Hasil untuk "Physical and theoretical chemistry"

Rileigh Bandy, Rebecca Morrison, Erin Mussoni et al.

During hypersonic flight, air reacts with a planetary re-entry vehicle's thermal protection system (TPS), creating reaction products that deplete the TPS. Reliable assessment of TPS performance depends on accurate ablation models. New finite-rate gas-surface chemistry models are advancing state-of-the-art in TPS ablation modeling, but model reductions that omit chemical species and reactions may be necessary in some cases for computational tractability. This work develops hybrid physics-based and data-driven enrichments to improve the predictive capability and quantify uncertainties in such low-fidelity models while maintaining computational tractability. We focus on discrepancies in predicted carbon monoxide production that arise because the low-fidelity model tracks only a subset of reactions. To address this, we embed targeted enrichments into the low-fidelity model to capture the influence of omitted reactions. Numerical results show that the hybrid enrichments significantly improve predictive accuracy while requiring the addition of only three reactions.

Rachel E. Gross, L. Ilsedore Cleeves

The chemistry within a protoplanetary disk is greatly affected by external radiation from the local stellar environment. Previous work has focused on extreme radiation fields, representative of the center of something like the Orion Nebula Cluster. However, even in such environments, many disks exist at the edges of a cluster where the lower stellar density leads to radiation fields weaker by orders of magnitude compared to the center. We present new chemical models of a T-Tauri disk in the presence of a moderately increased interstellar radiation field (ISRF). Such an environment has a background UV strength of 10 to 100 times higher than the galactic average ISRF. Moderate radiation fields are among the most prevalent disk-harboring environments and have interesting implications for the chemistry of the outer disk radii. We find that the external UV radiation creates an outer ionization front that impacts the cold disk chemistry to varying degrees, depending on outer disk structure. Certain molecules like C$^+$, N$_2$H$^+$, C, and CS are more strongly impacted by the ISRF in their abundance, column density, and observable emission. Other abundant species like HCO$^+$ and CO are less affected by the external UV flux in the outer disk under such moderate UV conditions. Further, we demonstrate that the chemistry occurring in the inner tens of au is relatively unchanged, which suggests that even in moderately externally irradiated disks, the inner disk chemistry may be more similar to isolated disks like those in, e.g., the Taurus and Lupus star-forming regions.

Noah S. J. Rogers, Allison L. Strom, Gwen C. Rudie et al.

Galaxies at Cosmic Noon (z$\sim$2-3) are characterized by rapid star formation that will lead to significant metal enrichment in the interstellar medium (ISM). While much observational evidence suggests that these galaxies are chemically distinct from those in the local Universe, directly measuring the ISM chemistry in large samples of high-z galaxies is only now possible with the observational capabilities of JWST. In this first key paper of the CECILIA program, we present the direct-method physical conditions and multi-element abundances in twenty galaxies at Cosmic Noon. Using a combination of archival Keck/MOSFIRE and new $\sim$30-hr NIRSpec spectroscopy, we measure multiple electron gas densities and the temperature structure from the O$^+$ and S$^{2+}$ ions. We find that n$_e$[O II] and n$_e$[S II] are comparable but elevated with respect to n$_e$ in local star-forming galaxies, and the simultaneous T$_e$[O II] and T$_e$[S III] generally agree with photoionization model T$_e$ scaling relations. The O abundances in the CECILIA galaxies range from 12+log(O/H)$=$7.76-8.81 (12-131% solar O/H), representing some of the highest direct-method metallicities and lowest T$_e$ (T$_e$[O II]$\approx$6500 K) measured with JWST to date. The CECILIA galaxies exhibit significantly sub-solar S/O and Ar/O a signature of predominant enrichment from core collapse supernovae. The N/O-O/H trends in the CECILIA galaxies generally agree with the abundance trends in local nebulae, but the large scatter in N/O could be sensitive to the star-formation history. The CECILIA observations demonstrate that exceptionally deep JWST spectroscopy can unveil the multi-element ISM abundance patterns in typical high-z galaxies.

