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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Effects of isoalcohol blending with gasoline on autoignition behavior in a rapid compression machine: Isopropanol and isobutanol

Alcohols, and particularly isoalcohols, are potentially advantageous blendstocks towards achieving efficient, low-carbon intensity internal combustion engines. Their use in advanced configurations, such as boosted spark-ignition or spark-assisted compression ignition, requires a comprehensive understanding of their blending effects on the low- and intermediate-temperature autoignition behavior of petroleum-derived gasoline. This work reports an experimental and modeling study of such autoignition characteristics quantified in a twin-piston rapid compression machine. Isopropanol and isobutanol are blended into a research-grade gasoline (FACE-F) at oxygenate blend levels of 0 to 30% vol/vol, with tests conducted at pressures of 20 and 40 bar, temperatures from 700 to 1000 K, and dilute stoichiometric fuel loadings. Changes to overall reactivity, including first-stage and main ignition times, and preliminary exothermicity are established, with comparisons made to previous measurements with ethanol-blended FACE-F gasoline. Furthermore, it is found that at low-temperature/NTC conditions (700–860 K) the isoalcohols suppress first-stage reactivity and associated heat release while main ignition times are extended. At NTC/intermediate-temperature (860–1000 K) conditions changes to fuel reactivity are less significant with isopropanol slightly suppressing reactivity and isobutanol promoting ignition. Detailed chemical kinetic modeling is used to interpret the experimental measurements. Overall trends of suppression or promotion in the blending behavior are reasonably captured by the model. Sensitivity and rate of production analyses indicate that at lower temperatures H-atom abstraction reactions from the surrogate fuel molecules (e.g., cyclopentane, isooctane) and the isoalcohols via OH are important leading to TC 3 H 6 OH and IC 4 H 8 OH–C radicals, for isopropanol and isobutanol respectively, which act as scavengers in the system. At higher temperatures, similar chemistries are dominant, but there is an increasing importance of abstraction by HO 2 . The kinetic modeling also indicates that the promoting effect of isobutanol at higher temperatures is due to the increased abstractions at the γ-sites, while at lower temperatures abstraction at the α-site leads to greater reactivity suppression.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Chemical kinetic interactions of NO with a multi-component gasoline surrogate: Experiments and modeling

Here this work reports an experimental and modeling study on the chemical kinetic interactions of NO with a multi-component gasoline surrogate, namely PACE-20, using a twin-piston rapid compression machine at a stochiometric fuel loading with 20% EGR (exhaust gas recirculation) by mass, pressures of 20 and 40 bar, and temperatures from 700 to 930 K. Five NO concentrations are investigated, namely 0, 20, 50, 70 and 150 ppm, where NO addition effects are characterized through changes in PACE-20 ignition reactivity and heat release characteristics. Experiments indicate that within the low-temperature regime, NO promotes low-temperature heat release rate and main ignition reactivity at low addition levels, with saturation or even inhibiting effects observed at >50 ppm NO addition, while within the NTC/intermediate-temperature regime, adding NO only promotes reactivity. A recently updated, detailed chemical kinetic model with chemistry specific to NOx/hydrocarbons interaction incorporated is used to simulate the experiments, and reasonable agreement is obtained. In-depth sensitivity and rate of production analyses are further performed. The results indicate that NO interacts with PACE-20 via two types of interaction: (a) direct interactions between NO and PACE-20 derivatives, primarily through NO+HO 2 ↔NO 2 +OH and RO 2 +NO↔RO+NO 2 , and (b) indirect interactions between PACE-20 derivatives and NO 2 produced from the direct interactions, primarily through R+NO 2 ↔RO+NO. The observed NO inhibiting effect at low temperatures and 150 ppm NO addition is attributed to the lack of HO 2 radicals to sustain NO consumption via NO+HO 2 ↔NO 2 +OH, and the take-up of inhibiting pathways via RO 2 +NO↔RO+NO 2 . The results also indicate that even with the presence of multiple fuel components, NOx/hydrocarbons interactions are highly selective, and are mainly initiated by the interactions between NO and RO 2 radicals from cyclopentane and ethanol, as well as between NO 2 and R radicals from toluene, 1,2,4-trimethylbenzene and 1-hexene. Further studies on these interactive reactions are therefore highly recommended.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Nanotwin formation in Ni-Mo-W alloys deposited by dc magnetron sputtering

