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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Palo Alto Research Center (PARC) and its partners will explore a targeted molten metal as a catalyst in a methane pyrolysis mist reactor to convert natural gas into hydrogen and solid carbon at a low cost without carbon dioxide emissions. The technology could augment or replace current H 2 production methods, while simultaneously sequestering carbon in high value materials.
Design and performance of a digital signal processing platform for enhancing off-the-shelf voltage and current sensors are presented. The platform was developed for enabling advanced distribution grid applications with existing low-cost sensors. This digital sensor system leverages a variety of off-the-shelf voltage and current sensing technologies, analog-to-digital conversion, and sensor correction algorithms to yield more accurate and reliable digital measurements that support a multitude of applications and use cases. Sample performance and accuracy results are provided, including measurements at power frequency and higher harmonics. The system extends the native performance of the medium voltage sensors used in the system, for example, broadening measurement bandwidth beyond a few kHz, appropriate for measuring switching surges and harmonics on medium voltage distribution systems. The digital sensor system is installed and characterized in the field with a high-bandwidth optical calibration system to determine site-dependent calibration and performance issues.
The entry cost for prototyping a composite component for manufacture using automated, high rate processes is prohibitively expensive in many cases, especially for small business, where tooling costs may be several $100k. Discussions with industry also indicate that many small companies, tier 1 and 2 suppliers, have an interest to mold composite parts but do not want to deal with the capital cost, material handling issues, and labor associated with dry fiber preforming operations. While the molders may locate near the end user for logistics reasons, it may be more cost effective for the performer to remain regional and invest in capital equipment to support preform automation, thus keeping costs to a minimum. This project was designed to explore and demonstrate several options to meet these industry needs. Dry fiber preforming approaches were evaluated which allow for low pressure resin infusion, single sided tooling options such a vacuum assisted resin transfer molding (VARTM) or low pressure resin transfer molding (RTM-light). Unlike sheet molding compound, SMC compression molding where typical molding pressures of 1000 psi are required to push material into the desired location; positioning of a dry fiber preform into the desired location on the tool allows for low molding pressures of 10-50 psi. Lower molding pressures allow for use of low cost, additive fabrication of polymeric tooling. Polymeric tooling is suitable for rapid part prototyping and limited production. Dry fiber preforming approaches evaluated included use of commercial chopped strand mat, robotic chopper gun deposition, and continuous fiber preform augmentation using tailored fiber placement (TFP). Use of chopped strand mat does not require a robotic deposition method, however a cutting table is generally required and there is typically 20-30% scrap generation. While various fiber areal weights are available, the preform is not readily optimized for minimal fiber use or weight savings. In contrast, a robotic chopper gun approach allows for localized deposition where fiber is required to meet structural requirements. The robotic method is highly automated and minimizes fiber scrap, however the capital cost of the equipment and engineering labor for programming can result in higher preform cost compared to chopped strand mat in certain cases depending on preform complexity. Dry fiber preforming using the robotic chopper gun method allows for creation of three dimensional forms. This approach may be ideal for molding in-house, or if the preforms stack together densely to allow for efficient shipping. Applications evaluated for this program considered trade-off between fabrication of a fully 3D preform versus production of a flat preform which is designed to readily drape into the final desired shape. Such a preform design greatly simplifies robotic programming and requires no specialized tooling. The flat preforms are easily stacked and shipped to the final molding location. Flat preforms are much easier to augment with TFP continuous fiber to provide local reinforcement. The demonstration and evaluation of these preforming and tooling methods were completed on three component applications. The first application was a battery box cover for an electric vehicle which was highly three dimensional. The second demonstrator article was comprised of complex contours and was used to demonstrate the use of TFP and RTM-light molding process. The third demonstration article was the roof of an operator’s cab for large construction equipment. The roof is relatively flat however it is comprised of complex changes in thickness which clearly demonstrate the advantage of robotic chopper gun approach as compared to using numerous preform layers of chopped strand mat. The cost trades for the various preforming methods are summarized to help guide the reader as to preforming method considerations. Finally, these demonstrations all used glass fiber roving. A fourth, exploratory task was added to evaluate the ability to make preforms using Zoltek’s carbon fiber split tow roving. We were able to adapt the chopper gun to make flat preforms for laminate testing, but further development effort would be required to make suitable preforms.
