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We demonstrate the determination of anharmonic acoustic phonon properties via second-order Raman scattering exemplarily on copper iodide single crystals. The origin of multi-phonon features from the second-order Raman spectra was assigned by the support of the calculated 2-phonon density of states. In this way, the temperature dependence of acoustic phonons was determined down to 10 K. To determine independently the harmonic contributions of respective acoustic phonons, density functional theory in quasi-harmonic approximation was used. Finally, the anharmonic contributions were determined. The results are in agreement with earlier publications and extend CuI’s determined acoustic phonon properties to lower temperatures with higher accuracy. This approach demonstrates that it is possible to characterize the acoustic anharmonicities via Raman scattering down to zero-temperature renormalization constants of at least 0.1 cm−1.
This talk explores the intricate connections between scientists, focusing on the networking dynamics within the realm of metal-organic frameworks (MOFs). The study delves into the collaborative networks formed among scientists, shedding light on the synergistic relationships that contribute to advancements in MOF research.
Mechanochemistry has become a compelling method for producing (new) molecule s and mate-rials, but the inner workings of the milling jars remain a fascinating mystery. Advances in this field include tailor-made chemical systems and real-time revelations using techniques such as XRD and Raman spectroscopy.
This talk will discuss our recent progress in using X-ray diffraction and sophisticated spectros-copy to observe reactions in various material systems during ball milling and extrusion in real-time.
The complexity of mechanochemical reactions spans multiple scales and requires a holistic ap-proach. The categorisation of reactions by investigative methods precedes the exploration of real-time analysis that reveals macroscopic processes using synchrotron techniques.
During this exploration, one resounding realisation remains: We are on the threshold of under-standing. The complexity of mechanochemistry requires a collective effort, drawing on the ex-pertise of a diverse community. As we unravel the web of mechanochemical phenomena, we acknowledge the collaborative nature of this ongoing journey.
Mechanochemistry emerges as a potent, environmentally friendly, and straightforward approach for crafting novel multicomponent crystal systems. Various milling parameters, including milling frequency, time, filling degree of the milling jar, ball diameter, vessel size, degree of milling ball filling, and material of jars, are recognized influencers on the mechanisms and rates of product formation. Despite the growing interest in mechanochemistry, there exists a gap in understanding the mechanistic aspects of mechanochemical reactivity and selectivity. To address this, diverse analytical methods and their combinations, such as powder X-ray diffraction, X-ray absorption spectroscopy, NMR, Raman spectroscopy, and thermography, have been developed for real-time, in situ monitoring of mechanochemical transformations.
This discussion centers on our recent findings, specifically investigating the formation of (polymorphic) cocrystals and metal-organic frameworks. Through these studies, we aim to unravel the impact of milling parameters and reaction sequences on the formation mechanism and kinetics. Notably, in the mechanochemical chlorination reaction of hydantoin, normalizing kinetic profiles to the volume of the milling ball unequivocally demonstrates the conservation of milling reaction kinetics. In this ball-milling transformation, physical kinetics outweigh chemical factors in determining reaction rates. Attempting to interpret such kinetics solely through chemical terms poses a risk of misinterpretation. Our results highlight that time-resolved in situ investigations of milling reactions provide a novel avenue for fine-tuning and optimizing mechanochemical processes.
Investigating the mechanism and kinetics of the mechanochemical synthesis of multi-component systems
(2023)
Mechanochemistry is a promising and environmentally friendly approach for synthesizing (novel) multicomponent crystal systems. Various milling parameters, such as milling frequency, milling time, and ball diameter have been shown to influence the mechanisms and rates of product formation. Despite increasing interest in mechanochemistry, there is still limited understanding of the underlying reactivity and selectivity mechanisms.
Various analytical techniques have been developed to gain insight into the mechanochemical transformations, including powder X-ray diffraction, X-ray adsorption spectroscopy, NMR, Raman spectroscopy and thermography. Using these techniques, we have studied the formation of (polymorphic) cocrystals, organometallic compounds and salts, and elucidated the influence of milling parameters and reaction sequences on the formation mechanism and kinetics.
For example, our study of the mechanochemical chlorination reaction of hydantoin revealed that normalisation of the kinetic profiles to the volume of the grinding ball clearly showed that physical kinetics dominate the reaction rates in a ball-milling transformation. Attempts to interpret such kinetics in purely chemical terms risk misinterpretation of the results.
Our results suggest that time-resolved in situ investigation of milling reactions is a promising way to fine-tune and optimise mechanochemical processes.
Mechanochemistry emerges as a potent, environmentally friendly, and straightforward approach for crafting novel multicomponent crystal systems. Various milling parameters, including milling frequency, time, filling degree of the milling jar, ball diameter, vessel size, degree of milling ball filling, and material of jars, are recognized influencers on the mechanisms and rates of product formation. Despite the growing interest in mechanochemistry, there exists a gap in understanding the mechanistic aspects of mechanochemical reactivity and selectivity. To address this, diverse analytical methods and their combinations, such as powder X-ray diffraction, X-ray absorption spectroscopy, NMR, Raman spectroscopy, and thermography, have been developed for real-time, in situ monitoring of mechanochemical transformations.
