Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data aggregation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Mapping and Synthesis of International Biomass Supply Assessments

This report, Mapping and Synthesis of International Biomass Supply Assessments (or Global Biomass Resource Assessment) is the first step in a long-term process to assemble data from around the globe into a virtual repository that can be updated and provide user-friendly access to the data. The Clean Energy Ministerial (CEM) Biofuture Platform Initiative recommended that research be completed to “address the need for internationally accepted benchmarks quantifying sustainable biomass feedstock supplies.” To act upon the CEM Biofuture recommendation, in 2024, the U.S. Department of Energy (DOE) commissioned Oak Ridge National Laboratory (ORNL) to prepare this report as the primary deliverable for a one-year assignment to assemble data into a citable form that could help resolve the persistent question presented related to bioenergy policy, “Is there enough sustainable biomass?” In response to that query, this report includes information received by August 2024 from national CEM representatives, collaborators, and public sources on current and future sustainable biomass supplies in 62 nations,and subsequently documents (a) the approach used by ORNL to analyze and categorize the information received in a manner that enables aggregation and comparability; and (b) recommendations for next steps and guidelines to help others update and harmonize future assessments of global sustainable biomass supplies.

09 BIOMASS FUELS↗
DOE OSTI2017

Community climate simulations to assess avoided impacts in 1.5 and 2 °C futures

The Paris Agreement of December 2015 stated a goal to pursue efforts to keep global temperatures below 1.5 °C above preindustrial levels and well below 2 °C. The IPCC was charged with assessing climate impacts at these temperature levels, but fully coupled equilibrium climate simulations do not currently exist to inform such assessments. Here, we produce a set of scenarios using a simple model designed to achieve long-term 1.5 and 2 °C temperatures in a stable climate. These scenarios are then used to produce century-scale ensemble simulations using the Community Earth System Model, providing impact-relevant long-term climate data for stabilization pathways at 1.5 and 2 °C levels and an overshoot 1.5 °C case, which are realized (for the 21st century) in the coupled model and are freely available to the community. We also describe the design of the simulations and a brief overview of their impact-relevant climate response. Exceedance of historical record temperature occurs with 60 % greater frequency in the 2 °C climate than in a 1.5 °C climate aggregated globally, and with twice the frequency in equatorial and arid regions. Extreme precipitation intensity is statistically significantly higher in a 2.0 °C climate than a 1.5 °C climate in some specific regions (but not all). The model exhibits large differences in the Arctic, which is ice-free with a frequency of 1 in 3 years in the 2.0 °C scenario, and 1 in 40 years in the 1.5 °C scenario. Significance of impact differences with respect to multi-model variability is not assessed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Non-Intrusive Load Monitoring of HVAC Components using Signal Unmixing

Heating, Ventilating and Air Conditioning units (HVAC) are a major electrical energy consumer in buildings. Monitoring of the operation and energy consumption of HVAC would increase the awareness of building owners and maintenance service providers of the condition and quality of performance of these units, enabling conditioned-based maintenance which would help achieving higher energy efficiency. In this paper, a novel non-intrusive load monitoring method based on group constrained non-negative matrix factorization is proposed for monitoring the different components of HVAC unit by only measuring the whole building aggregated power signal. At the first level of this hierarchical approach, power consumption of the building is decomposed to energy consumption of the HVAC unit and all the other electrical devices operating in the building such as lighting and plug loads. Then, the estimated power signal of the HVAC is used for estimating the power consumption profile of the HVAC major electrical loads such as compressors, condenser fans and indoor blower. Experiments conducted on real data collected from a building testbed maintained at the Oak Ridge National Laboratory (ORNL) demonstrate high accuracy on the disaggregation task.