This proposal centers in catalysts for the generation of hydrogen as a renewable energy source and other value- added carbon utilization. The goal of this project is to commercialize an LLNL-developed, low-cost 3D printable graphene oxide/MoS 2 ink tailored for DIW fabrication of a 3D catalyst electrode for hydrogen evolution. LLNL and MS will scale up and field test the catalyst inks for hydrogen evolution, benchmark them against non-3D printed electrodes, and adjust the compositions based on feedback to improve the technology's market potential and customer/user demands.
Quantifying methane emissions is essential for meeting near-term climate goals and is typically carried out using methane concentrations measured downwind of the source. One major source of methane that is important to observe and promptly remediate is fugitive emissions from oil and gas production sites but installing methane sensors at the thousands of sites within a production basin is expensive. In recent years, relatively inexpensive metal oxide sensors have been used to measure methane concentrations at production sites. Current methods used to calibrate metal oxide sensors have been shown to have significant shortcomings, resulting in limited confidence in methane concentrations generated by these sensors. To address this, we investigate using machine learning (ML) to generate a model that converts metal oxide sensor output to methane mixing ratios. To generate test data, two metal oxide sensors, TGS2600 and TGS2611, were collocated with a trace methane analyzer downwind of controlled methane releases. Over the duration of the measurements, the trace gas analyzer’s average methane mixing ratio was 2.40 ppm with a maximum of 147.6 ppm. The average calculated methane mixing ratios for the TGS2600 and TGS2611 using the ML algorithm were 2.42 ppm and 2.40 ppm, with maximum values of 117.5 ppm and 106.3 ppm, respectively. A comparison of histograms generated using the analyzer and metal oxide sensors mixing ratios shows overlap coefficients of 0.95 and 0.94 for the TGS2600 and TGS2611, respectively. Overall, our results showed there was a good agreement between the ML-derived metal oxide sensors’ mixing ratios and those generated using the more accurate trace gas analyzer. This suggests that the response of lower-cost sensors calibrated using ML could be used to generate mixing ratios with precision and accuracy comparable to higher priced trace methane analyzers. This would improve confidence in low-cost sensors’ response, reduce the cost of sensor deployment, and allow for timely and accurate tracking of methane emissions.
This report describes the development of two standard test methods: one for evaluating PM2.5 sensors; and the other for evaluating CO 2 Sensors. The standards were developed to provide a pathway for the use of reliable, low-cost indoor air quality sensors as occupant feedback, ventilation control, and overall indoor air quality improvement. This paper describes the development process and technical issues involved.
This manuscript presents the most recent results and findings to identify bond wire lift-offs and surface defects in high-power isolated gate bipolar junction transistor (IGBT) modules. The authors of this manuscript formerly proposed a low-cost, piezoelectric resonator-based measurement unit to detect bond wire related degradation in larger IGBT modules. Since high-power IGBT modules are expensive, it was apparent that evaluating our proposed method by inducing controlled damage to fresh (new) IGBTs may not be cost-effective especially when multiple sets of data need to be captured by inducing damage to different levels. In order to overcome this limitation, IGBT bond wires have been mimicked using a 3D printed enclosure, a PCB, and copper wires with dimensions very closely resembling a real IGBT. Using this method, multiple test devices can be built at the cost of a real IGBT, and the proposed technique could be fine-tuned without damaging expensive, real IGBTs. Our recent findings can be used to determine real IGBT degradation and bond wire lift-offs using only two sensors, as opposed to six transducers used in the first iteration of the setup. In addition to optimizing the sensor count, we have also identified the best possible locations of these sensors by attempting multiple placements inside the IGBT casing.
This paper investigates a grid-supportive load concept for small-scale residential appliances, focusing on a residential refrigerator. Power consumption is adjusted based on grid conditions to achieve IEEE-1547 grid support functions. Two key aspects are presented: a low-cost refrigerator converter with Lyapunov energy function-based local controllers for speed control, and the impact on a standard microgrid system, demonstrating advanced grid support from the load side. This method enhances grid resilience and reliability and can be extended to other residential loads. The study contributes to efficient and robust grid-supportive load management systems, showing promising performance. This approach has the potential to improve overall grid stability and can be adapted for various types of residential appliances. The modeling and simulations in MATLAB/Simulink and PLECS confirm the feasibility and effectiveness of the proposed solution. Future work will explore real-world implementation and scalability of this concept for broader applications.
