Recent advances in high-throughput experimentation for combinatorial studies have accelerated the discovery and analysis of materials across a wide range of compositions and synthesis conditions. However, many of the more powerful characterization methods are limited by speed, cost, availability, and/or resolution. To make efficient use of these methods, there is value in developing approaches for identifying critical compositions and conditions to be used as knowledge for follow-up characterization with high-precision techniques, such as micrometer-scale synchrotron-based X-ray diffraction (XRD). Here, we demonstrate the use of optical microscopy and reflectance spectroscopy to identify likely phase-change boundaries in thin film libraries. These methods are used to delineate possible metastable phase boundaries following lateral-gradient laser spike annealing (lg-LSA) of oxide materials. The set of boundaries are then compared with definitive determinations of structural transformations obtained using high-resolution XRD. We demonstrate that the optical methods detect more than 95% of the structural transformations in a composition-gradient La-Mn-O library and a GaO sample, both subject to an extensive set of lg-LSA anneals. Our results provide quantitative support for the value of optically detected transformations as data to guide subsequent structural characterization, ultimately accelerating and enhancing the efficient implementation of micrometer-resolution XRD experiments.

Combinatorial synthesis and high-throughput characterization of thin-film materials libraries enable to efficiently identify both photoelectrochemically active and inactive, as well as stable and instable systems for solar water splitting. This is shown on six ternary metal vanadate (M-V-O, M = Cu, Ag, W, Cr, Co, Fe) thin-film materials libraries, fabricated using combinatorial reactive magnetron cosputtering with subsequent annealing in air. By means of high-throughput characterization of these libraries correlations between composition, crystal structure, photocurrent density, and stability of the M-V-O systems in different electrolytes such as acidic, neutral and alkaline media were identified. The systems Cu-V-O and Ag-V-O are stable in alkaline electrolyte and exhibited photocurrents of 170 and 554 μA/cm, respectively, whereas the systems W-V-O, Cr-V-O, and Co-V-O are not stable in alkaline electrolyte. However, the Cr-V-O and Co-V-O systems showed an enlarged photoactive region in acidic electrolyte, albeit with very low photocurrents (<10 μA/cm). Complete data sets obtained from these different screening sets, including information on nonpromising systems, lays groundwork for their use to predict new systems for solar water splitting, for example, by machine learning.

The current COVID-19 pandemic caused by a novel coronavirus SARS-CoV-2 urgently calls for a working therapeutic. Here, we report a computation-based workflow for efficiently selecting a subset of FDA-approved drugs that can potentially bind to the SARS-CoV-2 main protease M. The workflow started with docking (using Autodock Vina) each of 1615 FDA-approved drugs to the M active site. This step selected 62 candidates with docking energies lower than -8.5 kcal/mol. Then, the 62 docked protein-drug complexes were subjected to 100 ns of molecular dynamics (MD) simulations in a molecular mechanics (MM) force field (CHARMM36). This step reduced the candidate pool to 26, based on the root-mean-square-deviations (RMSDs) of the drug molecules in the trajectories. Finally, we modeled the 26 drug molecules by a pseudoquantum mechanical (ANI) force field and ran 5 ns hybrid ANI/MM MD simulations of the 26 protein-drug complexes. ANI was trained by neural network models on quantum mechanical density functional theory (wB97X/6-31G(d)) data points. An RMSD cutoff winnowed down the pool to 12, and free energy analysis (MM/PBSA) produced the final selection of 9 drugs: dihydroergotamine, midostaurin, ziprasidone, etoposide, apixaban, fluorescein, tadalafil, rolapitant, and palbociclib. Of these, three are found to be active in literature reports of experimental studies. To provide physical insight into their mechanism of action, the interactions of the drug molecules with the protein are presented as 2D-interaction maps. These findings and mappings of drug-protein interactions may be potentially used to guide rational drug discovery against COVID-19.

A high throughput combinatorial synthesis utilizing inkjet printing of precursor inks was used to rapidly evaluate Bi-alloying into double perovskite oxides for enhanced visible light absorption. The fast visual screening of photo image scans of the library plates identifies 4-metal oxide compositions displaying an increase in light absorption, which subsequent UV-vis spectroscopy indicates is due to bandgap reduction. Structural characterization by X-ray diffraction (XRD) and Raman spectroscopy demonstrates that the visually darker composition range contains Bi-alloyed SmMnNiO (double perovskite structure), of the form (Bi,Sm)MnNiO. Bi alloying not only increases the visible absorption but also facilitates crystallization of this structure at the relatively low annealing temperature of 615 °C. Investigation of additional seven combinations of a rare earth (RE) and a transition metal (TM) with Bi and Mn indicates that Bi-alloying on the RE site occurs with similar effect in the family of rare earth oxide double perovskites.

