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Results for “Density functional theory simulations”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modeling separation of lanthanides via heterogeneous ligand binding

Individual lanthanide elements have physical/electronic/magnetic properties that make each useful for specific applications. Several of the lanthanides cations (Ln 3+ ) naturally occur together in the same ores. They are notoriously difficult to separate from each other due to their chemical similarity. Predicting the Ln 3+ differential binding energies (ΔΔE) or free energies (ΔΔG) at different binding sites, which are key figures of merit for separation applications, will help design of materials with lanthanide selectivity. We apply ab initio molecular dynamics (AIMD) simulations and density functional theory (DFT) to calculate ΔΔG for Ln 3+ coordinated to ligands in water and embedded in metal–organic frameworks (MOFs), and ΔΔE for Ln 3+ bonded to functionalized silica surfaces, thus circumventing the need for the computational costly absolute binding (free) energies ΔG and ΔE. Perturbative AIMD simulations of water-inundated simulation cells are applied to examine the selectivity of ligands towards adjacent Ln 3+ in the periodic table. Static DFT calculations with a full Ln 3+ first coordination shell, while less rigorous, show that all ligands examined with net negative charges are more selective towards the heavier lanthanides than a charge-neutral coordination shell made up of water molecules. Amine groups are predicted to be poor ligands for lanthanide-binding. Finally, we also address cooperative ion binding, i.e., using different ligands in concert to enhance lanthanide selectivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Experimental and Computational Studies on Superhard Material Rhenium Diboride under Ultrahigh Pressures

An emerging class of superhard materials for extreme environment applications are compounds formed by heavy transition metals with light elements. In this work, ultrahigh pressure experiments on transition metal rhenium diboride (ReB 2 ) were carried out in a diamond anvil cell under isothermal and non-hydrostatic compression. Two independent high-pressure experiments were carried out on ReB 2 for the first time up to a pressure of 241 GPa (volume compression V/V 0 = 0.731 ± 0.004), with platinum as an internal pressure standard in X-ray diffraction studies. The hexagonal phase of ReB 2 was stable under highest pressure, and the anisotropy between the a-axis and c-axis compression increases with pressure to 241 GPa. The measured equation of state (EOS) above the yield stress of ReB 2 is well represented by the bulk modulus K 0 = 364 GPa and its first pressure derivative K 0 ´ = 3.53. Corresponding density-functional-theory (DFT) simulations of the EOS and elastic constants agreed well with the experimental data. DFT results indicated that ReB 2 becomes more ductile with enhanced tendency towards metallic bonding under compression. The DFT results also showed strong crystal anisotropy up to the maximum pressure under study. The pressure-enhanced electron density distribution along the Re and B bond direction renders the material highly incompressible along the c-axis. Our study helps to establish the fundamental basis for anisotropic compression of ReB 2 under ultrahigh pressures

36 MATERIALS SCIENCE↗
DOE OSTI2020

Chemical and steric effects in simulating noncontact atomic force microscopy images of organic molecules on a Cu (111) substrate

Noncontact mode of atomic force microscopy (nc-AFM) employing a CO-functionalized tip is a very powerful tool for studying molecular structures. However, interpreting nc-AFM images for nonplanar molecules can sometimes be problematic. To illustrate and resolve the nature of such problematic systems, we employ real-space pseudopotentials constructed within density functional theory to simulate nc-AFM images. In this work, we focus on several representative nonplanar organic molecules (pentacene, naphthanthrone, olympicene, and 6-phenylhexa-1,3,5-triynybenzene (PHTB)) on a typical substrate: the Cu (111) surface. This substrate results in significant distortions in the molecular geometries of pentacene and naphthanthrone. Including these distortions in simulated nc-AFM imaging notably improves the agreement between the simulated and measured images. In naphthanthrone, the relatively large interaction between the O atom and the Cu substrate offers a straightforward explanation for the absence of the C = O bond in the measured image. Nonplanar features such as tilting or twisting are also apparent in olympicene and PHTB. A “triangular” bright feature associated with the –CH 2 group in olympicene appears in simulated and measured nc-AFM images. This feature is directly related to the tilting angle of the molecule with respect to the Cu substrate. The “defective” benzene ring feature and the faint ellipsoidal C ≡ C feature in PHTB can be ascribed to its twisted nature. The ability to simulate such subatomic images in nc-AFM reflects the accuracy and efficiency of calculating quantum forces in real space.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Comparing the Expense and Accuracy of Methods to Simulate Atomic Vibrations in Rubrene

Atomic vibrations can inform about materials properties from hole transport in organic semiconductors to correlated disorder in metal–organic frameworks. Currently, there are several methods for predicting these vibrations using simulations, but the accuracy–efficiency tradeoffs have not been examined in depth. In this work, rubrene is used as a model system to predict atomic vibrational properties using six different simulation methods: density functional theory, density functional tight binding, density functional tight binding with a Chebyshev polynomial-based correction, a trained machine learning model, a pretrained machine learning model called ANI-1, and a classical forcefield model. The accuracy of each method is evaluated by comparison to the experimental inelastic neutron scattering spectrum. All methods discussed here show some accuracy across a wide energy region, though the Chebyshev-corrected tight-binding method showed the optimal combination of high accuracy with low expense. We then offer broad simulation guidelines to yield efficient, accurate results for inelastic neutron scattering spectrum prediction.

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

Long-range hydrogen-binding effects of carbide interfaces in iron

A micromechanics model was developed to evaluate the elastic binding energy between carbide precipitates and hydrogen interstitials using Eshelby's equivalent inclusion method. In this work, density functional theory (DFT) simulations were performed to obtain the material-specific quantities, e.g., lattice constants and the elastic constants, for the continuum model. Using this model, we find that for coherent carbide precipitates, hydrogen atoms are more likely to bind on the broad surfaces of the disk-like precipitates, which is consistent with experimental observations. For semicoherent and incoherent precipitates, our model suggests that it is possible for semicoherent precipitates to have significant hydrogen binding capability while there is no hydrogen-binding capability of incoherent precipitates, which also agrees with experimental findings. In addition, several factors that influence the binding energies between hydrogen atoms and carbide precipitates were quantitatively analyzed, including the precipitate size, morphology, orientation, and interface. These collective results include both the position and the value of the strongest hydrogen-binding interaction for a wide range of carbide stoichiometries, which contributes to our understanding of hydrogen trapping in steel-based materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Energetics and Structure of Ag–Water Clusters Formed in Mordenite

Zeolite-supported Ag 0 clusters have broad applications from catalysis to medicine, necessitating a mechanistic understanding of the formation of Ag 0 clusters in situ. Density functional theory (DFT) simulations have been performed on silver, water, and silver–water clusters in silica mordenite (Si-MOR), to identify the role of the confinement on the structure and energetics of Ag 0 cluster formation. The most favorable binding energy in the 12-membered ring (MR) pore of the Si-MOR is a 10–15-atom Ag 0 cluster. Computational pair distribution function (PDF) data indicates that the Ag 0 and Ag 0 –H 2 O clusters formed in vacuum versus in Si-MOR exhibit structural differences. Additionally, when the Ag 0 cluster is confined, the density decreases and the surface area increases, hypothesized to be due to the limiting geometry of the 12-MR main channel. An energetic drive toward formation of larger Ag 0 clusters was also identified, with hydrated silver atoms generating higher energy structures. In conclusion, this work identifies mechanistic and structural insight into the role of nanoconfinement on formation of Ag 0 clusters in mordenite.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Selective and Stable Ethanol Synthesis via Electrochemical CO 2 Reduction in a Solid Electrolyte Reactor

Electrochemical CO 2 reduction to ethanol faces challenges such as low selectivity, a product mixture with liquid electrolyte, and poor catalyst/reactor stability. Here, we developed a grain-rich zinc-doped Cu 2 O precatalyst that presented a high ethanol Faradaic efficiency of over 40% under a current density of 350 mA·cm –2 . Our density functional theory (DFT) simulation suggested that Zn atoms inside the structure have a greater carbophilicity than the Cu atoms to help facilitate *CHCHO formation, a key reaction intermediate toward ethanol instead of other C 2 products. Here, a high Faradaic efficiency ratio between ethanol and ethylene (FE EtOH /FE C2H4 ) reached 2.34 in the zinc-doped Cu 2 O precatalyst, representing an over 4-fold improvement compared to bare Cu 2 O precatalyst. By integrating this Cu-based catalyst into a porous solid electrolyte (PSE) reactor with a salt-managing design, we achieved stable ethanol production for over 180 h under a current density of 250 mA·cm –2 while maintaining ethanol selectivity at ~30%.

