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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Technoeconomic Analysis of High-Value, Crystalline Silicon Photovoltaic Module Recycling Processes

Recycling is an important circular economy strategy, and for photovoltaics (PV), the one that has received the greatest research attention. Recycling of PV modules is required in Europe; everywhere else it competes in the market of end-of-life options, where cost is a primary decision factor. The vast majority of PV modules sold globally are crystalline-silicon (c-Si); most of the rest of the market are cadmium telluride thin film modules whose primary manufacturer runs its own commercial-scale recycling program. While many different c-Si recycling approaches and technologies have been proposed, they generally lack accompanying cost estimates or enough process information to model costs. Herein we develop detailed estimates of seven categories of capital and operating costs along with estimates of revenue from recovered materials for each step in two proposed recycling processes for c-Si PV modules. Using these results, we develop a hypothetical third recycling process merging process steps from the original two, estimate surcharges required to achieve minimum sustainable prices for each recycling process, as well as consider how economies of scale could reduce costs. Increasing the purity of and identifying higher-value markets for recovered materials are approaches to reducing costs and increasing recycling rates in voluntary markets.

crystalline silicon↗
DOE OSTI2022

Delivering real-time multi-modal materials analysis with enterprise beamlines

Contemporary advancements in low-cost automation and computation, reduced barrier to entry in developing artificial intelligence/machine learning (AI/ML), and increased ability to represent complex materials in digital form have led to a number of accelerated materials discovery platforms. However, many of these approaches operate with completely rigid vertical integration in an isolated feedback loop using limited modalities. In order to make a substantial impact on discovering new energy materials, AI-driven experiments must operate collaboratively with each other and researchers and over multiple measurement modalities. Herein, we describe the potential for an “internet of things” approach to self-driving enterprise beamlines that merges core information technologies, robotics, and multi-modal AI. The approach will enable full utility of light sources, collaborate effectively with other remote materials acceleration platforms, and help stride toward the world’s energy future.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multi-scale dynamics of magnetic flux tubes and inverse magnetic energy transfer

Here, we report on an analytical and numerical study of the dynamics of a three-dimensional array of identical magnetic flux tubes in the reduced-magnetohydrodynamic description of the plasma. We propose that the long-time evolution of this system is dictated by flux-tube mergers, and that such mergers are dynamically constrained by the conservation of the pertinent (ideal) invariants, viz. the magnetic potential and axial fluxes of each tube. We also propose that in the direction perpendicular to the merging plane, flux tubes evolve in a critically balanced fashion. These notions allow us to construct an analytical model for how quantities such as the magnetic energy and the energy-containing scale evolve as functions of time. Of particular importance is the conclusion that, like its two-dimensional counterpart, this system exhibits an inverse transfer of magnetic energy that terminates only at the system scale. We perform direct numerical simulations that confirm these predictions and reveal other interesting aspects of the evolution of the system. We find, for example, that the early time evolution is characterized by a sharp decay of the initial magnetic energy, which we attribute to the ubiquitous formation of current sheets. We also show that a quantitatively similar inverse transfer of magnetic energy is observed when the initial condition is a random, small-scale magnetic seed field.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Formation of transient high-β plasmas in a magnetized, weakly collisional regime

We present experimental data providing evidence for the formation of transient (~20 μs) plasmas that are simultaneously weakly magnetized (i.e. Hall magnetization parameter ωτ > 1) and dominated by thermal pressure (i.e. ratio of thermal-to-magnetic pressure β > 1). Particle collisional mean free paths are an appreciable fraction of the overall system size. These plasmas are formed via the head-on merging of two plasmas launched by magnetized coaxial guns. The ratio λ gun = μ 0 I gun /ψ gun of gun current I gun to applied magnetic flux ψ gun is an experimental knob for exploring the parameter space of β and ωτ. These experiments were conducted on the Big Red Ball at the Wisconsin Plasma Physics Laboratory. The transient formation of such plasmas can potentially open up new regimes for the laboratory study of weakly collisional, magnetized, high-β plasma physics; processes relevant to astrophysical objects and phenomena; and novel magnetized plasma targets for magneto-inertial fusion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Cluster Analysis of Combined EDS and EBSD Data to Solve Ambiguous Phase Identifications

