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Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and processparameter information. 715 individual prints are examined to reveal melt pool areas that exceeds 0.07 mm2and pyrometry signal over a threshold (375 pyrometry units) were more likely to have defects. These automated processes enable massive throughput of characterization techniques.

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Phase transformations are a challenging problem in materials science, which lead to changes in properties and may impact performance of material systems in various applications. We introduce a general framework for the analysis of particle growth kinetics by utilizing concepts from machine learning and graph theory. As a model system, we use image sequences of atomic force microscopy showing the crystallization of an amorphous fluoroelastomer film. To identify crystalline particles in an amorphous matrix and track the temporal evolution of the particle dispersion, we have developed quantitative methods of 2D analysis. 700 image sequences were analyzed using a neural network architecture, achieving 0.97 pixel-wise classification accuracy as a measure of the correctly classified pixels. The growth kinetics of isolated and impinged particles were tracked throughout time using these image sequences. The relationship between image sequences and spatiotemporal graph representations was explored to identify the proximity of crystallites from each other. The framework enables the analysis of all image sequences without the requirement of sampling for specific particles or timesteps for various materials systems.

X-ray diffraction patterns contain information about the atomistic structure and microstructure (defect population) of materials, extracting detailed information from diffraction patterns is complex, demanding and relies on prior knowledge. We hypothesize that deep-learning techniques can help to perform an effective and accurate analysis with high throughput rates. To demonstrate this concept, we applied a novel deep learning framework to determine the evolution of the β-phase volume fraction in a Ti–6Al–4V alloy during heat-treatment from video sequences of 2D diffraction patterns recorded in transmission and with highly monochromatic radiation in a synchrotron beamline. In particular, we studied the impact of network designon prediction reliability and computational performance. Networks of different architectures were trained using 3008 experimental 2D patterns. A well-tuned model was found to reproduce the phase fractions of another experimental data set, consisting of 1100 diffraction patterns, with a mean-square error as small as 2.6×10-4. The average prediction error of β-phase volume fraction was within 1.6×10-2(in each diffraction pattern) of the values obtained by conventional methods. Our work demonstrates that convolutional neural networks can evaluate high energy X-ray diffraction patterns with a remarkable level of reliability. Furthermore, it demonstrates the significance of network design on the reliability of predictions and computational performance. The most complex models do not necessarily result in highest accuracy and may even fail to learn from the data.

Phase transformations in materials systems can be tracked using atomic force microscopy (AFM), enabling the examination of surface properties and macroscale morphologies. In situ measurements investigating phase transformations generate large datasets of time-lapse image sequences. The interpretation of the resulting image sequences, guided by domain-knowledge, requires manual image processing using handcrafted masks. This approach is time-consuming and restricts the number of images that can be processed. In this study, we developed an automated image processing pipeline which integrates image detection and segmentation methods. We examine five time-series AFM videos of various fluoroelastomer phase transformations. The number of image sequences per video ranges from a hundred to a thousand image sequences. The resulting image processing pipeline aims to automatically classify and analyze images to enable batch processing. Using this pipeline, the growth of each individual fluoroelastomer crystallite can be tracked through time. We incorporated statistical analysis into the pipeline to investigate trends in phase transformations between different fluoroelastomer batches. Understanding these phase transformations is crucial, as it can provide valuable insights into manufacturing processes, improve product quality, and possibly lead to the development of more advanced fluoroelastomer formulations.

To optimize and extend the service life of polymeric materials in outdoor environments, a domain knowledge-based and data-driven approach was utilized to quantitatively investigate the temporal evolution of degradation modes, mechanisms, and rates under various stepwise accelerated exposure conditions. Six formulations of poly(methyl methacrylate) (PMMA) with different combinations of stabilizing additives, including one unstabilized formulation, were exposed in three accelerated weathering conditions. Degradation was dependent on wavelength as samples in UV light at 340 nm (UVA) exposure showed the most yellowing. The unstabilized PMMA formulation showed much higher yellowness index values (59.5) than stabilized PMMA formulations (2–12). Urbach edge analysis shows a shift toward longer wavelength from 285 to 500 nm with increasing exposure time and an increased absorbance around 400 nm of visible region as the unstabilized samples increase in yellowing. The degradation mechanisms of PMMA were tracked using induced absorbance to dose at specific wavelengths that correspond to known degradation mechanisms. The degradation pathway of PMMA was modeled in a framework using network structural equation modeling (netSEM). netSEM showed changes in degradation pathway as PMMA transition stages of degradation.

