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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Using the CYBER-Champ Model to Determine Cyber Competencies and Role Alignment

Cybersecurity roles, tasks, and skills are seen by many organizations as nonessential, abstract, or complicated. Although standards are in place, such as the National Institute of Standards and Technology (NIST) 800-181 document titled “Workforce Framework for Cybersecurity (NICE Framework)” available since September 2012, companies are still showing security vulnerabilities in their cyber networks (Hatzes, 2020). Barriers towards creating a cyber-ready workforce are not always due to a lack of resources, but often caused by organizational structure. The need for cyber-cognizant job postings, improved communication between Operational Technology (OT) and Information Technology (IT) employees, increases in staffing and funding for cyber teams, and better communication of standards and training can all contribute to increasing an organization’s cyber resilience. Cybersecurity Competency Health and Maturity Progression model (CYBER-CHAMP) is a model aimed at evaluating an organization’s structure and individual employee’s cyber knowledge and helps develop a plan to reach competency. This model was used to engage with organizations to promote proper cyber protocols and policies. The aim of this paper is to answer if it is possible for an organization to build a cyber-ready workforce by providing education and training options for current employees and prepare the future workforce to be ready on day one of employment. Both open source and firsthand interviews with organizations were used to conduct the research contained in this document. Further research may include additional development to the CYBER-CHAMP model and its delivery platform.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Genome-Wide Transcription Factor DNA Binding Sites and Gene Regulatory Networks in Clostridium thermocellum

Clostridium thermocellum is a thermophilic bacterium recognized for its natural ability to effectively deconstruct cellulosic biomass. While there is a large body of studies on the genetic engineering of this bacterium and its physiology to-date, there is limited knowledge in the transcriptional regulation in this organism and thermophilic bacteria in general. The study herein is the first report of a large-scale application of DNA-affinity purification sequencing (DAP-seq) to transcription factors (TFs) from a bacterium. We applied DAP-seq to > 90 TFs in C. thermocellum and detected genome-wide binding sites for 11 of them. We then compiled and aligned DNA binding sequences from these TFs to deduce the primary DNA-binding sequence motifs for each TF. These binding motifs are further validated with electrophoretic mobility shift assay (EMSA) and are used to identify individual TFs’ regulatory targets in C. thermocellum . Our results led to the discovery of novel, uncharacterized TFs as well as homologues of previously studied TFs including RexA-, LexA-, and LacI-type TFs. We then used these data to reconstruct gene regulatory networks for the 11 TFs individually, which resulted in a global network encompassing the TFs with some interconnections. As gene regulation governs and constrains how bacteria behave, our findings shed light on the roles of TFs delineated by their regulons, and potentially provides a means to enable rational, advanced genetic engineering of C. thermocellum and other organisms alike toward a desired phenotype.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Genome-wide Transcription Factor DNA Binding Sites and Gene Regulatory Networks in Clostridium thermocellum

Clostridium thermocellum is a thermophilic bacterium recognized for its natural ability to effectively deconstruct cellulosic biomass. While there is a large body of studies on the genetic engineering of this bacterium and its physiology to-date, there is limited knowledge in the transcriptional regulation in this organism and thermophilic bacteria in general. The study herein is the first report of a high-throughput application of DNA-affinity purification sequencing (DAP-seq) to transcription factors (TFs) from a thermophile. We applied DAP-seq to >90 TFs in C. thermocellum and detected genome-wide binding sites for 11 of them. We then compiled and aligned DNA binding sequences from these TFs to deduce the primary DNA-binding sequence motifs for each TF. These binding motifs are further validated with electrophoretic mobility shift assay (EMSA) and are used to identify individual TFs’ regulatory targets in C. thermocellum. Our results led to the discovery of novel, uncharacterized TFs as well as homologues of previously studied TFs including RexA-, LexA- and LacI-type TFs. We then used these data to reconstruct gene regulatory networks for the 11 TFs individually, which resulted in a global network encompassing the TFs with some interconnections. As gene regulation governs and constrains how bacteria behave, our findings shed light on the roles of TFs delineated by their regulons, and potentially provides a means to enable rational, advanced genetic engineering of C. thermocellum and other organisms alike towards a desired phenotype.

