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DOE OSTI2025

A mathematical approach to using the forgetting curve to evaluate experience and training factors in human reliability analysis

Traditional human reliability analysis (HRA) methods have difficulty dealing with the dynamic nature of factors such as time and rely on static and expert-judgment-based assessments of performance-shaping factors (PSFs) across limited levels. In this study, we introduce a mathematical approach for dynamically evaluating the experience and training PSF. Our proposed method integrates the psychological concept of the “forgetting curve” to evaluate how PSFs are impacted by the number of trainings and the time elapsed since training. To confirm the validity of the model, we provide experimental data fitted by identifying the quantitative relationship between training and human performance. This research enables dynamic and objective assessments, thus reducing reliance on subjective expert judgment and improving the accuracy of HRA.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2015

Multifidelity importance sampling

Estimating statistics of model outputs with the Monte Carlo method usually requires a large number of model evaluations. This leads to long runtimes if the model is expensive to evaluate. Importance sampling is one method that can lead to a reduction in the number of model evaluations. Importance sampling uses a biasing distribution to sample the model more efficiently, but generating such a biasing distribution can be difficult and usually also requires model evaluations. A different strategy to speed up Monte Carlo sampling is to replace the computationally expensive high-fidelity model with a computationally cheap surrogate model; however, because the surrogate model outputs are only approximations of the high-fidelity model outputs, the estimate obtained using a surrogate model is in general biased with respect to the estimate obtained using the high-fidelity model. We introduce a multifidelity importance sampling (MFIS) method, which combines evaluations of both the high-fidelity and a surrogate model. It uses a surrogate model to facilitate the construction of the biasing distribution, but relies on a small number of evaluations of the high-fidelity model to derive an unbiased estimate of the statistics of interest. We prove that the MFIS estimate is unbiased even in the absence of accuracy guarantees on the surrogate model itself. The MFIS method can be used with any type of surrogate model, such as projection-based reduced-order models and data-fit models. Moreover, the MFIS method is applicable to black-box models, i.e., where only inputs and the corresponding outputs of the high-fidelity and the surrogate model are available but not the details of the models themselves. We demonstrate on nonlinear and time-dependent problems that our MFIS method achieves speedups of up to several orders of magnitude compared to Monte Carlo with importance sampling that uses the high-fidelity model only.

42 ENGINEERING↗

DOE OSTI2019

A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels

A deep neural network was developed for the purpose of predicting thermal conductivity with a case study performed on irradiated nuclear fuel. Traditional thermal conductivity modeling approaches rely on existing theoretical frameworks that describe known, relevant phenomena that govern the microstructural evolution processes during neutron irradiation (such as recrystallization, and pore size, distribution and morphology). Current empirical modeling approaches, however, do not represent all irradiation test data well. We develop a machine learning approach to thermal conductivity modeling that does not require a priori knowledge of a specific material microstructure and system of interest. Our approach allows re-searchers to probe dependency of thermal conductivity on a variety of reactor operating and material conditions. The purpose of building such a model is to allow for improved predictive capabilities linking structure-property-processing-performance relationships in the system of interest (here, irradiated nuclear fuel), which could lead to improved experimental test planning and characterization. The uranium-molybdenum system is the fuel system studied in this work, and historic irradiation test data is leveraged for model development. Our model achieved a mean absolute percent error of approximately 10%. Results indicate our model generalizes well to never before seen data, and thus use of deep learning methods for material property predictions from limited, historic irradiation data is a viable approach. This work challenges the current paradigm in materials science, where material property models are based on limited experimental data fitted to low-dimensionality phenomenological models, or by simulations in which fundamental equations are explicitly solved. The work presented here aims to demonstrate the potential and limitations of machine learning in the field of materials science and material property modeling.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Neural Posterior Estimation for Scalable and Accurate Inverse Parameter Inference in Li-Ion Batteries

Diagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physics-based models to perform probabilistic parameter estimation via Bayesian calibration, diagnostics can account for the uncertainty due to model fitness, data noise, and the observability of any given parameter. However, Bayesian calibration in Li-ion batteries using electrochemical data is computationally intensive even when using a fast surrogate in place of physics-based models, requiring many thousands of model evaluations. A fully amortized alternative is neural posterior estimation (NPE). NPE shifts the computational burden from the parameter estimation step to data generation and model training, reducing the parameter estimation time from minutes to milliseconds, enabling real-time applications. The present work shows that NPE can infer parameters equally or more accurately than Bayesian calibration, even if it leads to higher voltage reconstruction errors. We also demonstrate that the higher computational costs for data generation are tractable even in high-dimensional cases (ranging from 6 to 27 estimated parameters). The NPE method also offers several interpretability advantages over Bayesian calibration, such as local parameter sensitivity to specific regions of the voltage curve. The NPE method is demonstrated using an experimental fast charge dataset, with parameter estimates validated against measurements of loss of lithium inventory and loss of active material. The implementation is made available in a companion repository (https://github.com/NatLabRockies/BatFIT).

25 ENERGY STORAGE↗

DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗

DOE OSTI2022

Spatiotemporal Metabolic Network Models Reveal Complex Autotroph-Heterotroph Biofilm Interactions Governed by Photon Incidences

Autotroph-heterotroph interactions are ubiquitous in natural environment and play a key role in controlling various essential ecosystem functions, such as production and utilization of organic matter, cycling of nitrogen, sulfur, and other chemical elements. Understanding how these biofilm metabolic interactions are constrained in space and time remains challenging because fully predictive models designed for this purpose are currently limited. Toward filling this gap, here we developed community metabolic network models for two autotroph-heterotroph biofilm consortia (termed UCC-A and UCC-O), which share a suite of common heterotrophic members but have a single distinct photoautotrophic cyanobacterium (Phormidesmis priestleyi str. ANA and Phormidium sp. OSCR) that provides organic carbon and nitrogen sources to support the growth of heterotrophic partners. After determining model parameters by data fitting using the spatiotemporal distributions of microbial abundances, we comparatively analyzed the resulting biofilm models to examine any fundamental differences in microbial interactions between the two consortia under the variation of key environmental variables: CO2 and photon levels. The UCC-A model predicted generally expected responses, i.e., the autotroph population increased in response to elevated levels of CO2 and photon, followed by increase in the heterotroph population. In contrast, the UCC-O model showed somewhat complicated dynamics, e.g., higher photon incidence rates resulted in the increase in autotroph population but decrease in heterotroph population due to the lowered provision of glucose from the autotroph. A further analysis showed that species coexistence was governed by the photon incidences rather than the carbon availability for UCC-O, which was the opposite for UCC-A.

Phalak, Poonam↗

DOE OSTI2021

A Primer on Dose-Response Data Modeling in Radiation Therapy

An overview of common approaches used to assess a dose response for radiation therapy–associated endpoints is presented, using lung toxicity data sets analyzed as a part of the High Dose per Fraction, Hypofractionated Treatment Effects in the Clinic effort as an example. Each component presented (eg, data-driven analysis, dose-response analysis, and calculating uncertainties on model prediction) is addressed using established approaches. Specifically, the maximum likelihood method was used to calculate best parameter values of the commonly used logistic model, the profile-likelihood to calculate confidence intervals on model parameters, and the likelihood ratio to determine whether the observed data fit is statistically significant. The bootstrap method was used to calculate confidence intervals for model predictions. Correlated behavior of model parameters and implication for interpreting dose response are discussed.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2026

