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DOE OSTI2025

An isotopic labeling investigation into the influence of the nitro group on LLM-105 thermal decomposition

Here, this work presents the first application of isotopically labeled LLM-105 (2,6-diamino-3,5-dinitropyrazine-1-oxide) to investigate thermal decomposition pathways. Specially synthesized LLM-105 isotopologues were utilized to isolate the influence of labeled 15 NO 2 nitro groups on the formation of lightgas products. Simultaneous differential scanning calorimetry, thermo-gravimetric, and mass spectrometry measurements were employed to track the evolution of product gases, enabling the direct comparison of isotopically shifted species with unlabeled LLM-105. Key findings show that C 2 N 2 production is mainly dependent on nitrogen sources from either the amine groups or the pyrazine ring (i.e., not the nitro groups). The formation of NO, N 2 , and N 2 O all involves the nitro groups to some extent. NO (nitric oxide) was found to be the predominant gas species directly formed from the nitro group of LLM-105. In contrast, mixed nitrogen isotopologues of N 2 and N 2 O (i.e., 14 N 15 N and 15 NNO) formed more readily in comparison to their pure counterparts (i.e., 15 N 2 and 15 N 2 O). This indicates the amine and/or pyrazine groups of LLM-105, in addition to the nitro group, are involved in the decomposition pathways forming N 2 and N 2 O. In addition, our investigation led to the discovery of two previously unreported decomposition products (CHO and HNCO), which were confirmed through hydrogen labelling utilizing deuterium isotopes. These results provide detailed speciation trends of gaseous products during LLM-105 decomposition, offering new insights into reaction pathways. Experimental data reported here will support the development of a detailed chemical kinetics model for LLM-105, essential for the safe handling of high explosives.

Chemistry - Chemical explosives↗

DOE OSTI2023

High-dimensional data analytics in civil engineering: A review on matrix and tensor decomposition

Recent developments in sensing and monitoring techniques have led to the generation of high-dimensional data in the field of civil engineering. High-dimensional data analytics methods have thus been developed to interpret such complex data. Among the different high-dimensional data analytics techniques, matrix and tensor decomposition methods have acquired a notable interest in the civil engineering community over the past decade. Due to their unique ability to deal with highly redundant and correlated data, these methods are establishing themselves as promising and efficient tools to analyze high-dimensional data in the civil engineering arena. In this paper, high-dimensional data is referred to as a data set in which the number of features is comparable or larger than the number of observations. This review paper aims to summarize the applications of matrix and tensor decomposition methods in civil engineering over the last decade. The survey begins with a general overview of matrix and tensor decomposition followed by highlighting their significance in the field. Afterward, various applications of these high-dimensional data analytics methods in civil engineering are presented, while the advantages offered by these methods are discussed. Lastly, challenges and potential research avenues for employing matrix and tensor decomposition and future emerging trends for their novel use are highlighted.

42 ENGINEERING↗

DOE OSTI2021

Parallel physics-informed neural networks via domain decomposition

Here we develop a distributed framework for the physics-informed neural networks (PINNs) based on two recent extensions, namely conservative PINNs (cPINNs) and extended PINNs (XPINNs), which employ domain decomposition in space and in time-space, respectively. This domain decomposition endows cPINNs and XPINNs with several advantages over the vanilla PINNs, such as parallelization capacity, large representation capacity, efficient hyperparameter tuning, and is particularly effective for multi-scale and multi-physics problems. Here, we present a parallel algorithm for cPINNs and XPINNs constructed with a hybrid programming model described by MPI + X, where X ∈ {CPUs, GPUs}. The main advantage of cPINN and XPINN over the more classical data and model parallel approaches is the flexibility of optimizing all hyperparameters of each neural network separately in each subdomain. We compare the performance of distributed cPINNs and XPINNs for various forward problems, using both weak and strong scalings. Our results indicate that for space domain decomposition, cPINNs are more efficient in terms of communication cost but XPINNs provide greater flexibility as they can also handle time-domain decomposition for any differential equations, and can deal with any arbitrarily shaped complex subdomains. To this end, we also present an application of the parallel XPINN method for solving an inverse diffusion problem with variable conductivity on the United States map, using ten regions as subdomains.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Role of phosphorus impurities in decomposition of La 2 NiO 4 –La 0.5 Ce 0.5 O 2-δ oxygen electrode in a solid oxide electrolysis cell