09 BIOMASS FUELS↗
DOE OSTI2025

CONCURRENT, CONDENSED STEIN VARIATIONAL GRADIENT DESCENT FOR UNCERTAINTY QUANTIFICATION OF NEURAL NETWORKS

In this work, we propose a Stein variational gradient descent (SVGD) method to concurrently sparsify, train, and provide uncertainty quantification (UQ) of a complexly parameterized model, such as a neural network (NN). It employs a graph reconciliation and condensation process to reduce complexity and increase similarity in the Stein ensemble of parameterizations. Therefore, the proposed concurrent, condensed SVGD (ccSVGD) method can provide UQ on parameters, not just outputs. Furthermore, the parameter reduction speeds up the convergence of the Stein gradient descent as it reduces the combinatorial complexity by aligning and differentiating the sensitivity to parameters. These properties are demonstrated with an illustrative example and an application to a mechanical response representation problem in solid mechanics.

42 ENGINEERING↗
DOE OSTI2025

Persistent Classification: Understanding Adversarial Attacks by Studying Decision Boundary Dynamics

ABSTRACT There are a number of hypotheses underlying the existence of adversarial examples for classification problems. These include the high‐dimensionality of the data, the high codimension in the ambient space of the data manifolds of interest, and that the structure of machine learning models may encourage classifiers to develop decision boundaries close to data points. This article proposes a new framework for studying adversarial examples that does not depend directly on the distance to the decision boundary. Similarly to the smoothed classifier literature, we define a (natural or adversarial) data point to be ( γ , σ)‐stable if the probability of the same classification is at least for points sampled in a Gaussian neighborhood of the point with a given standard deviation . We focus on studying the differences between persistence metrics along interpolants of natural and adversarial points. We show that adversarial examples have significantly lower persistence than natural examples for large neural networks in the context of the MNIST and ImageNet datasets. We connect this lack of persistence with decision boundary geometry by measuring angles of interpolants with respect to decision boundaries. Finally, we connect this approach with robustness by developing a manifold alignment gradient metric and demonstrating the increase in robustness that can be achieved when training with the addition of this metric.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Memory effect in composites of liquid crystal and silica aerosil

Aerosil silica nanoparticles dispersed in a liquid crystal (LC) possess the interesting property of keeping memory of an electric- or magnetic-field-induced orientation. Two types of memory have been identified: thermally erasable memory arising from the pinning of defect lines versus a 'permanent' memory where the orientation persists even after thermal cycling the samples up to the isotropic phase. To address the source of the latter type of memory, solid-state nuclear magnetic resonance spectroscopy and conventional x-ray diffraction (XRD) were first combined to characterize the LC orientational order as a function of multiple in-field temperature cycles. Microbeam XRD was then performed on aligned gels of different concentrations to gain knowledge of the structural properties at the origin of the memory effect. No detectable anisotropy of the gel or significant breaking of silica strands with heating ruled out the formation of an anisotropic silica network as the source of the permanent memory as previously proposed. Instead, support for a role of the surface memory effect, well known for planar substrates, in stabilizing the permanent memory was deduced from 'training' of the composites, that is, optimizing the orientational order through the thermal in-field cycling. The ability to train the composites is inversely proportional to the strength of the random-field disorder. The portion of thermally erasable memory also decreases as the silica density increases. We propose that the permanent memory originates from the surface memory effect operating at points of intersection in the silica network. These areas, where the LC is strongly confined with conflicted surface interactions, are trained to achieve an optimized orientation and subsequently act as sites from which the LC orientational order regrows after zero-field thermal cycling up to the isotropic phase.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Simultaneous x-ray and neutron 4D tomographic study of drying-driven hydro-mechanical behavior of cement-based materials at moderate temperatures

