Stable, conductive, and cost-effective semi-transparent electrodes for organic photovoltaics and beyond

Transparent conductive electrodes (TCEs) are a special class of material, as they are both transparent and conductive. In reality, however, these two important characteristics typically trade-off with one another and thus must be balanced. In terms of the material system, the selection of the TCE with the best performance is essential, which is evaluated by the so-called “figure of merit” (FOM). Hence, researchers have suggested various FOMs to rate the TCEs over the past 50 years. However, a more straightforwardly formulated FOM may help to assess a TCE's potential specifically for photovoltaic applications. Since the requirements for the latter are distinctly different compared to other optoelectronic devices, this thesis is devoted to bridging this gap. A novel and exact FOM is proposed that exclusively quantifies the impact of the TCE on photovoltaic performance. This exact FOM fulfills the aspired requirement of being a normalized, dimensionless, and proportional factor for the potential photovoltaic output power with respect to the Shockley-Queisser limit. Using this FOM, a set of current state-of-the-art semi-transparent electrodes is comprehensively assessed, where the spectral range in which photovoltaic materials operate is an important factor. Based on a comment from a colleague at the National Renewable Energy Laboratory, the exact FOM is extended into formulations that allow the assessment of different solar module geometries, including the addition of serial connections and additional metal grids. The thesis also includes a thorough study of the impact of poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) processing conditions on its work function. Various formulations are tested with regard to relative humidity levels, annealing temperatures, and solar cell performance. Finally, literature assessments and strategies for achieving highly conductive PEDOT:PSS TCEs are conducted at the end of the thesis

Studying Interactions of Gas Molecules with Nanomaterials Loaded in a Microwave Resonant Cavity

A resonant cavity operating in TE011 mode was used to study the adsorption response of single walled carbon nanotubes (SWCNTs) and other nanomaterials for different types of gas molecules. The range of the frequency signal as a probe was chosen as geometry dependent range between 9.1 -9.8 GHz. A highly specific range can be studied for further experiments dependent on the type of molecule being investigated. It was found that for different pressures of gases and for different types of nanomaterials, there was a different response in the shifts of the probe signal for each cycle of gassing and degassing of the cavity. This dissertation suggests that microwave spectroscopy of a complex medium of gases and carbon nanotubes can be used as a highly sensitive technique to determine the complex dielectric response of different polar as well as non-polar gases when subjected to intense electromagnetic fields within the cavity. Also, as part of the experimental work, a range of other micro-porous materials was tested using the residual gas analysis (RGA) technique to determine their intrinsic absorption/adsorption characteristics when under an ultra-high vacuum environment. The scientific results obtained from this investigation, led to the development of a chemical biological sensor prototype. The method proposed is to develop operational sensors to detect toxin gases for homeland security applications and also develop sniffers to detect toxin drugs for law enforcement agency personnel

Modeling of Soft Materials: Integrating Bond Graphs with Finite Element Analysis

Abstract In this paper, modeling of system dynamics during the soft contact interaction between a rigid body and a soft material is carried out using the Bond Graph technique and the Finite Element method. The Finite Element method is employed to discretize the elastic continuum and obtain material characteristics of inertia, stiffness and damping. An effort has been made to integrate the Bond Graph formulation for dynamic modeling and analysis with the advantages of the Finite Element method. The force-deformation relationship is represented using Cfields in Bond graphs, and its parameters are obtained from the Finite Element formulation. The model has been tested through simulation. Simulation code has been developed using MATLAB directly from the Bond graph

Response to Christopher P. Muzzillo's Comments on “Introduction of a Novel Figure of Merit for the Assessment of Transparent Conductive Electrodes in Photovoltaics: Exact and Approximate Form”

Similarities and differences between figure of merits (FOMs) for the assessment of transparent conductive electrodes (TCEs) are discussed. This article is a response to C. P. Muzzillo's comment on the introduction of the novel FOM (the so‐called exact FOM or Anand's FOM) and it deals with questions about how implicit and how exact the different approaches really are and whether specific application cases can be covered or not. While the exact FOM has been introduced to provide an upper limit of photovoltaic power conversion efficiency for the whole range of possible transmittance and sheet resistance values of transparent conductive oxides, Muzzillo's comment points out specific application cases, that have to be treated with more individual modeling. In this work, the authors adopt these application cases into the exact FOM to demonstrate its applicability. Furthermore, the FOM approximation given by Muzzillo is used and slightly refined, yielding an even better agreement with the exact FOM. In the end, it is concluded that both approaches are justified: Muzzillo's FOM for very practical applications and Anand's (exact) FOM for fundamental assessment. In this work, both approaches have been harmonized to yield an ultimate tool for the future development of TCEs for photovoltaics

