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Description
Titanium dioxide (TiO2) nanomaterial use is becoming more prevalent as is the likelihood of human exposure and environmental release. The goal of this thesis is to develop analytical techniques to quantify the level of TiO2 in complex matrices to support environmental, health, and safety research of TiO2 nanomaterials. A pharmacokinetic model showed that the inhalation of TiO2 nanomaterials caused the highest amount to be absorbed and distributed throughout the body. Smaller nanomaterials (< 5nm) accumulated in the kidneys before clearance. Nanoparticles of 25 nm diameter accumulated in the liver and spleen and were cleared from the body slower than smaller nanomaterials. A digestion method using nitric acid, hydrofluoric acid, and hydrogen peroxide was found to digest organic materials and TiO2 with a recovery of >80%. The samples were measured by inductively coupled plasma-mass spectrometry (ICP-MS) and the method detection limit was 600 ng of Ti. An intratracheal instillation study of TiO2 nanomaterials in rats found anatase TiO2 nanoparticles in the caudal lung lobe of rats 1 day post instillation at a concentration of 1.2 ug/mg dry tissue, the highest deposition rate of any TiO2 nanomaterial. For all TiO2 nanomaterial morphologies the concentrations in the caudal lobes were significantly higher than those in the cranial lobes. In a study of TiO2 concentration in food products, white colored foods or foods with a hard outer shell had higher concentrations of TiO2. Hostess Powdered Donettes were found to have the highest Ti mass per serving with 200 mg Ti. As much as 3.8% of the total TiO2 mass was able to pass through a 0.45 um indicating that some of the TiO2 is likely nanosized. In a study of TiO2 concentrations in personal care products and paints, the concentration of TiO2 was as high as 117 ug/mg in Benjamin Moore white paint and 70 ug/mg in a Neutrogena sunscreen. Greater than 6% of Ti in one sunscreen was able to pass through a 0.45 um filter. The nanosized TiO2 in food products and personal care products may release as much as 16 mg of nanosized TiO2 per individual per day to wastewater.
ContributorsWeir, Alex Alan (Author) / Westerhoff, Paul K (Thesis advisor) / Hristovski, Kiril (Committee member) / Herckes, Pierre (Committee member) / Arizona State University (Publisher)
Created2011
Description
Nitrate is the most prevalent water pollutant limiting the use of groundwater as a potable water source. The overarching goal of this dissertation was to leverage advances in nanotechnology to improve nitrate photocatalysis and transition treatment to the full-scale. The research objectives were to (1) examine commercial and synthesized photocatalysts, (2) determine the effect of water quality parameters (e.g., pH), (3) conduct responsible engineering by ensuring detection methods were in place for novel materials, and (4) develop a conceptual framework for designing nitrate-specific photocatalysts. The key issues for implementing photocatalysis for nitrate drinking water treatment were efficient nitrate removal at neutral pH and by-product selectivity toward nitrogen gases, rather than by-products that pose a human health concern (e.g., nitrite). Photocatalytic nitrate reduction was found to follow a series of proton-coupled electron transfers. The nitrate reduction rate was limited by the electron-hole recombination rate, and the addition of an electron donor (e.g., formate) was necessary to reduce the recombination rate and achieve efficient nitrate removal. Nano-sized photocatalysts with high surface areas mitigated the negative effects of competing aqueous anions. The key water quality parameter impacting by-product selectivity was pH. For pH < 4, the by-product selectivity was mostly N-gas with some NH4+, but this shifted to NO2- above pH = 4, which suggests the need for proton localization to move beyond NO2-. Co-catalysts that form a Schottky barrier, allowing for localization of electrons, were best for nitrate reduction. Silver was optimal in heterogeneous systems because of its ability to improve nitrate reduction activity and N-gas by-product selectivity, and graphene was optimal in two-electrode systems because of its ability to shuttle electrons to the working electrode. "Environmentally responsible use of nanomaterials" is to ensure that detection methods are in place for the nanomaterials tested. While methods exist for the metals and metal oxides examined, there are currently none for carbon nanotubes (CNTs) and graphene. Acknowledging that risk assessment encompasses dose-response and exposure, new analytical methods were developed for extracting and detecting CNTs and graphene in complex organic environmental (e.g., urban air) and biological matrices (e.g. rat lungs).
