SEARCH · Engineering Papers

Results for “Stack Design”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 667 records · Page 37

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Lateral Confinement in 2D Nanoplatelets: A Strategy to Expand the Colloidal Quantum Engineering Toolbox

Among colloidal nanocrystals, 2D nanoplatelets offer a unique set of properties with exceptionally narrow luminescence and low lasing thresholds. Furthermore, their anisotropic shape expands the playground for the complex design of heterostructures where spectra but also scattering rates can be engineered. A challenge that still remains is to combine shell growth which makes NPLs stable, with spectral tunability. Indeed, most reported shelled nanoplatelets end up being red emitters due to a loss of quantum confinement. Here, the combination of both lateral and in-plane confinements within a single heterostructure is explored. A CdS/CdSe/CdS/CdZnS core–crown–crown shell structure that enables yellow emission is grown and that is responsive to a large range of excitation including visible photons, X-ray photons, electron beams, and electrical excitations. k.p simulations predict that emission tunability of up to several 100 s of meV can be obtained in ideal structures. This material also displays stimulated emission resulting from bi-exciton emission with a low threshold. Once integrated into an LED stack, this material is compatible with sub-bandgap excitation and exhibits high luminance. Scaling of the electroluminescence properties by downsizing the pixel size is also investigated.

2D materials↗

DOE OSTI2021

Compensated Contacts for Three-Terminal Transistor Solar Cells

The heterojunction bipolar transistor solar cell (HBTSC) is an alternative to conventional double-junction solar cells that combines the advantages of three-terminal architectures with a compact design. A critical part of the HBTSC fabrication is the implementation of the middle contact, the one associated to the base layer in the transistor structure, because this layer can be damaged by the diffusion of metallic species during contact annealing. As a solution we propose to implement a "compensated contact", that is, to leave a thin (< 200 nm) portion of emitter or collector between the base layer and the contact metals and compensate its doping during contact annealing. Using an Au/Zn/Au metal structure to compensate a 150 nm thick portion of the collector, we obtain a specific contact resistance ~8 10-4 ..omega.. cm2. This value is fairly invariant for annealing temperatures between 400 and 460 degrees C and annealing times between 2 and 5 minutes, and it does not improve by adding Pd to the metallic stack. Optimizing the compensated contact technology opens the path to the realization of III-V-material-based HBTSCs with open-circuit voltages and fill factors comparable to those of conventional multijunction architectures.

contact resistance↗

DOE OSTI2023

In-Rod Sensor System Overview, Benefits and Recent Irradiation Test Results

Westinghouse is developing an In-Rod Sensor (IRS) System capable of measuring critical fuel rod parameters such as center-line fuel temperature, rod internal pressure and axial fuel pellet stack elongation without having penetrations to the fuel rod. The technology is similar to the Halden Reactor instrumentation, but the IRS system focuses on commercial fuel rod instrumentation. Both systems circumvent fuel rod penetrations, however the IRS system overcomes unique geometric challenges found in commercial fuel rod assemblies. The IRS could also play a key role in accelerated fuel qualification by allowing real-time measurements during separate effects tests, integral fuel testing or during irradiation of lead test rods or lead test assemblies. Combining these measurements with data acquired from targeted separate effects testing and multi-scale modelling could significantly reduce the time required to qualify new fuel and reduce the safety margin uncertainty of operating plants. The IRS is actively in development for Light Water Reactors (LWR) based fuel assemblies but can also be applied to Gen IV reactor designs with minimal modifications.

Carvajal, Jorge↗

DOE OSTI2021

Comprehensive deletion landscape of CRISPR-Cas9 identifies minimal RNA-guided DNA-binding modules

Proteins evolve through the modular rearrangement of elements known as domains. Extant, multidomain proteins are hypothesized to be the result of domain accretion, but there has been limited experimental validation of this idea. Here, we introduce a technique for genetic minimization by iterative size-exclusion and recombination (MISER) for comprehensively making all possible deletions of a protein. Using MISER, we generate a deletion landscape for the CRISPR protein Cas9. We find that the catalytically-dead Streptococcus pyogenes Cas9 can tolerate large single deletions in the REC2, REC3, HNH, and RuvC domains, while still functioning in vitro and in vivo, and that these deletions can be stacked together to engineer minimal, DNA-binding effector proteins. In total, our results demonstrate that extant proteins retain significant modularity from the accretion process and, as genetic size is a major limitation for viral delivery systems, establish a general technique to improve genome editing and gene therapy-based therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Ultra-grain refinement creates FCC pure cobalt with high strength and high ductility

