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This work analyzes rotational–vibrational spectra of scattering resonances in four ozone isotopomers in relation to the symmetry-driven isotope effect.
Insensitive high explosives based on TATB (1,3,5-triamino-2,4,6-trinitrobenzene) are needed in applications when safety is of paramount importance, but the basic material properties that give rise to its insensitivity are not fully understood. Molecular dynamics modeling using empirical force fields (FFs) has been the main route to characterize many complicated dynamical properties of TATB single crystal, but these FFs have not been comprehensively tested at extreme conditions typical of detonation. We collect a benchmark data set of (quasi)static TATB physical properties as determined by experiments and electronic structure calculations and apply this data set to validate four existing TATB FFs along with a new TATB FF that we develop here and denote as the CEA-LLNL-Missouri (CLM) FF. Benchmark data include vibrational spectra, the TATB crystal temperature–pressure–volume equation of state and lattice parameters, properties of TATB crystal polymorphs and transitions to the gaseous and liquid states, dimer energy landscapes, the pressure-dependent elastic tensor, and the energy landscape for inelastic deformation via sliding of TATB crystal layers. As a general assessment, we find that the two existing nonreactive FFs are more accurate in describing TATB’s physical properties compared to the two variants of the ReaxFF reactive FF considered. The new CLM FF is found to consistently yield similar or better agreement with experiments and electronic structure theory than any of the existing FF models, and it presents a distinct improvement in accurately modeling TATB elasticity and equation of state. So this work is expected to help improve the accuracy of FF-based modeling of complicated dynamic responses that ultimately govern the safety and performance characteristics of this material.
The vibrational dynamics of molecules and solids play a critical role in defining material properties, particularly their thermal behaviors. However, theoretical calculations of these dynamics are often computationally intensive, while experimental approaches can be technically complex and resource-demanding. Recent advancements in data-driven artificial intelligence (AI) methodologies have substantially enhanced the efficiency of these studies. This review explores the latest progress in AI-driven methods for investigating atomic vibrations, emphasizing their role in accelerating computations and enabling rapid predictions of lattice dynamics, phonon behaviors, molecular dynamics, and vibrational spectra. Key developments are discussed, including advancements in databases, structural representations, machine-learning interatomic potentials, graph neural networks, and other emerging approaches. Compared to traditional techniques, AI methods exhibit transformative potential, dramatically improving the efficiency and scope of research in materials science. The review concludes by highlighting the promising future of AI-driven innovations in the study of atomic vibrations.
VV10 is a powerful nonlocal density functional for long-range correlation that is used to include dispersion effects in many modern density functionals, such as the meta-generalized gradient approximation (mGGA), B97M-V, the hybrid GGA, ωB97X-V, and the hybrid mGGA, ωB97M-V. While energies and analytical gradients for VV10 are already widely available, this study reports the first derivation and efficient implementation of the analytical second derivatives of the VV10 energy. The additional compute cost of the VV10 contributions to analytical frequencies is shown to be small in all but the smallest basis sets for recommended grid sizes. Here, this study also reports the assessment of VV10-containing functionals for predicting harmonic frequencies using the analytical second derivative code. The contribution of VV10 to simulating harmonic frequencies is shown to be small for small molecules but important for systems where weak interactions are important, such as water clusters. In the latter cases, B97M-V, ωB97M-V, and ωB97X-V perform very well. The convergence of frequencies with respect to the grid size and atomic orbital basis set size is studied, and recommendations are reported. Finally, scaling factors to allow comparison of scaled harmonic frequencies with experimental fundamental frequencies and to predict zero-point vibrational energy are presented for some recently developed functionals (including r2SCAN, B97M-V, ωB97X-V, M06-SX, and ωB97M-V).
