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DOE OSTI2025

GRUMDN: A Multi-Task Model for Predicting Human Patterns-of-Life from Stay Transition Data

Understanding human patterns-of-life (PoL) is essential towards ensuring safe and secure indoor facility environment as well as outdoor urban environment. Prediction of human movement in between places of interest is vital in understanding human PoL. Movement between spaces maybe represented and detected in one of the two forms: 1) trajectories: locations measured at regular time intervals by mobile sensors, bluetooth or GPS sensors; or 2) stay transitions: semantic PoI (points of interest) and stay duration data measurable by eventbased sensors that collect data when a check-in or check-out event is detected. Stay transition data provides a more compressed data format compared to trajectories data, especially in situations with longer stay durations, while preserving the information necessary for PoL analysis. Now as introduced briefly in the paper, our deployed end application (Digital Twin of a facility with non-player characters, besides the interactive user in virtual reality) needed a well-performing and validated AI/ML model for simulating high quality stay transitions behavior. In this study we thus primarily present our findings with developing and validating that model, which is a multi-task neural network for stay transition prediction. The neural network consists of two heads, for corresponding two tasks of stay category prediction and stay duration prediction. We evaluated gated recurrent units and multi-layer perceptrons of varying network sizes for stay category prediction; while mixture density networks, noisy generator-only networks, and generative adversarial networks of varying network sizes for stay duration prediction. We have then evaluated four multi-task models, constructed by combining these specialized models, on their ability to predict stay transition data. We tested our models on datasets from two different cases: 1) a simulation-generated dataset of indoor movement within the HFIR (high flux isotope reactor) nuclear reactor facility at Oak Ridge National Laboratory (ORNL); and 2) the GeoLife human mobility dataset of outdoor urban movement available in literature. Our results indicate that GRUMDN, which combines gated recurrent units (GRU) for stay category prediction task, and mixture density networks (MDN) for stay duration prediction task, did overall outperform other multitask models and the current state-of-the-art.

Gunaratne, Chathika [ORNL] (ORCID:0000000225088745↗

DOE OSTI2020

Efficient Binary Static Code Data Flow Analysis Using Unsupervised Learning

The ever increasing need to ensure that code is reliably, efficiently and safely constructed has fueled the evolution of popular static binary code analysis tools. In identifying potential coding flaws in binaries, tools such as IDA Pro are used to disassemble the binaries into an opcode/assembly language format in support of manual static code analysis. Because of the highly manual and resource intensive nature involved with analyzing large binaries, the probability of overlooking potential coding irregularities and inefficiencies is quite high. In this paper, a light-weight data, unsupervised data flow methodology is described which uses highly-correlated data flow graph (CDFGs) to identify coding irregularities such that analysis time and required computing resources are minimized. Such analysis accuracy and efficiency gains are achieved by using a combination of graph analysis and unsupervised machine learning techniques which allows an analyst to focus on the most statistically significant flow patterns while performing binary static code analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Quantification of High‐Temperature Transition Al 2 O 3 and Their Phase Transformations**

Abstract High‐temperature treatment of γ‐Al 2 O 3 can lead to a series of polymorphic transformations, including the formation of δ‐Al 2 O 3 and θ‐Al 2 O 3 . Quantification of the microstructure in the range where δ‐ and θ‐Al 2 O 3 are formed represents a formidable challenge, as both phases accommodate a high degree of structural disorder. In this work, we explore the use of an XRD recursive‐stacking formalism for the quantification of high‐temperature transition aluminas. We formulate the recursive‐stacking methodology for modelling of disorder in δ‐Al 2 O 3 and twinning in θ‐Al 2 O 3 and show that explicitly accounting for the disorder is necessary to reliably model the XRD patterns of high‐temperature transition alumina. We also use the recursive stacking approach to study phase transformation during high‐temperature (1050 °C) treatment. We show that the two different intergrowth modes of δ‐Al 2 O 3 have different transformation characteristics and that a significant portion of δ‐Al 2 O 3 is stabilized with θ‐Al 2 O 3 even after prolonged high‐temperature exposures.

