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Chorpening, Benjamin

Publications and source records attributed to Chorpening, Benjamin.

At least 19 records

Quantum Sensing for Energy Applications

Quantum sensing is creating potentially transformative opportunities to exploit intricate quantum mechanical phenomena in new ways to make ultrasensitive measurements of multiple parameters. A growing interest in quantum sensing has created opportunities for its deployment to improve processes pertaining to energy production, distribution, and consumption. NETL is leveraging experimental and computational quantum tools to enhance sensitivity of hybrid quantum-classical ultrasensitive sensors for the detection of hydrocarbons and rare earth elements (REEs).

Paudel, Hari P.↗

A Panoramic View of Temperature and Field Distributions of the Structured Catalyst Under Microwave Irradiation Using Experimental and Modeling Approaches

This is a presentation covers NETL's recent research outcome in numerical modeling of microwave-assisted catalytic reaction. In this presentation, the intrinsic microwave-material interaction was demonstrated in this study through numerical modeling and experimental measurement. Different material combinations and reactor setups were investigated and compared. By combining numerical modeling with advanced measurement techniques, the microwave-material interaction and the relationship between electromagnetic field and heating can be better understood, which further benefits the development of microwave-assisted reactors and their application in process intensification.

Bai, Xinwei↗

Insights into Designing an Efficient and Reliable Microwave-Assisted Methane Dehydroaromatization Process: Effect of Microwave Absorber on Catalyst Performance

Microwave-assisted methane dehydroaromatization has the potential to address challenges of traditional dehydroaromatization reactions. However, catalysts for microwave-enhanced reaction systems require effective coupling of fields with the catalyst to produce heat and reach reaction temperatures. Here, this work presents an in-depth understanding of the effect of the addition of silicon carbide as a microwave absorber on catalyst performance among other variables, the viability of the microwave reactor configuration, and insights into designing an effective and reliable microwave-based methane dehydroaromatization process. The effect of other parameters including temperature, weight hourly space velocity, role of microwave absorber, and methane concentration during microwave-assisted methane dehydroaromatization reaction are studied. Mo/ZSM-5 was found to suffer from low permittivity and nonuniform heating under microwave conditions. Mixing silicon carbide powder as a microwave absorber with the catalyst was found to provide more uniform heating. When assessing the catalytic performance of the mixture, it was found that higher methane partial pressures at 2000 cc/g cat .h and a temperature range of 500-600°C produced the highest amount of benzene. The formation of graphitic carbon on the spent catalyst increased with temperature, gas-solid contact period, and methane concentration, which resulted in higher methane conversion and benzene selectivity. The study indicates that under microwave heating the presence of localized carbon enhanced catalyst life by coupling with microwave energy, leading to localized heating, and improving benzene selectivity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Advanced Reaction Systems

An overview of NETL's Gasification Systems R&D was provided at DOE's Hydrogen and Fuel Cells Technology Office (HFTO) Annual Merit Review (AMR) on May 7, 2024, in Arlington, VA. The objective of the Advanced Reaction Systems Portfolio is to design, develop, and analyze technologies to support the mission of the Gasification Program to enable the use of diverse feedstocks to produce hydrogen and other value-added products with net-zero greenhouse gas emissions.

Lewis, Eric↗

Advanced Reaction Systems

An overview of NETL's Gasification Systems R&D was provided at the 2024 FECM/NETL Spring R&D Project Review Meeting on April 24, 2024, in Pittsburgh, PA. The objective of the Advanced Reaction Systems Portfolio is to design, develop, and analyze technologies to support the mission of the Gasification Program to enable the use of diverse feedstocks to produce hydrogen and other value-added products with net-zero greenhouse gas emissions.