This discussion centers on our recent findings, specifically investigating the formation of (polymorphic) cocrystals and metal-organic frameworks. Through these studies, we aim to unravel the impact of milling parameters and reaction sequences on the formation mechanism and kinetics. Notably, in the mechanochemical chlorination reaction of hydantoin, normalizing kinetic profiles to the volume of the milling ball unequivocally demonstrates the conservation of milling reaction kinetics. In this ball-milling transformation, physical kinetics outweigh chemical factors in determining reaction rates. Attempting to interpret such kinetics solely through chemical terms poses a risk of misinterpretation. Our results highlight that time-resolved in situ investigations of milling reactions provide a novel avenue for fine-tuning and optimizing mechanochemical processes.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics for materials. More and more frequently, they are used as features in machine learning. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying these local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale and for machine-learning approaches. The latter relates to a general trend toward automation in density functional-based materials science. The lecture will demonstrate how our tools, that assess local atomic environments, helped to test and develop heuristics and design rules and an intuitive understanding of materials.
Mechanochemistry is an effective, environmentally benign, and facile method for the synthesis of new multicomponent crystal systems. Different milling parameters are known to affect the mechanisms and rates of product formation: milling frequency, milling time, filling degree of the milling jar, ball diameter and vessel size, degree of milling ball filling, and material of jars. The increasing interest in mechanochemistry is contrasted by a limited mechanistic understanding of the mechanochemical reactivity and selectivity.
Different analytical methods and their combinations have been developed for the time resolved in situ monitoring of mechanochemical transformations, including powder X-ray diffraction, X-ray adsorption spectroscopy, NMR, Raman spectroscopy, and thermography.
Here we will discuss our recent results investigating the formation of (poly-morphic) cocrystals[1,3], metal-organic compounds, and salts, thereby elucidating the influence of milling parameters and reaction sequences on the formation mechanism and kinetics. For the mechanochemical chlorination reaction of hydantoin normalizing the kinetic profiles to the volume of the milling ball showed clearly that milling reaction kinetics are conserved. Here physical kinetics dominate reaction rates in a ball-milling transformation. Attempting to interpret such kinetics in purely chemical terms risk misinterpreting the results.
Our results indicate that time-resolved in situ investigation of milling reactions offer a new approach to tune and optimize mechanochemical processes.
We show that mechanochemically driven polymorphic transformations can require extremely long induction periods, which can be tuned from hours to days by changing ball milling energy. The robust design and interpretation of ball milling experiments must account for this unexpected kinetics that arises from energetic phenomena unique to the solid state. Detailed thermal analysis, combined with DFT simulations, indicates that these marked induction periods are associated with processes of mechanical activation. Correspondingly, we show that the pre‐activation of reagents can also lead to marked changes in the length of induction periods. Our findings demonstrate a new dimension for exerting control over polymorphic transformations in organic crystals. We expect mechanical activation to have a much broader implication across organic solid‐state mechanochemistry.
To date, there are very few technologies available for the conversion of low-temperature waste heat into electricity. Thermomagnetic generators are one approach proposed more than a century ago. Such devices are based on a cyclic change of magnetization with temperature. This switches a magnetic flux and, according to Faraday’s law, induces a voltage.
Here we give an overview on our research, covering both materials and systems. We demonstrate that guiding the magnetic flux with an appropriate topology of the magnetic circuit improves the performance of thermomagnetic generators by orders of magnitude. Through a combination of experiments and simulations, we show that a pretzel-like topology results in a sign reversal of the magnetic flux. This avoids the drawbacks of previous designs, namely, magnetic stray fields, hysteresis and complex geometries of the thermomagnetic material.
Though magnetocaloric materials had been the first choice also for thermomagnetic generators, they require some different properties, which we illustrate with Ashby plots for materials selection. Experimentally we compare La-Fe-Co-Si and Gd plates in the same thermomagnetic generator. Furthermore, we discuss corrosion and deterioration under cyclic use is a severe problem occurring during operation. To amend this, composite plates using polymer as a matrix have been suggested previously.
Proton exchange membrane fuel cells (PEMFCs) are one of the most promising alternative green energy technologies that deliver high energy density without CO2 emissions. The proton conductivity of proton exchange membranes (PEM) contributes to the overall efficiency of a PEMFC. Materials being used as PEMs must exhibit high proton conductivity at the working conditions of the targeted PEMFC. To date, Nafion and Nafion-like polymers with acidic functionality are widely used as membrane materials due to their high proton conductivity in the range of 10-1 to 10-2 Scm-1 at higher relative humidity. However, these materials suffer from high costs, hazardous production process, and poor performance at high temperatures, limiting their versatility.
In this context, crystalline porous materials are recognized as promising proton conductors for the proton exchange membrane (PEM) in fuel cell technology, owing to their tunable framework structure. However, it is still challenging bulk synthesis for real-world applications of these materials. Herein, we present mechanochemical gram-scale synthesis of series of mixed ligand metal organic frameworks (MOFs) and metal hydrogen‐bonded organic frameworks (MHOFs) using phenylene diphosphonic acid and 1-hydroxyethylidene-1,1-diphosphonic acid with different bipyridyl type of ligands, respectively. In all cases, the existence of extensive hydrogen bonds with amphiprotic uncoordinated phosphonate hydroxyl and oxygen atoms, the frameworks exhibited high proton conductivity. The study demonstrates the potential of green mechanosynthesis for preparations of framework-based proton conducting materials in bulk scale for green energy generation.
