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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Linking the Spin Transition of Ferric Iron in δ‐(Al,Fe)OOH to Water Storage in the Lower Mantle

As the most massive geochemical reservoir, the lower mantle affects the Earth's budget of volatile elements, including hydrogen or H 2 O. The properties of minerals in the lower mantle are further affected by changes in the electronic configurations of iron cations, that is, by spin transitions. The feedback between spin transitions and potential storage of H 2 O in solid hydrous phases in the lower mantle, however, remains unexplored. By combining high‐pressure nuclear resonant inelastic X‐ray scattering and high‐pressure high‐temperature X‐ray diffraction experiments, we constrained the thermal equation of state of δ‐(Al,Fe)OOH, a member of the phase H solid solution. Based on the derived thermal equation of state of δ‐(Al,Fe)OOH and the underlying thermodynamic model, we calculate the excess Gibbs free energy that arises from the spin transition of ferric iron in this compound and evaluate the effect on phase equilibria. The results of our analysis show that the spin transition of ferric iron in phase H may significantly reduce the thermodynamic activity and hence the concentration of H 2 O in a coexisting hydrous melt. As a consequence, nominally anhydrous minerals of the lower mantle may become dehydrated in the presence of phase H. Our analysis further suggests that, under certain conditions, the spin transition may expand the thermal stability of Fe 3+ ‐bearing phase H and create a geochemical link between the storage of H 2 O in phase H and ferric iron in the lower mantle.

Buchen, Johannes [Univ. of Bayreuth (Germany); Cal↗
DOE OSTI2023

Crystal dissolution by particle detachment

Crystal dissolution, which is a fundamental process in both natural and technological settings, has been predominately viewed as a process of ion-by-ion detachment into a surrounding solvent. Here we report a mechanism of dissolution by particle detachment (DPD) that dominates in mesocrystals formed via crystallization by particle attachment (CPA). Using liquid phase electron microscopy to directly observe dissolution of hematite crystals — both compact rhombohedra and mesocrystals of coaligned nanoparticles — we find that the mesocrystals evolve into branched structures, which disintegrate as individual sub-particles detach. The resulting dissolution rates far exceed those for equivalent masses of compact single crystals. Applying a numerical generalization of the Gibbs-Thomson effect, we show that the physical drivers of DPD are curvature and strain inherently tied to the original CPA process. Based on the generality of the model, we anticipate that DPD is widespread for both natural minerals and synthetic crystals formed via CPA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine learning guided discovery of ternary compounds involving La and immiscible Co and Pb elements

Abstract Ternary compounds with an immiscible pair of elements are relatively unexplored but promising for novel quantum materials discovery. Exploring what third element and its ratio that can be added to make stable ternary compounds out of an immiscible pair of elements remains a great challenge. In this work, we combine a machine learning (ML) method with ab initio calculations to efficiently search for the energetically favorable ternary La-Co-Pb compounds containing immiscible elements Co and Pb. Three previously reported structures are correctly captured by our approach. Moreover, we predict a ground state La 3 CoPb compound and 57 low-energy La-Co-Pb ternary compounds. Attempts to synthesize La 3 CoPb via multiple techniques produce mixed or multi-phases samples with, at best, ambiguous signals of the predicted lowest-energy La 3 CoPb and the second lowest-energy La 18 Co 28 Pb 3 phases. The calculated results of Gibbs free energy are consistent with experiments, and will provide very useful guidance for further experimental synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Insights on the corrosion thermodynamics of chromium in molten LiF-NaF-KF eutectic salts