The evolution of coal porosity during pyrolysis

Gasification of coal, municipal waste, or other organic materials is a potential hydrogen source that entails complex thermal decomposition and transport processes. This study provides a multiscale analysis of these processes for sub-bituminous (Usibelli, Healy, Alaska) and lignite (Center, North Dakota) coals and provides data useful for process design. The chemistry, mineralogy, and pore structures of pyrolyzed coal and their evolution with thermal decomposition are discussed. Samples pyrolyzed at 200–1000 °C were analyzed by small-angle neutron scattering; ultra-small, small-, and wide-angle X-ray scattering; and other complementary techniques. Scanning electron microscopy showed new pores in the high-temperature-pyrolyzed material. Upon heating, the coals became progressively denser, and the concentration of hydrogen decreased. Changes in pore volume fell into three temperature ranges: an initial, low-temperature range that, for the Usibelli coal, involved an increase in overall porosity; a mid-temperature range associated with pore volume loss; and a high-temperature range associated with significant porosity increase and char formation. This transformation was paralleled by changes in fractal dimension and correlation length. The higher the pyrolysis temperature the greater the small-pore-volume fraction and overall surface area became. Pyrolysis increased the lateral size of coal crystallites, decreased the amorphous fraction, and increased the aromatics fraction and overall coal rank. Comparisons of neutron and X-ray scattering data and subsequent water uptake studies showed that pre-dried coals can re-hydrate relatively rapidly upon exposure to air, which can significantly affect the porosity calculated from small-angle-scattering data. Fits to the cumulative porosity curves provide a method for modeling the physical and chemical transformation of hydrogen-containing feedstock during gasification.

Anovitz, Lawrence {Larry} [ORNL] (ORCID:0000000226↗

DOE OSTI2022

Production of polyacrylonitrile/ionic covalent organic framework hybrid nanofibers for effective removal of chromium(VI) from water

Hexavalent Cr(VI) found in industrial wastewater is a proven carcinogen which causes serious health issues in humans around the world. This study presents a novel method to enhance the Cr(VI) oxyanion removal from wastewater by polyacrylonitrile (PAN) nanofibers through incorporation of a guanidinium-based ionic covalent organic framework (BT-DG) in the nanofibers structure. In this work, simple electrospinning technique was employed to produce PAN nanofibers and BT-DG was synthesized through condensation between benzene-1,3,5-tricarbaldehyde and N,N'-diaminoguanidine monohydrochloride. In-situ polymerization of BT-DG onto PAN nanofibers resulted in generation of hybrid PAN-BT-DG nanofibers. This modified PAN-BT-DG was characterized by obtaining its point of zero charge (PZC), differential scanning calorimeter (DSC), scanning electron microscopy (SEM) morphology and surface elements and oxidation states by X-ray photoelectron spectroscopy (XPS). PAN-BT-DG exhibited positive surface charge below pH 4, making it an outstanding adsorbent, for Cr(VI) removal. Cr(VI) adsorption onto PAN-BT-DG followed pseudo second order kinetics and adsorption data fitted well to Freundlich isotherm model. Highest Cr(VI) removal was obtained at 55 °C with a maximum Langmuir adsorption capacity of 173 mg/g at pH 3. Kinetic studies revealed that Cr(VI) adsorption onto PAN-BT-DG is endothermic and thermodynamically feasible. Desorption studies were conducted on PAN-BT-DG using 1 M NaOH as the stripping solvent and PAN-BT-DG exhibited excellent regeneration after five consecutive cycles.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Local structure of Fe in Fe-doped misfit-layered calcium cobaltite: An X-ray absorption spectroscopy study