The current popularity of photovoltaic (PV) systems is due in large part to their exceptional reliability and significantly lower cost than other energy sources. Studying cell and module degradation is key to promote further development in the state of the art. Fielded oraccelerated aged modules exhibit different failure modes, of which metallization degradation (contacts and interconnections) is prevalent. In this work, we discuss how multiscale characterization methods can be applied to a variety of module technologies that have been field exposed and have undergone accelerated age testing. These methods include performing characterization on the module level, cell level, and finally the materials level. The observed performance losses from the module- and cell-level characterization can be correlated with materials properties to find out the root cause of degradation. We recommend an initial nondestructive characterization suite, including module- and cell-level current–voltage ($I\text{--}V$), Suns–VOC, photoluminescence and electroluminescence imaging, quantum efficiency, ultraviolet fluorescence imaging, and thermal infrared imaging. Samples are then extracted from particularly degraded regions of the module and prepared for materials characterization techniques, such as top-down and cross-sectional scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, secondary ion mass spectrometry, Raman spectroscopy, and transmission electron microscopy, allowing a deeper look into the mechanism behind the metallization degradation. This article serves as an instructional review to introduce the different multiscale characterization methods and how they can be effectively applied to perform PV degradation studies. We also share some of our examples and discuss the strengths, limitations, and best practices for each of the characterization techniques.

Monitoring crop growth, soil conditions, and hydrological dynamics are imperative for sustainable agriculture and reduced environmental impacts. This interdisciplinary study integrates remote sensing, digital soil mapping, and hydrological data to elucidate intricate connections between these factors in the state of Ohio, USA. Advanced spatiotemporal analysis techniques were applied to key datasets, including the MODIS sensor satellite imagery, USDA crop data, soil datasets, Aster GDEM, and USGS stream gauge measurements. Vegetation indices derived from MODIS characterized crop-specific phenology and productivity patterns. Exploratory spatial data analysis show relationships of vegetation dynamics and soil properties, uncovering links between plant vigor, edaphic fertility, and nutrient distributions. Correlation analysis quantified these relationships and their seasonal evolution. Examination of stream gauge data revealed insights into spatiotemporal relationships of nutrient pollution and stream discharge. By synthesizing diverse geospatial data through cutting-edge data analytics, this work illuminated complex interactions between crop health, soil nutrients, and water quality in Ohio. The methodology and findings provide actionable perspectives to inform sustainable agricultural management and environmental policy. This study demonstrates the significant potential of open geospatial resources when integrated using a robust spatiotemporal framework. Integrating additional measurements and high-resolution data sources through advanced analytics and interactive visualizations could strengthen these insights.

Utilizing large-scale outdoor $I$–$V$ data streams, the authors demonstrated a feasible, repeatable method to construct outdoor time-series $I_{\text{sc}}$–$V_{\text{oc}}$ curves. Resulting Analytic $I_{\text{sc}}$–$V_{\text{oc}}$ curves are validated with laboratory $Suns$-$V_{\text{oc}}$ measurements. Comparing $I_{\text{sc}}$–$V_{\text{oc}}$ with real $I$–$V$ curves, power loss modes including uniform current loss, recombination loss, series resistance loss, and current mismatch loss can be quantified in units of watts or percent nominal power, making them directly comparable. Above power loss modes are calculated for long-term $I$–$V$ data from fielded modules in different climate zones, and dominant power loss modes were determined from the resulting time series. This method has been published as an open source package in R programming language.

Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research and development, and power plant operations and management. Predicting module electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification of both generalized and performance mechanism-specific EL image features, using pixel intensity-based and machine learning classification algorithms. From EL image features, we build predictive models for PV module power and series resistance, using time-series current-voltage (I-V) and EL data obtained stepwise on five brands of modules spanning three Si cell types through two accelerated exposures: damp heat (DH) (85°C/85% RH) and thermal cycling (TC) (IEC 61215). In total, 195 pairs of EL images and I-V characteristics were analyzed, yielding 11700 individual PV cell images. A convolutional neural network was built to classify cells by the severity of busbar corrosion with high accuracy (95%). Generalized power predictive models estimated the maximum power of PV modules from EL images with high confidence and an adjusted-R2 of 0.88, across all module brands and cell types in extended DH and TC exposures. Mechanistic degradation prediction was demonstrated by quantification of busbar corrosion in EL images of three module brands in DH, and subsequent modeling of series resistance using these mechanism-specific EL image features. For modules exhibiting busbar corrosion, we demonstrated series resistance predictive models with adjusted-R2 of up to 0.73.

Photovoltaic (PV) module backsheets degrade at different rates because of the specific polymeric materials and the local exposure environments of the installations. Studies of real-world backsheet degradation provide valuable information to understand backsheet degradation and failure. Field surveys of PV module backsheets were conducted on 1310 modules in four commercial PV power plant sites with different exposure times. The backsheet's local exposure environment is determined by its location along the rack length and depth, the modules’ elevation above the ground, and the ground cover albedo. Backsheets that are installed at the ends of the module rack length exhibit larger degradation rates (with yellowness index difference of 1.14 $\pm$ 0.45 and 7.80 $\pm$ 1.3 for backsheets of poly (ethylene terephthalate) and poly (ethylene naphthalate) as the air-side layer, respectively) than backsheets in the center section of the rack. A generalized spatio-temporal model was developed to predict the large-scale backsheet degradation of different backsheet polymers across time/age and location in the PV power plant rack. The model utilizes a cubic-spline relationship between backsheet degradation and rack length, and a quadratic relationship between backsheet discoloration and rack depth. This generalized spatio-temporal model predicts the outdoor backsheet degradation with an adjusted-$R^2$ range between 0.31 to 0.89. The similarity between this model and the spatial variation of the rear-side irradiance, indicates that the irradiance plays a significant role in outdoor backsheet degradation. The generalized spatio-temporal model can be used to evaluate large scale PV backsheet performance, as well as a guidance for PV site designers and operations and maintenance crews.

An automated data analysis pipeline is developed to preprocess electroluminescence (EL) module images, and parse the images into individual cells to be used as an input for machine learning algorithms. The dataset used in the study includes EL images of three 60 cell modules from each of five commercial brands at six steps of damp heat exposure, from 500 to 3000 h. Preprocessing of the original raw EL images includes lens distortion correction, filtering, thresholding, convex hull, regression fitting, and perspective transformation to produce planar indexed module and single cell images. Parsing of PV cells from each of the preprocessed 90 EL module images gives us 5400 cell images, which are function of module brand and damp heat exposure step. From the dataset, two unique degradation categories (“cracked” and “corroded”) were observed, while cells that did not degrade were classified as “good.” For supervised machine learning modeling, cell images were sorted into these three classes yielding 3550 images. A training and testing framework with 80:20 sampling ratio was generated using stratified sampling. Three machine learning algorithms (support vector machine, Random Forest, and convolutional neural network) were trained and tuned independently on the training set and then given the test set to predict the scores for each of the three models. Five-fold cross validation was done on training set to tune hyper-parameters of the models. Model prediction scores showed that convolutional neural network outperforms support vector machine and Random Forest for supervised PV cell classification.

