Unexpected formation of dinaphthoaza-17-crown-5 ether containing γ-aminopiperidine subunit

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Top-down synthesis of polyoxometalate-like sub-nanometer molybdenum-oxo clusters as high-performance electrocatalysts

The top-down fabrication of catalytically active molecular metal oxide anions, or polyoxometalates, is virtually unexplored, although these materials offer unique possibilities, for catalysis, energy conversion and storage. Here, we report a novel top-down route, which enables the scalable synthesis and deposition of sub-nanometer molybdenum-oxo clusters on electrically conductive mesoporous carbon. The new approach uses a unique redox-cycling process to convert crystalline Mo^IVO₂ particles into sub-nanometer molecular molybdenum-oxo clusters with a nuclearity of ∼1–20. The resulting molybdenum-oxo cluster/carbon composite shows outstanding, stable electrocatalytic performance for the oxygen reduction reaction with catalyst characteristics comparable to those of commercial Pt/C. This new material design could give access to a new class of highly reactive polyoxometalate-like metal oxo clusters as high-performance, earth abundant (electro-)catalysts.

The top-down synthesis and deposition of polyoxometalate-like clusters on porous carbon is reported together with the high electrocatalytic oxygen reduction reactivity of the composite.     

A Bis-Chelating / Ligand for the Synthesis of Heterobimetallic Platinum(II)/Rhenium(I) Complexes: Tools for the Optimization of a New Class of Platinum(II) Anticancer Agents

The two independent and coordination sites of a newly synthesized bis[2-(hydroxyphenyl)-1,2,4-triazole] platform have been exploited to prepare four monometallic neutral ()Pt^II complexes carrying DMSO, pyridine, triphenylphosphine, or N-heterocyclic carbene as the fourth ligand. Then, the second coordination site was used to introduce an IR-active rhenium tricarbonyl entity, affording the four corresponding heterobimetallic neutral Pt^II/Re^I complexes, as well as a cationic Pt^II/Re^I derivative. X-ray crystallographic studies showed that distortion of the organic platform occurred to accommodate the coordination geometry of both metal centers. No ligand exchange or transchelation occurred upon incubation of the Pt^II complexes in aqueous environment or in the presence of Fe^III, respectively. The antiproliferative activity of the ligand and complexes was first screened on the triple-negative breast cancer cell line MDA-MB-231. Then, the IC₅₀ values of the most active candidates were determined on a wider panel of human cancer cells (MDA-MB-231, MCF-7, and A2780), as well as on a nontumorigenic cell line (MCF-10A). Low micromolar activities were reached for the complexes carrying a DMSO ligand, making them the first examples of highly active, but hydrolytically stable, Pt^II complexes. Finally, the characteristic mid-IR signature of the {Re(CO)₃} fragment in the Pt/Re heterobimetallic complexes was used to quantify their uptake in breast cancer cells.     

Predicting polycyclic aromatic hydrocarbon formation with an automatically generated mechanism for acetylene pyrolysis

Using Reaction Mechanism Generator (RMG), we have automatically constructed a detailed mechanism for acetylene pyrolysis, which predicts formation of polycyclic aromatic hydrocarbons (PAHs) up to pyrene. To improve the data available for formation pathways from naphthalene to pyrene, new high‐pressure limit reaction rate coefficients and species thermochemistry were calculated using a combination of electronic structure data from the literature and new quantum calculations. Pressure‐dependent kinetics for the CH potential energy surface calculated by Zádor et al. were incorporated to ensure accurate pathways for acetylene initiation reactions. After adding these new data into the RMG database, a pressure‐dependent mechanism was generated in a single RMG simulation which captures chemistry from C to C. In general, the RMG‐generated model accurately predicts major species profiles in comparison to plug‐flow reactor data from the literature. The primary shortcoming of the model is that formation of anthracene, phenanthrene, and pyrene are underpredicted, and PAHs beyond pyrene are not captured. Reaction path analysis was performed for the RMG model to identify key pathways. Notable conclusions include the importance of accounting for the acetone impurity in acetylene in accurately predicting formation of odd‐carbon species, the remarkably low contribution of acetylene dimerization to vinylacetylene or diacetylene, and the dominance of the hydrogen abstraction CH addition (HACA) mechanism in the formation pathways to all PAH species in the model. This work demonstrates the improved ability of RMG to model PAH formation, while highlighting the need for more kinetics data for elementary reaction pathways to larger PAHs.     

Anti-Cancer Effects of Carnosine—A Dipeptide Molecule

Background: Carnosine is a dipeptide molecule (β-alanyl-l-histidine) with anti-inflammatory, antioxidant, anti-glycation, and chelating properties. It is used in exercise physiology as a food supplement to increase performance; however, in vitro evidence suggests that carnosine may exhibit anti-cancer properties. Methods: In this study, we investigated the effect of carnosine on breast, ovarian, colon, and leukemic cancer cell proliferation. We further examined U937 promonocytic, human myeloid leukemia cell phenotype, gene expression, and cytokine secretion to determine if these are linked to carnosine’s anti-proliferative properties. Results: Carnosine (1) inhibits breast, ovarian, colon, and leukemic cancer cell proliferation; (2) upregulates expression of pro-inflammatory molecules; (3) modulates cytokine secretion; and (4) alters U937 differentiation and phenotype. Conclusion: These effects may have implications for a role for carnosine in anti-cancer therapy.     

On the Statistical Calibration of Physical Models

We introduce a novel statistical calibration framework for physical models, relying on probabilistic embedding of model discrepancy error within the model. For clarity of illustration, we take the measurement errors out of consideration, calibrating a chemical model of interest with respect to a more detailed model, considered as “truth” for the present purpose. We employ Bayesian statistical methods for such model‐to‐model calibration and demonstrate their capabilities on simple synthetic models, leading to a well‐defined parameter estimation problem that employs approximate Bayesian computation. The method is then demonstrated on two case studies for calibration of kinetic rate parameters for methane air chemistry, where ignition time information from a detailed elementary‐step kinetic model is used to estimate rate coefficients of a simple chemical mechanism. We show that the calibrated model predictions fit the data and that uncertainty in these predictions is consistent in a mean‐square sense with the discrepancy from the detailed model data.