Lewis, Eric↗

Distributed Temperature Profiles of Silicon Carbide Catalyst Bed in a Microwave Reactor using Fiber-optic Sensor

Microwave heating is of great interest for reducing greenhouse gas emissions of catalytic chemical conversion processes because it can heat up the reactants rapidly and efficiently and accelerate reaction rates. In microwave-assisted catalytic thermochemical conversion processes, accurate internal temperature measurement would help facilitate effective process control. Metallic thermocouples interfere and spark in the microwave heating environment, so they are not an option. Infrared pyrometers typically provide a single temperature value by averaging over a finite surface area of the catalyst bed. In this work, we use fiber-optic sensors passing through the catalyst bed to provide more accurate temperatures along the height of the catalyst bed. The fiber optic sensors are immune to the electromagnetic microwave radiation and provide temperature profiles along their length. Here, custom fiber-optic sensors were coupled to the optical distributed temperature sensing devices to measure the temperature profiles along the height of the catalyst bed at high temperatures with ~1 mm spatial resolution. The sensing devices use optical frequency domain reflectometry (OFDR) technique for distributed temperature measurement. This work shows the temperature profiles along the height of the silicon carbide powder catalyst bed in the variable and fixed frequency microwave reactors.

Thapa, Juddha↗

A machine learning approach for determining temperature-dependent bandgap of metal oxides utilizing Allen–Heine–Cardona theory and O’Donnell model parameterization

To evaluate the high temperature sensing properties of metal oxide and perovskite materials suitable for use in combustion environments, it is necessary to understand the temperature dependence of their bandgaps. Although such temperature-driven changes can be calculated via the Allen–Heine–Cardona (AHC) theory, which assesses electron–phonon coupling for the bandgap correction at given temperatures, this approach is computationally demanding. Another approach to predict bandgap temperature-dependence is the O’Donnell model, which uses analytical expressions with multiple fitting parameters that require bandgap information at 0 K. This work employs data-driven Gaussian process regression (GPR) to predict the parameters employed in the O’Donnell model from a set of physical features. We use a sample of 54 metal oxides for which density functional theory has been performed to calculate the bandgap at 0 K, and the AHC calculations have been carried out to determine the shift in the bandgap at non-zero temperatures. As the AHC calculations are impractical for high-throughput screening of materials, the developed GPR model attempts to alleviate this issue by predicting the O'Donnell parameters purely from physical features. To mitigate the reliability issues arising from the very small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as quantify the uncertainty associated with the predictions. The method captures well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions of the O’Donnell parameters and, therefore, the bandgap as a function of temperature for any novel material.

36 MATERIALS SCIENCE↗

Hydrogen Safety Review for Gas Turbines, SOFC, and High Temperature Hydrogen Production

This report presents an introduction and summary on hydrogen safety issues, as they relate to hydrogen production, storage, and use in the energy sector, primarily as related to electricity generation by gas turbines, and solid oxide fuel cells (SOFC), and hydrogen production from gasification and steam methane reforming processes. The impetus behind this effort is the U.S. DOE goal for decarbonization of the power generation sector by the year 2035. In many cases, hydrogen is expected to replace natural gas as the primary fuel source (or form of energy storage) to achieve this goal. Advanced technologies for large-scale energy production using hydrogen as a fuel source which can be deployed over the next 5–10 years will be required. However, to develop and mature these hydrogen-based technologies, it will be critical to identify the safety issues related to widespread use of hydrogen as a fuel source, so that they may be addressed prior to widespread deployment.

08 HYDROGEN↗

Temperature Dependence of Band Gap Renormalization in High-T Sensor Materials via First-Principles and Experimental Corroboration

Understanding the temperature dependence of functional properties of high-T gas sensing materials is vital for their applications in combustion environments. The electron-phonon coupling that derives the electronic structure change with temperatures is a key property of interest as it affects other sensing responses. Herein, we assess the temperature dependence of band gap renormalization in metal oxides and perovskites by employing Allen-Heine-Cardona theory with first-principles simulations and corroborate with experimental observation. The calculated temperature-dependent band gap changes of these materials studied are in good agreement with in-house experimental data, proving that the theory can adequately predict renormalization on the band gap in the system of interest. The predicted and measured band gap variations are characterized using an analytical model, which can provide useful insights on the simulated zero-temperature band gaps. Based on the available data, a set of 53 metal oxides and perovskites were identified as potential high-T gas sensors. A machine learning model has been developed to predict the band-gap change by capturing the overall trend of the empirical parameters with respect to a reduced feature obtained by transforming the set of available physical features.

