Pair Distribution Function Data Research Articles

Large dynamic scissoring mode displacements coupled to band gap opening in the cubic phase of the methylammonium lead halide perovskites.

Hybrid perovskites are a rapidly growing research area, having reached photovoltaic power conversion efficiencies of over 25 %. There is a increasing consensus that the structures of these materials, and hence their electronic structures, can not be understood purely from the time and space averaged crystal structures observable by conventional methods. We apply a symmetry-motivated analysis method to analyse X-ray pair distribution function data of the cubic phases of the hybrid perovskites MAPb$X_3$ ($X$ = I, Br, Cl). We demonstrate that, even in the cubic phase, the local structure of the inorganic components of MAPb$X_3$ ($X$ = I, Br, Cl), are dominated by scissoring type deformations of the Pb$X_6$ octahedra. We find these modes to have a larger amplitude than equivalent distortions in the $A$-site deficient perovskite ScF$_3$ and demonstrate that they show a significant departure from the harmonic approximation. Calculations performed on an inorganic perovskite analogue, FrPbBr3show that the large amplitudes of the scissoring modes are coupled to a dynamic opening of the electronic band gap. Finally, we use density functional theory calculations to show that the organic MA cations reorientate to accommodate the large amplitude scissoring modes.

The Optimization of Pair Distribution Functions for the Evaluation of the Degree of Disorder and Physical Stability in Amorphous Solids.

The amorphous form of poorly soluble drugs is physically unstable and prone to crystallization, resulting in decreased solubility and bioavailability. However, the conventional accelerated stability test for amorphous drugs is time-consuming and inaccurate. Therefore, there is an urgent need to develop rapid and accurate stability assessment technology. This study used the antitumor drug nilotinib free base as a model drug. The degree of disorder and physical stability in the amorphous form was assessed by applying the pair distribution function (PDF) and principal component analysis (PCA) methods based on powder X-ray diffraction (PXRD) data. Specifically, the assessment conditions, such as the PDF interatomic distance range, PXRD detector type, and PXRD diffraction angle range were also optimized. The results showed that more reliable PCA data could be obtained when the PDF interatomic distance range was 0-15 Å. When the PXRD detector was a semiconductor-type detector, the PDF data obtained were more accurate than other detectors. When the PXRD diffraction angle range was 5-40°, the intermolecular arrangement of the amorphous drugs could be accurately predicted. Finally, the accelerated stability test also showed that under the above-optimized conditions, this method could accurately and rapidly assess the degree of disorder and physical stability in the amorphous form of drugs, which has obvious advantages compared with the accelerated stability test.

Characterization of Surfactant Spheroidal Micelle Structure for Pharmaceutical Applications: A Novel Analytical Framework.

We introduce an innovative theoretical framework tailored for the analysis of Pair Distribution Function (PDF) data derived from Small-Angle X-ray Scattering (SAXS) measurements of core-shell micelles. The new approach involves the exploitation of the first derivative of the PDF and the derivation of analytical equations to solve the core-shell micelle structure under the hypothesis of a spheroidal shape. These analytical equations enable us to determine the micelle's aggregation number, degree of ellipticity, and contrast in electron density between the core-shell and shell-buffer regions after having determined the whole micelle size and its shell size from the analysis of the first derivative of the PDF. We have formulated an overdetermined system of analytical equations based on the unknowns that characterize the micelle structure. This allows us to establish a Figure of Merit, which is utilized to identify the most reliable solution within the system of equations.

Atomically Precise Single-Site Catalysts via Exsolution in a Polyoxometalate-Metal-Organic-Framework Architecture.

Single-site catalysts (SSCs) achieve a high catalytic performance through atomically dispersed active sites. A challenge facing the development of SSCs is aggregation of active catalytic species. Reducing the loading of these sites to very low levels is a common strategy to mitigate aggregation and sintering; however, this limits the tools that can be used to characterize the SSCs. Here we report a sintering-resistant SSC with high loading that is achieved by incorporating Anderson-Evans polyoxometalate clusters (POMs, MMo6O24, M = Rh/Pt) within NU-1000, a Zr-based metal-organic framework (MOF). The dual confinement provided by isolating the active site within the POM, then isolating the POMs within the MOF, facilitates the formation of isolated noble metal sites with low coordination numbers via exsolution from the POM during activation. The high loading (up to 3.2 wt %) that can be achieved without sintering allowed the local structure transformation in the POM cluster and the surrounding MOF to be evaluated using in situ X-ray scattering with pair distribution function (PDF) analysis. Notably, the Rh/Pt···Mo distance in the active catalyst is shorter than the M···M bond lengths in the respective bulk metals. Models of the active cluster structure were identified based on the PDF data with complementary computation and X-ray absorption spectroscopy analysis.

