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Fatigue initiation in additively manufactured samples/parts often occurs at processed-induced defects such as lack-of-fusion (LoF), keyhole, or other morphological/microstructural defects that have unique characteristics and measurable qualities. Attempts at identifying and minimizing such defects have utilized optimized processing conditions along with in situ and ex situ characterization that includes metallography and/or X-ray computed tomography (XCT). This paper highlights the benefits of using fracture surface analyses to detect and quantify defects that may not be detected by metallography/XCT due to sectioning and resolution limits. In addition to using manual quantification of fatigue initiating LoF and keyhole defects on fracture surfaces, image-based machine learning using convolutional neural networks such as U-Net were also used to automate the process. Statistical analyses were used to identify the extreme cases of defects that initiated and accelerated fatigue and to model the distribution of defect size and shape characteristics to distinguish the type of defect. Initial results show agreement between trained machine learning models and ground truth data in defect segmentation, and the distributions of defect characteristics are distinguishable to particular process-induced defect types. [ABSTRACT FROM AUTHOR]

There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE's capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The document is a correction notice for an article titled "Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue" published in JOM: The Journal of The Minerals, Metals & Materials Society. The correction involves updating the author list to correct the name Arafath Nifar to Arafath Nihar. The correction notice was reported by multiple authors listed in the document. [Extracted from the article]

Rigorous statistical analysis of whole building, 15-minute interval, time series electricity data enables remote insights into buildings' operational characteristics. We developed select building markers and applied them to six commercial office buildings located in three different climate zones for comparison. The building markers reveal information about daily operational patterns, scheduling, and the ratio of base to peak load. Time series analysis, clustering, anomaly detection, diffusion index-based forecasting, first-order energy differential, data visualization and data mining techniques were used for marker development. The daily operational pattern marker identifies weekday and weekend energy consumption patterns and was used here to quantify opportunities for alternative weekend energy scheduling to reduce energy consumption. The scheduling marker recognizes the turn-on and turn-off times for HVAC and other scheduled equipment. Here, we quantified an alternative HVAC schedule can reduce on average 2.7% energy consumption in the office buildings. The base to peak load ratio marker identified that the selected office buildings could reduce their baseload by using more aggressive night and weekend temperature setbacks. Ultimately, these building marker functions may be employed on any whole building electricity datasets to gain insights to building operation and characteristics, enabling improved identification of potential energy savings measures. [ABSTRACT FROM AUTHOR]

The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high‐quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually. [ABSTRACT FROM AUTHOR]

The width tapered beam method for measuring fracture toughness has been contemplated for use in measuring the adhesion of photovoltaic materials, is promoted by it being a viable method for use directly on modules rather than to engineered test specimens. However, precise determination of crack length is often difficult as failure often involves tendril formation, cavitation voids, and other plastic deformation at the crack front in addition to obfuscation by opaque materials. Because fracture energy varies as the square of crack length, imprecise crack tip length results in large measurement uncertainties. In this work, we develop a method to estimate the crack tip length using the relationship between the applied force and the displacement of the load frame. The crack tip is determined for every data point and can be defined from a calibration curve related to the amount of energy put into the beam, or from a theoretically idealized estimation. Once a calibration curve is set up and the equations programmed, the fracture energy as a function of position on the newly formed crack interface can be accurately estimated. The test additionally provides much easier analysis and insight into the joint failure through more precise knowledge of fracture toughness at each de-bonded area. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The open circuit voltage (VOC) is a critical and common indicator of solar cell performance as well as degradation, for panel down to lab-scale photovoltaics. Detecting VOC at the nanoscale is much more challenging, however, due to experimental limitations on spatial resolution, voltage resolution, and/or measurement times. Accordingly, an approach based on Conductive Atomic Force Microscopy is implemented to directly detect the local VOC, notably for monocrystalline Passivated Emitter Rear Contact (PERC) cells which are the most common industrial-scale solar panel technology in production worldwide. This is demonstrated with cross-sectioned monocrystalline PERC cells around the entire circumference of a poly-aluminum-silicide via through the rear emitter. The VOC maps reveal a local back surface field extending ~ 2 μm into the underlying p-type Si absorber due to Al in-diffusion as designed. Such high spatial resolution methods for photovoltaic performance mapping are especially promising for directly visualizing the effects of processing parameters, as well as identifying signatures of degradation for silicon and other solar cell technologies. [ABSTRACT FROM AUTHOR]