09 BIOMASS FUELS↗
DOE OSTI2024

Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data

Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.

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

Electronic tuning of confined sub-nanometer cobalt oxide clusters boosting oxygen catalysis and rechargeable Zn–air batteries.

Reasonable design of robust bifunctional oxygen catalysts from an electronic structure perspective is intriguing and challenging for the development of high active rechargeable zinc-air batteries (ZABs). In this study, the favorable regulation of the electronic structure of the cobalt oxide nanoclusters was firstly predicted by density functional theory (DFT) simulation, and then experimentally verified by confining sub-nanometer CoOx clusters (0.86 nm) into the small pore of ZIF-8 derived N-doped nanomaterials (PNC) using a microporous MOFs confinement strategy. The confined effect of the MOF micropores not only enhanced the stability of the subnanometer cobalt oxide clusters, but also make it coupled with Co-Nx to further regulate the electronic structure of the former, synergistic resulting in enhanced ORR/OER actives. As a result, the optimized 0.05CoOx@PNC catalyst demonstrates outstanding bifunctional oxygen performance with a smaller potential gap of 0.67 V. Moreover, the rechargeable Zn-air batteries integrated 0.05CoOx@PNC air cathode displays encouraging performance with a peak power density of 157.1 mW cm(-2), a specific capacity of 887 mAh g(Zn)(-1)at 10 mA cm(-2) and long-term cyclability for over 200 h, significantly outperforming the benchmark electrode couple consisted of Pt/C/RuO2. DFT calculation further revealed that reducing particle size and coupling with Co-N could effectively regulate the charge distribution of CoOx nanoclusters and downshift the D-band center of Co adsorption sites in CoOx nanoclusters, which reduced the reaction barrier of intermediate O-2* and OH* and ORR/ OER over potential, thus accelerating the overall ORR/OER kinetic process. This work offers a novel reference for the construction of a robust sub-nanometer cluster catalysts in the field of ZABs.

Bifunctional oxygen electrocatalysts↗
DOE OSTI2020

Comparative study of Minnesota functionals performance on ferroelectric BaTiO 3 and PbTiO 3

Density functional theory based simulations are the leading tool for the computational investigation of ferroelectrics and the parametrization of their classical potentials. However, the predictions often depend strongly on the exchange correlation functional. The most popular choices, LDA and GGA, tend to underestimate or overestimate some structural, electric, and energy properties. These impede development of highly accurate classical potentials that extend the reach of first-principles simulations to finite temperatures and realistic sizes. In this work, we investigate the performance of recently developed Minnesota exchange correlation functionals on prototypical ferroelectrics BaTiO 3 , and PbTiO 3 , in comparison with some popular ones. We find that there exists strong correlation between predictions for some properties (tetragonality, phase energy difference, and polarization) by different functionals. Along the correlation line, we find a range of functionals (including some from the Minnesota suite) whose predictions fall in between those of LDA and GGA and, therefore, offer an improvement. A way to relatively rank functional performance with respect to chosen benchmarks is proposed and applied to identify the top performers for BaTiO 3 and PbTiO 3 . Here, the performance is found to be material dependent. Therefore, we propose that the performance assessment carried out in this work is employed to "screen" functionals prior to their use on ferroelectrics. Quick and computationally inexpensive, it is likely to lead to improved descriptions, especially for classical potential parametrization. The Minnesota functional suite is found to be suitable for this task.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Computational synthesis of 2D materials: A high-throughput approach to materials design