POMFinder: identifying polyoxometallate cluster structures from pair distribution function data using explainable machine learning.

Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.

Probing Molecular Packing of Amorphous Pharmaceutical Solids Using X-ray Atomic Pair Distribution Function and Solid-State NMR.

The structural investigation of amorphous pharmaceuticals is of paramount importance in comprehending their physicochemical stability. However, it has remained a relatively underexplored realm primarily due to the limited availability of high-resolution analytical tools. In this study, we utilized the combined power of X-ray pair distribution functions (PDFs) and solid-state nuclear magnetic resonance (ssNMR) techniques to probe the molecular packing of amorphous posaconazole and its amorphous solid dispersion at the molecular level. Leveraging synchrotron X-ray PDF data and employing the empirical potential structure refinement (EPSR) methodology, we unraveled the existence of a rigid conformation and discerned short-range intermolecular C-F contacts within amorphous posaconazole. Encouragingly, our ssNMR 19F-13C distance measurements offered corroborative evidence supporting these findings. Furthermore, employing principal component analysis on the X-ray PDF and ssNMR data sets enabled us to gain invaluable insights into the chemical nature of the intermolecular interactions governing the drug-polymer interplay. These outcomes not only furnish crucial structural insights facilitating the comprehension of the underlying mechanisms governing the physicochemical stability but also underscore the efficacy of synergistically harnessing X-ray PDF and ssNMR techniques, complemented by robust modeling strategies, to achieve a high-resolution exploration of amorphous structures.

Discovery of New Fast Oxygen Conductors: Bi2MO4x (M= rare earth, X= halogen) Via Unsupervised Machine Learning

Discovering new fast oxygen conductors is critical for developing many technologies, e.g., solid oxide fuel cells (SOFCs), solid oxide electrolysis cells (SOECs), proton ceramic fuel cells (PCFCs), and solid oxide air batteries, particularly for operation at lower temperatures (below 700 ˚C). By leveraging unsupervised machine learning techniques and large databases of computed materials data (e.g., Materials Project), we are able to rapidly explore and screen for new materials with high oxygen mobility. In this work, we used unsupervised machine learning analysis applied to features extracted from the structure of materials and discovered a new structural class of vacancy-mediated oxygen conductor. To do this, we applied the K-nearest neighbor (k-NN) algorithm on all the approximately 62,000 oxygen-containing compounds in the Materials Project database, using X-ray diffraction (XRD) and pair distribution function (PDF) data to represent the structural features. The neighbors of reference known for good oxygen conductors were screened with criteria relating to material stability, synthesizability, and electronic property dissimilarity (to ensure a novel material) compared to the reference material. Starting with the best-known oxygen conductor, the fluorite-type material, we examined the materials list of its nearest neighbors and identified the Bi2MO4X (M=Rare-earth element, X= halogen element) structural family as a novel group of fast oxygen conductors. In particular, Bi2LaO4Cl is a layered bismuth oxyhalide crystallized in the space group P4/mmm, adopting a “triple fluorite” layer [Bi2LaO4]+, balanced by the anion Cl- layer. The stability and mobility of oxygen vacancy in Bi2LaO4Cl were studied using Density Functional Theory (DFT) methods. 2+ oxygen vacancy is the most thermodynamically favorable defect type. Ab initio and machine learning-trained interatomic potential molecular dynamic simulations were performed to study the high temperature oxygen mobility in Bi2LaO4Cl. The simulation results show that Bi2LaO4Cl presents an extremely low migration barrier of 0.14 +/- 0.03 eV below 700 ˚C, suggesting 10 to 20 times higher ionic conductivity compared to yttria-stabilized zirconia (YSZ). Bi2LaO4Cl is an insulator with a wide band gap of 2.65 eV, indicating that Bi2LaO4Cl may be a promising electrolyte material with high oxygen ion conductivity and low electronic conductivity. This work demonstrates the power of combining unsupervised machine learning approaches with large materials databases for finding new fast oxygen conductors.