Although pure cobalt is generally known to have a hexagonal close-packed (HCP) structure at room temperature, we show that its high-temperature face-centered cubic (FCC) phase can be strongly stabilized through grain refinement, resulting in FCC pure cobalt at room temperature. Ultrafine-grained (UFG) FCC cobalt exhibits a hierarchical microstructure consisting of dense stacking fault (SF) networks in the dominant FCC grains and numerous SFs and thin FCC layers within a few HCP plates. This unique microstructure leads to a high tensile strength exceeding 1 GPa, together with a tensile elongation of over 35%, thereby surpassing the well-known strength–ductility trade-off of pure metals. In-situ synchrotron X-ray diffraction revealed that the UFG FCC cobalt exhibited a markedly enhanced deformation-induced FCC→HCP martensitic transformation, which provided sustained strain hardening through the transformation-induced plasticity (TRIP) effect. Furthermore, ultra-grain refinement dramatically suppressed premature void and crack formation, causing a transition in the fracture mode from brittle to ductile. These findings advance the fundamental understanding of the phase stability and TRIP-assisted deformation in elemental cobalt and offer new guidelines for the microstructure-driven design of high-performance cobalt-based structural alloys.

Metastability↗

DOE OSTI2026

Direct Determination of 3C/4H Silicon Carbide Heterophase Interfaces by Electron Ptychography

Silicon carbide is a technologically relevant material that can exist in various structural polymorphs, each with its own electronic properties. Through the controlled growth of abrupt interfaces between the polymorphs, systems with emergent properties arise from differences in local bonding and stacking sequences. Determining the structure of these interfaces provides important insights into predicting and ultimately controlling their properties. Here, in this study, we use multislice electron ptychography (MEP) to qualitatively and quantitatively analyze interfaces between 3C and 4H silicon carbide in three dimensions. Two distinct interfaces are investigated: a coherent interface between $(1\bar{1}1)$ (3C) and $(0001)$ (4H) , and an incoherent interface between $(1\bar{1}2)$ (3C) and $(1\bar{1}00)$ (4H) . At the coherent interface, MEP enables direct quantification of a sharp boundary with an inclined step, whereas at the incoherent interface, local atomic displacements are determined along dislocation cores. By quantifying the spatial distribution and atomic-scale structure of these interfaces in three dimensions, this study provides insight into the complexity of silicon carbide heteropolytype interfaces, paving the way for theoretical modeling and the development of design rules to achieve desired properties.

heterophase interface↗

DOE OSTI2023

Evaluation of Minimum Detectable Activities for Stack Sample Analyses

The Rad-NESHAP program takes samples at major point sources to track how much of select nuclides are being emitted out of the stack. Samples are taken at the site where the radiological operations are being performed. The samples are then sent to analytical laboratories for analysis to determine what nuclides exist in the sample. However, due to the sensitivity of the instruments, false positives are often reported. To reduce the number of false positives, the results reported from the analytical labs are compared to set detection limits. Anything below the set detection limit is considered part of the background while anything above the limit is considered a positive hit. Therefore, the detection limit must be set low enough that any false negatives do not result in a significant dose missed at the end of the year but high enough that there are not enough false positives to skew the annual dose being reported. All detection limits listed in this section for the analyses below were designed to meet the stated equivalent emissions rate and subsequent off-site doses. These minimum detectable activities (MDAs) were developed in the 1990s and documented in the memo cited for each analysis below. These limits and memos are all currently referenced in the Quality Assurance Project Plan (QAPP) for the Rad-NESHAP Compliance Task, which is being updated to a Project Implementation Plan (PIP) concurrently with this document. As part of this update process, the original MDAs defined in this section needed to be verified that they were still conservative and meet the off-site dose levels stated.

