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DOE OSTI2024

New dynamic diamond anvil cell for time-resolved radial x-ray diffraction

The dynamic diamond anvil cell (dDAC) is a recently developed experimental platform that has shown promise for studying the behavior of materials at strain rates ranging from intermediate to quasi-static and shock compression regimes. Combining dDAC with time-resolved x-ray diffraction (XRD) in the radial geometry (i.e., with incident x-rays perpendicular to the axis of compression) enables the study of material properties such as strength, texture evolution, and deformation mechanisms. This work describes a radial XRD dDAC setup at beamline P02.2 (Extreme Conditions Beamline) at DESY’s PETRA III synchrotron. Time-resolved radial XRD data are collected for titanium, zirconium, and zircon samples, demonstrating the ability to study the strength and texture of materials at compression rates above 300 GPa/s. In addition, the simultaneous optical imaging of the DAC sample chamber is demonstrated. The ability to conduct simultaneous radial XRD and optical imaging provides the opportunity to characterize plastic strain and deviatoric strain rates in the DAC at intermediate rates, exploring the strength and deformation mechanisms of materials in this regime.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

New dynamic diamond anvil cell for time-resolved radial x-ray diffraction

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Maximizing efficiency of dataset compression for machine learning potentials with information theory

Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets improve model accuracy and generalization but are computationally expensive to produce and train on, while smaller datasets risk discarding rare but important atomic environments and compromising MLIP accuracy/reliability. Here, we develop an information-theoretical framework to quantify the efficiency of dataset compression methods and propose an algorithm that maximizes this efficiency. By framing atomistic dataset compression as an instance of the minimum set cover (MSC) problem over atom-centered environments, our method identifies the smallest subset of structures that contains as much information as possible from the original dataset while pruning redundant information. The approach is extensively demonstrated on the GAP-20 and TM23 datasets and validated on 64 varied datasets from the ColabFit repository. Across all cases, MSC consistently retains outliers, preserves dataset diversity, and reproduces the long-tail distributions of forces even at high compression rates, outperforming other subsampling methods. Furthermore, MLIPs trained on MSC-compressed datasets exhibit reduced error for out-of-distribution data even in low-data regimes. We explain these results using an outlier analysis and show that such quantitative conclusions could not be achieved with conventional dimensionality reduction methods. The algorithm is implemented in the open-source QUESTS package and can be used for several tasks in atomistic modeling, from data subsampling, outlier detection, and training improved MLIPs at a lower cost.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Effect of Part Size, Displacement Rate, and Aging on Compressive Properties of Elastomeric Parts of Different Unit Cell Topologies Formed by Vat Photopolymerization Additive Manufacturing

Due to its ability to achieve geometric complexity at high resolution and low length scales, additive manufacturing (AM) has increasingly been used for fabricating cellular structures (e.g., foams and lattices) for a variety of applications. Specifically, elastomeric cellular structures offer tunability of compliance as well as energy absorption and dissipation characteristics. However, there are limited data available on compression properties for printed elastomeric cellular structures of different designs and testing parameters. In this work, the authors evaluate how unit cell topology, part size, the rate of compression, and aging affect the compressive response of polyurethane-based simple cubic, body-centered, and gyroid structures formed by vat photopolymerization AM. Finite element simulations incorporating hyperelastic and viscoelastic models were used to describe the data, and the simulated results compared well with the experimental data. Of the designs tested, only the parts with the body-centered unit cell exhibited differences in stress–strain responses at different part sizes. Of the compression rates tested, the highest displacement rate (1000 mm/min) often caused stiffer compressive behavior, indicating deviation from the quasi-static assumption and approaching the intermediate rate response. The cellular structures did not change in compression properties across five weeks of aging time, which is desirable for cushioning applications. This work advances knowledge on the structure–property relationships of printed elastomeric cellular materials, which will enable more predictable compressive properties that can be traced to specific unit cell designs.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Miniature Beryllium Split-Hopkinson Pressure Bars for Extending the Range of Achievable Strain-Rates

