Improving the Efficiency of Organic Solar Cells by Varying the Material Concentration in the Photoactive Layer

Polymer-fullerene bulk heterojunction solar cells have been a rapidly improving technology over the past decade. To further improve the relatively low energy conversion efficiencies of these solar cells, several modifications need to be made to the overall device structure. Emerging technologies include cells that are fabricated with interfacial layers to facilitate charge transport, and tandem structures are being introduced to harness the absorption spectrum of polymers with varying bandgap energies. When new structures are implemented, each layer of the cell must be optimized in order for the entire device to function efficiently. The most volatile layer of these devices is the photoactive layer solution of poly-3(hexylthiophene-2,5-diyl) (P3HT) and [6,6]-phenyl-C61-butyric acid methyl ester (PC 61BM). Even slight variations in pre-application and post-treatment will lead to large variations in the electrical, physical, and optical properties of the solar cell module. To improve the effectiveness of the photoactive layer, the material concentration of P3HT and PC61BM in the liquid phase, prior to application, was altered. The weight ratio of P3HT to PC61BM was kept at a constant 1 to 0.8, while the amounts of each dissolved in 2 mL of chlorobenzene were varied. Solar cells were fabricated, and J-V characterizations were performed to determine the electrical traits of the devices. Atomic force microscopy (AFM) measurements were done on the photoactive layer films to determine the physical characteristics of the films such as overall surface topology and RMS roughness. Also, variable angle spectroscopic ellipsometry (VASE) was used to determine film thickness and extinction coefficient of the active layers. To further understand the optical properties of the polymer-fullerene blend, the absorption spectrum of the films were calculated through UV-VIS spectrophotometry. It was found that an increased concentration of the polymer-fullerene blend prior to application increased overall device efficiency. A photoactive layer solution prepared with 30 mg P3HT and 24 mg PC61BM, when implemented in an organic solar cell, produced the optimal electrical, physical, and optical characteristics

Start-up vibration analysis for novelty detection on industrial gas turbines

This paper focuses on industrial application of start-up vibration signature analysis for novelty detection with experimental trials on industrial gas turbines (IGTs). Firstly, a representative vibration signature is extracted from healthy start-up vibration measurements through the use of an adaptive neuro-fuzzy inference system (ANFIS). Then, the first critical speed and the vibration level at the critical speed are located from the signature. Finally, two (s- and v-) health indices are introduced to detect and identify different novel/fault conditions from the IGT start-ups, in addition to traditional similarity measures, such as Euclidean distance and cross-correlation measures. Through a case study on IGTs, it is shown that the presented approach provides a convenient and efficient tool for IGT condition monitoring using start-up field data

Multi-region System Modelling by using Genetic Programming to Extract Rule Consequent Functions in a TSK Fuzzy System

[EN] This paper aims to build a fuzzy system by means of genetic programming, which is used to extract the relevant function for each rule consequent through symbolic regression. The employed TSK fuzzy system is complemented with a variational Bayesian Gaussian mixture clustering method, which identifies the domain partition, simultaneously specifying the number of rules as well as the parameters in the fuzzy sets. The genetic programming approach is accompanied with an orthogonal least square algorithm, to extract robust rule consequent functions for the fuzzy system. The proposed model is validated with a synthetic surface, and then with real data from a gas turbine compressor map case, which is compared with an adaptive neuro-fuzzy inference system model. The results have demonstrated the efficacy of the proposed approach for modelling system with small data or bifurcating dynamics, where the analytical equations are not available, such as those in a typical industrial setting.Research supported by EPSRC Grant EVES (EP/R029741/1).Zhang, Y.; Martínez-García, M.; Serrano, J.; Latimer, A. (2019). Multi-region System Modelling by using Genetic Programming to Extract Rule Consequent Functions in a TSK Fuzzy System. IEEE. 987-992. https://doi.org/10.1109/ICARM.2019.8834163S98799

Development of a steady-state thermodynamic model in Microsoft Excel for performance analysis of industrial gas turbines

In this paper, an off-design performance prediction model for a single shaft industrial gas turbine (IGT) using Microsoft Excel with Visual Basic for Applications (VBA) programming is presented. The modelling architecture is comprised of fundamental thermodynamic equations describing the performance of IGTs. A graphical user interface has been constructed to allow an easy interaction of the model to predict IGT performance at different operating conditions. Component characteristic maps for the compressor and turbine with a bilinear interpolation method have been implemented in the Excel modelling architecture. A commercial thermodynamic toolbox (Thermolib, EUtech Scientific Engineering GmbH) which is compatible with Simulink environment has been considered to validate Excel model of the IGT system. This Excel modelling architecture could be a valuable reference tool for engineers and students to understand IGT performance at different ambient and operating conditions

A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines

The aim of this paper is to introduce the bases of an intelligent fault diagnostic platform, which assists in detecting mechanical failures of Industrial Gas Turbines (IGTs). This comprises an integration of an expert system and its complementary signal processing techniques. The essential characteristic here is not to exclude humans (experts) from the diagnostic process, but rather to transfer their knowledge and experience to a computerized platform. The automated process executed by the computerized platform is to ensure the scalability and consistency in fault diagnosis; while the humans are required to corroborate the transparency and liability of the outcomes. In this paper, a Knowledge Transfer Platform (KTP) is proposed for fault diagnosis of industrial systems. It is then designed and tested for combustion fault diagnosis using field data of IGTs. The preliminary results have revealed the feasibility and efficacy of the proposed scheme, which has the potential to be further extended to a large industrial scale and to different engineering diagnostic applications