Nanotwinned metals and alloys have an attractive suite of properties, but the formation of the underlying nanostructure is generally dependent on the alloy chemistry and processing conditions. This study was undertaken to investigate the effect of deposition rate on nanotwin formation in sputter deposited Ni 84 Mo 11 W 5 that possess ultrahigh strength and stability. Specifically, deposition rates of 0.5-2.3 nm/s were examined and the grain size, texture, twin spacing and hardness were characterized. Minimal variations in the underlying nanostructure and attendant hardness were observed, elucidating a wide processing window and the importance of chemistry on the formation of nanotwins in this alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Transformative high entropy alloy conquers the strength-ductility paradigm by massive interface strengthening

Recent metastable alloy designs have demonstrated simultaneous attainment of high ultimate tensile strength (UTS) and ductility in high entropy alloys but with low yield strength. In this work, we present new strategy for improving the work hardenability and yield strength (YS) together in Fe 38.5 Mn 20 Co 20 Cr 15 Si 5 Cu 1.5 high entropy alloy (Cu-HEA). Drastic increase in the YS (1.5 GPa) is attributed to the formation of γ/ε, ε/ε (twin type) and ε/ε (plate type) interfaces in the microstructure due to extreme grain refinement whereas high UTS-ductility synergy (2.2 GPa, 15%) is attained by dynamic Hall-Petch hardening across these interfaces (i.e. massive interface strengthening) and transformation induced plasticity in γ phase. Thus, this harmonious combination of YS and UTS-ductility synergy in Cu-HEA outperform all structural materials till date. Therefore, deformation-induced massive interface strengthening is a new, yet cost-effective pathway for synergizing the benefits of advanced steels and high entropy alloys together in a material by conventional processing route.

36 MATERIALS SCIENCE↗
DOE OSTI2021

In-situ TEM observation of shear induced microstructure evolution in Cu-Nb alloy

Phase boundaries in multiphase alloys govern defect interaction and chemical intermixing across different phases during plastic deformation. Dynamic interaction of defects with phase boundaries in multiphase alloys, especially for immiscible alloys, has been topic of significant research interest in recent years. In this work, we describe a novel approach for carrying out in-situ TEM shear deformation to directly observe interfacial microstructural evolution of a Cu-Nb alloy. A unique double shear specimen geometry is microfabricated by a focused ion beam technique to apply shear deformation upon push loading inside the TEM. From the real-time observation, we discover that the phase boundary with a zigzag morphology effectively blocks stacking faults nucleated in a Cu grain from slipping into a Nb grain. Meanwhile, the Cu phase bears the most plastic deformation through slip or twinning mechanisms. This work sheds light on understanding the shear deformation and the behavior of phase boundaries in multiphase alloys during shear deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Nanoscale phase transition in impact indented Mo/Cu thin films

The study reveals a size effect in deformation mechanisms via unique structural transitions in sputter deposited fcc/bcc nanolayers under high strain rate impact nanoindentation as layer thickness is reduced from around 100-200 nm to around 5 nm. Here, the indented 5 nm Cu/5 nm Mo multilayer films characterized via cross-sectional scanning/transmission electron microscopy have revealed a phase transition in the 5 nm thick Mo layers from BCC to a coherent metastable FCC structure and fine deformation twins in the 5 nm Cu layers inside a shear band that is approximately 500 nm long and inclined to the layer interfaces. In contrast, thicker layers, 80 nm Cu/180 nm Mo, show different deformation behavior with uneven layer thickness reduction, shearing of Cu layers, and bending of the columnar Mo grains beneath the indentation without any sign of shear band and localized phase transformation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An atomistic structural description of the ferrielectric polar phase involving non-coplanar cation displacements

Materials with antipolar-polar transformation are attractive for their large functional responses. However, the antipolar state remains controversial in many materials. For example, recent studies on archetypical antiferroelectric (AFE) materials indicate an incomplete compensation of antiparallel dipoles, which prompted their alternative definition as ferrielectric. Here, we investigated the origin of the ferrielectric (FIE) state in a classical AFE material using X-ray and neutron total scattering. We show that the FIE state arises from 3-dimensional modulation of the cation-centric electric dipoles, which can be viewed as periodic arrangement of 180° twin boundaries with non-Ising characteristics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

