Experimental Screening Methods Research Articles

Discovery of Antimicrobial Lysins from the "Dark Matter" of Uncharacterized Phages Using Artificial Intelligence.

The rapid rise of antibiotic resistance and slow discovery of new antibiotics have threatened global health. While novel phage lysins have emerged as potential antibacterial agents, experimental screening methods for novel lysins pose significant challenges due to the enormous workload. Here, the first unified software package, namely DeepLysin, is developed to employ artificial intelligence for mining the vast genome reservoirs ("dark matter") for novel antibacterial phage lysins. Putative lysins are computationally screened from uncharacterized Staphylococcus aureus phages and 17 novel lysins are randomly selected for experimental validation. Seven candidates exhibit excellent in vitro antibacterial activity, with LLysSA9 exceeding that of the best-in-class alternative. The efficacy of LLysSA9 is further demonstrated in mouse bloodstream and wound infection models. Therefore, this study demonstrates the potential of integrating computational and experimental approaches to expedite the discovery of new antibacterial proteins for combating increasing antimicrobial resistance.

Discovering virtual antiperovskites as solid-state electrolytes through active learning

In surveying an extensive library of 18,133 hypothetical antiperovskites (X3BA), we address the challenges posed by conventional experimental and computational screening methods. We introduce a novel computational approach, leveraging an active learning framework that synergistically integrates genetic algorithms with Bayesian optimization. This method efficiently discerns thermodynamically stable antiperovskites, narrowing down the vast initial set to 43 compounds characterized by minimal energy above the hull (Eh<150 meV/atom). Subsequent evaluations of their band structure (Eg), electrochemical stability window (Vw), and room-temperature lithium ion conductivity (σRT), alongside the construction of a 4-dimensional Pareto frontier for Eh, Eg, Vw, and σRT, refined this list to 22 promising candidates, seven of which exhibit outstanding room-temperature ionic conductivity (>4 mS cm−1), marking them as potential candidates. Our methodology not only expedites the identification of superior antiperovskites but also establishes a groundbreaking paradigm for computational exploration in materials science.

Flavone Cocrystals: A Comprehensive Approach Integrating Experimental and Virtual Methods.

The dapsone/flavone cocrystal system served as a benchmark for both experimental and virtual screening methods. Expanding beyond this, two additional active pharmaceutical ingredients (APIs), sulfanilamide and sulfaguanidine, structurally related to dapsone were chosen to investigate the impact of substituents on cocrystal formation. The experimental screening involved mechanochemical methods, slurry experiments, hot-melt extrusion, and the contact preparation method. The virtual screening focused on crystal structure prediction (CSP), molecular complementarity, hydrogen-bond propensity, and molecular electrostatic potentials. The CSP studies not only indicated that each of the three APIs should form cocrystals with flavone but also reproduced the known single- and multicomponent phases. Experimentally, dapsone/flavone cocrystals ACC, BCC, CCC, and DCC were reproduced, CCC was identified as a nonstoichiometric hydrate, and a fifth cocrystal (ECC), a t-butanol solvate, was discovered. The cocrystal polymorphs ACC and BCC are enantiotripically related, and DCC, exhibiting a different stoichiometric ratio, is enthalpically stabilized over the other cocrystals. For the sulfaguanidine/flavone system, two novel, enantiotripically related cocrystals were identified. The crystal structures of two cocrystals and a flavone polymorph were solved from powder X-ray diffraction data, and the stability of all cocrystals was assessed through differential scanning calorimetry and lattice energy calculations. Despite computational indications, a diverse array of cocrystallization techniques did not result in a sulfanilamide/flavone cocrystal. The driving force behind dapsone's tendency to cocrystallize with flavone can be attributed to the overall strength of flavone interactions in the cocrystals. For sulfaguanidine, the potential to form strong API···API and API···coformer interactions in the cocrystal is a contributing factor. Furthermore, flavone was found to be trimorphic.