2D materials find promising applications in next-generation devices, however, large-scale, low-defect, and reproducible synthesis of 2D materials remains a challenging task. Here, to assist in the selection of suitable substrates for the synthesis of as-yet hypothetical 2D materials, we have developed an open-source high-throughput workflow package, Hetero2d, that searches for low-lattice mismatched substrate surfaces for any 2D material and determines the stability of these 2D-substrate heterostructures using density functional theory (DFT) simulations. Hetero2d automates the generation of 2D-substrate heterostructures, the creation of DFT input files, the submission and monitoring of computational jobs on supercomputing facilities, and the storage of relevant parameters alongside the post-processed results in a MongoDB database. We demonstrate the capability of Hetero2d in identifying stable 2D-substrate heterostructures for four 2D materials, namely 2H-MoS 2 , 1T- and 2H-NbO 2 , and hexagonal-ZnTe, considering 50 cubic elemental substrates. We find Cu, Hf, Mn, Nd, Ni, Pd, Re, Rh, Sc, Ta, Ti, V, W, Y, and Zr substrates sufficiently stabilize the formation energies of these 2D materials, with binding energies in the range of ~0.1–0.6 eV/atom. Upon examining the z-separation, the charge transfer, and the electronic density of states at the 2D-substrate interface, we find a covalent type bonding at the interface which suggests that these substrates can be used as contact materials for the 2D materials. Hetero2d is available on GitHub as an open-source package under the GNU license.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Understanding of three different polyvinylpyrrolidone (PVP) based battery binders blends on graphene surfaces from first principles via DFT simulations

Binders play a crucial role in binding electrodes, current collectors, and conductive agents during battery manufacturing. The selection of the binders and their blends considerably affect various properties such as mechanical durability, adhesion performance, ionic/electronic conductivities, and solid electrolyte interface stability for lithium-ion batteries. In this work, polyvinylidene di-fluoride (PVDF), polyacrylic acid (PAA), polyvinylpyrrolidone (PVP), and lithiated polyacrylic acid (Li-PAA) were selected as the model binders, and their blends were studied on graphene surfaces. Further, among them, PVDF is hardly soluble in water unlike the other binders, so in practice, the blend of PVP:PVDF is not thought to be a proper combination. However, the PVP:PVDF binder has also been included because the purpose of this work is to calculate several electronic properties such as binding energies, intermolecular interaction energies, bond critical points, electron density, and the Laplacian of electron density at critical bonding regions of all these blends at the molecular level via density functional theory (DFT) simulations in order to evaluate and compare how is the interaction strength and bonding type of three different lithium-ion batteries (LIBs) binder blends on graphene surfaces. Most stable binder pairs and their binding mechanisms on graphene surfaces were studied. The delocalization of lithium in Li-PAA was studied with the presence of water.

25 ENERGY STORAGE↗
DOE OSTI2024

High‐Energy LiNiO 2 Li Metal Batteries Enabled by Hybrid Electrolyte Consisting of Ionic Liquid and Weakly Solvating Fluorinated Ether

Abstract In pursuit of the highest possible energy density, researchers shift their focus to the ultimate anode material, lithium metal (Li 0 ), and high‐capacity cathode materials with high nickel content (Ni > 80%). The combination of these aggressive electrodes presents unprecedented challenges to the electrolyte. Here, we report a hybrid electrolyte consisting of a highly fluorinated ionic liquid and a weakly solvating fluorinated ether, whose hybridization structure enables the reversible operation of a battery chemistry based on Li 0 and LiNiO 2 (Ni = 100%), delivering nearly theoretical capacity of the latter (up to 249 mAh g −1 ) for >300 cycles with retention of 78.6% and in absence of unwanted morphological changes in both electrodes. Extensive characterization assisted by molecular dynamic simulation and density functional theory calculations reveals the function of the fluorinated ether to be far more profound than simple dilution and viscosity reduction. Instead, it induces drastic changes in Li + ‐solvation environment, the consequence of which engenders simultaneous stabilization of electrode/electrolyte and interfacing via formation of respective interfacial chemistries. This study further unlocks fundamental knowledge underneath the prevailing “diluent strategy” that is extensively applied by the electrolyte researchers and opens more design space for the next‐generation electrolytes and interphases for these coveted battery chemistries.