The drying of cement-based materials is intimately related to their durability, which has significant economic, social and environmental repercussions. The evolution of the saturation of the pore network and the associated drying shrinkage are in fact leading causes of cracking and of the ensuing penetration of aggressive chemicals. This process is highly heterogeneous, due to the thermo-hydric spatial gradients developing in the material from the exposed surfaces to its core and because of local effects, driven by the intrinsically heterogeneous micro-structure (e.g., by the distribution of pores and aggregates). It follows that macroscopic, sample-scale measurements cannot fully disclose the complexity of the underlying processes. In the last few decades, significant advances in full-field techniques have allowed an unprecedented insight into these local processes. For cement-based materials, x-ray and neutron tomography lend themselves as ideal, and highly complementary, tools for the study of their thermo-hydro-mechanical behavior. Notably, the high sensitivity to density variations of x-ray imaging gives access to the developments of fractures, in 4D (3D + time). On the other hand, neutron tomography allows the study of the evolution of the moisture field in 4D, thanks to its high hydrogen sensitivity. The combination of these two techniques provides a unique insight in thermo-hydro-mechanical couplings, e.g., the effect of cracks on the water content field. This contribution presents novel 5D datasets (3D tomographies along time, plus truly simultaneous x-ray and Neutron rapid acquisitions) in-operando of a cement paste and of a concrete sample heated at moderate temperatures (up to 140{sup ∘}C). The analysis of this 5D data-set (once aligned in time and across modalities) allows for example a quantification of the 4D moisture profiles which were found to predict an overall water loss at hydric equilibrium coherent with the corresponding analytical analysis. In the cement paste sample, the x-ray dataset captures the evolution of an extensive cracking network, opening and propagation toward the core of the sample. A novel analysis procedure is here proposed which allows the extraction of these fractures and the analysis of their interplay with local drying as captured through neutron imaging. This for example reveals the depth of penetration of drying in the vicinity of the fractures along time, which is essential for the assessment and calibration of hydro-mechanical coupled models.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A simulation framework for evaluating electronic order workflows in integrated health records

Electronic health record (EHR) systems are critical to modern healthcare delivery, yet the dynamic workflows that govern electronic order processing remain underexplored. Inefficiencies in these digital pathways can cause delays in care, repetitive workloads, and even patient harm. This study presents a discrete-event simulation framework used to reconstruct and evaluate EHR-based order workflows in a large integrated healthcare system. Using real-world data extracted from the Veterans Health Administration’s Corporate Data Warehouse, the authors mapped order events to standardized state transitions and modeled their progression across different facilities of varying complexity levels. After being calibrated with empirical distributions of transition times and validated against observed time-in-system metrics, the simulation demonstrates close alignment with historical performance. Scenario analyses reveal that resource capacity constraints significantly amplify the impact of electronic order surges, which are reflected in the disproportionate growth in backlogs and processing delays. Adjustments in transition probabilities further increased recirculation and extended workflow paths. Network-based analysis identified Reserved, InProgress, and Completed as structurally critical states that function as hubs within the process network but the transitions in-between also act as major bottlenecks. These results showcased the effectiveness of simulation-based approaches in monitoring EHR order processing performance and evaluating consequences of workflow changes on healthcare network resources planning. The proposed simulation framework provides a scalable data-driven tool to support operational decision-making and improve the efficiency of electronic order management in complex healthcare environments.

Engineering↗
DOE OSTI2020

Enabling fast charging of lithium-ion batteries through secondary- /dual- pore network: Part I - Analytical diffusion model

Battery performance is strongly correlated with electrode microstructural properties. Enabling fast charging of lithium-ion batteries requires improved through-plane ionic diffusion that can be achieved through, among other strategies, structured electrodes with a secondary- or dual-pore network (SPN). In this work, an analytical model investigates the impact of such an SPN on ionic diffusion with a composite electrode, considering various pore-channel geometries and comparing to standard electrodes with identical gravimetric- and volumetric-specific theoretical capacities. Relevant SPN design parameters and tortuosity coefficients are identified according to three optimization objectives that aim to balance the improved overall through-plane diffusion, thanks to the coarse aligned channels, and degraded in-plane diffusion because of the porous matrix densification required to maintain gravimetric- and volumetric-specific theoretical capacities. The model indicates that a relatively low amount of SPN is required and that electrodes with high through-plane tortuosity and low in-plane tortuosity benefit most from such architecture.