High-throughput calculations can be applied to a large number of compounds, in order to discover new useful materials. In the present work, ternary intermetallic compounds are investigated, to find new potentially interesting materials for thermoelectric applications. The screening of stable nonmetallic compounds required for such applications is performed by calculating their electronic structure, using DFT methods. In the first section, the study of the density of states at the Fermi level, of pure elements, binary and ternary compounds, leads to empirically chose the selection criterion to distinguish metals from nonmetals. In the second section, the TiNiSi structure-type is used as a case-study application, through the investigation of 570 possible compositions. The screening leads to the selection of 12 possible semiconductors. The Seebeck coefficient and the lattice thermal conductivity of the selected compounds are calculated in order to identify the most promising ones. Among them, TiNiSi, TaNiP, or HfCoP are shown to be worth a detailed experimental investigation.

Desorption electrospray ionization-mass spectrometry (DESI-MS) was used as a high-throughput experimentation (HTE) tool to rapidly identify derivatives of the biobased platform molecule triacetic acid lactone (TAL). TAL is a platform molecule capable of conversion to a wide range of useful commodity chemicals, agrochemicals, and advanced pharmaceutical intermediates. In the present study, a diverse family of aldol reaction mixtures were prepared in high-density microtiter plates with a liquid handling robot, then printed with a pin tool onto a PTFE surface for analysis by DESI-MS. Our DESI-MS results indicate that aldol products of TAL were obtained for each substrate tested, in good agreement with previously reported TAL reactivity. These HTE experiments also revealed solvent-dependent reactivity trends that facilitated reaction scale up. Our findings suggest that DESI-MS analysis can rapidly inform the selection of optimal reaction conditions from a wide variety of conditions for scale up using continuous synthesis conditions.

Rheumatoid arthritis (RA) is a chronic autoimmune disease, which is compared to "immortal cancer" in industry. Currently, SYK, BTK, and JAK are the three major targets of protein tyrosine kinase for this disease. According to existing research, marketed and research drugs for RA are mostly based on single target, which limits their efficacy. Therefore, designing multitarget or dual-target inhibitors provide new insights for the treatment of RA regarding of the specific association between SYK, BTK, and JAK from two signal transduction pathways. In this study, machine learning (XGBoost, SVM) and deep learning (DNN) models were combined for the first time to build a powerful integrated model for SYK, BTK, and JAK. The predictive power of the integrated model was proved to be superior to that of a single classifier. In order to accurately assess the generalization ability of the integrated model, comprehensive similarity analysis was performed on the training and the test set, and the prediction accuracy of the integrated model was specifically analyzed under different similarity thresholds. External validation was conducted using single-target and dual-target inhibitors, respectively. Results showed that our model not only obtained a high recall rate (97%) in single-target prediction, but also achieved a favorable yield (54.4%) in dual-target prediction. Furthermore, by clustering dual-target inhibitors, the prediction performance of model in various classes were proved, evaluating the applicability domain of the model in the dual-target drug screening. In summary, the integrated model proposed is promising to screen dual-target inhibitors of SYK/JAK or BTK/JAK as RA drugs, which is beneficial for the clinical treatment of rheumatoid arthritis.

We established a system for simultaneous measurements of photoelectrochemical (PEC) reaction and photoabsorption in a semiconductor photoelectrode. This system uses a photoacoustic technique and photoelectrodes with a film-thickness gradient that was prepared by electrophoretic deposition of tungsten(VI) oxide particles while pulling up a substrate. The system enabled high-throughput determination of optimum film thickness, and the results showed that irradiation direction has a significant influence on PEC performance for a photoelectrode with a thick film. Furthermore, the mechanism of enhancement of PEC performance by postnecking treatment was discussed.

A library of 66 perovskite BaSrCaTiO ( + + = 1) samples (ca. three grams per sample) was made in ca. 14 h using a high-throughput continuous hydrothermal flow synthesis system. The as-synthesized samples were collected from the outlet of the process and then cleaned and freeze-dried before being evaluated individually as oxygen reduction catalysts using a rotating disk electrode testing technique. To establish any correlations between physical and electrochemical characterization data, the as-synthesized samples were investigated using analytical methods including BET surface area, powder X-ray diffraction (PXRD) and in selected cases, transmission electron microscopy (TEM). The aforementioned approach was validated as being able to quickly identify oxygen reduction catalysts from new libraries of electrocatalysts.