25 ENERGY STORAGE↗
DOE OSTI2025

High-Temperature Gas Sensor Materials with Properties Predicted via First-Principles Calculations with Machine Learning Modeling and Experimental Corroboration

Understanding the temperature dependence of functional properties of sensing materials is vital for their applications in combustion environments. The electron-phonon coupling that derives the electronic structure change with temperatures is a key property of interest as it affects other sensing responses. Herein, we first assess the temperature dependence of band gap renormalization in sensing materials by employing Allen-Heine-Cardona (AHC) theory with density functional theory (DFT) simulations corroborated with experimental observation. As the AHC calculations are impractical for high-throughput screening of materials, we employ data-driven Gaussian process regression to predict the parameters employed in the O’Donnell empirical model from a set of physical features. To mitigate the reliability issues arising from the small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as to quantify the uncertainty associated with theoretical predictions. These models capture well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions and, therefore, the variation of bandgap as a function of temperature for other novel materials. The predicted candidates from machine learning models are further validated by experiments and DFT calculations.

bandgap renormalization↗
DOE OSTI2026

Disorder-induced magnetoelastic behaviors of MnTexSbyBi1-x-y alloys

This dataset contains input and output files from density functional theory (DFT) simulations used to study the disorder-induced magnetoelastic behaviors of MnTexSbyBi1-x-y (0 ≤ x + y ≤ 1) alloys and their binary end members MnTe, MnSb, and MnBi. The alloys adopt the hexagonal NiAs-type (nickeline) structure and span ternary (MnTexSb1-x, MnTexBi1-x, MnBixSb1-x), and quaternary compositions across the full MnTe–MnSb–MnBi composition triangle. For each alloy composition, the dataset provides DFT calculations in three magnetic configurations: A-type antiferromagnetic (AFM), C-type AFM, and ferromagnetic (FM). Every magnetic configuration folder contains the fully relaxed crystal structure (CONTCAR), VASP input parameters (INCAR), and the main VASP output file (OUTCAR), from which total electronic energies, Mn magnetic moments, lattice parameters, and percent volume changes between magnetic states are extracted. These data are used to construct compositional phase diagrams, evaluate thermodynamic stability (formability), and map magnetoelastic responses across the alloy space. For A-type AFM and FM configurations, additional data are provided as follows: (i) FORCE_CONSTANTS and thermal_properties.yaml files at the top level of A-type_AFM/ and FM/ folders — present only for compositions marked with an asterisk (*) in Table I of the main text. These are derived from Phonopy finite-displacement calculations on full disordered 128-atom supercells and provide vibrational free energy, entropy (Svib)contribution from explicit disorder calculations. (Table I of the associated main manuscript) (ii) A VCA/ subfolder within A-type_AFM/ and FM/, containing FORCE_CONSTANTS and thermal_properties.yaml from Virtual Crystal Approximation phonon calculations (without spin-orbit coupling). VCA data are available for all compositions and are used to estimate vibrational contributions to the Gibbs free energy across the full composition space. (iii) A SOC/ subfolder containing CONTCAR, INCAR, and OUTCAR from spin-orbit coupling calculations, providing relativistic corrections to electronic energies and lattice parameters (Tables S2–S3 of the SM, and Table I of the main manuscript). (iv) A SOC/VCA/ subfolder containing FORCE_CONSTANTS and thermal_properties.yaml from VCA phonon calculations performed within the SOC framework, combining relativistic and vibrational thermodynamic corrections. The computed properties are used to map the AFM–FM magnetic crossover near MnTe0.75Sb0.25, demonstrate disorder- and spin-induced phonon broadening, identify a semiconductor-to-metal crossover, and quantify the pronounced magnetoelastic volume response near the magnetic phase boundary.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Radiative and Nonradiative Recombinations in Organic Radical Emitters: The Effect of Guest–Host Interactions