ContributorsDoudrick, Kyle (Author) / Westerhoff, Paul (Thesis advisor) / Halden, Rolf (Committee member) / Hristovski, Kiril (Committee member) / Arizona State University (Publisher)
Created2013
Description
As engineered nanomaterials (NMs) become used in industry and commerce their loading to sewage will increase. However, the fate of widely used NMs in wastewater treatment plants (WWTPs) remains poorly understood. In this research, sequencing batch reactors (SBRs) were operated with hydraulic (HRT) and sludge (SRT) retention times representative of full-scale biological WWTPs for several weeks. NM loadings at the higher range of expected environmental concentrations were selected. To achieve the pseudo-equilibrium state concentration of NMs in biomass, SBR experiments needed to operate for more than three times the SRT value, approximately 18 days. Under the conditions tested, NMs had negligible effects on ability of the wastewater bacteria to biodegrade organic material, as measured by chemical oxygen demand (COD). NM mass balance closure was achieved by measuring NMs in liquid effluent and waste biosolids. All NMs were well removed at the typical biomass concentration (1~2 gSS/L). However, carboxy-terminated polymer coated silver nanoparticles (fn-Ag) were removed less effectively (88% removal) than hydroxylated fullerenes (fullerols; >90% removal), nano TiO2 (>95% removal) or aqueous fullerenes (nC60; >95% removal). Although most NMs did not settle out of the feed solution without bacteria present, approximately 65% of the titanium dioxide was removed even in the absence of biomass simply due to self-aggregation and settling. Experiments conducted over 4 months with daily loadings of nC60 showed that nC60 removal from solution depends on the biomass concentration. Under conditions representative of most suspended growth biological WWTPs (e.g., activated sludge), most of the NMs will accumulate in biosolids rather than in liquid effluent discharged to surface waters. Significant fractions of fn-Ag were associated with colloidal material which suggests that efficient particle separation processes (sedimentation or filtration) could further improve removal of NM from effluent. As most NMs appear to accumulate in biosolids, future research should examine the fate of NMs during disposal of WWTP biosolids, which may occur through composting or anaerobic digestion and/or land application, incineration, or landfill disposal.
ContributorsWang, Yifei (Author) / Westerhoff, Paul (Thesis advisor) / Krajmalnik-Brown, Rosa (Committee member) / Rittmann, Bruce (Committee member) / Hristovski, Kiril (Committee member) / Arizona State University (Publisher)
Created2012
Description
Large number of renewable energy based distributed energy resources(DERs) are integrated into the conventional power grid using power electronic interfaces. This causes increased need for efficient power conversion, advanced control, and DER situational awareness. In case of photovoltaic(PV) grid integration, power is processed in two stages, namely DC-DC and DC-AC. In this work, two novel soft-switching schemes for quadratic boost DC-DC converters are proposed for PV microinverter application. Both the schemes allow the converter to operate at higher switching frequency, reducing the converter size while still maintaining high power conversion efficiency. Further, to analyze the impact of high penetration DERs on the power system a real-time simulation platform has been developed in this work. A real, large distribution feeder with more than 8000 buses is considered for investigation. The practical challenges in the implementation of a real-time simulation (such as number of buses, simulation time step, and computational burden) and the corresponding solutions are discussed. The feeder under study has a large number of DERs leading to more than 200% instantaneous PV penetration. Opal-RT ePHASORSIM model of the distribution feeder and different types of DER models are discussed in detailed in this work. A novel DER-Edge-Cloud based three-level architecture is proposed for achieving solar situational awareness for the system operators and for real-time control of DERs. This is accomplished using a network of customized edge-intelligent-devices(EIDs) and end-to-end solar energy optimization platform(eSEOP). The proposed architecture attains superior data resolution, data transfer rate and low latency for the end-to-end communication. An advanced PV string inverter with control and communication capabilities exceeding those of state-of-the-art, commercial inverters has been developed to demonstrate the proposed real-time control. A power-hardware-in-loop(PHIL) and EID-in-loop(EIL) testbeds are developed to verify the impact of large number of controllable DERs on the distribution system under different operational modes such as volt-VAr, constant reactive power and constant power factor. Edge level data analytics and intelligent controls such as autonomous reactive power allocation strategy are implemented using EIL testbed for real-time monitoring and control. Finally, virtual oscillator control(VOC) for grid forming inverters and its operation under different X/R conditions are explored.