Flexible Research Platform↗
DOE OSTI2021

Scalable Pattern Matching in Metadata Graphs via Constraint Checking

Pattern matching is a fundamental tool for answering complex graph queries. Unfortunately, existing solutions have limited capabilities: They do not scale to process large graphs and/or support only a restricted set of search templates or usage scenarios. Moreover, the algorithms at the core of the existing techniques are not suitable for today’s graph processing infrastructures relying on horizontal scalability and shared-nothing clusters, as most of these algorithms are inherently sequential and difficult to parallelize. In this article we present an algorithmic pipeline that bases pattern matching on constraint checking. The key intuition is that each vertex and edge participating in a match has to meet a set of constraints implicitly specified by the search template. These constraints can be verified independently and typically are less expensive to compute than searching the full template. The pipeline we propose generates these constraints and iterates over them to eliminate all the vertices and edges that do not participate in any match, thus reducing the background graph to a subgraph that is the union of all template matches—the complete set of all vertices and edges that participate in at least one match. Additional analysis can be performed on this annotated, reduced graph, such as full match enumeration, match counting, or computing vertex/edge centrality. Furthermore, a vertex-centric formulation for constraint checking algorithms exists, and this makes it possible to harness existing high-performance, vertex-centric graph processing frameworks. This technique (i) enables highly scalable pattern matching in metadata (labeled) graphs; (ii) supports arbitrary patterns with 100% precision; (iii) enables tradeoffs between precision and time-to-solution, while always selects all vertices and edges that participate in matches, thus offering 100% recall; and (iv) supports a set of popular data analytics scenarios. We implement our approach on top of HavoqGT, an open-source asynchronous graph processing framework, and demonstrate its advantages through strong and weak scaling experiments on massive scale real-world (up to 257 billion edges) and synthetic (up to 4.4 trillion edges) labeled graphs, respectively, and at scales (1,024 nodes / 36,864 cores), orders of magnitude larger than used in the past for similar problems. This article serves two purposes: First, it synthesises the knowledge accumulated during a long-term project. Second, it presents new system features, usage scenarios, optimizations, and comparisons with related work that strengthen the confidence that pattern matching based on iterative pruning via constraint checking is an effective and scalable approach in practice. The new contributions include the following: (i) We demonstrate the ability of the constraint checking approach to efficiently support two additional search scenarios that often emerge in practice, interactive incremental search and exploratory search. (ii) We empirically compare our solution with two additional state-of-the-art systems, Arabsque and TriAD. (iii) We show the ability of our solution to accommodate a more diverse range of datasets with varying properties, e.g., scale, skewness, label distribution, and match frequency. (iv) We introduce or extend a number of system features (e.g., work aggregation, load balancing, and the ability to cap the generated traffic) and design optimizations and demonstrate their advantages with respect to improving performance and scalability. (v) We present bottleneck analysis and insights into artifacts that influence performance. (vi) We present a theoretical complexity argument that motivates the performance gains we observe.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Blending Education and Polymer Science: Semiautomated Creation of a Thermodynamic Property Database

Structured databases of chemical and physical properties play a central role in the everyday research activities of scientists and engineers. In materials science, researchers and engineers turn to these databases to quickly query, compare, and aggregate various properties, thereby allowing for the development or application of new materials. The vast majority of these databases have been generated manually, through decades of labor-intensive harvesting of information from the literature, yet while there are many examples of commonly used databases, a significant number of important properties remain locked within the tables, figures, and text of publications. The question addressed in our work is whether and to what extent the process of data collection can be automated. Students of the physical sciences and engineering are often confronted with the challenge of finding and applying property data from the literature, and a central aspect of their education is to develop the critical skills needed to identify such data and discern their meaning or validity. To address shortcomings associated with automated information extraction while simultaneously preparing the next generation of scientists for their future endeavors, we developed a novel course-based approach in which students develop skills in polymer chemistry and physics and apply their knowledge by assisting with the semiautomated creation of a thermodynamic property database.

Collaborative/Cooperative Learning↗
DOE OSTI2018

Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy

Microbial communities are critical to ecosystem function. A key objective of metagenomic studies is to analyse organism-specific metabolic pathways and reconstruct community interaction networks. This requires accurate assignment of assembled genome fragments to genomes. Existing binning methods often fail to reconstruct a reasonable number of genomes and report many bins of low quality and completeness. Furthermore, the performance of existing algorithms varies between samples and biotopes. Here, we present a dereplication, aggregation and scoring strategy, DAS Tool, that combines the strengths of a flexible set of established binning algorithms. DAS Tool applied to a constructed community generated more accurate bins than any automated method. Indeed, when applied to environmental and host-associated samples of different complexity, DAS Tool recovered substantially more near-complete genomes, including previously unreported lineages, than any single binning method alone. The ability to reconstruct many near-complete genomes from metagenomics data will greatly advance genome-centric analyses of ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Non-Intrusive Appliance Identification with Appliance-Specific Networks