Conventional Split Hopkinson Pressure Bars (SHPB) or “Kolsky” bars are often used for determining the high-rate compressive yield and failure strength of materials. However, for experiments generating very high strain-rates (>10 3 /s) miniaturization of the setup is often required for minimizing the effects of elastic wave dispersion in order to enable the inference of decreasingly short loading events from the data. Miniature aluminum and steel bars are often sufficient for meeting these requirements. However, for high enough strain-rates, miniaturization of steel or aluminum Kolsky bars may require prohibitively small diameter bars and test specimens that could become inappropriate for inferring representative properties of materials with large grain size relative to the test specimen size. The use of a beryllium Kolsky bar setup is expected to enable high rates to be accessible with larger diameter bars/specimen combinations due to the inherent physical properties of beryllium, which are expected to minimize the effects of elastic wave dispersion. For this reason, a series of beryllium Kolsky bars have been developed, and, in this paper, the dispersion characteristics of these bars are measured and compare the data with those of similarly sized 7075-T6 aluminum and C350 maraging steel. The results, which agree well with the theory, show no appreciable frequency dependence of the elastic wavespeed in the data from the beryllium bars, demonstrating its advantage over aluminum and steel in application to Kolsky bars.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Ensemble Kalman filter for data assimilation coupled with low-resolution computations techniques applied in fluid dynamics

This paper presents an innovative Reduced-order model (ROM) for merging experimental and simulation data using data assimilation (DA) to estimate the "True" state of a fluid dynamics system, leading to more accurate predictions. Our methodology introduces a novel approach by implementing the ensemble Kalman filter (EnKF) within a reduced-dimensional framework, grounded in a robust theoretical foundation and applied to fluid dynamics. To address the substantial computational demands of DA, the proposed ROM employs low-resolution (LR) techniques to drastically reduce computational costs. This innovative approach involves downsampling datasets for DA computations, followed by an advanced reconstruction technique based on low-cost singular value decomposition (lcSVD). The lcSVD method, a key innovation in this paper, has never been applied to DA before and offers a highly efficient way to enhance resolution with minimal computational resources. Our results demonstrate significant reductions in both computation time and RAM usage through these LR techniques without compromising the accuracy of the estimations. For instance, in a turbulent test case, for a data compression rate of 15.9, the LR approach can achieve a speed-up of 13.7 and a RAM compression of 90.9% while maintaining a low relative root mean square error (RRMSE) of 2.6%, compared to 0.8% in the high-resolution (HR) reference. Furthermore, we highlight the effectiveness of the EnKF in estimating and predicting the state of fluid flow systems based on limited observations and given low-fidelity numerical data. This paper highlights the potential of the proposed DA method in fluid dynamics applications, particularly for improving computational efficiency in CFD and related fields. Its ability to balance accuracy with low computational and memory costs makes it especially suitable for large-scale and real-time applications, such as environmental monitoring or engineering design. This method will be incorporated into ModelFLOWs-app.

Data Assimilation↗

DOE OSTI2020

Deformation behavior of annealed Cu64Zr36 metallic glass via molecular dynamics simulations

In the present work, we investigate the isothermal annealing process of Cu 64 Zr 36 metallic glass (MG) by means of molecular dynamics (MD) simulations, and characterize the compression properties under different temperatures and compressive rates. The pair distribution functions, the bond-orientational order, and the cluster-type index method are modeled and analyzed to characterize changes in the structure. Results of the modeling and analysis reveal that the MgZn 2 -type Laves, MgCu 2 -type Laves and five-fold local symmetry structures can be formed during the isothermal holding at 950 K. Study on the compression properties shows that at a fixed strain rate, the yield strength and yield drop all increase with decreasing temperature. At a fixed temperature, however, the increase of the strain rate leads to a noticeable increase in the yield strength at 600 K, but has little effect on yield strength at 10 K. Moreover, the modeling and analysis of the structure at a temperature of 10 K and strain-rate of 5 × 10 7 s -1 demonstrate that the order-disorder transformation initiates the shear band.