In research on photovoltaic (PV) device degradation, current–voltage ($I$–$V$) datasets carry a large amount of information in addition to the maximum power point. Performance parameters such as short-circuit current, open-circuit voltage, shunt resistance, series resistance, and fill factor are essential for diagnosing the performance and degradation of solar cells and modules. To enable the scaling of $I$–$V$ studies to millions of $I$–$V$ curves, we have developed a data-driven method to extract $I$–$V$ curve parameters and distributed this method as an open-source package in R. In contrast with the traditional practice of fitting the diode equation to $I$–$V$ curves individually, which requires solving a transcendental equation, this data-driven method can be applied to large volumes of $I$–$V$ data in a short time. Our data-driven feature extraction technique is tested on $I$–$V$ curves generated with the single-diode model and applied to $I$–$V$ curves with different data point densities collected from three different sources. This method has a high repeatability for extracting $I$–$V$ features, without requiring knowledge of the device or expected parameters to be input by the researcher. We also demonstrate how this method can be applied to large datasets and accommodates nonstandard $I$–$V$ curves including those showing artifacts of connection problems or shading where bypass diode activation produces multiple “steps.” These features together make the data-driven $I$–$V$ feature extraction method ideal for evaluating time-series $I$–$V$ data and analyzing power degradation mechanisms in PV modules through cross comparisons of the extracted parameters.

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.

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.

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.

The free energies, forces, and torques for all separations and mutual orientations are formulated in the full Lifshitz continuum theory of van der Waals interactions. Angular dependence of interactions is characterized for three different interaction geometries of bodies with morphological and/or material anisotropy, interacting across an isotropic aqueous medium: two infinite half-spaces; two half-spaces of composite media comprising parallel cylinder arrays; and an isolated pair of long, thin cylinders. The contributions to van der Waals interaction energy due to shape anisotropy and material anisotropy are isolated in detailed calculations and examined. Surprisingly, the effect of shape on interactions in the retarded regime results in a torque between arrays of cylinders that is stronger than that between half-spaces.

Costs associated with conventional photovoltaic (PV) installations (module manufacturing, mounts, wiring and installation labor, etc.) scale linearly with system area, and prudent design practices for increasing the light harvesting may significantly enhance the power output per unit area and cost per watt. The use of PV modules that have been augmented by the addition of a solar mirror provides an opportunity to improve light harvesting of a PV module and cost-per-watt considerations if the mirror is less expensive than the relative increase in power. In order to harvest greater insolation, an optimized design configuration between a flat-panel module and mirror is necessary for a fixed (nontracked) mirror-augmented photovoltaic (MAPV) system. A series of irradiance and energy harvest calculations were developed to screen various MAPV design configurations. We employed optical ray-tracing simulations to determine irradiance nonuniformities on the fixed MAPV system. The simulated results are compared with outdoor experimental field trial results. Over a three-month period of study, the fixed MAPV system produced 12% more power than an equivalent nonaugmented panel. An adjustable angle mounting system known as “time machine” was used to estimate yearly power production by adjusting the relative position of the MAPV system to represent varying times of the year. Current--voltage (I-V) curve tracing of test modules was performed during the field trials on augmented and nonaugmented modules for comparison. The experimental time machine result is in agreement with the irradiance simulation, which shows a gullwing curve of annual power output with peak production on the equinoxes and reduced production on the solstices.

We report on the design, construction, and initial performance measurements of a low-concentration photovoltaic system based on compound parabolic concentrators (CPCs). The system is approximately a 7× concentration system and uses commercially available laser groove buried contact monocrystalline silicon photovoltaic cells. The CPCs are fabricated using a second-surface aluminized acrylic mirror with proven weather durability. The asymmetric CPC optical design was driven by a balance between concentration factor, thermal issues, and optical angle of acceptance and was thoroughly evaluated by optical ray tracing. The design was targeted for a single-axis tracking system, with extruded aluminum heat sinks doubling as structural components. We fabricated a 120-cell (10 × 12) prototype array, and over three months of operation, we estimated an approximate peak total system power efficiency of 7.9%, limited mostly by the CPC optical efficiency (∼55%) and the cell conversion efficiency. We discuss several issues regarding system performance, reliability, and cost.