25 ENERGY STORAGE↗
DOE OSTI2018

Anisotropic antiferromagnetic order in the spin-orbit coupled trigonal-lattice Ca 2 Sr 2 IrO 6

Here, we used single-crystal x-ray and neutron diffraction to investigate the crystal and magnetic structures of trigonal lattice iridate Ca 2 Sr 2 IrO 6 . The crystal structure is determined to be R ¯3 with two distinct Ir sites. The system exhibits long-range antiferromagnetic order below T N = 13.1 K. The magnetic wave vector is identified as (0,0.5,1) with ferromagnetic coupling along the a axis and antiferromagnetic correlation along the b axis. Spins align dominantly within the basal plane along the [1,2,0] direction and tilt 34° toward the c axis. The ordered moment is 0.66(3) μ B /Ir, larger than other iridates where iridium ions form corner- or edge-sharing IrO 6 octahedral networks. The tilting angle is reduced to ≈19° when a magnetic field of 4.9 T is applied along the c axis. Density functional theory calculations confirm that the experimentally determined magnetic configuration is the most probable ground state with an insulating gap ~0.5 eV.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Integration of CeO 2 -Based Memristor with Vertically Aligned Nanocomposite Thin Film: Enabling Selective Conductive Filament Formation for High-Performance Electronic Synapses

The CeO 2 -based memristor has attracted significant attention due to its intrinsic resistive switching (RS) properties, large on/off ratio, and great plasticity, making it a promising candidate for artificial synapses. However, significant challenges such as high power consumption and poor device reliability hinder its broad application in neuromorphic microchips. To tackle these issues, in this work, we design a novel bilayer (BL) memristor by integrating a CeO 2 -based memristor with a Co-CeO 2 vertically aligned nanocomposite (VAN) layer and compare it with the single layer (SL) memristor. Preliminary electrical testing reveals that the BL memristor offers a reduced set/reset voltage (~67% lower), a higher on/off ratio (~5 × 10 2 ), enhanced device reliability, and improved device-to-device variation compared to the SL memristor. Insight from COMSOL simulation, coupled with microstructural analysis, provides a comprehensive elucidation on how the VAN layer facilitates the selective conductive filament (CF) formation. Subsequently, the plasticity of the BL memristor is evaluated through long-term potentiation/depression (LTP/LTD), paired-pulse facilitation (PPF), and spike-time-dependent plasticity (STDP). The spiking neural network (SNN) built upon the BL memristor achieves remarkable accuracy (~94%) after only 12 iterations, underscoring its potential for high-performance neural networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

EVIDENCE FOR TWO SEPARATE BUT INTERLACED COMPONENTS OF THE CHROMOSPHERIC MAGNETIC FIELD

Chromospheric fibrils are generally thought to trace out low-lying, mainly horizontal magnetic fields that fan out from flux concentrations in the photosphere. A high-resolution ({approx}0.''1 pixel{sup -1}) image, taken in the core of the Ca II 854.2 nm line and covering an unusually large area, shows the dark fibrils within an active region remnant as fine, looplike features that are aligned parallel to each other and have lengths comparable to a supergranular diameter. Comparison with simultaneous line-of-sight magnetograms confirms that the fibrils are centered above intranetwork areas (supergranular cell interiors), with one end rooted just inside the neighboring plage or strong unipolar network but the other endpoint less clearly defined. Focusing on a particular arcade-like structure lying entirely on one side of a filament channel (large-scale polarity inversion), we find that the total amount of positive-polarity flux underlying this 'fibril arcade' is {approx}50 times greater than the total amount of negative-polarity flux. Thus, if the fibrils represent closed loops, they must consist of very weak fields (in terms of total magnetic flux), which are interpenetrated by a more vertical field that contains most of the flux. This surprising result suggests that the fibrils in unipolar regions connect the network to the nearby intranetwork flux, while the bulk of the network flux links to remote regions of the opposite polarity, forming a second, higher canopy above the fibril canopy. The chromospheric field near the edge of the network thus has an interlaced structure resembling that in sunspot penumbrae.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Improving deep learning-based protein distance prediction in CASP14