36 MATERIALS SCIENCE↗

DOE OSTI2020

CONTROLLED END GAS AUTO IGNITION WITH EXHAUST GAS RECIRCULATION ON A STOICHIOMETRIC, SPARK IGNITED, NATURAL GAS ENGINE

The goal of this study is to address fundamental limitations to achieving diesel-like efficiencies in heavy duty on-highway natural gas (NG) engines. Engine knock and misfire are barriers to pathways leading to higher efficiency engines. This study explores enabling technologies for development of high efficiency stoichiometric, spark ignited, natural gas engines. These include design strategies for fast and stable combustion and higher dilution tolerance. Additionally, advanced control methodologies are implemented to maintain stable operation between knock and misfire limits. To implement controlled end- gas autoignition (C-EGAI) strategies a Combustion Intensity Metric (CIM) is used for ignition control with the use of a Woodward large engine control module (LECM). Tests were conducted using a single cylinder, variable compression ratio, cooperative fuel research (CFR) engine with baseline conditions of 900 RPM, engine load of 800 kPa indicated mean effective pressure (IMEP), and stoichiometric air/fuel ratio. Exhaust gas recirculation (EGR) tests were performed using a custom EGR system that simulates a high pressure EGR loop and can provide a range of EGR rates from 0 to 40%. The experimental measurements included the variance of EGR rate, compression ratio, engine speed, IMEP, and CIM. These five variables were optimized through a Modified Box- Benken design Surface Response Method (RSM), with brake efficiency as the merit function. A positive linear correlation between CIM and f-EGAI was identified. Consequently, CIM was used as the feedback control parameter for C-EGAI. As such, implementation of C-EGAI effectively allowed for the utilization of high EGR rates and CRs, controlling combustion between a narrower gap between knock and lean limits. The change from fixed to parametric ignition timing with CIM targeted select values of f-EGAI with an average coefficient of variance (COV) of peak pressure of 5.4. The RSM efficiency optimization concluded with operational conditions of 1080 RPM, 1150 kPa IMEP, 10.55:1 compression ratio, and 17.8% EGR rate with a brake efficiency of 21.3%. At this optimized point of peak performance, a f-EGAI for C-EGAI was observed at 34.1% heat release due to auto ignition, a knock onset crank angle value of 10.3° aTDC and ignition timing of - 24.7° aTDC. This work has demonstrated that combustion at a fixed f-EGAI can be maintained through advanced ignition control of CIM without experiencing heavy knocking events.

Cooperative Fuel Research Engine, Auto Ignition, E↗

DOE OSTI2023

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous highperformance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3× speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76 - 79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Design of a double-sided laser heating compatible piezoelectrically driven dynamic diamond anvil cell

Piezoelectrically driven dynamic diamond anvil cells are generally limited to use at room temperature due to spatial conflicts with the optical components necessary for double-sided laser heating. Here, this work describes the design of a double-sided laser-heating-compatible, piezoelectrically driven dynamic diamond anvil cell (LH-dDAC) that expands the range of experimentally accessible compression rate/temperature space. By developing a spatially in-parallel design, in which the diamond anvils and the piezo actuators lie alongside each other, the total length of the apparatus is reduced to 66.5 mm. The new LH-dDAC is capable of probing both overpressure and compression-rate effects on the kinetics of phase transitions. We illustrate the capability of the new LH-dDAC using the following cases: (1) a simultaneous pressure and temperature jump from 19.5 GPa and room temperature to 40 GPa and ≈1550 K and (2) a controlled compression ramp at ≈1850 K and ≈100 GPa/s. By pairing rapid compression with laser heating, the new LH-dDAC opens the door to studies of diffusionally controlled transformations at high pressures and temperatures.

Copley, J. A. [Princeton Univ., NJ (United States)↗

DOE OSTI2022

Effect of Moisture and Feedstock Variability on the Rheological Behavior of Corn Stover Particles

Continuous feeding, processing, and handling of biomass powders is pivotal to the economic viability of integrated biorefineries. However, current challenges associated with the operational reliability of bulk solids handling and transport greatly impact the process economics and ultimately the widespread commercialization of integrated biorefineries. In this work, we examine the effect of moisture and feedstock variability on the flow behavior of corn stover biomass particles. The total flow energy, compressibility, shear properties, and wall friction angles were measured for corn stover samples A and B containing 0%, 15%, 25%, 50%, and 75% (mass fraction) moisture contents using a FT4 powder rheometer. In general, the flowability of both A and B was reduced when moisture was present as indicated by the stability and variable flow rate, compressibility, and shear tests. The 15% moisture sample had the highest flow energy, revealing the interplay between the increased surface tension and looser packing both of which were caused by liquid bridging. The 75% moisture sample had the highest compressibility and the lowest flowability factor (ff c ). The trend in wall friction angle was found to be dependent upon the surface hydrophobicity of the wall material. The wall friction angle on the hydrophilic, stainless-steel surface increased with moisture and therefore poses additional handling challenges. On the other hand, the wall friction angle on the hydrophobic polymer surfaces were the lowest for samples with intermediate moisture contents. Sample B had greater bulk density, smaller compressibility, and greater flowability than the sample A , as suggested by the compressibility and shear tests. The wall friction angle of sample B was higher than or equal to that of sample A depending on both the type and the surface roughness of the wall material.

