ABSTRACT
Conical carbon nanofibers (CNFs) exist primarily as graphitic ribbons that fold into a cylindrical structure with the formation of a hollow core. Structural analysis aided by molecular modeling proves useful for obtaining a full picture of how the size of the central channel varies from fiber to fiber. From a geometrical perspective, conical CNFs possibly have cone tips that are nearly closed. On the other hand, their fiber wall thickness can be reduced to a minimum possible value that is determined solely by the apex angle, regardless of the outer diameter. A formula has been developed to express the number of carbon atoms present in conical CNFs in terms of measurable structural parameters. It appears that the energetically preferred fiber wall thickness increases not only with the apex angle, but also with the number of atoms in the constituent graphitic cones. The origin of the empirical observation that conical CNFs with small apex angles tend to have a large hollow core lies in the fact that in graphene sheets that are more highly curved the curvature-induced strain energy rises more rapidly as the fiber wall thickens.
ABSTRACT
In this paper, a novel discriminant methodology based on near infrared spectroscopic analysis technique and least square support vector machine was proposed for rapid and nondestructive discrimination of different types of Polyacrylamide. The diffuse reflectance spectra of samples of Non-ionic Polyacrylamide, Anionic Polyacrylamide and Cationic Polyacrylamide were measured. Then principal component analysis method was applied to reduce the dimension of the spectral data and extract of the principal compnents. The first three principal components were used for cluster analysis of the three different types of Polyacrylamide. Then those principal components were also used as inputs of least square support vector machine model. The optimization of the parameters and the number of principal components used as inputs of least square support vector machine model was performed through cross validation based on grid search. 60 samples of each type of Polyacrylamide were collected. Thus a total of 180 samples were obtained. 135 samples, 45 samples for each type of Polyacrylamide, were randomly split into a training set to build calibration model and the rest 45 samples were used as test set to evaluate the performance of the developed model. In addition, 5 Cationic Polyacrylamide samples and 5 Anionic Polyacrylamide samples adulterated with different proportion of Non-ionic Polyacrylamide were also prepared to show the feasibilty of the proposed method to discriminate the adulterated Polyacrylamide samples. The prediction error threshold for each type of Polyacrylamide was determined by F statistical significance test method based on the prediction error of the training set of corresponding type of Polyacrylamide in cross validation. The discrimination accuracy of the built model was 100% for prediction of the test set. The prediction of the model for the 10 mixing samples was also presented, and all mixing samples were accurately discriminated as adulterated samples. The overall results demonstrate that the discrimination method proposed in the present paper can rapidly and nondestructively discriminate the different types of Polyacrylamide and the adulterated Polyacrylamide samples, and offered a new approach to discriminate the types of Polyacrylamide.
ABSTRACT
By using the Rosemount gas analyzer and the test platform of fixed bed built by carbon furnace, the harmful gaseous compositions and the release rules of asphalt and mortar under high temperature rate were analyzed quantitatively based on infrared spectral analysis technology. The results indicated that the combustion process of the asphalt and mortar can be approximately divided into two stages stage of primary volatile combustion, and stage of secondary volatile release combined with fixed carbon combustion in isothermal condition with high heating rate. The major gaseous products are CO2, CO, NO, NO2 and SO2. the volatile content is one of the key factors affecting the release rules of gaseous combustion products in asphalt, and reducing the volatile content in asphalt materials can effectively reduce the generation of gaseous combustion products, especially CO.
