Titanium-catalyzed vinylic and allylic cf bond activation-scope, limitations and mechanistic insight

The hydrodefluorination (HDF) of fluoroalkenes in the presence of a variety of titanium catalysts was studied with respect to scope, selectivity, and mechanism. Optimization revealed that the catalyst requires low steric bulk and high electron density; secondary silanes serve as the preferred hydride source. A broad range of substrates yield partially fluorinated alkenes, such as previously unknown (Z)-1,2-(difluorovinyl)ferrocene. Mechanistic studies indicate a titanium(III) hydride as the active species, which forms a titanium(III) fluoride by H/F exchange with the substrate. The HDF step can follow both an insertion/elimination and a σ-bond metathesis mechanism; the E/Z selectivity is controlled by the substrate. The catalysts' ineffieciency towards fluoroallenes was rationalized by studying their reactivity towards Group?6 hydride complexes.     

Bonding Trends in Tetravalent Th–Pu Monosalen Complexes

The synthesis of three complex series of the form [AnCl₂(salen)(Pyx)₂] (H₂salen=N,N′-bis(salicylidene)ethylenediamine; Pyx=pyridine, 4-methylpyridine, 3,5-dimethylpyridine) with tetravalent early actinides (An=Th, U, Np, Pu) is reported with the goal to elucidate the affinity of these heavy elements for small neutral N-donor molecules. Structure determination by single-crystal XRD and characterization of bulk powders with infrared spectroscopy reveals isostructurality within each respective series and the same complex conformation in all reported structures. Although the trend of interatomic distances for An−Cl and An−N (imine nitrogen of salen or pyridyl nitrogen of Pyx) was found to reflect an ionic behavior, the trend of the An−O distances can only be described with additional covalent interactions for all elements heavier than thorium. All experimental results are supported by quantum chemical calculations, which confirm the mostly ionic character in the An−N and An−Cl bonds, as well as the highest degree of covalency of the An−O bonds. Structurally, the calculations indicate just minor electronic or steric effects of the additional Pyx substituents on the complex properties.     

Low-Frequency Ultrasound Coupled with High-Pressure Technologies: Impact of Hybridized Techniques on the Recovery of Phytochemical Compounds

The coupling of innovative technologies has emerged as a smart alternative for the process intensification of bioactive compound extraction from plant matrices. In this regard, the development of hybridized techniques based on the low-frequency and high-power ultrasound and high-pressure technologies, such as supercritical fluid extraction, pressurized liquids extraction, and gas-expanded liquids extraction, can enhance the recovery yields of phytochemicals due to their different action mechanisms. Therefore, this paper reviewed and discussed the current scenario in this field where ultrasound-related technologies are coupled with high-pressure techniques. The main findings, gaps, challenges, advances in knowledge, innovations, and future perspectives were highlighted.     

Polyfunctional Bis‐Lewis‐Acid‐/Bis‐Triazolium Catalysts for Stereoselective 1,4‐Additions of 2‐Oxindoles to Maleimides

Achieving enzyme‐like catalytic activity and stereoselectivity without the typically high substrate specificity of enzymes is a challenge in the development of artificial catalysts for asymmetric synthesis. Polyfunctional catalysts are considered to be a promising tool for achieving excellent catalytic efficiency. A polyfunctional catalyst system was developed, which incorporates two Lewis acidic/Brønsted basic cobalt centers in combination with triazolium moieties that are crucial for high reactivity and excellent stereoselectivity in the direct 1,4‐addition of oxindoles to maleimides. The catalyst is assembled through click chemistry and is readily recyclable through precipitation by making use of its charges. Kinetic studies support a cooperative mode of action. Diastereodivergency is achievable with either Boc‐protected or unprotected maleimide.     

Examining the acute effects of cediranib (RECENTIN, AZD2171) treatment in tumor models: a dynamic contrast-enhanced MRI study using gadopentate

Cediranib (RECENTIN, AZD2171) is a highly potent inhibitor of the tyrosine kinase activity associated with all three vascular endothelial growth factor (VEGF) receptors and is currently in Phase II/III clinical trials. Preclinically, cediranib inhibits VEGF signaling and angiogenesis in vivo and impedes solid tumor growth significantly. Clinically, changes observed using dynamic contrast-enhanced MRI (DCE-MRI) with gadopentate suggest that acute cediranib treatment compromises tumor hemodynamics. In this study, a DCE-MRI baseline scan using gadopentate was performed in nude rats bearing Lovo (human colorectal carcinoma) or C6 (rat glioma) tumors. Cediranib (3 mg/kg per day) or vehicle was then dosed orally (2, 26 and 50 h after the baseline scan; 12 rats per group) and a second scan acquired 2 h after the final dosing event. Mean values for K(trans) (Tofts and Kermode-derived) [Magn Reson Med 17 (1991) 357-67] and the initial area under the gadolinium concentration curve over the first 60 s (iAUC) were reduced significantly following cediranib treatment: K(trans) by 33% (P<.05) in both tumor models and iAUC by 23% (P>.05) and 33% (P>.005) in Lovo and C6, respectively. This is the first preclinical investigation to examine the effect of cediranib treatment on tumors by DCE-MRI with gadopentate.     

Surrogate optimization of deep neural networks for groundwater predictions

Sustainable management of groundwater resources under changing climatic conditions require an application of reliable and accurate predictions of groundwater levels. Mechanistic multi-scale, multi-physics simulation models are often too hard to use for this purpose, especially for groundwater managers who do not have access to the complex compute resources and data. Therefore, we analyzed the applicability and performance of four modern deep learning computational models for predictions of groundwater levels. We compare three methods for optimizing the models’ hyperparameters, including two surrogate model-based algorithms and a random sampling method. The models were tested using predictions of the groundwater level in Butte County, California, USA, taking into account the temporal variability of streamflow, precipitation, and ambient temperature. Our numerical study shows that the optimization of the hyperparameters can lead to reasonably accurate performance of all models (root mean squared errors of groundwater predictions of 2 meters or less), but the “simplest” network, namely a multilayer perceptron (MLP) performs overall better for learning and predicting groundwater data than the more advanced long short-term memory or convolutional neural networks in terms of prediction accuracy and time-to-solution, making the MLP a suitable candidate for groundwater prediction.