Replex Plastics aims to develop a low-cost, low-concentration photovoltaic module using a metallized acrylic reflector designed for use with an inclined single-axis tracker. An asymmetric compound parabolic concentrator is developed and analyzed optimizing the many factors impacting the design, such as tracking strategy, manufacturing process, and cell size. Ray tracing is used to improve the design as well as predict the performance. Results of the simulation closely match the tested performance of the prototype. The final design is an asymmetric compound parabolic concentrator mounted to an encapsulated silicon cell receiver with a system optical efficiency of 60%. The prototype concentrator achieves ~7x increase in power output over an encapsulated receiver with no reflector.

A system's van der Waals–London dispersion interactions are often ignored, poorly understood, or crudely approximated, despite their importance in determining the intrinsic properties and intermolecular forces present in a given system. There are several key barriers that contribute to this issue: 1) lack of the required full spectral optical properties, 2) lack of the proper geometrical formulation to give meaningful results, and 3) a perception that a full van der Waals–London dispersion calculation is somehow unwieldy or difficult to understand conceptually. However, the physical origin of the fundamental interactions for carbon nanotube systems can now be readily understood due to recent developments which have filled in the missing pieces and provided a complete conceptual framework. Specifically, our understanding is enhanced through a combination of a robust, ab-initio method to obtain optically anisotropic properties out to 30 electron Volts, proper extensions to the Lifshitz's formulations to include optical anisotropy with increasingly complex geometries, and a proper methodology for employing optical mixing rules to address multi-body and multi-component structures. Here we review this new framework to help end-users understand these interactions, with the goal of better system design and experimental prediction. Numerous examples are provided to show the impact of a material's intrinsic geometry, including optical anisotropy as a function of that geometry, and the effect of the size of the nanotube core and surfactant material present on its surface. We'll also introduce some new examples of how known trends in optical properties as a function of [n, m] can result in van der Waals interactions as a function of nanotube classification, radius, and other parameters. The concepts and framework presented are not limited to the nanotube community, and can be equally applied to other nanoscale or even biological systems.

Optical immersion lithography utilizes liquids with refractive indices >1 (the index of air) below the last lens element to enhance numerical aperture and resolution, enabling sub-40-nm feature patterning. This shift from conventional dry optical lithography introduces numerous challenges requiring innovations in materials at all imaging stack levels. In this article, we highlight the recent materials advances in photomasks, immersion fluids, topcoats, and photoresists. Some of the challenges encountered include the fluids' and photomask materials' UV durability, the high-index liquids' compatibility with topcoats and photoresists, and overall immersion imaging and defectivity performance. In addition, we include a section on novel materials and methods for double-patterning lithography—a technique that may further extend immersion technology by effectively doubling a less dense pattern's line density.

More than 50 fluorocarbon liquids are measured for transparency over the wavelength range 150 to 200 nm for the purpose of identifying a suitably transparent fluid for use in 157-nm liquid immersion lithography. Purification methods such as degasification, distillation, silica gel drying, and supercritical fluid fractionation are investigated to determine the impact of residual contaminants on absorbance. The purification processes are monitored by gas chromatography-mass spectrometry and Fourier-tranform infrared spectroscopy (for organics), 19F-nuclear magnetic resonance spectroscopy (for molecular structure), gel permeation chromatography (for molecular weight), Karl Fisher analysis (for water), and for residual dissolved oxygen. We find that in most cases, the absorbance is dominated by dissolved oxygen and water. Once the contaminant levels are reduced, the most transparent perfluoroether (PFE) measured is perfluoro-1,2-bis(2-methoxyethoxy)ethylene glycol (perfluorotriglyme) at 0.52 cm−1, the most transparent perfluoroalkane (PFA) measured is perfluorohexane at 1.1 cm−1, the most transparent hydrofluoroether (HFE) was 1-(1H-tetrafluoro)ethoxy-2-(1-trifluoromethyl)tetrafluoroethoxy-2-trifluoromethyl-1,1,2-trifluoroethane at 2.6 cm−1, and the lowest projected absorption coefficient for a hydrofluoroalkane (HFA) is decafluoro-2H,3H-pentane at <2 cm−1. Our chemical analysis shows that some impurities still remain in these materials, and further reductions in absorption are likely. Even so, our current absorption values should allow lens-to-wafer working distances (assuming 95 transmission) of 428, 203, 83, and 111 m, respectively, for the four classes of fluids. The identification of these four classes (PFEs, PFAs, HFEs, and HFAs) of fluids for potential use as 157-nm immersion fluids, each with their own ranges of viscosity, vapor pressure, refractive index, dn/dT,synthetic routes, and cost, should allow for flexibility in performing tradeoff analyses for various 157-nm immersion lithography engineering designs and cost of ownership estimates. © 2004 Society of Photo-Optical Instrumentation Engineers.