Noble metal-free nanoparticles (NPs) based on multi-principal element alloys (MPEAs) were synthesized using a one-step pulsed laser ablation in liquids (PLALs) method for the electrochemical reduction of CO2. Laser ablation was performed in pure water or poly-(diallyldimethylammonium chloride) (PDADMAC)-containing an aqueous solution of Al8Cr17Co17Cu8Fe17Ni33 MPEA targets. Transmission electron microscopy (TEM) measurements combined with energy dispersive X-ray (EDX) mapping were used to characterize the structure and composition of the laser-generated MPEA nanoparticles (MPEA-NPs). These results confirmed the presence of a characteristic elemental distribution of a core-shell phase structure as the predominant NP species. The electrocatalytic performance of the laser-generated MPEA-NPs was characterized by linear sweep voltammetry (LSV) demonstrating an enhanced electrocatalytic CO2 activity for PDADMAC-stabilized NPs. The findings of these investigations indicate that MPEAs have great potential to replace conventional, expensive noble metal electrocatalysts.
Mechanochemistry has proven to be a highly effective method for the synthesis of organic compounds. We studied the kinetics of the catalyst-free Knoevenagel reaction between 4-nitrobenzaldehyde and malononitrile, activated and driven by ball milling. The reaction was investigated in the absence of solvents (neat grinding) and in the presence of solvents with different polarities (liquid-assisted grinding). The reaction was monitored using time-resolved in situ Raman spectroscopy and powder X-ray diffraction (PXRD). Our results indicate a direct relationship between solvent polarity and reaction kinetics, with higher solvent polarity leading to faster product (2-(4-nitrobenzylidone)malononitrile) formation. For the first time, we were able to isolate and determine the structure of an intermediate 2-(hydroxy(4-nitrophenyl)methyl)malononitrile based on PXRD data.
The talk gives a short overview and explains basic principles of research ethics for PhD students. We examine the most important principles in the codes of conduct for good scientific practice on EU and national level. Based on these general rules, we give some hands-on practical tips for daily scientific work and discuss in detail one of the fields most susceptible to conflict, the topic of authorship in scientific publications.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, other recent workflow contributions to the Materials Project software infrastructure (pymatgen, atomate2) related to phonons and machine-learning potentials will be discussed.
In the current study, we investigate an interaction under high-pressure high-temperature of single phase fcc-, hcp- and bcc-structured high-entropy alloys with hydrogen, carbon and nitrogen to obtain high-entropy hydrides, carbides and nitrides. Structural changes in high-entropy alloys upon compression and heating in the presence of these light elements are in the focus of our investigation. An easy route to high-entropy hydrides, carbides and nitrides will open new synthetic horizons in compositionally complex materials. Our study suggests that high-entropy alloys form high- entropy hydrides mainly with a composition close to M:H 1:1 ratio. Hydrides can be obtained under compression with hydrogen as a pressure compression medium or using hydrogen fluid as reactive agent.
The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on “Shared Metadata and Data Formats for Big-Data Driven Materials Science”. We start from an operative definition of metadata, and the features that a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them.
The lattice enthalpies and monotropic relationship of two dehydroepiandrosterone (DEHA) polymorphs (forms I and II) were evaluated through a combination of differential scanning calorimetry (DSC), isothermal solution microcalorimetry, and drop-sublimation Calvet microcalorimetry experiments. The standard molar enthalpy of transition between both forms was determined as ΔtrsHom (II→I, 298.15 K) = - 0.90 ± 0.07 kJ mol-1 and ΔtrsHom (II→I, 417.8 K) = - 1.7 ± 1.0 kJ mol- 1, from measurements of standard molar enthalpies of solution in dimethyl sulfoxide and enthalpies of fusion, respectively. Drop-sublimation Calvet microcalorimetry experiments on form I led to ΔsubHom (cr I, 298.15 K) = 132.0±3.3 kJ mol - 1. This result, when combined with the more precise ΔtrsHom (II→I) value obtained by solution calorimetry, afforded ΔsubHom (cr II, 298.15 K) = 131.1±3.3 kJ mol - 1. The overall data indicate that on enthalpic grounds form I is more stable than form II from 298.15 K up to fusion. This conclusion, and the fact that DSC experiments indicated that form I has also a considerably higher temperature fusion, namely, Tfus(cr I)= 422.5±0.2 K and Tfus(cr II) = 413.1±0.2 K, suggest that the two polymorphs are monotropically related.
Knowing phonon properties is beneficial for predicting low thermal conductivity thermoelectric materials. Employing DFT consumes lots of computational resources. Using ML-driven interatomic potentials (MLIP, e.g., GAP) opens up a faster route, but most potentials are specifically tailored to a certain compound. We aim to generalize the MLIP generation in a Python code-based workflow, combining automatic DFT runs with automated GAP fits. Automation enables easier tests, benchmarks, and validation.
