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DOE OSTI2013

Carbon Data Assimilation with a Coupled Ensemble Kalman Filter (Final Technical Report)

We proposed (and accomplished) the development of an Ensemble Kalman Filter (EnKF) approach for the estimation of surface carbon fluxes as if they were parameters, augmenting the model with them. Our system is quite different from previous approaches, such as carbon flux inversions, 4D-Var, and EnKF with approximate background error covariance (Peters et al., 2008). We showed (using observing system simulation experiments, OSSEs) that these differences lead to a more accurate estimation of the evolving surface carbon fluxes at model grid-scale resolution. The main properties of the LETKF-C are: a) The carbon cycle LETKF is coupled with the simultaneous assimilation of the standard atmospheric variables, so that the ensemble wind transport of the CO2 provides an estimation of the carbon transport uncertainty. b) The use of an assimilation window (6hr) much shorter than the months-long windows used in other methods. This avoids the inevitable “blurring” of the signal that takes place in long windows due to turbulent mixing since the CO2 does not have time to mix before the next window. In this development we introduced new, advanced techniques that have since been adopted by the EnKF community (Kang, 2009, Kang et al., 2011, Kang et al. 2012). These advances include “variable localization” that reduces sampling errors in the estimation of the forecast error covariance, more advanced adaptive multiplicative and additive inflations, and vertical localization based on the time scale of the processes. The main result has been obtained using the LETKF-C with all these advances, and assimilating simulated atmospheric CO2 observations from different observing systems (surface flask observations of CO2 but no surface carbon fluxes observations, total column CO2 from GoSAT/OCO-2, and upper troposphere AIRS retrievals). After a spin-up of about one month, the LETKF-C succeeded in reconstructing the true evolving surface fluxes of carbon at a model grid resolution. When applied to the CAM3.5 model, the LETKF gave very promising results as well, although only one month is available.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter"

58 GEOSCIENCES↗

DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Seismic Observations of the OSIRIS-REx Sample Return Capsule Reentry: Deployment, Signal Characteristics, and Wavefield Phenomenology

Controlled spacecraft reentries from interplanetary trajectories provide rare, well-characterized hypersonic sources for advancing seismoacoustic observation techniques. Here we present seismic observations of the OSIRIS-REx sample return capsule (SRC) reentry on 24 September 2023, recorded by 16 three-component nodal seismometers deployed near Eureka, Nevada, at ground distances of 7–20 km from the capsule trajectory. Air-to-ground coupled signals are detected at all stations, exhibiting impulsive onsets consistent with ballistic shock arrivals from the descending Mach cone. We characterize the seismic wavefield through signal amplitude, period, waveform cross-correlation, and array processing. Signal periods decrease systematically with increasing distance from the trajectory within the airport array, indicating that higher-frequency content becomes more prominent at greater offsets, opposite to expectations from geometric spreading and atmospheric absorption. Seismic array processing identifies frequency-dependent back-azimuth variations whose origin remains unresolved; possible contributing factors include source geometry, scattering by fine-scale layered structure in the stratosphere, and near-surface effects. These observations document a spatially complex seismic wavefield from a well-characterized hypersonic line source and provide constraints for future modeling of atmospheric propagation and air-to-ground coupling.

OSIRIS-REx↗

DOE OSTI2016

High-Resolution, Non-Invasive Imaging of Upper Vocal Tract Articulators Compatible with Human Brain Recordings

A complete neurobiological understanding of speech motor control requires determination of the relationship between simultaneously recorded neural activity and the kinematics of the lips, jaw, tongue, and larynx. Many speech articulators are internal to the vocal tract, and therefore simultaneously tracking the kinematics of all articulators is nontrivial-especially in the context of human electrophysiology recordings. Here, we describe a noninvasive, multi-modal imaging system to monitor vocal tract kinematics, demonstrate this system in six speakers during production of nine American English vowels, and provide new analysis of such data. Classification and regression analysis revealed considerable variability in the articulator-to-acoustic relationship across speakers. Non-negative matrix factorization extracted basis sets capturing vocal tract shapes allowing for higher vowel classification accuracy than traditional methods. Statistical speech synthesis generated speech from vocal tract measurements, and we demonstrate perceptual identification. We demonstrate the capacity to predict lip kinematics from ventral sensorimotor cortical activity. These results demonstrate a multi-modal system to non-invasively monitor articulator kinematics during speech production, describe novel analytic methods for relating kinematic data to speech acoustics, and provide the first decoding of speech kinematics from electrocorticography. These advances will be critical for understanding the cortical basis of speech production and the creation of vocal prosthetics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