Revolutionizing material design for protonic ceramic fuel cells: Bridging the limitations of conventional experimental screening and machine learning methods

The commercial viability of protonic ceramic fuel cells (PCFCs) is contingent upon developing highly active and stable cathode materials. The conventional trial-and-error process is time-consuming and costly for cathode material development, while the availability of sufficient and reliable datasets limits the recently emerging machine learning (ML) method. Here, we propose a novel approach based on the experimental design paradigm (EDP) to efficiently facilitate PCFC cathode materials’ development with a minimal dataset. As a rigorous systematic statistical approach, we employ the EDP for strategic variation of multiple elements and measure their effect on desired performance characteristics. We generate empirical models that reveal the optimal concentrations and interactions of the elemental composition and performance characteristics. In this study, we select the BaCoαCeβFeγYζO3-δ series as a proof-of-concept, and the optimal composition, BaCo0.667Ce0.167Fe0.083Y0.083O3-δ, was promptly determined—guided by the EDP—using only 16 independent conditions and 32 randomized experimental runs. We further demonstrate the EDP’s versatility by optimizing the widely-used and high-performing Ba0.5Sr0.5Co0.8Fe0.2O3-δ cathode material for solid oxide fuel cells. Our results highlight the potential of the EDP for effectively designing superior materials for solid-state electrochemical power generation systems, offering a reliable and practical alternative to conventional trial-and-error screening and ML methods.

A genome-wide overexpression screen reveals Mycobacterium smegmatis growth inhibitors encoded by mycobacteriophage Hammy.

During infection, bacteriophages produce diverse gene products to overcome bacterial antiphage defenses, to outcompete other phages, and to take over cellular processes. Even in the best-studied model phages, the roles of most phage-encoded gene products are unknown, and the phage population represents a largely untapped reservoir of novel gene functions. Considering the sheer size of this population, experimental screening methods are needed to sort through the enormous collection of available sequences and identify gene products that can modulate bacterial behavior for downstream functional characterization. Here, we describe the construction of a plasmid-based overexpression library of 94 genes encoded by Hammy, a Cluster K mycobacteriophage closely related to those infecting clinically important mycobacteria. The arrayed library was systematically screened in a plate-based cytotoxicity assay, identifying a diverse set of 24 gene products (representing ∼25% of the Hammy genome) capable of inhibiting growth of the host bacterium Mycobacterium smegmatis. Half of these are related to growth inhibitors previously identified in related phage Waterfoul, supporting their functional conservation; the other genes represent novel additions to the list of known antimycobacterial growth inhibitors. This work, conducted as part of the HHMI-supported Science Education Alliance Gene-function Exploration by a Network of Emerging Scientists (SEA-GENES) project, highlights the value of parallel, comprehensive overexpression screens in exploring genome-wide patterns of phage gene function and novel interactions between phages and their hosts.

Construction of a QSAR Model Based on Flavonoids and Screening of Natural Pancreatic Lipase Inhibitors.

Pancreatic lipase (PL) is a key hydrolase in lipid metabolism. Inhibition of PL activity can intervene in obesity, a global sub-health disease. The natural product is considered a good alternative to chemically synthesized drugs due to its advantages, such as low side effects. However, traditional experimental screening methods are labor-intensive and cost-consuming, and there is an urgent need to develop high-throughput screening methods for the discovery of anti-PL natural products. In this study, a high-throughput virtual screening process for anti-PL natural products is provided. Firstly, a predictable anti-PL natural product QSAR model (R2train = 0.9444, R2test = 0.8962) were developed using the artificial intelligence drug design software MolAIcal based on genetic algorithms and their conformational relationships. 1068 highly similar (FS > 0.8) natural products were rapidly enriched based on the structure-activity similarity principle, combined with the QSAR model and the ADMET model, for rapid prediction of a total of five potentially efficient anti-PL natural products (IC50pre < 2 μM). Subsequently, molecular docking, molecular dynamics simulation, and MMGBSA free energy calculation were performed to not only reveal the interaction of candidate novel natural products with the amino acid residues of PL but also to validate the stability of these novel natural compounds bound to PL. In conclusion, this study greatly simplifies the screening and discovery of anti-PL natural products and accelerates the development of novel anti-obesity functional foods.

