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Results for “High precision control”

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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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Pulsed Laser Annealed Ga Hyperdoped Poly‐Si / SiO x Passivating Contacts for High‐Efficiency Monocrystalline Si Solar Cells

Polycrystalline Si ( poly ‐Si)‐based passivating contacts are promising candidates for high‐efficiency crystalline Si solar cells. We show that nanosecond‐scale pulsed laser melting (PLM) is an industrially viable technique to fabricate such contacts with precisely controlled dopant concentration profiles that exceed the solid solubility limit. We demonstrate that conventionally doped, hole‐selective poly ‐Si/SiO x contacts that provide poor surface passivation of c ‐Si can be replaced with Ga‐ or B‐doped contacts based on non‐equilibrium doping. We overcome the solid solubility limit for both dopants in poly ‐Si by rapid cooling and recrystallization over a timescale of ∼25 ns. We show an active Ga dopant concentration of ∼3 × 10 20 cm −3 in poly ‐Si which is six times higher than its solubility limit in c ‐Si, and a B dopant concentration as high as ∼10 21 cm −3 . We measure an implied open‐circuit voltage of 735 mV for Ga‐doped poly ‐Si/SiO x contacts on Czochralski Si with a low contact resistivity of 35.5 ± 2.4 mΩ cm 2 . Scanning spreading resistance microscopy and Kelvin probe force microscopy show large diffusion and drift current in the p ‐ n junction that contributes to the low contact resistivity. Our results suggest that PLM can be extended for hyperdoping of other semiconductors with low solubility atoms to enable high‐efficiency devices.

14 SOLAR ENERGY↗
DOE OSTI2025

Electroslag additive manufacturing: A pathway for high throughput near net shape production

Electroslag Additive Manufacturing (ESAM), a new high-throughput additive manufacturing (AM) method that combines Electroslag Strip Cladding (ESC) and wire arc AM (WAAM) is introduced. This combination enables the high deposition rate of ESC (more than 20 kg/h with a 60 mm strip electrode) to benefit from the precise geometric control of WAAM. As a precursor to ESAM, the ESC process is investigated in an AM context independently by evaluating both direct and staggered bead-stacking strategies and analyzing the microstructural and mechanical properties of each. This is followed by an ESAM demonstration producing an annular geometry by pairing ESC with gas tungsten arc welding (GTAW), wherein GTAW is utilized to construct annular walls that are subsequently infilled via ESC. The microstructure and mechanical properties of ESC-only AM are compared with that of the ESAM method and it is shown that printed integral retaining walls do not impact the resulting mechanical properties of ESAM. Furthermore, results indicate that ESAM-produced Alloy 625 parts exhibit tensile properties on par with cast counterparts, supporting the method’s scalability to components exceeding one metric ton, and possibly making ESAM a viable future manufacturing approach for competitive production of large-scale components currently manufactured by casting and forging.

Additive manufacturing↗
DOE OSTI2020

Metal-embedded fiber optic sensor packaging and signal demodulation scheme towards high-frequency dynamic measurements in harsh environments

Optical fiber-based sensors are commonly used for industrial monitoring and control processes due to their small size, resistance to electromagnetic interference, and ability to perform a wide variety of high-precision measurements. However, implementing optical fiber sensors in harsh environments is challenging because they are small and fragile. Proper packaging of fiber-optic sensors could extend their use to harsh environments, including at high temperature and under high radiation. Furthermore, conventional fiber optic-based measurement systems often use computationally expensive signal processing algorithms which hinder their use in high-frequency dynamic applications. This work reports on the design of an optical fiber-pressure sensor system based on low-coherence interferometry that uses a metal-embedded optical fiber to provide a robust sensor package. Additionally, a novel phase demodulation scheme is proposed to extract length changes from the deformation of a thin diaphragm within a Fabry–Pérot cavity to measure pressure. The sensor has been tested to ±100 kPa, has a theoretical linear response over a 270 kPa dynamic range (24 dB maximum signal-to-noise ratio), and can resolve pressure transients up to 3910 kPa/s. Sampling at 100 kHz, the present sensor can resolve 2 kPa dynamic pressures at frequencies up to 2 kHz. Faster transients on the order of tens to hundreds of kHz can theoretically be resolved at the expense of decreasing the maximum resolvable amplitude. A methodology for designing a LCI pressure sensor for a given application is outlined based on the limitations imposed by the Nyquist criterion, the diaphragm's resonant frequency, the LCI optoelectronics, and the phase demodulation scheme. The sensor is the first to implement a low-coherence light source and a Fabry–Pérot interferometer designed to provide real-time high-fidelity pressure measurements using a metal-embedded optical fiber. The demonstrated sensor provides a platform for sensing in harsh conditions such as in nuclear and energy applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Zr‐Porphyrin Metal–Organic Framework as nanoreactor for boosting the formation of hydrogen clathrates