Argyrodite-type materials have lately sparked a lot of research interest due to their thermoelectric properties. One promising candidate is canfieldite (Ag8SnS6), which has a Pna21 orthorhombic crystal structure at room temperature (RT). Recently, experimentalists have found a new low-temperature (LT) phase transition of canfieldite at 120K. Therefore, we investigate structural, vibrational and thermodynamic properties of Ag8SnS6 at room- and low-temperature employing density-functional theory (DFT) and lattice dynamics computations. Thermal properties calculations were based on the quasi-harmonic approximation (QHA) as implemented in phonopy. We achieve good agreement with experiments. Lattice parameters were overestimated by 2%, and thermal properties such as the constant-pressure heat capacity Cp are very close to experimental measurements. Our simulations also reveal a possible new phase transition at around 312 K. Furthermore, we compared RT and LT Ag8SnS6 Grüneisen parameters with some argyrodites analogues, Ag8TQ6 (T = Si, Ge, Ti and Sn; Q = S, Se), finding a relationship between the anharmonicity and low thermal conductivity.
Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material that exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). Therefore, it can be used to create composites with controllable expansion coefficients and prevent thermal stress, fatigue, cracking, and deformation at interfaces. We implement interdisciplinary research to analyze such material. We study the influence of the synthesis methods and their parameters on the sample's purity, crystallinity, and homogeneity. Moreover, we implement ab initio-based vibrational computations with partially treated anharmonicity in combination with experimental methods to follow temperature-induced structural changes and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder.
We demonstrate the determination of anharmonic acoustic phonon properties via second-order Raman scattering exemplarily on copper iodide single crystals. The origin of multi-phonon features from the second-order Raman spectra was assigned by the support of the calculated 2-phonon density of states. In this way, the temperature dependence of acoustic phonons was determined down to 10\,K. To determine independently the harmonic contributions of respective acoustic phonons, density functional theory (DFT) in quasi-harmonic approximation was used. Finally, the anharmonic contributions were determined. The results are in agreement with earlier publications and extend CuI's determined acoustic phonon properties to lower temperatures with higher accuracy. This approach demonstrates that it is possible to characterize the acoustic anharmonicities via Raman scattering down to zero-temperature renormalization constants of at least 0.1cm−1.
Physical adsorption at cryogenic temperature (cryoadsorption) is a reversible mechanism that can reduce the pressure of conventional compressed gas storage systems. Metal–organic framework (MOF) materials are remarkable candidates due to the combination of high specific surface area and density which, in some cases, provide a high volumetric storage capacity. However, such extensive use of MOFs for this application requires the selection of affordable structures, easy to produce and made from feasible metallic and organic components. Herein, we introduce a MOF database detailing the crystallographic and porous properties of 3600 existing MOFs made from industrially relevant metals and their organic composition. The comparison of the available minimum costs of linkers allowed the creation of a database to select affordable structures with high potential for volumetric hydrogen storage by cryoadsorption, considering their composition based on individual or mixed building blocks. A user interface, available online, facilitates the selection of MOFs based on the properties or names of structures and linkers.
An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto an atomic orbital basis. With the help of a fully automatic workflow, the VASP and LOBSTER software packages are used to generate the data. We then perform bonding analyses on 1520 compounds (insulators and semiconductors) and provide the results as a database. The projected densities of states and bonding indicators are benchmarked on standard density-functional theory computations and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine learning model for phononic properties, which shows an increase in prediction accuracies by 27% (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features.
Mechanical flexibility in molecular crystals is a fascinating behavior with potential for developing advanced technologies. However, the phenomenon of mechanical bending is poorly understood. We explore for the first time the atomistic origin of elastic bending in a single component organic crystal using a combination of μ-focus synchrotron X-ray diffraction and ab initio simulation.
Argyrodite-type materials have lately sparked a lot of research interest due to their thermoelectric properties. One promising candidate is canfieldite (Ag8SnS6), which has a Pna21 orthorhombic crystal structure at room temperature (RT). Recently, Slade group found a new low-temperature (LT) phase transition of canfieldite at 120K. Therefore, we investigate structural, vibrational and thermodynamic properties of Ag8SnS6 at room- and low-temperature employing density-functional theory (DFT) and lattice dynamics computations. Thermal properties calculations were based on the quasi-harmonic approximation (QHA) as implemented in phonopy. We achieve good agreement with experiments. Lattice parameters were overestimated by 2%, and thermal properties such as the constant-pressure heat capacity Cp are very close to experimental measurements. Our simulations also reveal a possible new phase transition at around 312 K. Furthermore, we compared RT and LT Ag8SnS6 Grüneisen parameters with some argyrodites analogues, Ag8TS6 (T = Si, Ge, Ti and Sn), finding a relationship between the anharmonicity and low thermal conductivity.
Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material which exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). The linear thermal expansion coefficient of ZrV2O7 is −7.1×10-6 K-. Therefore, it can be used to create composites with controllable expansion coefficients and prevent destruction by thermal shock.
