SEARCH · Engineering Papers

Results for “functioning”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,477 records · Page 82

All sources NASA NTRS DOE OSTI

DOE OSTI2011

Synthesis of Pt/PEI-MWCNT composite materials on polyethyleneimine-functionalized MWNTs as supports

Graphical abstract: Schematic diagram for (a) the preparation of Pt/MWCNT functionalized with PEI and (b) TEM of Pt/PEI-MWCNT. Highlights: Black-Right-Pointing-Pointer We prepared Pt/PEI/MWCNT where MWCNT was first functionalized by PEI (polyethyleneimine) followed by Pt deposit onto it. Black-Right-Pointing-Pointer PEI functionalization provided high density homogeneous functional groups on MWCNT's sidewall. Black-Right-Pointing-Pointer Cationic PEI leads to homogeneous dispersion in solutions such as water and organic solvents. Black-Right-Pointing-Pointer Pt/PEI/MWCNT catalyst exhibits excellent electrocatalytic activity compared to that of Pt/MWCNT catalyst obtained with polyvinylpyrrolidine (PVP). -- Abstract: Composite materials with highly dispersed platinum (Pt) nanoparticles on multiwalled carbon nanotubes (MWCNTs), functionalized with polyethyleneimine (PEI) by a noncovalent method were prepared. The PEI-functionalization provided high density homogeneous functional groups on the MWCNTs' sidewalls for binding Pt nanoparticles. Cationic PEI leads to homogeneous dispersion in solutions such as water and organic solvents. The effects of a reducing agent on the Pt nanoparticles that form on the surfaces of the MWCNT were studied by varying the molar ratio of NaOH to H{sub 2}PtCl{sub 6}. These composite materials were characterized with transmission electron micrograph (TEM), X-ray diffractometer (XRD) and X-ray photoelectron spectroscopy (XPS). The Pt/PEI-MWCNT catalyst exhibits excellent electrocatalytic activity and compared with Pt/PVP-MWCNT catalysts obtained with polyvinylpyrrolidone (PVP). Finally, the cyclic voltammogram of methanol electrooxidation for Pt/PEI-MWCNT shows better tolerance to CO and methanol oxidation to CO{sub 2} than of Pt/PVP-MWCNT.

36 MATERIALS SCIENCE↗

DOE OSTI2012

The effect of smoking cessation pharmacotherapies on pancreatic beta cell function

The goal of our study was to evaluate whether drugs currently used for smoking cessation (i.e., nicotine replacement therapy, varenicline [a partial agonist at nicotinic acetylcholine receptors (nAChR)] and bupropion [which acts in part as a nAChR antagonist]) can affect beta cell function and determine the mechanism(s) of this effect. INS-1E cells, a rat beta cell line, were treated with nicotine, varenicline and bupropion to determine their effects on beta cell function, mitochondrial electron transport chain enzyme activity and cellular/oxidative stress. Treatment of INS-1E cells with equimolar concentrations (1 μM) of three test compounds resulted in an ablation of normal glucose-stimulated insulin secretion by the cells. This disruption of normal beta cell function was associated with mitochondrial dysfunction since all three compounds tested significantly decreased the activity of mitochondrial electron transport chain enzyme activity. These results raise the possibility that the currently available smoking cessation pharmacotherapies may also have adverse effects on beta cell function and thus glycemic control in vivo. Therefore whether or not the use of nicotine replacement therapy, varenicline and bupropion can cause endocrine changes which are consistent with impaired pancreatic function warrants further investigation. -- Highlights: ► Smoking cessation drugs have the potential to disrupt beta cell function in vitro. ► The effects of nicotine, varenicline and bupropion are similar. ► The impaired beta cell function is mediated by mitochondrial dysfunction. ► If similar effects are seen in vivo, these drugs may increase the risk of diabetes.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Activating transcription factor 4 underlies the pathogenesis of arsenic trioxide-mediated impairment of macrophage innate immune functions