Abstract We report the first experimental evidence for rapid formation of hydrogen clathrates under mild pressure and temperature conditions within the cavities of a zirconium‐metalloporphyrin framework, specifically PCN‐222. PCN‐222 has been selected for its 1D mesoporous channels, high water‐stability, and proper hydrophilic behavior. Firstly, we optimize a microwave (MW)‐assisted method for the synthesis of nanosized PCN‐222 particles with precise structure control (exceptional homogeneity in morphology and crystalline phase purity), taking advantage of MW in terms of rapid/homogeneous heating, time and energy savings, as well as potential scalability of the synthetic method. Second, we explore the relevance of the large mesoporous 1D open channels within the PCN‐222 to promote the nucleation and growth of confined hydrogen clathrates. Experimental results show that PCN‐222 drives the nucleation process at a lower pressure than the bulk system (1.35 kbar vs 2 kbar), with fast kinetics (minutes), using pure water, and with a nearly complete water‐to‐hydrate conversion. Unfortunately, PCN‐222 cannot withstand these high pressures, which lead to a significant alteration of the mesoporous structure while the microporous network remains mainly unchanged.

Carrillo‐Carrión, Carolina↗
DOE OSTI2024

Zr‐Porphyrin Metal–Organic Framework as nanoreactor for boosting the formation of hydrogen clathrates

Abstract We report the first experimental evidence for rapid formation of hydrogen clathrates under mild pressure and temperature conditions within the cavities of a zirconium‐metalloporphyrin framework, specifically PCN‐222. PCN‐222 has been selected for its 1D mesoporous channels, high water‐stability, and proper hydrophilic behavior. Firstly, we optimize a microwave (MW)‐assisted method for the synthesis of nanosized PCN‐222 particles with precise structure control (exceptional homogeneity in morphology and crystalline phase purity), taking advantage of MW in terms of rapid/homogeneous heating, time and energy savings, as well as potential scalability of the synthetic method. Second, we explore the relevance of the large mesoporous 1D open channels within the PCN‐222 to promote the nucleation and growth of confined hydrogen clathrates. Experimental results show that PCN‐222 drives the nucleation process at a lower pressure than the bulk system (1.35 kbar vs 2 kbar), with fast kinetics (minutes), using pure water, and with a nearly complete water‐to‐hydrate conversion. Unfortunately, PCN‐222 cannot withstand these high pressures, which lead to a significant alteration of the mesoporous structure while the microporous network remains mainly unchanged.

Carrillo‐Carrión, Carolina↗
DOE OSTI2025

Controlling magnetism and transport at perovskite cobaltite interfaces via strain-tuned oxygen vacancy ordering

