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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

On the application of under-decimated filter banks

Maximally decimated filter banks have been extensively studied in the past. A filter bank is said to be under-decimated if the number of channels is more than the decimation ratio in the subbands. A maximally decimated filter bank is well known for its application in subband coding. Another application of maximally decimated filter banks is in block filtering. Convolution through block filtering has the advantages that parallelism is increased and data are processed at a lower rate. However, the computational complexity is comparable to that of direct convolution. More recently, another type of filter bank convolver has been developed. In this scheme, the convolution is performed in the subbands. Quantization and bit allocation of subband signals are based on signal variance, as in subband coding. Consequently, for a fixed rate, the result of convolution is more accurate than is direct convolution. This type of filter bank convolver also enjoys the advantages of block filtering, parallelism, and a lower working rate. Nevertheless, like block filtering, there is no computational saving. In this article, under-decimated systems are introduced to solve the problem. The new system is decimated only by half the number of channels. Two types of filter banks can be used in the under-decimated system: the discrete Fourier transform (DFT) filter banks and the cosine modulated filter banks. They are well known for their low complexity. In both cases, the system is approximately alias free, and the overall response is equivalent to a tunable multilevel filter. Properties of the DFT filter banks and the cosine modulated filter banks can be exploited to simultaneously achieve parallelism, computational saving, and a lower working rate. Furthermore, for both systems, the implementation cost of the analysis or synthesis bank is comparable to that of one prototype filter plus some low-complexity modulation matrices. The individual analysis and synthesis filters have complex coefficients in the DFT filter banks but have real coefficients in the cosine modulated filter banks.

Lin, Y.-P.↗
NASA NTRS1996

The Calculation of Accurate Harmonic Frequencies of Large Molecules: The Polycyclic Aromatic Hydrocarbons, a Case Study

The vibrational frequencies and infrared intensities of naphthalene neutral and cation are studied at the self-consistent-field (SCF), second-order Moller-Plesset (MP2), and density functional theory (DFT) levels using a variety of one-particle basis sets. Very accurate frequencies can be obtained at the DFT level in conjunction with large basis sets if they are scaled with two factors, one for the C-H stretches and a second for all other modes. We also find remarkably good agreement at the B3LYP/4-31G level using only one scale factor. Unlike the neutral PAHs where all methods do reasonably well for the intensities, only the DFT results are accurate for the PAH cations. The failure of the SCF and MP2 methods is caused by symmetry breaking and an inability to describe charge delocalization. We present several interesting cases of symmetry breaking in this study. An assessment is made as to whether an ensemble of PAH neutrals or cations could account for the unidentified infrared bands observed in many astronomical sources.

Bauschlicher, Charles W., Jr.↗
NASA NTRS1997

The Vibrational Frequencies of CaO2, ScO2, and TiO2: A Comparison of Theoretical Methods

The vibrational frequencies of several states of CaO2, ScO2, and TiO2 are computed at using density functional theory (DFT), the Hatree-Fock approach, second order Moller-Plesset perturbation theory (MP2), and the complete-active-space self-consistent-field theory. Three different functionals are used in the DFT calculations, including two hybrid functionals. The coupled cluster singles and doubles approach including the effect of unlinked triples, determined using perturbation theory, is applied to selected states. The Becke-Perdew 86 functional appears to be the cost effective method of choice, although even this functional does not perform well for one state of CaO2. The MP2 approach is significantly inferior to the DFT approaches.

Rosi, Marzio↗
NASA NTRS2004

Theoretical Investigation of OCN(-) Charge Transfer Complexes in Condensed Phase Media: Spectroscopic Properties in Amorphous Ice

Density functional theory (DFT) calculations of cyanate (OCN(-)) charge-transfer complexes were performed to model the "XCN" feature observed in interstellar icy grain mantles. OCN(-) charge-transfer complexes were formed from precursor combinations of HNCO or HOCN with either NH3 or H2O. Three different solvation strategies for realistically modeling the ice matrix environment were explored, including (1) continuum solvation, (2) pure DFT cluster calculations, and (3) an ONIOM DFT/PM3 cluster calculation. The model complexes were evaluated by their ability to reproduce seven spectroscopic measurements associated with XCN: the band origin of the OCN(-) asymmetric stretching mode, shifts in that frequency due to isotopic substitutions of C, N, O, and H, plus two weak features. The continuum solvent field method produced results consistent with some of the experimental data but failed to account for other behavior due to its limited capacity to describe molecular interactions with solvent. DFT cluster calculations successfully reproduced the available spectroscopic measurements very well. In particular, the deuterium shift showed excellent agreement in complexes where OCN(-) was fully solvated. Detailed studies of representative complexes including from two to twelve water molecules allowed the exploration of various possible solvation structures and provided insights into solvation trends. Moreover, complexes arising from cyanic or isocyanic acid in pure water suggested an alternative mechanism for the formation of OCN(-) charge-transfer complexes without the need for a strong base such as NH3 to be present. An extended ONIOM (B3LYP/PM3) cluster calculation was also performed to assess the impact of a more realistic environment on HNCO dissociation in pure water.

