The Application of Computational Chemistry and Chemometrics to Developing a Method for Online Monitoring of Polymer Degradation in the Manufacture of Bioresorbable Medical Implants

Bioresorbable polymers such as PLA have an important role to play in the development of temporary implantable medical devices with significant benefits over traditional therapies. However, development of new devices is hindered by high manufacturing costs associated with difficulties in processing the material. A major problem is the lack of insight on material degradation during processing. In this work, a method of quantifying degradation of PLA using IR spectroscopy coupled with computational chemistry and chemometric modeling is examined. It is shown that the method can predict the quantity of degradation products in solid-state samples with reasonably good accuracy, indicating the potential to adapt the method to developing an on-line sensor for monitoring PLA degradation in real-time during processing.

The application of Raman and anti-stokes Raman spectroscopy for in situ monitoring of structural changes in laser irradiated titanium dioxide materials

The use of Raman and anti-stokes Raman spectroscopy to investigate the effect of exposure to high power laser radiation on the crystalline phases of TiO₂ has been investigated. Measurement of the changes, over several time integrals, in the Raman and anti-stokes Raman of TiO₂ spectra with exposure to laser radiation is reported. Raman and anti-stokes Raman provide detail on both the structure and the kinetic process of changes in crystalline phases in the titania material. The effect of laser exposure resulted in the generation of increasing amounts of the rutile crystalline phase from the anatase crystalline phase during exposure. The Raman spectra displayed bands at 144 cm^-1 (A1g), 197 cm^-1 (Eg), 398 cm^-1 (B1g), 515 cm^-1 (A1g), and 640 cm^-1 (Eg) assigned to anatase which were replaced by bands at 143 cm^-1 (B1g), 235 cm^-1 (2 phonon process), 448 cm^-1 (Eg) and 612 cm^-1 (A1g) which were assigned to rutile. This indicated that laser irradiation of TiO₂ changes the crystalline phase from anatase to rutile. Raman and anti-stokes Raman are highly sensitive to the crystalline forms of TiO₂ and allow characterisation of the effect of laser irradiation upon TiO₂. This technique would also be applicable as an in situ method for monitoring changes during the laser irradiation process

Application of a cw quantum cascade laser CO2 analyser to catalytic oxidation reaction monitoring

Catalytic oxidation reaction monitoring has been performed for the first time with a trace gas carbon dioxide analyser based on a continuous wave (cw), thermoelectrically cooled (TEC), distributed feedback (DFB) quantum cascade laser (QCL) operating at around 2307 cm^-1. The reaction kinetics for carbon monoxide oxidation over a platinum catalyst supported on yttria-stabilised zirconia were followed by the QCL CO₂ analyser and showed that it is a powerful new tool for measuring low reaction rates associated with low surface area model catalysts operating at atmospheric pressures. A detection limit was determined of 40 ppb (1 standard deviation) for a 0.1 s average and a residual absorption standard deviation of 1.9×10^-4. © 2012 Springer-Verlag.

Monitoring growth in asthmatic children treated with high dose inhaled glucocorticoids does not predict adrenal suppression

Aims: To determine whether routine outpatient monitoring of growth predicts adrenal suppression in prepubertal children treated with high dose inhaled glucocorticoid.

Methods: Observational study of 35 prepubertal children (aged 4–10 years) treated with at least 1000 µg/day of inhaled budesonide or equivalent potency glucocorticoid for at least six months. Main outcome measures were: changes in HtSDS over 6 and 12 month periods preceding adrenal function testing, and increment and peak cortisol after stimulation by low dose tetracosactrin test. Adrenal suppression was defined as a peak cortisol 500 nmol/l.

Results: The areas under the receiver operator characteristic curves for a decrease in HtSDS as a predictor of adrenal insufficiency 6 and 12 months prior to adrenal testing were 0.50 (SE 0.10) and 0.59 (SE 0.10). Prediction values of an HtSDS change of –0.5 for adrenal insufficiency at 12 months prior to testing were: sensitivity 13%, specificity 95%, and positive likelihood ratio of 2.4. Peak cortisol reached correlated poorly with change in HtSDS ( = 0.23, p = 0.19 at 6 months; = 0.33, p = 0.06 at 12 months).

Conclusions: Monitoring growth does not enable prediction of which children treated with high dose inhaled glucocorticoids are at risk of potentially serious adrenal suppression. Both growth and adrenal function should be monitored in patients on high dose inhaled glucocorticoids. Further research is required to determine the optimal frequency of monitoring adrenal function.