The problem of noninstrusive load monitoring (NILM) is usually formulated as a single-channel blind source separation task, whose successful solution enable fast and convenient load identification and energy disaggregation. When applied at test time, NILM algorithms aim to identify the operating characteristics of individual appliances from an aggregate power measurement of the entire house. Recent advances in deep learning gave rise to many methods that mostly focus on learning a direct mapping from aggregate measurement to individual appliance power. However, these methods are not only computationally expensive, but they often suffer from overfitting and do not generalize very well. In this article, we propose a novel NILM method that leverages advances in statistical learning that have not been properly applied in this domain before. The proposed method consists of three stages: first, a Bayesian nonparametric learning-based approach for appliance state extraction; second, synthetic minority oversampling technique for data augmentation and mitigating the heavy imbalance in switching events; and third, appliance-specific lightweight long short-term memory networks for status classification for each appliance. Here, we adopt a “differential” input (the difference before and after the switching event) to reduce the complexity of network training and make the proposed method robust to multiappliance switching events. Experiments are conducted to demonstrate the effectiveness of the proposed method, achieving superior performance when compared to recent methods. An ablation study is conducted to demonstrate the effectiveness of each module of our method. Finally, we investigate the quality of generated synthetic samples.

42 ENGINEERING↗
DOE OSTI2021

A Network-Aware Distributed Energy Resource Aggregation Framework for Flexible, Cost-Optimal, and Resilient Operation

To efficiently use the ubiquitous behind-the-meter distributed energy resources (DERs) in distribution systems for providing grid services, this paper presents a hierarchical control framework for DER optimal aggregation and control. We first develop a convex optimization model to evaluate the DER flexibility, and then use a convex model-predictive-control based approach to dispatch those DERs. The hierarchical control framework consists of a utility controller, community aggregators and multiple home energy management systems. The flexibility of the DERs is evaluated by each controller in the hierarchy such that the resultant flexibility is feasible given its operational domain. Based on the determined flexibility, the hierarchical controllers then compute optimal setpoints for the DERs to help the distribution system regulate node voltages and provide other distribution grid services. Numerical simulations performed on a model of a real distribution feeder in Colorado, using actual DER data in a residential community demonstrate that the proposed approach can effectively alleviate voltage issues and support resilient operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC

Federated Learning (FL) is critical for edge and High Performance Computing (HPC) where data is not centralized and privacy is crucial. We present OmniFed, a modular framework designed around decoupling and clear separation of concerns for configuration, orchestration, communication, and training logic. Its architecture supports configuration-driven prototyping and code-level override-what-you-need customization. We also support different topologies, mixed communication protocols within a single deployment, and popular training algorithms. It also offers optional privacy mechanisms including Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Aggregation (SA), as well as compression strategies. These capabilities are exposed through well-defined extension points, allowing users to customize topology and orchestration, learning logic, and privacy/compression plugins, all while preserving the integrity of the core system. We evaluate multiple models and algorithms to measure various performance metrics. By unifying topology configuration, mixed-protocol communication, and pluggable modules in one stack, OmniFed streamlines FL deployment across heterogeneous environments. Github repository is available at https://github.com/at-aaims/OmniFed.

Tyagi, Sahil [ORNL] (ORCID:0009000783144745)↗
DOE OSTI2018

Investigation of protein quaternary structure via stoichiometry and symmetry information

