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A correlation factor applicable for converting inviscid aerodynamic normal-force coefficients of simple shapes in helium to equivalent coefficients in air is found by using calculations based on the shock-expansion method at Mach numbers of 12, 16, and 20.
The objective of this paper is to improve the power conversion efficiency of HIT solar cell using amorphous materials. A high efficiency amorphous material based on two dimensional heterojunction solar cell with thin intrinsic layer is designed and simulated at the research level using Synopsys/RSOFT-Solar Cell utility. The HIT structure composed of TCO/a-Si:H(p)/a-Si:H(i)/c-Si(n)/a-Si:H(i)/a-Si:H(n{sup +})/Ag is created by using of RSOFT CAD. Optical characterization of the cell is performed by Diffract MOD model based on RCWA (rigorous coupled wave analysis) algorithm. Electrical characterization of the cell is done by Solar cell utility using based on Ideal diode method. In addition, optimization of the different layer thickness in the HIT structure is executed to improve the absorption and thereby the photocurrent density. The proposed HIT solar cell structure resulted in an open circuit voltage of 0.751 V, a short circuit current density of 36.37 mA/cm{sup 2} and fill factor of 85.37% contributing to the total power conversion efficiency of 25.91% under AM1.5G. Simulation results showed that the power conversion efficiency is improved by 1.21% as compared to the reference HIT solar cell. This improvement in high efficiency is due to reduction of resistive losses, recombination losses at the hetero junction interface between intrinsic a-Si and c-Si, and optimization of the thicknesses in a-Si and c-Si layers.
Sugarcane is being enhanced as a bioenergy crop by engineering it to accumulate and store lipids along with polymeric sugars in vegetative tissues. However, there is no existing process that allows for processing this new crop to recover both lipid and cellulosic sugars from the oilcane bagasse. Therefore, a comprehensive investigation of two pretreatment methods—natural deep eutectic solvents (NADES) and chemical-free hydrothermal pretreatment (HT) was conducted to judge their suitability for recovering fermentable sugars, lipids, and lignin from bagasse. Two NADES, i.e., choline chloride: lactic acid (ChCl:LA) and betaine: lactic acid (BT:LA) were prepared using a 1:2 M ratio and were evaluated for pretreatment of oilcane bagasse at 10, 20, and 50 % (w/w) solids, followed by enzymatic hydrolysis at 10 % (w/w) solids. Notably, ChCl:LA NADES treatment at 10 % (w/w) solids at 140 °C for 2 h, solubilized 78.8 % of lignin and 80.4 % of hemicellulose and allowed 82.7 % enzymatic conversion of glucans to glucose. In contrast, HT pretreatment removed approximately 87.6 % of the hemicellulose and provided an enzymatic glucose yield of 69.7 %. Furthermore, ChCl:LA operated at 50 % solids loading the enriched lipids 2.6-fold (9.2 wt%) in recovered solids compared to HT (6.4 %) and BT:LA (5.1 %) pretreatment processes. NMR-HSQC and GPC analysis showed that ChCl:LA also cleaved the most lignin β–O–4 linkages and demonstrated lower molecular weight compared to HT. This study demonstrates that NADES pretreatment is an effective green processing method for recovering lipids, sugars, and lignin from bioenergy crops at high solid loading (50 % w/w) within the context of an integrated biorefinery.