Chronic arsenic exposure to humans is considered immunosuppressive with augmented susceptibility to several infectious diseases. The exact molecular mechanisms, however, remain unknown. Earlier, we showed the involvement of unfolded protein response (UPR) signaling in arsenic-mediated impairment of macrophage functions. Here, we show that activating transcription factor 4 (ATF4), a UPR transcription factor, regulates arsenic trioxide (ATO)-mediated dysregulation of macrophage functions. In ATO-treated ATF4{sup +/+} wild-type mice, a significant down-regulation of CD11b expression was associated with the reduced phagocytic functions of peritoneal and lung macrophages. This severe immuno-toxicity phenotype was not observed in ATO-treated ATF4{sup +/−} heterozygous mice. To confirm these observations, we demonstrated in Raw 264.7 cells that ATF4 knock-down rescues ATO-mediated impairment of macrophage functions including cytokine production, bacterial engulfment and clearance of engulfed bacteria. Sustained activation of ATF4 by ATO in macrophages induces apoptosis, while diminution of ATF4 expression protects against ATO-induced apoptotic cell death. Raw 264.7 cells treated with ATO also manifest dysregulated Ca{sup ++} homeostasis. ATO induces Ca{sup ++}-dependent calpain-1 and caspase-12 expression which together regulated macrophage apoptosis. Additionally, apoptosis was also induced by mitochondria-regulated pathway. Restoring ATO-impaired Ca{sup ++} homeostasis in ER/mitochondria by treatments with the inhibitors of inositol 1,4,5-trisphosphate receptor (IP3R) and voltage-dependent anion channel (VDAC) attenuate innate immune functions of macrophages. These studies identify a novel role for ATF4 in underlying pathogenesis of macrophage dysregulation and immuno-toxicity of arsenic. - Highlights: • ATF4 regulates arsenic-mediated impairment in macrophage functions. • Arsenic-mediated alterations in pulmonary macrophage are diminished in ATF4{sup +/−} mice. • Changes in macrophage functions can be attenuated by Ca{sup ++} homeostasis regulators.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Influenza B virus M2 protein can functionally replace its influenza A virus counterpart in promoting virus replication

The M2 protein (AM2 and BM2) of influenza A and B viruses function as a proton channel essential for viral replication. They also carry a cytoplasmic tail whose functions are not fully delineated. It is currently unknown whether these proteins could be replaced functionally in a viral context. Here, we generated single-cycle influenza A viruses (scIAV-ΔHA) carrying various M2-2A-mCherry constructs in the segment 4 (HA) and evaluated their growth in complementing cells. Intriguingly, the scIAV-ΔHA carrying AM2 and that bearing BM2 grew comparably well in MDCK-HA cells. Furthermore, while the virus carrying chimeric B-AM2 in which the BM2 transmembrane fused with the AM2 cytoplasmic tail produced robust infection, the one bearing the AM2 transmembrane fused with the BM2 cytoplasmic tail (A-BM2) exhibited severely impaired growth. Altogether, we demonstrate that AM2 and BM2 are functionally interchangeable and underscore the role of compatibility between transmembrane and cytoplasmic tail of the M2 protein. -- Highlights: •Flu A M2 protein (AM2) can be functionally replaced by that of Flu B (BM2). •Both AM2 and BM2 with extended cytoplasmic tail are functional. •Compatibility between the ion channel and the cytoplasmic tail is critical for M2 function. •M2 with higher ion channel activity may augment influenza virus replication.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Benchmark Calculations of Energetic Properties of Groups 4 and 6 Transition Metal Oxide Nanoclusters Including Comparison to Density Functional Theory

The heats of formation and the normalized clustering energies (NCEs) for the group 4 and group 6 transition metal oxide (TMO) trimers and tetramers have been calculated by the Feller-Peterson-Dixon (FPD) method. The heats of formation predicted by the FPD method do not differ much from those previously derived from the NCEs at the CCSD(T)/aT level except for the CrO3 nanoclusters. New and improved heats of formation for Cr3O9 and Cr4O12 were obtained using PW91 orbitals instead of Hartree-Fock (HF) orbitals. Diffuse functions are necessary to predict accurate heats of formation. The fluoride affinities (FAs) are calculated with the CCSD(T) method. The relative energies (REs) of different isomers, NCEs, electron affinities (EAs), and FAs of (MO2)n ( M = Ti, Zr, Hf, n = 1 – 4 ) and (MO3)n ( M = Cr, Mo, W, n = 1 – 3) clusters have been benchmarked with 55 exchange-correlation DFT functionals including both pure and hybrid types. The absolute errors of the DFT results are mostly less than ±10 kcal/mol for the NCEs and the EAs, and less than ±15 kcal/mol for the FAs. Hybrid functionals usually perform better than the pure functionals for the REs and NCEs. The performance of the two types of functionals in predicting EAs and FAs is comparable. The B1B95 and PBE1PBE functionals provide reliable energetic properties for most isomers. Long range corrected pure functionals usually give poor FAs. The standard deviation of the absolute error is always close to the mean errors and the probability distributions of the DFT errors are often not Gaussian (normal). The breadth of the distribution of errors and the maximum probability are dependent on the energy property and the isomer.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2023

