles in the range of 53-1500 microns, it was also noted that the architecture of the designed signal processing method allowed for a quicker online computation time when compared with other notable hybrid signal processing methods for particle sizing in the literature

Exploiting heterogeneous data for the estimation of particles size distribution in industrial plants

In industrial environments, it is often difficult and expensive to collect a good amount of data to adequately train expert systems for regression purposes. Therefore the usage of already available data, related to environments showing similar characteristics, could represent an effective approach to find a good balance between regression performance and the amount of data to gather for training. In this paper, the authors propose two alternative strategies for improving the regression performance by using heterogeneous data, i.e. data coming from diverse environments with respect to the one taken as reference for testing. These strategies are based on a standard machine learning algorithm, i.e. the Artificial Neural Network (ANN). The employed data came from measurements in industrial plants for energy production through the combustion of coal powder. The powder is transported in air within ducts and its size is detected by means of Acoustic Emissions (AE) produced by the impact of powder on the inner surface of the duct. The estimation of powder size distribution from AE signals is the task addressed in this work. Computer simulations show how the proposed strategies achieve a relevant improvement of regression performance with respect to the standard approach, using ANN directly on the dataset related to the reference plant

Overview of Instruments for Investigating Dust Interactions on Small Solar System Bodies by Landers and Rovers

Small Solar System bodies such as asteroids, comets and Mars\u27 moons Phobos and Deimos have relatively unknown regolith environments. It is hypothesized that dust preserved in the regolith on the surfaces will have similar mechanical properties to lunar dust because of similar formation processes from micrometeoric bombardment, low relative gravity for slow settling times, and virtually no weathering because there is no atmosphere