Recently, a random ternary copolymerization strategy has become a promising and efficient approach to develop high-performance polymer donors for polymer solar cells (PSCs). Here, in this study, a low-cost electron-withdrawing unit, 2,5-bis(4-(2-ethylhexyl)thiophen-2-yl)pyrazine (PZ-T), is incorporated into the polymer backbone of PM6 as the third component, and three D-A 1 -D-A 2 type terpolymers PMZ-10, PMZ-20, and PMZ-30 are synthesized by the random copolymerization strategy, with the PZ-T proportion of 10%, 20%, and 30%, respectively. The terpolymers exhibit downshifted highest occupied molecular orbital energy levels than PM6, which is beneficial for obtaining higher open-circuit voltage (V oc ) of the PSCs with the polymer as a donor. Importantly, the PSCs based on PMZ-10:Y6 demonstrate efficient exciton dissociation, higher and balanced electron/hole mobilities, desirable aggregation, and high power conversion efficiency of 18.23%, which is the highest efficiency among the terpolymer-based PSCs so far. The results indicate that the ternary copolymerization strategy with PZ-T as the second A-unit is an efficient approach to further improve the photovoltaic performance and reduce the synthetic cost of the D-A copolymer donors.
Current high-strength carbon fibers (CFs) are mostly derived from PAN polymer precursor, which are very expensive with limited usage in the high-end products. On the other hand, it has also stimulated research interest to develop new low-cost precursors and manufacturing process. Despite numerous studies, researchers have yet to develop a new polymeric precursor that is less expensive, melt-spinnable, and offers a high C-yield. In this three-year R&D project, we proposed three consecutive research phases, including (i) the development of new polyolefin-based precursors that can be thermally transformed to C material in one heating step under inert atmosphere and achieved a C-yield >80%, which is more than 60% higher than that of current PAN precursor, (ii) With the suitable new polyolefin-based precursor, we can focus on CF manufacturing process, including fiber spinning, stabilization, and carbonization, to form high quality carbon fibers with the desirable polymorphous morphology, and (iii) the investigation of thermal conversion process under tension for converting polyolefin-based precursor fibers to high tensile strength carbon fibers with specific graphite crystallite size, orientation of basal line, order-disorder ratio, fiber diameter, and reduced structural defect (voids). composites.
Recent record efficiency Si cells have had an interdigitated back contact (IBC) design where both positive and negative contacts are fabricated on the back side of the Si wafer. Many industry groups have investigated IBC cells but used photolithography to pattern the rear contacts although it is considered impractical for low cost, high volume manufacturing. Alternative patterning methods using lasers and mechanical masking have been utilized in fabrication of patterned regions in IBC Si solar cells to replace photolithography. Another exciting advance in Si solar cell technology has been replacing the high temperature diffusion of doped regions inside the crystalline Si wafer with low temperature deposition of very thin layers of doped amorphous hydrogenated Si (a-Si:H) on the Si wafer. This is called a Si heterojunction (HJ) device. The a-Si:H provides excellent passivation of surface defects and has produced the highest open circuit voltages (VOC) of any Si solar cell device structure. The Institute of Energy Conversion (IEC) demonstrated the first IBC-HJ solar cell combining these two strategies in 2007 and has fabricated IBC-HJ cells with 20% efficiency using three photolithography steps. Despite the demonstrated efficiency potential of this device by industry groups (>26%) there is no commercial production because of the challenges of patterning and processing the structure in an industrial environment. Our work sought to address that challenge. The objective of this 3 year project was to develop the processing for the IBC-HJ Si solar cell using lasers for patterning and contact formation instead of photolithography. Laser patterning enables rapid, contactless manufacturing of patterned regions. We intensively studied laser fired contacts, laser patterning of the a-Si multi-layer stacks and metal layers and the use of plasma shadow masks. After an exhaustive focus in the first year on the laser fired emitter (LFE) and contact (LFC), we were unable to achieve VOC greater than 660 mV, compared to the 720 mV required to meet our milestones. We discovered that an additional issue with our original IBC structure was the presence of an inversion layer at the back surface connecting the p and n regions which reduced the VOC and fill factor (FF). We developed innovative methods for characterizing the inversion layer. These two limitations in the original design lead to development of a new IBC-HJ process sequence and device structure which we called Plasma Masked Laser Processed (PMLP). It retained the original high efficiency features including manufacturability. The LFC was replaced with a standard n-type a-Si HJ contact. The inversion layer was eliminated by replacing the previous p-type stack in the gap with an n-type or SiN stack. The PMLP used laser ablation of a multi-layer a-Si stack followed by chemical etching to open the n-contact. It required deposition of a patterned stack through a mask in the plasma deposition chamber. This turned out to be a source of significant problems due to inevitable unwanted deposition ‘leakage’ under the mask. This formed a blocking contact on the emitter which significantly reduced Voc and FF. Several iterations in PMLP device structure and chemical etching steps resulted in a large improvements, e.g. the efficiency increasing from 3 to 15% and Voc from 450 to 660 mV but they were unable to completely eliminate it. The best IBC-HJ device we fabricated had only 15% efficiency. For perspective, our standard FHJ devices had 20% efficiency.