Nanoclusters add an additional dimension in which to look for promising catalyst candidates, since catalytic activity of materials often changes at the nanoscale. However, the large search space of relevant atomic sites exacerbates the challenge for computational screening methods and requires the development of new techniques for efficient exploration. We present an automated workflow that systematically manages simulations from the generation of nanoclusters through the submission of production jobs, to the prediction of adsorption energies. The presented workflow was designed to screen nanoclusters of arbitrary shapes and size, but in this work the search was restricted to bimetallic icosahedral clusters and the adsorption was exemplified on the hydrogen evolution reaction. We demonstrate the efficient exploration of nanocluster configurations and screening of adsorption energies with the aid of machine learning. The results show that the maximum of the -band Hilbert-transform ϵ is correlated strongly with adsorption energies and could be a useful screening property accessible at the nanocluster level.

Thin-film continuous composition spreads of Fe-Co-O were fabricated by reactive cosputtering from elemental Fe and Co targets in reactive Ar/O atmosphere using deposition temperatures ranging from 300 to 700 °C. Fused silica and platinized Si/SiO strips were used as substrates. Ti and Ta were investigated as adhesion layer for Pt and the fabrication of the Fe-Co-O films. The thin-film composition spreads were characterized by high-throughput electron-dispersive X-ray spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy, atomic force microscopy, scanning electron microscopy, and optical transmission spectroscopy. The Fe-content ranged from 28 to 72 at. %. The spinel phases FeCoO and FeCoO could be synthesized and stabilized at all deposition temperatures with a continuous variation in spinel composition in between. The dependence of the film surface microstructure on the deposition temperature and the composition was mapped. Moreover, the band gap values, ranging from 2.41 eV for FeCoO to 2.74 eV for FeCoO, show a continuous variation with the composition.

The power of ribosomes has increasingly been harnessed for the synthesis and selection of molecular libraries. Technologies, such as phage display, yeast display, and mRNA display, effectively couple genotype to phenotype for the molecular evolution of high affinity epitopes for many therapeutic targets. Genetic code expansion is central to the success of these technologies, allowing researchers to surpass the intrinsic capabilities of the ribosome and access new, genetically encoded materials for these selections. Here, we review techniques for the chemical expansion of genetically encoded libraries, their abilities and limits, and opportunities for further development. Importantly, we also discuss methods and metrics used to assess the efficiency of modification and library diversity with these new techniques.

A novel magnetic metal-organic framework (FeO@GlcA@Cu-MOF) has been prepared and characterized by spectroscopic, microscopic, and magnetic techniques. This magnetically separable catalyst exhibited high catalytic activity for nitrile hydration and the ability to reduce isothiocyanates, isocyanates, and with excellent activity and selectivity without any additional reducing agent.

Chemotherapy using natural compounds, such as resveratrol, curcumin, paclitaxel, docetaxel, etoposide, doxorubicin, and camptothecin, is of importance in cancer therapy because of the outstanding therapeutic activity and multitargeting capability of these compounds. However, poor solubility and bioavailability of natural compounds have limited their efficacy in cancer therapy. To circumvent this hurdle, nanocarriers have been designed to improve the antitumor activity of the aforementioned compounds. Nevertheless, cancer treatment is still a challenge, demanding novel strategies. It is well-known that a combination of natural products and gene therapy is advantageous over monotherapy. Delivery of multiple therapeutic agents/small interfering RNA (siRNA) as a potent gene-editing tool in cancer therapy can maximize the synergistic effects against tumor cells. In the present review, co-delivery of natural compounds/siRNA using nanovehicles are highlighted to provide a backdrop for future research.

The thermoelectric properties of bismuth telluride thin film (BTTF) was tuned by inducing internal strain through a combination of combinatorial gradient thermal annealing (COGTAN) and machine learning. BTTFs were synthesized via magnetron sputter coating and then treated by COGTAN. The crystal structure and thermoelectric properties, namely Seebeck coefficient and thermal conductivity, of the treated samples were analyzed via micropoint X-ray diffraction and scanning thermal probe microimaging, respectively. The obtained combinatorial data reveals the correlation between internal strain and the thermoelectric properties. The Seebeck coefficient of BTTF exhibits largest sensitivity, where the value ranges from 7.9 to -108 μV/K. To further explore the possibility to enhance Seebeck coefficient, the combinatorial data were subjected to machine learning. The trained model predicts that optimal strains of 3-4% and 1-2% along the - and -axis, respectively, significantly improve Seebeck coefficient. The technique demonstrated herein can be used to predict and enhance the performance of thermoelectric materials by inducing internal strain.

We propose to minimize the sampling time for high-throughput measurements of powder X-ray diffraction (XRD) and X-ray absorption fine structure (XAFS) in synchrotron radiation. The conventional synchrotron radiation powder X-ray diffraction method requires filling of a capillary tube, but a structure-refining diffraction pattern could be obtained by transferring the crushed powder to a tape and rotating the cassette-tape tool by ±5° around the sample position. XAFS spectra could also be measured with the sample attached to the tape. The time required for sample preparation was greatly reduced, which made high-throughput experiments with powders in synchrotron radiation experiments more accessible.

