Edward J. Wolfrum

Application of the Photocatalytic Chemistry of Titanium Dioxide to Disinfection and the Killing of Cancer Cells

Abstract This article will review the work that has been published on disinfection and the killing of cancer cells using photocatalytic chemistry with titanium dioxide (TiO2). This is an application of photocatalytic chemistry that has been under active investigation since 1985. Because the nature of the research is such that it brings together disparate disciplines, this review provides background on photocatalytic chemistry, fundamental characteristics of target organisms, potential applications, and the toxicology of titanium dioxide. Literature identified in searches done through September 1998 is included.

Life cycle environmental impacts of selected U.S. ethanol production and use pathways in 2022.

Projected life cycle greenhouse gas (GHG) emissions and net energy value (NEV) of high-ethanol blend fuel (E85) used to propel a passenger car in the United States are evaluated using attributional life cycle assessment. Input data represent national-average conditions projected to 2022 for ethanol produced from corn grain, corn stover, wheat straw, switchgrass, and forest residues. Three conversion technologies are assessed: advanced dry mill (corn grain), biochemical (switchgrass, corn stover, wheat straw), and thermochemical (forest residues). A reference case is compared against results from Monte Carlo uncertainty analysis. For this case, one kilometer traveled on E85 from the feedstock-to-ethanol pathways evaluated has 43%-57% lower GHG emissions than a car operated on conventional U.S. gasoline (base year 2005). Differences in NEV cluster by conversion technology rather than by feedstock. The reference case estimates of GHG and NEV skew to the tails of the estimated frequency distributions. Though not as optimistic as the reference case, the projected median GHG and NEV for all feedstock-to-E85 pathways evaluated offer significant improvement over conventional U.S. gasoline. Sensitivity analysis suggests that inputs to the feedstock production phase are the most influential parameters for GHG and NEV. Results from this study can be used to help focus research and development efforts.

Compositional Analysis of Lignocellulosic Feedstocks. 2. Method Uncertainties

The most common procedures for characterizing the chemical components of lignocellulosic feedstocks use a two-stage sulfuric acid hydrolysis to fractionate biomass for gravimetric and instrumental analyses. The uncertainty (i.e., dispersion of values from repeated measurement) in the primary data is of general interest to those with technical or financial interests in biomass conversion technology. The composition of a homogenized corn stover feedstock (154 replicate samples in 13 batches, by 7 analysts in 2 laboratories) was measured along with a National Institute of Standards and Technology (NIST) reference sugar cane bagasse, as a control, using this laboratorys suite of laboratory analytical procedures (LAPs). The uncertainty was evaluated by the statistical analysis of these data and is reported as the standard deviation of each component measurement. Censored and uncensored versions of these data sets are reported, as evidence was found for intermittent instrumental and equipment problems. The censored data are believed to represent the “best case” results of these analyses, whereas the uncensored data show how small method changes can strongly affect the uncertainties of these empirical methods. Relative standard deviations (RSD) of 1−3% are reported for glucan, xylan, lignin, extractives, and total component closure with the other minor components showing 4−10% RSD. The standard deviations seen with the corn stover and NIST bagasse materials were similar, which suggests that the uncertainties reported here are due more to the analytical method used than to the specific feedstock type being analyzed.

Algal Biomass Constituent Analysis: Method Uncertainties and Investigation of the Underlying Measuring Chemistries

Algal biomass compositional analysis data form the basis of a large number of techno-economic process analysis models that are used to investigate and compare different processes in algal biofuels production. However, the analytical methods used to generate these data are far from standardized. This work investigated the applicability of common methods for rapid chemical analysis of biomass samples with respect to accuracy and precision. This study measured lipids, protein, carbohydrates, ash, and moisture of a single algal biomass sample at 3 institutions by 8 independent researchers over 12 separate workdays. Results show statistically significant differences in the results from a given analytical method among laboratories but not between analysts at individual laboratories, suggesting consistent training is a critical issue for empirical analytical methods. Significantly different results from multiple lipid and protein measurements were found to be due to different measurement chemistries. We identified a set of compositional analysis procedures that are in best agreement with data obtained by more advanced analytical procedures. The methods described here and used for the round robin experiment do not require specialized instrumentation, and with detailed analytical documentation, the differences between laboratories can be markedly reduced.

Feasibility of spectroscopic characterization of algal lipids: chemometric correlation of NIR and FTIR spectra with exogenous lipids in algal biomass.

