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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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Stabilization and control of persistent current magnets using variable inductance
Ultra-stable, tunable magnetic fields are desirable for a wide range of applications in medical imaging, electron microscopy, quantum science, and atomic physics. Superconducting magnets operated in persistent current mode, with device current flowing in a closed superconducting loop disconnected from a power source, are a common approach for applications with the most stringent requirements on temporal field stability. In this work, we present a method for active control of this persistent current by means of dynamic inductance change within the superconducting circuit. For a first realization of this general technique, we consider a variable superconducting inductor placed in series with the main magnet. The inductor acts as a dynamic flux storage device capable of transferring flux to or from the main magnet through inductance change. This allows for fine and fast adjustments of the persistent current without the use of thermal switches that limit the speed and accuracy of many present-day methods. With first experiments employing this technique, we demonstrate stabilization of a 1.95 T Nb–Ti round lens for electron microscopy against decay resulting from residual losses in the superconducting circuit, and more generally show flexibility for precise control over the magnitude and waveform of the persistent current.
The possibility of utilizing electronic logic circuits for automatic medical monitoring
Technical aspects of using electronic logic circuits for automatic monitoring in space biology and medicine
DOME: Directional medical embedding vectors from Electronic Health Records
Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. Methods: We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. Results: We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHRembedding.
Ocular Screening System
An ocular screening system designed for safe, convenient screening of large groups was developed at Marshall Space Flight Center, leading to the formation of Medical Sciences Corporation. The system identifies visual defects accurately and inexpensively, and includes a photorefractor telephoto lens and an electronic flash. Medical Sciences Corporation is using the device to test at schools, industrial plants, etc.
Laser Welding in Electronic Packaging
The laser has proven its worth in numerous high reliability electronic packaging applications ranging from medical to missile electronics. In particular, the pulsed YAG laser is an extremely flexible and versatile too] capable of hermetically sealing microelectronics packages containing sensitive components without damaging them. This paper presents an overview of details that must be considered for successful use of laser welding when addressing electronic package sealing. These include; metallurgical considerations such as alloy and plating selection, weld joint configuration, design of optics, use of protective gases and control of thermal distortions. The primary limitations on use of laser welding electronic for packaging applications are economic ones. The laser itself is a relatively costly device when compared to competing welding equipment. Further, the cost of consumables and repairs can be significant. These facts have relegated laser welding to use only where it presents a distinct quality or reliability advantages over other techniques of electronic package sealing. Because of the unique noncontact and low heat inputs characteristics of laser welding, it is an ideal candidate for sealing electronic packages containing MEMS devices (microelectromechanical systems). This paper addresses how the unique advantages of the pulsed YAG laser can be used to simplify MEMS packaging and deliver a product of improved quality.
Manufacturing Processes for Permanent Magnets: Part II—Bonding and Emerging Methods
Abstract Permanent magnets produce magnetic fields and maintain the field even in the presence of an opposing magnetic field. They are widely used in electric machines, electronics, and medical devices. Part I reviews the conventional manufacturing processes for commercial magnets, including Nd-Fe-B, Sm-Co, alnico, and ferrite in cast and sintered forms. In Part II, bonding, emerging advanced manufacturing processes, as well as magnet recycling methods are briefly reviewed for their current status, challenges, and future directions.
Electrochemical Recovery of Rare-Earth Elements from Coal Fly Ash Using Ionic Liquids as both Extractant and Electrolyte
Rare-earth elements (REEs) are critical for medical technologies, electronics, and clean energy. Coal fly ash (CFA), a byproduct of coal combustion, offers a promising alternative REE source. However, efficient extraction and separation of REEs from CFA remain challenging due to the complex composition of CFA. This study introduces a sustainable method for REE recovery using a recyclable ionic liquid, betainium bis(trifluoromethylsulfonyl)imide ([Hbet]- [Tf 2 N]), which serves both as the extractant from CFA and as the electrolyte in electrodeposition. In the first stage, [Hbet][Tf 2 N] preferentially extracts REEs from CFA through leaching. In the second stage, the REE-enriched ionic liquid undergoes electrochemical deposition using amperometry techniques, where REEs are reduced and deposited onto the electrode. The deposition experiments were conducted from −0.5 to −2.0 V vs a Pt quasireference electrode in a three-electrode setup comprising titanium as the working electrode and platinum as both the reference and counter electrodes. Varying the applied potential enabled potential-dependent preferential REE deposition. At −0.5 V, neodymium (Nd) showed preferential recovery, reaching 25% with a separation factor of 37 over other REEs. In contrast, applying a more negative potential increased overall deposition, yielding ∼50% Nd recovery and 10−20% recovery for the remaining REEs. After recovery, the ionic liquid was regenerated and reused for a subsequent electrochemical recovery cycle. Overall, this study demonstrates a feasible approach for REE recovery from CFA waste, with potential to enhance resource utilization within the REE supply chain.