A laser ultrasonics-based approach for rapid screening of high entropy alloys

The primary objective of this seed project is to develop a laser ultrasonics-based characterization methodology for rapid metallic materials design and discovery via in situ determination of phases, microstructures and elastic properties with respect to temperature. Ultrasonic waves are strongly affected by material microstructure, and therefore, serve as a facile means to probe elastic properties, phase content and their size distributions and volume fractions. In this study, a laser ultrasonic technique will be used to systematically study the evolution of properties in a set of interrelated simple binary alloys and high entropy alloys (HEA). Phase transformations and microstructural changes inferred from the ultrasonic signals will be correlated with electron microscopy data and predictions using calculations of phase diagrams (CALPHAD). The non-contact and non-destructive ultrasonic testing approach developed in this study could overcome several limitations associated with current material characterization methods for materials discovery. It is expected that the products of the proposed work will be utilized to evaluate novel graded composition HEAs currently being developed at the Idaho National Laboratory (INL) using advanced manufacturing methods based on direct energy deposition and spark plasma sintering processes, and contribute to accelerate the discovery of HEAs.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Recovery Act: High-Temperature Circuit Boards for use in Geothermal Well Monitoring Applications

The U.S. Department of Energy is leading the development of alternative energy sources that will ensure the long-term energy independence of our nation. One of the key renewable resources currently being advanced is geothermal energy. To tap into the large potential offered by generating power from the heat of the earth, and for geothermal energy to be more widely used, it will be necessary to drill deeper wells to reach the hot, dry rock located up to 10 km beneath the earth’s surface. In this instance, water will be introduced into the well to create a geothermal reservoir. A geothermal well produced in this manner is referred to as an enhanced geothermal system (EGS). EGS reservoirs are typically at depths of 3 to 10 km, and the temperatures at these depths have become a limiting factor in the application of existing downhole technologies. These high temperatures are especially problematic for electronic systems such as downhole data-logging tools, which are used to map and characterize the fractures and high-permeability regions in underground formations. Information provided by these tools is assessed so that underground formations capable of providing geothermal energy can be identified, and the subsequent drilling operations can be accurately directed to those locations. The mapping of geothermal resources involves the design and fabrication of sensor packages, including the electronic control modules, to quantify downhole conditions (300°C temperature, high pressure, seismic activity, etc.). Because of the extreme depths at which these measurements are performed, it is most desirable to perform the sensor signal processing downhole and then transmit the information to the surface. This approach necessitates the use of high-temperature electronics that can operate in the downhole environment. Downhole signal processing in EGS wells will require the development and demonstration of circuit boards that can withstand the elevated temperatures found at these depths. At present, the highest-temperature commercially available circuit boards are based on polyimide materials, and those have maximum use temperatures of 200 to 250°C. In addition to thermal stability, downhole electronics must also be fabricated into high-aspect-ratio packages. For example, the multilayer assemblies produced at SNL were approximately 2.5 cm wide and 50 cm long. Because of this very high form factor, glass-fiber-reinforced polymers are much more desirable than multilayer ceramic modules (MCM). MCMs have many advantages for some applications, but are susceptible to damage induced by the mechanical and vibrational loads commonly experienced by data-logging tools. Thus, as EGS technology continues to advance, there is a strong need for multilayer electronics that can provide the necessary thermal performance while also being compatible with high-form-factor circuit designs. This project involved the design and development of high-temperature circuit materials, as well as the fabrication and testing of circuit components. The material development included the evaluation of various polymer/fiberglass composites, whereas the circuit components were tested using conventional microelectronic evaluation techniques. This effort targeted development of a new class of high-temperature multilayer circuit boards for use in downhole data-logging applications where temperatures are on the order of 300°C. This is consistent with DOE’s multiyear plan for advancing technologies for use in enhanced geothermal systems. Organic and inorganic polymer systems, both with glass reinforcements, were considered to provide the following performance at elevated temperatures: • Mechanical strength and durability • High dielectric strength and electrical resistivity • Thermal stability • Strong adhesion to copper to ensure the reliability of the multilayer assemblies • Processing characteristics that are consistent with state-of-the-art multilayer circuit board manufacturing practices

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Ultrasonic nondestructive diagnosis of lithium-ion batteries with multiple frequencies