To date, the primary sensing technology used to measure the vibration response has been accelerometers and strain gages mounted directly to the structure and using either wired or, more recently, wireless telemetry. Cost issues with these sensors and the associated data acquisition systems typically limit the numbers that are deployed on in situ structures. Although there are a few structures with larger sensing counts that in some cases exceed over 1000 sensors, more typical numbers range from ten to one hundred sensors resulting in low spatial resolution when they are applied to physically large systems. When one considers that nuclear power plant structures usually have complex geometries, material properties, connectivity and boundary conditions, it is clear these current approaches to vibration measurements can only provide limited information about a system’s dynamics response characteristics. As an alternative, many non-contact measurement technologies have emerged, including point wise measurement methods such as Global Positioning System (GPS), microwave interferometry, and laser Doppler vibrometry (LDV), as well as simultaneous full-field measurement methods such as electronic speckle pattern interferometry, holography interferometry, and muon tomography, some of which can provide high spatial resolution measurements. Among these methods, digital video imaging techniques have emerged as a feasible solution for full-field vibration measurements that provide significantly more detailed dynamic response information because every pixel becomes a measurement point. Furthermore, recent advances in image processing and computer vision algorithms have been successfully used to process video data for experimental and operational modal analysis. Such full-field measurements have the potential to significantly improve many current structural assessment procedures including system identification (modal parameter estimation), structural health monitoring, load reconstruction, model validation, and model updating. Furthermore, more recent full-field imaging techniques can be accomplished with relatively low-cost, commercially-available off-the-shelf cameras. However, these measurement procedures have other limitations that must be considered such as the ability to only measure visibly accessible points on a structure and a more limited dynamic range and bandwidth than can be achieved with accelerometers or strain gages.
As compared to conventional nuclear reactors, microreactors have the potential to significantly reduce construction timelines and capital costs, decreasing the barriers for advanced nuclear reactor technologies. However, the lower power output of these microreactors (typically < 20 MWe) creates challenging economics if operation and maintenance costs cannot be sufficiently reduced. The compact size of these designs presents an opportunity for comprehensive in-situ structural health monitoring to provide real-time feedback in order to reduce operational costs associated with maintenance and downtime. Many microreactor concepts use graphite for both in-core neutron moderation and as a structural material, which has typically required some form of periodic and laborious inspection. This report provides a description and assessment of recent work with graphite to couple acoustic-based experimental measurements and characterization with machine learning models to mature structural health monitoring capabilities and generate benefits for the nuclear microreactor industry. With resilient embedded sensors in development in other programs funded by the US Department of Energy’s Office of Nuclear Energy and elsewhere, the work described herein builds upon previously funded efforts to mature non-destructive testing technology that relates measured vibrational signatures to structural changes, using a combination of new experimental measurements and machine learning processing. Building on past successful demonstrations of predictive workflows to identify structural changes in a hexagonal stainless steel test article with excellent acoustic propagation, we first performed baseline characterization on graphite samples with canonical geometries to ensure compatibility and confidence in the applied techniques for a material with distinctly different mechanical properties. In contrast to efforts in previous years, we worked exclusively with unidirectional vibration data that is more comparable to those expected from the existing embedded sensor technologies which are suitable for deployment in a reactor setting. Established acoustic and modern machine-learning-based characterization approaches were applied to the resulting datasets from these simple geometries. Both approaches were found to be highly capable of detecting even small geometric irregularities amongst nominally identical samples. As such, we then moved to testing these approaches for detection of artificial local stress perturbations introduced into a more complex geometry: a hexagonal block with drilled holes. A main outcome of this work is that a generalizable ML workflow can be used to detect and predict the characteristics of small artificial anomalies in a graphite component with a relevant geometry. While this work was performed using surficial vibration data, we expect the approach to be flexible and viable for other monitoring scenarios, such as those with different arrangements or types of sensor arrays. As compared to previously funded efforts, an existing ML workflow based on neural networks was enhanced through the addition of recently developed Fourier neural operators. As applied to previously collected and new vibration datasets, prediction accuracies of anomaly characterizations were greatly improved with minimal added computational cost. As trained on small durations of vibration data (tens of seconds) collected over a realistic number of locations, the model was able to reliably determine the presence of a subtle stress anomaly and begin to provide location estimates. Such an approach is likely to be viable for more relevant reactor damage scenarios for graphite components, such as progressive crack growth or creep.
