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DOE OSTI2023

Investigation of process history and underlying phenomena associated with the synthesis of plutonium oxides using Vector Quantizing Variational Autoencoder

Accurate, high throughput, and unbiased analysis of plutonium oxide particles is needed for analysis of the phenomenology associated with process parameters in their synthesis. Compared to qualitative and taxonomic descriptors, quantitative descriptors of particle morphology through scanning electron microscopy (SEM) have shown success in analyzing process parameters of uranium oxides. Among other candidates, a neural network called a Vector Quantizing Variational Autoencoder (VQ-VAE) has shown the ability to quantitatively describe particle morphology to attain >85% accuracy in identifying uranium oxide processing routes. We utilize a VQ-VAE to quantitatively describe plutonium dioxide (PuO 2 ) particles created in a designed experiment and investigate their phenomenology and prediction of their process parameters. PuO 2 was calcined from Pu(III) oxalates that were precipitated under varying synthetic conditions that related to concentrations, temperature, addition and digestion times, precipitant feed, and strike order; the surface morphology of the resulting PuO 2 powders were analyzed by SEM. A pipeline was developed to extract and quantify useful image representations for individual particles with the VQ-VAE, then further reduce the dimensionality of the feature space using a bottlenecking neural network fit to perform multiple classification tasks simultaneously. The reduced feature space could predict process parameters with greater than 80% accuracies for some parameters with a single particle. They also showed utility for grouping particles with similar surface morphology characteristics together. Both the clustering and classification results reveal valuable information regarding which chemical process parameters chiefly influence the PuO 2 particle morphologies: strike order and oxalic acid feedstock. Doing the same analysis with multiple particles was shown to improve the classification accuracy on each process parameter over the use of a single particle, with statistically significant results generally seen with as few as four particles in a sample.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing

A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a unified methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More specifically, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (artificial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the final performance accuracy evaluation. The results showed that the application of linear regression coefficients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. Finally, the validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.

42 ENGINEERING↗

DOE OSTI2023

The cosmic web around the Coma cluster from constrained cosmological simulations

Galaxy clusters in the Universe occupy the important position of nodes of the cosmic web. They are connected among them by filaments, elongated structures composed of dark matter, galaxies, and gas. The connection of galaxy clusters to filaments is important, as it is related to the process of matter accretion onto the former. For this reason, investigating the connections to the cosmic web of massive clusters, especially well-known ones for which a lot of information is available, is a hot topic in astrophysics. In a previous work, we performed an analysis of the filament connections of the Coma cluster of galaxies, as detected from the observed galaxy distribution. In this work we resort to a numerical simulation whose initial conditions are constrained to reproduce the local Universe, including the region of the Coma cluster to interpret our observations in an evolutionary context. We detect the filaments connected to the simulated Coma cluster and perform an accurate comparison with the cosmic web configuration we detect in observations. We perform an analysis of the halos’ spatial and velocity distributions close to the filaments in the cluster outskirts. We conclude that, although not significantly larger than the average, the flux of accreting matter on the simulated Coma cluster is significantly more collimated close to the filaments with respect to the general isotropic accretion flux. This paper is the first example of such a result and the first installment in a series of publications which will explore the build-up of the Coma cluster system in connection to the filaments of the cosmic web as a function of redshift.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Selection function of clusters in Dark Energy Survey year 3 data from cross-matching with South Pole Telescope detections

Context. Galaxy clusters selected based on overdensities of galaxies in photometric surveys provide the largest cluster samples. However, modeling the selection function of such samples is complicated by noncluster members projected along the line of sight (projection effects) and the potential detection of unvirialized objects (contamination). Aims. We empirically constrained the magnitude of these effects by cross-matching galaxy clusters selected in the Dark Energy Survey data with the redMaPPer algorithm with significant detections in three South Pole Telescope surveys (SZ, pol-ECS, pol-500d). Methods. For matched clusters, we augmented the redMaPPer catalog with the SPT detection significance. For unmatched objects we used the SPT detection threshold as an upper limit on the SZe signature. Using a Bayesian population model applied to the collected multiwavelength data, we explored various physically motivated models to describe the relationship between observed richness and halo mass. Results. Our analysis reveals a clear preference for models with an additional skewed scatter component associated with projection effects over a purely log-normal scatter model. We rule out significant contamination by unvirialized objects at the high-richness end of the sample. While dedicated simulations offer a well-fitting calibration of projection effects, our findings suggest the presence of redshift-dependent trends that these simulations may not have captured. Our findings highlight that modeling the selection function of optically detected clusters remains a complicated challenge that requires a combination of simulation and data-driven approaches.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1992