Hydrogen energy-based electrochemical energy conversion technologies offer the promise of enabling a transition of the global energy landscape from fossil fuels to renewable energy. Here, we present a comprehensive review of the fundamentals of electrocatalysis in alkaline media and applications in alkaline-based energy technologies, particularly alkaline fuel cells and water electrolyzers. Anion exchange (alkaline) membrane fuel cells (AEMFCs) enable the use of nonprecious electrocatalysts for the sluggish oxygen reduction reaction (ORR), relative to proton exchange membrane fuel cells (PEMFCs), which require Pt-based electrocatalysts. However, the hydrogen oxidation reaction (HOR) kinetics is significantly slower in alkaline media than in acidic media. Understanding these phenomena requires applying theoretical and experimental methods to unravel molecular-level thermodynamics and kinetics of hydrogen and oxygen electrocatalysis and, particularly, the proton-coupled electron transfer (PCET) process that takes place in a proton-deficient alkaline media. Extensive electrochemical and spectroscopic studies, on single-crystal Pt and metal oxides, have contributed to the development of activity descriptors, as well as the identification of the nature of active sites, and the rate-determining steps of the HOR and ORR. Among these, the structure and reactivity of interfacial water serve as key potential and pH-dependent kinetic factors that are helping elucidate the origins of the HOR and ORR activity differences in acids and bases. Additionally, deliberately modulating and controlling catalyst-support interactions have provided valuable insights for enhancing catalyst accessibility and durability during operation. The design and synthesis of highly conductive and durable alkaline membranes/ionomers have enabled AEMFCs to reach initial performance metrics equal to or higher than those of PEMFCs. We emphasize the importance of using membrane electrode assemblies (MEAs) to integrate the often separately pursued/optimized electrocatalyst/support and membranes/ionomer components. Operando/in situ methods, at multiscales, and ab initio simulations provide a mechanistic understanding of electron, ion, and mass transport at catalyst/ionomer/membrane interfaces and the necessary guidance to achieve fuel cell operation in air over thousands of hours. We hope that this Review will serve as a roadmap for advancing the scientific understanding of the fundamental factors governing electrochemical energy conversion in alkaline media with the ultimate goal of achieving ultralow Pt or precious-metal-free high-performance and durable alkaline fuel cells and related technologies.
The sustainability of the biofuel industry depends on the development of a mature conversion technology on a national level that can take advantage of the economies of scale: the nth-plant. Defining the future location and supply logistics of conversion plants is imperative to ultimately transform the nation’s renewable biomass resources into cost-competitive, high-performance feedstock for production of biofuels and bioproducts. Since the US has put restrictions on production levels of conventional biofuels from edible resources, the nation needs to plan for the widespread accessibility and development of the cellulosic biofuel scenario. Conventional feedstock supply systems will be unable to handle cellulosic biomass nationwide, making it essential to expand the industry with an advanced feedstock supply system incorporating a distributed network of preprocessing depots and conversion plants, or biorefineries. Current studies are mostly limited to designing supply systems for specific regions of the country. We developed a national database with potential locations for depots and biorefineries to meet the nation’s target demand of cellulosic biofuel. Blended feedstock with switchgrass and corn stover (harvested by either a two- or three-pass method) are considered in a Mixed Integer Linear Programming model to deliver on-spec biomass that considers both, a desired quantity and quality at the biorefinery. A total delivered feedstock cost that is less than $79.07/dt (2016$) is evaluated for years 2022, 2030, and 2040. In 2022, 124 depots and 59 biorefineries could be supplied with 42.8 million dt of corn stover and switchgrass. In 2030 and 2040, the total accessible biomass could increase to 215% and 393% respectively when compared to 2022. However, an $8/dry tons reduction in targeted delivery cost could reduce total accessible biomass by 67%. Kansas, Nebraska, South Dakota and Texas were identified as potential states with a strong biofuel economy given that they had six or more biorefineries located in all scenarios. In some scenarios, Colorado, Alabama, Georgia, Minnesota, Mississippi and South Carolina would greatly benefit from a depot network as these could only deliver to a biorefinery in a nearby state. To elaborate the impact of a nationwide consideration, the findings were compared with existing literature for different US regions. We also present results for biorefinery capacities that are double, triple, and quadruple in size.
Ammonia (NH3) has garnered significant interest as an alternative fuel for meeting international emissions reduction mandates in sectors with high weight and distance requirements, such as shipping. Technical barriers and unanswered questions remain on the combustion strategies that can maximize ammonia utilization and minimize emissions. Prior research studies at the US Department of Energy’s Oak Ridge National Laboratory have shown strong performance with NH3 under dual-fuel mode using conventional diesel combustion (CDC) manifold air pressure settings. Diesel airflow was initially used to simplify retrofitting (no turbocharger modification), which resulted in air-fuel equivalence ratios (λ) greater than 1.5. To characterize potential improvements in dual-fuel NH3 combustion performance at richer in-cylinder conditions, a global λ sweep compared the use of early (E-pilot) and late (L-pilot) single diesel injections. The experiments were conducted at 1200 RPM and 12.8 ± 0.2 bar (75 % load), and λ was varied by decreasing the commanded air flow to the engine at greater than 90 % ammonia energy substitution level. A diesel injection timing sweep was conducted for both the injection strategies at fixed λ, and the timing with the lowest engine-out N2O emissions was identified. The results indicated an optimal balance between CO2,eq and thermal efficiency benefits both E-pilot and L-pilot injection strategy cases compared with CDC at a λ of 1.4. The indicated nitrogen-based emissions exhibited a strong correlation to the ratio of CA5–50 and ignition delay for L-pilot, but no apparent trend emerged for the E-pilot injection strategy at the tested boundary conditions. This dataset includes the raw experimental data and documentation of the experiment conditions and methods.