This study explored the decomposition mechanism of a La 2 NiO 4 (LNO) phase in the La 2 NiO 4 –La 0.5 Ce 0.5 O 2-δ (LNO-LDC) oxygen electrode in a solid oxide electrolysis cell (SOEC) after testing at 800 °C. Scanning electron microscopy and scanning transmission electron microscopy examinations of the LNO-LDC oxygen electrode before and after testing were undertaken. Other than phosphorus contamination in the form of P-rich grains and P-rich deposits along all grain boundaries (GBs), LNO and LDC phases were intact without degradation in the as-fabricated electrode. However, mild to aggressive LNO phase decomposition triggered by the phosphorus poisoned GBs was observed after testing at 800 °C for 900 h. The evolution of the LNO phase decomposition was noted beginning with the exsolution of Ni into the surrounding LNO matrix and GBs, forming La-rich and Ni-rich phases correspondingly, in the LNO. Importantly, this study illustrates a detailed decomposition progress of the LNO phase at the atomic level under an SOEC operation condition, and sheds light on how to ameliorate the fabrication process of SOECs to enhance their performance and durability.

25 ENERGY STORAGE↗

DOE OSTI2025

Effects of co-adsorbed water on different bond cleavages involved in acetic acid decomposition on Pt (111)

Acetic acid decomposition on Pt (111) in the presence of co-adsorbed water is a good model system for oxygenate decomposition on Pt (111) in aqueous phase, with application in hydrogen production and biomass conversion. Here, in this study, we present a density functional theory (DFT) theory calculation of how co-adsorbed water affects different bond cleavages of acetic acid decomposition on Pt (111). The presence of co-adsorbed water generally enhances O$-$H bond cleavage while inhibiting OC$-$O and OC$-$OH bond cleavage. The influence of co-adsorbed water on C$-$H bond cleavage varies the most and depends on the nature of the transition state and how co-adsorbed water stabilizes the initial and final state. Although these trends are useful as general guidance, they are not sufficient to predict the effect on a complex reaction network such as acetic acid decomposition on Pt (111) which has several parallel reaction paths with similar energies. In the absence of co-adsorbed water, the two lowest energy pathways are decarboxylation (DCX) and decarbonylation (DCN) pathways through a common CH 2 COO intermediate, in which the DCX pathway (C$-$H bond cleavage of CH 2 COO) is more favorable than DCN pathway (OC$-$O bond cleavage of CH 2 COO). In the presence of co-adsorbed water, the energy difference between Csingle bondH bond cleavage and OC$-$O bond cleavage of CH 2 COO increases, suggesting an increase in the favorability of acetic acid decarboxylation (formation of carbon dioxide) over acetic acid decarbonylation (formation of carbon monoxide) on Pt (111).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

ICTAC Kinetics Committee recommendations for analysis of thermal decomposition kinetics

Here in this review article, the Kinetics Committee of the International Confederation for Thermal Analysis and Calorimetry (ICTAC) delivers a collection of recommendations for the kinetic analysis of thermal decomposition processes. These recommendations specifically focus on the thermal decomposition processes in inorganic, organic, and polymeric materials, as well as biomass and solid fuels. A general introduction to the kinetic analysis of thermal decompositions studied by thermal analysis techniques is followed by individual sections that discuss thermal decomposition of specific classes of materials and respective kinetic approaches. In each section, various kinetic analysis procedures are introduced with regard to specific features of the reactions and explained progressively from simple to complex reactions with examples of practical kinetic analysis. These recommendations are expected to provide a guidance for performing reliable and meaningful kinetic analysis in terms of practical usefulness and physico-chemical significance of the results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Plasmon-Driven Ammonia Decomposition on Pd(111): Hole Transfer’s Role in Changing Rate-Limiting Steps