Van der Waals dispersive forces produce attractive interactions between bodies, playing an important role in many material systems influencing colloidal and emulsion stability, wetting behavior, and intergranular forces in glass–ceramic systems. It is of technological importance to accurately quantify these interactions, conveniently represented by the Hamaker constant,A. To set the current level of accuracy for determiningA, they were calculated from Lifshitz theory using full spectral data for muscovite mica, Al2O3, SiO2, Si3N4, and rutile TiO2, separated by vacuum or water. These were compared to Hamaker constants calculated from physical properties using the Tabor–Winterton approximation, a single oscillator model, a multiple oscillator model, andA’s calculated using force vs separation data from surface force apparatus and atomic force microscope studies. For materials with refractive indices between 1.4 and 1.8 separated by vacuum, all methods produce similar values, but for indices larger than 1.8 separated by vacuum, and any of these materials separated by water, results span a broader range. The present level of accuracy for the determination of Hamaker constants, here taken to be represented by the level of agreement between various methods, ranges from about 10% for the case of SiO2/vacuum/SiO2and TiO2/water/TiO2to a factor of approximately 7 for mica/water/mica.

The light-scattering performance of TiO2pigment depends intimately upon particle size, size distribution, shape and dispersion quality. TiO2particles exhibit complex shapes and have anisotropic optical constants, and particulate dispersions of TiO2are characterized by complicated microstructures such as aggregates and agglomerates. Despite this complexity, the theoretical understanding of light scattering by white pigment has been based upon Mie theory, which is restricted to the case of a single, optically isotropic sphere. We utilize a finite-element method which produces rigorous solutions to Maxwell's equations to determine computationally the light-scattering properties of complex particulate microstructures. This represents a significant step beyond the restrictions of Mie theory, providing a method to determine quantitatively the effects of particle shape, optical anisotropy, and interactions between neighboring particles upon the light-scattering properties of white pigments in coatings. In the present study, we use the finite-element method first to compute the light-scattering properties of a single, morphological rutile particle with a representative size and shape. These results are compared to the light-scattering properties of the optically isotropic, equivalent volume sphere using Mie theory. Neither the average index nor the weighted sum approximation offers clear advantages in this case. Second, the far-field light-scattering properties of two such particles interacting at near field are determined as a function of the interparticle separation. The agglomerated pair of particles exhibits a 20 decrease relative to the single particle in the scattering parameter associated with the hiding power of a paint film. The basis for this decrease is the same as for the crowding effect observed in extensive paint films. The results of both sets of computations are compared to Mie theory to determine the sizes of spherical particles with equivalent scattering cross sections. These comparisons highlight the inherent difficulties in using Mie theory to evaluate particle size by light-scattering methods.

The optical properties of alpha-Al2O3have been determined by two independent methods, vacuum ultraviolet (VUV) spectroscopy and electron energy-loss spectroscopy (EELS) over the energy range from 6 to 142 eV. For each experimental method two sets of high-quality data have been measured and analyzed from alpha-Al2O3. The evolution of the differences between the data for each experimental method and its associated analytical method indicate the reliability of the results, and the comparison between the methods emphasizes the relative advantages of each. VUV spectroscopy offers a higher energy resolution, resolving sharper features in the spectra, whereas EELS significantly extends the energy range, which has been exploited here to 140 eV but can be extended even further. Good overall consistency is found, but there are pronounced differences in the valence region from 10 to 30 eV, where strong absorptions are present, increasing the relative variability of the analysis. This demonstrates that accuracy estimates of the optical properties are meaningful only when supplied as a function of energy, because both strong intrinsic absorptions and weak experimental signals make accurate determinations difficult. Moreover, variations in the optical properties are observed most easily in the interband transition strength, Jcv, and are less evident when the optical properties are represented as the complex index of refraction. Because the optical properties can change with specimen origin and preparation and because of the details of data acquisition and numerical analysis procedures, assessing the contributions of each of these methods to our accurate knowledge of the optical constants is essential.