Improved Principal Component Monitoring of Large-Scale Processes

This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.

Statistical Monitoring of Dynamic Multivariate Processes: Part I: Modeling Auto- and Cross-Correlation

The work in this paper is of particular significance since it considers the problem of modelling cross- and auto-correlation in statistical process monitoring. The presence of both types of correlation can lead to fault insensitivity or false alarms, although in published literature to date, only autocorrelation has been broadly considered. The proposed method, which uses a Kalman innovation model, effectively removes both correlations. The paper (and Part 2 [2]) has emerged from work supported by EPSRC grant GR/S84354/01 and is of direct relevance to problems in several application areas including chemical, electrical, and mechanical process monitoring.

Improved Process Monitoring Using Nonlinear Principal Component Models

This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.

The design of a navigation guidance and control system for an unmanned surface vehicle for environmental monitoring

Maintaining the ecosystem is one of the main concerns in this modern age. With the fear of ever-increasing global warming, the UK is one of the key players to participate actively in taking measures to slow down at least its phenomenal rate. As an ingredient to this process, the Springer vehicle was designed and developed for environmental monitoring and pollutant tracking. This special issue paper highlighted the Springer hardware and software architecture including various navigational sensors, a speed controller, and an environmental monitoring unit. In addition, details regarding the modelling of the vessel were outlined based mainly on experimental data. The formulation of a fault tolerant multi-sensor data fusion technique was also presented. Moreover, control strategy based on a linear quadratic Gaussian controller was developed and simulated on the Springer model.
Gaussian controller is developed and simulated on the Springer model.

Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.

Development and Longer Term In Situ Evaluation of Fiber-Optic Sensors for Monitoring of Structural Concrete

In this paper, new solutions to the problem of making measurements, of carbonation and chloride ingress, in particular, in concrete structures are considered. The approach has focused on the design, development, and use of fiber-optic sensors (FOSs), recognizing the need in that conventional devices are often either inaccurate, expensive, or unsuitable for encapsulation in the material. The sensors have been designed to monitor, in situ and nondestructively, relevant physical, and chemical changes in cementitious materials. Three different types of FOS were constructed, tested, and evaluated specifically for this application, these being a temperature sensor (based on the fluorescence decay) and pH and chloride sensors, based on sol-gel (solidified gel) technology with appropriate impregnated indicators. The sensors were all designed to be inserted into the structures and evaluated under the harshest conditions, i.e., being mounted when the mortar is poured and thus tested in situ, with the temperature and pH sensors successfully embedded in mortar. The outcomes of these tests have shown that both the temperature sensor and the pH sensor were able to function correctly for the duration of the work - for over 18 months after placement. The laboratory tests on the chloride sensor showed it was able to make measurements but was not reversible, limiting its potential utility for in situ environments. Research is ongoing to refine the sensor performance and extend the testing.

Monitoring microbial sulfate reduction in porous media using multi-purpose electrodes

There is growing interest in the application of electrode-based measurements for monitoring microbial processes in the Earth using biogeophysical methods. In this study, reactive electrode measurements were combined to electrical geophysical measurements during microbial sulfate reduction occurring in a column of silica beads saturated with natural river water. Electrodic potential (EP), self potential (SP) and complex conductivity signals were recorded using a dual electrode design (Ag/AgCl metal as sensing/EP electrode, Ag/AgCl metal in KCl gel as reference/SP electrode). Open-circuit potentials, representing the tendency for electrochemical reactions to occur on the electrode surfaces, were recorded between sensing/EP electrode and reference/SP electrode and showed significant spatiotemporal variability associated with microbial activity. The dual electrode design isolates the microbial driven sulfide reactions to the sensing electrode and permits removal of any SP signal from the EP measurement. Based on the known sensitivity of a Ag electrode to dissolved sulfide, we interpret EP signals exceeding 550 mV recorded in this experiment in terms of bisulfide (HS-) concentration near multiple sensing electrodes. Complex conductivity measurements capture an imaginary conductivity (s?) signal interpreted as the response of microbial growth and biomass formation in the column. Our results suggest that the implementation of multipurpose electrodes, combining reactive measurements with electrical geophysical measurements, could improve efforts to monitor microbial processes in the Earth using electrodes.