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DOE OSTI2022

INGENIOUS - Great Basin Regional Dataset Compilation

This is the regional dataset compilation for the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project. The primary goal of this project is to accelerate discoveries of new, commercially viable hidden geothermal systems while reducing the exploration and development risks for all geothermal resources. These datasets will be used in INGENIOUS as input features for predicting geothermal favorability throughout the Great Basin study area. Datasets consist of shapefiles, geotiffs, tabular spreadsheets, and metadata that describe: 2-meter temperature probe surveys, quaternary faults and volcanic features, geodetic shear and dilation models, heat flow, magnetotellurics (conductance), magnetics, gravity, paleogeothermal features (such as sinter and tufa deposits), seismicity, spring and well temperatures, spring and well aqueous geochemistry analyses, thermal conductivity, and fault slip and dilation tendency. For additional project information, see the INGENIOUS project site linked in the submission. Terms of use: These datasets are provided "as is", and the contributors assume no responsibility for any errors or omissions. The user assumes the entire risk associated with their use of these data and bears all responsibility in determining whether these data are fit for their intended use. These datasets may be redistributed with attribution (see citation information below). Please refer to the license information on this page for full licensing terms and conditions.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Searching for Dwarf Galaxies in Gaia DR2 Phase-space Data Using Wavelet Transforms

We present a wavelet-based algorithm to identify dwarf galaxies in the Milky Way in Gaia DR2 data. Our algorithm detects overdensities in 4D position–proper-motion space, making it the first search to explicitly use velocity information to search for dwarf galaxy candidates. We optimize our algorithm and quantify its performance by searching for mock dwarfs injected into Gaia DR2 data and for known Milky Way satellite galaxies. Comparing our results with previous photometric searches, we find that our search is sensitive to undiscovered systems at Galactic latitudes |b| > 20° and with half-light radii larger than the 50% detection efficiency threshold for Pan-STARRS1 (PS1) at (i) absolute magnitudes of –7 < M V < –3 and distances of 32 kpc < D < 64 kpc, and (ii) M V < –4 and 64 kpc < D < 128 kpc. Based on these results, we predict that our search is expected to discover 5 ± 2 new satellite galaxies: four in the PS1 footprint and one outside the Dark Energy Survey and PS1 footprints. We apply our algorithm to the Gaia DR2 data set and recover ~830 high-significance candidates, out of which we identify a "gold standard" list of ~200 candidates based on cross-matching with potential candidates identified in a preliminary search using Gaia EDR3 data. All of our candidate lists are publicly distributed for future follow-up studies. Here, we show that improvements in astrometric measurements provided by Gaia EDR3 increase the sensitivity of this technique; we plan to continue to refine our candidate list using future data releases.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Exploring Geothermal Potential of Great Basin Sub-Regions: Preprint

The INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project aims to discover new, economically viable hidden geothermal systems in the Great Basin region by building on previous work in play fairway analysis and machine learning. A key objective of this project is to develop an exploration workflow to reduce geothermal exploration risks for hidden geothermal systems. A single preliminary play fairway workflow was developed from the assessment of the regional INGENIOUS geological, geophysical, and geochemical datasets. This workflow provided new preliminary predictive geothermal fairway maps for the INGENIOUS study area, which encompasses most of Nevada, western Utah, southern Idaho, southeastern Oregon, and easternmost California. However, a recent study (incorporating machine learning techniques) of a portion of Nevada identified four geologic domains and determined that the relative importance of individual datasets or features as indicators of geothermal potential may differ across these domains. The INGENIOUS study area includes a much larger and more geologically diverse region; therefore, additional geologic domains or sub-regions are expected. To assess the sub-regions in the INGENIOUS study area, principal component analysis and k-means clustering were applied. Preliminary results indicate that the INGENIOUS regional data cluster into groups that relate to different geologic domains in the Great Basin region. These include domains such as the Walker Lane, extensional western Great Basin region, broad lower strain region in the eastern Great Basin of western Utah and eastern Nevada, Quaternary volcanic fields, and the area adjacent to the Snake River Plain. These clusters are assessed to determine the key geologic drivers of the identified clusters. Understanding this variability can provide key insights for the exploration and characterization of hidden geothermal systems in the Great Basin region and could indicate the need to develop multiple geothermal conceptual models and play fairway workflows for the INGENIOUS study area.