A common problem in analytical scanning electron microscopy (SEM) using electron backscatter diffraction (EBSD) is the differentiation of phases with distinct chemistry but the same or very similar crystal structure. X-ray energy dispersive spectroscopy (EDS) is useful to help differentiate these phases of similar crystal structures but different elemental makeups. However, open, automated, and unbiased methods of differentiating phases of similar EBSD responses based on their EDS response are lacking. This paper describes a simple data analytics-based method, using a combination of singular value decomposition and cluster analysis, to merge simultaneously acquired EDS + EBSD information and automatically determine phases from both their crystal and elemental data. I use hexagonal TiB 2 ceramic contaminated with multiple crystallographically ambiguous but chemically distinct cubic phases to illustrate the method. Code, in the form of a Python 3 Jupyter Notebook, and the necessary data to replicate the analysis are provided as Supplementary material.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Data Science Meets Physical Organic Chemistry

At the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction. While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate. In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin-Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔ G ‡ = - RT ln[( S )/( R )], where ( S ) and ( R ) represent the concentrations of the enantiomeric products). We, and others, speculated that this important piece of information can be leveraged to guide reaction optimization in a quantitative way. Although traditional linear free energy relationships (LFERs), such as Hammett plots, have been used to illuminate important mechanistic features, we sought to develop data science derived tools to expand the power of LFERs in order to describe complex reactions frequently encountered in modern asymmetric catalysis. Specifically, we investigated whether enantioselectivity data from a reaction can be quantitatively connected to the attributes of reaction components, such as catalyst and substrate structural features, to harness data for asymmetric catalyst design. In this context, we developed a workflow to relate computationally derived features of reaction components to enantioselectivity using data science tools. The mathematical representation of molecules can incorporate many aspects of a transformation, such as molecular features from substrate, product, catalyst, and proposed transition states. Statistical models relating these features to reaction outputs can be used for various tasks, such as performance prediction of untested molecules. Perhaps most importantly, statistical models can guide the generation of mechanistic hypotheses that are embedded within complex patterns of reaction responses. Overall, merging traditional physical organic experiments with statistical modeling techniques creates a feedback loop that enables both evaluation of multiple mechanistic hypotheses and future catalyst design. In this Account, we highlight the evolution and application of this approach in the context of a collaborative program based on chiral phosphoric acid catalysts (CPAs) in asymmetric catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Toward Ideal Metal–Organic Framework Thin-Film Growth via Automated Layer-by-Layer Deposition: Examples Based on Perylene Diimide Linkers

Photoactive metal-organic framework (MOF) thin films offer an opportunity for translating the advantages of periodic, crystalline, and tailorable light-harvesting materials directly into devices such as those for photoelectrochemical solar energy conversion. In this study, we report the fabrication of light-absorbing perylene-diimide-containing pillared-paddlewheel MOF thin films using an automated layer-by-layer (LbL) deposition technique. Our focus here is on optimizing the growth of representative chromophoric MOFs as oriented films of uniform and predefined thickness. Growth was examined as a function of metal identity, pillaring ligand composition, and supporting-surface chemical functionality. Further, application of atomic force microscopy (AFM) and complementary techniques revealed that the surface-supported MOFs initially display island-type film growth (Volmer-Weber growth), resulting in comparatively rough films. Further growth is accompanied by the merging of islands, resulting in films that, depending on experimental details, can be remarkably smooth (i.e., roughness on the order of ±1 nm (one structural repeat unit in the pillaring direction)). These details include the use of 1,4-diazabicyclo[2.2.2]octane (DABCO) as a MOF pillar and ALD-grown zinc oxide as a film support (ALD = atomic layer deposition). Also helpful for mitigating island-type growth, at least in part, is the replacement of Zn 2+ by Cu 2+ as the metal component of the MOF. Notably, each of these adjustments entails replacing weaker chemical bonds with stronger ones.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Quantum Plasmonic Sensors

The extraordinary sensitivity of plasmonic sensors is well-known in the optics and photonics community. These sensors exploit simultaneously the enhancement and the localization of electromagnetic fields close to the interface between a metal and a dielectric. This enables, for example, the design of integrated biochemical sensors at scales far below the diffraction limit. Despite their practical realization and successful commercialization, the sensitivity and associated precision of plasmonic sensors are starting to reach their fundamental classical limit given by quantum fluctuations of light—known as the shot-noise limit. To improve the sensing performance of these sensors beyond the classical limit, quantum resources are increasingly being employed. This area of research has become known as “quantum plasmonic sensing”, and it has experienced substantial activity in recent years for applications in chemical and biological sensing. This review aims to cover both plasmonic and quantum techniques for sensing, and it shows how they have been merged to enhance the performance of plasmonic sensors beyond traditional methods. We discuss the general framework developed for quantum plasmonic sensing in recent years, covering the basic theory behind the advancements made, and describe the important works that made these advancements. We also describe several key works in detail, highlighting their motivation, the working principles behind them, and their future impact. The intention of the review is to set a foundation for a burgeoning field of research that is currently being explored out of intellectual curiosity and for a wide range of practical applications in biochemistry, medicine, and pharmaceutical research.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units

