6.0 Abteilungsleitung und andere
Filtern
Dokumenttyp
- Zeitschriftenartikel (91)
- Vortrag (72)
- Posterpräsentation (28)
- Forschungsdatensatz (20)
- Preprint (2)
- Video (1)
- Sonstiges (1)
Sprache
- Englisch (206)
- Deutsch (7)
- Mehrsprachig (2)
Schlagworte
- Automation (45)
- Mechanochemistry (44)
- DFT (19)
- Bonding analysis (18)
- Materials Informatics (15)
- Bonding Analysis (13)
- Computational Chemistry (13)
- Database (13)
- Phonons (13)
- Machine learning (11)
Organisationseinheit der BAM
- 6 Materialchemie (215)
- 6.0 Abteilungsleitung und andere (215)
- 6.3 Strukturanalytik (96)
- VP Vizepräsident (17)
- VP.1 eScience (17)
- 1 Analytische Chemie; Referenzmaterialien (7)
- 1.4 Prozessanalytik (6)
- 8 Zerstörungsfreie Prüfung (6)
- 8.5 Röntgenbildgebung (6)
- 5 Werkstofftechnik (4)
Paper des Monats
- ja (2)
Components produced using additive manufacturing can be marked for unique identification and secure authentication [1,2]. Serial numbers and machine-readable codes can be used to identify the component, and link digital product-related data (i.e., a digital product passport) to the actual components. The most prevailing solution consists of local process manipulation, such as printing a quick response (QR) code [3] or a set of blind holes on the surface of the internal cavity of hollow components. However, local manipulation of components may alter the properties, and external tagging features can be altered or even removed by post-processing treatments. This work therefore aims to provide a new methodology for identification, authentication, and traceability of additively manufactured (AM) components using microstructural features that are unique to each part. X-ray computed tomography (XCT) was employed to image the microstructural features of AlSi10Mg parts. Based on size and geometry, the most prominent features were selected to create a unique digital authenticator. We implemented a framework in Python using open-access modules that can successfully create a digital object authenticator using the segmented microstructure information from XCT. The authenticator is stored as a QR code, along with the 3D information of the selected features.
Photoresponsive molecular crystals have sparked interest owing to their potential application prospects. In this study, we achieved dual mechanical responses of a cyanostilbene-based crystal BN. The crystal exhibited both stress-induced mechanical flexibility and light-induced photomechanical bending. Two distinct mechanical responses are related to different underlying phenomena and were not interlinked. While elasticity is attributed to criss-cross packing arrangement in the isotropic structure, photomechanical bending is attributed to the formation of heterogeneous phase distribution due to monomer to dimer cycloaddition product formation. The crystal did not demonstrate photoswitching activity. However, it exhibited thermal reversibility, highlighting reversible dimer to monomer conversion.
AbstractUnderstanding the origin of low thermal conductivities in ionic conductors is essential for improving their thermoelectric efficiency, although accompanying high ionic conduction may present challenges for maintaining thermoelectric device integrity. This study investigates the thermal and ionic transport in Cu7PSe6, aiming to elucidate their fundamental origins and correlation with the structural and dynamic properties. Through a comprehensive approach including various characterization techniques and computational analyses, it is demonstrated that the low thermal conductivity in Cu7PSe6 arises from structural complexity, variations in bond strengths, and high lattice anharmonicity, leading to pronounced diffuson transport of heat and fast ionic conduction. It is found that upon increasing the temperature, the ionic conductivity increases significantly in Cu7PSe6, whereas the thermal conductivity remains nearly constant, revealing no direct correlation between ionic and thermal transport. This absence of direct influence suggests innovative design strategies in thermoelectric applications to enhance stability by diminishing ionic conduction, while maintaining low thermal conductivity, thereby linking the domains of solid‐state ionics and thermoelectrics. Thus, this study attempts to clarify the fundamental principles governing thermal and ionic transport in Cu+‐superionic conductors, similar to recent findings in Ag+ argyrodites.
Mechanochemistry is drawing attention from the pharmaceutical industry given its potential for sustainable material synthesis and manufacture. Scaling mechanochemical processes to industrial level remains a challenge due to an incomplete understanding of their underlying mechanisms. We here show how time-resolved in situ powder X-ray diffraction data, coupled with analytical kinetic modelling, provides a powerful approach to gain mechanistic insight into mechanochemical reactions. By using the ibuprofen–nicotinamide co-crystal mechanosynthesis as a benchmark system, we investigate the behaviour of the solids involved and identify the factors that promote the reaction. As mechanochemical mechanisms become increasingly clear, it promises to become a breakthrough in the industrial preparation of advanced pharmaceuticals.