Park, Jin-Young↗
NASA NTRS2003

Parallel Subconvolution Filtering Architectures

These architectures are based on methods of vector processing and the discrete-Fourier-transform/inverse-discrete- Fourier-transform (DFT-IDFT) overlap-and-save method, combined with time-block separation of digital filters into frequency-domain subfilters implemented by use of sub-convolutions. The parallel-processing method implemented in these architectures enables the use of relatively small DFT-IDFT pairs, while filter tap lengths are theoretically unlimited. The size of a DFT-IDFT pair is determined by the desired reduction in processing rate, rather than on the order of the filter that one seeks to implement. The emphasis in this report is on those aspects of the underlying theory and design rules that promote computational efficiency, parallel processing at reduced data rates, and simplification of the designs of very-large-scale integrated (VLSI) circuits needed to implement high-order filters and correlators.

Gray, Andrew A.↗
NASA NTRSDate not supplied

A Machine Learning-Derived Atomistic Potential for Y2Si2O7

Incorporation of SiC/SiC ceramic matrix composite (CMC) hot section components into aircraft engines promises to increase efficiency and safety. However, SiC/SiC CMCs are subject to water vapor-induced oxidation and recession at the high temperatures of engine operation, and thus environmental barrier coatings (EBCs) are required to reduce this degradation and enable their widespread adoption. An understanding of EBCs failure mechanisms, including thermochemical and thermomechanical mechanisms, is essential as coating degradation leads to reduced CMC component service life. Computational modeling approaches can provide insight into EBC material properties important for coating design. However, density functional theory (DFT) is computationally expensive and atomistic potentials are lacking for materials of interest. In this work, we utilize a machine learning approach and DFT training data to parameterize atomistic potentials for two candidate EBC materials, Y2Si2O7 and Yb2Si2O7. These potentials enable near DFT-accurate calculations of thermodynamic and thermomechanical properties essential to EBC design.

Cameron J Bodenschatz↗
DOE OSTI2020

Fifth-degree elastic energy for predictive continuum stress-strain relations and elastic instabilities under large strain and complex loading in silicon

Materials under complex loading develop large strains and often phase transformation via an elastic instability, as observed in both simple and complex systems. Here, we represent a material (exemplified for Si I) under large Lagrangian strains within a continuum description by a 5th-order elastic energy found by minimizing error relative to density functional theory (DFT) results. The Cauchy stress—Lagrangian strain curves for arbitrary complex loadings are in excellent correspondence with DFT results, including the elastic instability driving the Si I → II phase transformation (PT) and the shear instabilities. PT conditions for Si I → II under action of cubic axial stresses are linear in Cauchy stresses in agreement with DFT predictions. Such continuum elastic energy permits study of elastic instabilities and orientational dependence leading to different PTs, slip, twinning, or fracture, providing a fundamental basis for continuum physics simulations of crystal behavior under extreme loading.

Levitas, Valery↗
DOE OSTI2021

Ab Initio Study of Atomic and Electronic Structure of Promising Ba4XMn3O12 (X = Nb, Ce, Pr) Oxides for Solar Thermochemical Hydrogen Production