GEOTHERMAL ENERGY↗

DOE OSTI2025

Exploring Geothermal Potential of Great Basin Sub-Regions

exploration↗

DOE OSTI2021

Discovering Blind Geothermal Systems in the Great Basin Region: An Integrated Geologic and Geophysical Approach for Establishing Geothermal Play Fairways: All Phases

Most geothermal resources in the Great Basin region of the western USA are blind, and thus the discovery of new commercial-grade systems requires synthesis of favorable characteristics for geothermal activity. The geothermal play fairway concept involves integration of multiple parameters indicative of geothermal activity to identify promising areas for new development. This project integrated multiple datasets to apply the play fairway concept and assess geothermal potential in a large region of the Great Basin in Nevada. It is therefore referred to as the Nevada play fairway project. This project was a strong collaborative effort between several organizations, led by the Nevada Bureau of Mines and Geology at the University of Nevada, Reno, but with key support from the U.S. Geological Survey, ATLAS Geosciences, Inc,, Hi-Q Geophysical, Inc., Lawrence Berkeley National Laboratory, Utah Geological Survey, and Innovative Geothermal Ltd. In Budget Period 1 of this project, available data for nine geologic, geochemical, and geophysical parameters were initially synthesized to produce a new detailed geothermal potential map of 96,000 km2 from west-central to eastern Nevada. These parameters were grouped into subsets and individually weighted to delineate rankings for local permeability, intermediate permeability, regional permeability, and thermal potential, which collectively defined geothermal play fairways (i.e., most likely locations for significant geothermal fluid flow). This initial work was aimed at reducing the risks in regional exploration and therefore facilitating discovery of new commercial-grade systems in blind settings, as well as in areas with surface expressions of geothermal activity. Budget Period 2 of the project involved detailed analysis of some of the most promising areas identified in Phase 1. Twenty-four highly prospective areas, including both known undeveloped systems and previously undiscovered potential blind systems, were identified for further analysis. After reconnaissance of these areas, five of the most promising sites were selected for detailed studies. Multiple techniques were employed in the detailed studies, including geologic mapping, shallow temperature surveys, gravity surveys, Lidar, geochemical studies, seismic reflection analysis, and 3D modeling. The goal of the detailed studies was to identify specific areas with the highest likelihood for high permeability and thermal fluids, such that drill sites could be targeted. Three main sets of predictive maps were generated for each detailed study area: 1) play fairway maps, 2) play fairway error maps, and 3) direct evidence maps. Local- and intermediate-scale permeability models were revised to reflect results of the detailed geologic, geophysical, and geochemical analyses. Budget Period 3 of the project involved more detailed geophysical analyses and temperature-gradient (TG) drilling in southeastern Gabbs Valley and northern Granite Springs Valley, deemed the two most promising sites, with the goal of providing preliminary validation of the play fairway methodology. In southeastern Gabbs Valley, the collocation of a favorable structural setting (displacement transfer zone and fault intersections), Quaternary faults, intersecting and terminating gravity gradients, magnetic low, shallow (2 m) temperature anomaly, low resistivity anomaly, and promising geothermometry from nearby water wells provided evidence for a blind system. Drilling of six TG holes defines an apparent geothermal system at this locality with temperatures as high as 124°C at 152 m. This system is blind, with no surface hot springs, fumaroles, or paleo-geothermal deposits. For northern Granite Springs Valley, a favorable structural setting (termination of a major Quaternary normal fault), terminating gravity gradient, magnetic gradient, newly discovered sinter deposits, nearby warm water wells, previously drilled TG holes in the vicinity, and promising geothermometry suggest a hidden system. Drilling of six new TG holes yields temperatures of ~96°C at ~250 m, suggesting the presence of a geothermal system. Major lessons learned in the course of this project include: 1) initially identified sites commonly include multiple favorable structural settings at a finer scale; 2) promising sites in Cenozoic basins cannot be recognized without detailed geophysical surveys; and 3) play fairway analysis should be refined as the exploration program vectors into the most promising sites and finer-scale data are acquired. In addition to producing copious amounts of data, this project resulted in 16 published papers, 10 abstracts, more than 40 presentations across the U.S. and abroad (including several keynote addresses), 2 Masters theses, and 7 media reports.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Using Dark Fiber and Distributed Acoustic Sensing to Characterize a Geothermal System in the Imperial Valley, Southern California