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, the general trend toward automation in computational materials science and some of our recent contributions will be discussed.
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, the general trend toward automation in computational materials science and some of our recent contributions will be discussed.
Covalent organic frameworks (COFs) containing well-defined redox-active groups have become competitive materials for next-generation batteries. Although high potentials and rate performance can be expected, only a few examples of p-type COFs have been reported for charge storage to date with even fewer examples on the use of COFs in multivalent ion batteries. Herein, we report the synthesis of a p-type highly porous and crystalline azatruxene-based COF and its application as a positive electrode material in Li- and Mg-based batteries. When this material is used in Li-based half cells as a COF/carbon nanotube (CNT) electrode, a discharge potential of 3.9 V is obtained with discharge capacities of up to 70 mAh g−1 at a 2 C rate. In Mg batteries using a tetrakis(hexafluoroisopropyloxy)borate electrolyte, cycling proceeds with an averge discharge voltage of 2.9 V. Even at a fast current rate of 5 C, the capacity retention amounts to 84% over 1000 cycles.
High‐Entropy Alloys and Their Affinity with Hydrogen: From Cantor to Platinum Group Elements Alloys
(2024)
AbstractProperties of high‐entropy alloys are currently in the spotlight due to their promising applications. One of the least investigated aspects is the affinity of these alloys to hydrogen, its diffusion, and reactions. In this study, high pressure is applied at ambient temperature and stress‐induced diffusion of hydrogen is investigated into the structure of high‐entropy alloys (HEA) including the famous Cantor alloy as well as less known, but nevertheless important platinum group (PGM) alloys. By applying X‐ray diffraction to samples loaded into diamond anvil cells, a comparative investigation of transition element incorporating HEA alloys in Ne and H2 pressure‐transmitting media is performed at ambient temperature. Even under stresses far exceeding conventional industrial processes, both Cantor and PGM alloys show exceptional resistance to hydride formation, on par with widely used industrial grade Cu–Be alloys. The observations inspire optimism for practical HEA applications in hydrogen‐relevant industry and technology (e.g., coatings, etc), particularly those related to transport and storage.
Multicomponent reactions (MCRs) offer a platform to create different chemical structures and linkages for highly stable covalent organic frameworks (COFs). As an illustrative example, the multicomponent Povarov reaction generates 2,4-phenylquinoline from aldehydes and amines in the presence of electron-rich alkenes. In this study, we introduce a new domino reaction to generate unprecedented 2,3-phenylquinoline COFs in the presence of epoxystyrene. This work thus presents, for the first time, structural isomeric COFs produced by multicomponent domino and Povarov reactions. Furthermore, 2,3-phenylquinolines can undergo a Scholl reaction to form extended aromatic linkages. With this approach, we synthesize two thermally and chemically stable MCR-COFs and two heteropolyaromatic COFs using both domino and in situ domino and Scholl reactions. The structure and properties of these COFs are compared with the corresponding 2,4-phenylquinoline-linked COF and imine-COF, and their activity toward benzene and cyclohexane sorption and separation is investigated. The position of the pendant phenyl groups within the COF pore plays a crucial role in facilitating the industrially important sorption and separation of benzene over cyclohexane. This study opens a new avenue to construct heteropolyaromatic COFs via MCR reactions.
The impact of magnetism on chemical ordering in face-centered cubic CrCoNi medium entropy alloy is studied by a combination of ab initio simulations, machine learning potentials, and Monte Carlo simulations. Large magnetic energies are revealed for some mixed L12 /L10 type ordered configurations, which are rooted in strong nearest-neighbor magnetic exchange interactions and chemical bonding among the constituent elements. There is a delicate interplay between magnetism and stability of MoPt2 and L12 /L10 type of ordering which may explain opposing experimental and theoretical findings.