Atomic structure prediction and associated property calculations are the bedrock of chemical physics. Since high-fidelity ab initio modeling techniques for computing the structure and properties can be prohibitively expensive, this motivates the development of machine-learning (ML) models that make these predictions more efficiently. Training graph neural networks over large atomistic databases introduces unique computational challenges such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs such as social networks. We demonstrate a novel hardware-software co-design approach to scale up the training of atomistic graph neural networks (GNN) for structure and property prediction. First, to eliminate redundant computation and memory associated with alternative padding techniques and to improve throughput via minimizing communication, we formulate the effective coalescing of the batches of variable-size atomistic graphs as the bin packing problem and introduce a hardware-agnostic algorithm to pack these batches. In addition, we propose hardware-specific optimizations including a planner and vectorization for the gather-scatter operations targeted for Graphcore’s Intelligence Processing Unit (IPU), as well as model-specific optimizations such as merged communication collectives and optimized softplus. Putting these all together, we demonstrate the effectiveness of the proposed co-design approach by providing an implementation of a well-established atomistic GNN on the Graphcore IPUs. We evaluate the training performance on multiple atomistic graph databases with varying degrees of graph counts, sizes and sparsity. Here, we demonstrate that such a co-design approach can reduce the training time of atomistic GNNs and can improve the performance by up to 1.5× compared to the baseline implementation of the model on the IPUs. Additionally, we compare our IPU implementation with a Nvidia GPU-based implementation and show that our atomistic GNN implementation on the IPUs can run 1.8× faster on average compared to the execution time on the GPUs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Strategies to Obtain Reliable Energy Landscapes from Embedded Multireference Correlated Wavefunction Methods for Surface Reactions

Embedded correlated wavefunction (ECW) theory is a powerful tool for studying ground- and excited-state reaction mechanisms and associated energetics in heterogeneous catalysis. Several factors are important to obtaining reliable ECW energies, critically the construction of consistent active spaces (ASs) along reaction pathways when using a multireference correlated wavefunction (CW) method that relies on a subset of orbital spaces in the configuration interaction expansion to account for static electron correlation, e.g., complete AS self-consistent field theory, in addition to the adequate partitioning of the system into a cluster and environment, as well as the choice of a suitable basis set and number of states included in excited-state simulations. Here, in this work, we conducted a series of systematic studies to develop best-practice guidelines for ground- and excited-state ECW theory simulations, utilizing the decomposition of NH 3 on Pd(111) as an example. We determine that ECW theory results are relatively insensitive to cluster size, the aug-cc-pVDZ basis set provides an adequate compromise between computational complexity and accuracy, and that a fixed-clean-surface approximation holds well for the derivation of the embedding potential. Additionally, we demonstrate that a merging approach, which involves generating ASs from the molecular fragments at each configuration, is preferable to a creeping approach, which utilizes ASs from adjacent structures as an initial guess, for the generation of consistent potential energy curves involving open-d-shell metal surfaces, and, finally, we show that it is essential to include bands of excited states in their entirety when simulating excited-state reaction pathways.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

DONKEY: A Flexible and Accurate Algorithm for Clustering

We propose an accurate clustering algorithm suitable for the varied and multidimensional data sets that correspond to temporal snapshots from on-the-fly nonadiabatic trajectory-based simulations of photoexcited dynamics. The algorithm approximates the underlying probability density function using variable kernel density estimation, with local maxima corresponding to cluster centers. Each data point is then assigned to one of the maxima by employing a maximization procedure. Finally, clusters artificially separated by minor fluctuations in the probability density are merged. The algorithm does not require parameter tuning, which ensures flexibility and reduces the risk of bias. It is tested on several synthetic data sets, where it consistently outperforms conventional clustering algorithms. As a final example, the algorithm is applied to the excited dynamics of the norbornadiene ⇌ quadricyclane (C 7 H 8 ) molecular photoswitch, demonstrating how distinct reaction pathways can be identified.