Effect of Linker Structure and Functionalization on Secondary Gas Formation in Metal–Organic Frameworks

Rare-earth terephthalic acid (BDC)-based metal–organic frameworks (MOFs) are promising candidate materials for acid gas separation and adsorption from flue gas streams. However, previous simulations have shown that acid gases (H 2 O, NO 2 , and SO 2 ) react with the hydroxyl on the BDC linkers to form protonated acid gases as a potential degradation mechanism. Herein, gas-phase computational approaches were used to identify the formation energies of these secondary protonated acid gases across multiple BDC linker molecules. Formation energies for secondary protonated acid gases were evaluated using both density functional theory (DFT) and correlated wave function methods for varying BDC–gas reaction mechanisms. Upon validation of DFT to reproduce wave function calculation results, rotated conformational linkers and chemically functionalized BDC linkers with -OH, -NH 2 , and -SH were investigated. Furthermore, the calculations show that the rotational conformation affects the molecule stability. Double-functionalized BDC linkers, where two functional groups are substituted onto BDC, showed varied reaction energies depending on whether the functional groups donate or withdraw electrons from the aromatic system. Based on these results, BDC linker design must balance adsorption performance with degradation via linker dehydrogenation for the design of stable MOFs for acid gas separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Hydrogen Activation by Silica Supported Metal Ion Catalysts: Catalytic Properties of Metals and Performance of DFT Functionals

Single-site heterogeneous catalysts (SSHC) have received increasing attention due to their well-defined active sites and potentially high specific activity. Detailed computational studies were carried out on a set of potential SSHC’s, i.e., silica-supported metal ions, to investigate the reactivity of these catalysts with H2 as well as to evaluate the performance of density functional theory (DFT) methods in conjunction with triple-ζ quality basis sets (i.e., cc-pVTZ) on reaction energetics. The ions considered include 4d and 5d metals as well as several post-transition metal ions. A representative cluster model of silica is used to calculate reaction free energies of the metal hydride formation that results from the heterolytic cleavage of H2 on the M–O bond. The hydride formation free energy is previously shown to be strongly correlated with the catalytic activity of such catalysts for alkene hydrogenation. ONIOM calculations (CCSD(T)//MP2) are used to assess the accuracy and reliability of the MP2 results and it is found that MP2 is a suitable level of theory for gauging the performance of DFT functionals. The performance of various DFT functionals is assessed relative to MP2 results and it is found that the wB97xd and PBE0 functionals have the lowest standard deviation (STD) value while the MN12SX and PBE functionals have the lowest mean absolute deviation (MAD) values. The B3LYP functional is shown to have similar MAD and STD values as the top performing functionals. Potential active SSHC’s for exergonic hydrogen activation predicted in this study include mostly late and post transition metal ions, i.e., Au3+, Pd2+, Pt4+, Pd4+, Ir4+, Hg2+, Rh3+, Pb4+, Tl3+, In3+, Ir3+, Os4+, Cd2+, Ru2+, and Ga3+. This study provides important guidance to future computational studies of such catalyst systems.

Plascencia, Cesar↗

DOE OSTI2017

Multireference Density Functional Theory with Generalized Auxiliary Systems for Ground and Excited States