An Amorphous Lewis-acidic Zirconium Chlorofluoride as HF Shuttle: C-F Bond Activation and Formation
(2024)
In recent years, fluorine chemistry has gained increasing political attention. Owing to a growing shortage of fluorspar, a raw material used for producing fluorinated base chemicals, fluorospar has been named among the 30 critical raw materials in the EU.2 As such, it becomes increasingly important to recycle existing fluorinated compounds and make them available as sources of fluorine for reactions. Significant progress has been made in the field of C-F bond activation using heterogeneous catalysts such as aluminum chlorofluoride (ACF).3–5 However, the transfer of fluorine atoms from one molecule to another using heterogeneous catalysts has not yet been reported. In this study, we successfully synthesized amorphous zirconium chlorofluoride (ZCF), and we were able to establish a catalytic behavior of ZCF in C-F bond activation reactions. We present a heterogeneous catalyst that performs dehydrofluorination of a fluoroalkane and consecutive hydrofluorination of an alkyne at room temperature.
In recent years, many protocols in computational materials science have been automated and made available within software packages (primarily Python-based). This ranges from the automation of simple heuristics (oxidation states, coordination environments) to the automation of protocols, including multiple DFT and post-processing tools such as (an)harmonic phonon computations or bonding analysis. Such developments also shorten the time frames of projects after such developments have been made available and open new possibilities. For example, we can now easily make data-driven tests of well-known rules and heuristics or develop quantum chemistry-based materials descriptors for machine learning approaches. These tests and descriptors can have applications related to magnetic ground state predictions of materials relevant for spintronic applications or for predicting thermal properties relevant for thermal management in electronics. Combining high-throughput ab initio computations with fitting, fine-tuning machine learning models and predictions of such models within complex workflows is also possible and promises further acceleration in the field. In this talk, I will show our latest efforts to link automation with data-driven chemistry and materials science.
AbstractMulti-edge extended X-ray absorption fine structure (EXAFS) spectroscopy combined with reverse Monte Carlo (RMC) simulations was used to probe the details of element-specific local coordinations and component-dependent structure relaxations in single crystalline equiatomic CrMnFeCoNi high-entropy alloy as a function of the annealing temperature. Two representative states, namely a high-temperature state, created by annealing at 1373 K, and a low-temperature state, produced by long-term annealing at 993 K, were compared in detail. Specific features identified in atomic configurations of particular principal components indicate variations in the local environment distortions connected to different degrees of compositional disorder at the chosen representative temperatures. The detected changes provide new atomistic insights and correlate with the existence of kinks previously observed in the Arrhenius dependencies of component diffusion rates in the CrMnFeCoNi high-entropy alloy.
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, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
Zirconium chloro fluoride as catalyst for C-F bond activation and HF transfer of fluoroalkanes
(2024)
In this work1, we have successfully synthesised amorphous zirconium chloro fluoride (ZCF), which exhibits medium lewis acidity. In addition to investigating the local coordination sphere around the Zr atoms and the material properties, we were able to establish a catalytic behavior of ZCF in C-F bond activation reactions. We present the first heterogeneous catalyst that performs dehydrofluorination of a fluoroalkane and consecutive hydrofluorination of an alkyne at room temperature.
The LOBSTER (Deringer et al., 2011;Maintz et al., 2013 ,2016 ;Nelson et al., 2020 ) software aids in extracting quantum-chemical bonding information from materials by projecting the plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. LobsterEnv, a module implemented in pymatgen (Ong et al., 2013) by some of the authors of this package, facilitates the use of quantum-chemical bonding information obtained from LOBSTER calculations to identify neighbors and coordination environments. LobsterPy is a Python package that offers a set of convenient tools to further analyze and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the estimation of (anti) bonding contributions, generation of textual descriptions, and visualization of LOBSTER computation results. Since its first release, both LobsterPy and LobsterEnv capabilities have been extended significantly. Unlike earlier versions, which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs) (Dronskowski & Blöchl, 1993), both can now also analyze Crystal Orbital Overlap Populations (COOP) (Hughbanks & Hoffmann, 1983) and Crystal Orbital Bond Index (COBI) (Müller et al., 2021). Extracting the information about the most important orbitals contributing to the bonds is optional, and users can enable it as needed. Additionally, bonding-based features for machinelearning (ML) studies can be engineered via the sub-packages “featurize” and “structuregraphs”. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. LobsterPy has been used to produce the results in Ngo et al. (2023), Chen et al. (2024), Naik et al. (2023), and it is also part of Atomate2 (2023) bonding analysis workflow for generating bonding analysis data in a format compatible with the Materials Project (Jain et al., 2013) API.