09 BIOMASS FUELS↗

DOE OSTI2025

Effects of Critical Compression Ratio on Rating Gasoline Knock Propensity

It is common practice in the automotive industry to explore the knock limits of fuels on an engine by a comparison of the knock limited spark advance (KLSA) at threshold knock intensity. However, the knock propensity of gasolines can be rated by changing one of three metrics on a variable compression ratio Cooperative Fuels Research (CFR) octane rating engine while holding the other two variables constant: knock intensity, spark timing, and critical compression ratio. The operational differences between the standard research octane number (RON) rating and modern engine operation have been explored in three parts. The first part focused on the effects of lambda and knock characterization. The second part studied the effects of spark timing. This third part explores the knock ratings of several gasolines by comparing the critical compression ratios at constant combustion phasing and knock intensity. The threshold knock intensity was based on the standard octane rating D1 pickup or by maximum amplitude of pressure oscillations (MAPO) measured by a piezoelectric cylinder pressure transducer. Several Fuels for Advanced Combustion Engines (FACE) gasolines, primary reference fuels (PRFs), and toluene standardization fuels (TSFs) were tested on a CFR octane rating engine with advanced data acquisition equipment and a piezoelectric cylinder pressure transducer. These tests deviated from the ASTM D2699 standard octane rating procedure. For each test fuel, the CFR engine was operated at stoichiometry at a constant combustion phasing (CA50) and the compression ratio was modified until a threshold knock intensity was realized. It was found that the chemical composition of the fuels affected the relationship of critical compression ratios between the D1 knockmeter and piezoelectric pressure transducer knock intensity thresholds, as well as the measured combustion maximum pressure rise rate and spark timing setting for constant CA50. For highly aromatic fuels tested at a constant MAPO knock intensity threshold, it was found that the maximum pressure rise rate was two to three times higher than that of highly paraffinic fuels with similar RON and the spark advance was several crank angle degrees less for constant combustion phasing.

Kolodziej, Christopher P↗

DOE OSTI2023

Simultaneous measurements of volume, pressure, optical images, and crystal structure with a dynamic diamond anvil cell: A real-time event monitoring system

The dynamic diamond anvil cell (dDAC) technique has attracted great interest because it possibly provides a bridge between static and dynamic compression studies with fast, repeatable, and controllable compression rates. The dDAC can be a particularly useful tool to study the pathways and kinetics of phase transitions under dynamic pressurization if simultaneous measurements of physical quantities are possible as a function of time. We report the development of a real-time event monitoring (RTEM) system with dDAC, which can simultaneously record the volume, pressure, optical image, and structure of materials during dynamic compression runs. In particular, the volume measurement using both Fabry–Pérot interferogram and optical images facilitates the construction of an equation of state (EoS) using the dDAC in a home-laboratory. We also developed an in-line ruby pressure measurement (IRPM) system to be deployed at a synchrotron x-ray facility. This system provides simultaneous measurements of pressure and x-ray diffraction in low and narrow pressure ranges. The EoSs of ice VI obtained from the RTEM and the x-ray diffraction data with the IRPM are consistent with each other. The complementarity of both RTEM and IRPM systems will provide a great opportunity to scrutinize the detailed kinetic pathways of phase transitions using dDAC.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection

One of the fundamental assumptions of compressive sensing (CS) is that a signal can be reconstructed from a small number of samples by solving an optimization problem with the appropriate regularization term. Two standard regularization terms are the L1 norm and the total variation (TV) norm. We present a comparison of CS reconstruction results based on these two approaches in the context of chemical detection, and we demonstrate that optimization based on the L1 norm outperforms optimization based on the TV norm. Our comparison is driven by CS sampling, reconstruction, and chemical detection in two real-world datasets: the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset and the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Both datasets contain the release of a chemical simulant such as glacial acetic acid, triethyl phosphate, and sulfur hexafluoride. For chemical detection we use the adaptive coherence estimator (ACE) and bulk coherence, and we propose algorithmic ACE thresholds to define the presence or absence of a chemical of interest in both un-compressed data cubes and reconstructed data cubes. The un-compressed data cubes provide an approximate ground truth. We demonstrate that optimization based on either the L1 norm or TV norm results in successful chemical detection at a compression rate of 90%, but we show that L1 optimization is preferable. We present quantitative comparisons of chemical detection on reconstructions from the two methods, with an emphasis on the number of pixels with an ACE value above the threshold.