2019 MDA↗

DOE OSTI2022

Micro-fabricated mirrors with finesse exceeding one million

The Fabry–Perot resonator is one of the most widely used optical devices, enabling scientific and technological breakthroughs in diverse fields including cavity quantum electrodynamics, optical clocks, precision length metrology, and spectroscopy. Though resonator designs vary widely, all high-end applications benefit from mirrors with the lowest loss and highest finesse possible. Fabrication of the highest-finesse mirrors relies on centuries-old mechanical polishing techniques, which offer losses at the parts-per-million (ppm) level. However, no existing fabrication techniques are able to produce high-finesse resonators with the large range of mirror geometries needed for scalable quantum devices and next-generation compact atomic clocks. In this paper, we introduce a scalable approach to fabricate mirrors with ultrahigh finesse ( ≥ <#comment/> 10 6 ) and user-defined radius of curvature spanning of four orders of magnitude ( 10 − <#comment/> 4 − <#comment/> 10 0 m ). We employ photoresist reflow and reactive ion etching to shape and transfer mirror templates onto a substrate while maintaining sub-Angstrom roughness. This substrate is coated with a dielectric stack and used to create arrays of compact Fabry–Perot resonators with finesse values as high as 1.3 million and measured excess loss < <#comment/> 1 p p m . Optical ringdown measurements of 43 devices across five substrates reveal that the fabricated cavity mirrors—with both small and large radii of curvature—produce an average coating-limited finesse of 1.05 million. This versatile approach opens the door to scalable fabrication of high-finesse miniaturized Fabry–Perot cavities needed for emerging quantum optics and frequency metrology technologies.

42 ENGINEERING↗

DOE OSTI2022

Modeling Framework to Analyze Performance and Structural Reliability of Solid Oxide Electrolysis Cells

Solid oxide electrolysis cells (SOEC) have been receiving significant attention recently because of their high energy efficiency and fast hydrogen production. In this study a multi-physics model to simulate the SOEC performance and structural reliability of a state-of-the-art planar SOEC design was developed. The electrochemical reactions, fluid dynamics, species transport, electron transfer, and heat transfer were modeled in the commercial computational fluid dynamics (CFD) software STAR-CCM+. The thermomechanical analysis and the associated structural reliability evaluations were conducted using the commercial finite element analysis software ANSYS. The electrochemistry model was validated by using the experimentally obtained current-voltage (I-V) characteristics of the electrode-supported SOECs. The reliability analysis using a risk-of-rupture approach showed low failure probabilities under standard operating conditions considered in this study. For cells operated at voltages well above a thermoneutral voltage, the reliability evaluations indicated a potential risk of cell failure, but the damage was concentrated locally in specific areas of the cell which typically do not lead to total loss of cell function. The presented approach provides insights for evaluating representative cell and stack performances and structural reliability without intensive testing and for developing optimally performing and structurally reliable SOECs for efficient hydrogen generation.

25 ENERGY STORAGE↗

DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Wind

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production by conducting a statistical analysis of historical wind data over a five-year period (2020-2025) from a single 1.5MW turbine manufactured by General Electric (GE) located at NLR’s Flatirons Campus, to generate an experimental test profile that was deployed on a 1.25-MW proton exchange membrane type MC250 electrolyzer system manufactured by Nel Hydrogen . [1] While the electrolyzer balance-of-plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. The historical wind data provided several metrics, however, the analysis particularly focused on the measured power output by the wind turbine. The power output time series of data for each day was categorized by total energy generation and standard deviation, and the day that represented the highest combination of these two metrics was chosen – December 25th, 2022. This process was then repeated for a moving four-hour window within this day to identify the most statistically variable period. Finally, this four-hour period was scaled by 65% to match the 1.25 MW electrolyzer. The electrolysis system controls hydrogen production by varying DC current applied to the stack, from a maximum of 3000 A to a minimum safe operation of 300 A, or 10%. Because the current – voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical wind profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1 Hz frequency. For more details on the statistical analysis process, see the presentation labeled “ Public Reference Data for Megawatt-Scale Hydrogen Electrolysis” provided with each data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wind turbine electrolysis experiment and is formatted as follows: {technology}_{scaling factor}-{electrolyzer ramp rate in amperes/second} For instance, “wind-GE1.5MW_0.65-400.zip” represents the hour-long experiment using historical data from the wind-GE1.5MW turbine, scaled to 65%, with the electrolyzer power supply set to a maximum ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and wind power input. A PDF file detailing the historical wind data statistical analysis used to generate the wind profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all simulated wind experiments combined into one dataset labeled "combined_historical_wind_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis [1] nelhydrogen.com/product/mc-series-electrolyser .