The two-step metal oxide water-splitting cycle is one of the most viable approach for Solar Thermochemical Hydrogen (STCH) production. Challenges exist in finding suitable oxides that can satisfy thermodynamics of the STCH redox cycle under viable range of temperatures and partial pressures. Recently, BXM family members, i.e., Ba4NbMn3O12 (BNM), Ba4CeMn3O12 (BCM), Ba4PrMn3O12 (BPM) are discovered to exhibit promising STCH performance. However, the magnetic degrees of freedom of their experimental crystal structures is not characterized, and only hypothetical models exist for their electronic structure. We performed Monte-Carlo sampling for the magnetic spin arrangement of Mn atoms to provide explicit atomic structure models for their ground state 12R polytype and determined the most stable spin configuration for each member after structure relaxation using density functional theory (DFT) calculations. We also elucidate the most stable charge configuration (Mn3+/Mn4+ arrangement) for BNM among the existing hypothesis in literature. We investigate these strongly correlated oxides using different levels of theory (DFT+U, Hybrid-DFT) to develop better understanding of their electronic structure. These calculations allowed us to develop a model based on molecular orbital interactions that help explain the observed changes in their experimental x-ray spectroscopy measurements. Thereby, help link differences in electronic structure of BXM family members to their varying water-splitting behavior. These structure models will further facilitate future in-depth defect studies to model STCH redox cycle of these oxides.

atomic and magnetic structure↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

First Principle based Thermal Conductivity Modeling of Metal Alloys with Applications on Uranium Alloys

Thermal conductivity is an important materials property related to heat transport, which is essential to many applications, ranging from thermoelectrics to nuclear reactor materials. High- quality thermal conductivity data is critical to these materials and their associated technologies, including the nuclear fuel materials like traditional oxide fuels and metallic uranium (U) fuels, e.g. U-Zr and U-Mo alloys. And thermal conductivity modeling is widely used to interpolate or extrapolate experimental data, to obtain high-quality thermal conductivity data over wide temperature and composition range, and to understand the impacts of different factors including defects and microstructures. However, a general thermal conductivity model for metal alloys which can work regrading different phase components is still missing. Also, for U alloys, a thermal conductivity model working with different phases is needed. Therefore, in this work, we developed thermal conductivity models for metal alloys, based on ab-initio calculations, semi-classical physics rules, and limited experimental data. The goals of these models are to help obtain high- quality thermal conductivity data within a little experimental input as possible, have models that are extendable to varying microstructures, including different types of irradiation effects, and provide mechanistic understanding of heat transfer in the modeled alloys. In this work, our model solves several challenges in the development. The DFT-BTE approach is applied to decrease the reliance on experimental data and make the model extendable to composition changes and defects. A practical and efficient way to combine the DFT inputs with physics rules is pointed out in this work too. A staged approach, which starts from simple cases of elemental metals, then extends to solid solutions, different compound phases, and multi-phase mixtures, is presented to work with the metal alloys in different phase components. Our models are demonstrated respectively on aU for elemental metal model, on U-Zr and U-Mo alloys in aU temperature range for multi-phase mixture model, on high-temperature U-Zr, U-Nb, and U-Mo alloys in the body-centered cubic phase for concentrated solid solution model, and on irradiated U-Mo alloys for irradiated metal alloy model. All models show great agreement with experimental data. In these demonstrations, our model shows its advantages compared to previous empirical fitting model. Our model requires fewer experimental data, due to the inputs from DFT. The quantitative insights into the different physical factors are provided in our model, as it incorporates the electron and phonon scattering mechanisms. Our model also can be extended to incorporate the contributions of point defects, grain boundaries, and noble gas bubbles, to integrate their effects on heat transfer. This model can serve as both a foundation for understanding the more complex thermal conductivity of realistic U alloy fuels and a useful tool to guide further modeling of thermal conductivity to aid materials and device design.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Emergence of Local Magnetic Moment in Ternary TaWSe 2 Single Crystal via Atomic Clustering

Ternary transition metal dichalcogenides (TMDs) provide a versatile platform to explore novel electronic and magnetic ground states via compositional substitution and local structural modulations. Using a combination of scanning tunneling microscopy and spectroscopy (STM/S), magnetic property measurements, and density functional theory (DFT) calculations, the emergence of local magnetic moments driven by the clustering of Ta atoms in ternary TaWSe 2 single crystals is analyzed. STM topography reveals triangular clusters of Ta atoms embedded within W-rich regions of TaWSe 2 . These clusters exhibit a consistent shape and an orderly arrangement throughout the surfaces. DFT calculations show that these Ta clusters induce local strain, giving rise to localized magnetic moments. The magnetic behavior is further corroborated by temperature-dependent magnetization measurements, which exhibit a magnetic anomaly at ∼50 K. In conclusion, this study offers a pathway to engineer magnetism in TMD systems with potential applications in spintronic and quantum materials.

