Nicholas R. Wheeler

Mechanical enhancement via self-assembled nanostructures in polymer nanocomposites

This article reports the synthesis of a diacetylene containing organogelator for the use as a nanoscale filler for mechanical enhancement in a polymer nanocomposite. The tensile storage modulus increased when the self-assembled nanostructures were incorporated into a polymer matrix. Below the glass transition temperature, a modest increase of the tensile storage modulus was observed, but above the glass transition temperature, almost a two order of magnitude increase at the highest filler loadings was found. Additionally, the presence of the self-assembled nanofibers, increased the tensile storage modulus greater than a similar filler that does not form 1D nanofibers highlighting the importance of self-assembly to the mechanical reinforcement.

Degradation pathway models for photovoltaics module lifetime performance

Previously published accelerated testing data from Underwriter Labs, featuring measurements taken on 18 identical photovoltaic (PV) modules exposed to two stress conditions, were used to develop an analytical methodology. The results provide insight into active degradation mechanisms and pathways present in PV modules under accelerated testing conditions as indicated by statistically significant relationships between variables. Observed experimental results coincide with a domain knowledge based theoretical degradation pathway model informed by literature, and provide a basis for beginning to investigate the degradation modes and pathways truly present in modules and their effects on module performance over lifetime.

Photovoltaic lifetime and degradation science statistical pathway development: acrylic degradation

In order to optimize and extend the life of photovoltaics (PV) modules, scienti c and mechanistic statistical analytics must be performed on a large sample of materials, components and systems. Statistically signi - cant relationships were investigated between di erent mechanistically based variables to develop a statistical pathway diagram for the degradation of acrylic that is important in concentrating photovoltaics. The statisti- cally signi cant relationships were investigated using lifetime and degradation science using a domain knowledge semi-supervised generalized structural equation modeling (semi-gSEM. Predictive analytics and prognostics are informed from the statistical pathway diagram in order to predictively understand the lifetime of PV modules in di erent stress conditions and help with these critical lifetime technologies.

A Nonrelational Data Warehouse for the Analysis of Field and Laboratory Data From Multiple Heterogeneous Photovoltaic Test Sites

A nonrelational, distributed computing, data warehouse, and analytics environment (Energy-CRADLE) was developed for the analysis of field and laboratory data from multiple heterogeneous photovoltaic (PV) test sites. This data informatics and analytics infrastructure was designed to process diverse formats of PV performance data and climatic telemetry time-series data collected from a PV outdoor test network, i.e., the Solar Durability and Lifetime Extension global SunFarm network, as well as point-in-time laboratory spectral and image measurements of PV material samples. Using Hadoop/HBase for the distributed data warehouse, Energy-CRADLE does not have a predefined data table schema, which enables ingestion of data in diverse and changing formats. For easy data ingestion and data retrieval, Energy-CRADLE utilizes Hadoop streaming to enable Python MapReduce and provides a graphical user interface, i.e., py-CRADLE. By developing the Hadoop distributed computing platform and the HBase NoSQL database schema for solar energy, Energy-CRADLE exemplifies an integrated, scalable, secure, and user-friendly data informatics and analytics system for PV researchers. An example of Energy-CRADLE enabled scalable, data-driven, analytics is presented, where machine learning is used for anomaly detection across 2.2 million real-world current-voltage (I-V) curves of PV modules in three distinct Köppen-Geiger climatic zones.

Democratizing an electroluminescence imaging apparatus and analytics project for widespread data acquisition in photovoltaic materials

We present a description of an electroluminescence (EL) apparatus, easily sourced from commercially available components, with a quantitative image processing platform that demonstrates feasibility for the widespread utility of EL imaging as a characterization tool. We validated our system using a Gage R&R analysis to find a variance contribution by the measurement system of 80.56%, which is typically unacceptable, but through quantitative image processing and development of correction factors a variance contribution by the measurement system of 2.41% was obtained. We further validated the system by quantifying the signal-to-noise ratio (SNR) and found values consistent with other systems published in the literature, at SNR values of 10-100, albeit at exposure times of greater than 1 s compared to 10 ms for other systems. This SNR value range is acceptable for image feature recognition, providing the opportunity for widespread data acquisition and large scale data analytics of photovoltaics.

Hi-MC: a novel method for high-throughput mitochondrial haplogroup classification

Effective approaches for assessing mitochondrial DNA (mtDNA) variation are important to multiple scientific disciplines. Mitochondrial haplogroups characterize branch points in the phylogeny of mtDNA. Several tools exist for mitochondrial haplogroup classification. However, most require full or partial mtDNA sequence which is often cost prohibitive for studies with large sample sizes. The purpose of this study was to develop Hi-MC, a high-throughput method for mitochondrial haplogroup classification that is cost effective and applicable to large sample sizes making mitochondrial analysis more accessible in genetic studies. Using rigorous selection criteria, we defined and validated a custom panel of mtDNA single nucleotide polymorphisms that allows for accurate classification of European, African, and Native American mitochondrial haplogroups at broad resolution with minimal genotyping and cost. We demonstrate that Hi-MC performs well in samples of European, African, and Native American ancestries, and that Hi-MC performs comparably to a commonly used classifier. Implementation as a software package in R enables users to download and run the program locally, grants greater flexibility in the number of samples that can be run, and allows for easy expansion in future revisions. Hi-MC is available in the CRAN repository and the source code is freely available at https://github.com/vserch/himc.

A data science approach to understanding photovoltaic module degradation

The expected lifetime performance and degradation of photovoltaic (PV) modules is a major issue facing the levelized cost of electricity of PV as a competitive energy source. Studies that quantify the rates and mechanisms of performance degradation are needed not only for bankability and adoption of these promising technologies, but also for the diagnosis and improvement of their mechanistic degradation pathways. Towards this goal, a generalizable approach to degradation science studies utilizing data science principles has been developed and applied to c-Si PV modules. By combining domain knowledge and data derived insights, mechanistic degradation pathways are indicated that link environmental stressors to the degradation of PV module performance characteristics. Targeted studies guided by these results have yielded predictive equations describing rates of degradation, and further studies are underway to achieve this for additional mechanistic pathways of interest.