A large number of algal biofuels projects rely on a lipid screening technique for selecting a particular algal strain with which to work. We have developed a multivariate calibration model for predicting the levels of spiked neutral and polar lipids in microalgae, based on infrared (both near-infrared (NIR) and Fourier transform infrared (FTIR)) spectroscopy. The advantage of an infrared spectroscopic technique over traditional chemical methods is the direct, fast, and non-destructive nature of the screening method. This calibration model provides a fast and high-throughput method for determining lipid content, providing an alternative to laborious traditional wet chemical methods. We present data of a study based on nine levels of exogenous lipid spikes (between 1% and 3% (w/w)) of trilaurin as a triglyceride and phosphatidylcholine as a phospholipid model compound in lyophilized algal biomass. We used a chemometric approach to corrrelate the main spectral changes upon increasing phospholipid and triglyceride content in algal biomass collected from single species. A multivariate partial least squares (PLS) calibration model was built and improved upon with the addition of multiple species to the dataset. Our results show that NIR and FTIR spectra of biomass from four species can be used to accurately predict the levels of exogenously added lipids. It appears that the cross-species verification of the predictions is more accurate with the NIR models (R2 = 0.969 and 0.951 and RMECV = 0.182 and 0.227% for trilaurin and phosphatidylcholine spike respectively), compared with FTIR (R2 = 0.907 and 0.464 and RMECV = 0.302 and 0.767% for trilaurin and phosphatidylcholine spike, respectively). A fast high-throughput spectroscopic lipid fingerprinting method can be applied in a multitude of screening efforts that are ongoing in the microalgal research community.

Solar photocatalytic processes for the purification of water: State of development and barriers to commercialization

Abstract Semiconductor-based photocatalytic processes for removing hazardous chemicals from contaminated water have been studied for nearly 20 years. One goal of this research is to use the sun as the light source. This article assesses the state of development of solar heterogeneous photocatalytic processes for treating contaminated water and identifies key barriers that must be overcome for the technology to achieve commercial success. Some industry members estimate that the cost of using solar technology for waste treatment will need to be less than half the cost of a “conventional” technology in order to gain market acceptance. The number of applications that are near commercial viability could be expanded with significant progress in the improvement of the photo-efficiency of the photocatalytic process.

Bioreactor Design Studies for a Hydrogen-Producing Bacterium

Carbon monoxide (CO) can be metabolized by a number of microorganisms along with water to produce hydrogen (H2) and carbon dioxide. National Renewable Energy Laboratory researchers have isolated a number of bacteria that perform this so-called water-gas shift reaction at ambient temperatures. We performed experiments to measure the rate of CO conversion and H2 production in a trickle-bed reactor (TBR). The liquid recirculation rate and the reactor support material both affected the mass transfer coefficient, which controls the overall performance of the reactor. A simple reactor model taken from the literature was used to quantitatively compare the performance of the TBR geometry at two different size scales. Good agreement between the two reactor scales was obtained.

Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy

BackgroundObtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. Our objective was to use near-infrared (NIR) spectroscopy and partial least squares (PLS) multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. Major feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial cool season grasses, rice straw, and miscanthus.ResultsWe present individual model statistics to demonstrate model performance and validation samples to more accurately measure predictive quality of the models. The PLS-2 model for composition predicts glucan, xylan, lignin, and ash (wt%) with uncertainties similar to primary measurement methods. A PLS-2 model was developed to predict glucose and xylose release following pretreatment and enzymatic hydrolysis. An additional PLS-2 model was developed to predict glucan and xylan yield. PLS-1 models were developed to predict the sum of glucose/glucan and xylose/xylan for release and yield (grams per gram). The release and yield models have higher uncertainties than the primary methods used to develop the models.ConclusionIt is possible to build effective multispecies feedstock models for composition, as well as carbohydrate release and yield. The model for composition is useful for predicting glucan, xylan, lignin, and ash with good uncertainties. The release and yield models have higher uncertainties; however, these models are useful for rapidly screening sample populations to identify unusual samples.

A new method for the rapid determination of volatile organic compound breakthrough times for a sorbent at concentrations relevant to indoor air quality.

Abstract The use of sorbents has been proposed to remove volatile organic compounds (VOCs) present in ambient air at concentrations in the parts-per-billion (ppb) range, which is typical of indoor air quality applications. Sorbent materials, such as granular activated carbon and molecular sieves, are used to remove VOCs from gas streams in industrial applications, where VOC concentrations are typically in the parts-per-million range. A method for evaluating the VOC removal performance of sorbent materials using toluene concentrations in the ppb range is described. Breakthrough times for toluene at concentrations from 2 to 7500 ppb are presented for a hydrophobic molecular sieve at 25% relative humidity. By increasing the ratio of challenge gas flow rate to the mass of the sorbent bed and decreasing both the mass of sorbent in the bed and the sorbent particle size, this method reduces the required experimental times by a factor of up to several hundred compared with the proposed American Society of Heating, Refrigerating, and Air-Conditioning Engineers method, ASHRAE 145P, making sorbent performance evaluation for ppb-range VOC removal more convenient. The method can be applied to screen sorbent materials for application in the removal of VOCs from indoor air.

Calibration Transfer Among Sensor Arrays Designed for Monitoring Volatile Organic Compounds in Indoor Air Quality

Sensor arrays were constructed using commercially available heated tin oxide sensors (Figaro TGS2602) and exposed to a wide variety of volatile organic compounds (VOCs) in air streams at concentration levels in the range of 0.01-0.30 ppm, which is a range typical of indoor air quality studies. Partial least squares calibration models were developed using steady-state sensor array responses. These calibration models were used to detect, differentiate, and quantify different VOCs. The authors were able to successfully transfer single-component calibrations by sorting the sensors in each array by sensitivity prior to transfer. Future work will explore multicomponent calibration transfer