Chengfeng Zhou

Facile synthesis of soluble graphene quantum dots and its improved property in detecting heavy metal ions.

An effective approach to produce graphene quantum dots (GQDs) has been developed, which based on the cutting of graphene oxide (GO) powder into smaller pieces and being reduced by a green approach, using sodium polystyrene sulfonate (PSS) as a dispersant and l-ascorbic acid (l-AA) as the reducing agent, which is environmentally friendly. Then the as-prepared GQDs were further used for the detection of heavy metal ions Pb(2+). This kind of GQDs has greater solubility in water and is more biocompatible than GO that has been reduced by hydrazine hydrate. The few-layers of GQDs with defects and residual OH groups were shown to be particularly well suited for the determination of metal ions in the liquid phase using an electrochemical method, in which a remarkably low detection limit of 7×10(-9)M for Pb(2+) was achieved.

Prediction of mixed hardwood lignin and carbohydrate content using ATR-FTIR and FT-NIR

This study used Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and Fourier transform near-infrared (FT-NIR) spectroscopy with principal component regression (PCR) and partial least squares regression (PLS) to build hardwood prediction models. Wet chemistry analysis coupled with high performance liquid chromatography (HPLC) was employed to obtain the chemical composition of these samples. Spectra loadings were studied to identify key wavenumber in the prediction of chemical composition. NIR-PLS and FTIR-PLS performed the best for extractives, lignin and xylose, whose residual predictive deviation (RPD) values were all over 3 and indicates the potential for either instrument to provide superior prediction models with NIR performing slightly better. During testing, it was found that more accurate determination of holocellulose content was possible when HPLC was used. Independent chemometric models, for FT-NIR and ATR-FTIR, identified similar functional groups responsible for the prediction of chemical composition and suggested that coupling the two techniques could strengthen interpretation and prediction.

Near infrared spectroscopy calibration for wood chemistry: which chemometric technique is best for prediction and interpretation?

This paper addresses the precision in factor loadings during partial least squares (PLS) and principal components regression (PCR) of wood chemistry content from near infrared reflectance (NIR) spectra. The precision of the loadings is considered important because these estimates are often utilized to interpret chemometric models or selection of meaningful wavenumbers. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set. PLS and PCR, before and after 1st derivative pretreatment, was utilized for model building and loadings investigation. As demonstrated by others, PLS was found to provide better predictive diagnostics. However, PCR exhibited a more precise estimate of loading peaks which makes PCR better for interpretation. Application of the 1st derivative appeared to assist in improving both PCR and PLS loading precision, but due to the small sample size, the two chemometric methods could not be compared statistically. This work is important because to date most research works have committed to PLS because it yields better predictive performance. But this research suggests there is a tradeoff between better prediction and model interpretation. Future work is needed to compare PLS and PCR for a suite of spectral pretreatment techniques.

Monitoring Chemical Changes on the Surface of Kenaf Fiber during Degumming Process Using Infrared Microspectroscopy

Degumming is the dominant method to obtain lignocellulosic fibers in the textile industry. Traditionally, wet chemistry methods are used to monitor the evolution of major chemical components during the degumming process. However, these methods lack the ability to provide spatial information for these heterogeneous materials. In this study, besides wet chemistry and scanning electron microscopy (SEM) analysis, a Fourier-transform infrared microspectroscopy (FTIRM) method was employed to monitor the changes in spatial distribution of the main chemical components on the kenaf surface during a steam explosion followed by chemical degum process. The results showed that hemicellulose and lignin were degummed at different rates, and the mechanisms of their degumming are different. The infrared microspectral images revealed the distribution changes of chemical components on the fiber bundle surface during the process, indicating that FTIRM is an effective tool to analyze the degumming process and improve degumming methods.

Multivariate Calibration and Model Integrity for Wood Chemistry Using Fourier Transform Infrared Spectroscopy

This research addressed a rapid method to monitor hardwood chemical composition by applying Fourier transform infrared (FT-IR) spectroscopy, with particular interest in model performance for interpretation and prediction. Partial least squares (PLS) and principal components regression (PCR) were chosen as the primary models for comparison. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set to collect the original data. PLS was found to provide better predictive capability while PCR exhibited a more precise estimate of loading peaks and suggests that PCR is better for model interpretation of key underlying functional groups. Specifically, when PCR was utilized, an error in peak loading of ±15u2009cm−1 from the true mean was quantified. Application of the first derivative appeared to assist in improving both PCR and PLS loading precision. Research results identified the wavenumbers important in the prediction of extractives, lignin, cellulose, and hemicellulose and further demonstrated the utility in FT-IR for rapid monitoring of wood chemistry.

Classification and Identification of Plant Fibrous Material with Different Species Using near Infrared Technique—A New Way to Approach Determining Biomass Properties Accurately within Different Species

Plant fibrous material is a good resource in textile and other industries. Normally, several kinds of plant fibrous materials used in one process are needed to be identified and characterized in advance. It is easy to identify them when they are in raw condition. However, most of the materials are semi products which are ground, rotted or pre-hydrolyzed. To classify these samples which include different species with high accuracy is a big challenge. In this research, both qualitative and quantitative analysis methods were chosen to classify six different species of samples, including softwood, hardwood, bast, and aquatic plant. Soft Independent Modeling of Class Analogy (SIMCA) and partial least squares (PLS) were used. The algorithm to classify different species of samples using PLS was created independently in this research. Results found that the six species can be successfully classified using SIMCA and PLS methods, and these two methods show similar results. The identification rates of kenaf, ramie and pine are 100%, and the identification rates of lotus, eucalyptus and tallow are higher than 94%. It is also found that spectra loadings can help pick up best wavenumber ranges for constructing the NIR model. Inter material distance can show how close between two species. Scores graph is helpful to choose the principal components numbers during the model construction.