State-of-Charge Investigation of Lithium Manganese Dioxide Primary Batteries Utilizing X-ray Computed Tomography
Lithium primary batteries (LPBs) represent a class of energy storage devices, uniquely suited for mission-critical applications including emergency backup power, aerospace and defense electronics, implantable medical devices, and remote sensing. Despite their technological maturity and distinct advantages over rechargeable lithium-ion batteries (i.e., superior shelf life and operational simplicity), state-of-charge (SoC) estimation in these batteries remains a persistent challenge due to the lack of a reliable, quantifiable diagnostic technique. In this work, we investigate X-ray computed tomography (X-CT) as a transformative and accurate SoC estimation technique for LPBs. Results gained through X-CT measurements reveal systematic, quantifiable structural changes in cathode morphology and geometry during discharge, establishing a direct structural basis for SoC estimation, with a maximum relative standard deviation of 2.3%. This work establishes a pathway toward operando, imaging-driven SoC diagnostics that can significantly enhance the reliability of SoC estimation in LPBs.
Viability of Additively Manufactured Electrodes for Lithium-Ion Batteries
As the global economy becomes increasingly electrified, the demand for batteries and energy storage is expected to rise significantly, particularly in the transportation and electricity sectors. Lithium-ion batteries (LIBs) are currently the most advanced and widely used technology in this field. Traditionally, LIBs are manufactured using simple 2D planar geometries to maximize production efficiency and minimize costs. However, this approach limits energy density due to the restricted design flexibility of the electrodes. Additive manufacturing (AM) offers a promising solution to enhance the energy density and efficiency of LIBs by enabling the design of architectures that reduce diffusive losses and allow for a greater amount of active material to be incorporated within the same device footprint, thereby minimizing the use of inactive materials. Different AM techniques come with their own set of limitations, including printing speed, material compatibility, and scale, which must be considered when designing electrodes. Scalable and cost-effective methods are particularly important for electric vehicle batteries, while achieving higher energy densities in microbatteries is crucial for the miniaturization of wearable electronics and medical devices. Here, in this study, we simulate various 3D porous electrode designs for LIBs using graphite and nickel manganese cobalt oxide (NMC) electrodes. These designs are selected to represent structures that could be produced using different AM techniques, such as direct ink writing, fused deposition modeling, and stereolithography. Our results indicate that at higher charging rates and increased areal mass loading, 3D structures can outperform traditional 2D electrodes, although the benefits may diminish with more complex designs that are harder to manufacture. The observed gains in energy density are attributed to improved electrode utilization and reduced diffusive energy losses. This comprehensive analysis of structure–performance relationships will provide valuable insights to guide future research on 3D designs, material selection, and AM techniques for additively manufactured battery electrodes.
COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients
Background: The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods: Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results: Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion: Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.
Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients
Mortality prediction for intensive care unit (ICU) patients is crucial for improving outcomes and efficient utilization of resources. Accessibility of electronic health records (EHR) has enabled data-driven predictive modeling using machine learning. However, very few studies rely solely on unstructured clinical notes from the EHR for mortality prediction. In this work, we propose a framework to predict short, mid, and long-term mortality in adult ICU patients using unstructured clinical notes from the MIMIC III database, natural language processing (NLP), and machine learning (ML) models. Depending on the statistical description of the patients’ length of stay, we define the short-term as 48-hour and 4-day period, the mid-term as 7-day and 10-day period, and the long-term as 15-day and 30-day period after admission. We found that by only using clinical notes within the 24 hours of admission, our framework can achieve a high area under the receiver operating characteristics (AU-ROC) score for short, mid and long-term mortality prediction tasks. The test AU-ROC scores are 0.87, 0.83, 0.83, 0.82, 0.82, and 0.82 for 48-hour, 4-day, 7-day, 10-day, 15-day, and 30-day period mortality prediction, respectively. We also provide a comparative study among three types of feature extraction techniques from NLP: frequency-based technique, fixed embedding-based technique, and dynamic embedding-based technique. Lastly, we provide an interpretation of the NLP-based predictive models using feature-importance scores.
Experimental High Energy Physics at the University of Illinois
This research effort funded by the Office of High Energy Physics in the U.S. Department of Energy is aimed at exploring our universe at its most basic level. The goal of our effort is to learn more about how and why nature behaves the way it does. Within this effort, we are studying the smallest particles and the largest distances we can possibly observe. Our work in particular focuses on the search for new interactions that will help us understand how the universe came into being. Through this work, we collaborate with other scientists and engineers to develop new technologies that can be utilized throughout society. Benefits from our research include advances in medical technology, electronics, transportation, sustainability, information and computing as well as the advanced training of undergraduate and graduate students in science and engineering.
Amplifier can be adjusted to cancel unbalanced noise.
Amplifier circuit with network for automatic temperature compensation to cancel unbalanced noise useful in electrocardiography
Medical applications of nasa-developed science and technology quarterly progress report no. 2, 30 apr. - 31 jul. 1965
Medical applications of aerospace science and technology
Biomedical engineering units display systems for checkout and flight of gemini spacecraft.
Gemini ground station pulse code modulation display systems which make biochemical and environmental parameters available in binary codes
Heart rate patterns observed in medical monitoring.
Respiratory and slow wave heart rate patterns of individuals under aerospace flight stress situations recorded by medical monitoring techniques
The flight research program. i - long-range program to develop medical monitoring in flight.
Physiological parameter research and instrumentation development phases of long range program for in-flight monitoring in flight environment