A novel simplified procedure affords consistently 0.09-0.10 M solutions of distilled dimethyldioxirane in acetone: other than control of the reaction temperature below 15°C and vigorous mechanical stirring, no other precautions are mandatory.The importance of isolated dimethyldioxirane (Eq. 1) for preparative oxidation chemistry, as witnessed by the surge of activity2) since its isolation3) in 1985, demands a convenient method of isolation of this efficient oxidant. Presently, we provide a much simplified version of a recently reported procedure4), which should be of general interest and utility. Me (Eq. 1) H20. NaHC03 CH,COGH, + KHSO, pH Z 7 4 . 5 -1 O o C MeKey features of the new version are that even normal water (not distilled from EDTA) and commercial-grade acetone (directly from the metal canister) can be employed, that the high-efficiency

The synthesis of epoxides 2 by the reaction of the chloro-and methyl-substituted 2,3-dimethylbenzofurans 1 with dimethyldioxirane at low temperature is reported. These labile epoxides were spectroscopically characterized ( l H and 13C NMR) at subambient temperatures. Epoxidation of benzofuran l c affords a 31:69 mixture of epoxide 2 c and quinone methide 3c, the latter presumably being produced by valence isomerization of the epoxide. On warming up above -1O"C, the epoxides 2 suffer decomposition. Treatment of epoxide 2i with methanol yields the tautomeric mixture of adducts 4i and 4i'.

ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

No abstract

Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture.