Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns.

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.

Automated pipeline framework for processing of large-scale building energy time series data.

Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for "virtual" energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings' time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building's daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

Temporal evolution and pathway models of poly(ethylene-terephthalate) degradation under multi-factor accelerated weathering exposures.

Photolytic and hydrolytic degradation of poly(ethylene-terephthalate) (PET) polymers with different stabilizers were performed under multiple accelerated weathering exposures and changes in the polymers were monitored by various evaluation techniques. Yellowing was caused by photolytic degradation and haze formation was induced by combined effects of photolytic and hydrolytic degradation. The formation of light absorbing chromophores and bleaching of the UV stabilizer additive were recorded through optical spectroscopy. Chain scission and crystallization were found to be common mechanisms under both photolytic and hydrolytic conditions, based on the infrared absorption of the carbonyl (C = O) band and the trans ethylene glycol unit, respectively. The degradation mechanisms determined from these evaluations were then used to construct a set of degradation pathway network models using the network structural equation modeling (netSEM) approach. This method captured the temporal evolution of degradation by assessing statistically significant relationships between applied stressors, mechanistic variables, and performance level responses. Quantitative pathway equations provided the contributions from mechanistic variables to the response changes.

Multivariate multiple regression models of poly(ethylene-terephthalate) film degradation under outdoor and multi-stressor accelerated weathering exposures.

Developing materials for use in photovoltaic (PV) systems requires knowledge of their performance over the warranted lifetime of the PV system. Poly(ethylene-terephthalate) (PET) is a critical component of PV module backsheets due to its dielectric properties and low cost. However, PET is susceptible to environmental stressors and degrades over time. Changes in the physical properties of nine PET grades were modeled after outdoor and accelerated weathering exposures to characterize the degradation process of PET and assess the influence of stabilizing additives and weathering factors. Multivariate multiple regression (MMR) models were developed to quantify changes in color, gloss, and haze of the materials. Natural splines were used to capture the non-linear relationship between predictors and responses. Model performance was evaluated via adjusted-R2 and root mean squared error values from leave-one-out cross validation analysis. All models described over 85% of the variation in the data with low relative error. Model coefficients were used to assess the influence of weathering stressors and material additives on the property changes of films. Photodose was found to be the primary degradation stressor and moisture was found to increase the degradation rate of PET. Direct moisture contact was found to impose more stress on the material than airbone moisture (humidity). Increasing the concentration of TiO2 was found to generally decrease the degradation rate of PET and mitigate hydrolytic degradation. MMR models were compared to physics-based models and agreement was found between the two modeling approaches. Cross-correlation of accelerated exposures to outdoor exposures was achieved via determination of cross-correlation scale factors. Cross-correlation revealed that direct moisture contact is a key factor for reliable accelerated weathering testing and provided a quantitative method to determine when accelerated exposure results can be made more aggressive to better approximate outdoor exposure conditions.

A cross-sectional study of the temporal evolution of electricity consumption of six commercial buildings.

Current approaches to building efficiency diagnoses include conventional energy audit techniques that can be expensive and time consuming. In contrast, virtual energy audits of readily available 15-minute-interval building electricity consumption are being explored to provide quick, inexpensive, and useful insights into building operation characteristics. A cross sectional analysis of six buildings in two different climate zones provides methods for data cleaning, population-based building comparisons, and relationships (correlations) of weather and electricity consumption. Data cleaning methods have been developed to categorize and appropriately filter or correct anomalous data including outliers, missing data, and erroneous values (resulting in < 0.5% anomalies). The utility of a cross-sectional analysis of a sample set of building's electricity consumption is found through comparisons of baseload, daily consumption variance, and energy use intensity. Correlations of weather and electricity consumption 15-minute interval datasets show important relationships for the heating and cooling seasons using computed correlations of a Time-Specific-Averaged-Ordered Variable (exterior temperature) and corresponding averaged variables (electricity consumption)(TSAOV method). The TSAOV method is unique as it introduces time of day as a third variable while also minimizing randomness in both correlated variables through averaging. This study found that many of the pair-wise linear correlation analyses lacked strong relationships, prompting the development of the new TSAOV method to uncover the causal relationship between electricity and weather. We conclude that a combination of varied HVAC system operations, building thermal mass, plug load use, and building set point temperatures are likely responsible for the poor correlations in the prior studies, while the correlation of time-specific-averaged-ordered temperature and corresponding averaged variables method developed herein adequately accounts for these issues and enables discovery of strong linear pair-wise correlation R values. TSAOV correlations lay the foundation for a new approach to building studies, that mitigates plug load interferences and identifies more accurate insights into weather-energy relationship for all building types. Over all six buildings analyzed the TSAOV method reported very significant average correlations per building of 0.94 to 0.82 in magnitude. Our rigorous statistics-based methods applied to 15-minute-interval electricity data further enables virtual energy audits of buildings to quickly and inexpensively inform energy savings measures.

Organofunctional Silane Modification of Aluminum-Doped Zinc Oxide Surfaces as a Route to Stabilization.

Aluminum-doped zinc oxide (AZO) is a low-temperature processed transparent conductive oxide (TCO) made of earth abundant elements; its applications are currently limited by instability to heat, moisture, and acidic conditions. We demonstrate that the application of an organofunctional silane modifier mitigates AZO degradation and explore the interplay between performance and material composition and morphology. Specifically, we evaluate degradation of bare AZO and APTES (3-aminopropyltriethoxysilane)-modified AZO in response to damp heat (DH, 85 °C, 85% relative humidity) exposure over 1000 h and then demonstrate how surface modification impacts changes in electrical and optical properties and chemical composition in one of the most thorough studies to date. Hall measurements show that the resistivity of AZO increases due to a decrease in electron mobility, with no commensurate change in carrier concentration. APTES decelerates this electrical degradation, without affecting AZO optical properties. Percent transmission and yellowness index of an ensemble of bare and modified AZO are stable upon DH exposure, but haze increases slightly for a discrete sample of modified AZO. Atomic force microscopy (AFM) and optical profilometer (OP) measurements do not show evidence of pitting or delamination after 1000 h DH exposure but indicate a slight increase in surface roughness on both the nanometer and micrometer length scales. X-ray photoelectron spectroscopy data (XPS) reveal that the surface composition of bare and silanized AZO is stable over this time frame; oxygen vacancies, as measured by XPS, are also stable with DH exposure, which, together with transmission and Hall measurements, indicate stable carrier concentrations. However, after 1500 h of DH exposure, only bare AZO shows signs of catastrophic destruction. Comparison of the data presented herein to previous reports indicates that the initial AZO composition and microstructure dictate the degradation profile. This work demonstrates that surface modification slows the bulk degradation of AZO and provides insight into how the material can be more widely used as a transparent electrode in the next generation of optoelectronic devices.

Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures.

Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples' responses, the change in haze (%) depended on individual samples' responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R2 values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction.

Photon Management through Virus-Programmed Supramolecular Arrays.

Photon extraction and capture efficiency is a complex function of the material's composition, its molecular structure at the nanoscale, and the overall organization spanning multiple length scales. The architecture of the material defines the performance; nanostructured features within the materials enhance the energy efficiency. Photon capturing materials are largely produced through lithographic, top-down, manufacturing schemes; however, there are limits to the smallest dimension achievable using this technology. To overcome these technological barriers, a bottom-up nanomanufacturing is pursued. Inspired by the self-programmed assembly of virus arrays in host cells resulting in iridescence of infected organisms, virus-programmed, nanostructured arrays are studied to pave the way for new design principles in photon management and biology-inspired materials science. Using the nanoparticles formed by plant viruses in combination with charged polymers (dendrimers), a bottom-up approach is illustrated to prepare a family of broadband, low-angular dependent antireflection mesoscale layered materials for potential application as photon management coatings. Measurement and theory demonstrate antireflectance and phototrapping properties of the virus-programmed assembly. This opens up new bioengineering principles for the nanomanufacture of coatings and films for use in LED lighting and photovoltaics.

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.

Implication of the solvent effect, metal ions and topology in the electronic structure and hydrogen bonding of human telomeric G-quadruplex DNA.

We present a first-principles density functional study elucidating the effects of solvent, metal ions and topology on the electronic structure and hydrogen bonding of 12 well-designed three dimensional G-quadruplex (G4-DNA) models in different environments. Our study shows that the parallel strand structures are more stable in dry environments and aqueous solutions containing K(+) ions within the tetrad of guanine but conversely, that the anti-parallel structure is more stable in solutions containing the Na(+) ions within the tetrad of guanine. The presence of metal ions within the tetrad of the guanine channel always enhances the stability of the G4-DNA models. The parallel strand structures have larger HOMO-LUMO gaps than antiparallel structures, which are in the range of 0.98 eV to 3.11 eV. Partial charge calculations show that sugar and alkali ions are positively charged whereas nucleobases, PO4 groups and water molecules are all negatively charged. Partial charges on each functional group with different signs and magnitudes contribute differently to the electrostatic interactions involving G4-DNA and favor the parallel structure. A comparative study between specific pairs of different G4-DNA models shows that the Hoogsteen OH and NH hydrogen bonds in the guanine tetrad are significantly influenced by the presence of metal ions and water molecules, collectively affecting the structure and the stability of G4-DNA.

X-ray characterization of mesophases of human telomeric G-quadruplexes and other DNA analogues.

Observed in the folds of guanine-rich oligonucleotides, non-canonical G-quadruplex structures are based on G-quartets formed by hydrogen bonding and cation-coordination of guanosines. In dilute 5'-guanosine monophosphate (GMP) solutions, G-quartets form by the self-assembly of four GMP nucleotides. We use x-ray diffraction to characterize the columnar liquid-crystalline mesophases in concentrated solutions of various model G-quadruplexes. We then probe the transitions between mesophases by varying the PEG solution osmotic pressure, thus mimicking in vivo molecular crowding conditions. Using the GMP-quadruplex, built by the stacking of G-quartets with no covalent linking between them, as the baseline, we report the liquid-crystalline phase behaviors of two other related G-quadruplexes: (i) the intramolecular parallel-stranded G-quadruplex formed by the 22-mer four-repeat human telomeric sequence AG3(TTAG3)3 and (ii) the intermolecular parallel-stranded G-quadruplex formed by the TG4T oligonucleotides. Finally, we compare the mesophases of the G-quadruplexes, under PEG-induced crowding conditions, with the corresponding mesophases of the canonical duplex and triplex DNA analogues.

Microinverter Thermal Performance in the Real-World: Measurements and Modeling.

Real-world performance, durability and reliability of microinverters are critical concerns for microinverter-equipped photovoltaic systems. We conducted a data-driven study of the thermal performance of 24 new microinverters (Enphase M215) connected to 8 different brands of PV modules on dual-axis trackers at the Solar Durability and Lifetime Extension (SDLE) SunFarm at Case Western Reserve University, based on minute by minute power and thermal data from the microinverters and PV modules along with insolation and environmental data from July through October 2013. The analysis shows the strengths of the associations of microinverter temperature with ambient temperature, PV module temperature, irradiance and AC power of the PV systems. The importance of the covariates are rank ordered. A multiple regression model was developed and tested based on stable solar noon-time data, which gives both an overall function that predicts the temperature of microinverters under typical local conditions, and coefficients adjustments reecting refined prediction of the microinverter temperature connected to the 8 brands of PV modules in the study. The model allows for prediction of internal temperature for the Enphase M215 given similar climatic condition and can be expanded to predict microinverter temperature in fixed-rack and roof-top PV systems. This study is foundational in that similar models built on later stage data in the life of a device could reveal potential influencing factors in performance degradation.

Adhesive interactions of geckos with wet and dry fluoropolymer substrates.

Fluorinated substrates like Teflon® (poly(tetrafluoroethylene); PTFE) are well known for their role in creating non-stick surfaces. We showed previously that even geckos, which can stick to most surfaces under a wide variety of conditions, slip on PTFE. Surprisingly, however, geckos can stick reasonably well to PTFE if it is wet. In an effort to explain this effect, we have turned our attention to the role of substrate surface energy and roughness when shear adhesion occurs in media other than air. In this study, we removed the roughness component inherent to commercially available PTFE and tested geckos on relatively smooth wet and dry fluoropolymer substrates. We found that roughness had very little effect on shear adhesion in air or in water and that the level of fluorination was most important for shear adhesion, particularly in air. Surface energy calculations of the two fluorinated substrates and one control substrate using the Tabor-Winterton approximation and the Young-Dupré equation were used to determine the interfacial energy of the substrates. Using these interfacial energies we estimated the ratio of wet and dry normal adhesion for geckos clinging to the three substrates. Consistent with the results for rough PTFE, our predictions show a qualitative trend in shear adhesion based on fluorination, and the quantitative experimental differences highlight the unusually low shear adhesion of geckos on dry smooth fluorinated substrates, which is not captured by surface energy calculations. Our work has implications for bioinspired design of synthetics that can preferentially stick in water but not in air.

van der Waals Interactions on the Mesoscale: Open-Science Implementation, Anisotropy, Retardation, and Solvent Effects.

The self-assembly of heterogeneous mesoscale systems is mediated by long-range interactions, including van der Waals forces. Diverse mesoscale architectures, built of optically and morphologically anisotropic elements such as DNA, collagen, single-walled carbon nanotubes, and inorganic materials, require a tool to calculate the forces, torques, interaction energies, and Hamaker coefficients that govern assembly in such systems. The mesoscale Lifshitz theory of van der Waals interactions can accurately describe solvent and temperature effects, retardation, and optically and morphologically anisotropic materials for cylindrical and planar interaction geometries. The Gecko Hamaker open-science software implementation of this theory enables new and sophisticated insights into the properties of important organic/inorganic systems: interactions show an extended range of magnitudes and retardation rates, DNA interactions show an imprint of base pair composition, certain SWCNT interactions display retardation-dependent nonmonotonicity, and interactions are mapped across a range of material systems in order to facilitate rational mesoscale design.

Electronic structure and partial charge distribution of Doxorubicin in different molecular environments.

The electronic structure and partial charge of doxorubicin (DOX) in three different molecular environments-isolated, solvated, and intercalated in a DNA complex-are studied by first-principles density functional methods. It is shown that the addition of solvating water molecules to DOX, together with the proximity to and interaction with DNA, has a significant impact on the electronic structure as well as on the partial charge distribution. Significant improvement in estimating the DOX-DNA interaction energy is achieved. The results are further elucidated by resolving the total density of states and surface charge density into different functional groups. It is concluded that the presence of the solvent and the details of the interaction geometry matter greatly in determining the stability of DOX complexation. Ab initio calculations on realistic models are an important step toward a more accurate description of the long-range interactions in biomolecular systems.

Optical properties and electronic transitions of DNA oligonucleotides as a function of composition and stacking sequence.

The role of base pair composition and stacking sequence in the optical properties and electronic transitions of DNA is of fundamental interest. We present and compare the optical properties of DNA oligonucleotides (AT)10, (AT)5(GC)5, and (AT-GC)5 using both ab initio methods and UV-vis molar absorbance measurements. Our data indicate a strong dependence of both the position and intensity of UV absorbance features on oligonucleotide composition and stacking sequence. The partial densities of states for each oligonucleotide indicate that the valence band edge arises from a feature associated with the PO4(3-) complex anion, and the conduction band edge arises from anti-bonding states in DNA base pairs. The results show a strong correspondence between the ab initio and experimentally determined optical properties. These results highlight the benefit of full spectral analysis of DNA, as opposed to reductive methods that consider only the 260 nm absorbance (A260) or simple purity ratios, such as A260/A230 or A260/A280, and suggest that the slope of the absorption edge onset may provide a useful metric for the degree of base pair stacking in DNA. These insights may prove useful for applications in biology, bioelectronics, and mesoscale self-assembly.

Determination of the second virial coefficient of bovine serum albumin under varying pH and ionic strength by composition-gradient multi-angle static light scattering.

Composition-gradient multi-angle static light scattering (CG-MALS) is an emerging technique for the determination of intermolecular interactions via the second virial coefficient B22. With CG-MALS, detailed studies of the second virial coefficient can be carried out more accurately and effectively than with traditional methods. In addition, automated mixing, delivery and measurement enable high speed, continuous, fluctuation-free sample delivery and accurate results. Using CG-MALS we measure the second virial coefficient of bovine serum albumin (BSA) in aqueous solutions at various values of pH and ionic strength of a univalent salt (NaCl). The systematic variation of the second virial coefficient as a function of pH and NaCl strength reveals the net charge change and the isoelectric point of BSA under different solution conditions. The magnitude of the second virial coefficient decreases to 1.13 x 10(-5) ml*mol/g(2) near the isoelectric point of pH 4.6 and 25 mM NaCl. These results illuminate the role of fundamental long-range electrostatic and van der Waals forces in protein-protein interactions, specifically their dependence on pH and ionic strength.

Electronic structure, dielectric response, and surface charge distribution of RGD (1FUV) peptide.

Long and short range molecular interactions govern molecular recognition and self-assembly of biological macromolecules. Microscopic parameters in the theories of these molecular interactions are either phenomenological or need to be calculated within a microscopic theory. We report a unified methodology for the ab initio quantum mechanical (QM) calculation that yields all the microscopic parameters, namely the partial charges as well as the frequency-dependent dielectric response function, that can then be taken as input for macroscopic theories of electrostatic, polar, and van der Waals-London dispersion intermolecular forces. We apply this methodology to obtain the electronic structure of the cyclic tripeptide RGD-4C (1FUV). This ab initio unified methodology yields the relevant parameters entering the long range interactions of biological macromolecules, providing accurate data for the partial charge distribution and the frequency-dependent dielectric response function of this peptide. These microscopic parameters determine the range and strength of the intricate intermolecular interactions between potential docking sites of the RGD-4C ligand and its integrin receptor.

Dielectric response variation and the strength of van der Waals interactions.

Small changes in the dielectric response of a material result in substantial variations in the Hamaker coefficient of the van der Waals interactions, as demonstrated in a simplified approximate model as well as a realistic example of amorphous silica with and without an exciton peak. Variation of the dielectric response spectra at one particular frequency influences all terms in the Matsubara summation, making the total change in the Hamaker coefficient depend on the spectral changes not only at that frequency but also at the rest of the spectrum, properly weighted. The Matsubara terms most affected by the addition of a single peak are not those close to the position of the added peak, but are distributed doubly non-locally over the entire range of frequencies. A possibility of eliminating van der Waals interactions or at least drastically reducing them by spectral variation in a narrow regime of frequencies thus seems very remote.

Design rules for nanomedical engineering: from physical virology to the applications of virus-based materials in medicine.

Physical virology seeks to define the principles of physics underlying viral infections, traditionally focusing on the fundamental processes governing virus assembly, maturation, and disassembly. A detailed understanding of virus structure and assembly has facilitated the development and analysis of virus-based materials for medical applications. In this Physical Virology review article, we discuss the recent developments in nanomedicine that help us to understand how physical properties affect the in vivo fate and clinical impact of (virus-based) nanoparticles. We summarize and discuss the design rules that need to be considered for the successful development and translation of virus-based nanomaterials from bench to bedside.