Kovarik, Libor↗

DOE OSTI2023

An Extremely Stable and Soluble NH 2 -Substituted Anthraquinone Electrolyte for Aqueous Redox Flow Batteries

Aqueous redox flow batteries require long-term stable redox molecules for electrical energy storage. Anthraquinones, especially ether bond-decorated ones, experience two dominant decomposition pathways, including side-chain loss and anthrone formation. Here, with the aid of DFT calculations, we designed an anthraquinone (3-NH 2 -2-2PEAQ) bearing an ether substituent and a neighboring NH 2 group, which suppresses both of these decomposition mechanisms and exhibits a high solubility of 1.1 M. When paired with ferrocyanide in a full cell, this anthraquinone, at a concentration of 1.0 M, can operate at greater than 1 V with an extremely low capacity fade rate of 0.01%/day and a Coulombic efficiency above 99.8% while cycling for over a month. The synergistic effects of the ether and parent amino substituents are extremely sensitive to the precise substitution pattern of the anthraquinone. This study demonstrates the effectiveness of the judicious use of DFT-based prediction in rapidly identifying promising candidate electrolytes from a large chemical space.

25 ENERGY STORAGE↗

DOE OSTI2022

2022 AI Testbed Expeditions Report

By exploiting the coherent properties of a light source, coherent diffraction imaging (CDI) is able to obtain the sample image at a nanoscale resolution using the measured diffraction pattern. Bragg Coherent Diffraction Imaging (BCDI) has become valuable for recovering the displacement and strain field of crystals, providing a valuable tool in material science and solid-state physics. X-ray ptychography is another emerging CDI technique that can produce a high-resolution image of the extended sample and has become popular in many research areas (e.g., materials science, biology, electronics, and optics characterization). CDI including BCDI and ptychography has become an established technique in Synchrotron Facilities including the Advanced Photon Source (APS) and will greatly benefit from the 100x coherent flux increase of the upcoming APS Upgrade (APSU). The current image formation process in CDI employs iterative phase retrieval algorithms, which is a time-consuming and computationally expensive process. Especially after APSU, the traditional iterative methods will not be able to match the experimental data acquisition speed. We employ deep learning (DL) approach to replace the iterative approaches, therefore allowing hundreds of times faster recovery of the object. We developed AutoPhaseNN, a DL-based approach which learns to solve the inverse problem without labeled data. Taking 3D BCDI as a representative technique, AutoPhaseNN has been demonstrated to be one hundred times faster than traditional iterative phase retrieval methods while providing comparable image quality. The current network is trained with 64 x 64 x 64 data size, to achieve higher resolution imaging, we will need to scale the network to input and train/infer 3D arrays of size 256 x 256 x 256 (today) and of size 2560x2560x2560 (APSU). However, the scalability of the network is restricted due to the memory-intensive training process. To perform the training for a 256 x 256 x 256 data size, the required memory exceeds the capacity of the current machine. In this project, we explore using Sambanova system to train the network for the direct data inversion for CDI.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community

Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. In this work, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Emergence of lignin-carbohydrate interactions during plant stem maturation visualized by solid-state NMR

Lignification waterproofs and strengthens secondary plant cell walls but increases the energy cost of sugar release for biofuels. The physical association between lignin and the carbohydrate scaffold that accommodates lignin polymerization, along with the distinct roles of lignin units and carbohydrate partners during lignification, remain unclear. Here, we map lignin-carbohydrate spatial proximity by solid-state NMR in 13 C-labeled Arabidopsis inflorescence stems during secondary cell wall formation. Analyses include wild-type plants and mutants that selectively or globally disrupt lignin biosynthesis. Mature walls in basal regions show enrichment of S-lignin and dense carbohydrate-lignin packing. Acetylated xylan predominantly associates with S-lignin, while methylated pectin unexpectedly interacts with G-lignin during early-stage lignification. The importance of S-lignin in stabilizing the carbohydrate-lignin interface is highlighted by weak lignin-carbohydrate contacts and compromised mechanical properties in the low-S fah1 mutant, whereas the ref3 mutant, despite reduced lignin content, remains unaffected due to a high S/G ratio. Thus, molecular mixing patterns, rather than lignin content, critically determine the structure and properties of lignocellulosic materials.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Impact of prescribed fire on soil microbial communities in a Southern Appalachian Forest clear-cut