Here, ammonia (NH 3 ) has the potential to be a hydrogen carrier because it can be transported and stored with ease, but only if it also can be decomposed easily when needed. Understanding how to control the frequently rate-limiting N–H bond breaking and N–N bond forming on catalytic surfaces may help design efficient means for NH 3 decomposition. Yuan et al. recently demonstrated photocatalytically selective N–H bond breaking in NH 3 on plasmon-driven aluminum–palladium (Al–Pd) antenna–reactor heterostructures. Using embedded correlated wavefunction (ECW) theory, we predict that the rate-determining step (RDS) for NH 3 decomposition on Pd(111) via thermocatalysis (dissociating the first N–H bond, *NH 3 → *NH 2 + *H, in the ground state, where * means adsorbed) differs from that via photocatalysis (dissociating the second N–H bond, *NH 2 → *NH + *H, in the excited state). This result is consistent with the measured catalytic efficiency and selectivity of NH 3 -deuterium (D 2 ) exchange reactions (an indirect way to measure N–H bond breaking) on Al–Pd heterodimers. We also determine the origin of the observed selectivity of thermocatalysis and photocatalysis on Pd(111) toward doubly deuterated (NHD 2 ) and monodeuterated (NH 2 D) products, respectively, and explore viability of the full NH 3 decomposition path, also via ECW theory. Additionally, we predict that the associative desorption of *N as N 2 from Pd(111) is extremely difficult in thermocatalysis at least at low surface coverages; metal-to-adsorbate hole transfer in photocatalysis stabilizes the transition state for the first N–H bond dissociation, shifting the RDS to the second N–H bond breaking. Furthermore, the redistribution of electrons around *N upon excitation reduces the electron density in the Pd–N bonds, which may lower the barrier for N 2 associative desorption in photocatalysis. Thus, light-induced, plasmon-mediated, excited-state hole transfer may provide an efficient mechanism to accelerate NH 3 decomposition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Role of Decomposition Product Ions in Hysteretic Behavior of Metal Halide Perovskite

Ion migration is one of the most debated mechanisms and credited with multiple observed phenomena and performance in metal halide perovskites (MHPs) semiconductor devices. However, to date, the migration of ions and their effects on MHPs are not still fully understood, largely due to a lack of direct observations of temporal ion migration. In this work, using direct observation of ion migration in-operando, we observe the hysteretic migration behavior of intrinsic ions (i.e., CH 3 NH 3 + and I – ) as well as reveal the migration behavior of CH 3 NH 3 + decomposition ions. We find that CH 3 NH 3 + decomposition products can be affected by light and accumulate at the interfaces under bias. These MHP decomposition products are tightly related to the device performance and stability. Complementary results of time-resolved Kelvin probe force microscopy (tr-KPFM) demonstrate a correlation between dynamics of these interfacial ions and charge carriers. Overall, we find that there are a number of mobile ions including CH 3 NH 3 + decomposition products in MHPs that need to be taken into account when measuring MHP device responses (e.g., charge dynamics) and should be considered in future optimization studies of MHP semiconductor devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Tensor decompositions for count data that leverage stochastic and deterministic optimization

There is growing interest to extend low-rank matrix decompositions to multi-way arrays, or tensors. One fundamental low-rank tensor decomposition is the canonical polyadic decomposition (CPD). The challenge of fitting a low-rank, nonnegative CPD model to Poisson-distributed count data is of particular interest. Several popular algorithms use local search methods to approximate the maximum likelihood estimator (MLE) of the Poisson CPD model. Here, this work presents two new algorithms that extend state-of-the-art local methods for Poisson CPD. Hybrid GCP-CPAPR combines Generalized Canonical Decomposition (GCP) with stochastic optimization and CP Alternating Poisson Regression (CPAPR), a deterministic algorithm, to increase the probability of converging to the MLE over either method used alone. Restarted CPAPR with SVDrop uses a heuristic based on the singular values of the CPD model unfoldings to identify convergence toward optimizers that are not the MLE and restarts within the feasible domain of the optimization problem, thus reducing overall computational cost when using a multi-start strategy. We provide empirical evidence that indicates our approaches outperform existing methods with respect to converging to the Poisson CPD MLE.

