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Experimental measurements ol N₂ resonant charge transfer cross sections were performed. It was found that the energy of electrons used to produce the N₂⁺ ions is an important variable with respect to cross section. An examination of the experimental precision was performed and it was found that the precision of measurement was insufficient to determine the exact form of this relationship. The effect of ion energy (collisional energy) was too small to be seen. Modulated detection was used to improve precision and permit measurement at high noise levels. A description of the apparatus is provided. Consideration of other systems and the suitability of their resonant charge transfer reactions for experimental investigation is discussed. Various theoretical models for estimation of cross section were examined. / M.S.

Total charge-transfer cross sections have been obtained in the N₂⁺ - N₂ system with relative ion energies at seven different values between 9 and 441 eV. Data is obtained to examine the curvature and structural relation between total cross section versus ion energy. The effect of ion beam excitation on the cross sections was studied by varying the electron ionization energy in the mass spectrometer ion source over electron energies at eight different values, between 11.6 and 32.1 eV. The dependence of total cross section on the neutralization chamber gas pressure was examined by obtaining data at four different pressure values from 9.9 to 19.9 x 10⁻⁵ torr. Subsequent data treatment provided 56 different cross section values that are with and theoretical results of other investigators. / Ph. D.

Currently in the marketplace, one of the important goals is to improve quality, and reliability. There is great interest in the engineering community to develop a field calibration technique concerning piezoelectric pressure sensor to reduce cost and improve reliability. This paper summarizes the algorithm used to characterize and develop a model for a piezoelectric pressure transducer. The basic concept of the method is to excite the sensor using an electric force to capture the signature characteristic of the pressure transducer. This document presents the frequency curve fitted model based on the high frequency excitation of the piezoelectric pressure transducer. It also presents the time domain model of the sensor. The time domain response of the frequency curve fitted model obtained in parallel with the frequency response of the time domain model and the comparison results are discussed. Moreover, the relation between model parameters and sensitivity extensively is investigated. In order to detect damage and monitor the condition of the sensor on line the resonance frequency comparison method is presented. The relationship between sensitivity and the resonance frequency characteristic of the sensor extensively is investigated. The method of resonance monitoring greatly reduces the cost of hardware. This work concludes with a software implementation of the signature comparison of the sensor based on a study of the experimental data. The software would be implemented in the control system. / Master of Science

The work in this thesis is focused on the development and validation of an automotive battery monitoring system that estimates the health of a lead-acid battery during engine cranking and provides a low state of health (SOH) warning of potential battery failure. A reliable SOH estimation should assist users in preventing a sudden battery failure and planning for battery replacement in a timely manner. Most commercial battery health estimation systems use the impedance of a battery to estimate the SOH with battery voltage and current; however, using a current sensor increases the installation cost of a system due to parts and labor. The battery SOH estimation method with the battery terminal voltage during engine cranking was previously proposed. The proposed SOH estimation system intends to improve existing methods. The proposed method requires battery voltages and temperature for a reliable SOH estimation. Without the need for a costly current sensor, the proposed SOH monitoring system is cost-effective and useful for automotive applications. Measurement results presented in this thesis show that the proposed SOH monitoring system is more effective in evaluating the health of a lead-acid battery than existing methods. A low power microcontroller equipped prototype implements the proposed SOH algorithm on a high performance ARM Cortex-M4F based MCU, TM4C123GH6PM. The power dissipation of the final prototype is approximately 144 mW during an active state and 36 mW during a sleep state. With the reliability of the proposed method and low power dissipation of the prototype, the proposed system is suitable for an on-board battery monitoring as there is no on-board warning that estimates the health of a battery in modern cars. / Master of Science

DNA is a potential component in molecular electronics. To explore this end, there has been an incredible amount of research on how well DNA conducts and by what mechanism. There has also been a tremendous amount of research to find new uses for it in nanoscale electronics. DNA's self-assembly and recognition properties have found a unique place in this area. We predict, using a tight-binding model, that spin-dependent transport can be observed in short DNA molecules sandwiched between ferromagnetic contacts. In particular, we show that a DNA spin-valve can be realized with magnetoresistance values of as much as 26% for Ni and 16% for Fe contacts. Spin-dependent transport can broaden the possible applications of DNA as a component in molecular electronics and shed new light into the transport properties of this important biological molecule. / Master of Science

This thesis describes the development of a system to continuously monitor the battery in a car and warn the user of an upcoming battery failure. An automotive battery endures enormous strain when it starts the engine, and when it supplies loads without the engine running. Note that the current during a cranking event often exceeds 500 Amperes. Despite the strains, a car battery still typically lasts 4-6 years before requiring replacement. There is often no warning of when a battery should be replaced and there is never a good time for a battery failure. All currently available lead-acid battery monitoring systems use voltage and current sensing to monitor battery impedance and estimate battery health. However, such a system is costly due to the current sensor and typically requires an expert to operate the system. This thesis describes a prototype system to monitor battery state of health and provide advance warning of an upcoming battery failure using only voltage sensing. The prototype measures the voltage during a cranking event and determines if the battery is healthy or not. The voltage of an unhealthy battery will drop lower than a healthy one, and it will not recover as quickly. The major contributions of the proposed research to the field are an algorithm to predict automotive battery state-of-health that is temperature-dependent and a prototype implementation of the algorithm on an ARM processor development board. / Master of Science