The Imperial Valley, CA, is a tectonically active transtensional basin located south of the Salton Sea; the area hosts numerous geothermal fields, including significant hidden hydrothermal resources without surface manifestations. Development of inexpensive, rugged, and highly sensitive exploration techniques for undiscovered geothermal systems is critical for accelerating geothermal power deployment as well as unlocking a low-carbon energy future. We present a case study utilizing distributed acoustic sensing (DAS) and ambient noise interferometry for geothermal reservoir imaging, utilizing unlit fiber-optic telecommunication infrastructure (dark fiber). The study exploits two days of passive DAS data acquired in early November 2020 over a ~28-km section of fiber from Calipatria, CA to Imperial, CA. We apply ambient noise interferometry to retrieve coherent signals from DAS records and develop a bin stacking technique to attenuate the effects from persistent localized noise sources and to enhance retrieval of coherent surface waves. As a result, we are able to obtain high-resolution two-dimensional (2D) S wave velocity ($V_s$) structure to 3 km depth, based on joint inversion of both the fundamental and higher overtones. We observe a previously unmapped high $V_s$ and low $V_p$/$V_s$ ratio feature beneath the Brawley geothermal system, which we interpret to be a zone of hydrothermal mineralization and lower porosity. This interpretation is consistent with a host of other measurements including surface heat flow, gravity anomalies, and available borehole wireline data. These results demonstrate the potential utility of DAS deployed on dark fiber for geothermal system exploration and characterization in the appropriate geological settings.

Vp/Vs imaging↗

DOE OSTI2023

Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis

Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. As a result, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Characterizing Signatures of Geothermal Exploration Data with Machine Learning Techniques: An Application to the Nevada Play Fairway Analysis

We are introducing machine learning methods to the play fairway analysis to generate geothermal potential maps to support the evaluation of geothermal resource potential and the exploration for undiscovered blind geothermal systems in the Nevada Great Basin region. Our project aims to identify new ways to combine the play fairway data and empirically organize relationships between feature weights and labels in an improved workflow. As a means of doing this, we introduce machine learning methods to evaluate the influence of certain geological and geophysical features/feature sets in predicting geothermal favorability. This report highlights promising approaches based on supervised and unsupervised learning methods. First, we demonstrate a filter method applied to supervised classification modeling. The supervised filter method is based on permutation analysis to evaluate every possible feature combination/drop out scenario and rank feature influence based on the performance variance of supervised classification models. Additionally, we present an unsupervised factor analysis based on principal component analysis coupled with a semi-supervised kmeans clustering algorithm. This analysis allows us to identify the optimal number of groups/clusters for training sites and structural settings to identify feature patterns including correlation, variance, and latent and dominant feature relationships. The results from these methods offer a promising avenue for identifying favorable sources of predictive information to identify the locations of blind geothermal systems and furthering our understanding of complex geothermal feature and label relationships in the Great Basin region and beyond.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Hybrid and Inorganic Vacancy-Ordered Double Perovskites A 2 WCl 6

We report hybrid and all-inorganic, vacancy-ordered double perovskites of d 2 W 4+ with the formula A 2 WCl 6 (A = CH 3 NH 3 + , Rb + , and Cs + ). These compounds, which are reddish in color, can be distinguished from structurally similar compounds obtained by hydrothermal methods on the basis of structure, spectroscopic, and magnetic properties. The latter are green and incorporate oxygen, with the actual formula Cs 2 WO x Cl 6–x and distinct optical absorption and emission behavior. Furthermore, the local-moment magnetism of the pure-red d 2 compounds reported here does not correspond to the appropriate Kotani model, suggesting as-yet undiscovered physics in these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Discovery of a Hybrid System for Photocatalytic CO 2 Reduction via Attachment of a Molecular Cobalt-Quaterpyridine Complex to a Crystalline Carbon Nitride