The overall objective of this project was to develop multiscale models for understanding and eventually designing complex processes for renewables. To the best of our knowledge, our work is the first attempt at modeling complex reacting systems, whose performance relies on underlying multiscale mathematics and developing rigorous mathematical techniques and computational algorithms to study such models. Our specific application lies at the heart of biofuels initiatives of DOE and entails modeling of catalytic systems, to enable economic, environmentally benign, and efficient conversion of biomass into either hydrogen or valuable chemicals.
The present invention relates to a protonic ceramic fuel cell and a method of making the same. More specifically, the method relates to a cost-effective route which utilizes a single moderate-temperature (less than or equal to about 1400° C.) sintering step to achieve the sandwich structure of a PCFC single cell (dense electrolyte, porous anode, and porous cathode bone). The PCFC layers are stably connected together by the intergrowth of proton conducting ceramic phases. The resulted PCFC single cell exhibits excellent performance (about 450 mW/cm2 at about 500° C.) and stability (greater than about 50 days) at intermediate temperatures (less than or equal to about 600° C.). The present invention also relates to a two step method for forming a PCFC, and the resulting PCFC.
Methods for producing non-platinum group metal electrocatalysts effective for the reduction of oxygen in fuel cells and other electrochemical reactions and electrocatalysts as may be produced by these methods are described. The electrocatalysts can be formed according to low to medium temperature formation methods and may be particularly attractive for use in low-temperature fuel cells.
A system and method for decoding an unknown automotive controller area network (“CAN”) message definitions. CAN data vehicle signal mappings are typically held in secret and varied by automotive model and year. Without knowledge of the mappings, the wealth of real-time vehicle data hidden in the automotive CAN packets is uninterpretable—impeding research, after-market tuning, efficiency and performance monitoring, fault diagnosis, and privacy-related technologies. This technology can ascertain the CAN signals' boundaries (start bit and length), endianness (byte ordering), signedness (binary-to-integer encoding) from raw CAN data. This allows conversion of CAN data to time series. Interpreting the translated CAN data's physical meaning and finding a linear mapping to standard units (e.g., knowing the signal is speed and scaling values to represent units of miles per hour) can be achieved for many signals by leveraging diagnostic standards to obtain real-time measurements of in-vehicle systems. The system and method can be integrated into lightweight hardware enabling an OBD-II plugin for real-time in-vehicle CAN decoding or run on standard computers. The system can output a standard DBC file with the signal definition information.
We are grateful for the Department of Energy for support of this research related to the improvement of power conversion efficiency of organic photovoltaic architectures over the years. As we noted at the beginning and now, the fundamental photophysical pathways that connect the absorption of light to the production of charge carriers remain elusive in spite of continuing experimental and theoretical efforts. Our investigations were directed toward gaining information about the mechanism of this ultrafast, long-range charge separation in photovoltaic systems with non-fullerene acceptors. This problem is still of high importance for the organic photovoltaics because solving this problem will allow us to minimize the contribution of the geminate recombination and trapping the charges in bound charge transfer states. Extremely low charge separation driving energy specific to non-fullerene acceptor systems reduces the energy loss in organic photovoltaic cells making them competitive with inorganic and perovskite photovoltaic systems. A major accomplishment of this study was to detail the long-range ballistic charge separation in donor-non-fullerene acceptor photovoltaic systems. We have successfully probed the spatial structure of ultrafast long-range (~40nm) ballistic charge separation in donor-non-fullerene acceptor photovoltaic system. We performed a systematic investigation of the delocalized states in their connection to the efficient long-range charge separation using the pulse pair interferometric near-field microscopy method. This method possesses unique capability of detecting coherent effects with spatial resolution of few tens of nanometers. These measurements allowed us the determination of delocalized excitations locally in a thin film photovoltaic system and to be connected to the long-range charge separation and the power conversion efficiency. We also investigated long-lived coupled triplet pairs in a singlet fission process for the possibility of concerted extraction of two electrons from triplet entangled states. New experimental and theoretical approaches of the concerted double electron extraction were carried out. The impact of this work is the development of important structure-property relationships to the design criteria of more efficient organic photovoltaic cells in accordance with the BES mission.