Escalating wildfire frequency and severity, exacerbated by shifting climate patterns, pose significant ecological and economic challenges. Prescribed burns, a common forest management tool, aim to mitigate wildfire risks and protect biodiversity. Nevertheless, understanding the impact of prescribed burns on soil and microbial communities in temperate mixed forests, considering temporal dynamics and slash fuel types, remains crucial. Our study, conducted at the University of Tennessee Forest Resources AgResearch and Education Center in Oak Ridge, TN, employed controlled burns across various treatments, and the findings indicate that low-intensity prescribed burns have none or minimal short-term effects on soil parameters but may alter soil nutrient concentrations, as evidenced by significant changes in porewater acetate, formate, and nitrate concentrations. These burns also induce shifts in microbial community structure and diversity, with Proteobacteria and Acidobacteria increasing significantly post-fire, possibly aiding soil recovery. In contrast, Verrucomicrobia showed a notable decrease over time, and other specific microbial taxa correlated with soil pH, porewater nitrate, ammonium , and phosphate concentrations. Our research contributes to understanding the intricate relationships between prescribed fire, soil dynamics, and microbial responses in temperate mixed forests in the Southern Appalachian Region, which is valuable for informed land management practices in the face of evolving environmental challenges.

Microbiology↗

DOE OSTI2021

Controlled Fabrication of Quality ZnO NWs/CNTs and ZnO NWs/Gr Heterostructures via Direct Two-Step CVD Method

A novel and advanced approach of growing zinc oxide nanowires (ZnO NWs) directly on single-walled carbon nanotubes (SWCNTs) and graphene (Gr) surfaces has been demonstrated through the successful formation of 1D–1D and 1D–2D heterostructure interfaces. The direct two-step chemical vapor deposition (CVD) method was utilized to ensure high-quality materials’ synthesis and scalable production of different architectures. Iron-based universal compound molecular ink was used as a catalyst in both processes (a) to form a monolayer of horizontally defined networks of SWCNTs interfaced with vertically oriented ZnO NWs and (b) to grow densely packed ZnO NWs directly on a graphene surface. We show here that our universal compound molecular ink is efficient and selective in the direct synthesis of ZnO NWs/CNTs and ZnO NWs/Gr heterostructures. Heterostructures were also selectively patterned through different fabrication techniques and grown in predefined locations, demonstrating an ability to control materials’ placement and morphology. Several characterization tools were employed to interrogate the prepared heterostructures. ZnO NWs were shown to grow uniformly over the network of SWCNTs, and much denser packed vertically oriented ZnO NWs were produced on graphene thin films. Such heterostructures can be used widely in many potential applications, such as photocatalysts, supercapacitors, solar cells, piezoelectric or thermal actuators, as well as chemical or biological sensors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2020

Completion design improvement using a deep convolutional network

Maximizing stimulated natural and hydraulic fracture network is one of the primary hydraulic fracturing concerns for economic production from a horizontal shale gas well. Geomechanical facies and preexisting fractures in each stage are identified based on similarities in formation characteristics to optimize the locations of perforation clusters. This often requires analyzing large volumes of drilling, Logging While Drilling (LWD) and Measurement While Drilling (MWD) data. In this paper, we develop a methodology that calculates the mechanical specific energy (MSE) using real-time drill string acceleration signals directly from its definition. High resolution vibration signals have been collected using a tri-axial accerlometer, which was an auxiliary tool included in acoustic borehole imager. This technique provides a cost-efficient solution for engineered completion design. Furthermore, we adopt deep Convolutional Neural Network (CNN) with signal processing to build a data pipeline that effectively extracts patterns from dynamic acceleration signals for rock lateral MSE classification. First, we apply discrete wavelet transform and Short-Time Fourier Transform (STFT) for signal denoising and pattern recognition. Then we construct an image dataset using multi-scale image fusion at pixel level from 3 sensor channels, including axial, lateral acceleration spectrograms and zero-padded revolutions per minute (RPM). The resulted RGB image dataset includes 4,000 images of 5 MSE ranges with various rock strength conditions. Our results demonstrate that the proposed deep learning model can achieve more than 90% classification accuracy. The deep learning results, as a reference source, were applied in selected Marcellus Shale Energy and Environmental Lab (MSEEL) wells engineered completion located in the Marcellus shale gas site.