The topology of large-scale structure. VI - Slices of the universe

Results of an investigation of the topology of large-scale structure in two observed slices of the universe are presented. Both slices pass through the Coma cluster and their depths are 100 and 230/h Mpc. The present topology study shows that the largest void in the CfA slice is divided into two smaller voids by a statistically significant line of galaxies. The topology of toy models like the white noise and bubble models is shown to be inconsistent with that of the observed slices. A large N-body simulation was made of the biased cloud dark matter model and the slices are simulated by matching them in selection functions and boundary conditions. The genus curves for these simulated slices are spongelike and have a small shift in the direction of a meatball topology like those of observed slices.

Park, Changbom↗

NASA NTRS2012

Using Statistical Multivariable Models to Understand the Relationship Between Interplanetary Coronal Mass Ejecta and Magnetic Flux Ropes

In-situ measurements of interplanetary coronal mass ejections (ICMEs) display a wide range of properties. A distinct subset, "magnetic clouds" (MCs), are readily identifiable by a smooth rotation in an enhanced magnetic field, together with an unusually low solar wind proton temperature. In this study, we analyze Ulysses spacecraft measurements to systematically investigate five possible explanations for why some ICMEs are observed to be MCs and others are not: i) An observational selection effect; that is, all ICMEs do in fact contain MCs, but the trajectory of the spacecraft through the ICME determines whether the MC is actually encountered; ii) interactions of an erupting flux rope (PR) with itself or between neighboring FRs, which produce complex structures in which the coherent magnetic structure has been destroyed; iii) an evolutionary process, such as relaxation to a low plasma-beta state that leads to the formation of an MC; iv) the existence of two (or more) intrinsic initiation mechanisms, some of which produce MCs and some that do not; or v) MCs are just an easily identifiable limit in an otherwise corntinuous spectrum of structures. We apply quantitative statistical models to assess these ideas. In particular, we use the Akaike information criterion (AIC) to rank the candidate models and a Gaussian mixture model (GMM) to uncover any intrinsic clustering of the data. Using a logistic regression, we find that plasma-beta, CME width, and the ratio O(sup 7) / O(sup 6) are the most significant predictor variables for the presence of an MC. Moreover, the propensity for an event to be identified as an MC decreases with heliocentric distance. These results tend to refute ideas ii) and iii). GMM clustering analysis further identifies three distinct groups of ICMEs; two of which match (at the 86% level) with events independently identified as MCs, and a third that matches with non-MCs (68 % overlap), Thus, idea v) is not supported. Choosing between ideas i) and iv) is more challenging, since they may effectively be indistinguishable from one another by a single in-situ spacecraft. We offer some suggestions on how future studies may address this.

Riley, P.↗

DOE OSTI2025

Measurement of $\nu_\mu$ CC Interactions With Two-Proton Final State in MINERvA

This dissertation presents a measurement of charged–current (CC) muon–neutrino interactions with exactly two protons and no pions in the final state (CC~$2p\,0\pi$), using data collected by the MINERvA detector in the NuMI medium–energy beam at Fermilab. Such two–proton topologies are a sensitive probe of nuclear dynamics in the few–GeV regime, including multi–nucleon correlations (npnh, notably $2p2h$) and intranuclear final–state interactions (FSI) such as pion absorption and nucleon rescattering. A precise experimental characterization of these processes is essential both for neutrino–interaction theory and for reducing systematic uncertainties in oscillation experiments that rely on accurate modeling of neutrino–nucleus interactions. Events are selected by requiring a $\nu_\mu$ CC interaction with a reconstructed $\mu^-$ and two proton tracks originating from a common vertex in MINERvA’s finely segmented scintillator tracker, with no reconstructed mesons. Muon charge and momentum are constrained by matching to the MINOS Near Detector, while proton identification exploits energy–loss profiles and stopping–proton features. Backgrounds from pion–producing channels that enter the signal region through FSI or reconstruction effects are constrained with data–driven sidebands (Michel–electron and isolated–cluster “blob” samples) and tuned via a simultaneous fit across signal and sideband regions. To correct detector resolution and acceptance effects, the analysis employs iterative Bayesian unfolding with extensive validation: statistical pseudo–experiments, and robustness checks against generator systematic “universes” and additional strong shape warps. Single–differential cross sections are reported for three observables tailored to the two–proton final state: the opening–angle cosine $\cos\!\left(\theta_{pp}\right)$, the leading–proton momentum, and the subleading–proton momentum. Systematic uncertainties include contributions from neutrino flux, interaction modeling (e.g., npnh and resonance parameters, pion FSI), and detector response (calibration, reconstruction efficiencies). The resulting distributions provide targeted constraints on the interplay of multi–nucleon dynamics and FSI that shape CC~$2p\,0\pi$ final states on hydrocarbon. Comparisons to modern GENIE–based simulations highlight kinematic regions where model components require refinement. These measurements thus inform generator tuning and improve the reliability of neutrino–energy reconstruction strategies for current and future long–baseline oscillation programs.