AutoBEM: A scalable framework for nationwide building energy simulation and retrofit evaluation in the United States

This paper presents AutoBEM, an integrated, automated framework for nationwide building energy modeling and retrofit evaluation in the United States. Unlike prior UBEM platforms that either rely primarily on representative stock sampling or operate at city scale, AutoBEM automates the generation of building-resolved, physics-based EnergyPlus/OpenStudio simulation models at national scale using GIS-derived geometry, prototype-based assumptions, and standardized scalable workflows. Leveraging the Model America dataset and high-performance computing, AutoBEM generates and simulates energy models for 122.9 million buildings, representing 97.8% of the U.S. building stock. These models are being made publicly and freely available as the Model America v1.0 (MAv1) dataset. AutoBEM supports detailed, building-level assessments of energy consumption, CO2 emissions, and post-processed anthropogenic heat emissions (AHE), and evaluates 151 energy conservation measures (ECMs) using localized utility pricing and building characteristics. In addition, AutoBEM incorporates both typical and future climate conditions through integration with Typical Meteorological Year (TMY) and Future TMY (fTMY) weather data derived from IPCC scenarios. In a case study of Phoenix, Arizona, AutoBEM identified several high-efficiency HVAC upgrades and selected envelope measures with short modeled payback periods (1.5 years) for certain building types and standards. Simulations under future climate scenarios (SSP5–RCP8.5) project an 11.3% increase in electricity use and a 32% reduction in natural gas demand by 2100, underscoring the need for climate-adaptive retrofit planning. By enabling reproducible, bottom-up, and location-specific analysis at scale, AutoBEM provides a step toward a national digital twin of the built environment and supports data-driven screening and planning for decarbonization, resilience, and energy equity.

Li, Hang [ORNL] (ORCID:0000000306001920)↗
DOE OSTI2023

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. Here, this paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Microstructure and Composition of Passivating Interfaces in Silicon Heterojunction Solar Modules Weathered in Different Climates

Sanyo/Panasonic patented silicon heterojunction with intrinsic thin layer (HIT) solar cells in the 1990's, which demonstrated world record photovoltaic (PV) efficiency around 2014 and inspired the newer generations of silicon heterojunction technology (SHJ) as well as the current world record back-contact PV cell designs. The high-quality passivation strategy, utilizing ultra-thin layers of hydrogenated amorphous silicon (a-Si:H), leads to high voltages and long carrier lifetimes, but these qualities may degrade over time as the modules operate outdoors. Here, we investigate local microstructure and composition at the interface layers of cells from HIT modules weathered in a hot, humid climate (Florida, USA) and temperate climate (Colorado, USA) for 10 years. We employ a comprehensive set of high-resolution electron microscopy imaging and spectroscopy to directly resolve structure and composition in these cells down to the nanoscale. We show features such as alignment of the In2O3 transparent conducting oxide and a-Si:H layers on the textured c-Si facets, interfacial oxidation at the a-Si:H/c-Si and In2O3/a-Si:H interfaces, and twinned c-Si resulting from epitaxial growth into the a-Si:H layer. Our results raise potential degradation mechanisms in these outdoor-weathered modules, but the root cause of electrical loss remains uncertain due to the very small changes in aged samples. This study shows that many atomic scale features at the In2O3/a-Si:H/c-Si interfaces are surprisingly robust, and the electrical losses with aging may either be attributed to other pathways or be very sensitive to the subtle chemical and microstructural features observed here.