Material characterization, leading to application, requires pure, homogenous samples of high crystallinity via a reliable synthesis route. While there is a selection of described syntheses in the literature, it still needs to be addressed which synthesis route leads to truly pure and homogenous samples. Here, we study the influence of the synthesis methods (solid-state, sol-gel, solvothermal) and their parameters on the sample's purity, crystallinity, and homogeneity. The reproducibility of results and data obtained with scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry, and thermogravimetric analysis (DSC/TGA) were analyzed extensively. The sol-gel method proves superior to the solid-state method and produces higher-quality samples over varying parameters. Sample purity also plays an important role in NTE micro and macro-scale characterizations that explain the impact of porosity versus structural changes.
Moreover, we implement ab-initio-based vibrational computations with partially treated anharmonicity (quasi-harmonic approximation, temperature-dependent effective harmonic potentials) in combination with experimental methods to follow and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder, microstructure, and defects. Khosrovani et al. and Korthuis et al., in a series of diffraction experiments, attributed the thermal contraction of ZrV2O7 to the transverse thermal motion of oxygen atoms in V-O-V linkages. In addition to previous explanations, we hypothesize that local disorder develops in ZrV2O7 crystals during heating.
We are working on the experimental ZrV2O7 development and discuss difficulties one might face in the process as well as high-quality sample significance in further investigation. The obtained samples are currently used in the ongoing research of structure analysis and the negative thermal expansion mechanism.
Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material which exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). The linear thermal expansion coefficient of ZrV2O7 is −7.1×10-6 K-. Therefore, it can be used to create composites with controllable expansion coefficients and prevent destruction by thermal shock.
Material characterization, leading to application, requires pure, homogenous samples of high crystallinity via a reliable synthesis route. While there is a selection of described syntheses in the literature, it still needs to be addressed which synthesis route leads to truly pure and homogenous samples. Here, we study the influence of the synthesis methods (solid-state, sol-gel, solvothermal) and their parameters on the sample's purity, crystallinity, and homogeneity. The reproducibility of results and data obtained with scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry, and thermogravimetric analysis (DSC/TGA) were analyzed extensively. The sol-gel method proves superior to the solid-state method and produces higher-quality samples over varying parameters. Sample purity also plays an important role in NTE micro and macro-scale characterizations that explain the impact of porosity versus structural changes.
Moreover, we implement ab-initio-based vibrational computations with partially treated anharmonicity (quasi-harmonic approximation, temperature-dependent effective harmonic potentials) in combination with experimental methods to follow and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder, microstructure, and defects. Khosrovani et al. and Korthuis et al., in a series of diffraction experiments, attributed the thermal contraction of ZrV2O7 to the transverse thermal motion of oxygen atoms in V-O-V linkages. In addition to previous explanations, we hypothesize that local disorder develops in ZrV2O7 crystals during heating.
We are working on the experimental ZrV2O7 development and discuss difficulties one might face in the process as well as high-quality sample significance in further investigation. The obtained samples are currently used in the ongoing research of structure analysis and the negative thermal expansion mechanism.
Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material which exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). The linear thermal expansion coefficient of ZrV2O7 is −7.1×10-6 K-1. Therefore, it can be used to create composites with controllable expansion coefficients and prevent destruction by thermal shock.
Material characterization, leading to application, requires pure, homogenous samples of high crystallinity via a reliable synthesis route. While there is a selection of described syntheses in the literature, it still needs to be addressed which synthesis route leads to truly pure and homogenous samples. Here, we study the influence of the synthesis methods (solid-state, sol-gel, solvothermal) and their parameters on the sample's purity, crystallinity, and homogeneity. The reproducibility of results and data obtained with scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry, and thermogravimetric analysis (DSC/TGA) were analyzed extensively. The sol-gel method proves superior to the solid-state method and produces higher-quality samples over varying parameters. Sample purity also plays an important role in NTE micro and macro-scale characterizations that explain the impact of porosity versus structural changes.
Moreover, we implement ab-initio-based vibrational computations with partially treated anharmonicity (quasi-harmonic approximation, temperature-dependent effective harmonic potentials) in combination with experimental methods to follow and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder, microstructure, and defects. Khosrovani et al. and Korthuis et al., in a series of diffraction experiments, attributed the thermal contraction of ZrV2O7 to the transverse thermal motion of oxygen atoms in V-O-V linkages. In addition to previous explanations, we hypothesize that local disorder develops in ZrV2O7 crystals during heating.
We are working on the experimental ZrV2O7 development and discuss difficulties one might face in the process as well as high-quality sample significance in further investigation. The obtained samples are currently used in the ongoing research of structure analysis and the negative thermal expansion mechanism.
Understanding and Machine Learning of Materials Properties with Quantum-Chemical Bonding Analysis
(2023)
Bonds and local atomic environments are crucial descriptors for material properties. They have been used to create design rules for materials and are used as features in machine learning of material properties. This talk will show how our recently developed tools, that automatically perform quantum chemical bond analysis and enable the study of chemical bonds and local atomic environments, accelerate and improve the development of such heuristics and machine-learned models for materials properties.