To describe static correlation, we create a new approach to density functional theory (DFT), which uses a generalized auxiliary system that is of a different symmetry, such as particle number or spin, from that of the physical system. The total energy of the physical system consists of two parts: the energy of the auxiliary system, which is determined with a chosen density functional approximation (DFA), and the excitation energy from an approximate linear response theory that restores the symmetry to that of the physical system, thus rigorously leading to a multideterminant description of the physical system. The electron density of the physical system is different from that of the auxiliary system and is uniquely determined from the functional derivative of the total energy with respect to the external potential. Our energy functional is thus an implicit functional of the physical system density, but an explicit functional of the auxiliary system density. We demonstrate that the total energy minimum and stationary states, describing the ground and excited states of the physical system, can be obtained by a self-consistent optimization with respect to the explicit variable, the generalized Kohn–Sham noninteracting density matrix. We have developed the generalized optimized effective potential method for the self-consistent optimization. Among options of the auxiliary system and the associated linear response theory, reformulated versions of the particle–particle random phase approximation (pp-RPA) and the spin-flip time-dependent density functional theory (SF-TDDFT) are selected for illustration of principle. Numerical findings show that our multireference DFT successfully describes static correlation in bond dissociation and double bond rotation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Tailoring Surface Properties through in Situ Functionality Gradients in Reactively Modified Poly(2-vinyl-4,4-dimethyl azlactone) Thin Films

Generating physical or chemical gradients in thin-film scaffolds is an efficient approach for screening and optimizing an interfacial structure or chemical functionality to create tailored surfaces that are useful because of their wetting, antifouling, or barrier properties. In this paper, the relationship between the structure of poly(2-vinyl-4,4-dimethyl azlactone) (PVDMA) brushes created by the preferential assembly of poly(glycidyl methacrylate)-block-PVDMA diblock copolymers and the ability to chemically modify the PVDMA chains in situ to create a gradient in functionality are examined to investigate how the extent of functionalization affects the interfacial and surface properties. The introduction of a chemical gradient by controlled immersion allows reactive modification to generate position-dependent properties that are assessed by ellipsometry, attenuated total reflectance-Fourier transform infrared spectroscopy, contact angle measurements, and atomic force microscopy imaging. After functionalization of the azlactone rings with n-alkyl amines, ellipsometry confirms an increase in thickness and contact angle measurements support an increase in hydrophobicity along the substrate. These results are used to establish relationships between layer thickness, reaction time, position, and the extent of functionalization and demonstrate that gradual immersion into the functionalizing solution results in a linear change in chemical functionality along the surface. Finally, these findings broadly support efforts to produce tailored surfaces by in situ chemical modification, having application as tailored membranes, protein resistant surfaces, or sensors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Array-Based Machine Learning for Functional Group Detection in Electron Ionization Mass Spectrometry

Mass spectrometry is a ubiquitous technique capable of complex chemical analysis. The fragmentation patterns that appear in mass spectrometry are an excellent target for artificial intelligence methods to automate and expedite the analysis of data to identify targets such as functional groups. To develop this approach, we trained models on electron ionization (a reproducible hard fragmentation technique) mass spectra so that not only the final model accuracies but also the reasoning behind model assignments could be evaluated. The convolutional neural network (CNN) models were trained on 2D images of the spectra using transfer learning of Inception V3, and the logistic regression models were trained using array-based data and Scikit Learn implementation in Python. Our training dataset consisted of 21,166 mass spectra from the United States’ National Institute of Standards and Technology (NIST) Webbook. The data was used to train models to identify functional groups, both specific (e.g., amines, esters) and generalized classifications (aromatics, oxygen-containing functional groups, and nitrogen-containing functional groups). We found that the highest final accuracies on identifying new data were observed using logistic regression rather than transfer learning on CNN models. It was also determined that the mass range most beneficial for functional group analysis is 0–100 m/z. We also found success in correctly identifying functional groups of example molecules selected from both the NIST database and experimental data. Beyond functional group analysis, we also have developed a methodology to identify impactful fragments for the accurate detection of the models’ targets. The results demonstrate a potential pathway for analyzing and screening substantial amounts of mass spectral data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Introduction of Functionality, Selection of Topology, and Enhancement of Gas Adsorption in Multivariate Metal–Organic Framework-177

Metal–organic framework-177 (MOF-177) is one of the most porous materials whose structure is composed of octahedral Zn 4 O(-COO) 6 and triangular 1,3,5-benzenetribenzoate (BTB) units to make a three-dimensional extended network based on the qom topology. This topology violates a long-standing thesis where highly symmetric building units are expected to yield highly symmetric networks. In the case of octahedron and triangle combinations, MOFs based on pyrite (pyr) and rutile (rtl) nets were expected instead of qom. In this study, we have made 24 MOF-177 structures with different functional groups on the triangular BTB linker, having one or more functionalities. We find that the position of the functional groups on the BTB unit allows the selection for a specific net (qom, pyr, and rtl), and that mixing of functionalities (-H, -NH 2 , and -C 4 H 4 ) is an important strategy for the incorporation of a specific functionality (-NO 2 ) into MOF-177 where otherwise incorporation of such functionality would be difficult. Such mixing of functionalities to make multivariate MOF-177 structures leads to enhancement of hydrogen uptake by 25%.