14 SOLAR ENERGY↗
DOE OSTI2019

Correlating results from high resolution EBSD with TEM- and ECCI-based dislocation microscopy: Approaching single dislocation sensitivity via noise reduction

High resolution electron backscatter diffraction (HREBSD), an SEM-based diffraction technique, may be used to measure the lattice distortion of a crystalline material and to infer the geometrically necessary dislocation content. Uncertainty in the image correlation process used to compare diffraction patterns leads to an uneven distribution of measurement noise in terms of the lattice distortion, which results in erroneous identification of dislocation type and density. This work presents a method of reducing noise in HREBSD dislocation measurements by removing the effect of the most problematic components of the measured distortion. The method is then validated by comparing with TEM analysis of dislocation pile-ups near a twin boundary in austenitic stainless steel and with ECCI analysis near a nano-indentation on a tantalum oligocrystal. Finally, the HREBSD dislocation microscopy technique is able to resolve individual dislocations visible in TEM and ECCI and correctly identify their Burgers vectors.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Imaging Polarity in Two Dimensional Materials by Breaking Friedel's Law

Friedel's law guarantees an inversion-symmetric diffraction pattern for thin, light materials where a kinematic approximation or a single-scattering model holds. Typically, breaking Friedel symmetry is ascribed to multiple scattering events within thick, non-centrosymmetric crystals. However, two-dimensional (2D) materials such as a single monolayer of MoS 2 can also violate Friedel's law, with unexpected contrast between conjugate Bragg peaks. Here, we show analytically that retaining higher order terms in the power series expansion of the scattered wavefunction can describe the anomalous contrast between hkl and $\bar{hkl}$ peaks that occurs in 2D crystals with broken in-plane inversion symmetry. These higher-order terms describe multiple scattering paths starting from the same atom in an atomically thin material. Furthermore, 2D materials containing heavy elements, such as WS 2 , always act as strong phase objects, violating Friedel's law no matter how high the energy of the incident electron beam. Experimentally, this understanding can enhance diffraction-based techniques to provide rapid imaging of polarity, twin domains, in-plane rotations, or other polar textures in 2D materials.

2D materials↗
DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Reduced Order Modeling conditioned on monitored features for response and error bounds estimation in engineered systems

Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of their function as surrogates for computationally intensive digital twinning simulators. Although purely data-driven methods are available for ROM construction, schemes that allow to retain a portion of the physics tend to enhance the interpretability and generalization of ROMs. However, physics-based techniques can adversely scale when dealing with nonlinear systems that feature parametric dependencies. This study introduces a generative physics-based ROM that is suited for nonlinear systems with parametric dependencies and is additionally able to provide numerical error bounds associated with the respective estimates. A main contribution of this work is the conditioning of these parametric ROMs to features that can be derived from monitoring measurements, feasibly in an online fashion. This is contrary to most existing ROM schemes, which remain restricted to the prescription of the physics-based, and usually a priori unknown, system parameters. Our work utilizes conditional Variational Autoencoders to continuously map the required reduction bases to a feature vector extracted from limited output measurements, while additionally allowing for a probabilistic assessment of the ROM-estimated Quantities of Interest. An auxiliary task using a neural network-based parametrization of suitable probability distributions is introduced to re-establish the link with physical model parameters. We verify the proposed scheme on a series of simulated case studies incorporating effects of geometric and material nonlinearity under parametric dependencies related to system properties and input load characteristics.

Conditional VAEs↗
DOE OSTI2022

Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching

Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Disorderly Conduct of Benzamide IV: Crystallographic and Computational Analysis of High Entropy Polymorphs of Small Molecules

Benzamide, a simple derivative of benzoic acid and a common intermediate of pharmaceutical compounds, was reported to form two polymorphs in 1832, but the single crystal structure of the more stable form was not solved until 1959. Nearly 50 years later, the second form was characterized by powder diffraction, followed shortly thereafter by characterization of a third form, a polytype of the most thermodynamically stable Form I. These two new forms, Forms II and III, are metastable. Herein, we describe a fourth polymorph, Form IV, discovered by melt crystallization concurrently with its crystallization under confinement at small length scales (<10 nm), where it is stable indefinitely. Form III exists under confinement in larger pores, and melting point data for different pore sizes corroborate the existence of Form IV below 10 nm. Form IV is highly disordered, precluding indexing of powder diffraction data other than hk0 reflections. Nonetheless, a combination of powder X-ray diffraction and computational crystal structure prediction reveals that Form IV contains a 2D motif resembling that of Form II, but with longrange order in the third dimension masked by ubiquitous stacking faults. This approach relies on distilling a large number of candidate structures to a few possible disorder models based on benzamide tetrads that organize in 2D parquet-like tiles, with organization along the third dimension, that can be modeled with various stacking fault configurations having distinct intermolecular interactions and translations in the dimension orthogonal to the tiling planes. These observations reveal a bewildering crystallographic complexity for such a simple molecule. Nonetheless, the approach described herein demonstrates that challenging structures that may be abandoned prematurely because of poor crystallinity, twinning, or disorder can be solved.