CPAPR↗

DOE OSTI2026

Decomposition of methane diluted with inert gas in an RF discharge cell

Decomposition of methane using non-thermal plasmas is an attractive route for producing hydrogen-rich gases and valuable carbon nanomaterials. Understanding how plasma discharge modes influence methane decomposition in optimizing plasma-assisted chemical conversion remains unexplored. This study explores the coupling between the discharge structure and product selectivity in RF capacitively coupled discharges operating in methane/inert gas mixtures in the pressure range of 2–3 torr. Under our experimental conditions the discharge exhibits mode transitions from uniform to striated in Ar and Kr and from diffuse to contracted in Ar and Kr with <5% CH 4 . The discharges in He and Ne remained uniform under our operating conditions, and their mixtures with CH 4 remained diffuse. A 0-d model for Ar/CH 4 discharge established a threshold for contraction while also asserting the importance of Ar$^{*}_{\textrm{m}}$ in the dissociation and ionization processes. The highest degree of methane decomposition, >99.7% with the main products of acetylene and graphitized solid carbon was achieved in the contracted discharge mode for both Kr or Ar with ⩽5% CH 4 . We demonstrate that contraction can play a crucial role in the effective decomposition of methane with value-added products and that both the electronic and thermal properties of plasma gas are responsible for this effect.

RF plasma↗

DOE OSTI2025

Multi-omics of a model bacterial consortium deciphers details of chitin decomposition in soil

Soil microorganisms interact to carry out decomposition of complex organic carbon and nitrogen compounds, such as chitin, but the high diversity and complexity of the soil microbiome and habitat have posed a challenge to elucidating such interactions. Here, we sought to address this challenge by analysis of a model soil consortium (MSC-2) consisting of eight soil bacterial species. Our aim was to elucidate the specific roles of the member species during chitin metabolism. Samples were collected from MSC-2 incubated in chitin-enriched soil over 3 months. Multi-omics was used to understand how the community composition, transcripts, proteins, and chitin decomposition shifted over time. The data clearly and consistently revealed a temporal shift during chitin decomposition with defined contributions by individual species. A Streptomyces genus member (sp001905665) was a key player in early steps of chitin decomposition, with other MSC-2 members being central in carrying out later steps. These results illustrate how multi-omics applied to a defined consortium untangles the interactions between soil microorganisms.

chitin↗

DOE OSTI2020

Generalized Canonical Polyadic Tensor Decomposition

Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized canonical polyadic (GCP) low-rank tensor decomposition that allows other loss functions besides squared error. For instance, we can use logistic loss or Kullback--Leibler divergence, enabling tensor decomposition for binary or count data. We present a variety of statistically motivated loss functions for various scenarios. We provide a generalized framework for computing gradients and handling missing data that enables the use of standard optimization methods for fitting the model. Furthermore, we demonstrate the flexibility of the GCP decomposition on several real-world examples including interactions in a social network, neural activity in a mouse, and monthly rainfall measurements in India.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Parallel Randomized Tucker Decomposition Algorithms

The Tucker tensor decomposition is a natural extension of the singular value decomposition (SVD) to multiway data. Here, we propose to accelerate Tucker tensor decomposition algorithms by using randomization and parallelization. We present two algorithms that scale to large data and many processors, significantly reduce both computation and communication cost compared to previous deterministic and randomized approaches, and obtain nearly the same approximation errors. The key idea in our algorithms is to perform randomized sketches with Kronecker-structured random matrices, which reduces computation compared to unstructured matrices and can be implemented using a fundamental tensor computational kernel. We provide probabilistic error analysis of our algorithms and implement a new parallel algorithm for the structured randomized sketch. Our experimental results demonstrate that our combination of randomization and parallelization achieves accurate Tucker decompositions much faster than alternative approaches. We observe up to a 16X speedup over the fastest deterministic parallel implementation on 3D simulation data.

