English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In recent years, the study of properties of ionic liquids (ILs) and their  structures has developed to a great extent. Among the common physicochemical  properties of pure ILs, electric conductivity (EC) is of crucial importance  for both practical and fundamental levels. In order to develop effective  models for predicting EC value of various ILs, the relationship between the  structural descriptors and the EC of thirty-five ionic liquids at different  temperatures has been investigated by multi linear regression (MLR) and back  propagation artificial neural network (ANN), respectively. As a result, a  three layer ANN with four variables selected by MLR model as input node was  set up successfully. The descriptors selected by MLR were suitable and  significant to be the input nodes of the ANN model in this study. And the  calculated ionic conductivities by ANN model with high correlation  coefficient and low root mean squared error were quantitative in good  agreement with the experimental values, and it was proved better than the MLR  model

Prediction of electric conductivity for ionic liquids by two chemometrics methods