Aluminum nitride is a promising substrate material for AlGaN‐based UV‐LED. In order to develop a robust growth processing route for AlN single crystals, fundamental studies of solution growth experiments using Ni‐Al alloy melts as a new solution system were performed. Al can be stably kept in solution the Ni‐Al liquid even at high temperature; in addition, the driving force of the AlN formation reaction from solution can be controlled by solution composition and temperature. To investigate AlN crystal growth behavior we developed an in situ observation system using an electromagnetic levitation technique. AlN formation behavior, including nucleation and growth, was quantitatively analyzed by an image processing pipeline. The nucleation rate of AlN decreased with increasing growth temperature and decreasing aluminum composition. In addition, hexagonal c‐axis oriented AlN crystal successfully grew on the levitated Ni‐40 mol%Al droplet reacted at low driving force (1960 K), on the other hand, AlN crystal with dendritic morphology appeared on the sample with higher driving force (Ni‐50 mol%Al, 1960 K). Thus, the nucleation rate and crystal morphology were dominated by the driving force of the AlN formation reaction. [ABSTRACT FROM AUTHOR]

Recognition of spatially varying optical properties is a necessity when studying the van der Waals–London dispersion (vdW-Ld) interactions of carbon nanotubes (CNTs) that have surfactant coatings, tubes within tubes, and/or substantial core sizes. The ideal way to address these radially dependent optical properties would be to have an analytical add-a-layer solution in cylindrical coordinates similar to the one readily available for the plane-plane geometry. However, such a formulation does not exist nor does it appear trivial to be obtained exactly. The best and most pragmatic alternative for end-users is to take the optical spectra of the many components and to use a spectral mixing formulation so as to create effective solid-cylinder spectra for use in the far-limit regime. The near-limit regime at “contact” is dominated by the optical properties of the outermost layer, and thus no spectral mixing is required. Specifically we use a combination of a parallel capacitor in the axial direction and the Bruggeman effective medium in the radial direction. We then analyze the impact of using this mixing formulation upon the effective vdW-Ld spectra and the resulting Hamaker coefficients for small and large diameter single walled CNTs (SWCNTs) in both the near- and far-limit regions. We also test the spectra of a [16,0,s+7,0,s] multiwalled CNT (MWCNT) with an effective MWCNT spectrum created by mixing its [16,0,s] and [7,0,s] SWCNT components to demonstrate nonlinear coupling effects that exist between neighboring layers. Although this paper is primarily on nanotubes, the strategies, implementation, and analysis presented are applicable and likely necessary to any system where one needs to resolve spatially varying optical properties in a particular Lifshitz formulation. [ABSTRACT FROM AUTHOR]

Data‐driven science and technology have helped achieve meaningful technological advancements in areas such as materials/drug discovery and health care, but efforts to apply high‐end data science algorithms to the areas of glass and ceramics are still limited. Many glass and ceramic researchers are interested in enhancing their work by using more data and data analytics to develop better functional materials more efficiently. Simultaneously, the data science community is looking for a way to access materials data resources to test and validate their advanced computational learning algorithms. To address this issue, The American Ceramic Society (ACerS) convened a Glass and Ceramic Data Science Workshop in February 2018, sponsored by the National Institute for Standards and Technology (NIST) Advanced Manufacturing Technologies (AMTech) program. The workshop brought together a select group of leaders in the data science, informatics, and glass and ceramics communities, ACerS, and Nexight Group to identify the greatest opportunities and mechanisms for facilitating increased collaboration and coordination between these communities. This article summarizes workshop discussions about the current challenges that limit interactions and collaboration between the glass and ceramic and data science communities, opportunities for a coordinated approach that leverages existing knowledge in both communities, and a clear path toward the enhanced use of data science technologies for functional glass and ceramic research and development. [ABSTRACT FROM AUTHOR]

A materials data analytics (MDA) methodology was developed in this study to evaluate publicly available information on 9% Cr family steel and to handle nonlinear relationships and the sparsity in materials data for this alloy class. The overarching goal is to accelerate the design process as well as to reduce the time and expense associated with qualification testing of new alloys for fossil energy applications. Data entries in the analyzed data set for 82 iron‐base alloy compositions, several processing parameters, and results of tensile mechanical tests selected for this study were arranged in 34 columns by 915 rows. While detailed microstructural information was not available, it is assumed that the compositional space for the 9 to 12% Cr steels is limited such that all data entries have a tempered martensitic microstructure during service. Establishing a hierarchy of first‐order trends in the publicly available data requires the MDA to filter out the biases. Complexity of the phase transformations and microstructure evolution in the multicomponent alloys (using 21 chemical elements) with major influence on mechanical properties, leads to inefficiency in direct application of unbiased linear regression across the entire data space. To address the nonlinearity, analyses of tensile data were performed in composition‐based clusters. Clusters corresponding to moderately frequent patterns and maximized information gain were further refined by using p‐norm distance measures, matching the alloy classification groups adopted by industry. The evolutionary method of propagating an ensemble of competing cluster‐based models proved to be a viable option in dealing with scarce, multidimensional data. [ABSTRACT FROM AUTHOR]