Numerical Analysis of Friction Stir Welding on an Alumunium Butt Joint

In this paper, we present a three-dimensional numerical analysis of friction stir welding on an alumunium butt joint. A thin sheet of aluminum marking material was embedded into the 6061-aluminum alloy panel and its rear weld path. The positions after friction stir welding were investigated by metallographic techniques. Looking at the visualized material flow pattern, a three-dimensional model was developed to numerically simulate the temperature profile and plastic effects. The calculated velocity profile for plastic flow in the immediate vicinity of the tool generally agrees with the visualized results. Increasing the tool speed while maintaining a constant tool feed rate increases the material flow near the pin. The shape and size of the predicted weld zone match the experimentally measured ones

Detecting Crop Health using Machine Learning Techniques in Smart Agriculture System

699-706The crop diseases can’t detected accurately by only analysing separate disease basis. Only with the help of making comprehensive analysis framework, users can get the predictions of most expected diseases. In this research, IOT and machine learning based technique capable of processing acquisition, analysis and detection of crop health information in the same platform is introduced. The proposed system supports distinguished services by monitoring crop and also managed its data, devices and models. This system also supports data sharing and communication with the help of IOT using unmanned aerial vehicle (UAV) and maintains high communication standards even in bad communication environment. Therefore, IOT and machine learning ensures the high accuracy of disease prediction in crop. The proposed integrated system is capable of detecting health of crop through analysis of multi-spectral images captured through the IOT associated UAV. The various machine learning is also applied to test the performance of our system and compared with the existing disease detection methods

An Optimized Approach for Feature Extraction in Multi-Relational Statistical Learning

537-542Various features come from relational data often used to enhance the prediction of statistical models. The features increases as the feature space increases. We proposed a framework, which generates the features for feature selection using support vector machine with (1) augmentation of relational concepts using classification-type approach (2) various strategy to generate features. Classification are used to increase the productivity of feature space by adding new techniques used to create new features and lead to enhance the accuracy of the model. The feature generation in run-time lead to the building of models with higher accuracy despite generating features in advance. Our results in different applications of data mining in different relations are far better from existing results

Detecting Crop Health using Machine Learning Techniques in Smart Agriculture System

The crop diseases can’t detected accurately by only analysing separate disease basis. Only with the help of making comprehensive analysis framework, users can get the predictions of most expected diseases. In this research, IOT and machine learning based technique capable of processing acquisition, analysis and detection of crop health information in the same platform is introduced. The proposed system supports distinguished services by monitoring crop and also managed its data, devices and models. This system also supports data sharing and communication with the help of IOT using unmanned aerial vehicle (UAV) and maintains high communication standards even in bad communication environment. Therefore, IOT and machine learning ensures the high accuracy of disease prediction in crop. The proposed integrated system is capable of detecting health of crop through analysis of multi-spectral images captured through the IOT associated UAV. The various machine learning is also applied to test the performance of our system and compared with the existing disease detection methods

An Optimized Approach for Feature Extraction in Multi-Relational Statistical Learning

Various features come from relational data often used to enhance the prediction of statistical models. The features increases as the feature space increases. We proposed a framework, which generates the features for feature selection using support vector machine with (1) augmentation of relational concepts using classification-type approach (2) various strategy to generate features. Classification are used to increase the productivity of feature space by adding new techniques used to create new features and lead to enhance the accuracy of the model. The feature generation in run-time lead to the building of models with higher accuracy despite generating features in advance. Our results in different applications of data mining in different relations are far better from existing results

Performance and stability of organic solar cells bearing nitrogen containing electron extraction layers

Charge extraction and transport layers represent an important component of organic solar cells. Many different material groups are reported for these layers. Two important classes are metal oxides and organic materials. Many of these organic materials which are used as electron extraction layers (EELs) are nitrogen containing. Therefore, it has been decided to study a broad array of—to the largest part so far not reported—amine and imine containing organic materials as EELs in organic solar cells and compare them with an archetypical metal oxide electron transport layer (ETL). It enables certain structure–property relationships to be obtained for the EELs and to understand what determines their performance to a large part. Furthermore, their effect on the stability of organic solar cells is studied and they are found to be reasonable replacements as a cheap, quickly processable, environmentally friendly, biocompatible, and biodegradable alternative as compared with ETLs