The Protein Data Bank (PDB) is the single worldwide archive of experimentally-determined three-dimensional (3D) structures of proteins and nucleic acids. As of January 2017, the PDB housed more than 125,000 structures and was growing by more than 11,000 structures annually. Since the 3D structure of a protein is vital to understand the mechanisms of biological processes, diseases, and drug design, correct oligomeric assembly information is of critical importance. Unfortunately, the biologically relevant oligomeric form of a 3D structure is not directly obtainable by X-ray crystallography, whilst in solution methods (NMR or single particle EM) it is known from the experiment. Instead, this information may be provided by the PDB Depositor as metadata coming from additional experiments, be inferred by sequence-sequence comparisons with similar proteins of known oligomeric state, or predicted using software, such as PISA (Proteins, Interfaces, Structures and Assemblies) or EPPIC (Evolutionary Protein Protein Interface Classifier). Despite significant efforts by professional PDB Biocurators during data deposition, there remain a number of structures in the archive with incorrect quaternary structure descriptions (or annotations). Further investigation is, therefore, needed to evaluate the correctness of quaternary structure annotations. In this study, we aim to identify the most probable oligomeric states for proteins represented in the PDB. Our approach evaluated the performance of four independent prediction methods, including text mining of primary publications, inference from homologous protein structures, and two computational methods (PISA and EPPIC). Aggregating predictions to give consensus results outperformed all four of the independent prediction methods, yielding 83% correct, 9% wrong, and 8% inconclusive predictions, when tested with a well-curated benchmark dataset. We have developed a freely-available web-based tool to make this approach accessible to researchers and PDB Biocurators.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Chloroplast SRP43 autonomously protects chlorophyll biosynthesis proteins against heat shock

The assembly of light-harvesting chlorophyll-binding proteins (LHCPs) is coordinated with chlorophyll biosynthesis during chloroplast development. The ATP-independent chaperone known as chloroplast signal recognition particle 43 (cpSRP43) mediates post-translational LHCP targeting to the thylakoid membrane and also participates in tetrapyrrole biosynthesis (TBS). How these distinct actions of cpSRP43 are controlled has remained unclear. Here, we demonstrate that cpSRP43 effectively protects several TBS proteins from heat-induced aggregation and enhances their stability during leaf greening and heat shock. While the substrate-binding domain of cpSRP43 is sufficient for chaperoning LHCPs, the stabilization of TBS clients requires the chromodomain 2 of the protein. Strikingly, cpSRP54—which activates cpSRP43’s LHCP-targeted function—inhibits the chaperone activity of cpSRP43 towards TBS proteins. High temperature weakens the interaction of cpSRP54 with cpSRP43, thus freeing cpSRP43 to interact with and protect the integrity of TBS proteins. Our data indicate that the temperature sensitivity of the cpSRP43–cpSRP54 complex enables cpSRP43 to serve as an autonomous chaperone for the thermoprotection of TBS proteins.

09 BIOMASS FUELS↗
DOE OSTI2025

Virtual Power Plants: Pilots, Challenges, and Innovations Shaping Future Development

Virtual Power Plants (VPPs) aggregate distributed energy resources (DERs) to provide grid services traditionally delivered by centralized power plants. This article reviews the current state of VPP deployment, highlighting business models, compensation mechanisms, and global pilot projects. While VPPs offer benefits such as grid flexibility, resilience, and cost savings, challenges remain in communication infrastructure, regulatory frameworks, market access, and customer engagement. To address these, we propose a scalable, privacy-preserving hierarchical VPP architecture that coordinates with distribution utilities and preserves customer data. We also present the Integrated T&D Control Room of the Future as a key test bed for validating and accelerating VPP adoption. These innovations can help transition VPPs from pilot programs to integral components of a modern, reliable power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

The Influences of Neutron Irradiation on Aggregate Induced Degradation of Concrete