DFT↗
DOE OSTI2025

Revealing Robust Room Temperature Ferromagnetism in Gd‐Doped Few‐Layered MoS 2 Thin Films

2D MoS 2 holds great promise for spintronics, yet is limited by intrinsic diamagnetism. This study demonstrates inducing ferromagnetic behavior in MoS 2 films doped with 0.47% Gd, achieving an ultrahigh saturation magnetization of 454 emu/cm 3 in a few‐layered film over 11‐times higher than bulk films (40 nm). Raman spectroscopy, X‐ray photoelectron spectroscopy, X‐ray magnetic circular dichroism, and density functional theory (DFT) calculations reveal an interplay between Gd dopants and Mo, S vacancies (V 1Mo+2S ), leading to the formation of bound magnetic polarons (BMPs) that drive ferromagnetic ordering. H 2 S annealing and DFT calculations reveal that defect healing reduces the saturation magnetization by 83%. High sulfur migration barrier in few‐layered films helps preserve BMPs, thereby sustaining ferromagnetism, whereas lower migration barriers in bulk films lead to suppression. These findings highlight the synergy between Gd doping and defect engineering in achieving ultrahigh room‐temperature ferromagnetism, offering a scalable strategy for developing high‐performance 2D magnetic materials for spintronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Surface Amorphization of Vanadium Dioxide (B) for K-ion Battery

Given the merits of low cost, fast ionic transport in electrolyte, and high operating voltage, potassium ion batteries (PIBs) are promising alternatives to lithium-ion batteries. However, developing suitable electrode materials that can reversibly accommodate large potassium ions is a great challenge. In this paper, guided by density functional theory (DFT) calculations, it is demonstrated that the strategy of interfacial engineering via surface amorphization of VO 2 (B) nanorods (SA-VO 2 ), which results in the formation of a crystalline core/amorphous shell heterostructure, enables superior K + storage performance in terms of large capacity, outstanding rate capability, and long cycle stability working as an anode for PIBs. DFT calculations reveal that the created crystalline/amorphous heterointerface in SA-VO 2 can substantially lower the surface energy, narrow the band gap, and reduce the K + diffusion barrier of VO 2 (B). These conditions enable enhanced K + storage capacity and rapid K + /electron transfer, which result in large capacity and outstanding rate capability. Using in situ X-ray diffraction and in situ transmission electron microscopy complemented by ex situ microscopic and spectroscopic techniques, it is unveiled that the superior cycling stability originates from the excellent phase reversibility with negligible strain response and robust mechanical behavior of SA-VO 2 upon (de)potassiation.

25 ENERGY STORAGE↗
DOE OSTI2025

Engineering PdAu/CeO 2 Alloy/Oxide Interfaces for Selective Methane‐to‐Methanol Conversion with Water

The direct conversion of methane-to-methanol remains a critical challenge in methane valorization. In this study, we unveil the crucial role of PdAu/CeO 2 catalysts in enabling selective methane transformation under mild conditions, using only water as the sole oxidant. Through a combination of experimental techniques, including XPS and catalytic testing, alongside density functional theory (DFT) calculations, we demonstrate that a Pd 0.3 Au 0.7 /CeO 2 catalyst, which predominantly exposes isolated Pd atoms, achieves remarkable methanol selectivity (∼80%) at 500 K with a 1:1 methane-to-water ratio. While Pd/CeO 2 efficiently activates methane, its tendency for overreaction leads to complete methanol decomposition, thereby limiting selectivity. Alloying Pd with Au on ceria mitigates this over-reactivity, preventing methanol degradation while maintaining sufficient catalytic activity. The PdAu/CeO 2 composite exhibits a synergistic effect: Pd in contact with the ceria support facilitates methane activation and water dissociation, while Au fine-tunes reactivity to promote methanol formation. DFT calculations confirm that isolated Pd sites at the PdAu/CeO 2 interface play a key role in balancing activity and selectivity. This work underscores the importance of alloy/oxide interfaces in controlling selective methane conversion with water and offers valuable insights for designing highly efficient catalysts for methanol synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Dual Chemical Looping/Catalytic Process for Alkylation of Benzene With Ethane and Propane Yielding Ethylbenzene and Cumene Over Copper‐Containing Mordenite

