Understanding the Role Thin Film Interfaces Play in Solar Cell Performance and Stability

As more efficient and cost-effective photovoltaic (PV) architectures are developed, solar becomes an ever more competitive and viable replacement for fossil fuels. Full grid electrification necessitates the development of efficient, reliable, cost-effective technologies - and there is room for many different kinds of PV in this expanding market. The practical challenges and constraints of terawatt PV production have brought scalability and durability into sharp scientific focus. From a materials perspective, there are commonalities in the materials questions and challenges across different PV technologies. Whereas most PV technology is referred to by the absorber layer - e.g. silicon, or perovskite solar cells, other layers in the device are equally important to performance and durability. These layers are often composed of metal oxides, and are common across device technologies - for example, interfacial layers (such as charge transport layers, CTLs), and transparent conducting oxides (TCOs) used as electrodes.This work addresses materials oxide characterization and its relationship to materials and device performance and degradation across PV technologies. Among the most promising PVs to date are two technologies with different levels of thin film incorporation: silicon heterojunction (SHJ) and perovskite PV. SHJ cells are part of the industrial Si PV portfolio, and perovskite cells are starting to be commercially produced after a decade of intensive research. However, there are well-known stability and cost limitations associated with each technology. Understanding the thin film materials science in these devices, and using that understanding to enhance device performance and stability is key to more reliable and cost effective electricity production. Under practical aging conditions, careful materials-level characterization is required to understand the degradation mechanisms of these materials and the complex effects of aging on the multilayer system. This work details the effects of practical degradation challenges such as damp heat (DH) exposure and encapsulation degradation on the stability of inorganic metal oxides in both the SHJ and perovskite thin film photovoltaic architectures. For perovskite devices, MAPbI3 films were deposited by spin coating onto a range of substrates and CTLs; substrates of glass and indium tin oxide (ITO) were paired with metal oxides (MOs) including MoOX, NiOX, and ZnO. SE and SEM were used to characterize the surface and bulk properties of the perovskite films. Results demonstrate that the underlying layers affect the rate of absorber degradation when exposed to heat and moisture. Unencapsulated SHJ cells (a subset of which were exposed to low concentrations of acetic acid under an applied voltage) were aged under DH 85°C/85% relative humidity conditions. The contact-ITO interface was examined via optical profilometry (OP), spectroscopic ellipsometry (SE), and scanning electron microscopy (SEM). SHJ cells have interfaces unique to their architecture, namely the c-Si/a-Si:H and a-Si:H/ITO interfaces at the top of the device. Examining the degradation of unencapsulated SHJ cells and comparing those results to historical degradation profiles of encapsulated SHJ cells in addition to the simulated effects of acetic acid exposure will help to decouple the effects of encapsulation on ITO stability in this architecture. It is well known that ethylene vinyl acetate (EVA) encapsulation degrades and produces acetic acid upon exposure to heat and humidity. When under an applied voltage, even small concentrations of acetic acid can quickly corrode the contact-ITO interface, leading to decreases in efficiency and increases in series resistance of the cell. Here, XPS was used to monitor the changes in the front contact of the SHJ cells during DH and acetic acid exposure. Changes to the materials will be correlated to changes in device performance under the same aging conditions

Unlocking Insights into Crop Growth and Nutrient Distribution: A Geospatial Analysis Approach Using Satellite Imagery and Soil Data

Accurate monitoring of crop growth and nutrient distribution is crucial for optimizing agricultural practices, promoting a sustainable environment, and ensuring long-term food production. In this study, we propose a novel and comprehensive approach to monitor crop growth and nutrient distribution in large-scale agricultural landscapes. Our methodology combines advanced geospatial and temporal analysis techniques, providing valuable insights into the intricate relationships between crop health, soil nutrients, and other essential soil properties. To monitor vegetation dynamics, we obtained data from the IBM EIS (Environment Intelligence Suite) and processed it using our HPC (High-Performance Computing) infrastructure. This is ingested into our CRADLE (Common Research Analytics and Data Lifecycle Environment). The IBM EIS consists of vast amounts of geospatial data curated from diverse sources, readily available for analysis. Leveraging the Normalized Difference Vegetation Index (NDVI) algorithm and MODIS Aqua satellite imagery, we classified vegetation on a daily basis, yielding a detailed assessment of land use and growth. Additionally, by integrating MODIS Aqua data with USDA Historical Crop planting data, we can identify the dominant crops in each region and monitor their growth and health across Texas and Ohio during 2019. To investigate soil properties and their influence on crop health, we utilize prominent soil databases from IBM EIS such as The Soil Survey Geographic Database (SSURGO) and the World Soil Information Service (WoSIS). These databases provide essential information on key soil properties, including pH, texture, water holding capacity, and organic carbon. By correlating these properties with soil nitrogen content, we can assess their interdependencies and infer their impacts on crop health. Furthermore, we analyze the correlation between crop health and nitrogen content, gaining valuable insights into the effects of soil nitrogen on crop well-being. By integrating remote sensing technology, soil science, and data science, this interdisciplinary study contributes to the development of sustainable agricultural management strategies. The findings of this research enhance food production capabilities and provide valuable information for policy decision-making, ultimately promoting environmental conservation within large-scale agricultural systems