Nature of GaOx Shells Grown on Silica by Atomic Layer Deposition.

Gallia-based shells with a thickness varying from a submonolayer to ca. 2.5 nm were prepared by atomic layer deposition (ALD) using trimethylgallium, ozone, and partially dehydroxylated silica, followed by calcination at 500 °C. Insight into the atomic-scale structure of these shells was obtained by high-field 71Ga solid-state nuclear magnetic resonance (NMR) experiments and the modeling of X-ray differential pair distribution function data, complemented by Ga K-edge X-ray absorption spectroscopy and 29Si dynamic nuclear polarization surface enhanced NMR spectroscopy (DNP SENS) studies. When applying one ALD cycle, the grown submonolayer contains mostly tetracoordinate Ga sites with Si atoms in the second coordination sphere ([4]Ga(Si)) and, according to 15N DNP SENS using pyridine as the probe molecule, both strong Lewis acid sites (LAS) and strong Brønsted acid sites (BAS), consistent with the formation of gallosilicate Ga-O-Si and Ga-μ2-OH-Si species. The shells obtained using five and ten ALD cycles display characteristics of amorphous gallia (GaOx), i.e., an increased relative fraction of pentacoordinate sites ([5]Ga(Ga)), the presence of mild LAS, and a decreased relative abundance of strong BAS. The prepared Ga1-, Ga5-, and Ga10-SiO2-500 materials catalyze the dehydrogenation of isobutane to isobutene, and their catalytic performance correlates with the relative abundance and strength of LAS and BAS, viz., Ga1-SiO2-500, a material with a higher relative fraction of strong LAS, is more active and stable compared to Ga5- and Ga10-SiO2-500. In contrast, related ALD-derived Al1-, Al5-, and Al10-SiO2-500 materials do not catalyze the dehydrogenation of isobutane and this correlates with the lack of strong LAS in these materials that instead feature abundant strong BAS formed via the atomic-scale mixing of Al sites with silica, leading to Al-μ2-OH-Si sites. Our results suggest that [4]Ga(Si) sites provide strong Lewis acidity and drive the dehydrogenation activity, while the appearance of [5]Ga(Ga) sites with mild Lewis activity is associated with catalyst deactivation through coking. Overall, the atomic-level insights into the structure of the GaOx-based materials prepared in this work provide a guide to design active Ga-based catalysts by a rational tailoring of Lewis and Brønsted acidity (nature, strength, and abundance).

Analyzing the atomic pair distribution function of BMT-based nanopowders via X-ray diffraction

In this study, we investigate the three-dimensional atomic structure of 0.65[Ba(Mg1/3, Ti2/3)]−0.35[BaTiO3] (BMT-BT) using x-ray diffraction (XRD) with molybdenum radiation (MoKα 1), Rietveld refinement, and the atomic pair distribution function (PDF) technique. We provide an analysis of the advantages of the PDF method for studying the unit cell of electroceramics. The XRD results show that BMT has a bulk crystal structure with a cubic perovskite structure. The new structural information enhances our understanding of the dielectric properties of BMT. The PDF analysis reveals that the average interatomic bond distance in the octahedral TiO6 atomic bond is around 2.8 Å, and the first peak at r = 1.39 Å corresponds to the nearest neighbor M-O distance (M = Ti, Mg, Nb). Additionally, the peak at r = 2.4 Å corresponds to the Ba-O and O-O distances in the MO6 octahedron. Peaks in the PDF data below 1 Å are ignored as they have no physical implications.

Symmetry-mode analysis for local structure investigations using pair distribution function data

Symmetry-adapted distortion modes provide a natural way of describing distorted structures derived from higher-symmetry parent phases. Structural refinements using symmetry-mode amplitudes as fit variables have been used for at least ten years in Rietveld refinements of the average crystal structure from diffraction data; more recently, this approach has also been used for investigations of the local structure using real-space pair distribution function (PDF) data. Here, the value of performing symmetry-mode fits to PDF data is further demonstrated through the successful application of this method to two topical materials: TiSe2, where a subtle but long-range structural distortion driven by the formation of a charge-density wave is detected, and MnTe, where a large but highly localized structural distortion is characterized in terms of symmetry-lowering displacements of the Te atoms. The analysis is performed using fully open-source code within the DiffPy framework via two packages developed for this work: isopydistort, which provides a scriptable interface to the ISODISTORT web application for group theoretical calculations, and isopytools, which converts the ISODISTORT output into a DiffPy-compatible format for subsequent fitting and analysis. These developments expand the potential impact of symmetry-adapted PDF analysis by enabling high-throughput analysis and removing the need for any commercial software.