Reactor cavity concrete, the primary support structure for the reactor pressure vessel (RPV), is exposed to chronic neutron/gamma radiation during normal operation. During long term operation, the interaction of neutron irradiation with the concrete’s constituents may cause premature degradation of the concrete structure, as highlighted by a review of concrete properties in nuclear power plant environments. At high fluence levels, as in biological shielding, neutron irradiation may cause amorphization of the crystalline components of concrete, i.e., mineral aggregates, and volume changes, which are typically expansive in nature. Volume expansion can generate tensile stresses within concrete which can induce microcracking and/or result in debonding of aggregate with the matrix. On the other hand, structural disordering can result in changes in the stability of the phase in aqueous environments. This report presents the findings of investigations performed over 3 years through the collaboration of researchers from University of California, Los Angeles, Arizona State University, and Oak Ridge National Laboratory. The first part of the study addresses the effect of neutron irradiation on the durability of aggregates. Comparisons of aqueous dissolution rates, as a function of pH and temperature, were performed for aggregates that are either pristine (i.e., non-irradiated) or exposed to Ar+ ion irradiation at specified doses. These experimental results were coupled with molecular dynamics simulations of atomic scale changes in the crystallographic structure of the mineral and any resulting changes in physical properties (e.g., density) that result. The second section focuses on macroscopic effects of such changes in aggregate behavior in concrete degradation, primarily through changes in the concrete’s microstructure. Particularly, the effects akin to those originating from alkali-silica reaction (ASR) were evaluated. The project is structured to encompass 6 main tasks, as follows: Task 1: Selection of aggregates in relation to their abundance and mineralogy, and their irradiation Task 2: Molecular dynamics (MD): The effects of irradiation on the atomic disordering of aggregates Task 3: The influences of irradiation on aggregate reactivity Task 4: Reactive-transport simulation of expansive product formation in concrete microstructures Task 5: X-FEM-based modeling of damage evolution due to irradiation-induced aggregates reactions Task 6: Benchmarking, verification and validation of the simulation modules Thus, a rigorous understanding of irradiation-assisted mineral alterations was used to develop a mechanistic degradation model for concrete exposed to neutrons, while considering aspects of: chemical reaction (dissolution and precipitation), mass and ion transport, and damage evolutions. The models developed were embedded in a multi-physics aging code for nuclear components and structures called Grizzly, which allows for benchmarking, verification, and validation against expansion data acquired on concrete undergoing alkali-silica reaction, a physically/chemically equivalent form of degradation that develops in the absence of radiation. These findings provide insight into the sensitivities of minerals to irradiation, by highlighting characteristics of concretes and power plants subject to such risks. The research will aid the nuclear power industry to anticipate and mitigate such risks in light of second license renewals.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Resilience Metrics Framework for Solar Photovoltaics

This presentation was given at the Photovoltaic Specialist Conference (PVSC) 54 in New Orleans, Louisiana. Photovoltaic (PV) systems are routinely exposed to extreme weather, including wind and hail storms. Historically, most systems have proven to be resilient to such events, but some storms have damaged PV systems, leading to physical and financial loss. Storm hardening measures and specific system attributes can reduce this risk. This work introduces a set of resilience metrics and a framework for quantifying, comparing, and predicting PV system resilience. The framework is divided into two parts: 1) predictive, attribute metrics based on site and component characteristics, and 2) impact metrics that assess post-storm performance. Metrics are weighted and aggregated, producing hazard-specific resilience scores. We derive damage functions from storm-impacted PV systems, establishing a baseline against which post-storm performance can be compared. This damage was widely variable across hail and wind intensities, and field hail damage was less than predicted by laboratory tests, suggesting that system features - in addition to storm conditions - influence damage likelihood. Finally, the metrics framework is demonstrated using three case studies of storm damaged PV systems. Although additional data are needed to create attribute specific damage functions and establish metric weights, this study presents a methodology for evaluating PV resilience and contributes new damage functions to the literature.

14 SOLAR ENERGY↗
DOE OSTI2024

Relating Molecular Properties to the Persistence of Marine Dissolved Organic Matter with Liquid Chromatography–Ultrahigh-Resolution Mass Spectrometry

Marine dissolved organic matter (DOM) contains a complex mixture of small molecules that eludes rapid biological degradation. Spatial and temporal variations in the abundance of DOM reflect the existence of fractions that are removed from the ocean over different time scales, ranging from seconds to millennia. However, it remains unknown whether the intrinsic chemical properties of these organic components relate to their persistence. Here, we elucidate and compare the molecular compositions of distinct DOM fractions with different lability along a water column in the North Atlantic Gyre. Our analysis utilized ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry at 21 T coupled to liquid chromatography and a novel data pipeline developed in CoreMS that generates molecular formula assignments and metrics of isomeric complexity. Clustering analysis binned 14 857 distinct molecular components into groups that correspond to the depth distribution of semilabile, semirefractory, and refractory fractions of DOM. The more labile fractions were concentrated near the ocean surface and contained more aliphatic, hydrophobic, and reduced molecules than the refractory fraction, which occurred uniformly throughout the water column. These findings suggest that processes that selectively remove hydrophobic compounds, such as aggregation and particle sorption, contribute to variable removal rates of marine DOM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Leveraging Topological Events in Tracking Graphs for Understanding Particle Diffusion