Zhihao Zhang, Xiao-Ming Cao

Deep learning has advanced efficient chemical process simulations on the surfaces, accelerating high-throughput materials screening and rational design in heterogeneous catalysis, energy storage and conversion, and gas separation. However, the accuracy of the deep learning model generally depends on the quality of the training data. Unfortunately, precise experimental data in surface chemistry, such as adsorption energies, are scarce, while accurate quantum chemistry simulations remain computationally prohibitive for large-scale studies. Herein, we present a deep learning model of DOS Transformer for Adsorption (DOTA) for efficient surface chemistry simulations with chemical accuracy. It enables the alignment of experimental data and multi-fidelity quantum chemistry calculation data by capturing latent orbital interaction patterns based on the map between local density of states (LDOS) and adsorption energy. This minimizes the reliance on scarce high-precision training data in surface chemistry to accomplish efficient prediction of adsorption energies rivaling the high-precision experimental data, resolving the long-standing challenge of "CO puzzle". It provides a robust framework for efficient materials screening, effectively bridging the gap between computational and experimental data.

Siddharth M. Narayanan, James D. Braza, Ryan-Rhys Griffiths et al.

Reasoning models are large language models that emit a long chain-of-thought before answering, providing both higher accuracy and explicit reasoning for their response. A major question has been whether language model reasoning generalizes beyond mathematics, programming, and logic, where most previous work has focused. We demonstrate that reasoning models can be post-trained for chemistry without additional domain pretraining, and require substantially less data compared to contemporary domain-specific models. We report ether0, a 24B parameter LLM (based on Mistral-Small-24B) that can reason in natural language and respond with chemical structures. This reasoning model was trained with reinforcement learning on 640,730 experimentally-grounded chemistry problems across 375 tasks ranging from synthesizability, to blood-brain barrier permeability, to human receptor activity, to scent. Our model exceeds general-purpose chemistry models, frontier models, and human experts on molecular design tasks. It is also more data efficient relative to specialized models. We anticipate that this method can be applied to train data-efficient language models specialized for tasks across a wide variety of scientific domains.

Akkinepally Bhargav, Sri Harisha Bairi, Nadar Nandini Robin et al.

Electrode materials comprising SnO2 quantum dots embedded within ZnO hexagonal prisms were successfully synthesized for building cost-effective energy-storage devices. Extensive structural and functional characterizations were performed to assess the electrochemical performance of the electrodes. SEM–EDS results confirm a uniform distribution of SnO2 quantum dots across ZnO. The integration of SnO2 quantum dots with ZnO hexagonal prisms markedly improved the electrochemical behavior. The analysis of electrode functionality conducted in a 3 M KOH electrolyte revealed specific capacitances of 949.26 and 700.68 F g⁻1 for SnO2@ZnO and ZnO electrodes, respectively, under a current density of 2 A g⁻1. After undergoing 5,000 cycles at a current density of 15 A g⁻1, the SnO2@ZnO and ZnO electrodes displayed impressive cycling stability, maintaining specific capacitance retention rates of 89.9 and 92.2%, respectively. Additionally, a symmetric supercapacitor (SSC) device constructed using the SnO2@ZnO electrode showcased exceptional performance, exhibiting a specific capacitance of 83 F g⁻1 at 1.2 A g⁻1. Impressive power and energy densities were achieved by the device, with values reaching 2,808 and 70.2 W kg⁻1, respectively. Notably, the SnO2@ZnO SSC device maintained a capacity preservation of 75% throughout 5,000 galvanostatic charge–discharge sequences. The outcomes highlight the potential of SnO2@ZnO hexagonal prisms as candidates for energy-storage applications, offering scalability and cost-effectiveness. The proposed approach enhances the electrochemical performance while ensuring affordability, facilitating the creation of effective and financially feasible energy storage solutions.