A Combination of Virtual and Experimental Screening Tools for the Prediction of Nitrofurantoin Multicomponent Crystals with Pyridine Derivatives

Thirty-four binary systems of nitrofurantoin with pyridine derivatives were analyzed by combining virtual (molecular complementarity prediction and hydrogen bond propensity calculations) and experimental (liquid-assisted grinding) screening methods. A new modification of the hydrogen bond propensity calculation method (the integrated hydrogen bond propensity calculation method) with significantly improved virtual screening efficiency was proposed. Novel cocrystals of nitrofurantoin with 3-aminopyridine and 2-(1H-Imidazol-2-yl)pyridine were discovered. The crystal structures of the new cocrystals were determined from single-crystal X-ray diffraction data, and the hydrogen bond patterns were studied in conjunction with the Molecular Electrostatic Potential maps of the components. The nitrofurantoin cocrystal with 3-aminopyridine was found to exist in two polymorphic modifications. The origins of the different stability of the polymorphic forms were rationalized both in terms of total lattice enthalpy and free energy derived from periodic DFT-D3 calculations and in terms of the non-covalent interaction energy distribution in crystal.

BinderSpace: A package for sequence space analyses for datasets of affinity-selected oligonucleotides and peptide-based molecules.

Discovery of target-binding molecules, such as aptamers and peptides, is usually performed with the use of high-throughput experimental screening methods. These methods typically generate large datasets of sequences of target-binding molecules, which can be enriched with high affinity binders. However, the identification of the highest affinity binders from these large datasets often requires additional low-throughput experiments or other approaches. Bioinformatics-based analyses could be helpful to better understand these large datasets and identify the parts of the sequence space enriched with high affinity binders. BinderSpace is an open-source Python package that performs motif analysis, sequence space visualization, clustering analyses, and sequence extraction from clusters of interest. The motif analysis, resulting in text-based and visual output of motifs, can also provide heat maps of previously measured user-defined functional properties for all the motif-containing molecules. Users can also run principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) analyses on whole datasets and on motif-related subsets of the data. Functionally important sequences can also be highlighted in the resulting PCA and t-SNE maps. If points (sequences) in two-dimensional maps in PCA or t-SNE space form clusters, users can perform clustering analyses on their data, and extract sequences from clusters of interest. We demonstrate the use of BinderSpace on a dataset of oligonucleotides binding to single-wall carbon nanotubes in the presence and absence of a bioanalyte, and on a dataset of cyclic peptidomimetics binding to bovine carbonic anhydrase protein. BinderSpace is openly accessible to the public via the GitHub website: https://github.com/vukoviclab/BinderSpace.

Continuous Manufacturing and Molecular Modeling of Pharmaceutical Amorphous Solid Dispersions.

Amorphous solid dispersions enhance solubility and oral bioavailability of poorly water-soluble drugs. The escalating number of drugs with poor aqueous solubility, poor dissolution, and poor oral bioavailability is an unresolved problem that requires adequate interventions. This review article highlights recent solubility and bioavailability enhancement advances using amorphous solid dispersions (ASDs). The review also highlights the mechanism of enhanced dissolution and the challenges faced by ASD-based products, such as stability and scale-up. The role of process analytical technology (PAT) supporting continuous manufacturing is highlighted. Accurately predicting interactions between the drug and polymeric carrier requires long experimental screening methods, and this is a space where computational tools hold significant potential. Recent advancements in data science, computational tools, and easy access to high-end computation power are set to accelerate ASD-based research. Hence, particular emphasis has been given to molecular modeling techniques that can address some of the unsolved questions related to ASDs. With the advancement in PAT tools and artificial intelligence, there is an increasing interest in the continuous manufacturing of pharmaceuticals. ASDs are a suitable option for continuous manufacturing, as production of a drug product from an ASD by direct compression is a reality, where the addition of multiple excipients is easy to avoid. Significant attention is necessary for ongoing clinical studies based on ASDs, which is paving the way for the approval of many new ASDs and their introduction into the market.