Radical-carrying organic molecules have received significant attention to bypass the issue related to harvesting triplet excitons in current light-emitting materials. While the computational efforts conducted so far have treated these radical emitters as isolated entities, in actual devices, they are embedded in a host matrix and subject to emitter–host interactions. In this study, by combining molecular dynamics simulations and density functional theory calculations, the impact of the host matrix on the optoelectronic performance of radical emitters is evaluated, taking as a representative example the (4-ncarbazolyl-2,6-dichlorophenyl)bis(2,4,6-trichlorophenyl)-methyl (TTM-3NCz) radical emitter dispersed in a 4,4-bis(carbazol-9-yl)biphenyl (CBP) host. A morphological analysis shows that steric effects around the radical centers, carried by the TTM electron-poor moieties of the emitters, disfavor π–π interactions with the host molecules, which leads to random intermolecular orientations around the TTM moieties. The 3NCz electron-rich moieties of the emitters, however, have much lesser spatial hindrance for intermolecular π–π stacking, which modulates the structural and electronic properties of the emitters in the host matrix. The influence of dynamic and static disorders on the radiative and nonradiative recombination processes is also investigated and it is found that the rates of nonradiative recombination are small, which opens the way to 100% internal quantum efficiency for the doublet-based emission process.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Identifying Dense NiSe 2 /CoSe 2 Heterointerfaces Coupled with Surface High-Valence Bimetallic Sites for Synergistically Enhanced Oxygen Electrocatalysis

Constructing heterostructures with abundant interfaces is essential for integrating the multiple functionalities in single entities. Herein, the synthesis of NiSe 2 /CoSe 2 heterostructures with different interfacial densities via an innovative strategy of successive ion injection is reported. The resulting hybrid electrocatalyst with dense heterointerfaces exhibits superior electrocatalytic properties in an alkaline electrolyte, superior to other benchmarks and precious metal catalysts. Advanced synchrotron techniques, post structural characterizations, and density functional theory (DFT) simulations reveal that the introduction of atomic-level interfaces can lower the oxidation overpotential of bimetallic Ni and Co active sites (whereas Ni$^{2+}$ can be more easily activated than Co$^{2+}$) and induce the electronic interaction between the core selenides and surface in situ generated oxides/hydroxides, which play a critical role in synergistically reducing energetic barriers and accelerating reaction kinetics for catalyzing the oxygen evolution. Hence, the heterointerface structure facilitates the catalytic performance enhancement via increasing the intrinsic reactivity of metallic atoms and enhancing the synergistic effect between the inner selenides and surface oxidation species. This work not only complements the understanding on the origins of the activity of electrocatalysts based on metal selenides, but also sheds light on further surface and interfacial engineering of advanced hybrid materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Defect-Promoted Ni-Based Layer Double Hydroxides with Enhanced Deprotonation Capability for Efficient Biomass Electrooxidation

Ni-based hydroxides are promising electrocatalysts for biomass oxidation reactions, supplanting the oxygen evolution reaction (OER) due to lower overpotentials while producing value-added chemicals. The identification and subsequent engineering of their catalytically active sites are essential to facilitate these anodic reactions. Herein, the proportional relationship between catalysts’ deprotonation propensity and Faradic efficiency of 5-hydroxymethylfurfural (5-HMF)-to-2,5 furandicarboxylic acid (FDCA, FEFDCA) is revealed by thorough density functional theory (DFT) simulations and atomic-scale characterizations, including in situ synchrotron diffraction and spectroscopy methods. The deprotonation capability of ultrathin layer-double hydroxides (UT-LDHs) is regulated by tuning the covalency of metal (M)-oxygen (O) motifs through defect site engineering and selection of M 3+ co-chemistry. NiMn UT-LDHs show an ultrahigh FE FDCA of 99% at 1.37 V versus reversible hydrogen electrode (RHE) and retain a high FE FDCA of 92.7% in the OER-operating window at 1.52 V, about 2× that of NiFe UT-LDHs (49.5%) at 1.52 V. Ni–O and Mn–O motifs function as dual active sites for HMF electrooxidation, where the continuous deprotonation of Mn–OH sites plays a dominant role in achieving high selectivity while suppressing OER at high potentials. The results showcase a universal concept of modulating competing anodic reactions in aqueous biomass electrolysis by electronically engineering the deprotonation behavior of metal hydroxides, anticipated to be translatable across various biomass substrates.

36 MATERIALS SCIENCE↗