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.[5,6] Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
Crystalline materials that exhibit reversible mechanical responses upon exposure to external stimuli have garnered significant attention owing to their potential applications in various fields. Herein, we report a crystal of (2Z,2′Z)-2,2′-(1,4-phenylene)bis(3-(4-bromophenyl)acrylonitrile) (DSBr), which displays simultaneous elasto-plastic behaviour and reversible thermosalient effects. While elasto-plastic behaviour is attributed to underlying packing features, reversible thermosalient expansion is attributed to uniaxial expansion mediated by heat. Exceptional length increase and contraction upon cooling is due to the restorative nature of weak interactions through a cooperative effect. The cooperative movement of molecules is reflected in the unidirectional expansion of the habit plane. Thermosalient reversible expansion–contraction in elasto-plastic crystals have not been discussed in the literature so far. Detailed analysis reported herein provides a comprehensive understanding of the underlying mechanism of flexibility and thermosalient responses. This crystal's unique blend of reversible thermal expansion with flexibility holds substantial promise for applications in flexible thermal actuators.
This study investigates the mechanochemical reactions between AgBr 3-picolylamine and 4-picolylamine. The use of different stoichiometry ratios of the reagents allows [(AgBr)(n-pica)]n and [(AgBr)2(n-pica)]n to be obtained, and we report the new structures of [(AgBr)2(3-pica)]n and [(AgBr)2(4-pica)]n which are characterized by the presence of the following: (a) infinite inorganic chains, (b) silver atom coordinated only by bromide atoms and (c) argentophilic interactions. Furthermore, we studied the interconversion of [(AgBr)(n-pica)]n/[(AgBr)2(n-pica)]n by mechanochemical and thermal properties. The in situ experiments suggest that [(AgBr)(3-pica)]n is kinetically favoured while [(AgBr)2(3-pica)]n is converted into [(AgBr)(3-pica)]n only with a high excess of the ligand. Finally, the liquid nature of the ligands is not sufficient to assist the grinding process, and the complete reaction is observed with the addition of a small quantity of acetonitrile.
Jobflow is a free, open-source library for writing and executing workflows. Complex workflows can be defined using simple python functions and executed locally or on arbitrary computing resources using the FireWorks workflow manager.
Some features that distinguish jobflow are dynamic workflows, easy compositing and connecting of workflows, and the ability to store workflow outputs across multiple databases.
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.
We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch
other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of filesystem- and cloud-based databases without the data storage process being part of the underlying workflow logic itself. Jobflow has been intentionally designed to be fully independent of the choice of workflow manager used to dispatch the calculations on remote computing resources. At the time of writing, Jobflow
workflows can be executed either locally or across distributed compute environments via an adapter to the FireWorks package, and Jobflow fully supports the integration of additional workflow execution adapters in the future.
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.
A cobalt(III) complex, [Co(L)3](DMF) (1) of Schiff base ligand HL, 2-((E)-(benzylimino)methyl)-4-bromophenol is prepared and single crystal X-ray structural analysis have also been performed. The structures of complex 1 showed hexa-coordinated mononuclear systems that adopt octahedral geometry. The complex has also exhibited the supramolecular networks through non-covalent interactions like H-bonding, C–Hπ stacking. Moreover, the complex 1 is very effective in the catalytic fixation of carbon dioxide in propergyl alcohols to produce α-alkylidene cyclic carbonates. The catalytic production of α-alkylidene cyclic carbonates have been carried out through carboxylative cyclization of propargyl alcohols using CO2 balloon of 1 atm pressure at 80 ◦C. Solvent free condition (green synthesis) made this catalytic protocol eco-friendly towards the environment. Utilizing various substrates of propargyl alcohols moderate to high percentage yield (62–95%) of respective α-alkylidene cyclic carbonates product have been isolated over this catalytic reaction. Besides, the theoretical calculations (DFT) was performed for the prediction of probable mechanism of the catalytic reaction
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 development of more powerful and more efficient lithium-ion batteries (LIBs) is a key area in battery research, aiming to support the ever-increasing demand for energy storage systems. To better understand the causes and mechanisms of degradation, and thus the diminishing cycling performance and lifetime often observed in LIBs, in operando techniques are essential, because battery chemistry can be monitored non-invasively, in real time. Moreover, there is increasing interest in developing new battery chemistries. Beyond LIBs, sodium ion batteries (NIBs) have gained increasing interest in recent years, as they are a promising candidate to complement LIBs, owing to their improved sustainability and lower cost, while still maintaining high energy density.[1] Initial phases of NIB commercialisation have occurred in the past year. However, for the widespread commercialisation of NIBs, there are still challenges that need to be overcome in developing optimized electrode materials and electrolytes. For the development of such materials and greater understanding of sodium storage mechanisms, solid electrolyte interface (SEI) formation and stability, and degradation processes, in operando methodologies are crucial.
Among the techniques available for in operando analysis, nuclear magnetic resonance spectroscopy (NMR) and imaging (MRI) are becoming increasingly used to characterize the chemical composition of battery materials, study the growth and distribution of dendrites, and investigate battery storage and degradation mechanisms. In situ and in operando 1H, 7Li and 23Na NMR and MRI have recently been used to study LIBs and NIBs, identifying chemical changes in Li and Na species respectively, in metallic, quasimetallic and electrolytic environment as well as directly and indirectly studying dendrite formation in both systems.[2-4] The ability of NMR and MRI to probe battery systems across multiple environments can further be complemented by the enhanced spatial resolution of micro-computed X-ray tomography (μ-CT) which can provide insight into battery material microstructure and defect distribution.
Here, we report in operando 1H and 7Li NMR and MRI experiments that investigate LIB performance, and the identification of changes in the Li signal during charge cycling, as well as the observation of signals in both 1H and 7Li NMR spectra that we attribute to diminishing battery performance, capacity loss and degradation. Additionally, recent operando methodology are adapted and implemented to study Sn based anodes in NIBs. 23Na spectroscopy is performed to monitor the formation and evolution of peaks assigned to stages of Na insertion into Sn, while 1H MRI is used to indirectly visualize the volume expansion of Sn anodes during charge cycling. Battery operation and degradation is further explored in these NIBs, using μ-CT, where the anode is directly visualized to a higher resolution and the loss of electrolyte in the cell, during cycling is observed
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 0.1 cm⁻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.
Laser powder bed fusion (PBF-LB/M) is a metal-based additive manufacturing (AM) process that utilizes a laser to melt powder particles selectively and form the desired product through a layer-by-layer fabrication process. It allows access to a vast new design space and can produce parts with far greater geometrical complexity than traditional methods. However, PBF-LB/M induces inevitable microstructural defects, such as voids and pores, that can adversely affect the quality and performance of the manufactured components. Hence, knowledge of pore formation, types, and suppression is essential for successful future AM applications. This research investigates the formation of different types of pores and their quantification from X-ray micro-computed tomography images of a formerly reported sample. It also examines the influence of the volumetric energy density (VED) on the size, shape, and location of pores for 316L stainless steel parts produced by the PBF-LB/M process.
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.
The huge atomic heterogeneity of high-entropy materials along with a possibility to unravel the behavior of individual components at the atomic scale suggests a great promise in designing new compositionally complex systems with the desired multi-functionality. Herein, we apply multi-edge X-ray absorption spectroscopy (extended X-ray absorption fine structure (EXAFS), X-ray absorption near edge structure (XANES), and X-ray magnetic circular dichroism (XMCD)) to probe the structural, electronic, and magnetic properties of all individual constituents in the single-phase face-centered cubic (fcc)-structured nanocrystalline thin film of Cr20Mn26Fe18Co19Ni17 (at.%) high-entropy alloy on the local scale. The local crystallographic ordering and component-dependent lattice displacements were explored within the reverse Monte Carlo approach applied to EXAFS spectra collected at the K absorption edges of several constituents at room temperature. A homogeneous short-range fcc atomic environment around the absorbers of each type with very similar statistically averaged interatomic distances (2.54–2.55 Å) to their nearest-neighbors and enlarged structural relaxations of Cr atoms were revealed. XANES and XMCD spectra collected at the L2,3 absorption edges of all principal components at low temperature from the oxidized and in situ cleaned surfaces were used to probe the oxidation states, the changes in the electronic structure, and magnetic behavior of all constituents at the surface and in the sub-surface volume of the film. The spin and orbital magnetic moments of Fe, Co, and Ni components were quantitatively evaluated. The presence of magnetic phase transitions and the co-existence of different magnetic phases were uncovered by conventional magnetometry in a broad temperature range.
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 tutorial is aimed at developers who would like to develop workflows with Jobflow. This could include contributions to atomate2 and quacc. Jobflow workflows can also be executed with Fireworks on Supercomputers.
This tutorial includes information on how to write a job for jobflows, how to connect jobs to a workflow including dynamic features and how to store job results in databases. The structure of the workflow is inspired by workflows that have been developed for atomate2 and quacc.