Covalent docking and molecular dynamics simulations reveal the specificity-shifting mutations Ala237Arg and Ala237Lys in TEM beta-lactamase.

The rate of modern drug discovery using experimental screening methods still lags behind the rate at which pathogens mutate, underscoring the need for fast and accurate predictive simulations of protein evolution. Multidrug-resistant bacteria evade our defenses by expressing a series of proteins, the most famous of which is the 29-kilodalton enzyme, TEM β-lactamase. Considering these challenges, we applied a covalent docking heuristic to measure the effects of all possible alanine 237 substitutions in TEM due to this codon's importance for catalysis and effects on the binding affinities of commercially-available β-lactam compounds. In addition to the usual mutations that reduce substrate binding due to steric hindrance, we identified two distinctive specificity-shifting TEM mutations, Ala237Arg and Ala237Lys, and their respective modes of action. Notably, we discovered and verified through minimum inhibitory concentration assays that, while these mutations and their bulkier side chains lead to steric clashes that curtail ampicillin binding, these same groups foster salt bridges with the negatively-charged side-chain of the cephalosporin cefixime, widely used in the clinic to treat multi-resistant bacterial infections. To measure the stability of these unexpected interactions, we used molecular dynamics simulations and found the binding modes to be stable despite the application of biasing forces. Finally, we found that both TEM mutants also bind strongly to other drugs containing negatively-charged R-groups, such as carumonam and ceftibuten. As with cefixime, this increased binding affinity stems from a salt bridge between the compounds' negative moieties and the positively-charged side chain of the arginine or lysine, suggesting a shared mechanism. In addition to reaffirming the power of using simulations as molecular microscopes, our results can guide the rational design of next-generation β-lactam antibiotics and bring the community closer to retaking the lead against the recurrent threat of multidrug-resistant pathogens.

Personalized Risk-Based Screening Design for Comparative Two-Arm Group Sequential Clinical Trials.

Personalized medicine has been emerging to take into account individual variability in genes and environment. In the era of personalized medicine, it is critical to incorporate the patients’ characteristics and improve the clinical benefit for patients. The patients’ characteristics are incorporated in adaptive randomization to identify patients who are expected to get more benefit from the treatment and optimize the treatment allocation. However, it is challenging to control potential selection bias from using observed efficacy data and the effect of prognostic covariates in adaptive randomization. This paper proposes a personalized risk-based screening design using Bayesian covariate-adjusted response-adaptive randomization that compares the experimental screening method to a standard screening method based on indicators of having a disease. Personalized risk-based allocation probability is built for adaptive randomization, and Bayesian adaptive decision rules are calibrated to preserve error rates. A simulation study shows that the proposed design controls error rates and yields a much smaller number of failures and a larger number of patients allocated to a better intervention compared to existing randomized controlled trial designs. Therefore, the proposed design performs well for randomized controlled clinical trials under personalized medicine.

Peptide Tethering: Pocket-Directed Fragment Screening for Peptidomimetic Inhibitor Discovery.

Constrained peptides have proven to be a rich source of ligands for protein surfaces, but are often limited in their binding potency. Deployment of nonnatural side chains that access unoccupied crevices on the receptor surface offers a potential avenue to enhance binding affinity. We recently described a computational approach to create topographic maps of protein surfaces to guide the design of nonnatural side chains [J. Am. Chem. Soc. 2017, 139, 15560]. The computational method, AlphaSpace, was used to predict peptide ligands for the KIX domain of the p300/CBP coactivator. KIX has been the subject of numerous ligand discovery strategies, but potent inhibitors of its interaction with transcription factors remain difficult to access. Although the computational approach provided a significant enhancement in the binding affinity of the peptide, fine-tuning of nonnatural side chains required an experimental screening method. Here we implement a peptide-tethering strategy to screen fragments as nonnatural side chains on conformationally defined peptides. The combined computational-experimental approach offers a general framework for optimizing peptidomimetics as inhibitors of protein-protein interactions.