New insights into pertinent Fe-complexes for the synthesis of iron via the instant polyol process
(2023)
Chemically synthesized iron is in demand for biomedical applications due to its large saturation magnetization compared to iron oxides. The polyol process, suitable for obtaining Co and Ni particles and their alloys, is laborious in synthesizing Fe. The reaction yields iron oxides, and the reaction pathway remains unexplored. This study shows that a vicinal polyol, such as 1,2-propanediol, is suitable for obtaining Fe rather than 1,3-propanediol owing to the formation of a reducible Fe intermediate complex. X-ray absorption spectroscopy analysis reveals the ferric octahedral geometry and tetrahedral geometry in the ferrous state of the reaction intermediates in 1,2-propanediol and 1,3-propanediol, respectively. The final product obtained using a vicinal polyol is Fe with a γ-Fe2O3 shell, while the terminal polyol is favourable for Fe3O4. The distinct Fe–Fe and Fe–O bond lengths suggest the presence of a carboxylate group and a terminal alkoxide ligand in the intermediate of 1,2-propanediol. A large Fe–Fe bond distance suggests diiron complexes with bidentate carboxylate bridges. Prominent high-spin and low-spin states indicate the possibility of transition, which favors the reduction of iron ions in the reaction using 1,2-propanediol.
Mechanochemistry provides polymorphs that are difficult to obtain by conventional solution-based methods. Based on the nicotinamide and pimelic acid cocrystal, we demonstrated that the ball size, frequency, and temperature are essential parameters. Our results indicate that fine-tuning the energy input during a mechanochemical reaction can provide control over polymorphism.
Investigating the phononic properties is beneficial for predicting low thermal conductivity thermoelectric materials.1–3 Employing density functional theory4 takes many calculation steps and consumes a lot of computational resources.5,6 Using machine learning driven interatomic potentials (MLIP, e.g., Gaussian approximation potential,8 GAP) opens up a faster route to phonons7 but in most cases, the potentials are specifically tailored for a certain compound. In this work, we automate the generation of such MLIPs in a Python code-based workflow, based on the automation tools atomate29 and pymatgen10 which combines the automatic DFT computations with the automated fitting of GAPs. Automation enables easier testing, benchmarking and validation.11 We aim to provide the workflow-generated potentials for storage in databases.
Understanding the chemistry and nature of individual chemical bonds is essential for materials design. Bonding analysis via the LOBSTER software package has provided valuable insights into the properties of materials for thermoelectric and catalysis applications. Thus, the data generated from
bonding analysis becomes an invaluable asset that could be utilized as features in large-scale data analysis and machine learning of material properties. However, no systematic studies exist that conducted high-throughput materials simulations to curate and validate bonding data obtained from LOBSTER. Here we present an approach to constructing such a large database consisting of quantum-chemical bonding information.
Argyrodite-type materials have lately sparked a lot of research interest due to their thermoelectric properties.One promising candidate is canfieldite (Ag8SnS6), which has a Pna21 orthorhombic crystal structure at room temperature (RT). Recently, a new low-temperature (LT) phase transition of canfieldite at 120K has been found. Here, we investigate structural, vibrational and thermodynamic properties of Ag8SnS6 at room- and low-temperature employing density functional theory (DFT) and lattice dynamics computations. Thermal properties calculations were based on the quasi-harmonic approximation (QHA) as implemented in phonopy. We achieve good agreement with experiments. Thermal properties such as the constant-pressure heat capacity (Cp) and thermal conductivity are very close to experimental measurements. Furthermore, we compared RT and LT Ag8SnS6 Grüneisen parameters with argyrodites analogues, Ag8XS6 (X = Sn, Si, Ge), to arrive at an improved
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics for materials. More and more frequently, they are used as features in machine learning. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying these local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale and for machine-learning approaches. The latter relates to a general trend toward automation in density functional-based materials science. The lecture will demonstrate how our tools, that assess local atomic environments, helped to test and develop heuristics and design rules and an intuitive understanding of materials.
Crystalline porous materials are recognized as promising proton conductors for the proton exchange membrane (PEM) in fuel cell technology owing to their tunable framework structure. However, it is still a challenging bulk synthesis for real-world applications of these materials. Herein, we report the mechanochemical gram-scale synthesis of two isostructural metal hydrogen-bonded organic frameworks (MHOFs) of Co(II) and Ni(II) based on 1-hydroxyethylidenediphosphonic acid (HEDPH4) with 2,2′-bipyridine (2,2′-bipy): Co(HEDPH3)2(2,2′-bipy)·H2O (1) and Ni(HEDPH3)2(2,2′-bipy)·H2O (2). In situ monitoring of the mechanochemical synthesis using different synchrotron-based techniques revealed a one-step mechanism – the starting materials are directly converted to the product. With the existence of extensive hydrogen bonds with amphiprotic uncoordinated phosphonate hydroxyl and oxygen atoms, both frameworks exhibited proton conduction in the range of 10–4 S cm–1 at room temperature under humid conditions. This study demonstrates the potential of green mechanosynthesis for bulk material preparation of framework-based solid-state proton conductors.