Syrotenko, Vladyslav S. [Tufts U.]↗

NASA NTRS2023

Cosmological Shocks Around Galaxy Clusters: A Coherent Investigation With DES, SPT & ACT

We search for signatures of cosmological shocks in gas pressure profiles of galaxy clusters using the cluster catalogues from three surveys: the Dark Energy Survey (DES) Year 3, the South Pole Telescope (SPT) SZ survey, and the Atacama Cosmology Telescope (ACT) data releases 4, 5, and 6, and using thermal Sunyaev–Zeldovich (SZ) maps from SPT and ACT. The combined cluster sample contains around 105 clusters with mass and redshift ranges 10 13.7 200m /M ⊙ <10 15.5 and 0.1 < z < 2, and the total sky coverage of the maps is ≈15000deg 2 ⁠. We find a clear pressure deficit at R/R 200m ≈ 1.1 in SZ profiles around both ACT and SPT clusters, estimated at 6σ significance, which is qualitatively consistent with a shock-induced thermal non-equilibrium between electrons and ions. The feature is not as clearly determined in profiles around DES clusters. We verify that measurements using SPT or ACT maps are consistent across all scales, including in the deficit feature. The SZ profiles of optically selected and SZ-selected clusters are also consistent for higher mass clusters. Those of less massive, optically selected clusters are suppressed on small scales by factors of 2–5 compared to predictions, and we discuss possible interpretations of this behaviour. An oriented stacking of clusters – where the orientation is inferred from the SZ image, the brightest cluster galaxy, or the surrounding large-scale structure measured using galaxy catalogues – shows the normalization of the one-halo and two-halo terms vary with orientation. Finally, the location of the pressure deficit feature is statistically consistent with existing estimates of the splashback radius.

galaxies↗

DOE OSTI2021

Eridanus II: A Fossil from Reionization with an Off-center Star Cluster

In this work, we present deep Hubble Space Telescope (HST) photometry of the ultra-faint dwarf galaxy Eridanus II (Eri II). Eri II, which has an absolute magnitude of M V = -7.1, is located at a distance of 339 kpc, just beyond the virial radius of the Milky Way. We determine the star formation history of Eri II and measure the structure of the galaxy and its star cluster. We find that a star formation history consisting of two bursts, constrained to match the spectroscopic metallicity distribution of the galaxy, accurately describes the Eri II stellar population. The best-fit model implies a rapid truncation of star formation at early times, with >80% of the stellar mass in place before z ~ 6. A small fraction of the stars could be as young as 8 Gyr, but this population is not statistically significant; Monte Carlo simulations recover a component younger than 9 Gyr only 15% of the time, where they represent an average of 7 ± 4% of the population. These results are consistent with theoretical expectations for quenching by reionization. The HST depth and angular resolution enable us to show that Eri II’s cluster is offset from the center of the galaxy by a projected distance of 23 ± 3 pc. This offset could be an indication of a small (~50–75 pc) dark matter core in Eri II. Moreover, we demonstrate that the cluster has a high ellipticity of and is aligned with the orientation of Eri II within 3° ± 6°, likely due to tides. The stellar population of the cluster is indistinguishable from that of Eri II itself.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