The thermodynamic stability of chromium in the Cr 0 , Cr 2+ , and Cr 3+ oxidation states considering various F - ion coordination compounds was investigated in molten LiF–NaF-KF (FLiNaK) eutectic salt. Potential fluoride ion activity (F - and CrF 3 - ) diagrams were constructed to predict the most stable Cr oxidation states as a function of anion activity, solvation state of chromium ions and potential at 600 °C. The Gibbs free energies of these compounds were estimated by utilizing a combined cyclic voltammetry – Nernst theory analysis approach in FLiNaK salt. To verify the constructed diagrams, X-ray diffraction was utilized after exposure of Cr at various applied potentials to determine whether compounds detected in solidified FLiNaK salts agreed with thermodynamic calculations. This work aims to identify key thermodynamic factors that are significant to chromium corrosion for molten salt nuclear reactor applications. The F - stability region spans the entire region where spontaneous corrosion of Cr occurs. Cr may be oxidized to Cr 2+ and Cr 3+ in the presence HF (due to moisture as an impurity) spontaneously except some conditions of p 1/2 H 2 /a HF etc. This situation is not changed qualitatively for various states of solvation between oxidized Cr solute in F - solvent and this aspect is substantially similar for the two cases (pair 1: Cr 0 /CrF 3 - / CrF 6 3- ; pair 2: Cr 0 /CrF 4 2- /CrF 5 2- ) considered in this work.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantum mechanical dataset of 836k neutral closed-shell molecules with up to 5 heavy atoms from C, N, O, F, Si, P, S, Cl, Br

Abstract We introduce the Vector-QM24 (VQM24) dataset comprehensively covering all possible neutral closed-shell small organic and inorganic molecules with up to five heavy (p-block) atoms: C, N, O, F, Si, P, S, Cl, Br. All valid stoichiometries, Lewis-rule-consistent graphs, and stable conformers (identified via GFN2-xTB) were enumerated combinatorially, yielding 577k conformational isomers spanning 258k constitutional isomers and 5,599 unique stoichiometries. DFT (ωB97X-D3/cc-pVDZ) optimizations were performed for all, and diffusion quantum Monte Carlo (DMC@PBE0(ccECP/cc-pVQZ)) energies are provided for 10,793 lowest-energy conformers with up to 4 heavy atoms. VQM24 includes structures, vibrational modes, rotational constants, thermodynamic properties (Gibbs free energies, enthalpies, ZPVEs, entropies, heat capacities), and electronic properties such as atomization, electron interaction, exchange-correlation, dispersion energies, multipole moments (dipole to hexadecapole), alchemical potentials, Mulliken charges, and wavefunctions. Machine learning models of atomization energies on this dataset reveal significantly higher complexity than QM9, with none achieving chemical accuracy. VQM24 offers a rigorous, high-fidelity benchmark for evaluating quantum machine learning models.

Science & Technology - Other Topics↗
DOE OSTI2020

Predicting the influence of particle size on the glass transition temperature and viscosity of secondary organic material

Abstract Atmospheric aerosols can assume liquid, amorphous semi-solid or glassy, and crystalline phase states. Particle phase state plays a critical role in understanding and predicting aerosol impacts on human health, visibility, cloud formation, and climate. Melting point depression increases with decreasing particle diameter and is predicted by the Gibbs–Thompson relationship. This work reviews existing data on the melting point depression to constrain a simple parameterization of the process. The parameter $$\xi $$ ξ describes the degree to which particle size lowers the melting point and is found to vary between 300 and 1800 K nm for a wide range of particle compositions. The parameterization is used together with existing frameworks for modeling the temperature and RH dependence of viscosity to predict the influence of particle size on the glass transition temperature and viscosity of secondary organic aerosol formed from the oxidation of $$\alpha $$ α -pinene. Literature data are broadly consistent with the predictions. The model predicts a sharp decrease in viscosity for particles less than 100 nm in diameter. It is computationally efficient and suitable for inclusion in models to evaluate the potential influence of the phase change on atmospheric processes. New experimental data of the size-dependence of particle viscosity for atmospheric aerosol mimics are needed to thoroughly validate the predictions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Connectivity-informed drainage network generation using deep convolution generative adversarial networks

Abstract Stochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation. In this study, Deep Convolutional Generative Adversarial Networks (DCGANs) were applied to quickly reproduce drainage networks from the already generated network samples without repetitive long modeling of the stochastic network model, Gibb’s model. In particular, we developed a novel connectivity-informed method that converts the drainage network images to the directional information of flow on each node of the drainage network, and then transforms it into multiple binary layers where the connectivity constraints between nodes in the drainage network are stored. DCGANs trained with three different types of training samples were compared; (1) original drainage network images, (2) their corresponding directional information only, and (3) the connectivity-informed directional information. A comparison of generated images demonstrated that the novel connectivity-informed method outperformed the other two methods by training DCGANs more effectively and better reproducing accurate drainage networks due to its compact representation of the network complexity and connectivity. This work highlights that DCGANs can be applicable for high contrast images common in earth and material sciences where the network, fractures, and other high contrast features are important.