Diverse lignin samples have been subjected to a catalytic aerobic oxidation process, followed by formic-acid-induced hydrolytic depolymerization. The yield of monomeric aromatic compounds varies depending on the lignin plant source and pretreatment method. The best results are obtained from poplar lignin isolated via a acidolysis pretreatment method, which gives 42 wt% yield of low-molecular-weight aromatics. Use of other pretreatment methods and/or use of maple and maize lignins afford yields of aromatics ranging from 3 to 31 wt%. These results thus establish useful references for the development of improved oxidation/depolymerization protocols.
Perovskite solar cells (PSCs) have becoma a leading low‐cost photovoltaic technology, achieving power conversion efficiencies (PCEs) of up to 26.1%. However, their commercialization is hindered by stability issues and the need for controlled processing environments. Carbon‐electrode‐based PSCs (C‐PSCs) offer enhanced stability and cost‐effectiveness compared to traditional metal‐electrode PSCs, i.e., Au and Ag. However, processing challenges persist, particularly in air conditions where moisture sensitivity poses a significant hurdle. Herein, a novel air processing technique is presented for planar C‐PSCs that incorporates antisolvent vapors, such as chlorobenzene, into a controlled air‐quenching process. This method effectively mitigates moisture‐induced instability, resulting in champion PCEs exceeding 20% and robust stability under ambient conditions. The approach retains 80% of initial efficiency after 30 h of operation at maximum power point without encapsulation. This antisolvent‐mediated air‐quenching technique represents a significant advancement in the scalable production of C‐PSCs, paving the way for future large‐scale deployment.
Purpose: To further reduce CT dose, a practical sparse-view acquisition scheme is proposed to provide the same attenuation estimation as higher dose for PET imaging in the extended scan field-of-view. Methods: CT scans are often used for PET attenuation correction and can be acquired at very low CT radiation dose. Low dose techniques often employ low tube voltage/current accompanied with a smooth filter before backprojection to reduce CT image noise. These techniques can introduce bias in the conversion from HU to attenuation values, especially in the extended CT scan field-of-view (FOV). In this work, we propose an ultra-low dose CT technique for PET attenuation correction based on sparse-view acquisition. That is, instead of an acquisition of full amount of views, only a fraction of views are acquired. We tested this technique on a 64-slice GE CT scanner using multiple phantoms. CT scan FOV truncation completion was performed based on the published water-cylinder extrapolation algorithm. A number of continuous views per rotation: 984 (full), 246, 123, 82 and 62 have been tested, corresponding to a CT dose reduction of none, 4x, 8x, 12x and 16x. We also simulated sparse-view acquisition by skipping views from the fully-acquired view data. Results: FBP reconstruction with Q. AC filter on reduced views in the full extended scan field-of-view possesses similar image quality to the reconstruction on acquired full view data. The results showed a further potential for dose reduction compared to the full acquisition, without sacrificing any significant attenuation support to the PET. Conclusion: With the proposed sparse-view method, one can potential achieve at least 2x more CT dose reduction compared to the current Ultra-Low Dose (ULD) PET/CT protocol. A pre-scan based dose modulation scheme can be combined with the above sparse-view approaches, which can even further reduce the CT scan dose during a PET/CT exam.