algorithms↗
DOE OSTI2025

Recent Improvements to the NWChem COSMO Module

This work presents recent developments in the COSMO solvation model implementation in NWChem. A new cavity construction approach, based on the solvent-excluding surface (SES) and utilizing the well-established GEPOL algorithm, has been introduced. Additionally, a straightforward procedure to merge surface segments that are too close─often a source of numerical artifacts─has been implemented. The available methods for correcting outlying surface charges have also been reviewed and improved. To validate the new implementation, we computed dielectric solvation energies for a chemically diverse set of approximately 100 molecules, including neutral species, small ions, and common ionic liquid components. Results were compared to those from GAMESS using the double-cavity method as a reference. Although the double-cavity approach can be regarded as more accurate, the simpler correction schemes available in NWChem─based on scaling factors or Lagrange multipliers─can achieve excellent agreement if the potential is also properly corrected, with mean unsigned deviations of around 0.15 kcal/mol. Predictions of typical vapor–liquid and liquid–liquid equilibria using a COSMO-SAC variant based on NWChem also showed very promising results.

Cavities↗
DOE OSTI2019

Structure–Property Effects in the Generation of Transient Aqueous Benzoic Acid Anhydrides by Carbodiimide Fuels

The design of dissipative systems, which operate out-of-equilibrium by consuming chemical fuels, is difficult. As yet, there are a few examples of privileged fuel chemistries that can be broadly applied in abiotic systems in the same way that ATP hydrolysis is exploited throughout biochemistry. The key issue is that designing nonequilibrium systems is inherently about balancing the relative rates of coupled reactions. The use of carbodiimides as fuels to generate transient aqueous carboxylic anhydrides has recently been used in examples of new nonequilibrium materials and supramolecular assemblies. Here, we explore the kinetics of formation and decomposition of a series of benzoic anhydrides generated from the corresponding acids and EDC under typical conditions (EDC = N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide hydrochloride). The reactions can be described by a simple mechanism that merges known behavior for the two processes independently. Structure–property effects in these systems are dominated by differences in the anhydride decomposition rate. The kinetic parameters allow trends in concentration-dependent properties to be simulated, such as reaction lifetimes, peak anhydride concentrations, and yields. For key properties, there are diminishing returns with the addition of increasing amounts of fuel. These results should provide useful guidelines for the design of functional systems making use of this chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Combined Crossed Molecular Beam and Theoretical Investigation of the Elementary Reaction of Tricarbon (C 3 (X 1 Σ g + )) with Diacetylene (C 4 H 2 (X 1 Σ g + )): Gas Phase Formation of the Heptatriynylidyne Radical ( l -C 7 H(X 2 Π))

An elucidation of the underlying formation pathways to acyclic hydrocarbons such as polyynes (C n H 2 ), cumulenes (C n H 2 ), and linear resonantly stabilized linear radicals (l-C n H) is indispensable to understand the hydrocarbon chemistry in extreme low and high temperature environments. In this study, we exploited the crossed molecular beam technique to investigate the reaction of tricarbon C 3 (X 1 Σ g + ) with diacetylene (butadiyne; HCCCCH; X 1 Σ g + ) at a collision energy of 47 ± 1 kJ mol ⁻1 . The experimental data were merged with ab initio calculations of the singlet C 7 H 2 potential energy surface (PES) revealing that the reaction is initiated via the formation of an initial van der Waals reactant complex in the entrance channel. Subsequent rearrangements lead to various carbene-type and cyclic intermediates via ring-opening, ring-closure, and hydrogen migration processes eventually forming acyclic C 7 H 2 isomers prior to their barrierless unimolecular decomposition to the most stable linear isomer, heptatriynylidyne (C 7 H, X 2 Π) in an overall endoergic reaction (+57 kJ mol ⁻1 ). The reaction exhibits strong similarities to the tricarbon – acetylene (C 3 – C 2 H 2 ). Furthermore, the significant energy threshold suggests that the tricarbon reaction with (poly)acetylenes forming resonantly stabilized linear radicals are open in high-temperature environments such as combustion flames and circumstellar envelopes of carbon stars and planetary nebulae as their descendants; however, these reactions are closed in low-temperature environments as in cold molecular clouds and hydrocarbon-rich atmospheres of planets and their moons such as in Titan.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The Singlet–Triplet Gap of Cyclobutadiene: The CIPSI-Driven CC( P ; Q ) Study

