Search NASA⌕ Search

SEARCH · Search NASA

Results for “Performance Rating Method”

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.

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

At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Challenges in Clinical Trial Implementation: Results from a Survey of the National Accreditation Program of Breast Centers (NAPBC)

Background: Although the results of clinical trials often guide best practices, changing clinical practice based on clinical trial results can be challenging. The objective of this study was to examine provider-reported barriers to adopting best clinical practices according to clinical trial data. Methods: A cross-sectional survey was conducted of providers from the National Accreditation Program for Breast Centers about barriers that prevent the incorporation of trial findings. Descriptive analyses and multivariable analyses were performed to determine provider characteristics that were significantly associated with reported barriers. Results: Overall, 383 institutions participated (63.5% response rate), with a total of 1226 physicians responding to the survey (80% response rate). Providers identified national guidelines and meetings as the most compelling way to receive practice-changing information. They reported the following internal barriers to trial implementation: patient preference (45%), strongly held beliefs by partners/colleagues (37%), and insufficient time to discuss new practices (30%). External barriers preventing trial implementation included a lack of agreement from multidisciplinary tumor boards (32%), fear of reimbursement loss (23%), and resistance from clinical staff (20%). Reported barriers differed by provider specialty, with plastic surgeons and radiation oncologists reporting that strongly held beliefs by partners/colleagues and disagreement from multidisciplinary tumor boards were the most significant factors preventing clinical trial implementation. Conclusions: Physician beliefs and patient preferences are the most frequently reported barriers to clinical trial implementation. Tactics to better educate providers about how to explain new clinical trial data to their patients and colleagues are needed.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Magnetic Partitioning Nanofluid for Rare Earth Extraction from Geothermal Fluids

Rare earth metals are critical materials in a wide variety of applications in generating and storing renewable energy and in designing more energy efficient devices. Extracting rare earth metals from geothermal brines is a very challenging problem due to the low concentrations of these elements and engineering challenges with traditional chemical separations methods involving packed sorbent beds or membranes that would impede large volumetric flow rates of geothermal fluids transitioning through the plant. We are demonstrating a simple and highly cost-effective nanofluid-based method for extracting rare earth metals from geothermal brines. Core-shell composite nanoparticles are produced that contain a magnetic iron oxide core surrounded by a shell made of silica or metal-organic framework (MOF) sorbent functionalized with chelating ligands selective for the rare earth elements. By introducing the nanoparticles at low concentration (≈0.05 wt%) into the geothermal brine after it passes through the plant heat exchanger, the brine is exposed to a very high concentration of chelating sites on the nanoparticles without need to pass through a large and costly traditional packed bed or membrane system where pressure drop and parasitic pumping power losses are significant issues. Instead, after a short residence time flowing with the brine, the particles are effectively separated out with an electromagnet and standard extraction methods are then applied to strip the rare earth metals from the nanoparticles, which are then recycled back to the geothermal plant. Recovery efficiency for the rare earths at ppm level has now been measured for both silica and MOF sorbents functionalized with a variety of chelating ligands. A detailed preliminary techno-economic performance analysis of extraction systems using both sorbents showed potential to generate a promising internal rate of return (IRR) up to 20%.

58 GEOSCIENCES↗
DOE OSTI2024

Evaluation of In-Situ AM Process Monitoring Techniques and Potential for Detecting Process Anomalies and Undesirable Microstructures

The US Department of Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program is pursuing rapid qualification of new materials for fabrication of nuclear relevant components using advanced manufacturing techniques. Particular interest is placed on code-qualifying stainless steel (SS) 316H processed by laser powder bed fusion (LPBF) additive manufacturing. A paradigm that incorporates data from in-situ sensing during the printing, ex-situ characterization, and advanced artificial intelligence–based models was established under the Transformation Challenge Reactor (TCR) program to develop a pedigree for each fabricated component that could be tracked from the feedstock to the component’s release for application. Under the TCR program, the Peregrine software was developed as a tool for incorporating the vast amounts of in-situ and ex-situ characterization data collected; all data stored on a rapidly growing digital platform. The digital platform allows for users to link site-specific process anomalies to the macro- and microstructure. The platform will eventually be able to predict component performance, which will be crucial to qualifying materials and components in risk-averse industries such as those supporting and building nuclear reactors. Current in-situ process monitoring techniques that are already integrated with software like Peregrine are advantageous for identifying process anomalies including powder spatter, component edge swelling, recoating-build interactions, and so on. However, additional data are required to fully predict the resulting microstructures needed for identifying relationships to component performance. The rapid cooling rates observed in LPBF are some of the highest of any bulk manufacturing process, resulting in heterogenous microstructures and typically causing anisotropy in mechanical properties. Moreover, evolved residual thermal stresses are high, which can cause severe defects such as delamination or cracking. Therefore, other in-situ monitoring methods are warranted for exploration to measure and map the thermal history, and potentially the stress state, of each build. This report summarizes different in-situ monitoring strategies proposed for LPBF with a focus on the more developed sensor systems. Novel capabilities for measuring melt pool temperatures are also addressed to better inform modeling efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2021

PlasmidMaker: a Versatile, Automated, and High Throughput End-to-End Platform for Plasmid Construction

Plasmids are used extensively in basic and applied biology. However, design and construction of plasmids, specifically the ones carrying complex genetic information, remains one of the most time-consuming, labor-intensive, and rate-limiting steps in performing sophisticated biological experiments. Here, we report the development of a versatile, robust, automated end-to-end platform named PlasmidMaker that allows error-free construction of plasmids with virtually any sequences in a high-throughput manner. This platform consists of a most versatile DNA assembly method using Pyrococcus furiosus Argonaute ( Pf Ago)-based artificial restriction enzymes, a user-friendly frontend for plasmid design, and a backend that streamlines the workflow and integration with a robotic system. As a proof of concept, we used this platform to generate ~100 plasmids from six different species ranging from 5 to 18 kb in size from up to 11 DNA fragments within 3 days. PlasmidMaker should greatly expand the potential of synthetic biology.