Estimating gas turbine compressor discharge temperature using Bayesian neuro-fuzzy modelling

The objective of this paper is to estimate the compressor discharge temperature measurements on an industrial gas turbine that is undergoing commissioning at site, using a data-driven model which is built using the test bed measurements of the engine. This paper proposes a Bayesian neuro-fuzzy modelling (BNFM) approach, which combines the adaptive neuro-fuzzy inference system (ANFIS) and variational Bayesian Gaussian mixture model (VBGMM) techniques. A data-driven compressor model is built using ANFIS, and VBGMM is applied in the set-up stage to automatically select the number of input membership functions in the fuzzy system. The efficacy of the proposed BFNM approach is established through experimental trials of a sub-15MW gas turbine, and the results, from the model that is built using test bed data, are shown to be promising for estimating the compressor discharge temperatures on the gas turbine during commissioning

Racial Discrepancies in Pulse Oximetry Reading and Their Effects on Self-monitoring Devices Usage and Clinical Decision-Making

INTRODUCTION: As technology use rises and the use of pulse oximetry data increases, the demand for accurate oxygen saturation (SpO2) readings is paramount to ensure health equity among all populations. Pulse oximetry is a non-invasive tool used to monitor SpO2. Self-monitoring devices, such as SMART devices, allow for portable and cost-effective utilization; therefore, self-monitoring device usage and pulse oximetry data are quickly becoming more available to patients and their providers. Pulse oximetry is a critical component used when evaluating the severity of arterial deoxygenation. Providers often use data from pulse oximetry to determine treatment options. Recent studies have found discrepancies in pulse oximeter reading among Black patients, posing a problem for both patients and their providers. We hypothesize that self-monitoring devices can affect mortality rates among Black patients if these disparities are not addressed. OBJECTIVES: The aim of this study is to investigate how racial discrepancies in pulse oximetry reading among self-monitoring devices can affect mortality rates among Black patients in the United States. METHODS: The design of this study is a systematic review and data extraction of relevant articles that discuss the use of self-monitoring devices to determine oxygen saturation and relevant racial disparities associated with health outcomes. RESULTS: Searches identified 123 citations with relative pulse oximetry data in relation to race. Some of the data extraction provided significant evidence that there are disparities present among reading provided by self-monitoring, pulse oximetry devices and Black patients. CONCLUSION This is a research proposal that is still ongoing. Current independent reviews of individual articles are still being analyzed

Improving maternal mortality reporting at the community level with a 4‐question modified reproductive age mortality survey (RAMOS)

ObjectiveTo investigate the identification of maternal deaths at the community level using the reproductive age mortality survey (RAMOS) in all households in which a women of reproductive age (WRA) died and to determine the most concise subset of questions for identifying a pregnancy‐related death for further investigation.MethodsA full RAMOS survey was conducted with the families of 46 deceased WRA who died between 2005 and July 2009 and was compared with the cause of death confirmed by the maternal mortality review committee to establish the number of maternal mortalities. The positive predictive value (PPV) of each RAMOS question for identifying a maternal death was determined.ResultsCompared with years of voluntary reporting, active surveillance for maternal deaths doubled their identification. In addition, 4 questions from the full RAMOS have the highest PPV for a maternal death including the question: “Was she pregnant within the last 6 weeks?” which had a 100% PPV and a 100% negative predictive value.ConclusionActive identification of maternal mortality at the community level by using a 4‐question modified RAMOS that is systematically administered in the local language by health workers can increase understanding of the extent of maternal mortality in rural Ghana.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135380/1/ijgo29.pd

The Rhodococcus equi virulence protein VapA disrupts endolysosome function and stimulates lysosome biogenesis

Rhodococcus equi (R. equi) is an important pulmonary pathogen in foals that often leads to the death of the horse. The bacterium harbors a virulence plasmid that encodes numerous virulence-associated proteins (Vaps) including VapA that is essential for intracellular survival inside macrophages. However, little is known about the precise function of VapA. Here, we demonstrate that VapA causes perturbation to late endocytic organelles with swollen endolysosome organelles having reduced Cathepsin B activity and an accumulation of LBPA, LC3 and Rab7. The data are indicative of a loss of endolysosomal function, which leads cells to upregulate lysosome biogenesis to compensate for the loss of functional endolysosomes. Although there is a high degree of homology of the core region of VapA to other Vap proteins, only the highly conserved core region of VapA, and not VapD of VapG, gives the observed effects on endolysosomes. This is the first demonstration of how VapA works and implies that VapA aids R. equi survival by reducing the impact of lysosomes on phagocytosed bacteria

Performance analysis of a twin shaft Industrial Gas Turbine at fouling conditions

In this study, the performance of a twin-shaft Industrial Gas Turbine (IGT) at fouling conditions is simulated through a Simulink model based on fundamental thermodynamics. Engine measurements across a twin-shaft IGT system during compressor fouling conditions were considered to validate this study. By implementing correlation coefficients in the compressor model, it is possible to predict the performance of the IGT system during compressor fouling conditions. The change of compressor air flow and the compressor efficiency in the twin-shaft IGT during fouling conditions is estimated. The results show that the reduction of air flow rate is the dominating parameter in the decrease of power generation in an IGT under fouled conditions. The model can provide an insight into the effect of compressor fouling conditions on system IGT performance