Statistical Analysis and Degradation Pathway Modeling of Photovoltaic Minimodules with Varied Packaging Strategies

Degradation pathway models constructed using network structural equation modeling (netSEM) are used to study degradation modes and pathways active in photovoltaic (PV) system variants in exposure conditions of high humidity and temperature. This data-driven modeling technique enables the exploration of simultaneous pairwise and multiple regression relationships between variables in which several degradation modes are active in specific variants and exposure conditions. Durable and degrading variants are identified from the netSEM degradation mechanisms and pathways, along with potential ways to mitigate these pathways. A combination of domain knowledge and netSEM modeling shows that corrosion is the primary cause of the power loss in these glass/backsheet PV minimodules. We show successful implementation of netSEM to elucidate the relationships between variables in PV systems and predict a specific service lifetime. The results from pairwise relationships and multiple regression show consistency. This work presents a greater opportunity to be expanded to other materials systems

Degradation Science: Mesoscopic Evolution and Temporal Analytics of Photovoltaic Energy Materials

Based on recent advances in nanoscience, data science and the availability of massive real-world datastreams, the mesoscopic evolution of mesoscopic energy materials can now be more fully studied. The temporal evolution is vastly complex in time and length scales and is fundamentally challenging to scientific understanding of degradation mechanisms and pathways responsible for energy materials evolution over lifetime. We propose a paradigm shift towards mesoscopic evolution modeling, based on physical and statistical models, that would integrate laboratory studies and real-world massive datastreams into a stress/mechanism/response framework with predictive capabilities. These epidemiological studies encompass the variability in properties that affect performance of material ensembles. Mesoscopic evolution modeling is shown to encompass the heterogeneity of these materials and systems, and enables the discrimination of the fast dynamics of their functional use and the slow and/or rare events of their degradation. We delineate paths forward for degradation science

Standalone vertex finding in the ATLAS muon spectrometer

A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to bbar b final states, and pp collision data at √s = 7 TeV collected with the ATLAS detector at the LHC during 2011

Measurements of Higgs boson production and couplings in diboson final states with the ATLAS detector at the LHC

Measurements are presented of production properties and couplings of the recently discovered Higgs boson using the decays into boson pairs, H →γ γ, H → Z Z∗ →4l and H →W W∗ →lνlν. The results are based on the complete pp collision data sample recorded by the ATLAS experiment at the CERN Large Hadron Collider at centre-of-mass energies of √s = 7 TeV and √s = 8 TeV, corresponding to an integrated luminosity of about 25 fb−1. Evidence for Higgs boson production through vector-boson fusion is reported. Results of combined fits probing Higgs boson couplings to fermions and bosons, as well as anomalous contributions to loop-induced production and decay modes, are presented. All measurements are consistent with expectations for the Standard Model Higgs boson

Measurement of the top quark-pair production cross section with ATLAS in pp collisions at \sqrt{s}=7\TeV

A measurement of the production cross-section for top quark pairs(\ttbar) in $pp$ collisions at \sqrt{s}=7 \TeV is presented using data recorded with the ATLAS detector at the Large Hadron Collider. Events are selected in two different topologies: single lepton (electron $e$ or muon $\mu$) with large missing transverse energy and at least four jets, and dilepton ($ee$, $\mu\mu$ or $e\mu$) with large missing transverse energy and at least two jets. In a data sample of 2.9 pb-1, 37 candidate events are observed in the single-lepton topology and 9 events in the dilepton topology. The corresponding expected backgrounds from non-\ttbar Standard Model processes are estimated using data-driven methods and determined to be $12.2 \pm 3.9$ events and $2.5 \pm 0.6$ events, respectively. The kinematic properties of the selected events are consistent with SM \ttbar production. The inclusive top quark pair production cross-section is measured to be \sigmattbar=145 \pm 31 ^{+42}_{-27} pb where the first uncertainty is statistical and the second systematic. The measurement agrees with perturbative QCD calculations.Comment: 30 pages plus author list (50 pages total), 9 figures, 11 tables, CERN-PH number and final journal adde