The Evolution of AGN Activity in Brightest Cluster Galaxies

We present the results of an analysis of Wide-field Infrared Survey Explorer (WISE) observations of the full 2500 deg 2 South Pole Telescope (SPT)-Sunyaev–Zel'dovich cluster sample. We describe a process for identifying active galactic nuclei (AGN) in brightest cluster galaxies (BCGs) based on WISE mid-IR color and redshift. Applying this technique to the BCGs of the SPT-SZ sample, we calculate the AGN-hosting BCG fraction, which is defined as the fraction of BCGs hosting bright central AGNs over all possible BCGs. Assuming an evolving single-burst stellar population model, we find statistically significant evidence (>99.9%) for a mid-IR excess at high redshift compared to low redshift, suggesting that the fraction of AGN-hosting BCGs increases with redshift over the range of 0 < z < 1.3. The best-fit redshift trend of the AGN-hosting BCG fraction has the form (1 + z) 4.1±1.0 . These results are consistent with previous studies in galaxy clusters as well as as in field galaxies. One way to explain this result is that member galaxies at high redshift tend to have more cold gas. While BCGs in nearby galaxy clusters grow mostly by dry mergers with cluster members, leading to no increase in AGN activity, BCGs at high redshift could primarily merge with gas-rich satellites, providing fuel for feeding AGNs. If this observed increase in AGN activity is linked to gas-rich mergers rather than ICM cooling, we would expect to see an increase in scatter in the P cav versus L cool relation at z > 1. Last, this work confirms that the runaway cooling phase, as predicted by the classical cooling-flow model, in the Phoenix cluster is extremely rare and most BCGs have low (relative to Eddington) black hole accretion rates.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Imaging systematics induced by galaxy subsample fluctuation: New systematics at second order

Imaging systematics refers to the inhomogeneous distribution of a galaxy sample caused by varying observing conditions and astrophysical foregrounds. Current mitigation methods correct the galaxy density fluctuations $n$ gal /$\bar{n}$ gal caused by imaging systematics assuming that all galaxies in a sample have the same $n$ gal /$\bar{n}$ gal . Under this assumption, the corrected sample cannot perfectly recover the true correlation function. Here, we name this effect subsample systematics. For a galaxy sample, even if its overall sample statistics [redshift distribution 𝑛⁡(𝑧), galaxy bias 𝑏⁡(𝑧)], are accurately measured, 𝑛⁡(𝑧), 𝑏⁡(𝑧) can still vary across the observed footprint. It makes the correlation function amplitude of galaxy clustering higher, while correlation functions for galaxy-galaxy lensing and cosmic shear do not have noticeable change. Such a combination could potentially degenerate with physical signals on small angular scales, such as the amplitude of galaxy clustering, the impact of neutrino mass on the matter power spectrum, etc. subsample systematics cannot be corrected using imaging systematics mitigation approaches that rely on the cross-correlation signal between imaging systematics maps and the observed galaxy density field. In this paper, we derive formulated expressions of subsample systematics, demonstrating its fundamental difference with other imaging systematics. We also provide several toy models to visualize this effect. Finally, we discuss a potential method to estimate and mitigate subsample systematics by forward modeling its behavior using synthetic source injection.

Kong, Hui [The Barcelona Institute of Science and ↗

DOE OSTI2025

Cosmological preference for a negative neutrino mass

The most precise determination of the sum of neutrino masses from cosmological data, derived from analysis of the cosmic microwave background (CMB) and baryon acoustic acoustic oscillations (BAO) from the Dark Energy Spectroscopic Instrument (DESI), favors a value below the minimum inferred from neutrino flavor oscillation experiments. We explore which data is most responsible of this puzzling aspect of the current constraints on neutrino mass and whether it is related to other anomalies in cosmology. We demonstrate conclusively that the preference for negative neutrino masses is a consequence of larger than expected lensing of the CMB in both the two- and four-point lensing statistics. Furthermore, we show that this preference is robust to changes in likelihoods of the BAO and CMB optical depth analyses given the available data. We then show that this excess clustering is not easily explained by changes to the expansion history and is likely distinct from the preference for for dynamical dark energy in DESI BAO data. Finally, we discuss how future data may impact these results, including an analysis of Planck CMB with mock DESI 5-year data. Here, we conclude that the negative neutrino mass preference is likely to persist even as more cosmological data is collected in the near future.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