Ibos, Léa, Schulz, Emmanuelle

Recent developments in asymmetric catalysis involve the heterogeneization of chiral complexes so that they can be easily immobilized on different supports. This account focuses on procedures that lead to the formation of non-covalent interactions between the chiral active sites and the chosen support, as they generally involve less tedious synthetic modifications and can allow either the chiral catalyst or the support to be easily recovered, at will. As a result, electrostatic interactions between inorganic supports and chiral organometallic complexes have been described to lead to efficient and recoverable catalysts. More recently, other weak interactions, such as ${\pi }$-interactions, but also donor–acceptor complexes, have been implicated for implementation of robust immobilizing procedures. There are also many examples of the use of coordination links, such as those present in metal organic frameworks for supported asymmetric catalysis, which pave the way for efficient cooperative or tandem asymmetric catalysis.

Öyküm N. Avcı, Luca Sementa, Alessandro Fortunelli

The surface configurations of the low-index facets of a set of spinel oxides are investigated using DFT+U calculations to derive surface energies and predict equilibrium nanoparticle shapes via the Wulff construction. Two very different conditions are investigated, corresponding to application either in heterogeneous catalysis or in electrocatalysis. First, the bare stoichiometric surfaces of NiFe₂O₄, CoFe₂O₄, NiCo₂O₄, and ZnCo₂O₄ spinels are studied to model their use as high-temperature oxidation catalysts. Second, focusing attention on the electrochemical oxygen evolution reaction (OER) and on the CoFe₂O₄ inverse spinel as the most promising OER catalyst, we generate surface configurations by adsorbing OER intermediates and, in an innovative study, we recalculate surface energies taking into account adsorption and environmental conditions, i.e., applied electrode potential and O₂ pressure. We predict that under OER operating conditions, (111) facets are dominant in CoFe₂O₄ nanoparticle shapes, in fair agreement with microscopy measurements. Importantly, in the OER case, we predict a strong dependence of nanoparticle shape upon O₂ pressure. Increasing O₂ pressure increases the size of the higher-index (111) and (110) facets at the expense of the (001) more catalytically active facet, whereas the opposite occurs at low O₂ pressure. These predictions should be experimentally verifiable and help define the optimal OER operative conditions.

Jihua Chen, Yue Yuan, Amir Koushyar Ziabari et al.

Artificial Intelligence (AI) approaches are increasingly being applied to more and more domains of Science, Engineering, Chemistry, and Industries to not only improve efficiencies and enhance productivity, but also enable new capabilities. The new opportunities range from automated molecule design and screening, properties prediction, gaining insights of chemical reactions, to computer-aided design, predictive maintenance of systems, robotics, and autonomous vehicles. This review focuses on the new applications of AI in manufacturing and healthcare. For the Manufacturing Industries, we focus on AI and algorithms for (1) Battery, (2) Flow Chemistry, (3) Additive Manufacturing, (4) Sensors, and (5) Machine Vision. For Healthcare applications, we focus on: (1) Medical Vision (2) Diagnosis, (3) Protein Design, and (4) Drug Discovery. In the end, related topics are discussed, including physics integrated machine learning, model explainability, security, and governance during model deployment.