Optimization of Enzymatic Preparation Process of Hypoglycemic Peptide from Moringa oleifera Seeds by Response Surface Methodology and Its Hypoglycemic Activities Evaluation in Vitro

In order to improve the resource utilization of Moringa oleifera seeds powder, enzymatic method was used to prepare hypoglycemic peptide from Moringa oleifera seeds. Taking the the degree of proteolysis and α-glucosidase inhibition rate as the response value, single-factor experimental screening and response surface method was used to optimize the optimum enzymatic hydrolysis time, liquid-to-material ratio, hydrolysis pH, enzyme addition, and enzymatic hydrolysis temperature. The hypoglycemic peptide with a molecular weight of <3 kDa were prepared by coarse separation by ultrafiltration, and their inhibitory effects on human liver cancer cells (HepG2 cells) were analyzed by MTT method. The results showed that the optimal conditions of enzymatic hydrolysis were as follows: Time 4.6 h, ratio of liquid to material 40.5:1, pH8.3, enzyme dosage 5.5%, temperature 55 ℃, and the inhibition rate of α-glucosidase was 23.62%±0.14%. The α-glucosidase inhibition rate of the ultrafiltration fraction with a molecular weight of less than 3 kDa had an IC50 value of 5.56 mg/mL. When the concentration of ultrafiltration fraction was 300 μg/mL, the proliferation of HepG2 cells was significantly inhibited after 48 h(P<0.05). The research can lay the foundation for the further separation and purification of hypoglycemic peptide from Moringa oleifera seeds.

Characterization of an Al-Cu-Mg-Zn multi principal element alloy by experimental and computational screening methods

We present a combined experimental and computational investigation of phase stability and mechanical properties in the Al-Cu-Mg-Zn quaternary system. Samples containing different relative compositions were prepared using magnetron sputtering and investigated by electron microscopic and X-ray based methods. To classify the technical relevance of the samples, the indentation hardness was measured. The phase stability was studied computationally using a cluster expansion approach based on density functional theory (DFT) methods in a comprehensive screening of the structural and stoichiometric configuration space. Upon decreasing Cu concentration, a transition from an FCC to a mixed FCC/BCC crystal system and significant changes in the mechanical properties depending on Valence Electron Concentration (VEC) and atomic size differences (δr) was observed experimentally. The corresponding crystallographic phases were assigned by XRD and the experimentally observed phase transition was confirmed by the computational screening of formation energies. Since to date, quaternary complex light metal alloy systems cannot be reliably predicted, this is an important step towards a priori modelling of this class of materials.

Characterization of an Al-Cu-Mg-Zn Multi Principal Element Alloy by Experimental and Computational Screening Methods

We present a combined experimental and computational investigation of phase stability and mechanical properties in the Al-Cu-Mg-Zn quaternary system. Samples containing different relative compositions were prepared using magnetron sputtering and investigated by electron microscopic and X-ray based methods. To classify the technical relevance of the samples, the indentation hardness was measured. The phase stability was studied computationally using a cluster expansion approach based on density functional theory (DFT) methods to allow for a comprehensive screening of the configuration space. Upon decreasing Cu concentration, a transition from an fcc to a mixed fcc/bcc crystal system and significant changes in the mechanical properties depending on Valence Electron Concentration (VEC) and atomic size differences (δr) was observed experimentally. The corresponding crystallographic phases were assigned by XRD and the experimentally observed phase transition could be confirmed by computing formation energies for the corresponding ground-state structures. Since to date, quaternary complex light metal alloy systems cannot be reliably predicted, this is an important step towards a priori modelling of this class of materials.