03 NATURAL GAS↗

DOE OSTI2024

From phase decomposition to evaporation: A multi-modal evaluation of thermally degraded model lightweight high-entropy alloy

Lightweight high-entropy alloys (LHEAs) have the potential to replace conventional lightweight materials due to their superior mechanical properties and thermal stability. However, the thermal degradation pattern of LHEAs from phase decomposition to evaporation is not clear. We develop a new Al-based dual phase (FCC + HCP) LHEA—AlTi 0.45 CuZn, and further investigate its thermal degradation behavior for potential high-temperature structural applications. Using multimodal advanced characterization techniques such as differential scanning calorimetry/thermogravimetric analysis, scanning/transmission electron microscopy, and synchrotron X-ray diffraction/pair distribution function (XRD/PDF), a sequence of thermal degradation events beyond the thermal phase stability limit—between 250 and 360 °C—is observed. These include phase decomposition at ~360 °C, Zn evaporation at ~750 °C, and LHEA melting at 880 °C which results in ~25% cumulative weight loss. The formation of Al-Ti phase off the AlTi 0.45 CuZn matrix is due to the largest negative mixing enthalpy for Al-Ti than other binary pairs. Similarly, Zn evaporation from AlTi 0.45 CuZn LHEA is due to its faster evaporation rate than other constituent elements. The high-resolution synchrotron XRD and PDF results support the aforementioned observations; in addition, they reveal local atomic arrangements, local strain, and sluggish grain growth in the LHEA. Among other LHEAs of close density range (5.55 ≤ ρ ≤ 5.85 g/cc), the investigated LHEA exhibits outstanding nano-indentation hardness values due to the coupled grain size effect and HCP phase strengthening of the FCC matrix. As the search for LHEAs for lightweight applications grows, this study shows the potential use of AlTi 0.45 CuZn LHEA for structural applications even at elevated temperatures.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Formation of a Strong Heterogeneous Aluminum Lewis Acid on Silica

Abstract Al(OC(CF 3 ) 3 )(PhF) reacts with silanols present on partially dehydroxylated silica to form well‐defined ≡SiOAl(OC(CF 3 ) 3 ) 2 (O(Si≡) 2 ) ( 1 ). 27 Al NMR and DFT calculations with a small cluster model to approximate the silica surface show that the aluminum in 1 adopts a distorted trigonal bipyramidal coordination geometry by coordinating to a nearby siloxane bridge and a fluorine from the alkoxide. Fluoride ion affinity (FIA) calculations follow experimental trends and show that 1 is a stronger Lewis acid than B(C 6 F 5 ) 3 and Al(OC(CF 3 ) 3 )(PhF) but is weaker than Al(OC(CF 3 ) 3 ) and i Pr 3 Si + . Cp 2 Zr(CH 3 ) 2 reacts with 1 to form [Cp 2 ZrCH 3 ][≡SiOAl(OC(CF 3 ) 3 ) 2 (CH 3 )] ( 3 ) by methide abstraction. This reactivity pattern is similar to reactions of organometallics with the proposed strong Lewis acid sites present on Al 2 O 3 .