Mridula Kuppa, Roger Ghanem, Marco Panesi

This work presents a novel framework for physically consistent model error characterization and operator learning for reduced-order models of non-equilibrium chemical kinetics. By leveraging the Bayesian framework, we identify and infer sources of model and parametric uncertainty within the Coarse-Graining Methodology across a range of initial conditions. The model error is embedded into the chemical kinetics model to ensure that its propagation to quantities of interest remains physically consistent. For operator learning, we develop a methodology that separates time dynamics from other input parameters. Karhunen-Loeve Expansion (KLE) is employed to capture time dynamics, yielding temporal modes, while Polynomial Chaos Expansion (PCE) is subsequently used to map model error and input parameters to KLE coefficients. The proposed model offers three significant advantages: i) Separating time dynamics from other inputs ensures stability of chemistry surrogate when coupled with fluid solvers; ii) The framework fully accounts for model and parametric uncertainty, enabling robust probabilistic predictions; iii) The surrogate model is highly interpretable, with visualizable time modes and a PCE component that facilitates analytical calculation of sensitivity indices. We apply this framework to O2-O chemistry system under hypersonic flight conditions, validating it in both a 0D adiabatic reactor and coupled simulations with a fluid solver in a 1D shock case. Results demonstrate that the surrogate is stable during time integration, delivers physically consistent probabilistic predictions accounting for model and parametric uncertainty, and achieves maximum relative error below 10%. This work represents a significant step forward in enabling probabilistic predictions of non-equilibrium chemistry with coupled fluid solvers, offering a physically accurate approach for hypersonic flow predictions.

Ryosuke NAKAZATO, Keeko MATSUMOTO, Noboru YAMAGUCHI et al.

Carbon dioxide electrochemical reduction (CO2ER) has attracted considerable attention as a technology to recycle CO2 into raw materials for chemicals using renewable energies. We recently found that Zn-Al layered double hydroxides (Zn-Al LDH) have the CO-forming CO2ER activity. However, the activity was only evaluated by using the liquid-phase CO2ER. In this study, Ni-Al and Ni-Fe LDHs as well as Zn-Al LDH were synthesized using a facile coprecipitation process and the gas-phase CO2ER with the LDH-loaded gas-diffusion electrode (GDE) was examined. The products were characterized by XRD, STEM-EDX, BF-TEM and ATR-IR spectroscopy. In the ATR-IR results, the interaction of CO2 with Zn-Al LDH showed a different carbonates evolution with respect to other LDHs, suggesting a different electrocatalytic activity. The LDH-loaded GDE was prepared by simple drop-casting of a catalyst ink onto carbon paper. For gas-phase CO2ER, only Zn-Al LDH exhibited the CO2ER activity for carbon monoxide (CO) formation. By using different potassium salt electrolytes affording neutral to strongly basic conditions, such as KCl, KHCO3 and KOH, the gas-phase CO2ER with Zn-Al LDH-loaded GDE showed 1.3 to 2.1 times higher partial current density for CO formation than the liquid-phase CO2ER.

J. Alfredo Collazos

Neutron stars are one of the most extreme objects in the universe, with densities that can exceed those of atomic nuclei and gravitational fields that are among the strongest known. Theoretical and observational research on neutron stars has revealed a wealth of information about their structural characteristics and physical properties. The structural characteristics of neutron stars are determined by the equations of state that describe the relationship between their density, pressure, and energy. These equations of state are still not well understood, and ongoing theoretical research aims to refine our understanding of the behavior of matter under these extreme conditions. Observational research on neutron stars, such as measurements of their masses and radii, can provide valuable constraints on the properties of the equation of state. The physical properties of neutron stars are also of great interest to researchers. Neutron stars have strong magnetic fields, which can produce observable effects such as pulsations and emission of X-rays and gamma rays. The surface temperature of neutron stars can also provide insight into their thermal properties, while observations of their gravitational fields can test predictions of Einstein's theory of general relativity. Observational research on neutron stars is carried out using a variety of techniques, including radio and X-ray telescopes, gravitational wave detectors, and optical telescopes. These observations are often combined with theoretical models to gain a more complete understanding of the properties of neutron stars.

Katerina P. Hilleke, Eva Zurek

An overview of the behavior of materials at high pressure is presented, starting from the effects on single atoms driving electronic transitions and changes in periodic trends. A range of high-pressure-induced phenomena in the solid state are then discussed building on the atomic changes, including bizarre electronic structures, electrides, compounds of noble gases, changes in elemental miscibility, and strange structural and bonding configurations. In the final section, the field of high pressure superconductivity is discussed, as high pressure phases have generated immense study and excitement as some of their critical superconducting temperatures approach room temperature.