42 ENGINEERING↗
DOE OSTI2022

A theoretical investigation of the effect of Ga alloying on thermodynamic stability, electronic-structure, and oxidation resistance of Ti 2 AlC MAX phase

We present a systematic investigation of thermodynamic stability, phase-reaction, and chemical activity of Al containing disordered Ti 2 (Al-Ga)C MAX phases using machine-learning driven high-throughput framework to understand the oxidation resistance behavior with increasing temperature and exposure to static oxygen. The A-site (at Al) disordering of Ti 2 AlC with Ga shows significant change in the chemical activity of Al with increasing temperature and exposure to static oxygen, which is expected to enable surface segregation of Al, thereby, the formation of Al 2 O 3 and improved oxidation resistance. We performed in-depth convex hull analysis of ternary Ti-Al-C, Ti-Ga-C, and Ti-Al-Ga-C based MAX phase, and provide detailed contribution arising from electronic, chemical and vibrational entropies. The thermodynamic analysis shows change in the Gibbs formation enthalpy (ΔG form ) at higher temperatures, which implies an interplay of temperature-dependent enthalpy and entropic contributions in oxidation resistance Ga doped Ti 2 AlC MAX phases. A detailed electronic structure and chemical bonding analysis using crystal orbital Hamilton population method reveal the origin of change in phases stability and in oxidation resistance in disorder Ti2(Al 1-x Ga x )C MAX phases. Our electronic structure analysis correlate well with the change in oxidation resistance of Ga doped MAX phases. We believe our study provides a useful guideline to understand to role of alloying on electronic, thermodynamic, and oxidation related mechanisms of bulk MAX phases, which can work as a precursor to understand oxidation behavior of twodimensional MAX phases, i.e., MXenes (transition metal carbides, carbonitrides and nitrides).

36 MATERIALS SCIENCE↗
DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A hybrid calorimetry-simulation model of mixing enthalpy for molten salt

Calorimetric determination of enthalpies of mixing (ΔH mix ) in multicomponent molten salts is often interpreted using empirical models that lack physically meaningful parameters. However, for improving pyrochemical separation of spent nuclear fuel, where lanthanides are major fission products and critical elements, a deeper thermodynamic understanding of the link between excess thermodynamic properties and solvation structure is critically needed. In this work, we implement a hybrid and physics-informed framework, MIVM+Calorimetry+AIMD, which integrates experimentally measured ΔH mix (via high temperature drop calorimetry) with solvation structures from ab initio molecular dynamics (AIMD). This approach is demonstrated using LaCl 3 mixed with eutectic LiCl-KCl (58 mol% – 42 mol%) at 873 K and 1133 K. MIVM-derived parameters enable extrapolation of excess Gibbs energy and La 3+ activity across compositions. In contrast, direct ΔH mix predictions from AIMD and polarizable ion model simulations deviate significantly. By incorporating experimentally benchmarked solvation structures into an interpretable thermodynamic model, the MIVM+Calorimetry+AIMD formalism achieves higher accuracy and generalizable method for studying molten salts, offering a robust path for understanding and optimizing molten salt chemistry relevant to nuclear fuel cycles and separation science.