Thin films of two types of high-entropy oxides (HEOs) have been deposited on 76.2 mm Si wafers using combinatorial sputter deposition. In one type of the oxides, (MgZnMnCoNi)O, all the metals have a stable divalent oxidation state and similar cationic radii. In the second type of oxides, (CrFeMnCoNi)O, the metals are more diverse in the atomic radius and valence state, and have good solubility in their sub-binary and ternary oxide systems. The resulting HEO thin films were characterized using several high-throughput analytical techniques. The microstructure, composition, and electrical conductivity obtained on defined grid maps were obtained for the first time across large compositional ranges. The crystalline structure of the films was observed as a function of the metallic elements in the composition spreads, that is, the Mn and Zn in (MgZnMnCoNi)O and Mn and Ni in (CrFeMnCoNi)O. The (MgZnMnCoNi)O sample was observed to form two-phase structures, except single spinel structure was found in (MgZnMnCoNi)O over a range of Mn > 12 at. % and Zn < 44 at. %, while (CrFeMnCoNi)O was always observed to form two-phase structures. Composition-controlled crystalline structure is not only experimentally demonstrated but also supported by density function theory calculation.

This work presents the first use of yeast-displayed protein targets for screening mRNA-display libraries of cyclic and linear peptides. The WW domains of Yes-Associated Protein 1 (WW-YAP) and mitochondrial import receptor subunit TOM22 were adopted as protein targets. Yeast cells displaying WW-YAP or TOM22 were magnetized with iron oxide nanoparticles to enable the isolation of target-binding mRNA-peptide fusions. Equilibrium adsorption studies were conducted to estimate the binding affinity () of select WW-YAP-binding peptides: values of 37 and 4 μM were obtained for cyclo[M-AFRLC-K] and its linear cognate, and 40 and 3 μM for cyclo[M-LDFVNHRSRG-K] and its linear cognate, respectively. TOM22-binding peptide cyclo[M-PELNRAI-K] was conjugated to magnetic beads and incubated with yeast cells expressing TOM22 and luciferase. A luciferase-based assay showed a 4.5-fold higher binding of TOM22 yeast compared to control cells. This work demonstrates that integrating mRNA- and yeast-display accelerates the discovery of peptide ligands.

Nucleic acid aptamers are single-stranded oligonucleotides that may be evolved for affinity and specificity for their targets and can be easily produced, regenerated, and stabilized. In this study, we adapted Ni-NTA (nickle-charged nitrilotriacetic acid) affinity-chromatography in the development of single-stranded DNA aptamers against N-cadherin protein by systematic evolution of ligands by exponential enrichment (SELEX). After ten rounds of selection, two aptamers, designated NS13 and NC23, were selected, which showed low dissociation constants of 93 and 174 nM, respectively. The 5'-carboxyfluorescein-labeled NS13 was used for the sensitive detection of N-cadherin protein by the enzyme-linked oligonucleotide assay (ELONA) method.

Relative role of enthalpy and entropy in the stabilization of senary FCC Al-Co-Cr-Fe-Ni-Mn high entropy alloys was investigated via a high throughput combinatorial solid-to-solid diffusion couple approach. Many off-equiatomic compositions of FCC AlCoCrFeNiMn were generated by the diffusing Al and Ni in equiatomic CoCrFeNiMn alloy, i.e., the AlNi vs CoCrFeNiMn diffusion couple, annealed at 900°, 1000°, 1100°, and 1200 °C. Above 1000 °C, the solubility limit of Al in off-equiatomic AlCoCrFeNiMn alloy was determined to be higher than the solubility limit of Al in equiatomic AlCoCrFeNiMn alloy. Compositions corresponding to the highest solubility limit of Al in off-equiatomic AlCoCrFeNiMn alloy exhibited a lower free energy of mixing, i.e., higher thermodynamic stability, than equiatomic AlCoCrFeNiMn compositions, at 1100 °C and above. Therefore, the role of enthalpy was estimated to be significant in achieving higher thermodynamic stability in off-equiatomic alloys, since they always have lower entropy of mixing than their equiatomic counterparts. The magnitude of interdiffusion coefficients of individual elements in Al-Co-Cr-Fe-Ni-Mn alloys were compared to the interdiffusion coefficients in relevant quinary, quaternary, and ternary solvent-based alloys. Interdiffusion coefficients were not necessarily lower in FCC Al-Co-Cr-Fe-Ni-Mn alloys; therefore no sluggish diffusion was observed in FCC HEA, but diffusion of individual elements in BCC Al-Co-Cr-Fe-Ni-Mn alloy followed the sluggish diffusion hypothesis except for Ni. All compositions in the FCC Al-Co-Cr-Fe-Ni-Mn alloy were observed to comply with existing empirical single phase formation rules in high entropy alloys.