Tucker decompositions↗

DOE OSTI2023

Tensor Decompositions for Count Data that Leverage Stochastic and Deterministic Optimization

There is growing interest to extend low-rank matrix decompositions to multi-way arrays, or tensors. One fundamental low-rank tensor decomposition is the canonical polyadic decomposition (CPD). The challenge of fitting a low-rank, nonnegative CPD model to Poisson-distributed count data is of particular interest. Several popular algorithms use local search methods to approximate the global maximum likelihood estimator from local minima. Simultaneously, a recent trend in theoretical computer science and numerical linear algebra leverages randomization to solve very large, hard problems. The typical approach is to use randomization for a fast approximation and determinism for refinement to yield effective algorithms with theoretical guarantees. Two popular algorithms for Poisson CPD reflect that emergent dichotomy: CP Alternating Poisson Regression is a deterministic algorithm and Generalized Canonical Polyadic decomposition makes use of stochastic algorithms in several variants. This work extends recent work to develop two new methods that leverage randomized and deterministic algorithms for improved accuracy and performance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Effect of elevation, season and accelerated snowmelt on biogeochemical processes during isolated conifer needle litter decomposition

Increased drought and temperatures associated with climate change have implications for ecosystem stress with risk for enhanced carbon release in sensitive biomes. Litter decomposition is a key component of biogeochemical cycling in terrestrial ecosystems, but questions remain regarding the local response of decomposition processes to climate change. This is particularly complex in mountain ecosystems where the variable nature of the slope, aspect, soil type, and snowmelt dynamics play a role. Hence, the goal of this study was to determine the role of elevation, soil type, seasonal shifts in soil moisture, and snowmelt timing on litter decomposition processes. Experimental plots containing replicate deployments of harvested lodgepole and spruce needle litter alongside needle-free controls were established in open meadows at three elevations ranging from 2,800–3,500 m in Crested Butte, Colorado. Soil biogeochemistry variables including gas flux, porewater chemistry, and microbial ecology were monitored over three climatically variable years that shifted from high monsoon rains to drought. Results indicated that elevation and soil type influenced baseline soil biogeochemical indicators; however, needle mass loss and chemical composition were consistent across the 700 m elevation gradient. Rates of gas flux were analogously consistent across a 300 m elevation gradient. The additional variable of early snowmelt by 2–3 weeks had little impact on needle chemistry, microbial composition and gas flux; however, it did result in increased dissolved organic carbon in lodgepole porewater collections suggesting a potential for aqueous export. In contrast to elevation, needle presence and seasonal variability of soil moisture and temperature both played significant roles in soil carbon fluxes. Furthermore, during a pronounced period of lower moisture and higher temperatures, bacterial community diversity increased across elevation with new members supplanting more dominant taxa. Microbial ecological resilience was demonstrated with a return to pre-drought structure and abundance after snowmelt rewetting the following year. These results show similar decomposition processes across a 700 m elevation gradient and reveal the sensitivity but resilience of soil microbial ecology to low moisture conditions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

A comparison of lodgepole and spruce needle chemistry impacts on terrestrial biogeochemical processes during isolated decomposition