While recent reports have demonstrated the attachment of molecular catalysts to amorphous, graphitic carbon nitrides (g-CN) for light-driven CO 2 reduction, approaches to the utilization of crystalline carbon nitrides have remained undiscovered. Herein, a functional hybrid photocatalyst system has been found using a crystalline carbon nitride semiconductor, poly(triazine imide) lithium chloride (PTI-LiCl), with a surface-attached CoCl 2 (qpy-Ph-COOH) catalyst for CO 2 reduction. The molecular catalyst attaches to PTI-LiCl at concentrations from 0.10 to 4.30 wt % and exhibits ∼96% selectivity for CO production in a CO 2 -saturated, aqueous 0.5 M KHCO 3 solution. Optimal loadings were found to be within 0.42–1.04 wt % with rates between 1,400 and 1,550 μmol CO/g·h at an irradiance of 172 mW/cm 2 (λ = 390 nm) and apparent quantum yields of ∼2%. This optimized loading is postulated to represent a balance between maximal turnover frequency (TOF; 300+ h –1 ) and excess catalyst that can limit excited-electron lifetimes, as probed via transient absorption spectroscopy. An increase in the incident irradiance yields a concomitant increase in the TOFs and CO rates only for the higher catalyst loadings, reaching up to 2,149 μmol CO/g·h with a more efficient use of the catalyst surface capacity. The lower catalyst loadings, by comparison, already function at maximal TOFs. Higher surface loadings are also found to help mitigate deactivation of the molecular catalysts during extended catalytic testing (>24 h) owing to the greater net surface capacity for CO 2 reduction, thus representing an effective strategy to extend lifetime. The hybrid particles can be deposited onto an FTO substrate to yield ∼60% Faradaic efficiency for photoelectrochemical CO production at −1.2 V vs Ag/AgCl bias. In conclusion, these results demonstrate the synergistic combination of a crystalline carbon nitride with a molecular catalyst that achieves among the highest known rates in carbon-nitride systems for the light-driven CO 2 reduction to CO in aqueous solution with >95% selectivity.

CO2 reduction↗

DOE OSTI2022

Identifying candidate structured RNAs in CRISPR operons

Noncoding RNAs with secondary structures play important roles in CRISPR-Cas systems. Many of these structures likely remain undiscovered. We used a large-scale comparative genomics approach to predict 156 novel candidate structured RNAs from 36,111 CRISPR-Cas systems. A number of these were found to overlap with coding genes, including palindromic candidates that overlapped with a variety of Cas genes in type I and III systems. Among these 156 candidates, we identified 46 new models of CRISPR direct repeats and 1 tracrRNA. This tracrRNA model occasionally overlapped with predicted cas9 coding regions, emphasizing the importance of expanding our search windows for novel structure RNAs in coding regions. We also demonstrated that the antirepeat sequence in this tracrRNA model can be used to accurately assign thousands of predicted CRISPR arrays to type II-C systems. This study highlights the importance of unbiased identification of candidate structured RNAs across CRISPR-Cas systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems

The high complexity and multiscale nature of many engineered systems—such as those in nuclear power plants—make representing and forecasting their dynamic behavior challenging. Physics-based models can be overly complex and computationally intractable, whereas machine learning (ML) tools are often data-hungry and prone to unphysical solutions. This study proposes a knowledge-informed ML-aided hybrid residual modeling approach that offers accurate and efficient time series forecasting for the operation of dynamical engineered systems. Hybrid residual modeling entails a baseline solution from domain knowledge and known physics expressions about the system dynamics integrated with an ML model to capture undiscovered information from the mismatch (i.e., residuals) between true states from measurements and baseline-predicted outputs. This study further quantifies the ML model uncertainty to provide trustworthy solutions. Real-time operational data from thermal-hydraulic flow loops of the cryogenic moderator system in Oak Ridge National Laboratory’s Spallation Neutron Source facility were used to demonstrate the potential of knowledge-informed uncertainty-aware ML in real-world applications. The state variables of the cryogenic helium loop were modeled with (1) first principles–based system identification (sysID), (2) long short-term memory (LSTM) neural network, and (3) hybrid sysID (baseline) + LSTM (residual). The superior predictive capability of the sysID+LSTM model versus stand-alone sysID and LSTM is confirmed by average performance metrics and individual data points across different prediction horizons. By creating a robust representation of the underlying physical system, the widely applicable hybrid residual modeling approach will enable the future development of digital twins for performance prediction, prognostics, and operation control.