Solid oxide fuel cells (SOFCs) represent next-generation energy sources with high energy conversion efficiencies, low pollutant emissions, good flexibility with a wide variety of fuels, and excellent modularity suitable for distributed power generation. As an electrochemical energy conversion device, the SOFC’s performance and reliability depend sensitively on the catalytic activity and stability of electrode materials. To date, however, the development of electrode materials and microstructures is still based largely on trial-and-error methods because of the inadequate understanding of electrode process mechanisms. Therefore, the identification of key descriptors/properties for electrode materials or functional heterogeneous interfaces, especially under in situ/operando conditions, may provide guidance for the design of optimal electrode materials and microstructures. Here, Raman spectroscopy is ideally suited for the probing and mapping of chemical species present on electrode surfaces under operating conditions. And to boost the sensitivity toward electrode surface species, the surface-enhanced Raman spectroscopy (SERS) technique can be employed, in which thermally robust SERS probes (e.g., Ag@SiO 2 core–shell nanoparticles) are designed to make in situ/operando analysis possible. Furthermore, this review summarizes recent progresses in the investigation of SOFC electrode materials through Raman spectroscopic techniques, including topics of early stage carbon deposition (coking), coking-resistant anode modification, sulfur poisoning, and cathode degradation. In addition, future perspectives for utilizing the in situ/operando SERS for investigations of other electrochemical surfaces and interfaces are also discussed.
Biomass such as agricultural residues, energy crops and yard waste has significant potential to be used as renewable feedstocks for production of fuels, chemicals and energy. However, in a given location, biomass availability, cost and quality can vary markedly. Strategies to manage these traits must be identified and implemented so that consistent low-cost and high-quality feedstocks can be delivered to biorefineries year round. In this study, we examine air classification as a method to mitigate high ash concentrations in corn stover, switchgrass, and grass clippings. Formulation techniques were then used to produce blends that met ash quality and biomass quantity specifications at the lowest possible cost for biopower and biochemical conversion applications. It was found that air classification can separate the biomass into light fractions which contain concentrated amounts of elemental ash components introduced through soil contamination such as sodium, alumina, silica, iron and titania; and heavy fractions that are depleted in these components and have relatively lower total ash content. Light fractions of corn stover and grass clippings were found to be suitable for combustion applications since they had less propensity to slag than the whole biomass material. The remaining heavy fractions of corn stover or grass clippings could then be blended with switchgrass to produce blends that met the 5% total ash specifications suggested for biochemical conversions. However, ternary blends of the three feedstocks were not possible due to the high ash content of grass clippings. Lastly, it was determined that air classification by itself was not suitable to prepare these feedstocks for pyrolysis due to high ash content.
A pretreatment protocol is presented that allows for the quantitative conversion and subsequent in situ spectroscopic analysis of [Re(CO)3]+ species in simulated Hanford tank waste. The protocol encompasses adding a simulated waste sample containing the non-emissive [Re(CO)3]+ species to a developer solution that enables the rapid, quantitative conversion of the non-emissive species to a luminescent species which can then be detected spectroscopically. The [Re(CO)3]+ species concentration in an alkaline, simulated Hanford tank waste supernatant can be quantified by the standard addition method. In a test case, the [Re(CO)3]+ species was measured to be at a concentration of 38.9 µM, which was a difference of 2.01% from the actual concentration of 39.7 µM.
High-temperature molten salt-cooled reactors (MSCRs) are a promising next-generation nuclear technology option, offering efficient power conversion and inherent safety features. However, the reliability of these systems depends on the robust operation of heat exchangers (HXs), which are susceptible to failure due to temperature gradients and channel plugging caused by fluid freezing. Conventional monitoring methods, relying on inlet and outlet measurements, lack the spatial resolution needed to detect early-stage faults. We propose a novel design of a compact salt-to-salt matrix-type HX design consisting of interleaved arrays of parallel tubes, with integrated synthetic fiber optic distributed temperature sensing (DTS) to enable localized detection of incipient faults. To evaluate performance of this design, we generate high-fidelity synthetic data using heat transfer computational modeling to simulate channel plugging, and introduce sensor noise for realistic modeling of measurements. The dataset comprises of 97% normal operation and 3% anomaly cases, with each anomaly class representing 1% of the data. These early anomalies result in overlapping temperature profiles between normal and faulty channels, producing a non-separable dataset that challenges traditional classification techniques. We benchmark eight supervised machine learning (ML) models and demonstrate that XGBoost achieves the highest performance. To improve transparency, we develop an explainability framework combining Shapley values and partially ordered sets (POSETs) to quantify and structurally analyze feature importance. This approach identifies both dominant predictors and ambiguous feature relationships, enhancing trust and interpretability. Our results highlight the potential of combining DTS and explainable ML with intelligent feature selection to improve predictive maintenance and ensure operational resilience in advanced nuclear systems.