Park, Jongwoo↗

Development of green-colour-emitting pyrotechnics as a core for 3D temperature imaging sensors inside coal boilers

Efficiency in the control operation of the boilers for coal and coal with biomass can be further improved if the flue gas temperature distribution can be better characterized. This is very difficult in these harsh environmental systems, where spatially resolved measurements are nearly impossible with solid-state sensors. Here, in this work, we evaluate the development of pyrotechnic compounds that would serve as the basis for a novel optical mapping of the temperature inside coal boilers. For this purpose, various green-colour-emitting pyrotechnics using BaCl 2 · 2H 2 O and Ba(NO 3 ) 2 as the green light source were prepared, as this colour offers a distinct signal from the combustion-based background in the boiler. These pyrotechnics were characterized using thermogravimetric analysis (TGA) and X-ray diffraction (XRD) and tested using a flat-flame burner. Furthermore, the composition was varied to evaluate the effect of different metal fuels such as Sn, Co, and Mg, as well as various binders such as ethylcellulose, shellac, parlon, and PVC on green light emission. The emission intensity and the apparent ignition temperature were strongly dependent on the metal type, with Mg showing higher intensities. On the other hand, the effect of the binder showed that the ignition behaviour, emission intensity, and spectral purity were influenced by the nature and exothermicity of the binder. The addition of other potential green light-producing materials, such as boric acid, increased the intensity of emission by 17% for a BaCl 2 · 2H 2 O-based composition. This study identified prospective compositions with intense and bright green-colour emissions that have high spectral purities.

42 ENGINEERING↗

Applicability of Allen–Heine–Cardona Theory on MO x Metal Oxides and ABO 3 Perovskites: Toward High-Temperature Optoelectronic Applications [plus supplemental information]

We report characterizing the temperature-dependent electronic structures of optoelectronic materials is vital for their application in high-temperature processes. Allen–Heine–Cardona (AHC) theory is an established theory to determine the impact of electron–phonon coupling on the temperature renormalization of the electronic structure. A question of fundamental interest is how applicable this approach can be in a broad range of materials. Herein, we establish the consensus on the temperature dependence of band gaps between AHC predictions and experimental measurements, in a versatile set of nine MO x metal oxides and six ABO 3 perovskites that are utilized as gas sensors. Both calculations and measurements point to the appreciable band gap downshifts within the temperature range relevant to typical high-temperature applications in all examined materials. The correlation between the band gaps and temperatures was quantified at zero- and finite-temperature limits using the analytical parameters of the O’Donnell model. We found fairly comparative agreements between AHC- and experiment-derived parameters, especially for the parameters (S and $\langle\hbar$ω$\rangle$) defining the temperature dependence behavior of band gaps at the finite-temperature regime. Our findings provide important implications for the first-principles modeling of the electronic structures and their temperature dependency, which should be the focus toward high-temperature optoelectronic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided.

97 MATHEMATICS AND COMPUTING↗

Quantifying Temperature Dependence of Electronic Band Gaps and Optical Properties in SnO 2 and SnO via First-Principles Simulations

Tin metal oxides SnO x ( x = 1, 2) have gained interest as gas-sensing materials. For their applications as high-temperature sensors, however, a better understanding of their temperature dependence sensing responses is needed. Here, we comparatively quantify the temperature-dependent electronic band gaps and optical properties of SnO 2 and SnO using first-principles calculations. Without considering the temperature effect, SnO 2 and SnO are predicted to have direct and indirect band gaps of 2.18 and 1.66 eV, respectively, at the PBE + U -GGA level that we employed. The temperature effect on the electronic and optical properties is captured by taking account of the electron–phonon interaction. Band gap renormalization with temperature is calculated via the Allen–Heine–Cardona theory. For both oxides, we find a monotonic decrease in the electronic band gap such that renormalization at zero point (0 K) is ~–0.17 and ~–0.52 eV at 1000 K. These results are also analyzed by employing an analytical equation that helps characterize the band gap shift with temperature. In addition, the optical properties at finite temperatures are simulated using the frozen-phonon method that combines electron–phonon coupling with the momentum matrix. As temperature increases, the optical property spectra are smoothed because of the smearing effect, which diminishes optical constants at shorter wavelengths. Our results are of interest for high-temperature functional materials in applications of optical detection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