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Carbamazepine Dihydroxybenzoic Acid Cocrystals: Exploring Packing Interactions and Reaction Kinetics
(2021)
Herein, we present the mechanochemical formation of three new cocrystals containing the active pharmaceutical ingredient carbamazepine and dihydroxybenzoic acids as coformers (CBZ:2,4-DHBA 1:1, CBZ:2,5-DHBA 1:1, and CBZ:2,6-DHBA 1:1). Rietveld methods were used for three different purposes: (i) refining all structures solved using powder X-ray diffraction, (ii) performing a quantitative phase analysis of the diffraction data collected from ex situ mechanochemical reactions at different milling times, and (iii) determining the cocrystallization kinetic profiles. The rate of cocrystallization was found to be higher for the formation of CBZ:2,4-DHBA and CBZ:2,6-DHBA, reaching an equilibrium after 600 s of milling. In the case of CBZ:2,5-DHBA a short induction period of 20 s was detected prior to the start of the reaction and an equilibrium was reached after 1200 s. An empirical trend between the rate of cocrystallization and the structural complexity of the cocrystal product was found. The slowest cocrystallization rate observed for CBZ:2,5-DHBA corresponds to the crystal structure deviating substantially from the hydrogen-bonding motif found in the reactants.
High-throughput computations are nowadays an established way to suggest new candidate materials for applications to experimentalists. Due to new packages for automation and access to databases of computed materials properties, these studies became more and more complex over the last years. Besides suggesting new candidate materials for applications, they also offer a way to understanding the materials properties based on chemical bonds. For example, we have recently used orbital-based bonding analysis to understand the results of high-throughput studies for spintronic materials, ferroelectric materials and photovoltaic materials in detail. To do so, we have developed Python tools for high-throughput bonding analysis with the programs VASP and Lobster (see www.cohp.de). They are based on the Python packages pymatgen, atomate, and custodian. This implementation will be discussed within the talk. We also expect that these tools offer possibilities to arrive at new descriptors based on chemical bonds for materials properties.
We obtained concomitant dimorphic forms of Anthracene Schiffbase (N-(anthracen-9-yl methylene)-2,5- dichloroaniline) from hexane solvent. Two polymorphs can be differentiated by their morphology and mechanical properties. One form is long acicular type and elastically bendable while another form is block shaped and brittle in nature. Mechanical property is attributed to underlying crystal packing. Hirsh- feld analysis and energy framework calculations were done to corroborate structure-property correlation of two forms.
Nitrapyrin (NP) is applied to cultivated soils to inhibit the enzymatic activity of ammonia monooxygenase (AMO), but its poor aqueous solubility and high volatility severely limit its application. β-Cyclodextrin (β-CD) is commonly used to form inclusion complexes with hydrophobic molecules, improving water solubility and stability upon complexation. Here we report on the mechanochemical synthesis of the inclusion complex β-CD·NP, characterized via a combination of solid-state techniques, including exsitu and in situ X-ray diffraction, Raman and NMR spectroscopies, transmission electron microscopy, and energy dispersive X-ray spectroscopy. The pure inhibitor NP was also structurally characterized. The β-CD·NP complex presents improved solubility and thermal stability, and still inhibits the enzymatic activity of AMO with high efficacy. All results indicate that the inclusion of NP into β-CD represents a viable route for the preparation of a novel class of inhibitors, with improved properties related to stability, water solubility, and good inhibition activity.
Mechanochemistry is increasingly used for synthesizing soft matter materials including metal organic compounds and cocrystals.1 The ever-increasing interest in this method is contrasted by a limited mechanistic understanding of the mechanochemical reactivity and selectivity. Time-resolved in situ investigations of milling reactions provide direct insights in the underlying mechanisms.2-4 We recently introduced different setups enabling in situ investigation of mechanochemical reactions using synchrotron XRD combined with Raman spectroscopy and thermography. The presented setup allows the detection of crystalline, amorphous, eutectic, and liquid intermediates. Furthermore, the chemical composition of the reaction mixture was found to be directly correlated with changes in the temperature profile of the reaction. The resulting deeper kinetic and thermodynamic understanding of milling processes is the key for future optimization of mechanochemical syntheses. In this contribution, we will discuss our recent results investigating the formation of (polymorphic) cocrystals and coordination polymers.2,3,5 Our results indicate that in situ investigation of milling reactions offer a new approach to tune and optimize mechanochemical syntheses.
Mechanochemistry has emerged as one of the most interesting synthetic protocols to produce new materials. The development of mechanochemistry as a synthetic method is supported by excellent research by many groups worldwide in a wide range of applications. The potential of mechanochemistry is also reflected in the inclusion in IUPAC’s 10 chemical innovations that will change our world’.[1] Solvent-free methodologies lead to unique chemical processes during synthesis with the consequent formation of martials with new properties.2 In this contribution, we will discuss our recent results investigating the formation of (polymorphic) cocrystals, coordination polymers, metal oxides and metal nanoparticles.[3-8] We introduced different setups enabling in situ investigation of mechanochemical reactions using synchrotron XRD combined with Raman spectroscopy and thermography.
Superhydrophobic surfaces can be quickly formed with supramolecular materials. Incorporating low-molecular-weight gelators (LMWGs) with perfluorinated chains generates xerogel coatings with low surface energies and high roughness. Here, we examine and compare the properties of the xerogel coatings formed with eight different LMWGs. These LMWGs all have a trans-1,2-diamidocyclohexane core and two perfluorinated ponytails, whose lengths vary from three to ten carbon atoms (CF3 to CF10). Investigation of the xerogels aims to provide in-depth information on the chain length effect. LMWGs with a higher degree of fluorination (CF7 to CF10) form superhydrophobic xerogel coatings with very low surface energies. Scanning electron microscopy images of the coatings show that the aggregates of CF5 and CF7 are fibrous, while the others are crystal-like. Aggregates of CF10 are particularly small and further assemble into a porous structure on the micrometer scale. To test their stabilities, the xerogel coatings were flushed multiple times with a standardized water flush test. The removal of material from the surface in these flushes was monitored by a combination of the water contact angle, contact angle hysteresis, and coating thickness measurements. A new method based on image processing techniques was developed to reliably determine the change of the coating thickness. The CF7, CF9, and CF10 surfaces show consistent hydrophobicity and coating durability after repetitive flushing tests. The length of the perfluorinated side chains thus has a significant effect on the morphology of the deposited xerogel coatings, their roughness, and, in consequence, their hydrophobicity and mechanical durability.
The stability of materials is a very important property for materials discovery. In recent years, we have explored several approaches for advancing the prediction of the stability of crystals (e.g., classical chemical heuristics and machine-learned interatomic potentials). For example, we have assessed a famous chemical heuristic – the Pauling rules – regarding their usefulness for structure prediction. They are only of limited predictive power, i.e. the rules 2 to 5 only work for 13 % of all tested oxides. Furthermore, we have shown that machine-learned interatomic potentials can be used to predict phonon properties of a range of silicon allotropes accurately and, therefore, they can also be used to assess the dynamic stability of materials. To do so, we have developed new schemes to build reference databases for machine learned interatomic potentials.