Mechanically flexible single crystals of molecular materials offer potential for a multitude of new directions in advanced materials design. Before the full potential of such materials can be exploited, insight into their mechanisms of action must be better understood. Such insight can be only obtained through synergistic use of advanced experimentation and simulation. We herein report the first detailed mechanistic study of elasto-plastic flexibility in a molecular solid. An atomistic origin for this mechanical behaviour is proposed through a combination of atomic force microscopy, μ-focus synchrotron X-ray diffraction, Raman spectroscopy, ab initio simulation, and computed elastic tensors. Our findings suggest that elastic and plastic bending are intimately linked and result from extensions of the same molecular deformations. The proposed mechanism bridges the gap between contested mechanisms, suggesting its applicability as a general mechanism for elastic and plastic bending in organic molecular crystals.
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. This open-source software package powers the Materials Project.
In this particular contribution, the handling of obital-resolved "ICOHPLIST.lobster" files from Lobster was implemented in the software package (github handle: @JaGeo).
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database.
Here we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output files data stored as a dictionary.
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. It consists of two kinds of json files. Smaller lightweight JSONS consists of summarized bonding information for each of the compounds. The files are named as per ID numbers in the materials project database.
Here we provide also the larger computational data json files for 700 compounds. This files consists of all important LOBSTER computation output files data stored as dictionary.
Talk about my recent research on data-driven chemical understanding with geometrical and quantum-chemical bonding analysis.
Optimizing the Green Synthesis of ZIF-8 by Reactive Extrusion Using In Situ Raman Spectroscopy
(2023)
We report the scale-up of a batch solid synthesis of zeolitic imidazolate framework-8 (ZIF-8) for reactive extrusion. The crystalline product forms in the extruder directly under the mixture of solid 2-methylimidazole and basic zinc carbonate in the presence of a catalytic amount of liquid. The process parameters such as temperature, liquid type, feeding rate, and linker excess were optimized using the setup specifically designed for in situ Raman spectroscopy. Highly crystalline ZIF-8 with a Brunauer–Emmett–Teller (BET) surface area of 1816 m2 g–1 was quantitatively prepared at mild temperature using a catalytic amount of ethanol and a small excess of the linker. Finally, we developed a simple and comprehensive approach to evaluating the environmental friendliness and scalability of metal–organic framework (MOF) syntheses in view of their large-scale production.
A deep insight into the chemistry and nature of individual chemical bonds is essential for understanding materials. Bonding analysis is expected to provide important features for large-scale data analysis and machine learning of material properties. Such information on chemical bonds can be calculated using the LOBSTER (www.cohp.de) software package, which post-processes data from modern density functional theory computations by projecting plane wave-based wave functions onto a local atomic orbital basis. We have performed bonding analysis on 1520 compounds (insulators and semiconductors) using a fully automated workflow combining the VASP and LOBSTER software packages. We then automatically evaluated the data with LobsterPy (https://github.com/jageo/lobsterpy) and provide results as a database. The projected densities of states and bonding indicators are benchmarked on VASP projections and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine-learning model for phononic properties, which shows an increase in prediction accuracies by 27 % (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features.
This talk highlights a proof-of-concept that demonstrates the ability to calculate high-resolution Fourier transforms. These can be combined with multi-scale modeling to simulate scattering over a wide range, from small-angle scattering to XRD and PDF.
The preprint documenting this is available on the ArXiv here:
https://doi.org/10.48550/arXiv.2303.13435
The Jupyter notebook, VASP calculation details and MOUSE measured scattering patterns are available from this Zenodo repository: https://dx.doi.org/10.5281/zenodo.7764045
## Summary:
This notebook and associated datasets (including VASP details) accompany a manuscript available on the ArXiv (https://doi.org/10.48550/arXiv.2303.13435) and hopefully soon in a journal as short communication as well. Most of the details needed to understand this notebook are explained in that paper with the same title as above. For convenience, the abstract is repeated here:
## Paper abstract:
We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for Pair Distribution Function (PDF) analysis. Such simulated patterns can be used to test holistic analysis models, and, since the diffraction intensity is presented coupled to the scattering intensity, may offer a novel pathway for determining the degree of crystallinity.
The ``Ultima Ratio'' strategy is demonstrated on a 64-nm Metal Organic Framework (MOF) particle, calculated from $Q<0.01$\,$\mathrm{nm}^{-1}$ up to $Q\approx150$\,$\mathrm{nm}^{-1}$, with a resolution of 0.16\,\AA. The computations exploit a modified 3D Fast Fourier Transform (3D-FFT), whose modifications enable the transformations of matrices at least up to $8000^3$ voxels in size. Multiple of these modified 3D-FFTs are combined to improve the low-$Q$ behaviour.
The resulting curve is compared to a wide-range scattering pattern measured on a polydisperse MOF powder.
While computationally intensive, the approach is expected to be useful for simulating scattering from a wide range of realistic, complex structures, from (poly-)crystalline particles to hierarchical, multicomponent structures such as viruses and catalysts.
We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for Pair Distribution Function (PDF) analysis. Such simulated patterns can be used to test holistic analysis models, and, since the diffraction intensity is on the same scale as the scattering intensity, may offer a novel pathway for determining the degree of crystallinity.