A capacitive relative humidity sensor includes a first electrode and a second electrode, where at least the one of the electrodes is a water-permeable electrode. The electrodes act in conjunction with a water-sensitive hygroscopic dielectric material. The design enables low-cost and simple fabrication of fast-responding and stable relative humidity sensors using digital fabrication techniques.
Qubit readout is a critical operation in quantum computing systems, which maps the analog response of qubits into discrete classical states. Deep neural networks (DNNs) have recently emerged as a promising solution to improve readout accuracy . Prior hardware implementations of DNN-based readout are resource-intensive and suffer from high inference latency, limiting their practical use in low-latency decoding and quantum error correction (QEC) loops. This paper proposes LUNA, a fast and efficient superconducting qubit readout accelerator that combines low-cost integrator-based preprocessing with Look-Up Table (LUT) based neural networks for classification. The architecture uses simple integrators for dimensionality reduction with minimal hardware overhead, and employs LogicNets (DNNs synthesized into LUT logic) to drastically reduce resource usage while enabling ultra-low-latency inference. We integrate this with a differential evolution based exploration and optimization framework to identify high-quality design points. Our results show up to a 10.95x reduction in area and 30% lower latency with little to no loss in fidelity compared to the state-of-the-art. LUNA enables scalable, low-footprint, and high-speed qubit readout, supporting the development of larger and more reliable quantum computing systems.
K-12 school buildings account for the highest energy consumption within the public sector. Implementing advanced HVAC controls in grid-interactive K-12 schools could bring substantial economic advantages and grid flexibility. Our previous study demonstrated that a low-cost model predictive control (MPC) solution, which coordinates multiple packaged units, can enable demand flexibility without major hardware upgrades. However, a significant gap remains between academic pilots and market-ready scalable solutions. This paper extends the previous single-site pilot to a multi-site demonstration involving three school campuses (95 total units) through a commercial technology transfer process. Addressing the challenge of verifying performance with sparse field data, we present a new statistical approach using Bayesian methods to estimate the MPC’s effect on peak demand. Unlike traditional methods, this approach robustly quantifies uncertainty in non-normal, limited datasets. The results confirm the solution’s replicability, achieving a 21.6–38.9% reduction in HVAC peak demand (10.8–22.1% at the site-level) with > 98% probability across diverse locations. Finally, we document critical barriers to scaling software-as-a-service (SaaS) solutions–such as API instability and diverse legacy systems–and offer practical strategies to accelerate the commercial adoption of grid-interactive efficient buildings.