ContributorsKorada, Nikhil (Author) / Ayyanar, Raja (Thesis advisor) / Lei, Qin (Committee member) / Wu, Meng (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2022
Description
Operational efficiency of solar energy farms requires detailed analytics and information on each panel regarding voltage, current, temperature, and irradiance. Monitoring utility-scale solar arrays was shown to minimize the cost of maintenance and help optimize the performance of photovoltaic (PV) arrays under various conditions. This dissertation describes a project that focuses on the development of machine learning and neural network algorithms. It also describes an 18kW solar array testbed for the purpose of PV monitoring and control. The use of the 18kW Sensor Signal and Information Processing (SenSIP) PV testbed which consists of 104 modules fitted with smart monitoring devices (SMDs) is described in detail. Each of the SMDs has embedded, a wireless transceiver, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. Data is obtained in real time using the SenSIP PV testbed. Machine learning and neural network algorithms for PV fault classification is are studied in depth. More specifically, the development of a series of customized neural networks for detection and classification of solar array faults that include soiling, shading, degradation, short circuits and standard test conditions is considered. The evaluation of fault detection and classification methods using metrics such as accuracy, confusion matrices, and the Risk Priority Number (RPN) is performed. The examination and assessment the classification performance of customized neural networks with dropout regularizers is presented in detail. The development and evaluation of neural network pruning strategies and illustration of the trade-off between fault classification model accuracy and algorithm complexity is studied. This study includes data from the National Renewable Energy Laboratory (NREL) database and also real-time data collected from the SenSIP testbed at MTW under various loading and shading conditions. The overall approach for detection and classification promises to elevate the performance and robustness of PV arrays.
ContributorsRao, Sunil (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2021
Description
The volume of end-of-life photovoltaic (PV) modules is increasing as the global PV market increases, and the global PV waste streams are expected to reach 250,000 metric tons by the end of 2020. If the recycling processes are not in place, there would be 60 million tons of end-of-life PV modules lying in the landfills by 2050, that may not become a not-so-sustainable way of sourcing energy since all PV modules could contain certain amount of toxic substances. Currently in the United States, PV modules are categorized as general waste and can be disposed in landfills. However, potential leaching of toxic chemicals and materials, if any, from broken end-of-life modules may pose health or environmental risks. There is no standard procedure to remove samples from PV modules for chemical toxicity testing in the Toxicity Characteristic Leaching Procedure (TCLP) laboratories as per EPA 1311 standard. The main objective of this thesis is to develop an unbiased sampling approach for the TCLP testing of PV modules. The TCLP testing was concentrated only for the laminate part of the modules, as they are already existing recycling technologies for the frame and junction box components of PV modules. Four different sample removal methods have been applied to the laminates of five different module manufacturers: coring approach, cell-cut approach, strip-cut approach, and hybrid approach. These removed samples were sent to two different TCLP laboratories, and TCLP results were tested for repeatability within a lab and reproducibility between the labs. The pros and cons of each sample removal method have been explored and the influence of sample removal methods on the variability of TCLP results has been discussed. To reduce the variability of TCLP results to an acceptable level, additional improvements in the coring approach, the best of the four tested options, are still needed.
