LabNotes - August 2013
Collaborative Technology Demonstrates Potential in Diabetes Testing
Diabetes is a disease afflicting tens of millions of people. Current diagnostic testing can include expensive doctor’s visits and invasive testing, which makes diagnosing and monitoring the disease anything but straightforward. One symptom of diabetes is “fruity” breath, which may occur when the body cannot produce or use insulin effectively.
Glucose is our body’s main source of energy, but when deprived of this fuel – as in diabetics – it turns to the next best thing, fat. Fat metabolism results in the body producing ketones, specifically acetone, which is associated with side effects including fruity-smelling breath. While everyone produces a certain level of acetone through normal, daily metabolic processes, diabetics produce it in larger amounts and also exhale it at a higher rate than non-diabetics, which is what produces the “fruity” aroma that indicates high blood glucose levels.
A recent R&D innovation from the National Energy Technology Laboratory and its Regional University Alliance (NETL-RUA) has the potential to simplify diagnosis of diabetes through acetone detection. The NETL-RUA has developed a new hybrid nanostructure material that may be used in an inexpensive “breathalyzer” to test for and monitor diabetes.
The word breathalyzer provokes images of testing for intoxicated driving. But in fact, the new technology would be used in breathalyzers to monitor blood sugar, rather than blood alcohol. When used as a sensing tool in a breath analyzer, the NETL-RUA hybrid nanostructure could offer a way for millions of diabetics to trade the pain and hassle of finger sticks for a non-invasive testing solution.
|An illustration of (left) an acetone molecule adsorbed on a titanium dioxide cluster at the surface of a carbon nanotube, and (right) a hypothetical diabetes breathalyzer device.
NETL-RUA researchers discovered that by bolstering titanium dioxide—the same ingredient found in most sunscreens—with carbon nanotubes, or CNTs, they could produce a sensor that can detect acetone vapors at parts per million levels. Carbon nanotube (CNT)-based sensors are extremely small, inexpensive, consume little to no power, and are compatible with complementary metal-oxide-semiconductor (CMOS) technology, which allows further incorporation into modern electronic devices, like smart phones. These advantages make CNT-based sensors ideal for chemical sensing and non-invasive medical diagnostic tools.
CNTs are also highly conductive. Titanium dioxide is highly refractive and highly absorbent to UV rays. The research team combined titanium dioxide with CNTs to form a hybrid nanostructure, which was deposited on a silicon substrate with gold contacts for testing. When exposed to UV light and acetone vapor, the electrical conductivity of the hybrid decreased as acetone concentrations increased from 2 to 20 parts per million (ppm); researchers also calculated the detection limit of the hybrid material at 0.4 ppm acetone. The sensitivity and electrical response are in a useful range for diagnosis and monitoring of diabetes.
The NETL-RUA team—Alexander Star, principal investigator and an associate professor of chemistry at University of Pittsburgh; Dan Sorescu, a research physicist at the National Energy Technology Laboratory; and Mengning Ding, a Pitt graduate student in chemistry—are now developing a prototype sensor. Successful tests on human breath samples could mean a game-changing medical technology is on the way. It is anticipated that the portable sensors could facilitate the diabetes research and clinical practice related to this disease.
Contact: Dan Sorescu, 412-386-4827
Quantifying Uncertainty in Computer Model Predictions
The U.S. Department of Energy has great interest in technologies that will lead to reducing the CO2 emissions of fossil-fuel-burning power plants. Advanced energy technologies such as Integrated Gasification Combined Cycle (IGCC) and Carbon Capture and Storage (CCS) can potentially lead to the clean and efficient use of fossil fuels to power our nation. The development of new energy technologies, however, takes a long time, as the technologies need to be tested at multiple scales, progressing from lab scale to pilot scale to demonstration scale before widespread deployment. In addition to developing new energy technologies, NETL’s research is working to reduce the cost and time of technology development.
Advanced modeling and simulation capabilities can significantly reduce the time and cost of the development and deployment of energy technologies. In particular, modeling and simulation can be used to increase the confidence as technologies are scaled up, such as, for example, when designing a 285 MWe gasifier based on data generated from a 13 MWth pilot-scale gasifier. This allows the rapid scale-up of technologies, reducing or even avoiding costly intermediate-scale testing. New designs can be tested with the help of simulations to ensure reliable operation under a variety of operating conditions. However, before simulation results can be used with confidence for scale-up, the reliability of the predictions must be established. Therefore, in 2011, NETL initiated work on the verification, validation and uncertainty quantification of multiphase computational fluid dynamics (CFD) models that underpin the simulation of several advanced energy technologies, adapting methods developed for other applications such as the stewardship of the nuclear stockpile. This involves exploring “how to make models as useful as possible by quantifying how wrong they are” as stated in a National Academies report, the basic idea being quantifying the uncertainty in the predictions.
| Comparison of the actual results and MARS based response surface generated for 1024 sample runs.
Multiphase CFD models, for example, have the ability to predict the performance of scaled-up fluidized bed reactors, but they must be validated with data from small, pilot-scale units. The validation studies usually report the ability of the model to agree with measured values in qualitative terms (e.g. , “good” agreement). Because various sources of uncertainty unavoidably get introduced by the time a numerical solution is computed, even though multiphase CFD models are based on a set of deterministic mathematical equations, the ideal of a “perfect” agreement between model and experiment is practically unachievable.
NETL’s objective is to demonstrate how a comprehensive uncertainty quantification method can be adopted for describing the validity of multiphase CFD models. A gasifier simulation, for example, uses a set of input parameters taken from the design (e.g. , geometry specifications, gas/solid flow rates, and composition) and laboratory measurements (e.g. , chemical reaction rates) and predicts the quantity of interest (e.g. , carbon conversion, pressure drop). There exist a number of challenges when applying uncertainty quantification techniques. In multiphase flows, for example, many uncertain parameters exist. Another challenge may be the computational cost, requiring a compromise in terms of the grid resolution used. Since the governing physics in multiphase flows is more complex than in single phase flow simulations, the computational cost increase plays a key role in the determination of adequate sampling technique and number of samples.
Using a framework established by earlier researchers in this field, NETL researchers apply the following steps to describe the validity of the models they use and the differences observed in predicted vs. observed phenomena: (1) identify and characterize the sources of uncertainty as being uncertainty due to inherent variation in a quantity (aleatory) or uncertainty due to information missing on the part of modelers or experimenters (epistemic); (2) understand the propagation of uncertainties using quasi-Monte Carlo, Latin hypercube, orthogonal arrays, etc. calculations; (3) estimate uncertainties due to numerical approximations (e.g. , discretization errors); (4) estimate uncertainty in experimental data; and (5) estimate model form uncertainty.
Some preliminary results of this research were published in two 2013 papers titled “Validation and Uncertainty Quantification of a Multiphase Computational Fluid Dynamics Model” and “Applying Uncertainty Quantification to Multiphase Flow Computational Fluid Dynamics,” that were published in Industrial & Engineering Chemistry Research journal and in Powder Technology journal, respectively.
Contact: Mehrdad Shahnam, 304-285-4546 and Madhava Syamlal, 304-285-4685