Anastasios Stergiou, Ioanna K. Sideri, Martha Kafetzi et al.

Development of graphene/perovskite heterostructures mediated by polymeric materials may constitute a robust strategy to resolve the environmental instability of metal halide perovskites and provide barrierless charge transport. Herein, a straightforward approach for the growth of perovskite nano-crystals and their electronic communication with graphene is presented. Methylammonium lead bromide (CH₃NH₃PbBr₃) nano-crystals were grown in a poly[styrene-co-(2-(dimethylamino)ethyl methacrylate)], P[St-co-DMAEMA], bi-functional random co-polymer matrix and non-covalently immobilized on graphene. P[St-co-DMAEMA] was selected as a bi-modal polymer capable to stabilize the perovskite nano-crystals via electrostatic interactions between the tri-alkylamine amine sites of the co-polymer and the A-site vacancies of the perovskite and simultaneously enable Van der Waals attractive interactions between the aromatic arene sites of the co-polymer and the surface of graphene. The newly synthesized CH₃NH₃PbBr₃/co-polymer and graphene/CH₃NH₃PbBr₃/co-polymer ensembles were formed by physical mixing of the components in organic media at room temperature. Complementary characterization by dynamic light scattering, microscopy, and energy-dispersive X-ray spectroscopy revealed the formation of uniform spherical perovskite nano-crystals immobilized on the graphene nano-sheets. Complementary photophysical characterization by UV-Vis absorption, steady-state, and time-resolved fluorescence spectroscopy unveiled the photophysical properties of the CH₃NH₃PbBr₃/co-polymer colloid perovskite solution and verified the electronic communication within the graphene/CH₃NH₃PbBr₃/co-polymer ensembles at the ground and excited states.

Ahmad Peyvan, Khemraj Shukla, Jesse Chan et al.

We compare high-order methods including spectral difference (SD), flux reconstruction (FR), the entropy-stable discontinuous Galerkin spectral element method (ES-DGSEM), modal discontinuous Galerkin methods, and WENO to select the best candidate to simulate strong shock waves characteristic of hypersonic flows. We consider several benchmarks, including the Leblanc and modified shock-density wave interaction problems that require robust stabilization and positivity-preserving properties for a successful flow realization. We also perform simulations of the three-species Sod problem with simplified chemistry with the chemical reaction source terms introduced in the Euler equations. The ES-DGSEM scheme exhibits the highest stability, negligible numerical oscillations, and requires the least computational effort in resolving reactive flow regimes with strong shock waves. Therefore, we extend the ES-DGSEM to hypersonic Euler equations by deriving a new set of two-point entropy conservative fluxes for a five-species gas model. Stabilization for capturing strong shock waves occurs by blending high-order entropy conservative fluxes with low-order finite volume fluxes constructed using the HLLC Riemann solver. The hypersonic Euler solver is verified using the non-equilibrium chemistry Sod problem. To this end, we adopt the Mutation++ library to compute the reaction source terms, thermodynamic properties, and transport coefficients. We also investigate the effect of real chemistry versus ideal chemistry, and the results demonstrate that the ideal chemistry assumption fails at high temperatures, hence real chemistry must be employed for accurate predictions. Finally, we consider a viscous hypersonic flow problem to verify the transport coefficients and reaction source terms determined by the Mutation++ library.

Daniel Kelshaw, Luca Magri

Measurements on dynamical systems, experimental or otherwise, are often subjected to inaccuracies capable of introducing corruption; removal of which is a problem of fundamental importance in the physical sciences. In this work we propose physics-informed convolutional neural networks for stationary corruption removal, providing the means to extract physical solutions from data, given access to partial ground-truth observations at collocation points. We showcase the methodology for 2D incompressible Navier-Stokes equations in the chaotic-turbulent flow regime, demonstrating robustness to modality and magnitude of corruption.