CAHS: Context-Aware Homology Search

Protein homology search is foundational to bioinformatics: it supports annotation transfer, structure/function inference, and evolutionary analysis over rapidly expanding sequence repositories (e.g., UniProtKB). Profile hidden Markov models (pHMMs), as implemented in HMMER, remain the most widely trusted approach because they provide statistically calibrated E-values; however, their gap behavior is fixed once a profile is trained, despite biological evidence that insertion/deletion tolerance varies across flexible loops and intrinsically disordered regions. We present CAHS (Context-Aware Homology Search), a lightweight query-time adapter for pHMM search that incorporates learned and biologically motivated signals without changing HMMER's downstream search pipeline or its calibrated E-value reporting. Given a query sequence, CAHS computes per-residue representations from a protein language model and a disorder predictor, maps these to profile coordinates, and modulates only match-state transition rows (gap-open and gap-extension probabilities) while preserving Plan7 constraints. We comprehensively evaluate CAHS across six structurally diverse protein families and multi-domain architectures against a 570k-sequence target corpus. CAHS expands detection capability, retrieving thousands of additional remote homologs at relaxed thresholds by maintaining alignment quality through flexible regions. For multi-domain proteins, context-aware modulation resolves 94% of fragmented alignments. Crucially, CAHS preserves hit-set invariance at stringent operating points (E<10-10), demonstrating increased statistical confidence without inflating false positives. Furthermore, sharper statistical distinction between homologs and background noise during early filter stages yields up to a 3.87× acceleration in end-to-end wall-clock time on high-performance computing clusters. Overall, CAHS illustrates a practical AI-for-science design pattern: augmenting a trusted probabilistic model with query-specific learned signals to improve interpretable, reproducible inference in data-rich biology.

Bhattaram, Swethasree [Georgia Institute of Techno↗

DOE OSTI2025

Discovery and Spectroscopic Confirmation of Aquarius III: A Low-mass Milky Way Satellite Galaxy

Abstract We present the discovery of Aquarius III, an ultra-faint Milky Way satellite galaxy identified in the second data release of the DECam Local Volume Exploration survey. Based on deeper follow-up imaging with DECam, we find that Aquarius III is a low-luminosity ( M V = − 2.5 − 0.5 + 0.3 ; L V = 850 − 260 + 380 L ⊙ ), extended ( r 1 / 2 = 41 − 8 + 9 pc) stellar system located in the outer halo ( D ⊙ = 85 ± 4 kpc). From medium-resolution Keck/DEIMOS spectroscopy, we identify 11 member stars and measure a mean heliocentric radial velocity of v sys = − 13.1 − 0.9 + 1.0 km s − 1 for the system and place an upper limit of σ v < 3.5 km s −1 ( σ v < 1.6 km s −1 ) on its velocity dispersion at the 95% (68%) credible level. Based on calcium-triplet metallicities of the six brightest red giant members, we find that Aquarius III is very metal-poor ([Fe/H]= − 2.61 ± 0.21) with a statistically significant metallicity spread ( σ [ Fe / H ] = 0.46 − 0.14 + 0.26 dex). We interpret this metallicity spread as strong evidence that the system is a dwarf galaxy as opposed to a star cluster. Combining our velocity measurement with Gaia proper motions, we find that Aquarius III is currently situated near its orbital pericenter in the outer halo ( r peri = 78 ± 7 kpc) and that it is plausibly on first infall onto the Milky Way. This orbital history likely precludes significant tidal disruption from the Galactic disk, notably unlike other satellites with comparably low velocity dispersion limits in the literature. Thus, if further velocity measurements confirm that its velocity dispersion is truly below σ v ≲ 2 km s −1 , Aquarius III may serve as a useful laboratory for probing galaxy formation physics in low-mass halos.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

The SRG/eROSITA All-Sky Survey: Constraints on the structure growth from cluster number counts