ContributorsLeslie, Joswin (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Kuitche, Joseph (Committee member) / Arizona State University (Publisher)
Created2018
Description
Flame retardants (FRs) are applied to variety of consumer products such as textiles and polymers for fire prevention and fire safety. Substantial research is ongoing to replace traditional FRs with alternative materials that are less toxic, present higher flame retardancy and result in lower overall exposure as there are potential health concerns in case of exposure to popular FRs. Carbonaceous nanomaterials (CNMs) such as carbon nanotubes (CNTs) and graphene oxide (GO) have been studied and applied to polymer composites and electronics extensively due to their remarkable properties. Hence CNMs are considered as potential alternative materials that present high flame retardancy. In this research, different kinds of CNMs coatings on polyester fabric are produced and evaluated for their use as flame retardants. To monitor the mass loading of CNMs coated on the fabric, a two-step analytical method for quantifying CNMs embedded in polymer composites was developed. This method consisted of polymer dissolution process using organic solvents followed by subsequent programmed thermal analysis (PTA). This quantification technique was applicable to CNTs with and without high metal impurities in a broad range of polymers. Various types of CNMs were coated on polyester fabric and the efficacy of coatings as flame retardant was evaluated. The oxygen content of CNMs emerged as a critical parameter impacting flame retardancy with higher oxygen content resulting in less FR efficacy. The most performant nanomaterials, multi-walled carbon nanotubes (MWCNTs) and amine functionalized multi-walled carbon nantoubes (NH2-MWCNT) showed similar FR properties to current flame retardants with low mass loading (0.18 g/m2) and hence are promising alternatives that warrant further investigation. Chemical/physical modification of MWCNTs was conducted to produce well-dispersed MWCNT solutions without involving oxygen for uniform FR coating. The MWCNTs coating was studied to evaluate the durability of the coating and the impact on the efficacy during use phase by conducting mechanical abrasion and washing test. Approximately 50% and 40% of MWCNTs were released from 1 set of mechanical abrasion and washing test respectively. The losses during simulated usage impacted the flame retardancy negatively.
ContributorsNosaka, Takayuki (Author) / Herckes, Pierre (Thesis advisor) / Westerhoff, Paul (Committee member) / Wang, Qing Hua (Committee member) / Arizona State University (Publisher)
Created2018
Description
Utility scale solar energy is generated by photovoltaic (PV) cell arrays, which are often deployed in remote areas. A PV array monitoring system is considered where smart sensors are attached to the PV modules and transmit data to a monitoring station through wireless links. These smart monitoring devices may be used for fault detection and management of connection topologies. In this thesis, a compact hardware simulator of the smart PV array monitoring system is described. The voltage, current, irradiance, and temperature of each PV module are monitored and the status of each panel along with all data is transmitted to a mobile device. LabVIEW and Arduino board programs have been developed to display and visualize the monitoring data from all sensors. All data is saved on servers and mobile devices and desktops can easily access analytics from anywhere. Various PV array conditions including shading, faults, and loading are simulated and demonstrated.
Additionally, Electrical mismatch between modules in a PV array due to partial shading causes energy losses beyond the shaded module, as unshaded modules are forced to operate away from their maximum power point in order to compensate for the shading. An irradiance estimation algorithm is presented for use in a mismatch mitigation system. Irradiance is estimated using measurements of module voltage, current, and back surface temperature. These estimates may be used to optimize an array’s electrical configuration and reduce the mismatch losses caused by partial shading. Propagation of error in the estimation is examined; it is found that accuracy is sufficient for use in the proposed mismatch mitigation application.
Additionally, Electrical mismatch between modules in a PV array due to partial shading causes energy losses beyond the shaded module, as unshaded modules are forced to operate away from their maximum power point in order to compensate for the shading. An irradiance estimation algorithm is presented for use in a mismatch mitigation system. Irradiance is estimated using measurements of module voltage, current, and back surface temperature. These estimates may be used to optimize an array’s electrical configuration and reduce the mismatch losses caused by partial shading. Propagation of error in the estimation is examined; it is found that accuracy is sufficient for use in the proposed mismatch mitigation application.
ContributorsPeshin, Shwetang (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2016
Description
Ion exchange sorbents embedded with metal oxide nanoparticles can have high affinity and high capacity to simultaneously remove multiple oxygenated anion contaminants from drinking water. This research pursued answering the question, “Can synthesis methods of nano-composite sorbents be improved to increase sustainability and feasibility to remove hexavalent chromium and arsenic simultaneously from groundwater compared to existing sorbents?” Preliminary nano-composite sorbents outperformed existing sorbents in equilibrium tests, but struggled in packed bed applications and at low influent concentrations. The synthesis process was then tailored for weak base anion exchange (WBAX) while comparing titanium dioxide against iron hydroxide nanoparticles (Ti-WBAX and Fe-WBAX, respectively). Increasing metal precursor concentration increased the metal content of the created sorbents, but pollutant removal performance and usable surface area declined due to pore blockage and nanoparticle agglomeration. An acid-post rinse was required for Fe-WBAX to restore chromium removal capacity. Anticipatory life cycle assessment identified critical design constraints to improve environmental and human health performance like minimizing oven heating time, improving pollutant removal capacity, and efficiently reusing metal precursor solution. The life cycle environmental impact of Ti-WBAX was lower than Fe-WBAX as well as a mixed bed of WBAX and granular ferric hydroxide for all studied categories. A separate life cycle assessment found the total number of cancer and non-cancer cases prevented by drinking safer water outweighed those created by manufacture and use of water treatment materials and energy. However, treatment relocated who bore the health risk, concentrated it in a sub-population, and changed the primary manifestation from cancer to non-cancer disease. This tradeoff was partially mitigated by avoiding use of pH control chemicals. When properly synthesized, Fe-WBAX and Ti-WBAX sorbents maintained chromium removal capacity while significantly increasing arsenic removal capacity compared to the parent resin. The hybrid sorbent performance was demonstrated in packed beds using a challenging water matrix and low pollutant influent conditions. Breakthrough curves hint that the hexavalent chromium is removed by anion exchange and the arsenic is removed by metal oxide sorption. Overall, the hybrid nano-sorbent synthesis methods increased sustainability, improved sorbent characteristics, and increased simultaneous removal of chromium and arsenic for drinking water.
