Brian K. Via

Fabrication and characterization of a triple functionalization of graphene oxide with Fe3O4, folic acid and doxorubicin as dual-targeted drug nanocarrier.

A novel triple functionalized drug delivery system was synthesized by encapsulation of superparamagnetic graphene oxide (GO) and doxorubicin (DOX) with folic acid (FA) conjugated chitosan (CHI). The carrier exhibited a high loading efficiency (0.98 mg/mg), a high saturation magnetization (10.5 emu/g) and a prolonged release rate. A real-time monitoring method on the drug release from graphene oxide (GO) was reported using DOX as the model drug. The release mechanism of DOX at different pH was investigated via monitoring the time dependency of the accumulative drug release. Results show that the drug release of DOX was pH sensitive as observed at pH 5.3 and pH 7.4 PBS solutions, the lower pH values lead to weaker hydrogen bonds and degradation of CHI, and thus result in a higher release rate of DOX. Especially, this system could be applied as a dual-targeted drug nanocarrier by combined biological (active) and magnetical (passive) targeting capabilities. Our research suggests that a novel triple functionalized, pH-responsive nanocarrier for anticancer drug has been synthesized.

Synthesis of strongly green-photoluminescent graphene quantum dots for drug carrier.

A novel approach has been developed for the preparation of strongly green-photoluminescent graphene quantum dots (GQDs-PEG) which have been surface-passivated by polyethylene glycol. The photoluminescence (PL) quantum yield of the GQDs-PEG with 400 nm excitation was about 18.8%, which was higher than other GQDs reported in the literature. More importantly, the surface-passivated PEG on GQDs can not only enhance PL intensity but also load drug by hydrogen bonding. Moreover, the high specific surface area of GQDs-PEG endowed them high loading capability (2.5 mg/mg) to carry drug. The results demonstrated that the GQDs-PEG were suitable for drug carrier and cell imaging.

Modeling for proximate analysis and heating value of torrefied biomass with vibration spectroscopy.

The goal of this study was to characterize the changes in biomass with torrefaction for near infrared reflectance (NIR) and attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy for sweetgum, loblolly pine, and switchgrass. Calibration models were built for the prediction of proximate analysis after torrefaction. Two dimensional (2D) correlation spectroscopy between NIR and FTIR was found to precisely explain the depolymerization at key functional groups located within hemicellulose, cellulose, and lignin. This novel 2D technique also demonstrated the possibility of assigning key NIR wavenumbers based on mid IR spectra. Hemicellulose based wavenumbers were found to be most sensitive to torrefaction severity with complete degradation at 250-275°C. Lignin associated wavenumbers exhibited the least degradation to severity but was still detected with 2D correlation spectroscopy. Finally, calibration models for proximate analysis were performed and while both systems could be used for rapid monitoring, NIR performed better than FTIR.

WITHIN TREE VARIATION OF LIGNIN, EXTRACTIVES, AND MICROFIBRIL ANGLE COUPLED WITH THE THEORETICAL AND NEAR INFRARED MODELING OF MICROFIBRIL ANGLE

A theoretical model was built predicting the relationship between microfibril angle and lignin content at the Angstrom (A) level. Both theoretical and statistical examination of experimental data supports a square root transformation of lignin to predict microfibril angle. The experimental material used came from 10 longleaf pine (Pinus palustris) trees. Klason lignin (n=70), microfibril angle (n=70), and extractives (n=100) were measured and reported at different ring numbers and heights. All three traits were strongly influenced by ring age from pith while microfibril angle and extractives exhibited more of a height effect than lignin. As such, the multivariate response of the three traits were different in the axial direction than the radial direction supporting that care needs to be taken when defining juvenile wood within the tree. The root mean square error of calibration (RMSEC) for microfibril angle of the theoretical model (RMSEC = 9.8) was almost as low as the least squares regression model (RMSEC = 9.35). Microfibril angle calibrations were also built from NIR absorbance and showed a strong likeness to theoretical and experimental models (RMSEC = 9.0). As a result, theoretical and experimental work provided evidence that lignin content played a significant role in how NIR absorbance relates to microfibril angle. Additionally, the large variation in extractives content coupled with sampling procedure proved important when developing NIR based calibration equations for lignin and microfibril angle.

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.

Genetic improvement of fiber length and coarseness based on paper product performance and material variability - a review

Improvement of specific gravity through tree breeding was an early choice made in the mid 20th century due to its ease of measurement and impact on pulp yield and lumber strength and stiffness. This was often the first, and in many cases, the only wood quality trait selected for. However, from a product standpoint, increased specific gravity has shown to lower many paper strength and stiffness properties and has been assumed to be directly attributable to increased fiber coarseness. As a result, it is currently not clear which fiber trait would best benefit a tree improvement program for paper products. This review found coarseness to be perhaps more important to paper strength and stiffness whereas tracheid length showed better promise from a breeding point of view due to its independence from specific gravity. However, both traits possessed strong heritability and influence on product performance and thus both would be beneficial to breed for depending on organizational goals and end product mix. The objective of this paper is to review and prioritize coarseness and tracheid length from both an end use and raw material perspective. To aid in prioritization, the variation, correlation, and heritability of both traits were reviewed along with significant genetic and phenotypic correlations. Variation trends within and between families as well as within a tree were reviewed.

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.

Rapid assessment of coniferous biomass lignin–carbohydrates with near-infrared spectroscopy

The main objective of this research was to construct accurate near-infrared reflectance (NIR) models of wood chemistry. Wet chemistry procedures and high-performance liquid chromatography methods were employed to analyze the chemical composition of southern pine. The NIR spectra were collected from 21 wood samples, which were milled down to different particle size classes. NIR calibration and prediction models were established using two modeling methods with different pretreatments. Furthermore, the spectrum range used in the NIR models was refined to achieve higher prediction accuracy. Results showed that NIR model precision could be improved considerably by decreasing the particle size to a very fine powder coupled with a targeted spectrum range. Superior prediction models for lignin and holocellulose content were constructed, while models for extractives and cellulose contents were also strong.

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.

Non-destructive prediction of the properties of forest biomass for chemical and bioenergy applications using near infrared spectroscopy

Forest biomass will play a key role as a feedstock for bioproducts as the bioeconomy develops. Rapid assessment of this heterogeneous resource will help determine its suitability as feedstock for specific applications, aid in feedstock improvement programmes and enable better process control that will optimise the biorefinery process. In this study, near infrared spectroscopy coupled with partial least-squares regression was used to predict important chemical and thermal reactivity properties of biomass made up of needles, twigs, branches, bark and wood of Pinus taeda (loblolly pine). Models developed with the raw spectra for property prediction used between three and eight factors to yield R2 values ranging from a low of 0.34 for higher heat values to a high of 0.92 for volatile matter. Pretreating the raw spectra with first derivatives improved the fit statistics for all properties (i.e. min 0.57, max 0.92; with two or three factors). The best-performing models were for extractives, lignin, glucose, cellulose, volatile matter and fixed carbon (R2 ≥ 0.80, residual predictive deviation/ratio of performance to deviation ≥1.5). This study provided the capacity to predict multiple chemical and thermal/energy traits from a single spectrum across an array of materials that differ considerably in chemistry type and distribution. Models developed should be able to rapidly predict the studied properties of similar biomass types. This will be useful in rapidly allocating feedstocks that optimise biomass conversion technologies.