Zhao, Xingang↗

DOE OSTI2021

Extraordinarily strong magneto-responsiveness in phase-separated LaFe 2 Si

Materials responding vigorously to minor variations of external stimuli with negligible hysteresis could revolutionize many of the energy technologies, including refrigeration, actuation, and sensing. Here, we report a combined experimental and theoretical study of a two-phase composite, naturally formed at the LaFe 2 Si stoichiometry, which exhibits a nearly anhysteretic, two-step first-order ferromagnetic-to-paramagnetic phase transformation with enhanced sensitivity to an external magnetic field. Other unusual properties include a large plateau-like positive magnetoresistance, magnetic-field-induced temperature and entropy changes occurring over a wide temperature range, and a Griffiths-like phase associated with short-range ferromagnetic clustering in the paramagnetic state. The heat capacity, magnetization, Mössbauer spectroscopy, and electrical resistivity, all exhibit characteristic, unusually sharp, first-order discontinuities even in magnetic fields as high as 100 kOe. We expect that similar phenomena could be designed in other mixed-phase systems, leading to novel functionalities, such as giant caloric effects in many yet undiscovered or/and underperforming intermetallic compounds.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Hidden transport phenomena in an ultraclean correlated metal

Advancements in materials synthesis have been key to unveil the quantum nature of electronic properties in solids by providing experimental reference points for a correct theoretical description. Here, we report hidden transport phenomena emerging in the ultraclean limit of the archetypical correlated electron system SrVO 3 . The low temperature, low magnetic field transport was found to be dominated by anisotropic scattering, whereas, at high temperature, we find a yet undiscovered phase that exhibits clear deviations from the expected Landau Fermi liquid, which is reminiscent of strange-metal physics in materials on the verge of a Mott transition. Further, the high sample purity enabled accessing the high magnetic field transport regime at low temperature, which revealed an anomalously high Hall coefficient. Taken with the strong anisotropic scattering, this presents a more complex picture of SrVO 3 that deviates from a simple Landau Fermi liquid. These hidden transport anomalies observed in the ultraclean limit prompt a theoretical reexamination of this canonical correlated electron system beyond the Landau Fermi liquid paradigm, and more generally serves as an experimental basis to refine theoretical methods to capture such nontrivial experimental consequences emerging in correlated electron systems.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Engineering a new tripartite split-ccGFP system from Corynactis californica for detecting protein–protein interactions

Protein-protein interactions (PPIs) are critical to a range of biological processes and, consequently, aberrant interactions are implicated in many disorders. The study of the complex networks of PPIs promises to elucidate undiscovered roles in cellular processes and the mechanisms of disease. To accomplish this, tools to effectively sense PPIs are necessary. Effective PPI sensors must rapidly detect interactions in real-time with high sensitivity without perturbing the proteins of interest (POIs) under study. Split fluorescent proteins have previously been used to successfully monitor PPIs, in part due to the small size of the tags. Here, we developed an optimized tripartite split GFP system based on Corynactis californica GFP (ccGFP) to detect PPIs in vitro. In this sensor system, ccGFP fragments ccGFP10 and ccGFP11 are tagged to two POIs. PPIs can then be detected via fluorescence by complementation to the third fragment, ccGFP1-9, which reconstitutes functional ccGFP. The optimized ccGFP system shows improved detection kinetics and pH and temperature stability compared to a previous system. We then validated the sensor by monitoring PPIs in two model systems: attractive/repulsive coiled-coils and rapamycin-inducible FRB/FKBP heterodimerization. Finally, we developed an anti-tripartite ccGFP single-chain variable fragment (scFv), which could enable versatile detection of identified protein-protein complexes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Twisted bilayer graphene. III. Interacting Hamiltonian and exact symmetries