This study investigates the isolated decomposition of spruce and lodgepole conifer needles to enhance our understanding of how needle litter impacts near-surface terrestrial biogeochemical processes. Harvested needles were exported to a subalpine meadow to enable a discrete analysis of the decomposition processes over 2 years. Initial chemistry revealed the lodgepole needles to be less recalcitrant with a lower carbon to nitrogen (C:N) ratio. Total C and N fundamentally shifted within needle species over time with decreased C:N ratios for spruce and increased ratios for lodgepole. Differences in chemistry correlated with CO 2 production and soil microbial communities. The most pronounced trends were associated with lodgepole needles in comparison to the spruce and needle-free controls. Increased organic carbon and nitrogen concentrations associated with needle presence in soil extractions further corroborate the results with clear biogeochemical signatures in association with needle chemistry. Interestingly, no clear differentiation was observed as a function of bark beetle impacted spruce needles vs those derived from healthy spruce trees despite initial differences in needle chemistry. These results reveal that the inherent chemistry associated with tree species has a greater impact on soil biogeochemical signatures during isolated needle decomposition. By extension, biogeochemical shifts associated with bark beetle infestation are likely driven more by changes such as the cessation of rhizospheric processes than by needle litter decomposition.

Bark Beetle Disturbance↗

DOE OSTI2025

Water‐mediated Decomposition Pathways of ε‐Hexanitrohexaazaisowurtzitane

The dominant initial decomposition mechanism of 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (HNIW) is understood from the literature to be the N─NO 2 bond scission unimolecular mechanism. Here we have found a water-mediated HONO release initial decomposition mechanism energetically competitive with the N─NO 2 bond scission mechanism modeled using ab initio nudged elastic band simulations. The activation energy of the water-mediated HONO mechanism was calculated to be 138.9 kJ/mol with the Perdew, Burke, and Ernzerhof (PBE) functional (177.8 with PBE0), while the double NO 2 release mechanism is 171.9 kJ/mol (203.3 with PBE0). Branching secondary, tertiary, and quaternary decomposition steps were also discovered including oxidation of HONO or H 2 O causing ring-opening that leads to C─N bond breaking and N 2 O or NO release. The reaction rate of HONO oxidation is much faster than HONO release, making the HONO concentration low. The Helmholtz free energy barrier of the double NO 2 release mechanism is lower than the water-mediated HONO release above 438 K (165°C) due to the vibrational contribution to the free energy. This helps explain why the literature reports more NO 2 release at higher temperatures. The present study reveals a physical mechanism for how water can catalyze the decomposition of HNIW at low enough temperatures, and reveals secondary, tertiary, and quaternary reaction mechanisms leading to NO x release. In conclusion, as water is realistically always present, the reported barriers are important for building kinetic models needed to ensure the safety and reliability of HNIW.

Steele, Brad A. [Lawrence Livermore National Labor↗

DOE OSTI2021

The Stellar decomposition: A compact representation for simplicial complexes and beyond

Here, we introduce the Stellar decomposition, a model for efficient topological data structures over a broad range of simplicial and cell complexes. A Stellar decomposition of a complex is a collection of regions indexing the complex’s vertices and cells such that each region has sufficient information to locally reconstruct the star of its vertices, i.e., the cells incident in the region’s vertices. Stellar decompositions are general in that they can compactly represent and efficiently traverse arbitrary complexes with a manifold or non-manifold domain. They are scalable to complexes in high dimension and of large size, and they enable users to easily construct tailored application-dependent data structures using a fraction of the memory required by a corresponding global topological data structure on the complex. As a concrete realization of this model for spatially embedded complexes, we introduce the Stellar tree, which combines a nested spatial tree with a simple tuning parameter to control the number of vertices in a region. Stellar trees exploit the complex’s spatial locality by reordering vertex and cell indices according to the spatial decomposition and by compressing sequential ranges of indices. Stellar trees are competitive with state-of-the-art topological data structures for manifold simplicial complexes and offer significant improvements for cell complexes and non-manifold simplicial complexes. We conclude with a high-level description of several mesh processing and analysis applications that utilize Stellar trees to process large datasets.

97 MATHEMATICS AND COMPUTING↗