Abstract Single particle tracking (SPT) of fluorescent molecules provides significant insights into the diffusion and relative motion of tagged proteins and other structures of interest in biology. However, despite the latest advances in high‐resolution microscopy, individual particles are typically not distinguished from clusters of particles. This lack of resolution obscures potential evidence for how merging and splitting of particles affect their diffusion and any implications on the biological environment. The particle tracks are typically decomposed into individual segments at observed merge and split events, and analysis is performed without knowing the true count of particles in the resulting segments. Here, we address the challenges in analyzing particle tracks in the context of cancer biology. In particular, we study the tracks of KRAS protein, which is implicated in nearly 20% of all human cancers, and whose clustering and aggregation have been linked to the signaling pathway leading to uncontrolled cell growth. We present a new analysis approach for particle tracks by representing them as tracking graphs and using topological events – merging and splitting, to disambiguate the tracks. Using this analysis, we infer a lower bound on the count of particles as they cluster and create conditional distributions of diffusion speeds before and after merge and split events. Using thousands of time‐steps of simulated and in‐vitro SPT data, we demonstrate the efficacy of our method, as it offers the biologists a new, detailed look into the relationship between KRAS clustering and diffusion speeds.

McDonald, T.↗
DOE OSTI2018

Flow Mechanisms and Diffusion Combustion of Turbulent Jets

The problem of flow and combustion of turbulent jets of fuel gas in the external medium of an oxidant (air) is solved with regard to the existence of the actual boundary of the turbulent flow region of a jet. Based on the ideas of the friction force of the external flow acting on the boundary of a jet, the entrainment equation for the external medium is derived that closes the system of equations of motion of turbulent jets. The physical meaning of the dissipation rate of the turbulent energy of a jet is interpreted as the work of the friction force. To describe the combustion kinetics, the limit of instantaneous reactions corresponding to the diffusion combustion mode is used. Calculations of the effective reaction rates for reactants and the volumes occupied by them are based on the representation of a turbulent medium as an aggregation of independent turbulent particles—vortices—whose random contacts lead to the mixing and combustion of reacting substances [31]. The concomitant phenomena of flow and combustion are analyzed, including radiation effects. In particular, it is shown that the apparent increase in the combustion temperature with increasing Reynolds number is in fact attributed to the relative decrease of thermal radiation losses. Qualitative agreement is obtained between the results of the theoretical calculations of the length of a combustion torch and experimental data.

42 ENGINEERING↗
DOE OSTI2021

Hydroxylpyromorphite, a mineral important to lead remediation: Modern description and characterization

Abstract Hydroxylpyromorphite, Pb5(PO4)3(OH), has been documented in the literature as a synthetic and naturally occurring phase for some time but has not previously been formally described as a mineral. It is fully described here for the first time using crystals collected underground in the Copps mine, Gogebic County, Michigan. Hydroxylpyromorphite occurs as aggregates of randomly oriented hexagonal prisms, primarily between about 20–35 μm in length and 6–10 μm in diameter. The mineral is colorless and translucent with vitreous luster and white streak. The Mohs hardness is ~3½–4; the tenacity is brittle, the fracture is irregular, and indistinct cleavage was observed on {001}. Electron microprobe analyses provided the empirical formula Pb4.97(PO4)3(OH0.69F0.33Cl0.06)Σ1.08. The calculated density using the measured composition is 7.32 g/cm3. Powder X-ray diffraction data for the type material is compared to data previously reported for hydroxylpyromorphite from the talc mine at Rabenwald, Austria, and from Whytes Cleuch, Wanlockhead, Scotland. Hydroxylpyromorphite is hexagonal, P63/m, at 100 K, a = 9.7872(14), c = 7.3070(10) Å, V = 606.16(19) Å3, and Z = 2. The structure [R1 = 0.0181 for 494 F>4σ(F) reflections] reveals that hydroxylpyromorphite adopts a column anion arrangement distinct from other members of the apatite supergroup due to the presence of fluorine and steric constraints imposed by stereoactive lone-pair electrons of Pb2+ cations. The F– anion sites are displaced slightly from hydroxyl oxygen anions, which allows for stronger hydrogen-bonding interactions that may in turn stabilize the observed column-anion arrangement and overall structure. Our modern characterization of hydroxylpyromorphite provides deeper understanding to a mineral useful for remediation of lead-contaminated water.

Geochemistry & Geophysics↗