Over the past two decades, research on transforming lignocellulosic biomass into small molecule chemicals byusing catalytic liquefaction has made great progress. Notably, in recent years it has been found the producti...Over the past two decades, research on transforming lignocellulosic biomass into small molecule chemicals byusing catalytic liquefaction has made great progress. Notably, in recent years it has been found the production of smallmolecule chemicals through directional liquefaction of lignocellulosic biomass. Understanding the liquefactionmechanism of lignocellulosic biomass is highly important. In this review, the liquefaction mechanism of lignocellulosicbiomass and model compounds of cellulose are described, and some problems and suggestions to address them aredescribed.展开更多
Epoxy resins are a group of important materials that have been used everywhere,and development of new materials of this kind with optimal mechanical properties from either bio-resources or industrial precursors has dr...Epoxy resins are a group of important materials that have been used everywhere,and development of new materials of this kind with optimal mechanical properties from either bio-resources or industrial precursors has drawn great focus from scientists and engineers.By reacting different kinds of epoxy adhesives and curatives,massive kinds of epoxy resins with different characteristics are produced.Determination of original mixing ratio of epoxy adhesives and corresponding curatives of their curing products is useful in controlling and examining these materials.Here in this work,we described an efficient method based on Raman spectrometry and machine learning algorithms for rapid molar composition determination of epoxy resins.Original mixing ratio of epoxy adhesives and curatives could be calculated simply via Raman spectra of the products.Raman spectral data scanned during curing procedure was fed to random forest(RF)classification to calculate weights of Raman shift features and reduce data dimensionality,then spectral data of selected features were processed by partial least squares regression(PLSR)for model training and composition ratio determination.It turned out that ratio predictions of our model fit well to their actual values,with a coefficient of determination(R2)of 0.9926,and a root mean squared error(RMSE)of 0.0305.展开更多
文摘Over the past two decades, research on transforming lignocellulosic biomass into small molecule chemicals byusing catalytic liquefaction has made great progress. Notably, in recent years it has been found the production of smallmolecule chemicals through directional liquefaction of lignocellulosic biomass. Understanding the liquefactionmechanism of lignocellulosic biomass is highly important. In this review, the liquefaction mechanism of lignocellulosicbiomass and model compounds of cellulose are described, and some problems and suggestions to address them aredescribed.
基金National Natural Science Foundation of China(No.31670577).
文摘Epoxy resins are a group of important materials that have been used everywhere,and development of new materials of this kind with optimal mechanical properties from either bio-resources or industrial precursors has drawn great focus from scientists and engineers.By reacting different kinds of epoxy adhesives and curatives,massive kinds of epoxy resins with different characteristics are produced.Determination of original mixing ratio of epoxy adhesives and corresponding curatives of their curing products is useful in controlling and examining these materials.Here in this work,we described an efficient method based on Raman spectrometry and machine learning algorithms for rapid molar composition determination of epoxy resins.Original mixing ratio of epoxy adhesives and curatives could be calculated simply via Raman spectra of the products.Raman spectral data scanned during curing procedure was fed to random forest(RF)classification to calculate weights of Raman shift features and reduce data dimensionality,then spectral data of selected features were processed by partial least squares regression(PLSR)for model training and composition ratio determination.It turned out that ratio predictions of our model fit well to their actual values,with a coefficient of determination(R2)of 0.9926,and a root mean squared error(RMSE)of 0.0305.