Modeling Short-Range Order in Disordered Rocksalt Cathodes by Pair Distribution Function Analysis

Pair distribution function (PDF) analysis is a powerful technique for the characterization of short-range order (SRO) in disordered materials. Accurate interpretation of experimental PDF data is critically reliant on the development of structural models that can account for local variations in site occupancies and bond lengths. To this end, we outline an approach to model SRO using first-principles calculations based on the cluster-expansion formalism. These methods are validated on neutron scattering data from two disordered rocksalt oxyfluorides, Li1.3Mn0.4Ti0.3O1.7F0.3 and Li1.3Mn0.4Nb0.2Ti0.1O1.7F0.3. For each composition, we demonstrate that an average structure without any SRO fails to reproduce several key features in the experimental PDF. To pinpoint the origin of the suspected SRO in these materials, configurational and displacive effects were separately investigated using two disparate models. Special quasi-random structures were relaxed using density functional theory to account for local changes in bond lengths while maintaining a near-random ionic configuration. This leads to slightly improved accuracy but still misrepresents asymmetry in the first few peaks of the PDF. Monte Carlo simulations were performed to model configurational SRO on a fixed lattice, which by itself is shown to have a minimal influence on the PDF. Instead, we find that it is the bond length relaxations within environments created by SRO which controls the details of the PDF, thereby highlighting the subtle but important coupling between configurational and displacive SRO in disordered materials.

Pair distribution function analysis of discrete nanomaterials in PDFgui

Pair distribution functions (PDFs) are a leading tool for atomic structure analysis of nanomaterials. However, the most widely used programs for refining atomic structure against PDF data are based on extended crystallographic models, which cannot be applied to discrete, whole nanoparticles. This work describes a straightforward approach to simulate and refine atomistic models of discrete clusters and nanoparticles employing widely used PDF modelling programs such as PDFgui [Farrow et al. (2007). J. Phys. Condens. Matter, 19, 335219] that utilize extended crystallographic models. In this approach, the whole particle to be modelled is contained within an expanded, and otherwise empty, unit cell that is sufficiently large to avoid correlations between atoms in neighbouring unit cells over the r range analysed. The PDF of the particle is simulated as a composite using two conventional `phases': one that calculates the atom–atom correlations and one that approximates the local number density. This approach is first validated for large nanoparticles that are well modelled by a conventional shape factor model, and then applied to simulate the PDF of discrete particles and low-dimensional materials (graphene and MXene) and to model the experimental PDF data for single-layer FeS nanosheets. A comparison of this approach with the DiffPy-CMI program [Juhás et al. (2015). Acta Cryst. A71, 562–568], which calculates the PDF of discrete species, shows that the composite modelling approach is equally or more accurate. Example input files for implementing this approach within PDFgui and TOPAS [Coelho (2018). J. Appl. Cryst. 51, 210–218], and recommendations for selecting model parameters for reliable application of this refinement strategy, are provided.

A spherical harmonics method for processing anisotropic X-ray atomic pair distribution functions

In this work, a general spherical harmonics method for the extraction of anisotropic pair distribution functions (PDFs) is described. In the structural study of functional crystallized materials, there is an urgent need for investigation of local structures under the application of external stimuli, such as an electric field and stress. A well established technique for local structural studies is PDF analysis, but the extraction of X-ray PDF data is usually based on angular integrations of isotropic X-ray structure functions, which is no longer valid for the anisotropic responses of materials under orientation-dependent stimuli. Therefore, an advanced spherical harmonics method has been developed, to transform 2D X-ray total-scattering data into anisotropic PDF data on the basis of the 3D diffraction geometry and a Fourier transform. The electric-field-induced local structural change in PbZr0.54Ti0.46O3 ceramics is presented to demonstrate the effectiveness of this method.