The "Ultima Ratio" strategy is demonstrated on a 64-nm Metal Organic Framework (MOF) particle, calculated from Q < 0.01 1/nm up to Q < 150 1/nm, with a resolution of 0.16 Angstrom. The computations exploit a modified 3D Fast Fourier Transform (3D-FFT), whose modifications enable the transformations of matrices at least up to 8000^3 voxels in size. Multiple of these modified 3D-FFTs are combined to improve the low-Q behaviour. The resulting curve is compared to a wide-range scattering pattern measured on a polydisperse MOF powder. While computationally intensive, the approach is expected to be useful for simulating scattering from a wide range of realistic, complex structures, from (poly-)crystalline particles to hierarchical, multicomponent structures such as viruses and catalysts.
Vibrational properties play a key role in determining the stability and thermal conductivity behaviour of materials. The quasi- harmonic approximation gives insight into the phononic properties of a compound, but in the established way, i.e. density functional theory based methods, it takes many calculation steps and consumes a lot of resources to arrive at the desired results. Machine learning (ML) trained interatomic potentials (e.g. Gaussian approximation potential, GAP) pose an alternative to the traditional computation way of phonons. We develop a Python code based workflow which combines automation tools like atomate2 with ML to ease providing interactomic potentials for (quantum chemical) computations and databases.
Chemical bonding and coordination environments are crucial descriptors of material properties. They have previously been applied to creating chemical design guidelines and chemical heuristics. They are currently being used as features in machine learning more and more frequently. I will discuss implementations and algorithms (ChemEnv and LobsterEnv) for identifying these coordination environments based on geometrical characteristics and chemical bond quantum chemical analysis. I will demonstrate how these techniques helped in testing chemical heuristics like the Pauling rule and thereby improved our understanding of chemistry. I will also show how these tools can be used to create new design guidelines and a new understanding of chemistry. To use quantum-chemical bonding analysis on a large-scale and for machine-learning approaches, fully automatic workflows and analysis tools have been developed. After presenting the capabilities of these tools, I will also point out how these developments relate to the general trend towards automation in the field of density functional based materials science.
Soda lakes of the East African Rift Valley are hyperalkaline, hypersaline lakes extremely enriched in Na+, K+, Cl−, CO32−, HCO3−, and SiO2. In this paper, we investigate the chemical evolution in these lakes and the production of chemical sediments by salt precipitation via evaporation. Water samples from tributary springs and three lakes (Magadi, Nasikie Engida and Natron) have been experimentally studied by in-situ X-ray diffraction during evaporation experiments to characterize the sequence of mineral precipitation. These data are complemented by ex-situ diffraction studies, chemical analyses and thermodynamic hydrochemical calculations producing detailed information on the activity of all solution species and the saturation state of all minerals potentially generated by the given composition. Major minerals precipitating from these samples are sodium carbonates/bicarbonates as well as halite. The CO3/HCO3 ratio, controlled by pH, is the main factor defining the Na‑carbonates precipitation sequence: in lake brines where CO3/HCO3 > 1, trona precipitates first whereas in hot springs, where CO3/HCO3 ≪ 1, nahcolite precipitates instead of trona, which forms later via partial dissolution of nahcolite. Precipitation of nahcolite is possible only at lower pH values (pCO2 higher than −2.7) explaining the distribution of trona and nahcolite in current lakes and the stratigraphic sequences. Later, during evaporation, thermonatrite precipitates, normally at the same time as halite, at a very high pH (>11.2) after significant depletion of HCO3− due to trona precipitation. The precipitation of these soluble minerals increases the pH of the brine and is the main factor contributing to the hyperalkaline and hypersaline character of the lakes. Villiaumite, sylvite, alkaline earth carbonates, fluorapatite and silica are also predicted to precipitate, but most of them have not been observed in evaporation experiments, either because of the small amount of precipitates produced, kinetic effects delaying the nucleation of some phases, or by biologically induced effects in the lake chemistry that are not considered in our calculations. Even in these cases, the chemical composition in the corresponding ions allows for discussion on their accumulation and the eventual precipitation of these phases. The coupling of in-situ and ex-situ experiments and geochemical modelling is key to understanding the hydrogeochemical and hydroclimatic conditions of soda lakes, evaporite settings, and potentially soda oceans of early Earth and other extraterrestrial bodies.
This paper reports on the effect of the solvent viscosity on the formation of gold nanoparticles (Au NPs) during Sputtering onto Liquid (SoL) process. All other parameters related to the plasma and the host liquid are kept constant. SoL is a simple highly reproducible approach for preparation of colloidal dispersions of small naked NPs. The properties of the final product are determined by both the sputtering parameters and the host liquid characteristics. As a model system we chose to sputter a gold target by a direct-current magnetron discharge onto a line of polymerized rapeseed oils having similar surface tension (32.6 ― 33.1 mJ·m-2 at RT). It was found that well dispersed Au NPs grow in the bulk solution of oils with low viscosities (below 630 cP at 25 °C) while gold films form onto the surface of high viscosity liquids (more than 1000 cP at 25 °C). The mean diameter of the individual Au NPs slightly increases with oil viscosity and is in range about 2.1―2.5 nm according to transmission electron microscopy.