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.

Mirror augmented photovoltaic (MAPV) systems utilize low cost mirrors to couple more light into a photovoltaic (PV) absorber. By increasing the light absorbed, they are expected to produce less expensive electricity. As a substrate candidate for back surface reflector mirrors, two grades of PMMA have been exposed to UV stress from two sources at two intensities for two doses in an effort to see the response of materials under different states of stress and after exposure to different amounts of total stress. By developing a framework for correlating stresses, such as short wave ultraviolet radiation, with responses, such as induced absorbance and yellowing, mirror durability we have made progress in developing lifetime and degradation science using mirror durability as a case study. All of the samples showed similarities in their degradation characteristics. The UV stress acceleration factor was quantized as 10.2 in short wave ultraviolet irradiance, and 15.8 in total shortwave UV dose. The effects of UV absorbers in protecting the polymer from degradation are discussed. Further study into degradation mechanisms will elucidate the exact phenomena that contribute to these material responses to stress.

Water-based immersion lithography using ArF illumination is able to provide optical solutions as far as the 45-nm node, but is not able to achieve the 38- or 32-nm nodes as currently defined. Achieving these lithographic nodes will require new, higher refractive index fluids to replace the water used in first-generation immersion systems. We have developed a number of such second-generation high-index fluids for immersion lithography at 193 nm. These highly transparent fluids have 193-nm indices up to 1.664. To understand the behavior and performance of different fluid classes, we use spectral index measurements to characterize the index dispersion, coupled with Urbach absorption edge analysis and Lorentz Oscillator modeling. Interference imaging printers have long been available, and they now have a new use: a rapid, cost-effective way to develop immersion lithography, particularly at extremely high resolutions. Although interference printers will never replace classical lens-based lithography systems for semiconductor device production, they do offer a way to develop resist and fluid technology at a relatively low cost. Their simple image-forming format offers easy access to the basic physics of advanced imaging. Issues such as polarization of the image-forming light rays, fluid/resist interaction during exposure, topcoat film performance, and resist line edge roughness (LER) at extremely high resolutions, can all be readily studied. 32-nm 1:1 line/space (L/S) imaging is demonstrated using two of the second-generation fluids. These resolutions are well beyond current lens-based system capabilities. Results on the performance of various resists and topcoats are also reported for 32-nm L/S features.

Water-based immersion lithography using ArF illumination is able to provide optical solutions as far as the 45-nm node, but is not able to achieve the 38- or 32-nm nodes as currently defined. Achieving these lithographic nodes will require new, higher refractive index fluids to replace the water used in first-generation immersion systems. We have developed a number of such second-generation high-index fluids for immersion lithography at 193 nm. These highly transparent fluids have 193-nm indices up to 1.664. To understand the behavior and performance of different fluid classes, we use spectral index measurements to characterize the index dispersion, coupled with Urbach absorption edge analysis and Lorentz Oscillator modeling. Interference imaging printers have long been available, and they now have a new use: a rapid, cost-effective way to develop immersion lithography, particularly at extremely high resolutions. Although interference printers will never replace classical lens-based lithography systems for semiconductor device production, they do offer a way to develop resist and fluid technology at a relatively low cost. Their simple image-forming format offers easy access to the basic physics of advanced imaging. Issues such as polarization of the image-forming light rays, fluid/resist interaction during exposure, topcoat film performance, and resist line edge roughness (LER) at extremely high resolutions, can all be readily studied. 32-nm 1:1line/space (L/S) imaging is demonstrated using two of the second-generation fluids. These resolutions are well beyond current lens-based system capabilities. Results on the performance of various resists and topcoats are also reported for 32-nm L/S features.