Accurately estimating the state of charge (SoC) in battery management systems (BMSs) requires the measurement of numerous parameters and advanced algorithms. This work studies multifrequency ultrasonic waves to estimate the SoC of Li-ion batteries by sensing the material changes during charge/discharge. A pouch-type LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) graphite battery cell is designed and fabricated with a capacity of 2.4 Ah. Different ultrasonic testing setups are explored to determine the optimal testing parameters for the battery. An ultrasonic monitoring system is developed to monitor the battery during charge/discharge at 750 kHz, 1 MHz, and 1.5 MHz. Signal processing algorithms are proposed for extracting three ultrasonic features—amplitude, wave velocity, and attenuation. In a three-cycle test, the amplitude histories do not show clear correlations with the SoC. The wave velocities of all three frequencies have an approximately linear relationship with the SoC, which can be used for SoC estimation. Hysteresis behavior is observed for the wave velocity in terms of a larger slope in the discharge process and velocity drop after a close charge/discharge cycle. The wave attenuation is able to capture the material phase transitions during charge/discharge.

25 ENERGY STORAGE↗

DOE OSTI2016

Direct measurement of the propagation velocity of defects using coherent X-rays

The properties of artificially grown thin films are often strongly affected by the dynamic relationships between surface growth processes and subsurface structure. Coherent mixing of X-ray signals promises to provide an approach to better understand such processes. Here, we demonstrate the continuously variable mixing of surface and bulk scattering signals during realtime studies of sputter deposition of a-Si and a-WSi2 films by controlling the X-ray penetration and escape depths in coherent grazing-incidence small-angle X-ray scattering. Under conditions where the X-ray signal comes from both the growth surface and the thin film bulk, oscillations in temporal correlations arise from coherent interference between scattering from stationary bulk features and from the advancing surface. We also observe evidence that elongated bulk features propagate upwards at the same velocity as the surface. Moreover, a highly surface-sensitive mode is demonstrated that can access the surface dynamics independently of the subsurface structure.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Time and Frequency Analysis of Load Profile Data

Technology advancements and integration of modern advanced metering systems can monitor, forecast, inform, control, and operate the building's mechanical, electrical, and plumbing (MEP) systems. They offer a higher level of information, which can contribute to making smart buildings more energy efficient and to making them closer to becoming grid-interactive energy efficient buildings (GEB). This paper builds on the ongoing research on variability analysis of a case study building with a 1-minute load profile and examines the Discrete Wavelet Transform (DWT) process in the frequency domain to quantify the signal's energy in each bandwidth, with respect to each end-use category. Moreover, the amount of variability in the total variability is not similar among the end-use categories. This information is needed to understand the behavior of the variability in the frequency domain for future applications, such as generating synthetic load profiles with a similar frequency spectrum as the measured signal.

decomposition↗

DOE OSTI2013

Dark Matter Annihilation at the Galactic Center

Observations by the WMAP and PLANCK satellites have provided extraordinarily accurate observations on the densities of baryonic matter, dark matter, and dark energy in the universe. These observations indicate that our universe is composed of approximately five times as much dark matter as baryonic matter. However, efforts to detect a particle responsible for the energy density of dark matter have been unsuccessful. Theoretical models have indicated that a leading candidate for the dark matter is the lightest supersymmetric particle, which may be stable due to a conserved R-parity. This dark matter particle would still be capable of interacting with baryons via weak-force interactions in the early universe, a process which was found to naturally explain the observed relic abundance of dark matter today. These residual annihilations can persist, albeit at a much lower rate, in the present universe, providing a detectable signal from dark matter annihilation events which occur throughout the universe. Simulations calculating the distribution of dark matter in our galaxy almost universally predict the galactic center of the Milky Way Galaxy (GC) to provide the brightest signal from dark matter annihilation due to its relative proximity and large simulated dark matter density. Recent advances in telescope technology have allowed for the first multiwavelength analysis of the GC, with suitable effective exposure, angular resolution, and energy resolution in order to detect dark matter particles with properties similar to those predicted by the WIMP miracle. In this work, I describe ongoing efforts which have successfully detected an excess in gamma-ray emission from the region immediately surrounding the GC, which is difficult to describe in terms of standard diffuse emission predicted in the GC region. While the jury is still out on any dark matter interpretation of this excess, I describe several related observations which may indicate a dark matter origin. Finally, I discuss the role of future telescopes in differentiating a dark matter model from astrophysical emission.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures

We present that EPA-MOVES (Motor Vehicle Emission Simulator) is often integrated with traffic simulators to assess emission levels of large-scale urban networks with signalized intersections. High variations in speed profiles exist in the context of congested urban networks with signalized intersections. The traditional average-speed-based emission estimation technique with EPA-MOVES provides faster execution while underestimates the emissions in most cases because of ignoring the speed variation at congested networks with signalized intersections. In contrast, the atomic second-by-second speed profile (i.e., the trajectory of each vehicle)-based technique provides accurate emissions at the cost of excessive computational power and time. We addressed this issue by developing a novel method to determine the link-driving-schedules (LDSs) for the EPA-MOVES tool. Our research developed a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the LDS for EPA-MOVES that is capable of producing emission estimates better than the average-speed-based technique with execution time faster than the atomic speed profile approach. We applied the HC-DTW on a sample data from a signalized corridor and found that HC-DTW can significantly reduce computational time without compromising the accuracy. The developed technique in this research can substantially contribute to the EPA-MOVES-based emission estimation process for large-scale urban transportation network by reducing the computational time with reasonably accurate estimates. This method is highly appropriate for transportation networks with higher variation in speed such as signalized intersections. Lastly, experimental results show error difference ranging from 2% to 8% for most pollutants except PM 10 .

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2014

Overexpression of FABP3 inhibits human bone marrow derived mesenchymal stem cell proliferation but enhances their survival in hypoxia

Studying the proliferative ability of human bone marrow derived mesenchymal stem cells in hypoxic conditions can help us achieve the effective regeneration of ischemic injured myocardium. Cardiac-type fatty acid binding protein (FABP3) is a specific biomarker of muscle and heart tissue injury. This protein is purported to be involved in early myocardial development, adult myocardial tissue repair and responsible for the modulation of cell growth and proliferation. We have investigated the role of FABP3 in human bone marrow derived mesenchymal stem cells under ischemic conditions. MSCs from 12 donors were cultured either in standard normoxic or modified hypoxic conditions, and the differential expression of FABP3 was tested by quantitative {sup RT}PCR and western blot. We also established stable FABP3 expression in MSCs and searched for variation in cellular proliferation and differentiation bioprocesses affected by hypoxic conditions. We identified: (1) the FABP3 differential expression pattern in the MSCs under hypoxic conditions; (2) over-expression of FABP3 inhibited the growth and proliferation of the MSCs; however, improved their survival in low oxygen environments; (3) the cell growth factors and positive cell cycle regulation genes, such as PCNA, APC, CCNB1, CCNB2 and CDC6 were all down-regulated; while the key negative cell cycle regulation genes TP53, BRCA1, CASP3 and CDKN1A were significantly up-regulated in the cells with FABP3 overexpression. Our data suggested that FABP3 was up-regulated under hypoxia; also negatively regulated the cell metabolic process and the mitotic cell cycle. Overexpression of FABP3 inhibited cell growth and proliferation via negative regulation of the cell cycle and down-regulation of cell growth factors, but enhances cell survival in hypoxic or ischemic conditions. - Highlights: • FABP3 expression pattern was studied in 12 human hypoxic-MSCs. • FABP3 mRNA and proteins are upregulated in the MSCs under hypoxic conditions. • Overexpression of FABP3 inhibits cell growth but advanced the MSC survival under hypoxia. • Overexpression of FABP3 down-regulate the cell cycle and stem cell signaling pathways.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos, and cosmic rays cover a wide range of wavelengths and timescales. Combining and processing these datasets that vary in volume, speed, and dimensionality requires new modes of instrument coordination, funding, and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal-processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets has powered the rise of artificial intelligence. This digital revolution now powers a multibillion dollar industry, with far-reaching implications in technology and society. In this chapter, we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in multi-messenger astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017 to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data.