No / The reversible in situ formation of a self-assembly building block (naphthalenediimide (NDI)–dipeptide conjugate) by enzymatic condensation of NDI-functionalized tyrosine (NDI-Y) and phenylalanine-amide (F-NH2) to form NDI-YF-NH2 is described. This coupled biocatalytic condensation/assembly approach is thermodynamically driven and gives rise to nanostructures with optimized supramolecular interactions as evidenced by substantial aggregation induced emission upon assembly. Furthermore, in the presence of di-hydroxy/alkoxy naphthalene donors, efficient charge-transfer complexes are produced. The dynamic formation of NDI-YF-NH2 and electronic and H-bonding interactions are analyzed and characterized by different methods. Microscopy (TEM and AFM) and rheology are used to characterize the formed nanostructures. Dynamic nanostructures, whose formation and function are driven by free-energy minimization, are inherently self-healing and provide opportunities for the development of aqueous adaptive nanotechnology.

The automotive industry is undertaking major technological steps in an effort to reduce emissions and fight climate change. To reduce the reliability on fossil fuels a lot of research is invested into electric motors (EM) and their applications. One such application is plug-in hybrid electric vehicles (PHEV), in which internal combustion engines (ICE) and EM are used in combination, and take turns to propel the vehicle based on driving conditions. The main optimization problem of PHEV is to decide when to use which motor. If this optimization is done with respect to emissions, the entire electric charge should be used up before the end of the trip. But if the charge is used up too early, latter driving segments for which the optimal choice would have been to use the EM will have to be done using the ICE. To address this optimization problem, we studied the fuel consumption during different driving conditions. These driving conditions are characterized by hundreds of sensors which collect data about the state of the vehicle continuously when driving. From these data, we constructed 150 seconds segments, including e.g. vehicle speed, before new descriptive features were engineered for each segment, e.g. max vehicle speed. By using the characteristics of typical driving conditions specified by the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), segments were labelled as a highway or city road segments. To reduce the dimensions without losing information, principle component analysis was conducted, and a Gaussian mixture model was used to uncover hidden structures in the data. Three machine learning regression models were trained and tested: a linear mixed model, a kernel ridge regression model with linear kernel function, and lastly a kernel ridge regression model with an RBF kernel function. By splitting the data into a training set and a test set the models were evaluated on data which they have not been trained on. The model performance and explanation rate obtained for each model, such as R2, Mean Absolute Error and Mean Squared Error, were compared to find the best model. The study shows that the fuel consumption can be modelled by the sensor data of a PHEV test fleet where 6 features contributes to an explanation ratio of 0.5, thus having highest impact on the fuel consumption. One needs to keep in mind the data were collected during the Covid-19 outbreak where travel patterns were not considered to be normal. No regression model can explain the real world better than what the underlying data does. / Fordonsindustrin vidtar stora tekniska steg för att minska utsläppen och bekämpa klimatförändringar. För att minska tillförlitligheten på fossila bränslen investeras en hel del forskning i elmotorer (EM) och deras tillämpningar. En sådan applikation är laddhybrider (PHEV), där förbränningsmotorer (ICE) och EM används i kombination, och turas om för att driva fordonet baserat på rådande körförhållanden. PHEV: s huvudoptimeringsproblem är att bestämma när man ska använda vilken motor. Om denna optimering görs med avseende på utsläpp bör hela den elektriska laddningen användas innan resan är slut. Men om laddningen används för tidigt måste senare delar av resan, för vilka det optimala valet hade varit att använda EM, göras med ICE. För att ta itu med detta optimeringsproblem, studerade vi bränsleförbrukningen under olika körförhållanden. Dessa körförhållanden kännetecknas av hundratals sensorer som samlar in data om fordonets tillstånd kontinuerligt vid körning. Från dessa data konstruerade vi 150 sekunder segment, inkluderandes exempelvis fordonshastighet, innan nya beskrivande attribut konstruerades för varje segment, exempelvis högsta fordonshastighet. Genom att använda egenskaperna för typiska körförhållanden som specificerats av Worldwide Harmonized Light Vehicles Test Cycle (WLTC), märktes segment som motorvägs- eller stadsvägsegment. För att minska dimensioner på data utan att förlora information, användes principal component analysis och en Gaussian Mixture model för att avslöja dolda strukturer i data. Tre maskininlärnings regressionsmodeller skapades och testades: en linjär blandad modell, en kernel ridge regression modell med linjär kernel funktion och slutligen en en kernel ridge regression modell med RBF kernel funktion. Genom att dela upp informationen i ett tränings set och ett test set utvärderades de tre modellerna på data som de inte har tränats på. För utvärdering och förklaringsgrad av varje modell användes, R2, Mean Absolute Error och Mean Squared Error. Studien visar att bränsleförbrukningen kan modelleras av sensordata för en PHEV-testflotta där 6 stycken attribut har en förklaringsgrad av 0.5 och därmed har störst inflytande på bränsleförbrukningen . Man måste komma ihåg att all data samlades in under Covid-19-utbrottet där resmönster inte ansågs vara normala och att ingen regressionsmodell kan förklara den verkliga världen bättre än vad underliggande data gör.