Complex oxides such as perovskite cobaltites exhibit rich phenomena at interfaces due to the complex interplay between their structural, defect, electronic, and magnetic degrees of freedom. Here, we study this interplay here in the ferromagnetic metallic cobaltite La 1−𝑥 ⁢Sr 𝑥⁢ CoO 3−𝛿 , using specific substrates to vary both the heteroepitaxial strain (compressive vs tensile) and growth orientation ((001) vs (110)). Transmission electron microscopy, electron energy-loss spectroscopy, high-resolution X-ray diffraction, magnetometry, polarized neutron reflectometry, and electronic magnetotransport measurements are applied. Lattice mismatch and growth orientation are found to enable the precise control of interfacial oxygen vacancy ordering in La 1−𝑥⁢ Sr 𝑥 ⁢CoO 3−𝛿 , thus dictating strain relaxation and oxygen vacancy depth profiles, in turn controlling thickness-dependent magnetic and electronic properties. In particular, compressive strain and (110) orientations minimize deleterious magnetic/electronic dead layer effects, leading to the optimization of interfacial magnetism and transport. Strain and orientation tuning of oxygen vacancy ordering are thus established as powerful means to control physical properties at cobaltite-based interfaces, relevant to several fields.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The effect of remnant CdSe layers on the performance of CdSeTe/CdTe photovoltaic devices

Thin film CdTe-based photovoltaic devices have achieved high efficiency above 22 %. The recent improvement in efficiency is due to Se alloying in the CdTe absorbers to form a CdSeTe/CdTe structure. The subsequent band gap grading increases the short circuit current density. The Se can be introduced by depositing a precursor thin film of either CdSe or a CdSeTe alloy and then diffusing the Se into the CdTe during the high temperature cadmium chloride activation process. Using CdSe is preferred because it is easier to control the Se concentration. However, during fabrication of the CdSeTe/CdTe devices, the CdSe thickness needs to be precisely controlled to prevent the retention of a CdSe remnant layer after the activation treatment. Retention of a remnant CdSe layer causes a dramatic reduction in device efficiency. In this work, we show that the reduction in efficiency is caused by a number of factors. The remnant CdSe layer is n-type which moves the position of the p-n junction. Also, it is widely thought that the CdSe remnants are photo-inactive. In this work, we clarify that the individual CdSe grains are actually highly photo-active. However, the grain sizes in the CdSe remnant and the adjacent CdSeTe layer are very small resulting in a high grain boundary area. Although the grain boundaries are passivated with chlorine, cathodoluminescence imaging and electrical measurements show that this is only partially effective. Also, EQE measurements show that the remnant CdSe causes parasitic absorption. Overall, the remnant CdSe layer causes a reduction in short circuit current density and device efficiency. The thickness of the CdSe precursor layer and the cadmium chloride activation process conditions must be precisely optimised to ensure that all the CdSe is consumed and inter-diffused to form the CdSeTe alloy for highest efficiency devices.

CdSe remnant↗
DOE OSTI2023

High-Precision Calculation of Nanoparticle (Nanocrystal) Potentials of Mean Force and Internal Energies

Thermodynamic stability assessment of nanocrystal systems requires precise free energy calculations. This study highlights the importance of meticulous control over various factors, including the thermostat, time step, potential cutoff, initial configuration, sampling method, and overall simulation duration. Free energy computations in dry (solvent-free) systems are on the order of several hundred $k_BT$ but can be obtained with consistent accuracy. However, calculation of internal energies becomes challenging, as they are typically much larger in magnitude than free energies and exhibit significant noise and reduced reliability. To address this limitation, we propose a new internal energy estimate that drastically reduces the noise. Here, we also present formulas that enable the optimization of the parameters of the harmonic bias potential for optimal convergence. Finally, we discuss the implications of these findings for the computation of free energies in nanocrystal clusters and superlattices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Vertically Aligned Single-Crystalline CoFe 2 O 4 Nanobrush Architectures with High Magnetization and Tailored Magnetic Anisotropy

Micrometer-tall vertically aligned single-crystalline CoFe 2 O 4 nanobrush architectures with extraordinarily large aspect ratio have been achieved by the precise control of a kinetic and thermodynamic non-equilibrium pulsed laser epitaxy process. Direct observations by scanning transmission electron microscopy reveal that the nanobrush crystal is mostly defect-free by nature, and epitaxially connected to the substrate through a continuous 2D interface layer. In contrast, periodic dislocations and lattice defects such as anti-phase boundaries and twin boundaries are frequently observed in the 2D interface layer, suggesting that interface misfit strain relaxation under a non-equilibrium growth condition plays a critical role in the self-assembly of such artificial architectures. Further, magnetic property measurements have found that the nanobrushes exhibit a saturation magnetization value of 6.16 μB/f.u., which is much higher than the bulk value. The discovery not only enables insights into an effective route for fabricating unconventional high-quality nanostructures, but also demonstrates a novel magnetic architecture with potential applications in nanomagnetic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Fabrication of silicon-tipped fiber-optic temperature sensors using aerogel-assisted glass soldering with precise laser heating