Artificial Intelligence↗

DOE OSTI2025

A split ribozyme system for in vivo plant RNA imaging and genetic engineering

RNA plays a central role in plants, governing various cellular and physiological processes. Monitoring its dynamic abundance provides a discerning understanding of molecular mechanisms underlying plant responses to internal (developmental) and external (environmental) stimuli, paving the way for advances in plant biotechnology to engineer crops with improved resilience, quality and productivity. In general, traditional methods for analysis of RNA abundance in plants require destructive, labour-intensive and time-consuming assays. To overcome these limitations, we developed a transformative innovation for in vivo RNA imaging in plants. Specifically, we established a synthetic split ribozyme system that converts various RNA signals to orthogonal protein outputs, enabling in vivo visualisation of various RNA signals in plants. We demonstrated the utility of this system in transient expression experiments (i.e., leaf infiltration in Nicotiana benthamiana ) to detect RNAs derived from transgenes and tobacco rattle virus, respectively. Also, we successfully engineered a split ribozyme-based biosensor in Arabidopsis thaliana for in vivo visualisation of endogenous gene expression at the cellular level, demonstrating the feasibility of multi-scale (e.g., cellular and tissue level) RNA imaging in plants. Furthermore, we developed a platform for easy incorporation of different protein outputs, allowing for flexible choice of reporters to optimise the detection of target RNAs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Physics-Based Real-time Analysis and Control to Achieve Transient-Free Operations for the ITER Era (Final Report)

This is the final report for the early career award for Prof. Egemen Kolemen that aimed to develop the physics and technical basis of real-time transient – i.e. disruptions and Edge Localized Modes (ELMs) – avoidance and feedback control at the DIII-D nuclear fusion facility for the ITER era. During this project, we reconstructed high quality real-time DIII-D equilibria using kinetic information from the motional Stark effect (MSE), Thomson Scattering (TS), and Charge Exchange Recombination (CER) spectroscopy. These equilibria were analyzed in real-time to determine the low-n ideal MHD stability and the pedestal stability thresholds, and to obtain plasma response models. These calculations were used to understand which physical process evolves to lead to the onset of disruption in ITER-relevant scenarios at DIII-D, and how disruption can be predicted using stability analysis as opposed to the current methods of correlation of various signals, which are descriptive but lack predictive power. In the disruption studies, we focused on tearing modes, since these constitute the vast majority of disruptions in this regime. With this knowledge, real-time disruption avoidance algorithms were developed and tested. The enhanced situation awareness and physics insight were used in advanced real-time feedback control of pedestal and core profiles, which allowed the pursuit and sustainment of transient-free high-confinement regimes that cannot be achieved via feed-forward design based on in-between shot analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

A laser ultrasonics-based approach for rapid screening of high entropy alloys

This project demonstrates a laser ultrasonics-based characterization methodology for rapid metallic materials design and discovery via in situ determination of phases, microstructures and elastic properties with respect to temperature. Ultrasonic waves are strongly affected by material microstructure, and therefore serve as a facile means to probe elastic properties, phase content and their size distributions and volume fractions. In this study, a laser ultrasonic technique is used to systematically study the evolution of properties in a set of interrelated simple binary alloys and high entropy alloys (HEA). Phase transformations and microstructural changes inferred from the ultrasonic signals will be correlated with electron microscopy data and predictions using calculations of phase diagrams (CALPHAD). The non-contact and non-destructive ultrasonic testing approach developed in this study could overcome several limitations associated with current material characterization methods for materials discovery. It is expected that laser ultrasonics-based methodology developed in this work will be utilized to evaluate novel graded composition HEAs currently being developed at the Idaho National Laboratory (INL) using advanced manufacturing methods based on direct energy deposition and spark plasma sintering processes, and contribute to accelerate the discovery of HEAs.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Charge Transfer Processes in OPV Materials as Revealed by EPR Spectroscopy

Understanding charge separation and charge transport at a molecular level is crucial for improving the efficiency of organic photovoltaic (OPV) cells. Under illumination of Bulk Heterojunction (BHJ) blends of polymers and fullerenes, various paramagnetic species are formed including polymer and fullerene radicals, radical pairs, and photoexcited triplet states. Light-induced Electron Paramagnetic Resonance (EPR) spectroscopy is ideally suited to study these states in BHJ due to its selectivity in probing the paramagnetic intermediates. Some advanced EPR techniques like light-induced ENDOR spectroscopy and pulsed techniques allow the determination of hyperfine coupling tensors, while high-frequency EPR allows the EPR signals of the individual species to be resolved and their g-tensors to be determined. In these magnetic resonance parameters reveal details about the delocalization of the positive polaron on the various polymer donors which is important for the efficient charge separation in BHJ systems. Time-resolved EPR can contribute to the study of the dynamics of charge separation, charge transfer and recombination in BHJ by probing the unique spectral signatures of charge transfer and triplet states. Furthermore, the potential of the EPR also allows characterization of the intermediates and products of BHJ degradation.

36 MATERIALS SCIENCE↗