An accurate determination of singlet−triplet gaps in biradicals, including cyclobutadiene in the automerization barrier region where one has to balance the substantial nondynamical many-electron correlation effects characterizing the singlet ground state with the predominantly dynamical correlations of the lowest-energy triplet, remains a challenge for many quantum chemistry methods. High-level coupled-cluster (CC) approaches, such as the CC method with a full treatment of singly, doubly, and triply excited clusters (CCSDT), are often capable of providing reliable results, but routine application of such methods is hindered by their high computational costs. We have recently proposed a practical alternative to converging the CCSDT energetics at small fractions of the computational effort, even when electron correlations become stronger and connected triply excited clusters are larger and nonperturbative, by merging the CC(P;Q) moment expansions with the selected configuration interaction methodology abbreviated as CIPSI. We demonstrate that one can accurately approximate the highly accurate CCSDT potential surfaces characterizing the lowest singlet and triplet states of cyclobutadiene along the automerization coordinate and the gap between them using tiny fractions of triply excited cluster amplitudes identified with the help of relatively inexpensive CIPSI Hamiltonian diagonalizations.

Basis sets↗
DOE OSTI2024

Hexagonal Lattices of HIV Capsid Proteins Explored by Simulations Based on a Thermodynamically Consistent Model

HIV capsid proteins (CAs) may self-assemble into a variety of shapes under in vivo and in vitro conditions. Here, we employed simulations based on a residue-level coarse-grained (CG) model with full conformational flexibility to investigate hexagonal lattices, which are the underlying structural pattern for CA aggregations. Facilitated by enhanced sampling simulations to rigorously calculate CA dimerization and polymerization affinities, we calibrated our model to reproduce the experimentally measured affinities. Here using the calibrated model, we performed unbiased simulations on several large systems consisting of 1512 CA subunits, allowing reversible binding and unbinding of the CAs in a thermodynamically consistent manner. In one simulation, a preassembled hexagonal CA sheet developed spontaneous curvatures reminiscent of those observed in experiments, and the edges of the sheet exhibited local curvatures larger than those of the interior. In other simulations starting with randomly distributed CAs at different concentrations, existing CA assemblies grew by binding free capsomeres to the edges and by merging with other assemblies. At high CA concentrations, rapid establishment of predominant aggregates was followed by much slower adjustments toward more regular hexagonal lattices, with increasing numbers of intact CA hexamers and pentamers being formed. Our approach of adapting a general CG model to specific systems by using experimental binding data represents a practical and effective strategy for simulating and elucidating intricate protein aggregations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Observation of [VCu1–Ini2+VCu1–] Defect Triplets in Cu-Deficient CuInS2

Copper indium disulfide (CuInS2) is a semiconductor with a direct energy band gap of 1.53 eV, an optimal value for highly efficient thin-film solar cells. But it has reached only ~ 11 % power conversion efficiency, far less than the theoretically achievable value of ~ 30 %. The cause of this low performance is not understood. A single crystal grown from 1 mol% Cu-deficient melt was studied, using atomic resolution high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) and electron dispersive spectroscopy (EDS). While the bulk crystal is exactly stoichiometric CuInS2, it contains nanometer-thick, structurally coherent, Cu-deficient interphases that form along rotational twin boundaries in the {112} plane. Transition zones from bulk crystal to interphase are observed, where In is seen to move from its normal site InIn in the chalcopyrite structure to a tetrahedral interstitial site Ini, while Cu remains in its normal CuCu position. Two InIn rows of the bulk crystal merge into one row of Ini, causing excess Ini in the interphase. The concentrations of CuCu and Ini reflect a ratio of Cu vacancies, VCu, to excess Ini of ~ 2. Their relative lattice positions, and the high electrical resistivity of the crystal, suggests that VCu and excess Ini ‘precipitate’ as self-compensating, electrically neutral, [VCu1- Ini2+ VCu1-] defect triplets. This is the first atomic-level observation of the ordered defect that has been invoked as the basic structural modifier in chalcopyrite compound homologs. The interphases introduce an optical gap of 1.47 eV. Electron trapping in band tail states, evident from a photoconductivity exponent of 0.54, is the likely cause of an unusually low electron mobility of 0.1 cm2V-1s-1. The overall result is that making CuInS2 slightly copper-poor inserts nanometer-thick layers of the interphase into the bulk crystal. This study shows that apparently conflicting results of the effect of Cu deficiency on CuInS2 thin-film solar cells may be resolved by analyzing structure and composition at nanometer spatial resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