Covalent Docking and Molecular Dynamics Simulations Reveal the Resistance-Granting Mutations A237R/K in TEM Beta-Lactamase

The rate of modern drug discovery using experimental screening methods still lags far behind the rate at which pathogens mutate, highlighting the dire need for accurate and fast predictive simulations of macromolecular evolution. Particularly threatening, multidrug-resistant bacteria evade our defenses by expressing a series of resistance factors, the most famous of which is the 29 kilodaltons antibiotic cleaver, TEM beta-lactamase. Rising to this challenge, we applied biophysical simulations and predictions to identify two new resistance-conferring mutations in TEM and their respective modes of action. Using homology modeling and traditional all-atom molecular dynamics simulations, we predicted the structures of 50 TEM mutants, then deployed covalent docking to estimate their binding affinities against 85 beta-lactam-containing drugs to identify potential substrate specificity shifts. Finally, we used binding pose metadynamics and normal mode analyses to further scan the most promising results (validated against an empirical fitness dataset). This heuristic identified putative mechanisms for the previously-unreported, resistance-conferring mutations A237R and A237K. We also uncovered concerted structural rearrangements that could explain the specificity shifts observed in the clinically-entrenched mutations R164S/G/D. In addition to reaffirming the power of using simulations as molecular microscopes, our results could prove useful for guiding the rational design of next-generation beta-lactam antibiotics and bring the community closer to retaking the lead against the recurrent threat of multidrug-resistant pathogens.

An Accessible Method for Screening Aerosol Filtration Identifies Poor-Performing Commercial Masks and Respirators

Background: The COVID-19 pandemic has presented an acute need for masks, specifically N95 respirators to be used by healthcare workers contacting COVID-19 patients and medical masks to be used by the public. The shortage of regulation NIOSH-tested masks make it necessary to find alternatives to protect frontline healthcare workers. Many of the available alternatives have not been NIOSH-certified or tested in the United States, leaving room for question as to whether they are performing at a level that is protecting the wearer from SARS-CoV-2 transmission. In addition, currently there are many counterfeit N95 and other masks being produced and sold as authentic products. It has become critical to be able to test these masks from non-traditional suppliers as well as the many masks that are being donated to hospitals at this time in order to ensure the safety of healthcare workers. Methods: For situations where regulatory methods are not accessible, we present an experimental setup and screening method to evaluate mask filtration and breathability quickly via a cost-effective approach that could be replicated in communities of need without the extensive research infrastructure necessary for regulation mask tests. The experimental approaches presented here examined both mask aerosol filtration performance and mask flow impedance to ensure breathability, and mask fit tests were conducted in tandem but are not the focus of this study. Findings: Tests conducted of 28 non-regulation masks using this setup reveal that a number of commercially- available masks in hospital inventories perform similarly to N95 masks for aerosol filtration of 0.2 μm and above. There are also a range of masks with relatively-weaker filtration efficiencies. Yet, a subset of commercially-available masks have poor filtration performance relative to N95 or similar masks at typical breathing velocities. All masks functioned acceptably with regards to breathability (i.e. flow impedance) and impedance was not correlated with filtration efficiency. Interpretation: With a small set of simplified tests, organizations with critical personal protective equipment (PPE) shortages and uncertainties about their mask/respirator inventories can quickly evaluate the efficacy of their masks relative to N95-regulation masks to make informed decisions about which PPE to use and future procurement. This is essential since the results of this study use an actual diverse inventory of masks/respirators from a hospital network and show significant variability in their performance for filtering airborne aerosols that could contain SARS-CoV-2 and a subset of masks that would not be acceptable for frontline workers.

Identification of a previously unreported co-crystal form of acetazolamide: a combination of multiple experimental and virtual screening methods.