Goncharov, Vitaliy G. [Washington State Univ., Pul↗
DOE OSTI2020

Freezing water at constant volume and under confinement

Water expands upon freezing. What happens when water is cooled below 0 °C in an undeformable, constant-volume container? This is a fundamental question in materials thermodynamics, and is also relevant in biological, geological, and technological applications in which ice forms under nano-, meso-, or macroscale confinement. Here, we analyze the phase-equilibria and kinetic behaviors of water and ice-1h in an isochoric (constant-volume) system. By making use of the Helmholtz potential F (temperature, volume), in contrast to the Gibbs potential G (temperature, pressure), we demonstrate significant changes in phase behavior when the specific volume of the container is constrained below that of ice-1h. We construct a T–V (temperature–volume) phase diagram for water and ice that features a broad two-phase equilibrium region, and we further derive an isochoric nucleation theory that reveals the existence of a critical confinement volume, on the order of microns, below which ice-1h is kinetically prohibited from forming.

42 ENGINEERING↗
DOE OSTI2020

Effects of catalyst droplets on wire growth and the resulting branched structures during VLS growth

Vapor-liquid-solid (VLS) method is vastly employed to grow hierarchical structures with unique properties. However, key questions remain, such as what controls the branched structures and what the roles of catalyst droplet size are during the growth. Here, an in-depth understanding of the kinetics of the nucleation, growth, and subsequent coalescence processes of Bi liquid catalyst droplets is provided by direct observation of PbSe branched wire growth in an environmental transmission electron microscope. This brings a kinetic control of the branch density by varying the parameters, such as temperature. In addition, the dependence of wire growth rate on the catalyst droplet size is revealed, i.e., the smaller the catalyst size the larger the wire growth rate, unlike the wire growth controlled by Gibbs-Thomson effect, possibly due to different mass transport pathways and atomic surface diffusion. These results extend the fundamental understanding of the VLS growth mechanism of branched structures and benefit the structure design of hierarchical materials with tailored properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Reactivity descriptors in acid catalysis: acid strength, proton affinity and host–guest interactions

Brønsted acids mediate chemical transformations via proton transfer to bound species and interactions between the conjugate anion and bound cationic intermediates and transition states that are also stabilized by van der Waals forces within voids of molecular dimensions in inorganic hosts. This Feature Article describes the relevant descriptors of reactivity in terms of the properties of acids and molecules that determine their ability to donate and accept protons and to reorganize their respective charges to optimize their interactions at bound states. The deprotonation energy (DPE) of the acids and the protonation energy (Eprot) of the gaseous analogs of bound intermediates and transition states reflect their respective properties as species present at non-interacting distances. These properties accurately describe the reactivity of acids of a given type, such as polyoxometalates (POM) with a given type of addenda atom but different central atoms and heterosilicates, for different families of reactions. They do not fully capture, however, differences among acid types (e.g., Mo and W POM, heterosilicates, mineral acids) for diverse types of chemical transformations (e.g., elimination, isomerization, dimerization, condensation). The incompleteness of such descriptors reflects their inability to describe how protonated molecular species and conjugate anions restructure their respective charges when present as a binding pair at interacting distances. Such interaction energies represent electrostatic forces that depend on charge distributions in the cations and anions and the ability to reorganize the distributions to maximize the interactions. In the case of deprotonation, the electrostatic and charge reorganization components of DPE for various acids solely reflect the ability of the conjugate anion to accept and distribute the negative charge, a characteristic unique of each type of solid acid and specifically of the composition of its extended conjugate anion framework. The energy required to accept and rearrange the positive charge in bound intermediates and transition states reflects, in turn, their respective ability to recover the ionic and covalent components of DPE, the energy required to detach proton from conjugate anions. The DPE components and the recovery fractions together lead to a modified DPE, which captures only the part of DPE that remains unrecovered by the ion-pair interactions at bound intermediates and transition states, as the unifying descriptor for broad families of acids and reactions. The electrostatic and charge reorganization energies involved in these general descriptors are placed in historical context by assessing their connections to the heuristics of hard–soft acid–base displacements. Further development of these concepts requires benchmarking and extension of electrostatic and reorganization components of energies for a more diverse set of reaction types and acid families and advancement of methods for more efficient calculations of electrostatic interactions. Reactivity descriptors must also account for dispersive interactions between host cavities and guest molecules, requiring a framework analogous to the one described here for ion-pair interactions; these dispersive interactions depend on the fit between their shapes and sizes as well as their ‘‘structural stiffness’’ that determines the ability to modify the shapes of molecules and voids to minimize free energy. Entropy considerations and estimates of their dependence on properties of catalysts and molecules are also required for accurately determining Gibbs free energies that ultimately determine reaction rates.

