ACS Combinatorial Science最新文献

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.

A novel magnetic metal–organic framework (Fe₃O₄@[email?protected]) 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 isocyanides with excellent activity and selectivity without any additional reducing agent.

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 library of 66 perovskite Ba_xSr_yCa_zTiO₃ (x + y + z = 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.

Thin-film continuous composition spreads of Fe–Co–O were fabricated by reactive cosputtering from elemental Fe and Co targets in reactive Ar/O₂ atmosphere using deposition temperatures ranging from 300 to 700 °C. Fused silica and platinized Si/SiO₂ 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 Fe₂CoO₄ and FeCo₂O₄ 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 FeCo₂O₄ to 2.74 eV for Fe₂CoO₄, show a continuous variation with the composition.

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.

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.

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_x, all the metals have a stable divalent oxidation state and similar cationic radii. In the second type of oxides, (CrFeMnCoNi)O_x, 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_x and Mn and Ni in (CrFeMnCoNi)O_x. The (MgZnMnCoNi)O_x sample was observed to form two-phase structures, except single spinel structure was found in (MgZnMnCoNi)O_x over a range of Mn > 12 at. % and Zn < 44 at. %, while (CrFeMnCoNi)O_x 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.

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 a- and c-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.