Samudrala, Kavyasripriya K.↗

DOE OSTI2022

Formation of a Strong Heterogeneous Aluminum Lewis Acid on Silica

Samudrala, Kavyasripriya K.↗

DOE OSTI2021

SPRUCE Diurnal and Seasonal Patterns of Water Potential in S1 Bog and SPRUCE Experimental Plot Vegetation beginning in 2010

This data set reports on water potential from excised terminal shoots or leaves (Mainthemum only) of vegetation at the SPRUCE site located in the S1 bog. Pretreatment measurements were collected from outside plots within the S1 bog. Post treatments were collected from inside SPRUCE plots. Water potential was measured on the overstory canopy species, consisting of the evergreen Picea mariana (Mill.) B.S.P. (black spruce) and deciduous Larix laricina (Du Roi) K. Koch (tamarack) trees, and ericaceous shrubs < 1 m tall, consisting of the evergreen Rhododendron groenlandicum (Oeder) Kron & Judd (Labrador tea) and Chamaedaphne calyculata (L.) Moench. (leatherleaf). Some measurement campaigns also included the evergreen Kalmia polifolia, Wangenh. (bog laurel), the deciduous Vaccinium angustifolium (Aiton) (blueberry), and the herbaceous Maianthemum trifolium (L.) Sloboda (three-leaf false Solomon’s seal). This dataset contains one data file in comma-separated (*.csv) format. This is the second release of water potential data from the site with additional data from 2018 and 2019. These data cover 2010-06-22 to 2019-07-16. Further results will be added to this data set and released to the public periodically as quality assurance and publication of results are accomplished. There are 10 experimental plots in SPRUCE: five temperature treatments (+0, +2.25, +4.5, +6.75, +9°C) at ambient CO2, and the same five temperature treatments at elevated CO2 (+500 ppm). These data span the pre- and post-treatment periods when enclosed plots were exposed to warming and elevated carbon dioxide (CO2) within the SPRUCE experiment. The pretreatment samples were collected from south end of the S1 bog prior to SPRUCE boardwalk construction, then throughout the bog once the boardwalks were completed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

High-Resolution XFEL Structure of the Soluble Methane Monooxygenase Hydroxylase Complex with its Regulatory Component at Ambient Temperature in Two Oxidation States

Soluble methane monooxygenase (sMMO) is a multicomponent metalloenzyme that catalyzes the conversion of methane to methanol at ambient temperature using a nonheme, oxygen-bridged dinuclear iron cluster in the active site. Structural changes in the hydroxylase component (sMMOH) containing the diiron cluster caused by complex formation with a regulatory component (MMOB) and by iron reduction are important for the regulation of O 2 activation and substrate hydroxylation. Structural studies of metalloenzymes using traditional synchrotron-based X-ray crystallography are often complicated by partial X-ray-induced photoreduction of the metal center, thereby obviating determination of the structure of the enzyme in pure oxidation states. Here, microcrystals of the sMMOH:MMOB complex from Methylosinus trichosporium OB3b were serially exposed to X-ray free electron laser (XFEL) pulses, where the ≤35 fs duration of exposure of an individual crystal yields diffraction data before photoreduction-induced structural changes can manifest. Merging diffraction patterns obtained from thousands of crystals generates radiation damage-free, 1.95 Å resolution crystal structures for the fully oxidized and fully reduced states of the sMMOH:MMOB complex for the first time. The results provide new insight into the manner by which the diiron cluster and the active site environment are reorganized by the regulatory protein component in order to enhance the steps of oxygen activation and methane oxidation. Furthermore, this study also emphasizes the value of XFEL and serial femtosecond crystallography (SFX) methods for investigating the structures of metalloenzymes with radiation sensitive metal active sites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Electron scattering with ethane adsorbed on rare gas multilayers: Hole transfer, coulomb decay, and ion dissociation