membrane, carbon capture, materials and chemistry ↗

DOE OSTI2019

Function-driven single-cell genomics uncovers cellulose-degrading bacteria from the rare biosphere

Assigning a functional role to a microorganism has historically relied on cultivation of isolates or detection of environmental genome-based biomarkers using a posteriori knowledge of function. However, the emerging field of function-driven single-cell genomics aims to expand this paradigm by identifying and capturing individual microbes based on their in situ functions or traits. To identify and characterize yet uncultivated microbial taxa involved in cellulose degradation, we developed and benchmarked a function-driven single-cell screen, which we applied to a microbial community inhabiting the Great Boiling Spring (GBS) Geothermal Field, northwest Nevada. Our approach involved recruiting microbes to fluorescently labeled cellulose particles, and then isolating single microbe-bound particles via fluorescence-activated cell sorting. The microbial community profiles prior to sorting were determined via bulk sample 16S rRNA gene amplicon sequencing. The flow-sorted cellulose-bound microbes were subjected to whole genome amplification and shotgun sequencing, followed by phylogenetic placement. Next, putative cellulase genes were identified, expressed and tested for activity against derivatives of cellulose and xylose. Alongside typical cellulose degraders, including members of the Actinobacteria, Bacteroidetes, and Chloroflexi, we found divergent cellulases encoded in the genome of a recently described candidate phylum from the rare biosphere, Goldbacteria, and validated their cellulase activity. As this genome represents a species-level organism with novel and phylogenetically distinct cellulolytic activity, we propose the name Candidatus ‘Cellulosimonas argentiregionis’. We expect that this function-driven single-cell approach can be extended to a broad range of substrates, linking microbial taxonomy directly to in situ function.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

FunDiff: diffusion models over function spaces for physics-informed generative modeling

Recent advances in generative modeling-particularly diffusion models and flow matching-have been widely used for synthesizing discrete data such as images and videos. However, adapting these models to physical applications remains challenging, as the quantities of interest are continuous functions governed by complex physical laws. To address this, we introduce FunDiff, an efficient and robust framework for generative modeling in function spaces. FunDiff combines a latent diffusion process with a function autoencoder architecture to handle input functions with varying discretizations, generates continuous functions that can be evaluated at arbitrary locations, and seamlessly incorporate physical priors. These priors are enforced through architectural constraints or physics-informed loss functions, ensuring that generated samples satisfy fundamental physical laws. We theoretically establish minimax optimality guarantees for density estimation in function spaces, demonstrating that diffusion-based estimators achieve optimal convergence rates under suitable regularity conditions. We further demonstrate the practical effectiveness of FunDiff across diverse applications in fluid dynamics and solid mechanics. Empirical results indicate that our method can generate physically consistent samples with high fidelity to the target distribution, and exhibit robustness to noisy and low-resolution data.

Wang, Sifan [Yale University, New Haven, CT (Unite↗

DOE OSTI2021

The three major axes of terrestrial ecosystem function

The leaf economics spectrum and the global spectrum of plant forms and functions revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability. Here we derive a set of ecosystem functions from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Supervised learning of a chemistry functional with damped dispersion

Abstract Kohn–Sham density functional theory is widely used in chemistry, but no functional can accurately predict the whole range of chemical properties, although recent progress by some doubly hybrid functionals comes close. Here, we optimized a singly hybrid functional called CF22D with higher across-the-board accuracy for chemistry than most of the existing non-doubly hybrid functionals by using a flexible functional form that combines a global hybrid meta-nonseparable gradient approximation that depends on density and occupied orbitals with a damped dispersion term that depends on geometry. We optimized this energy functional by using a large database and performance-triggered iterative supervised training. We combined several databases to create a very large, combined database whose use demonstrated the good performance of CF22D on barrier heights, isomerization energies, thermochemistry, noncovalent interactions, radical and nonradical chemistry, small and large systems, simple and complex systems and transition-metal chemistry.

