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Data-driven materials design aims to predict and optimize material properties, such as stability and thermal conductivity, which are influenced by vibrational behavior. Approaches like DFT are computationally demanding and have limitations for phonon calculations. Machine learning-driven interatomic potentials (MLIP), like the Gaussian approximation potential (GAP), offer a more efficient alternative.1–8 We developed a Python workflow to automate MLIP generation using the Materials Project database.9 DFT computations, MLIP fitting and benchmark steps are automated.10,11 This approach accelerates phonon calculations and supports testing different data generation strategies and hyperparameters, and further validation12 is planned. Our goal is to provide open-source code and share these potentials.
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.