Positive ion desorption following electron impact dissociative ionization of ethane adsorbed on Ar, Kr, and Xe multilayers has been studied as a function of incident electron energy from threshold to 100 eV. Based on the dependence of ion yields on the identity of the rare gas, it is likely that the majority of ethane molecules undergo indirect ionization following hole transfer from the ionized underlying rare gas. Further, this has also been corroborated by density of states calculations showing the energetic alignment of the outer valence states of ethane and the condensed rare gas ionization energies. Due to the near-resonant nature of charge transfer for single-hole states, the ethane molecular ion is excited to different final ionic states on different rare gases, which leads to differences in ion desorption yields and branching ratios. The quantitative yields increase with increasing ionization energy gap between the rare gas and ethane, in the order Ar > Kr >Xe. The large increase in yields from 25 eV onwards for all rare gases is likely due to the formation and decay of two-hole states on neighboring rare gas and ethane molecules due to interatomic and intermolecular Coulomb decay (ICD) and not electron transfer mediated decay (ETMD). The ICD and ETMD pathways become accessible when the incoming electron has sufficient energy to excite the inner valence ns level of the rare gas to a Rydberg state or ionize it. The experimental findings are supported by calculations of thresholds, density of states for the final configurations of these processes, and coupling strengths for hole transfer between ethane and rare gases. The fragment ion branching ratios vary with energy from threshold to about 35 eV, showing the fragmentation pattern changes with the mode of hole transfer and availability of excess energy. Sigma C–C bonds are more likely to break than C–H bonds in the mid-20 eV range, and this effect is most pronounced for Xe, followed by Kr, and then Ar.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Hydrologic windows into the crystalline basement and their controls on groundwater flow patterns across the Paradox Basin, western USA

Conceptual models of sedimentary basin groundwater flow systems typically assume that the crystalline basement acts as an impermeable boundary and can be neglected. In this study, we use hydrologic models constrained by isotopic and geochemical datasets to argue that the La Sal Mountains, Utah, USA, act as a hydrologic window into the Paradox Basin’s lower aquifer system and underlying crystalline basement. We conducted a sensitivity study in which we varied crystalline basement/laccolith permeability as well as fault zone connectivity along a cross-sectional transect from the La Sal Mountains to Lisbon Valley. When the crystalline basement/laccolith units are set at relatively permeable levels (10–14 m 2 ), simulated tracers that include total dissolved solids, oxygen isotopic composition of pore fluids (δ 18 O), and groundwater residence times are in closest agreement with field measurements. Model results indicate that pore fluids in the basal aquifer system underlying the Paradox Formation confining unit are a mixture of relatively young meteoric fluids and older Paradox Formation brines. The presence of faults did not significantly modify fluid exchange between the upper and lower aquifer systems. This was due, in part, to underpressuring within the Paradox Formation. Our study concludes that the Paradox Basin represents a regional recharge area for the Colorado Plateau, with groundwater discharge occurring along the Colorado River within the Grand Canyon some 375 km away to the southwest. This is only possible with a permeable crystalline basement. Our findings help explain the genesis of Mississippi Valley-type ore deposits of the US Midcontinent, where the presence of a permeable basement may be useful in addressing issues related to solute mass and energy balance.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Chlorine and potassium enrichment in the Cassiopeia A supernova remnant

The elements in the Universe are synthesized primarily in stars and supernovae, where nuclear fusion favours the production of even-Z elements. In contrast, odd-Z elements are less abundant and their yields are highly dependent on detailed stellar physics, making theoretical predictions of their cosmic abundance uncertain. In particular, the origin of odd-Z elements such as phosphorus (P), chlorine (Cl) and potassium (K), which are important for planet formation and life, is poorly understood. While the abundances of these elements in Milky Way stars are close to solar values, supernova explosion models systematically underestimate their production by up to an order of magnitude, indicating that key mechanisms for odd-Z nucleosynthesis are currently missing from theoretical models. Here we report the observation of P, Cl and K in the Cassiopeia A supernova remnant using high-resolution X-ray spectroscopy with X-Ray Imaging and Spectroscopy Mission data, with the detection of K at above the 6σ level being the most significant finding. Supernova explosion models of normal massive stars cannot explain the element abundance pattern, especially the high abundances of Cl and K, while models that include stellar rotation, binary interactions or shell mergers agree closely with the observations. Our observations suggest that such stellar activity plays an important role in supplying these elements to the Universe.

Astronomy and AstroPhysics↗