Given the sustained demand for alkylated aromatics and the strained olefin market, there is an urgent need to develop efficient one‐step processes for the direct alkylation of aromatics using alkanes instead of olefins. Such technologies offer greater energy efficiency and sustainability by eliminating the need for separate, energy‐intensive alkane dehydrogenation steps. In this work, we report a dual chemical looping / catalytic process that couples alkane dehydrogenation with aromatic alkylation over a copper‐containing mordenite yielding up to 25% of alkylated aromatics with >97% selectivity per cycle. In situ MAS NMR and FTIR spectroscopies combined with DFT calculations showed that the alkylation of benzene with alkanes proceeds via a π‐bounded Cu(I)‐olefin intermediate, which subsequently interacts with benzene, catalyzed by Brønsted acid sites, leading to alkylated products that readily desorb from the active material into the gas phase. DFT calculations show that alkylation mediated solely by Cu(I) has prohibitively high barriers (>1.8 eV), whereas a bi‐functional pathway involving both Cu(I) and Brønsted acid sites can proceed with significantly lower barrier (0.8 eV) through a concerted C–C bond formation and proton transfer step.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Radiation‐Induced Defects in Uranyl Trinitrate Solids

Abstract Actinides are inherently radioactive; thus, ionizing radiation is emitted by these elements can have profound effects on its surrounding chemical environment through the formation of free radical species. While previous work has noted that the presence of free radicals in the system impacts the redox state of the actinides, there is little atomistic understanding of how these metal cations interact with free radicals. Herein, we explore the effects of radiation (UV and γ) on three U(VI) trinitrate complexes, M[UO 2 (NO 3 ) 3 ] (where M=K + , Rb + , Cs + ), and their respective nitrate salts in the solid state via electron paramagnetic resonance (EPR) and Raman spectroscopy paired with Density Functional Theory (DFT) methods. We find that the alkali salts form nitrate radicals under UV and γ irradiation, but also note the presence of additional degradation products. M[UO 2 (NO 3 ) 3 ] solids also form nitrate radicals and additional DFT calculations indicate the species corresponds to a change from the bidentate bound nitrate anion into a monodentate NO 3 • radical. Computational studies also highlight the need to include the second sphere coordination environment around the [UO 2 (NO 3 ) 3 ] 0,1 species to gain agreement between the experimental and predicted EPR signatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Density Functional Theory Guided Investigation of Ligand‐Induced Neptunyl‐Neptunyl Interactions

Abstract Actinyl‐actinyl interactions are particularly prevalent for the pentavalent neptunyl cation (Np(V)O 2 ) + where these interactions appear either as a T ‐ or D ‐shape (diamond‐shape). T ‐shaped interactions have been previously identified in high concentration Np(V) solutions containing simple anions (NO 3 − , ClO 4 − , Cl − ) whereas D ‐shaped have only been isolated in the solid‐state in the presence of carboxylate ligands. In this study, Density Functional Theory (DFT) calculations were paired with Raman spectroscopy to evaluate the formation of D ‐shaped interactions in the presence of aliphatic (R=H (formate), CH 3 (acetate), CH 2 CH 3 (propionate)) and aromatic (R=C 6 H 5 (benzoate), C 6 H 4 OH (4‐hydroxybenzoate), C 5 H 4 N (isonicotinate)) carboxylate ligands. DFT studies indicate that the Δ G to form hydrated T ‐ and D ‐shaped forms are not spontaneous but become so with the addition of the carboxylate ligands. Raman spectra of the Np(V) carboxylate solutions contained vibrational modes associated with the D ‐shaped interactions, but spectral changes observed over time indicate a dynamic system. Crystallization experiments from the Np(V) carboxylate systems confirmed the presence of D‐ shaped dimers for the aromatic carboxylates, suggesting that the choice of the anion in solution favors actinyl‐actinyl interactions even at low concentrations (≤20 mM) of Np(V).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Role of Surface Termination in the Structural and Electronic Properties of Sc$_2$CT$_\textrm{x}$ MXene

Graphene-like layered transition metal carbides, nitrides, or carbonitrides, called MXenes, obey the stoichiometric formula of M n+1 X n T x , where M is an early transition metal such as scandium (Sc), n is a natural number, X is C, N, or CN, and T x is a functional group such as –O, –F, or –OH that passivates the surface of the MXene. The electronic structure of bare Sc 2 C and functionalized Sc 2 CT x MXenes are explored by performing first-principles density functional theory (DFT) calculations. The bare Sc 2 C is metallic, but less stable than its passivated structure. The Sc 2 C MXene has an interlayer 2D electron gas not bound to Sc or C atoms but free to move, making it an electride. DFT calculations show that functionalization can open an energy gap in Sc 2 CT x MXenes. The size and type (direct versus indirect) of the bandgap vary with the functional groups, which provides a means for opening and tuning of the band gap.

DFT↗