This project will develop a process to combine AM via direct ink writing (DIW) technology for CFC printing and to use design optimization tools pioneered at LLNL, with advances in resin/composite formulation enabled by chemical and nano-material modification to produce lightweight low-cost CNG tanks. Our approach will yield sub-scale prototype composite pressure tanks equivalent to Type-5 CNG vessel designs that demonstrate a potential cost-benefit advantage. Central to our vision is using agile AM and design based on computationally informed DIW of both short and continuous CF, further coupled with high-performance thermoset polymer matrixes modified by emergent nanomaterials. Our single-stage, multi-material AM technology, combined with a decreased volume fraction of CF and an increased proportion of economically advantaged short fiber, all together drive the reduction in manufacturing time and overall cost. Importantly, reductions in continuous fiber and overall fiber volume fraction will be achieved without detriment to the mechanical strength of the composite vessel. This will be achieved by employing a single process using multi materials grading involving a thermoset resin “ink” modified with aligned nanoplatelets to leverage the efficient tortuous-path gas barrier effect, printed as an inner flexible gas barrier as the initial stage in our manufacturing process before compositionally grading the AM feedstock in real-time to transition to a rigid, structural CF-filled resin. The proposed hybrid construction is projected to achieve pressure ratings at a service range of 2,900–3,600 psi with a 3× burst safety factor comparable to conventional filament-wound composite tanks with an estimated 30–50% reduction in total manufacturing cost.
Aluminum is the most earth-abundant metal, trivalent, and inert in ambient air; it also has a density approximately five times that of lithium at room temperature, making it attractive for cost-effective, long-duration storage in batteries. Here, we investigate structural requirements and physicochemical and transport properties of ionic liquid (IL) electrolytes thought to enable high reversibility of Al battery anodes. We find that intentionally designed, low-cost IL analogs, including ammonium-based molten salts, offer comparable Al anode reversibility to state-of-the-art imidazolium-based IL melts. A critical ratio of solvated Al-ion species is required to balance the effects of Lewis acidity needed to continuously etch native Al 2 O 3 and form a stable solid electrolyte interphase on Al. Our findings open new opportunities for developing simple, cost-effective, room-temperature Al batteries that enable long-duration electrical energy storage.
Air leakage through the building envelope in the U.S. accounts for about four quads of energy annually, costing approximately $40 billion per year. However, a high-fidelity and non-intrusive method to detect air leakage has not been demonstrated to date. Here, in this paper, we propose a novel non-intrusive and low-cost method called Transient Infrared (IR) Imaging (TIRI) that can rapidly and accurately identify air leakage locations and relative rates on building envelopes. When the interior and exterior temperatures are different, and a small internal pressure pulse is created by HVAC, the temperature at locations with air leakages will change rapidly, while the areas without a leakage do not change. Based on a heat transfer model, we have derived the temperature change as a function of time after the HVAC is turned on. By tracking the temperature change, which depends on leakage rate and size, we have obtained the air leakage map in the case studies. Using an exterior door as an example, we took transient IR images in different seasons and different times of the day, and successfully obtained the leakage map in all the scenarios. Successfully obtained the air leakage map even when the indoor-outdoor air temperature difference is as small as 2 °C. We have also realized a detection speed of 10s and demonstrated that this method also worked for windows, which have mirror-like IR reflections. Our TIRI method will accelerate the improvement of airtightness in buildings, save building energy, and help reduce greenhouse gas emissions.
Reflection fiber temperature sensors functionalized with plasmonic nanocomposite material using intensity-based modulation are demonstrated for the first time. Characteristic temperature optical response of the reflective fiber sensor is experimentally tested using Au-incorporated nanocomposite thin films deposited on the fiber tip, and theoretically validated using a thin-film-optic-based optical waveguide model. By optimizing the Au concentration in a dielectric matrix, Au nanoparticles (NP) exhibit a localized surface plasmon resonance (LSPR) absorption band in a visible wavelength that shows a temperature sensitivity ~0.025%/°C as a result of electron–electron and electron–phonon scattering of Au NP and the surrounding matrix. Detailed optical material properties of the on-fiber sensor film are characterized using scanning electron microscopy (SEM) and focused-ion beam (FIB)-assisted transmission electron microscopy (TEM). Airy’s expression of transmission and reflection using complex optical constants of layered media is used to model the reflective optical waveguide. A low-cost wireless interrogator based on a photodiode transimpedance-amplifier (TIA) circuit with a low-pass filter is designed to integrate with the sensor. The converted analog voltage is wirelessly transmitted via 2.4 GHz Serial Peripheral Interface (SPI) protocols. Feasibility is demonstrated for portable, remotely interrogated next-generation fiber optic temperature sensors with future capability for monitoring additional parameters of interest.