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

FAIR2: A framework for addressing discrimination bias in social data science

[EN] Building upon the FAIR principles of (meta)data (Findable, Accessible, Interoperable and Reusable) and drawing from research in the social, health, and data sciences, we propose a framework -FAIR2 (Frame, Articulate, Identify, Report) - for identifying and addressing discrimination bias in social data science. We illustrate how FAIR2 enriches data science with experiential knowledge, clarifies assumptions about discrimination with causal graphs and systematically analyzes sources of bias in the data, leading to a more ethical use of data and analytics for the public interest. FAIR2 can be applied in the classroom to prepare a new and diverse generation of data scientists. In this era of big data and advanced analytics, we argue that without an explicit framework to identify and address discrimination bias, data science will not realize its potential of advancing social justice.This work was generously funded by grant #015865 from the Public Interest Technology University Network - New America Foundation.Richter, F.; Nelson, E.; Coury, N.; Bruckman, L.; Knighton, S. (2023). FAIR2: A framework for addressing discrimination bias in social data science. Editorial Universitat Politècnica de València. 327-335. https://doi.org/10.4995/CARMA2023.2023.1640032733

Knowledge Management and Semantic Reasoning: Ontology and Information Theory Enable the Construction of Knowledge Bases and Knowledge Graphs

FAIR (Findable, Accessible, Interoperable, Reusable) principles are guidelines Wilkinson, et. al. (2016) proposed for data governance and stewardship. Ontology is a powerful tool that can achieve many aspects of all four FAIR principles. Unfortunately, there is a misconception about ontology that it is only useful for establishing FAIR data. We need to think beyond data to answer the question “So what?” after an ontology is developed. It is critical to apply FAIR principles to results, analysis, and models, which is where the concept of digital thread comes in. FAIRified results, analysis, and models can be stored in a knowledge base and represented in a knowledge graph (KG), a flexible and extensible representation of knowledge, capable of inductive and deductive reasoning via the inherent structure that allows semantic reasoning, as well as the semantics applied by an ontology as the underlying schema layer. This versatile data structure can also be combined with principles of information theory that can refine the patterns and relationships by minimizing the uncertainties and randomness of the data. In essence, we supply a KG with a knowledge base and a semantic reasoning engine to infer new patterns and relationships as new knowledge, which can be imported back into the knowledge base

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

Knowledge Management of Historical Data: Ontology Development for Chemical Reactions

Knowledge management of the literature and historical data is critical to accelerated drug and materials discovery. Currently, literature knowledge is scattered in journal articles in various formats: diagrams, texts, plots, etc. Historical data from past experiments are saved in a number of local computers under confusing folder structures with ambiguous file names. To manage and organize historical data and knowledge, our group (SDLE) at CWRU follows FAIR (Findable, Accessible, Interoperable, Reusable) principles, which outline the best practices for data stewardship and data provenance, and ontology, a formal representation of terms and concepts and their relationships, as a tool to improve interoperability. Knowledge graphs, constructed from graph data structure, are built from historical knowledge and domain ontology, which acts as a schema layer, and are capable of inductive reasoning via graph traversal. In this project, an ontology for conducting a chemical reaction or synthesis is developed by mapping terms from multiple common mid-level ontologies from the chemistry domain such as Chemical Entities with Biological Interest (ChEBI), National Cancer Institute thesaurus (NCIt), Chemical Method Ontology (CHMO), etc. The ontology is built using FAIRmaterials, a package available in R and Python developed by SDLE students. The resulting ontology will be used to build a knowledge graph on the nitration of aromatic compounds with flow chemistry

Materials Data Science Ontology (MDS-Onto): Unifying Domain Knowledge in Materials and Applied Data Science

Ontologies have gained popularity in the scientific community as a means of standardizing concepts and terminology used in metadata across different institutions to facilitate data comprehension, sharing, and reuse. Despite the existence of frameworks and guidelines for building ontologies, the processes and standards used to develop ontologies still differ significantly, particularly in Materials Science. Our goal with the MDS-Onto Framework is to provide a unified and automated system for ontology development in the Materials and Data Sciences. This framework offers recommendations on where to publish ontologies online, how to best integrate them within the semantic web, and which formats to store and share ontologies. The framework aims to enhance the findability and interoperability of these ontologies. One critical component of the MDS-Onto Framework is the bilingual FAIRmaterials Python and R package, a practical and user-friendly tool for scientists to create and visualize ontologies effectively. We also present two domain ontologies created with our framework, X-ray diffraction and Photovoltaics(PV), to demonstrate the practical application and steps for implementing materials in ontology creation and merging. These cases highlight our framework\u27s feasibility and efficiency