In the search for new co-crystal forms, many studies only consider one method of co-crystallisation which may lead to incorrect results. In this work, we demonstrate the efficiency of applying multiple experimental and virtual screening methods for a more comprehensive search for co-crystals of acetazolamide. A new co-crystal of acetazolamide with 4-aminobenzoic acid ([ACZ + PABA] (1 : 1)) was discovered, although previously, it had been found in the blind spot of the liquid-assisted grinding (LAG) screening method. The new co-crystal was investigated by different analytical techniques, including the powder and single crystal X-ray diffraction, differential scanning calorimetry, dissolution and solubility methods. The specific features of the mechanochemical formation process for [ACZ + PABA] (1 : 1) were studied. It was found that the appearance of the blind spot of the LAG screening method can be caused by a number of reasons; among those are the high sensitivity to the solvent choice and the low rate of the reagent conversion into the reaction product. A comparison of the ACZ co-crystals with 4-aminobenzoic and 4-hydroxybenzoic acids revealed their close resemblance in terms of the packing energy gain and the driving force of co-crystallization. Therefore, the experimental problems in the formation of the [ACZ + PABA] (1 : 1) co-crystal were associated with a number of kinetic reasons, e.g. the high energy barrier of the nucleation process and the low growth rate of the co-crystal. Using the co-crystal screening of acetazolamide as an example, the effectiveness of five different virtual methods for predicting co-crystal formation was assessed. In order to carry out the virtual screening based on the formation thermodynamics of a hypothetical co-crystal, for the first time ever we studied the ACZ sublimation process. Four out of the five virtual screening methods confirm the formation of the new [ACZ + PABA] (1 : 1) co-crystal.

A preformulation co-crystal screening case study: Polymorphic co-crystals of an imidazopyridazine antimalarial drug lead with the coformer succinic acid

A crystal engineering approach combined with experimental screening methods was employed as a preformulation step for producing expectedly useful forms of a novel antimalarial drug lead (MMV652103). In previous experiments, this drug lead had shown promise both in vitro and in vivo against the human malaria parasite Plasmodium falciparum. Various new forms of MMV652103 were produced earlier, including co-crystals, a salt, polymorphs and a hydrated form and these were reported elsewhere. Three distinct new forms are reported here, of which two are conformational and synthon (2:1) co-crystal polymorphs produced with the coformer succinic acid, while the third is a co-crystal with a different stoichiometric ratio (1:1) of the same components. The crystal structures of the co-crystal polymorphs were elucidated by single-crystal X-ray diffraction. The different forms were also characterised by powder X-ray diffraction, Fourier transform infrared spectroscopy, 1H nuclear magnetic resonance spectroscopy, hot stage microscopy, differential thermal analysis, differential scanning calorimetry, thermogravimetric analysis and in some cases variable-temperature powder X-ray diffraction. It was determined that the polymorphs are monotropically related. Methods were developed to prepare each of these forms reproducibly and rapidly identify each form.

High-Throughput Experimental Study of Wurtzite Mn1-x Zn x O Alloys for Water Splitting Applications.

We used high-throughput experimental screening methods to unveil the physical and chemical properties of Mn1–xZnxO wurtzite alloys and identify their appropriate composition for effective water splitting application. The Mn1–xZnxO thin films were synthesized using combinatorial pulsed laser deposition, permitting for characterization of a wide range of compositions with x varying from 0 to 1. The solubility limit of ZnO in MnO was determined using the disappearing phase method from X-ray diffraction and X-ray fluorescence data and found to increase with decreasing substrate temperature due to kinetic limitations of the thin-film growth at relatively low temperature. Optical measurements indicate the strong reduction of the optical band gap down to 2.1 eV at x = 0.5 associated with the rock salt-to-wurtzite structural transition in Mn1–xZnxO alloys. Transmission electron microscopy results show evidence of a homogeneous wurtzite alloy system for a broad range of Mn1–xZnxO compositions above x = 0.4. The wurtzite Mn1–xZnxO samples with the 0.4 < x < 0.6 range were studied as anodes for photoelectrochemical water splitting, with a maximum current density of 340 μA cm–2 for 673 nm-thick films. These Mn1–xZnxO films were stable in pH = 10, showing no evidence of photocorrosion or degradation after 24 h under water oxidation conditions. Doping Mn1–xZnxO materials with Ga dramatically increases the electrical conductivity of Mn1–xZnxO up to ∼1.9 S/cm for x = 0.48, but these doped samples are not active in water splitting. Mott–Schottky and UPS/XPS measurements show that the presence of dopant atoms reduces the space charge region and increases the number of mid-gap surface states. Overall, this study demonstrates that Mn1–xZnxO alloys hold promise for photoelectrochemical water splitting, which could be enhanced with further tailoring of their electronic properties.