Polycrystalline Ca{sub 3}Co{sub 4−x}Fe{sub x}O{sub 9+δ} ceramics (x=0, 0.01, 0.03, 0.05) were fabricated using a simple thermal hydro-decomposition method and a spark plasma sintering technique. Thermoelectric property measurements showed that increasing Fe concentration resulted in a decrease in electrical resistivity, thermopower and thermal conductivity, leading to an improvement in the dimensionless figure-of-merit, >35% for x=0.05 at 1073 K. An X-ray absorption spectroscopy technique was used to investigate the local structure of Fe ions in the Ca{sub 3}Co{sub 4−x}Fe{sub x}O{sub 9+δ} structure for the first time. By fitting data from the extended X-ray absorption fine structure (EXAFS) spectra and analyzing the X-ray absorption near-edge structure (XANES) spectra incorporated with first principle simulation, it was shown that Fe was substituted for Co in the the Ca{sub 2}CoO{sub 3} (rocksalt, RS) layer rather than in the CoO{sub 2} layer. Variation in the thermoelectric properties as a function of Fe concentration was attributed to charge transfer between the CoO{sub 2} and the RS layers. The origin of the preferential Fe substitution site was investigated considering the ionic radii of Co and Fe and the total energy of the system. - Graphical abstract: The Fe K-edge XANES spectra of: (a) experimental result in comparison to the simulated spectra when Fe atoms were substituted in the RS layer; (b) with magnetic moment; (c) without magnetic moment, and in the CoO{sub 2} layer; (d) with magnetic moment and (e) without magnetic moment. Highlights: • Synthesis, structural studies, and thermoelectric properties of Ca{sub 3}Co{sub 4−x}Fe{sub x}O{sub 9+δ}. • Direct evidence for the local structure of the Fe ions in the Ca{sub 3}Co{sub 4−x}Fe{sub x}O{sub 9+δ} using XAS analysis. • EXAFS and XANES analysis showed that Fe was likely to be situated in the RS layer structure. • Changes in TE property with Fe content was due to charge transfer between the CoO{sub 2} and the RS layers. • Total energy calculation showed energetically favorable Fe substitution in the RS layer.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Uniform Cu{sub 2}Cl(OH){sub 3} hierarchical microspheres: A novel adsorbent for methylene blue adsorptive removal from aqueous solution

Using the solution phase method without any surfactants or templates, the hierarchical of Cu{sub 2}Cl(OH){sub 3} microspheres were synthesized by freeze drying. The size and surface area of the microspheres are ca. 1–2 µm and 76.61 m{sup 2} g{sup −1}, respectively. A possible formation mechanism is presented based on the experimental results. Methylene blue was chosen to investigate the adsorption capacity of the as-prepared adsorbent. The effects of various experimental parameters, such as pH, initial dye concentration, and contact time were investigated. The results showed that the dye removal increased with the increasing in the initial concentration of the dye and also increased in the amount of microspheres used and initial pH. Adsorption data fitted well with the Freundlich adsorption isotherm. The thermodynamic analysis presented the exothermic, spontaneous and more ordered arrangement process. The microspheres could be employed effective for removal of dyes from aqueous solution. - Graphical abstract: The single-crystalline hierarchical Cu{sub 2}Cl(OH){sub 3} spheres can be prepared for the first time by using a template-free process through freeze-drying. Meanwhile, the hierarchical spheres exhibited high adsorption capacity to methylene blue. Display Omitted - Highlights: • Cu{sub 2}Cl(OH){sub 3} microspheres were successfully synthesized through a freeze drying process. • A possible formation mechanism of hierarchical microspheres was presented. • The Cu{sub 2}Cl(OH){sub 3} microspheres have high methylene blue adsorption capacity. • Methylene blue adsorption is a spontaneous and exothermic process. • The adsorption mechanism of microspheres onto dye was proposed in detail.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Intercalation of organic molecules in 2D copper (II) nitroprusside: Intermolecular interactions and magnetic properties

This contribution discusses the intercalation of imidazole and its 2-ethyl derivative, and pyridine in 2D copper nitroprusside. In the interlayer region, neighboring molecules remain interacting throu gh their dipole and quadrupole moments, which supports the solid 3D crystal structure. The crystal structure of this series of intercalation compounds was solved and refined from powder X-ray diffraction patterns complemented with spectroscopic information. The intermolecular interactions were studied from the refined crystal structures and low temperature magnetic measurements. Due to strong attractive forces between neighboring molecules, the resulting π–π cloud overlapping enables the ferromagnetic coupling between metal centers on neighboring layers, which was actually observed for the solids containing imidazole and pyridine as intercalated molecules. For these two solids, the magnetic data were properly described with a model of six neighbors. For the solid containing 2-ethylimidazole and for 2D copper nitroprusside, a model of four neighbors in a plane is sufficient to obtain a reliable data fitting. - Highlights: • Intercalation of organic molecules in 2D copper (II) nitroprusside. • Molecular properties of intercalation compounds of 2D copper (II) nitroprusside. • Magnetic properties of hybrid inorganic–organic solids. • Hybrid inorganic–organic 3D framework.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2011