Liu, Yiwei (ORCID:0000000288126163)↗

DOE OSTI2018

How accurate are static polarizability predictions from density functional theory? An assessment over 132 species at equilibrium geometry

Static polarizabilities are the first response of the electron density to electric fields, and are therefore important for predicting intermolecular and molecule-field interactions. They also offer a global measure of the accuracy of the treatment of excited states by density functionals in a formally exact manner. We have developed a database of benchmark static polarizabilities for 132 small species at equilibrium geometry, using coupled cluster theory through triple excitations (extrapolated to the complete basis set limit), for the purpose of developing and assessing density functionals. The performance of 60 popular and recent functionals are also assessed, which indicates that double hybrid functionals perform the best, having RMS relative errors in the range of 2.5-3.8%. Many hybrid functionals also give quite reasonable estimates with 4-5% RMS relative error. A few meta-GGAs like mBEEF and MVS yield performance comparable to hybrids, indicating potential for improved excited state predictions relative to typical local functionals. Some recent functionals however are found to be prone to catastrophic failure (possibly as a consequence of overparameterization), indicating a need for caution in applying these.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2021

Challenges for density functional theory: calculation of CO adsorption on electrocatalytically relevant metals

Density Functional Theory (DFT) is currently the most tractable choice of theoretical model used to understand the mechanistic pathways for electrocatalytic processes such as CO 2 or CO reduction. In this work, we assess the performance of two DFT functionals designed specifically to describe surface interactions, RTPSS and RPBE, as well as two popular meta-GGA functionals, SCAN and B97M-rV, that have not been a priori optimized for better interfacial properties. We assess all four functionals against available experimental data for prediction of bulk and bare surface properties on four electrocatalytically relevant metals, Au, Ag, Cu, and Pt, and for binding CO to surfaces of these metals. To partially mitigate issues such as thermal and anharmonic corrections associated with comparing computations with experiments, molecular benchmarks against high level quantum chemistry are reported for CO complexes with Au, Ag, Cu and Pt atoms, as well as the CO–water complex and the water dimer. Overall, we find that the surface modified RPBE functional performs reliably for many of the benchmarks examined here, and the meta-GGA functionals also show promising results. Specifically B97M-rV predicts the correct site preference for CO binding on Ag and Au (the only functional tested here to do so), while RTPSS performs well for surface relaxations and binding of CO on Pt and Cu.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Assessing cathode property prediction via exchange-correlation functionals with and without long-range dispersion corrections

In this work, we benchmark calculated interlayer spacings, average topotactic voltages, thermodynamic stabilities, and band gaps in layered lithium transition-metal oxides (TMOs) and their de-lithiated counterparts, which are used in lithium-ion batteries as positive electrode materials, against available experimental data. Specifically, we examine the accuracy of properties calculated within density functional theory (DFT) using eight different treatments of electron exchange-correlation: the strongly constrained and appropriately normed (SCAN) and Perdew–Burke–Ernzerhof (PBE) density functionals, Hubbard-U-corrected SCAN and PBE (i.e., SCAN+U and PBE+U), and SCAN(+U) and PBE(+U) with added long-range dispersion (D) interactions (i.e., DFT(+U)+D). van der Waals interactions are included respectively via the revised Vydrov-Van Voorhis (rVV10) for SCAN(+U) and the DFT-D3 for PBE(+U). We find that SCAN-based functionals predict larger voltages due to an underestimation of stability of the MO2 systems, while also predicting smaller interlayer spacings compared to their PBE-based counterparts. Furthermore, adding dispersion corrections to PBE has a greater effect on voltage predictions and interlayer spacings than with SCAN, indicating that DFT-SCAN – despite being a ground-state theory – fortuitously captures some short and medium-range dispersion interactions better than PBE. While SCAN-based and PBE-based functionals yield qualitatively similar band gap predictions, there is no significant quantitative improvement of SCAN-based functionals over the corresponding PBE-based versions. Finally, we expect SCAN-based functionals to yield more accurate property predictions than the respective PBE-based functionals for most TMOs, given SCAN's stronger theoretical underpinning and better predictions of systematic trends in interlayer spacings, intercalation voltages, and band gaps obtained in this work.

25 ENERGY STORAGE↗