Hyperspectral imagery, L1-norm, total variation, A↗

DOE OSTI2020

A data-driven approach to sampling matrix selection for compressive sensing

Sampling is a fundamental aspect of any implementation of compressive sensing. Typically, the choice of sampling method is guided by the reconstruction basis. However, this approach can be problematic with respect to certain hardware constraints and is not responsive to domain-specific context. We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression. We focus on the Walsh-Hadamard sampling basis for its relevance to hardware constraints, but our approach applies to any sampling basis of interest. We illustrate the effectiveness of our method on the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset, the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset, and a Colorado State University Swiss Ranger depth image dataset. The spectral datasets consist of simulant experiments, including releases of chemicals such as GAA and SF6. We combine our sampling and reconstruction with the adaptive coherence estimator (ACE) and bulk coherence for chemical detection and we incorporate an algorithmic threshold for ACE values to determine the presence or absence of a chemical. We compare results across sampling methods in this context. We have successful chemical detection at a compression rate of 90%. For all three datasets, we compare our sampling approach to standard orderings of sampling basis such as random, sequency, and an analog of sequency that we term `frequency.' In one instance, the peak signal to noise ratio was improved by over 30% across a test set of depth images.

compressive sensing, hyperspectral imaging, Walsh-↗

DOE OSTI2020

Phase transformation in boron under shock compression

Using first-principles molecular dynamics, we calculated the equation of state and shock Hugoniot of various boron phases. Here, we find a large mismatch between Hugoniots based on existing knowledge of the equilibrium phase diagram and those measured by shock experiments, which could be reconciled if the α-B 12 /β phases are significantly over-pressurized in boron under shock compression. Our results also indicate that there exist an anomaly and negative Clapeyron slope along the melting curve of boron at 100 GPa and 1500–3000 K. These results enable an in-depth understanding of matter under shock compression, in particular the significance of compression-rate dependence of phase transitions and kinetic effects in experimental measurements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Computational investigation of water glasses using machine-learning potentials

The molecular origins of water’s anomalous properties have long been a subject of scientific inquiry. The liquid–liquid phase transition hypothesis, which posits the existence of distinct low-density and high-density liquid states separated by a first-order phase transition terminating at a critical point, has gained increasing experimental and computational support and offers a thermodynamically consistent framework for many of water’s anomalies. However, experimental challenges in avoiding crystallization near the postulated liquid–liquid critical point have focused attention to water’s canonical glassy states: low-density and high-density amorphous ice. Here, we use two Deep Potential machine-learning models, trained on the Strongly Constrained and Appropriately Normed density functional and the highly accurate Many-Body Polarizable potential, to conduct an investigation of water’s glassy phenomenology based on quantum mechanical calculations. Despite not being explicitly trained on amorphous ices, both models accurately capture the structure and transformation of the water glasses, including their interconversion along different thermodynamic paths. Isobaric quenching of liquid water at various pressures generates a continuum of intermediate amorphous ices and density fluctuations increase near the liquid–liquid critical pressure. The glass transition temperatures of the amorphous ices produced at different pressures exhibit two distinct branches, corresponding to low-density and high-density amorphous ice behaviors, consistent with experiment and the liquid–liquid transition hypothesis. Extrapolating transformation pressures from isothermal compressions to experimental compression rates brings our simulations into excellent agreement with data. Our findings demonstrate that machine-learning potentials trained on equilibrium phases can effectively model nonequilibrium glassy behavior and pave the way for studying long-timescale, out-of-equilibrium processes with quantum mechanical accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Anisotropic strength behavior of single-crystal TATB

High-rate strength behavior plays an important role in the shock initiation of high explosives, with plastic deformation serving to localize heat into hot spots and as a mechanochemical means to enhance reactivity. Recent simulations predict that detonation-like shocks produce highly reactive nanoscale shear bands in the layered crystalline explosive TATB (1,3,5-triamino-2,4,6-trinitrobenzene), but the thresholds leading to this response are poorly understood. We utilize molecular dynamics to simulate the high-rate compressive stress–strain response of TATB, with a focus on understanding flow behavior. Here, the dependence of strength on pressure and loading axis (crystal orientation) is explored. The deformation mechanisms fall broadly into two categories, with compression along crystal layers activating a buckling/twinning mode and compression normal to the layers producing nanoscale shear bands. Despite the complexity of the underlying mechanisms, the crystal exhibits relatively straightforward stress–strain curves. Most of the crystal orientations studied show rapid strain softening following the onset of yielding, which settles to a steady flow state. Trajectories are analyzed using five metrics for local states and structural order, but most of these metrics yield similar distributions for these deformation mechanisms. On the other hand, a recently proposed measure of intramolecular strain energy is found to most cleanly distinguish between these mechanisms, while also providing a plausible connection with mechanochemically accelerated decomposition kinetics. Localization of intramolecular strain energy is found to depend strongly on crystal orientation and pressure.

36 MATERIALS SCIENCE↗