DeepStruc: towards structure solution from pair distribution function data using deep generative models.

Structure solution of nanostructured materials that have limited long-range order remains a bottleneck in materials development. We present a deep learning algorithm, DeepStruc, that can solve a simple monometallic nanoparticle structure directly from a Pair Distribution Function (PDF) obtained from total scattering data by using a conditional variational autoencoder. We first apply DeepStruc to PDFs from seven different structure types of monometallic nanoparticles, and show that structures can be solved from both simulated and experimental PDFs, including PDFs from nanoparticles that are not present in the training distribution. We also apply DeepStruc to a system of hcp, fcc and stacking faulted nanoparticles, where DeepStruc recognizes stacking faulted nanoparticles as an interpolation between hcp and fcc nanoparticles and is able to solve stacking faulted structures from PDFs. Our findings suggests that DeepStruc is a step towards a general approach for structure solution of nanomaterials.

Node Distortion as a Tunable Mechanism for Negative Thermal Expansion in Metal–Organic Frameworks

Chemically functionalized series of metal-organic frameworks (MOFs), with subtle differences in local structure but divergent properties, provide a valuable opportunity to explore how local chemistry can be coupled to long-range structure and functionality. Using in situ synchrotron X-ray total scattering, with powder diffraction and pair distribution function (PDF) analysis, we investigate the temperature dependence of the local- and long-range structure of MOFs based on NU-1000, in which Zr6O8 nodes are coordinated by different capping ligands (H2O/OH, Cl- ions, formate, acetylacetonate, and hexafluoroacetylacetonate). We show that the local distortion of the Zr6 nodes depends on the lability of the ligand and contributes to a negative thermal expansion (NTE) of the extended framework. Using multivariate data analyses, involving non-negative matrix factorization (NMF), we demonstrate a new mechanism for NTE: progressive increase in the population of a smaller, distorted node state with increasing temperature leads to global contraction of the framework. The transformation between discrete node states is noncooperative and not ordered within the lattice, i.e., a solid solution of regular and distorted nodes. Density functional theory calculations show that removal of ligands from the node can lead to distortions consistent with the Zr···Zr distances observed in the experiment PDF data. Control of the node distortion imparted by the nonlinker ligand in turn controls the NTE behavior. These results reveal a mechanism to control the dynamic structure of MOFs based on local chemistry.

Magnetic pair distribution function data using polarized neutrons and ad hoc corrections

We report the first example of magnetic pair distribution function (mPDF) data obtained through the use of neutron polarization analysis. Using the antiferromagnetic semiconductor MnTe as a test case, we present high-quality mPDF data collected on the HYSPEC instrument at the Spallation Neutron Source using longitudinal polarization analysis to isolate the magnetic scattering cross section. Clean mPDF patterns are obtained for MnTe in both the magnetically ordered state and the correlated paramagnet state, where only short-range magnetic order is present. We also demonstrate significant improvement in the quality of high-resolution mPDF data through the application of ad hoc corrections that require only minimal human input, minimizing potential sources of error in the data processing procedure. We briefly discuss the current limitations and future outlook of mPDF analysis using polarized neutrons. Overall, this work provides a useful benchmark for mPDF analysis using polarized neutrons and provides an encouraging picture of the potential for routine collection of high-quality mPDF data.

Special quasirandom structures description of the local structure of disordered Bi0.5K0.5TiO3

Polar nanoregions (PNRs) are believed to play a decisive role in the local and macroscopic polarization in relaxor ferroelectrics. The limited microscopic understanding of the structure and dynamics of PNRs hampers the rational design of new lead-free materials. Here, the local structure of A-site disordered Bi0.5K0.5TiO3 (BKT) is investigated using synchrotron x-ray and neutron pair distribution function (PDF) analysis and density functional theory (DFT) optimized special quasirandom structures (SQSs). DFT-relaxed SQS with a 4 × 4 × 4 supercell size can reproduce the experimental PDFs of disordered BKT, as well as the partial PDFs and total polarization, with comparable results to those reported from a combined analysis of x-ray and neutron PDF data with large-box reverse Monte Carlo methods. We find that small Bi3+-rich polar clusters are likely to be the microscopic origin of relaxor behavior in disordered BKT, and that the existence of large polar nanoregions (PNRs) is not necessary to explain the relaxor properties. Our results also highlight the great potential of the SQS approach to gain a nanoscale-to-microscopic understanding of other relaxor solid solutions.