We demonstrate the fabrication of fiber-optic Fabry–Perot interferometer (FPI) temperature sensors by bonding a small silicon diaphragm to the tip of an optical fiber using low melting point glass powders heated by a 980 nm laser on an aerogel substrate. The heating laser is delivered to the silicon FPI using an optical fiber, while the silicon temperature is being monitored using a 1550 nm white-light system, providing localized heating with precise temperature control. The use of an aerogel substrate greatly improves the heating efficiency by reducing the thermal loss of the bonding parts to the ambient environment. A desirable temperature for bonding can be achieved with relatively small heating laser power. The bonding process is carried out in an open space at room temperature for convenient optical alignment. The precise temperature control ensures minimum perturbation to the optical alignment and no induced thermal damage to the optical parts during the bonding process. For demonstration, we fabricated a low-finesse and high-finesse silicon FPI sensor and characterized their measurement resolution and temperature capability. The results show that the fabrication method has a good potential for high-precision fabrication of fiber-optic sensors.

Sheng, Qiwen↗
DOE OSTI2020

Ring-opening metathesis polymerization of cobaltocenium derivative to prepare anion exchange membrane with high ionic conductivity

Abstract An efficient method to incorporate a cobaltocenium cation into polymers for anion exchange membranes is described. A cobaltocenium-containing norbornene derivative has been synthesized. It can be homopolymerized, or copolymerized with norbornene, by ring-opening metathesis polymerization (ROMP) with quantitative yields. The composition of the random copolymers can be precisely controlled by adjusting the feed ratio of the monomers. Additionally, the resulting polymers can be cast into thin robust membranes. The morphology of the membranes is characterized by humidity controlled SAXS and TEM, showing disordered interconnected cobaltocenium domains. The high ionic conductivity suggests that such membranes are good candidates for anion exchange membranes.

42 ENGINEERING↗
DOE OSTI2021

BOOSTR: A Dataset for Accelerator Control Systems

The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab’s Rapid-Cycling Synchrotron (RCS) operating at 15 Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster’s gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection.

Kafkes, Diana (ORCID:000000021716463X)↗
DOE OSTI2021

BOOSTR: A Dataset for Accelerator Control Systems

The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab's Rapid-Cycling Synchrotron (RCS) operating at 15~Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster's gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Electronic and catalytic engineering in two-dimensional vdW metal–organic frameworks through alloying

Bimetallic metal-organic framework (MOF s ) alloys, in which heterogeneous metal clusters are incorporated into their backbone, are capable of highly selective separations and catalysis. Due to limitations in our fundamental understanding of their alloying, however, established methods result in phase-separated or amorphous two-dimensional (2D) MOF s or lack precise control over alloy ratios. Here, our results demonstrate 2D MOF alloys where metal cation ratios (M 1 and M 2 ) in M 1 xM 2 1-x BDC (M 1 or M 2 = Zn, Cu, Ni, Co, Fe, Mn) can be engineered on demand by controlling the metal salt dissociation constants. Resulting MOF alloys exhibit a highly 2D nature with excellent crystallinity and minute control over metal cation ratios. Overall, our experimental and theoretical results show that their electronic bandgaps and photoexcited carrier lifetimes can be engineered by metal cation alloying. Interestingly, 2D alloyed MOF s enable high-efficiency photo-catalytic water reduction performance in Co/Ni MOF alloys owing to the spatially separated metal clusters in 2D MOF alloys.