Recent advancements in methods used in wide-area surveys have demonstrated the reliability of the number density of galaxy clusters as a viable tool for precision cosmology. Beyond testing the current cosmological paradigm, cluster number counts can also be used to investigate the discrepancies currently affecting cosmological measurements. In particular, cosmological studies based on cosmic shear and other large-scale structure probes routinely find a value for the amplitude of the fluctuations in the universe S 8 = σ 8 (Ω m /0.3) 0.5 smaller than the one inferred from the primary cosmic microwave background. In this work, we investigate this tension by measuring structure evolution across cosmic time as probed by the number counts of massive halos with the first SRG/eROSITA All-Sky Survey cluster catalog in the western Galactic hemisphere, complemented with the overlapping Dark Energy Survey Year-3, Kilo-Degree Survey, and Hyper Suprime-Cam data for weak lensing mass calibration, by implementing two different parameterizations and a model-agnostic method. In the first model, we measured the cosmic linear growth index as γ = 1.19 ± 0.21, which is in tension with the standard value of γ = 0.55 but in good statistical agreement with other large-scale structure probes. The second model is a phenomenological scenario in which we rescale the linear matter power spectrum at low redshift to investigate a potential reduction of structure formation, and it provided similar results. Finally, in a third strategy, we considered a standard ΛCDM cosmology, but we separated the cluster catalog into five redshift bins, measuring the cosmological parameters in each and inferring the evolution of the structure formation, finding hints of a reduction. Interestingly, the S 8 value inferred from the number counts of the cluster eRASS1 when we add a degree of freedom to the matter power spectrum recovers the value inferred by cosmic shear studies. The observed reduction in the growth rate or systematic uncertainties associated with various measurements may account for the discrepancy in the S 8 values suggested between cosmic shear probes and eROSITA cluster number counts and Planck CMB measurements.

cosmological parameters↗

DOE OSTI2023

Statistical 3D morphology characterization of vaterite microspheres produced by engineered Escherichia coli

Hollow vaterite microspheres are important materials for biomedical applications such as drug delivery and regenerative medicine owing to their biocompatibility, high specific surface area, and ability to encapsulate a large number of bioactive molecules and compounds. We demonstrated that hollow vaterite microspheres are produced by an Escherichia coli strain engineered with a urease gene cluster from the ureolytic bacteria Sporosarcina pasteurii in the presence of bovine serum albumin. We characterized the 3D nanoscale morphology of five biogenic hollow vaterite microspheres using 3D high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) tomography. Using automated high-throughput HAADF-STEM imaging across several sample tilt orientations, we show that the microspheres evolved from a smaller more ellipsoidal shape to a larger more spherical shape while the internal hollow core increased in size and remained relatively spherical, indicating that the microspheres produced by this engineered strain likely do not contain the bacteria. The statistical 3D morphology information demonstrates the potential for using biogenic calcium carbonate mineralization to produce hollow vaterite microspheres with controlled morphologies.

3D morphology↗

DOE OSTI2022

Overall Performance Losses and Activated Mechanisms in Double Glass and Glass-backsheet Photovoltaic Modules with Monofacial and Bifacial PERC Cells, under Accelerated Exposures

Commercial PV modules have various packaging choices nowadays, which influence their long-term reliability. This study compared the degradation behaviors of sixteen module variants from two brands with varying encapsulant materials (EVA or POE), encapsulant types, module architectures (GB or DG), and cell types (monofacial or bifacial) using null hypothesis testing to determine statistical significant findings. The modules were exposed for 2,520 hours under two accelerated exposures: modified damp heat (mDH) and modified damp heat with full-spectrum light (mDH+FSL). For both brands, two DG module variants with UV-Cutoff rear encapsulant are found to have significantly lower average power loss than the module variants of EVA+GB with opaque rear encapsulant after each accelerated exposure. Metallization interconnect corrosion is identified as the primary degradation mechanism. Furthermore, unsupervised hierarchical clustering finds that the degradation behaviors of modules from one brand with a more strict manufacturing quality control depends on module architectures only.

14 SOLAR ENERGY↗

DOE OSTI2020

Wave function methods for canonical ensemble thermal averages in correlated many-fermion systems

We present a wave function representation for the canonical ensemble thermal density matrix by projecting the thermofield double state against the desired number of particles. Furthermore, the resulting canonical thermal state obeys an imaginary-time evolution equation. Starting with the mean-field approximation, where the canonical thermal state becomes an antisymmetrized geminal power (AGP) wave function, we explore two different schemes to add correlation: by number-projecting a correlated grand-canonical thermal state and by adding correlation to the number-projected mean-field state. As benchmark examples, we use number-projected configuration interaction and an AGP-based perturbation theory to study the hydrogen molecule in a minimal basis and the six-site Hubbard model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