08 HYDROGEN↗

DOE OSTI2020

On the Marriage of Asynchronous Many Task Runtimes and Big Data: A Glance

The rise of the accelerator-based architectures and reconfigurable computing have showcased the weakness of software stack toolchains that still maintain a static view of the hardware instead of relying on a symbiotic relationship between static (e.g., compilers) and dynamic tools (e.g., runtimes). In the past decades, this need has given rise to adaptive runtimes with increasingly finer computational tasks. These finer tasks help to take advantage of the hardware by switching out when a long latency operation is encountered (because of the deeper memory hierarchies and new memory technologies that might target streaming instead of random access), thus trading off idle time for unrelated work. Examples of these finer task runtimes are Asynchronous Many Task (AMT) runtimes, in which highly efficient computational graphs run on a variety of hardware. Due to its inherent latency tolerant characteristics, Latency-sensitive applications, such as Graph Analytics and Big Data can effectively use these runtimes. This paper aims to present an example of how the careful design of an AMT can exploit the hardware substrate when faced with high latency applications such as the ones given in the Big Data domain. Moreover, with its introspection and adaptive capabilities, we aim to show the power of these runtimes when facing the changing requirements of the application workloads. We use the Performance Open Community Runtime (P-OCR) as our vehicle to demonstrate the concepts presented here.

adaptive runtime, big data analysis↗

DOE OSTI2024

Exploring the benefits of using co-packaged optics in data center and AI supercomputer networks: a simulation-based analysis [Invited]

We investigate the advantages of using co-packaged optics in next-generation data center and AI supercomputer networks. The increased escape bandwidth offered by co-packaged optics provides multiple possibilities for building 50T switches and beyond, expanding the opportunities in both the data center and supercomputing domains. Furthermore, this provides network architects with the opportunity to expand their design space and develop simplified networks with enhanced network locality properties. Co-packaging at the switch and server points enables networks with double capacity while reducing the switch count by 64% compared to state-of-the-art systems. We evaluate these concepts through discrete-event simulations using all-to-all and all-reduce traffic patterns that simulate collective communications commonly found in network-bound applications. Initially, we investigate the all-to-all overhead involved in distributing the virtual machines of the applications across multiple leaf switches and compare it to the scenario in which all VMs are placed under a single switch. Subsequently, we evaluate the performance of an AI supercomputing cluster by simulating both patterns for different message sizes, while also varying the number of participating nodes. The results suggest that networks with improved locality properties become increasingly important as the network stack operates at higher speeds; for a stack latency of 1.25 µs, placing the applications under multiple switches can result in up to 68% higher completion times than placing them under a single switch. For AI supercomputers, significant improvements are observed in the mean server throughput, reaching more than 90% for configurations involving 256 nodes and message sizes of at least 128 KiB.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Effect of Molecular Conformation on Intermolecular Interactions and Photovoltaic Performances of Giant Molecule Acceptors

The molecular conformation of giant molecule acceptors (GMAs) plays a significant role in regulating the intermolecular interactions and their photovoltaic performances in organic solar cells (OSCs). For the linear GMA GT‐l, the stronger homo‐molecular interaction causes its aggregation being weakly affected by the donor, thus forming an ordered molecular stacking and proper phase separation in its blend film. The star‐shaped GMA GT‐s‐based blend film shows a dominant hetero‐molecular interaction that suppresses the aggregation of the donor and acceptor, resulting in smaller phase separation and more uniform vertical phase distribution. While for another star‐shaped GMA GTs, the weakest hetero‐molecular interaction causes its blend film to form larger phase separation. Therefore, the GT‐l based OSC with PM6 as donor shows the highest charge mobilities, the fastest charge transfer (CT) process, reduced energy loss and less charge recombination, contributing to a higher power conversion efficiency (PCE) of 19.03%. Comparatively, the PCEs of the OSCs based on GTs and GT‐s are 18.05% and 17.58% respectively. Notably, all the three GMAs based OSCs show excellent thermal stability and long‐term storage stability. This study provides a facile strategy by tuning the linking unit and its connecting mode for designing highly efficient and stable organic photovoltaic materials.

Chemistry↗

DOE OSTI2022

A Visual Comparison of Silent Error Propagation

High-performance computing (HPC) systems play a critical role in facilitating scientific discoveries. Their scale and complexity (e.g., the number of computational units and software stack) continue to grow as new systems are expected to process increasingly more data and reduce computing time. However, with more processing elements, the probability that these systems will experience a random bit-flip error that corrupts a program's output also increases, which is often recognized as silent data corruption. Analyzing the resiliency of HPC applications in extreme-scale computing to silent data corruption is crucial but difficult. An HPC application often contains a large number of computation units that need to be tested, and error propagation caused by error corruption is complex and difficult to interpret. Here, to accommodate this challenge, we propose an interactive visualization system that helps HPC researchers understand the resiliency of HPC applications and compare their error propagation. Our system models an application's error propagation to study a program's resiliency by constructing and visualizing its fault tolerance boundary. Coordinating with multiple interactive designs, our system enables domain experts to efficiently explore the complicated spatial and temporal correlation between error propagations. At the end, the system integrated a nonmonotonic error propagation analysis with an adjustable graph propagation visualization to help domain experts examine the details of error propagation and answer such questions as why an error is mitigated or amplified by program execution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Self‐Standing Chiral Covalent Organic Framework Thin Films with Full‐Color Tunable Guest‐Induced Circularly Polarized Luminescence

Abstract Exploring self‐standing chiral covalent organic framework (COF) thin films with controllable circularly polarized luminescence (CPL) is of paramount significance but remains a challenging task. Herein, we demonstrate the first example of self‐standing chiral COF films employing a polymerization‐dispersion‐filtration strategy. Pristine, low‐quality chiral COF films were produced by interfacial polymerization and then re‐dispersed into COF colloidal solutions. Via vacuum assisted assembly, these COF colloids were densely stacked and assembled into self‐standing, pure chiral COF films ( L‐ / D‐ CCOF‐F) that were transparent, smooth, crack‐free and highly crystalline. These films were tunable in thicknesses, areas, and roughness, along with strong diffuse reflectance circular dichroism (DRCD) and cyan CPL signals, showing an intrinsic luminescence asymmetric factor ( g lum ) of ~4.3×10 −3 . Furthermore, these COF films served as host adsorbents to load various achiral organic dye guests through adsorption. The effective chiral transfer and energy transfer between CCOF‐F and achiral fluorescent dyes endowed the dyes with strong chirality and tunable DRCD, resulting in intense, full‐color‐tunable solid‐state CPL. Notably, the ordered arrangement of dye guest molecules within the preferentially oriented chiral pores of CCOF‐F contributed to an amplified | g lum | factor of up to 7.2×10 −2 , which is state‐of‐the‐art for COF‐based CPL materials. This work provides new insights into the design and fabrication of self‐standing chiral COF films, demonstrating their great potential for chiroptical applications.

Tang, Xihao↗

DOE OSTI2025

TPCpp-10M: Simulated proton-proton collisions in a time projection chamber for AI foundation models

Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large scale datasets, as well as standardized evaluation tasks and metrics. Furthermore, the specialized knowledge and software typically required to process particle physics data pose significant barriers to interdisciplinary collaboration with the broader machine learning community. This work introduces a large, openly accessible dataset of 10 million simulated proton-proton collisions, designed to support self-supervised training of foundation models. To facilitate ease of use, the dataset is provided in a common NumPy format. In addition, it includes 70,000 labeled examples spanning three well defined downstream tasks: track finding, particle identification, and noise tagging, to enable systematic evaluation of the foundation model's adaptability. The simulated data are generated using the Pythia Monte Carlo event generator at a center of mass energy of $\sqrt{s}$ = 200 GeV and processed with Geant4 to include realistic detector conditions and signal emulation in the sPHENIX Time Projection Chamber at the Relativistic Heavy Ion Collider, located at Brookhaven National Laboratory. This dataset resource establishes a common ground for interdisciplinary research, enabling machine learning scientists and physicists alike to explore scaling behaviors, assess transferability, and accelerate progress toward foundation models in nuclear and high energy physics. The complete simulation and reconstruction chain is reproducible with the sPHENIX software stack. All data and code locations are provided under Data Accessibility.

Data Analysis, Statistics and Probability (physics↗

DOE OSTI2025

Deep learning model for fast, science-based forecasting of fluid migration along faults in geologic carbon storage scenarios

Effective long-term geologic storage depends on robust site selection and credible, science-based forecasting of subsurface behavior to ensure storage integrity. For this work, we develop a deep learning–based reduced-order model (ROM) to quantify potential carbon dioxide (CO₂) and brine migration through geological faults. The ROM combines a Transformer model for binary classification and a Stacked Ensemble for regression, trained on a comprehensive dataset generated from 1400 physics-based reservoir simulations. Key geologic and operational parameters—including fault geometry, reservoir structure, and injection conditions—were systematically varied to capture a wide range of fluid migration scenarios. The ROM accurately predicts the onset of migration, cumulative migration volumes of both CO₂ and brine, and associated migration rates, as compared to an independent set of validation simulations, while significantly reducing computational cost compared to traditional simulation methods. Model performance was evaluated across diverse fault configurations, revealing that shallow reservoir geometry and fault angle are among the most influential factors governing migration behavior. Sensitivity analysis using SHapley Additive exPlanations (SHAP) provided interpretability, revealing distinct patterns in how geological and operational features drive transient versus cumulative migration outcomes. The ROM’s ability to rapidly simulate fault migration scenarios enables efficient sensitivity analyses, scenario evaluations, and decision support for site selection and monitoring design. This approach enhances the safety, scalability, and long-term operational performance of geologic carbon storage (GCS) systems by providing a robust, interpretable tool for predicting subsurface fluid migration and assessing fault-related migration potential.

42 ENGINEERING↗

DOE OSTI2023

Ultra-fast and sensitive magneto-optical hydrogen sensors using a magnetic nano-cap array

Magnetism in curved nano-geometries has opened a new path to design novel characteristics and phenomena that could greatly impact the future fundamental and applied studies of materials. Here, in this report, a hexagonally-packed array of magnetic nano-caps (or nano-patches, NP) exhibiting unique magnetic anisotropy and optimal nanostructure for fast hydrogen kinetics is explored as a sensing element for the spark-free magnetic-circular-dichroism (MCD) H 2 sensor. The MCD H 2 sensor outperforms the state-of-the-art optical sensors reported to date and satisfies the most challenging performance targets imposed by US Department of Energy. In particular, a Pd 67 Co 33 NP sensor exhibits response time of t 90 < 0.9 s, recovery time of t 10 < 9.0 s over the 1-100 mbar H 2 partial pressure range, and limit of detection (LOD) of ~1 ppm. The LOD improves to <700 ppb, when the signal-to-noise ratio is enhanced by stacking three NP arrays. Using a 30-nm TAF (Teflon AF 2400) polymer coating, the sorption kinetics of the Pd 67 Co 33 /TAF sensor are significantly accelerated, with t 90 < 0.4 s and t 10 < 2.8 s over the same pressure range. When the Pd 67 Co 33 /TAF sensor is further coated with a 100-nm poly(methyl methacrylate) (PMMA) layer, a rapid sorption time of t 90 < 0.5 s, LOD < 1 ppm and excellent sensor accuracy (<2.5% full scale) are maintained, while the sensor obtains strong selectivity against interference gases and moisture and a negligible aging effect. The MCD nano-cap sensor platform may have a great impact on the future deployment of H 2 fuel, H 2 environmental monitoring sensors, and fast proton-based magneto-ionic devices.

Fast and sensitive hydrogen sensors↗