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

Superconductivity in Y 4 RuGe 8 with a Vacancy-Ordered CeNiSi 2 -Type Superstructure

In this work, we report a new compound, Y 4 RuGe 8 , with a transition metal vacancy-ordered CeNiSi 2 -type superstructure, which has a superconducting transition at 1.3 K. Y 4 RuGe 8 crystals were grown by indium flux at relatively low temperatures (below 1273 K), which makes it possible to stabilize such a vacancy-ordered phase. The crystal structure of Y 4 RuGe 8 was solved by single-crystal X-ray diffraction and confirmed by transmission electron microscopy. The as-grown Y 4 RuGe 8 crystals are always twinned, crystallizing in the space group $P\bar{1}$(no. 2) with the lattice parameters a = 5.7680(1) Å, b = 8.2042(2) Å, c = 11.5093(3) Å, α = 79.696(1)degrees, β = 88.491(1)degrees, and γ = 79.637(2)degrees; this structure is a superstructure deriving from the higher symmetry CeNiSi 2 -type structure (Cmcm, no. 63) due to the ordering of Ru vacancies. The ordering of Ru sites breaks slightly distorted Ge planes in the CeNiSi 2 prototype into infinite cis-trans Ge chains in Y 4 RuGe 8 . The presence of bulk superconductivity in Y 4 RuGe 8 is well supported by zero resistance and a jump in specific heat at the critical transition temperature. The Sommerfeld coefficient (19 mJ K -2 mol -1 ) of the specific heat is greater than that (11 mJ K -2 mol -1 ) estimated using the bare density of states (4.7 states/eV/f.u.) from first-principles calculations. The ab initio calculations indicate that 4d electrons of both Y and Ru and 4p electrons of Ge are the main contributors to the total density of states at the Fermi level in Y 4 RuGe 8 .

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

Growth and Characterization of Epitaxial FeWO 4 Thin Films with Controlled Oxygen Stoichiometry

Here, we report the growth of single-phase epitaxial FeWO 4 thin films, using plasma-assisted molecular beam epitaxy, and investigate structural, optical, and electronic properties. The FeWO 4 films grow in (100) orientation on c-plane sapphire (0001) substrates and exhibit 3 rotational twin variants where FeWO 4 [001] is aligned to sapphire [100] equivalent in-plane directions. X-ray diffraction measurements indicate that the epitaxial FeWO 4 (100) structure is optimized when 80-100 W of rf power is applied to an atomic oxygen source during growth, yielding films with minimal strain and impurity phases or other orientations. In films grown with 120 W of rf power, FeWO 4 crystallites develop inhomogeneous and homogeneous strains and are potentially contaminated with Fe 3+ oxide phase impurities. In films grown with 60 W of rf power, FeWO 4 crystallites do not form fully epitaxial layers. X-ray photoelectron spectroscopy indicates that the structural changes are correlated with the Fe 3+ /Fe 2+ oxidation state ratio increasing from 0.6-1.4 with rf power from 60-120 W. X-ray fluorescence spectroscopy indicates that the Fe/W composition ratio is also increasing from 1.1-1.8 with rf power from 60-120 W. Ultraviolet and visible optical absorption spectra indicate a 1.8 ± 0.1 eV band gap with an additional interband absorption feature at 3.1 ± 0.1 eV in the 80-100 W films, with similar onsets observed in the 60 W films. In the 120 W films, the higher lying transition is shifted to 2.7 ± 0.1 eV due to the Fe 3+ enrichment. Electrical resistivity decreases over 2 orders of magnitude with oxidation from 10 4 -10 5 Ω cm in 60 W films to 120 ± 10 Ω cm in 120 W films. Thermopower measurements show p-type to n-type conductivity conversion when oxidation states shift from Fe 2+ majority in the 100 W films to Fe 3+ majority in the 120 W films. We conclude that electron polaron hopping driven by Fe 3+ is a dominant transport mechanism and a source of n-type conductivity in overoxidized FeWO 4 films.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