We derive the explicit Hamiltonian of twisted bilayer graphene (TBG) with Coulomb interaction projected into the flat bands and study the symmetries of the Hamiltonian. First, we show that all projected TBG Hamiltonians can be written as positive semidefinite Hamiltonians, an example of which was found in work by Kang and Vafek [Phys. Rev. Lett. 122, 246401 (2019)]. We then prove that the interacting TBG Hamiltonian exhibits an exact U(4) symmetry in the exactly flat band (nonchiral-flat) limit. We further define, besides a first chiral limit where the AA stacking hopping is zero, a second chiral limit where the AB/BA stacking hopping is zero. In the first chiral-flat limit (or second chiral-flat limit) with exactly flat bands, the TBG is enhanced to have an exact U(4) × U(4) symmetry, whose generators are different between the two chiral limits. While in the first chiral limit and in the nonchiral case these symmetries have been found in work by Bultinck et al. [Phys. Rev. X 10, 031034 (2020)], for the eight lowest bands, we here prove that they are valid for projection into any 8 n max particle-hole symmetric TBG bands, with n max > 1 being the practical case for small twist angles < 1°. Furthermore, in the first or second chiral-nonflat limit without flat bands, an exact U(4) symmetry still remains. We also elucidate the link between the U(4) symmetry presented here and the similar but different U(4) of Kang and Vafek [Phys. Rev. Lett. 122, 246401 (2019)]. Additionally, we show that our projected Hamiltonian can be viewed as the normal-ordered Coulomb interaction plus a Hartree-Fock term from passive bands, and exhibits a many-body particle-hole symmetry which renders the physics symmetric around charge neutrality. We also provide an efficient parametrization of the interacting Hamiltonian. The existence of two chiral limits with an enlarged symmetry suggests a possible duality of the model yet undiscovered.

2-dimensional systems↗

DOE OSTI2021

Metal Halide Scaffolded Assemblies of Organic Molecules with Enhanced Emission and Room Temperature Phosphorescence

Ionically bonded organic metal halide hybrids have emerged as versatile multicomponent material systems exhibiting unique and useful properties. The unlimited combinations of organic cations and metal halides lead to the tremendous structural diversity of this class of materials, which could unlock many undiscovered properties of both organic cations and metal halides. Here we report the synthesis and characterization of a series benzoquinolinium (BZQ) metal halides with a general formula (BZQ)Pb 2 X 5 (X = Cl, Br), in which metal halides form a unique two-dimensional (2D) structure. These BZQ metal halides are found to exhibit enhanced photoluminescence and stability as compared to the pristine BZQ halides, due to the scaffolding effects of 2D metal halides. Optical characterizations and theoretical calculations reveal that BZQ + cations are responsible for the emissions in these hybrid materials. Changing the halide from Cl to Br introduces heavy atom effects, resulting in yellow room temperature phosphorescence (RTP) from BZQ + cations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Observation of a chiral wave function in the twofold-degenerate quadruple Weyl system BaPtGe

Topological states in quantum materials are defined by bulk wave functions that possess nontrivial topological invariants. While edge modes are widely presented as signatures of nontrivial topology, how bulk wave functions can manifest explicitly topological properties remains unresolved. Here, using high-resolution inelastic x-ray spectroscopy (IXS) combined with first principles calculations, we report experimental signatures of chiral wave functions in the bulk phonon spectrum of BaPtGe, which we show to host a previously undiscovered twofold-degenerate quadruple Weyl node. The chirality of the degenerate phononic wave function yields a nontrivial phonon dynamical structure factor, S(Q,ω), along high-symmetry directions, that is in excellent agreement with numerical and model calculations. Furthermore, our results establish IXS as a powerful tool to uncover topological wave functions, providing a key missing ingredient in the study of topological quantum matter.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