ContributorsGifford, James McKay (Author) / Westerhoff, Paul (Thesis advisor) / Hristovski, Kiril (Thesis advisor) / Chester, Mikhail (Committee member) / Arizona State University (Publisher)
Created2016
Description
To increase the deployment of photovoltaic (PV) systems, a higher level of performance for PV modules should be sought. Soiling, or dust accumulation on the PV modules, is one of the conditions that negatively affect the performance of the PV modules by reducing the light incident onto the surface of the PV module. This thesis presents two studies that focus on investigating the soiling effect on the performance of the PV modules installed in Metro Phoenix area.
The first study was conducted to investigate the optimum cleaning frequency for cleaning PV modules installed in Mesa, AZ. By monitoring the soiling loss of PV modules mounted on a mock rooftop at ASU-PRL, a detailed soiling modeling was obtained. Same setup was also used for other soiling-related investigations like studying the effect of soiling density on angle of incidence (AOI) dependence, the climatological relevance (CR) to soiling, and spatial variation of the soiling loss. During the first dry season (May to June), the daily soiling rate was found as -0.061% for 20o tilted modules. Based on the obtained soiling rate, cleaning PV modules, when the soiling is just due to dust on 20o tilted residential arrays, was found economically not justifiable.
The second study focuses on evaluating the soiling loss in different locations of Metro Phoenix area of Arizona. The main goal behind the second study was to validate the daily soiling rate obtained from the mock rooftop setup in the first part of this thesis. By collaborating with local solar panel cleaning companies, soiling data for six residential systems in 5 different cities in and around Phoenix was collected, processed, and analyzed. The range of daily soiling rate in the Phoenix area was found as -0.057% to -0.085% for 13-28o tilted arrays. The soiling rate found in the first part of the thesis (-0.061%) for 20o tilted array, was validated since it falls within the range obtained from the second part of the thesis.
The first study was conducted to investigate the optimum cleaning frequency for cleaning PV modules installed in Mesa, AZ. By monitoring the soiling loss of PV modules mounted on a mock rooftop at ASU-PRL, a detailed soiling modeling was obtained. Same setup was also used for other soiling-related investigations like studying the effect of soiling density on angle of incidence (AOI) dependence, the climatological relevance (CR) to soiling, and spatial variation of the soiling loss. During the first dry season (May to June), the daily soiling rate was found as -0.061% for 20o tilted modules. Based on the obtained soiling rate, cleaning PV modules, when the soiling is just due to dust on 20o tilted residential arrays, was found economically not justifiable.
The second study focuses on evaluating the soiling loss in different locations of Metro Phoenix area of Arizona. The main goal behind the second study was to validate the daily soiling rate obtained from the mock rooftop setup in the first part of this thesis. By collaborating with local solar panel cleaning companies, soiling data for six residential systems in 5 different cities in and around Phoenix was collected, processed, and analyzed. The range of daily soiling rate in the Phoenix area was found as -0.057% to -0.085% for 13-28o tilted arrays. The soiling rate found in the first part of the thesis (-0.061%) for 20o tilted array, was validated since it falls within the range obtained from the second part of the thesis.
ContributorsNaeem, Mohammad Hussain (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2014