This tutorial is also connected to google collab so that you can execute the code via their services.
Please access the tutorial here: https://jageo.github.io/Advanced_Jobflow_Tutorial/intro.html
Mechanically dual-responsive molecular crystals have gained ubstantial interest among researchers due to their significant applications in various fields. However, incorporation of disparate mechanical responses in the same crystalline material is still a challenging issue and is continuously being explored. Here, we report long acicular crystals derived from 4-bromobenzhydrazide and 9-anthraldehyde exhibit both stressinduced mechanical bending and blue-light induced photomechanical bending. Furthermore, the crystals show thermal back isomerization upon gradual heating. These two distinct mechanical responses in the crystalline phase have been studied and their structure–property correlation have been established.
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.
X-ray absorption spectroscopy (XAS) provides a unique, atom-specific tool to probe the electronic structure of solids. By surmounting long-held limitations of powder-based XAS using a dynamically averaged powder in a Resonant Acoustic Mixer (RAM), we demonstrate how time-resolved in situ (TRIS) XAS provides unprecedented detail of mechanochemical synthesis. The use of a custom-designed dispersive XAS (DXAS) setup allows us to increase the time resolution over existing fluorescence measurements from ∼15 min to 2 s for a complete absorption spectrum. Hence, we here establish TRIS-XAS as a viable method for studying mechanochemical reactions and sampling reaction kinetics. The generality of our approach is demonstrated through RAM-induced (i) bottom-up Au nanoparticle mechanosynthesis and (ii) the synthesis of a prototypical metal organic framework, ZIF-8. Moreover, we demonstrate that our approach also works with the addition of a stainless steel milling ball, opening the door to using TRIS-DXAS for following conventional ball milling reactions. We expect that our TRIS-DXAS approach will become an essential part of the mechanochemical tool box.
Clerodin was isolated from the medicinal plant Clerodendrum infortunatum, and CSD search showed the first crystal structure of clerodin by a single-crystal X-ray diffraction study. We checked its binding potential with target proteins by docking and conducted network pharmacology analysis, ADMET analysis, in silico pathway analysis, normal mode analysis (NMA), and cytotoxic activity studies to evaluate clerodin as a potential anticancer agent. The cell viability studies of clerodin on the human breast carcinoma cell line (MCF-7) showed toxicity on MCF-7 cells but no toxicity toward normal human lymphocyte cells (HLCs). The anticancer mechanism of clerodin was validated by its enhanced capacity to produce intracellular reactive oxygen species (ROS) and to lower the reduced glutathione content in MCF-7 cells.
In the pursuit of discovering materials with desirable properties, extending the available material libraries is crucial. High-throughput simulations have become an integral part in designing new materials in the past decades. However, there is no straightforward way of distinguishing synthesizable materials from all the proposed candidates. This project focuses on employing AI-driven methods to estimate synthesizability of materials.
Up to now, material scientists and engineers have relied on domain knowledge as well as empirical heuristics to guess the stability and synthesizability of molecules and crystals. The famous Pauling rules of crystal stability are an example of such heuristics. However, after the accelerating material discovery in all the years since Pauling, these rules now fail to account for the stability of most known crystals. A new predictive set of heuristics for crystal stability/synthesizability is unlikely to be uncovered by human perception, given the magnitude and dimensionality of crystallographic data. Hence, a data-driven approach should be proposed to find a predictive model or set of heuristics which differentiate synthesizable crystal structures from the rest. The main challenge of this research problem is the lack of a negative set for classification. Here, there are two classes of data: the positive class which contains synthesizable materials and the negative class which contains materials which are not synthesizable. While the data from the positive class is simply the data of crystals which have been experimentally synthesized, we do not have access to data points which are certainly unsynthesizable. Strictly speaking, if an attempt of synthesizing a crystal fails, it does not necessarily follow that the crystal is not synthesizable. Also, there is no database available which contains the intended crystal structures of unsuccessful synthesis attempts.
This project proposes a semi-supervised learning scheme to predict crystal synthesizability. The ML model is trained on experimental and theoretical crystal data. The initial featurization focuses on local environments which is inspired by the Pauling Rules. The experimental data points are downloaded through the Pymatgen API from the Materials Project database which contains relaxed structures recorded in Inorganic Crystal Structure Database – ICSD. The theoretical data is queried from select databases accessible through the Optimade project’s API.