Deshlahra, Prashant↗
DOE OSTI2020

Solvent manipulation of the pre-reduction metal–ligand complex and particle-ligand binding for controlled synthesis of Pd nanoparticles

Understanding how to control the nucleation and growth rates is crucial for designing nanoparticles with specific sizes and shapes. Herein, we show that the nucleation and growth rates are correlated with the thermodynamics of metal–ligand/solvent binding for the pre-reduction complex and the surface of the nanoparticle, respectively. To obtain these correlations, we measured the nucleation and growth rates by in situ small angle X-ray scattering during the synthesis of colloidal Pd nanoparticles in the presence of trioctylphosphine in solvents of varying coordinating ability. The results show that the nucleation rate decreased, while the growth rate increased in the following order, toluene, piperidine, 3,4-lutidine and pyridine, leading to a large increase in the final nanoparticle size (from 1.4 nm in toluene to 5.0 nm in pyridine). Using density functional theory (DFT), complemented by 31 P nuclear magnetic resonance and X-ray absorption spectroscopy, we calculated the reduction Gibbs free energies of the solvent-dependent dominant pre-reduction complex and the solvent-nanoparticle binding energy. The results indicate that lower nucleation rates originate from solvent coordination which stabilizes the pre-reduction complex and increases its reduction free energy. At the same time, DFT calculations suggest that the solvent coordination affects the effective capping of the surface where stronger binding solvents slow the nanoparticle growth by lowering the number of active sites (not already bound by trioctylphosphine). The findings represent a promising advancement towards understanding the microscopic connection between the metal–ligand thermodynamic interactions and the kinetics of nucleation and growth to control the size of colloidal metal nanoparticles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Lithium cuprate, a multifunctional material for NO selective catalytic reduction by CO with subsequent carbon oxide capture at moderate temperatures

Li 2 CuO 2 was evaluated as a possible catalyst for the NO selective catalytic reduction (NO SCR) by CO. The initial idea was supported by ab initio thermodynamic analysis. This analysis showed that NO SCR was favored by using Li 2 CuO 2 as a catalyst, based on the calculated heat of reaction and Gibbs free energy. The NO SCR reaction was evaluated under dynamic, isothermal and cyclic conditions using different NO : CO ratios (2 : 1, 1 : 1 and 1 : 2). The dynamic and isothermal experiments showed that Li 2 CuO 2 does reduce NO in the presence of CO to reach higher conversions (>90%) between 400 and 450 °C. However, it must be mentioned that the catalytic process was reduced once some of the produced CO 2 was captured on the ceramic. At that point, Li 2 CuO 2 acts as a bifunctional material, catalyzing the NO SCR reaction and capturing some of the produced CO 2 . Moreover, cyclic experiments evidenced high NO reduction. Finally, a different set of experiments showed that this catalytic reduction performed differently if NO and CO were previously fed. Finally, when lithium cuprate is previously carbonated (initial CO feed), the catalytic reaction does not complete. In contrast, when there was a previous nitrate formation (initial NO feed), the reaction reached 100% efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A comparative study of Bi, Sb, and BiSb for electrochemical nitrogen reduction leading to a new catalyst design strategy