Abstract Motivation Accurate prediction of residue–residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance prediction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment. Results Benchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions [i.e. classifying distances between two residues into two categories: in contact (<8 Angstrom) and not in contact otherwise] and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the precision of distance classification. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps. Availability and implementation The software package, source code and data of DeepDist2 are freely available at https://github.com/multicom-toolbox/deepdist and https://zenodo.org/record/4712084#.YIIM13VKhQM. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Modular HF Isolated MV String Inverters Enable a New Paradigm for Large PV Farms

The “Modular HF Isolated MV String Inverters Enable a New Paradigm for Large PV Farms” project focuses on exploring alternative power converter and system-level plant configurations to achieve the lowest cost and highest energy output for a given solar plus storage (e.g., PV + battery) plant, including the use of medium voltage (MV) collection while taking into account detailed models of all elements. To realize this objective, four approaches were utilized (i) employ a novel Medium Voltage String Inverter (MVSI) topology (soft switching solid state transformer – S4T) to convert 1000 Vdc to 4.16 kVac; (ii) plant collection using standard, low-cost overhead MV distribution network; (iii) enable energy storage integration without additional converter cost to achieve dispatchability of the PV resource; and (iv) provide advanced functionality (autonomous operation, track ISO signals for dynamic balancing and ancillary services, and PV farm operation as a virtual grid resource). Subsequently and in alignment with the previously mentioned approaches, the project was structured in five efforts (i) S4T MVSI simulation and design; (ii) system analysis and storage optimization; (iii) financial analysis; (iv) power converter prototype build and test; and (v) regulatory and commercial impact study. The outcomes provided by each effort can be summarized as follows (i) Project explored the use of MV AC distribution architecture for hybrid PV+storage utility-scale PV farms; (ii) Detailed loss and LCOE analysis for AC and DC side BESS architecture, including multiple converter topologies, as well as for proposed MVSI/MDCT systems; (iii) MVSI was built and holds promise but needs lower-cost high-voltage Si-C devices, which does not seem possible in the near term; (iv) MDCT provides a simpler modular building block – validated through HIL and farm level modeling, simulation and experimental validation; (v) 300 kVA MDCT prototype built and tested, technology is being commercialized; and (vi) Regulatory model of utility building PV plants, where PV panels are treated as DC generation (IPP), seems viable and can allow improved grid integration.

14 SOLAR ENERGY↗
DOE OSTI2011

Spatially resolved distribution function and the medium-range order in metallic liquid and glass

The structural description of disordered systems has been a longstanding challenge in physical science. We propose an atomic cluster alignment method to reveal the development of three-dimensional topological ordering in a metallic liquid as it undercools to form a glass. By analyzing molecular dynamic (MD) simulation trajectories of a Cu 64.5 Zr 35.5 alloy, we show that medium-range order (MRO) develops in the liquid as it approaches the glass transition. Specifically, around Cu sites, we observe 'Bergman triacontahedron' packing (icosahedron, dodecahedron and icosahedron) that extends out to the fourth shell, forming an interpenetrating backbone network in the glass. The discovery of Bergman-type MRO from our order-mining technique provides unique insights into the topological ordering near the glass transition and the relationship between metallic glasses and quasicrystals.