Manipulating thermal properties of materials can be interpreted as the control of how vibrations of atoms (known as phonons) scatter in a crystal lattice. Compared to a perfect crystal, crystalline solids with defects are expected to have shorter phonon mean free paths caused by point defect scattering, leading to lower lattice thermal conductivities than those without defects. While this is true in many cases, alloying can increase the phonon mean free path in the Cd-doped AgSnSbSe 3 system to increase the lattice thermal conductivity from 0.65 to 1.05 W m –1 K –1 by replacing 18% of the Sb sites with Cd. It is found that the presence of lone pair electrons leads to the off-centering of cations from the centrosymmetric position of a cubic lattice. X-ray pair distribution function analysis reveals that this structural distortion is relieved when the electronic configuration of the dopant element cannot produce lone pair electrons. Furthermore, a decrease in the Grüneisen parameter with doping is experimentally confirmed, establishing a relationship between the stereochemical activity of lone pair electrons and the lattice anharmonicity. The observed “harmonic” behavior with doping suggests that lone pair electrons must be preserved to effectively suppress phonon transport in these systems.
Due to the large lattice mismatch between gold and nickel, gold–nickel surface alloys can form unique nonstoichiometric overlayer structures characterized by a moiré pattern and subsurface defects. For this work, we performed density functional theory (DFT) calculations to study the adsorption of molecular oxygen, atomic hydrogen, and atomic carbon on a gold–nickel(111) surface alloy with 0.46 monolayer gold randomly distributed in the surface layer. We observed six distinct adsorption structures for molecular oxygen characterized by intramolecular stretching frequencies of <700, 729, 795, 857, 929, and 1004 cm –1 , which describe well the experimentally observed high-resolution electron energy-loss spectra. Surface atomic hydrogen adsorption is associated with adsorbate–surface modes in the ∼1000 cm –1 range, while subsurface hydrogen can have features as low as ∼400 cm –1 . We observed a unique adsorption structure for atomic carbon inside the surface dislocation loop defect, which explains the experimentally observed low carbon-surface mode at ∼340 cm –1 . Our study sheds light on the unique adsorption properties of the gold–nickel surface alloys and helps with rationalizing vibrational frequency experimental studies for this system.
Molecular catalyst functionalized semiconductor quantum dots (QDs) are a promising modular platform for developing novel hybrid photocatalysts. The interaction between adsorbed catalyst vibrations and the QD electron intraband absorption can influence the photophysical properties of both the QD and the catalysts and potentially their photocatalysis. In CdSe QDs functionalized by the CO 2 reduction catalyst, Re(CO) 3 (4,4’-bipyridine-COOH)Cl, we observe that the transient Fano resonance signal resulting from coupling of the catalyst CO stretching mode and the QD conduction band electron mid-IR intraband absorption appears on an ultrafast time scale and decays with the electron population, irrespective of the occurrence of photoreduced catalysts. The Fano asymmetry factor increases with an increase in the adsorbed catalyst number and a decrease in QD sizes. The latter can be attributed to an enhanced charge transfer interaction between the more strongly quantum-confined QD conduction band and catalyst LUMO levels. These results provide a more in-depth understanding of interactions in excited QD-catalyst hybrid photocatalysts.
Plutonium exhibits a particularly complex and rich aqueous chemistry due to a dynamic redox equilibrium that can result in the coexistence of four different oxidation states in aqueous solution. Within this equilibrium, pentavalent plutonium is among the most soluble and dominant oxidation states of Pu in neutral to alkaline aqueous solutions of relevance to environmental chemistries and the extreme chemical environments encountered in high-level radioactive wastes. Despite this, the fundamental chemistry of Pu(V) under such conditions remains poorly understood. Here, in this study, we demonstrate the redox accessibility of Pu(V) in alkaline media based on the observations of an electrochemically reversible Pu(VI/V) couple at E 1/2 = 0.19 V vs Hg/HgO (0.29 V vs NHE), allowing us to electrolytically generate Pu(V) in alkaline solution. These Pu(V) solutions remained stable under ambient conditions for at least 9 days before precipitation of crystalline Na 2 [Pu V O 2 (OH) 3 ]·2H 2 O, the first structurally characterized Pu(V) hydroxide. The modest potential of the Pu(VI/V) redox couple allowed us to reproduce Na 2 [Pu V O 2 (OH) 3 ]·2H 2 O synthetically as well as isolate two additional novel Pu(V)-bearing hydroxide phases Na[Pu V O 2 (OH) 2 (H 2 O)]·2.5H 2 O and K 2 [Pu V O 2 (OH) 3 ]·2H 2 O as single crystals from alkaline reactions under mild hydrothermal conditions (<200 °C) using Na 2 O 2 and KO 2 as reductants. This reactivity is rationalized and informed by our electrochemical measurements. The properties of the Pu(V) solids were further characterized by using vibrational and optical spectroscopy.