Substitution of Copper by Magnesium in Malachite: Insights into the Synthesis and Structural Effects.

The phase width of the copper hydroxycarbonate malachite, Cu2CO3(OH)2, upon substitution with magnesium has been studied in detail. In extension of a previous study on amorphous precursors, the introduction of a hydrothermal aging step allowed the retrieval of crystalline hydroxycarbonate samples with up to 37 atom % Mg (metal content) that are suitable candidates as precursors to Cu/MgO catalysts for CO hydrogenation. Simultaneous refinements of X-ray powder diffraction and pair distribution function (PDF) data as well as complementary spectroscopic insight (X-ray absorption and infrared spectroscopy) revealed that samples with up to 18 atom % Mg are phase-pure magnesian malachites but the magnesium content can be increased beyond this threshold when mcguinnessite (CuMgCO3(OH)2) is accepted as a side phase. In a complementary study, a continuous increase of the magnesium fraction was found during aging and the corresponding structural evolution was studied by means of PDF. These findings add significant insight into the aging chemistry of crystalline Cu,Mg hydroxycarbonates. Furthermore, both phase-pure magnesian malachite and mcguinnessite-containing samples with up to 37 atom % Mg have been examined by thermogravimetry, X-ray powder diffraction, and N2 physisorption and were found to be promising candidates for use as precursors for the preparation of Cu/MgO catalysts.

Atomic-Layer-Deposited Aluminum Oxide Thin Films Probed with X-ray Scattering and Compared to Molecular Dynamics and Density Functional Theory Models.

A better understanding of amorphous aluminum oxide's structure and electronic properties is obtained through combined experimental and computational approaches. Grazing incidence X-ray scattering measurements were carried out on aluminum oxide thin films grown using thermal atomic layer deposition. The corresponding pair distribution functions (PDFs) showed structures similar to previously reported PDFs of solid-state amorphous alumina and molten alumina. Structural models based on crystalline alumina polymorphs (PDFgui) and amorphous alumina (molecular dynamics, MD) were examined for structural comparisons to the experimental PDF data. Smaller MD models were optimized and verified against larger models to allow for quantum chemical electronic structure calculations. The electronic structure of the amorphous alumina models yields additional insight into the band structure and electronic defects present in amorphous alumina that are not present in crystalline samples.

Nano-crystalline precursor formation, stability, and transformation to mullite-type visible-light photocatalysts

A new precursor for the formation of mullite-type visible-light active photocatalyst Bi2Al4O9 has been identified. The crystal structure of the organic–inorganic hybrid perovskite can be described using the hexagonal setting of the rhombohedral unit cell with lattice parameters a = 1.1342(2) nm, c = 2.746(1) nm, and V = 3.059(2) nm3. The presence of di-nitro-glycerin as organic component, which is centered together with two bismuth atoms at the A-sites of the ABX3-type perovskite, suggests for doubling of the a- and c-lattice parameters compared to isostructural BiAlO3 perovskite. The nano-crystalline precursor with the chemical composition [Bi2(C3H5N2O7)]Al4[O9(□1-x(H2O)x)3] (□: vacancies) decomposes at 540(10) K to a quantum-crystalline phase with an average crystallite size of 1.4(1) nm, refined from X-ray powder data Bragg reflections and confirmed by atomic pair distribution function data analysis. Further heating enables a controlled formation of quantum- or nano-crystalline mullite-type phases, depending on temperature and time. The same precursor structure could also be obtained as iron-containing phase and for Al/Fe solid-solution samples. UV/Vis diffuse reflectance spectroscopy suggests an indirect band-gap transition energy of 3.50(3) eV calculated by the Reflectance-Absorption-Tauc-DASF (RATD) method. Temperature-dependent UV/Vis allows to follow the change of band-gap energy across all associated phase transformations. The long- and short-range appearance of each phase has been presented using X-ray Bragg scattering and total scattering data analyses. This is supported by Raman and infrared spectroscopic investigations complemented by density functional theory (DFT) calculations. Moreover, the theoretical calculation confirms the incorporated di-nitro-glycerin. Thermal stabilities of the phases are investigated by using thermal analysis and temperature-dependent X-ray diffraction.