36 MATERIALS SCIENCE↗
DOE OSTI2013

A Community-Based Approach to Leading the Nation in Smart Energy Use

Project Objectives The AEP Ohio gridSMART® Demonstration Project (Project) achieved the following objectives: • Built a secure, interoperable, and integrated smart grid infrastructure in northeast central Ohio that demonstrated the ability to maximize distribution system efficiency and reliability and consumer use of demand response programs that reduced energy consumption, peak demand, and fossil fuel emissions. • Actively attracted, educated, enlisted, and retained consumers in innovative business models that provided tools and information reducing consumption and peak demand. • Provided the U.S. Department of Energy (DOE) information to evaluate technologies and preferred smart grid business models to be extended nationally. Project Description Ohio Power Company (the surviving company of a merger with Columbus Southern Power Company), doing business as AEP Ohio (AEP Ohio), took a community-based approach and incorporated a full suite of advanced smart grid technologies for 110,000 consumers in an area selected for its concentration and diversity of distribution infrastructure and consumers. It was organized and aligned around: • Technology, implementation, and operations • Consumer and stakeholder acceptance • Data management and benefit assessment Combined, these functional areas served as the foundation of the Project to integrate commercially available products, innovative technologies, and new consumer products and services within a secure two-way communication network between the utility and consumers. The Project included Advanced Metering Infrastructure (AMI), Distribution Management System (DMS), Distribution Automation Circuit Reconfiguration (DACR), Volt VAR Optimization (VVO), and Consumer Programs (CP). These technologies were combined with two-way consumer communication and information sharing, demand response, dynamic pricing, and consumer products, such as plug-in electric vehicles and smart appliances. In addition, the Project incorporated comprehensive cyber security capabilities, interoperability, and a data assessment that, with grid simulation capabilities, made the demonstration results an adaptable, integrated solution for AEP Ohio and the nation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Thermomagnetic Processing of Liquid-Crystalline Epoxy Resins and Their Mechanical Characterization Using Nanoindentation

A thermomagnetic processing method was used to produce a biphenyl-based liquid-crystalline epoxy resin (LCER) with oriented liquid-crystalline (LC) domains. The orientation of the LCER was confirmed and quantified using two-dimensional X-ray diffraction. The effect of molecular alignment on the mechanical and thermomechanical properties of the LCER was investigated using nanoindentation and thermomechanical analysis, respectively. The effect of the orientation on the fracture behavior was also examined. Lastly, the results showed that macroscopic orientation of the LC domains was achieved, resulting in an epoxy network with an anisotropic modulus, hardness, creep behavior, and thermal expansion.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Valence and conduction band offsets at amorphous hexagonal boron nitride interfaces with silicon network dielectrics

To facilitate the design of heterostructure devices employing hexagonal/sp{sup 2} boron nitride, x-ray photoelectron spectroscopy has been utilized in conjunction with prior reflection electron energy loss spectroscopy measurements to determine the valence and conduction band offsets (VBOs and CBOs) present at interfaces formed between amorphous hydrogenated sp{sup 2} boron nitride (a-BN:H) and various low- and high-dielectric-constant (k) amorphous hydrogenated silicon network dielectric materials (a-SiX:H, X = O, N, C). For a-BN:H interfaces formed with wide-band-gap a-SiO{sub 2} and low-k a-SiOC:H materials (E{sub g} ≅ 8.2−8.8 eV), a type I band alignment was observed where the a-BN:H band gap (E{sub g} = 5.5 ± 0.2 eV) was bracketed by a relatively large VBO and CBO of ∼1.9 and 1.2 eV, respectively. Similarly, a type I alignment was observed between a-BN:H and high-k a-SiC:H where the a-SiC:H band gap (E{sub g} = 2.6 ± 0.2 eV) was bracketed by a-BN:H with VBO and CBO of 1.0 ± 0.1 and 1.9 ± 0.2 eV, respectively. The addition of O or N to a-SiC:H was observed to decrease the VBO and increase the CBO with a-BN:H. For high-k a-SiN:H (E{sub g} = 3.3 ± 0.2 eV) interfaces with a-BN:H, a slightly staggered type II band alignment was observed with VBO and CBO of 0.1 ± 0.1 and −2.3 ± 0.2 eV, respectively. The measured a-BN:H VBOs were found to be consistent with those deduced via application of the commutative and transitive rules to VBOs reported for a-BN:H, a-SiC:H, a-SiN:H, and a-SiO{sub 2} interfaces with Si (100)

36 MATERIALS SCIENCE↗