On the preparation of as-produced and purified single-walled carbon nanotube samples for standardized X-ray diffraction characterization

The aim of this research was to specify proper sample conditioning for acquiring representative X-ray diffraction (XRD) profiles for single-walled carbon nanotube (SWCNT) samples. In doing so, a specimen preparation method for quantitative XRD characterization of as-produced and purified arc-discharge SWCNT samples has been identified. Series of powder XRD profiles were collected at different temperatures, states, and points of time to establish appropriate conditions for acquiring XRD profiles without inducing much change to the specimen. It was concluded that heating in the 300-450 deg. C range for 20 minutes, preferably vacuum-assisted, and then sealing the sample is an appropriate XRD specimen preparation technique for purified arc-discharge SWCNT samples, while raw samples do not require preconditioning for characterization. - Graphical Abstract: A sample preparation method for XRD characterization of as-produced and purified arc-discharge SWCNT samples is identified. The preparation technique seeks to acquire representative XRD profiles without inducing changes to the samples. Purified samples required 20 minutes of heating at (300-450)deg. C, while raw samples did not require preconditioning for characterization. Highlights: {yields} Purification routines may induce adsorption onto the SWCNT samples. {yields} Heating a SWCNT sample may result in material loss, desorption, and SWCNTs closing. {yields} Raw arc-discharge samples do not require preparation for XRD characterization. {yields} Heating is appropriate specimen preparation for purified and heat-treated samples. {yields} XRD data fitting is required for structural analysis of SWCNT bundles.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2011

Evaluation and modeling of thermal kinetic degradation for PVA doped PbS quantum dot

Highlights: {yields} Synthesis of PVA doped PbS quantum dots. {yields} Data fitting using integral and differential thermal kinetic models for calculating activation energy. {yields} Prediction of thermal degradation using iso-conversion model. -- Abstract: The kinetic analysis of the thermogravimetric curves for the thermal decomposition processes of PVA/PbS was performed. The samples were heated in nitrogen, with three different heating rates: 10, 20 and 30 {sup o}C min{sup -1}. Various forms of non-isothermal methods of analysis for determining the kinetic parameters were used. The differential and integral models were used to calculate the activation energies. Comparing with pure PVA, the results showed that the maximum activation energy of thermal degradation is achieved for PVA/PbS nanocomposite. Isoconversion model is used for predicting the thermal degradation acceleration. The results showed that the acceleration of thermal degradation for pure PVA was faster than PVA/PbS nanocomposite.

36 MATERIALS SCIENCE↗

DOE OSTI2018

The relation of magnetic properties and anomalous Hall behaviors in Mn4N (200) epitaxial films

Highlights: • Ferrimagnetic Mn4N(200) were grown on MgO(100) by plasma assisted molecular beam epitaxy. • We elucidate the relation of nitrogen vacancies and the magnetism by adjusting the growth pressure. • We state the relation of longitudinal resistivity and temperature as ρ{sub xx}∝T{sup 1.9994} (T ≤ 86K) and ρ{sub xx}∝T{sup 0.3068} (T > 86K) by data fitting. • We introduce a method to estimate the Debye temperature of Mn{sub 4}N materials and the residual resistance of 0 K. • We verify the linear relation between anomalou Hall resistivity (ρ{sub AH}) and magnetization at low magnetic field. - Abstract: The Mn{sub 4}N(200) films were grown on MgO(100) substrates by molecular beam epitaxy. The saturated magnetizations are 0.735 μB/f.u. and 0.892 μB/f.u. under pressure of 7.5 × 10{sup −6} mbar–9.5 × 10{sup −6} mbar. Increasing of growth pressure enhances the magnetism of Mn{sub 4}N due to the reduction of nitrogen vacancies. Debye temperature (T{sub D}) of Mn{sub 4}N is estimated to be 86 K. The longitudinal resistivity is linear to T{sup 1.9994} when T D , which coincides with electron-electron scattering of ferromagnetic coupling. The longitudinal resistivity originates from electron-electron scattering below T{sub D} since electron-phonon scattering is frozen. The longitudinal resistivity at 0 K is about 10% lower than 5 K as 7.085 μΩ cm. Mn{sub 4}N films show obvious anomalous Hall Effect in perpendicular magnetic fields. The anomalous Hall resistivity (ρ{sub AH}) of Mn{sub 4}N film is obtained by subtracting the ordinary Hall resistivity from transverse resistivity. The ρ{sub AH} is verified to be linear to magnetization at low magnetic field strength region (0–9 KOe).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2015