Over the past decade, a combination of crystal structure prediction techniques and experimental synthetic work has thoroughly explored the phase diagrams of binary hydrides under pressure. The fruitfulness of this dual approach is demonstrated in the recent identification of several superconducting hydrides with T c s approaching room temperature. We start with an overview of the computational procedures for predicting stable structures and estimating their propensity for superconductivity. A survey of phases with high T c reveals some common structural features that appear conducive to the strong coupling of the electronic structure with atomic vibrations that leads to superconductivity. We discuss the stability and superconducting properties of phases containing two of these—molecular H 2 units mixed with atomic H and hydrogenic clathrate-like cages—as well as more unique motifs. Lastly, we argue that ternary hydride phases, whose exploration is still in its infancy, are a promising route to achieve simultaneous superconductivity at high temperatures and stability at low pressures. Several ternary hydrides arise from the addition of a third element to a known binary hydride structure through site mixing or onto a new site, and several more are based on altogether new structural motifs.
Important effects appear in low and high temperature magnetometry of multiferroic BiFeO 3 (BFO) nanoparticles as sizes are reduced from 105 nm to 18 nm. In these particles, a magnetic transition appears close to 200 K probably associated with a spin reorientation which is absent in bigger particles. At higher temperatures and up to the bulk ferroelectric Curie temperature (1000 K), two additional magnetic transitions can be identified. Around T N 600 K, a size-dependent magnetic transition appears, associated with an antiferromagnetic transition. A model based on atomic vibration instability can describe the variation of T N with nanoparticle size. Furthermore, for certain sizes, the properties are dominated by the glassy state arising from the disorder produced by the formation of a core-shell structure. This implies that the magnetic behavior of nanoparticles is strongly affected by their crystal structure close to the transition temperature. Finally, the structural disorder which couples to the magnetic properties add control to the magnetic and ferroelectric properties of BFO nanoparticles.
Although interfacial solvation plays an important role in determining carbon dioxide reduction (CO 2 R) kinetics, present understanding of the potential dependent properties of the electrochemical double layer under conditions relevant for CO 2 R remains limited. This article summarizes the development and recent applications of plasmon-enhanced vibrational sum frequency generation (VSFG) spectroscopy to study the effects of cation hydration and interfacial solvation on CO 2 R using CO as a vibrational Stark reporter. Results show that electrolyte cations retain their entire solvation shell upon adsorption to inactive sites, while active sites retain only a single water layer between the gold surface and the cation. Measurements also show that the total interfacial electric field can be separated into two contributions: one from the electrochemical double layer (Stern field) and another from the polar solvation environment (Onsager field). Surprisingly, correlating VSFG spectra with reaction kinetics reveals that it is the solvation-mediated Onsager field that governs the chemical reactivity at the electrode/electrolyte interface. Measuring the interfacial water spectra during electrocatalysis also provides evidence for the proton source during H2 evolution, which competes with CO 2 R in aqueous electrolyte. These findings highlight the importance of directly probing cation hydration and interfacial solvation, which mediates reaction kinetics at electrochemical interfaces.
Semiempirical energy relations provide a means of estimating thermodynamic properties. Specifically, linear scaling relationships (LSRs) and Brønsted–Evans–Polanyi (BEP) relationships correlate adsorption energies between adsorbates across surfaces and reaction energies with activation barriers, respectively. Although vibrational scaling relations (VSRs) exist between adsorbates at identical sites, scaling between vibrational frequencies of adsorbed local minima and transition states is lacking. Here, we present density functional theory calculations for AH X (A = C, N, O) diffusions on transition metal surfaces and reveal linear scaling between frequencies of local minima and the transition state between those minima. Using d-band theory and linear muffin tin orbital theory (LMTO), we derive the slopes of these transition state vibrational scaling relations (TSVSRs) and, in so doing, provide a rigorous theory extending the original BEP relations developed for solution chemistry to surface chemistry. Furthermore, with a single reference DFT calculation, we predict the slopes and quantify uncertainty in the predictions.