Heritage data for the class of 9 to 12 wt pct Cr steels are studied using data science to quantify the statistically significant relationships among multiple processing/microstructure and performance variables. The effort is undertaken to find new martensitic steels for creep life of 10 5 hours or greater at 650 °C and 100 MPa using machine learning. Linear regression and lasso regression were utilized to identify alloying elements that contribute towards better creep strength. Visualization techniques such as t-distributed stochastic neighbor embedding and pair-wire element specific comparisons were utilized to explore information gaps that exist within the data and are in conflict with existing domain knowledge. Combining all results suggest that the next alloy design to be explored should be 9 wt pct Cr with high W (2 to 3 wt pct) and high Co (2 to 3 wt pct) for creep life of 10 5 hours or greater at 650 °C, 100 MPa. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Functional graded materials (FGM) allow for reconciliation of conflicting design constraints at different locations in the material. This optimization requires a priori knowledge of how different architectural measures are interdependent and combine to control material performance. In this work, an aluminum FGM was used as a model system to present a new network modeling approach that captures the relationship between design parameters and allows an easy interpretation. The approach, in an un-biased manner, successfully captured the expected relationships and was capable of predicting the hardness as a function of composition. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents the heteroepitaxial growth of ultrawide bandgap β-Ga2O3 thin films on c-plane sapphire substrates by low pressure chemical vapor deposition. N-type conductivity in silicon (Si)-doped β-Ga2O3 films grown on sapphire substrate is demonstrated. The thin films were synthesized using high purity metallic gallium (Ga) and oxygen (O2) as precursors. The morphology, crystal quality, and properties of the as-grown thin films were characterized and analyzed by field emission scanning electron microscopy, X-ray diffraction, electron backscatter diffraction, photoluminescence and optical, photoluminescence excitation spectroscopy, and temperature dependent van der Pauw/Hall measurement. The optical bandgap is ∼4.76 eV, and room temperature electron mobility of 42.35 cm²/V s was measured for a Si-doped heteroepitaxial β-Ga2O3 film with a doping concentration of 1.32×1018 cm-3. [ABSTRACT FROM AUTHOR]

The authors demonstrate the feasibility of quantifying cell-level performance heterogeneity from module-level I-V curves by determining conditions of bypass diode turn-on. Analysis of these curves falls outside of typical diode-based models of photovoltaic (PV) performance. The authors show that this approach can leverage statistical and machine learning techniques for broad application to massive datasets, and combine those insights with simulations and laboratory-based experiments to provide useful information into the metastability of the interfaces of a PV cell. The authors find good agreement between the experimentally determined curves and the simulated curves, which guide the variable selection in the massive dataset collected from sites in Cleveland, OH, USA, the Negev Desert, Israel, Isla Gran Canaria, Spain, and Mount Zugspitze, Germany. [ABSTRACT FROM AUTHOR]

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 O…H and N…H 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A challenging task in computational biophysics is to ascertain the solvent effect on the electronic structure and interatomic bonding at the atomistic level. Simulations must be carried out on reasonably large biomolecules for accurate calculations to yield valid results. We report the results of a calculation on collagen model in the form of a peptide under three different environments: vacuum, solvated and with neutral and charged sites. Quantitative results and analysis of the partial charge (PC) distribution on each amino acid are discussed. A significant charge transfer of more than 1 electron from protein to water molecules is found with similar results when the model contains charged sites. The main contributions to the interatomic bonding are from hydrogen bonds (HBs) between water-water and water-protein pairs. A connection between PC and HBs can be established since the nonpolar amino acids form no HBs and have the smallest PC and vice versa. The ab initio PC obtained are used in the NAMD simulation showing significant improvement over the default values as reflected in the root mean square deviation of atomic positions in the MD steps and the total free energy in energy minimization. These results could facilitate the interpretation of data on interaction of various ligands in charged proteins in relation to isoelectric points. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 PO43− 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. [ABSTRACT FROM AUTHOR]