Bridging a gap between continuum-QCD and ab initio predictions of hadron observables

Within contemporary hadron physics there are two common methods for determining the momentum-dependence of the interaction between quarks: the top-down approach, which works toward an ab initio computation of the interaction via direct analysis of the gauge-sector gap equations; and the bottom-up scheme, which aims to infer the interaction by fitting data within a well-defined truncation of those equations in the matter sector that are relevant to bound-state properties. We unite these two approaches by demonstrating that the renormalisation-group-invariant running-interaction predicted by contemporary analyses of QCD's gauge sector coincides with that required in order to describe ground-state hadron observables using a nonperturbative truncation of QCD's Dyson–Schwinger equations in the matter sector. This bridges a gap that had lain between nonperturbative continuum-QCD and the ab initio prediction of bound-state properties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Effect of stereochemistry on nanoscale assembly of ABA triblock copolymers with crystallizable blocks

We report on the nanoscale assembly of poly(lactic acid)-b-poly(ethylene oxide)-b-poly(lactic acid) (PLA-PEO-PLA) triblock copolymers in water, focusing on the effect of stereochemistry, where the PLA blocks are statistical copolymers of l-lactide and d-lactide with l/d ratios of 100/0, 95/5, 90/10, 85/15, 75/25, and 50/50. Small-angle neutron scattering (SANS) shows a nearly constant d-spacing as concentration varies in triblock systems with l/d ratios of 90/10 and 95/5, which we attribute to inhomogeneity in the structure of these gels, supported by previous USANS and confocal microscopy studies. The SANS data fit well to a core-shell ellipsoid form factor model with a hard-sphere structure factor. Polymeric micelles with l/d ratios from 75/25 to 85/15 displayed very high aggregation numbers, consistent with a strong interaction between PLA chains and the enhanced storage modulus observed in rheological studies of these systems. While the 90/10 and 95/5 samples showed lower aggregation numbers, their SANS profiles shows close spacing between micelles, which may promote a high fraction of intermicellar bridging chains, also consistent with a higher storage modulus. Overall, these results provide insight into the micellar assembly behavior of block copolymers with a crystallizable block, and indicate that tuning stereochemistry of PLA-based block copolymers is an effective means of modifying micellar properties for specific applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Near-field transport imaging applied to photovoltaic materials

We developed and applied a new analytical technique - near-field transport imaging (NF-TI or simply TI) - to photovoltaic materials. Charge-carrier transport is an important factor in solar cell performance, and TI is an innovative approach that integrates a scanning electron microscope with a near-field scanning optical microscope, providing the possibility to study luminescence associated with recombination and transport with high spatial resolution. In this paper, we describe in detail the technical barriers we had to overcome to develop the technique for routine application and the data-fitting procedure used to calculate minority-carrier diffusion length values. The diffusion length measured by TI agrees well with the results calculated by time-resolved photoluminescence on well-controlled gallium arsenide (GaAs) thin-film samples. We report for the first time on measurements on thin-film cadmium telluride using this technique, including the determination of effective carrier diffusion length, as well as the first near-field imaging of the effect of a single localized defect on carrier transport and recombination in a GaAs heterostructure. Furthermore, by changing the scanning setup, we were able to demonstrate near-field cathodoluminescence (CL), and correlated the results with standard CL measurements. In conclusion, the TI technique shows great potential for mapping transport properties in solar cell materials with high spatial resolution.

14 SOLAR ENERGY↗