Selective Formation of a Trisubstituted Alkene Motif by trans-Hydrostannation/Stille Coupling: Application to the Total Synthesis and Late-Stage Modification of 5,6-Dihydrocineromycin B

Countless natural products of polyketide origin have an E-configured 2-methyl-but-2-en-1-ol substructure. An unconventional entry into this important motif was developed as part of a concise total synthesis of 5,6-dihydrocineromycin B. The choice of this particular target was inspired by a recent study, which suggested that the cineromycin family of antibiotics might have overlooked lead qualities, although our biodata do not necessarily support this view. The new approach consists of a sequence of alkyne metathesis followed by a hydroxy-directed trans-hydrostannation and a largely unprecedented methyl-Stille coupling. The excellent yield and remarkable selectivity with which the signature trisubstituted alkene site of the target was procured is noteworthy considering the rather poor outcome of a classical ring-closing metathesis reaction. Moreover, the unorthodox ruthenium-catalyzed trans-hydrostannation is shown to be a versatile handle for diversity-oriented synthesis

Gold- or Silver-Catalyzed Syntheses of Pyrones and Pyridine Derivatives: Mechanistic and Synthetic Aspects

3-Oxo-5-alkynoic acid esters, on treatment with a carbophilic catalyst, undergo 6-endo-dig cyclization reactions to furnish either 2-pyrones or 4-pyrones in high yields. The regiochemical course can be dialed in by the proper choice of the alcohol part of the ester and the π-acid. This transformation is compatible with a variety of acid-sensitive groups as witnessed by a number of exigent applications to the total synthesis of natural products, including pseudopyronine A, hispidine, phellinin A, the radininol family, neurymenolide, violapyrone, wailupemycin and an unnamed brominated 4-pyrone of marine origin. Although the reaction proceeds well in neutral medium, the rate is largely increased when HOAc is used as solvent or co-solvent, which is thought to favor the protodeauration of the reactive alkenyl-gold intermediates as the likely rate-determining step of the catalytic cycle. Such intermediates are prone to undergo diauration as an off-cycle event that sequesters the catalyst; this notion is consistent with literature data and supported by the isolation of the gem-diaurated complexes 12 and 15. Furthermore, silver catalysis allowed access to be gained to 2-alkoxypyridine and 2-alkoxyisoquinoline derivatives starting from readily available imidate precursors

Polyunsaturated C-Glycosidic 4-Hydroxy-2-pyrone Derivatives: Total Synthesis Shows that Putative Orevactaene Is Likely Identical with Epipyrone A

Orevactaene and epipyrone A were previously thought to comprise the same polyunsaturated tail but notably different C-glycosylated 4-hydroxy-2-pyrone head groups. Total synthesis now shows that the signature bicyclic framework assigned to orevactaene is a chimera; the compound is almost certainly identical with epipyrone A, whose previously unknown stereochemistry has also been established during this study. Key to success was the ready formation of the bicyclic core of putative orevactaene by a sequence of two alkyne cycloisomerization reactions using tungsten and gold catalysis. Equally important was the flexibility in the assembly process gained by the use of heterobimetallic polyunsaturated modules whose termini could be selectively and consecutively addressed in a practical one-pot cross-coupling sequence

A Convolutional Neural Network Approach for Estimation of Li-Ion Battery State of Health from Charge Profiles

Intelligent and pragmatic state-of-health (SOH) estimation is critical for the safe and reliable operation of Li-ion batteries, which recently have become ubiquitous for applications such as electrified vehicles, smart grids, smartphones, as well as manned and unmanned aerial vehicles. This paper introduces a convolutional neural network (CNN)-based framework for directly estimating SOH from voltage, current, and temperature measured while the battery is charging. The CNN is trained with data from as many as 28 cells, which were aged at two temperatures using randomized usage profiles. CNNs with between 1 and 6 layers and between 32 and 256 neurons were investigated, and the training data was augmented with noise and error as well to improve accuracy. Importantly, the algorithm was validated for partial charges, as would be common for many applications. Full charges starting between 0 and 95% SOC as well as for multiple ranges ending at less than 100% SOC were tested. The proposed CNN SOH estimation framework achieved a mean average error (MAE) as low as 0.8% over the life of the battery, and still achieved a reasonable MAE of 1.6% when a very small charge window of 85% to 97% SOC was used. While the CNN algorithm is shown to estimate SOH very accurately with partial charge data and two temperatures, further studies could also investigate a wider temperature range and multiple different charge currents or constant power charging

A Convolutional Neural Network Approach for Estimation of Li-Ion Battery State of Health from Charge Profiles

Intelligent and pragmatic state-of-health (SOH) estimation is critical for the safe and reliable operation of Li-ion batteries, which recently have become ubiquitous for applications such as electrified vehicles, smart grids, smartphones, as well as manned and unmanned aerial vehicles. This paper introduces a convolutional neural network (CNN)-based framework for directly estimating SOH from voltage, current, and temperature measured while the battery is charging. The CNN is trained with data from as many as 28 cells, which were aged at two temperatures using randomized usage profiles. CNNs with between 1 and 6 layers and between 32 and 256 neurons were investigated, and the training data was augmented with noise and error as well to improve accuracy. Importantly, the algorithm was validated for partial charges, as would be common for many applications. Full charges starting between 0 and 95% SOC as well as for multiple ranges ending at less than 100% SOC were tested. The proposed CNN SOH estimation framework achieved a mean average error (MAE) as low as 0.8% over the life of the battery, and still achieved a reasonable MAE of 1.6% when a very small charge window of 85% to 97% SOC was used. While the CNN algorithm is shown to estimate SOH very accurately with partial charge data and two temperatures, further studies could also investigate a wider temperature range and multiple different charge currents or constant power charging