Abstract Polyurethane foam (PUF) can absorb sound by air friction and structural vibrations of cell walls. It is widely used as acoustic material in the automotive industry. According to existing studies, graphene oxide (GO) can improve the acoustic properties of PUF. However, GO tends to coalesce inside PUF. In this paper, straw fibers were modified with different concentrations of GO solution to embed GO on the surface of plant fibers. The GO was dispersed in the PUF using straw fibers. In addition, some studies show that the wrinkled morphology of the GO surface is believed to play a mechanical interlocking role in improving interfacial bonding, thus enhancing the stress transfer between the plant fibers and the polyurethane (PU) matrix and improving the mechanical properties of the PU composites. The acoustic and mechanical properties of the modified PU materials were observed by treating plant fibers with appropriate amounts of GO solution. The experimental study shows that the sample modified with 1 g L −1 GO solution reaches a maximum average sound absorption coefficient of 0.5838 when the straw fiber content reaches 0.5 g, and the sample modified with 2 g L −1 GO solution reaches a maximum transmission loss of 10.5333 when the straw fiber content reaches 1 g. © 2022 Society of Industrial Chemistry.
Accurate pK a prediction is critical for understanding chemical reactivity and molecular properties across a wide range of applications. Computational approaches usually invoke a harmonic treatment of the vibrational modes for zero-point energies, as well as thermal and entropic contributions. Herein, we present a general protocol for relative pK a prediction that incorporates the significant anharmonic effects using nuclear–electronic orbital (NEO) theory. This protocol is validated against experimental data for a range of molecules in acetonitrile, including protonated nitrogen bases, nitrophenols, anilines, and diamines, as well as cobalt electrocatalysts. For simple acids, the NEO approach offers only a slight improvement over conventional density functional theory with the standard harmonic vibrational treatment, whereas for hydrogen-bonded acids, the NEO approach offers more significantly improved performance at a comparable computational cost. This accessible methodology provides a practical route for accurate pKa prediction in challenging systems and is extendable to related thermodynamic properties such as hydricities and proton-coupled redox potentials.
Understanding phonons in α-RuCl 3 is critical to analyze the controversy around the observation of the half-integer thermal quantum Hall effect. While many studies have focused on the magnetic excitations in α-RuCl 3 , its vibrational excitation spectrum has remained relatively unexplored. We investigate the phonon structure of α-RuCl 3 via inelastic neutron-scattering experiments and density-functional-theory calculations. Our results show excellent agreement between experiment and first-principles calculations. After validating our theoretical model, we extrapolate the low-energy phonon properties. We find that the phonons in α-RuCl 3 that either propagate or vibrate in the out-of-plane direction have significantly reduced velocities and therefore have the potential to dominate the observability of the elusive half-integer plateaus in the thermal Hall conductance. In addition, we use low-energy interlayer phonons to resolve the low-temperature stacking structure of our large crystal of α-RuCl 3 , which we find to be consistent with that of the R$\bar{3}$ space group, in agreement with neutron diffraction.
Abstract We report a ternary hybrid photocatalyst architecture with tailored interfaces that boost the utilization of solar energy for photochemical CO 2 reduction by synergizing electron and heat flows in the photocatalyst. The photocatalyst comprises cobalt phthalocyanine (CoPc) molecules assembled on multiwalled carbon nanotubes (CNTs) that are decorated with nearly monodispersed cadmium sulfide quantum dots (CdS QDs). The CdS QDs absorb visible light and generate electron‐hole pairs. The CNTs rapidly transfer the photogenerated electrons from CdS to CoPc. The CoPc molecules then selectively reduce CO 2 to CO. The interfacial dynamics and catalytic behavior are clearly revealed by time‐resolved and in situ vibrational spectroscopies. In addition to serving as electron highways, the black body property of the CNT component can create local photothermal heating to activate amine‐captured CO 2 , namely carbamates, for direct photochemical conversion without additional energy input.