2D materials↗
DOE OSTI2025

Optimizing on-ramp merging for connected and automated vehicles: A hierarchical approach using deep reinforcement learning and optimal control

On-ramp merging for Connected and Automated Vehicles (CAVs) presents significant challenges in dynamic traffic environments. Traditional methods and recent learning-based approaches often fail to simultaneously address decision-making complexity and execution precision under fluctuating conditions. This study introduces a novel hierarchical framework that combines: (1) a high-level Deep Reinforcement Learning (DRL) module that coordinates merging sequences through Virtual Traffic Signals (VTS) with Yield/Green phases and (2) a low-level optimal controller generating collision-free speed trajectories via pseudospectral convex optimization. A convolutional autoencoder compresses high-dimensional traffic states to enhance responsiveness. Extensive simulations demonstrate a 12.5% improvement in mainline throughput a 28% reduction in emergency braking events, and 31.66% lower fuel consumption compared to baseline methods. Furthermore, the framework’s effectiveness in coordinating CAV merges highlights its potential for real-world deployment. Future work will extend validation to multi-lane scenarios with mixed traffic and large-scale multiple merging points.

Connected and automated vehicles↗
DOE OSTI2023

Extrusion parameter control optimization for DIW 3D printing using image analysis techniques

Material extrusion is a well-recognized facet of additive manufacturing that involves the fabrication of parts through the deposition of structural material from an extrusion head from a bulk supply. In the subdivision of Direct Ink Writing (DIW) additive manufacturing, challenges arise when the structural material is flowable, synchronous extrusion control and tool movement becomes critical for achieving high-quality parts with low defect populations. DIW techniques are most used in laboratory settings using expensive custom instruments and may require specialized 3D slicing software. Here, in this study, the fabrication of an inexpensive, consumer-friendly progressive cavity pump dispensing system is detailed, in which can create high-quality parts by executing G-code commands produced from a commercial slicing software. The precision and repeatability of the movement-synchronized material extrusion is demonstrated through a series of optimization schemes, entailing the alteration of various control parameters, which directly affect the extrusion properties demonstrated during a print. In situ diagnostics were implemented to evaluate the results of the established optimization experiment. Using a machine vision technique, images of the optimization prints are processed. Following this, a supervised machine learning model was trained to autonomously judge whether or not the extrusion parameters produced a passing or failing result. The machine learning scheme serves as a preliminary benchmark for future layer-by-layer evaluation of more complex DIW parts. The construction of the printer and development of in situ characterization capabilities demonstrates the ability for this printer to create high-fidelity DIW parts for a fraction of the price of other systems.

42 ENGINEERING↗
DOE OSTI2021

An exploration of benchtop X–ray emission spectroscopy for precise characterization of the sulfur redox state in cementitious materials

The evolution of sulfur chemistry in cements is best known in the bailiwick of failure mechanisms via sulfate attack but is equally important for its contributions to the reduction capacity of cementitious materials often used for immobilizing nuclear waste streams destined for long-term storage, e.g., cementitious waste forms (CWF). The total reduction capacity of CWFs, encompassing contributions from both S and Fe reductants, and its implications toward radionuclide immobilization is most often studied by destructive wet chemistry methods requiring acid digestion in the presence of Ce(IV) and subsequent titration and colorimetric interpretation. Here, we investigate a similarly analytical but nondestructive alternative, benchtop high resolution wavelength-dispersive x-ray fluorescence spectroscopy, most commonly known as x-ray emission spectroscopy (XES), for probing the bulk sulfur oxidation state distribution. We present here an initial investigation of S XES, including an improved experimental protocol for lab XES of inhomogeneous samples, both as a complement to the Ce(IV) test and for new scientific opportunities that it enables for observing changes in sulfur chemistry. We discuss future improvements and opportunities, including: (1) the practical challenges associated with coordinating XES and Ce(IV) liquid extraction for a more comprehensive perspective on reduction capacity and for a high-precision evaluation of uncertainties in the Ce(IV) test; and (2) new opportunities, due to the nondestructive nature of XES, for controlled evolution studies aimed at elucidating specific chemical responses of CWF’s exposed to invasive gas or liquid species or to accelerated aging by radiative dose or thermal treatment.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models

One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time.

machine learning↗