Recent studies identified Bi as one of the most promising non-noble metal elements that can promote the electrochemical N 2 reduction reaction (ENRR) to produce NH 3 . The electronic features that make Bi a promising ENRR catalyst may also be owned by Sb that belongs to the same group as Bi. Thus, the ENRR properties of Bi, Sb, and a BiSb alloy were investigated comparatively to identify common characteristics that facilitate the ENRR. These catalysts were prepared as uniform coating layers on high surface area carbon felt electrodes, which could serve as both regular electrodes and pseudo-gas diffusion electrodes. The experimental results demonstrated that while Bi and Sb show comparable ENRR performances, the formation of a BiSb alloy distinctively increases the faradic efficiency for NH 3 production. Additionally, the X-ray photoelectron spectroscopy results revealed that Bi in BiSb possesses a partial positive charge while Sb in BiSb possesses a partial negative charge, which can impact the way the catalyst surface interacts with the reactants and reaction intermediates of the ENRR and hydrogen evolution reaction (HER), the major competing reaction with the ENRR. Computational investigations including the Bader charge analysis and Gibbs free energy calculations for the elemental steps of the ENRR and HER provided an explanation of how the formation of a BiSb alloy can change the selectivity for the ENRR. The combined experimental and theoretical results and discussion contained in this study lead to a new strategy for designing efficient metal catalysts for the ENRR. Additionally, this study investigated how the use of gas phase and dissolved N 2 affected the ENRR performances of the Bi, Sb, and BiSb catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High throughput exploration of the oxidation landscape in high entropy alloys

We report high entropy alloys (HEAs) have gained interest for structural applications in extreme environments. With a potentially vast chemical and phase space, there are significant opportunities to discover superior performing alloys. Crucial for most high-temperature applications is understanding and mitigating the oxidation behavior of these chemically complex alloys. Most experimental and computational HEA studies have focused on a limited set of compositions and only a fraction these compositions have been characterized for oxidation. We present a high-throughput framework that utilizes density-functional theory (DFT) in concert with a combined machine-learning model and grand-canonical linear programming for assessing phase stability, phase-fraction, chemical activity and high-temperature survivability of arbitrary HEAs. This framework considers temperature dependent contributions to the Gibbs energy of the competing phases arising from short-range order and vibrational entropy. We demonstrate the effectiveness of the framework by assessing the thermodynamic stability, oxidation behavior, chemical activity, and phase decomposition of body-centered cubic Mo-W-Ta-Ti-Zr refractory HEAs. A total of 51 compositions were analyzed and ranked in order of their survivability based on the Pareto-front analysis. Oxidation was performed at 1373 K on fours samples in air showing the difference in oxidation behavior determined experimentally through scale thickness and their mass changes. The insights on oxidation behavior presented in this work will enable the fast assessment of technologically useful HEAs needed for future structural application in extreme conditions

36 MATERIALS SCIENCE↗
DOE OSTI2023

Single and dual metal atom catalysts for enhanced singlet oxygen generation and oxygen reduction reaction

We demonstrate rational design of graphene-supported single and dual metal atom catalysts (SACs and DACs) for photocatalytic applications, such as singlet oxygen ( 1 O 2 ) sensitization and H 2 O 2 production. Here we combine density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations with experimental verifications. We found a synergistic effect between triplet sensitization and triplet–triplet (Dexter) energy transfer; both play a role in the photocatalytic activity through the volcano plot of 3d transition metal SACs. More specifically, FeN 4 -SAC exhibits a low ISC energy gap (ΔE ISC ) of 0.039 eV, compared with 0.108 eV for FeNiN8-DACs, both possessing a high Bader charge transfer of 0.366 e- and 0.405 e-, respectively. Guided by these computational results, we synthesized a series of SACs and a DAC and confirmed their structures with scanning transmission electron microscopy (STEM) along with the X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). We then confirm their band structures with low-energy inverse photoemission spectroscopy (LEIPS) and UV-vis-NIR. Subsequently, we synthesized the catalysts for the photooxygenation of anthracene and two-electron oxygen reduction reaction (ORR) to measure their photocatalytic activity. We found that H 2 O 2 production through the two-electron ORR competes with the 1 O 2 generation through Dexter energy transfer. FeN 4 -SAC demonstrates a high photooxygenation conversion of 86% and a high 1 O 2 quantum yield of 1.04, obtained from electron spin resonance (ESR) spectroscopy, with low H 2 O 2 production. In contrast, NiN 4 -SAC exhibits a low 1 O 2 generation and a high H 2 O 2 production mainly because of the high Gibbs free energy of the OOH* intermediate. In conclusion, this work proposes an effective DFT-guided strategy for designing SACs and DACs for various photocatalytic applications.

36 MATERIALS SCIENCE↗