Enghiad, Behnam↗
DOE OSTI2017

Experimental Evaluation of an Oxidizing Hazard in the Presence of Sorbents

Oxidizing chemicals are commonly found in waste generated from actinide separations and other chemical processes. Because they can create and/or increase the combustion of combustible materials, they need to be experimentally evaluated, in order to provide a highly reliable assessment of their potential hazards. Two sets of experiments were performed using a modified approach to the Environmental Protection Agency (EPA) SW-846 Method 1040. In the first set of experiments (scoping studies), the purpose was to determine the relative strength of some selected oxidizers. In the second set of experiments, the potential of the strongest tested oxidizer to increase the burning rate or burning intensity of some selected sorbents was investigated. The overall goal of this work was to conservatively identify oxidizer/sorbent mixtures producing non-oxidizer results. A few results from this experimental study are presented herein. More exhaustive information on this work can be found in supporting documentation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Comparing EGF Methods for Estimating Corner Frequency and Stress Drop from P-wave Spectra

Empirical Green's functions (EGF) are widely applied to correct earthquake spectra for attenuation and other path effects in order to estimate corner frequencies and stress drops, but these source parameter estimates often exhibit poor agreement between different studies. We investigate this issue by analyzing a compact cluster of over 3000 aftershocks of the 1992 Landers earthquake. We apply and compare two different analysis and modeling methods: (1) the spectral decomposition and global EGF fitting approach, and (2) a more traditional EGF method of modeling spectral ratios. We find that spectral decomposition yields event terms that are consistent with stacks of spectral ratios for individual events, but source-parameter estimates nonetheless vary between the methods. The main source of differences comes from the modeling approach used to estimate the EGF. The global EGF-fitting approach suffers from parameter tradeoffs among the absolute stress drop, the stress-drop scaling with moment, and the high-frequency falloff rate, but has the advantage that the relative spectral shapes and stress drops among the different events in the cluster are well-resolved even if their absolute levels are not. The spectral-ratio approach solves for a different EGF for each target event without imposing any constraint on the corner frequency, f c , of the smaller events, and so can produce biased results for target-event f c . Placing constraints on the small-event f c improves the performance of the spectral-ratio method and allows the two methods to yield very similar results.

58 GEOSCIENCES↗
DOE OSTI2016

Machine- z : Rapid machine-learned redshift indicator for Swift gamma-ray bursts

Studies of high-redshift gamma-ray bursts (GRBs) provide important information about the early Universe such as the rates of stellar collapsars and mergers, the metallicity content, constraints on the re-ionization period, and probes of the Hubble expansion. Rapid selection of high-z candidates from GRB samples reported in real time by dedicated space missions such as Swift is the key to identifying the most distant bursts before the optical afterglow becomes too dim to warrant a good spectrum. Here, we introduce ‘machine-z’, a redshift prediction algorithm and a ‘high-z’ classifier for Swift GRBs based on machine learning. Our method relies exclusively on canonical data commonly available within the first few hours after the GRB trigger. Using a sample of 284 bursts with measured redshifts, we trained a randomized ensemble of decision trees (random forest) to perform both regression and classification. Cross-validated performance studies show that the correlation coefficient between machine-z predictions and the true redshift is nearly 0.6. At the same time, our high-z classifier can achieve 80 per cent recall of true high-redshift bursts, while incurring a false positive rate of 20 per cent. With 40 per cent false positive rate the classifier can achieve ~100 per cent recall. As a result, the most reliable selection of high-redshift GRBs is obtained by combining predictions from both the high-z classifier and the machine-z regressor.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study

Background: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. Objective: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? Methods: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies. Results: Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence. Conclusions: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Vision-based localization for cooperative robot-CNC hybrid manufacturing

Wire and arc additive manufacturing (WAAM) has shown promise in recent years for producing large-scale parts with higher deposition rates than other additive processes. WAAM is often combined with subtractive machining to form a hybrid manufacturing process. This hybrid process can be realized by retrofitting computer numerical control (CNC) machines with deposition heads, adding spindles and deposition heads to robots, or developing part localization methods to transfer parts from an additive cell to a CNC machine. Here, a novel, robot-CNC hybrid configuration is introduced where a maneuverable robot is placed in front of a CNC machine to deposit material within the machine envelop. Furthermore, this method removes the need for part localization and the extensive machine modifications required for retrofitting; however, the problem of robot localization is also added. In this work, the effects of error in vision-based, contactless robot localization on machining parameters in a robot-machine hybrid process were studied. Performance was characterized on an implementation of this system using classical computer vision techniques. In addition, machining simulations were conducted to evaluate the effects of image-induced error on chip thickness, material removal rate, and machining allowance. Initial tests show that computer vision could adequately locate a robot for the hybrid WAAM process within .5 mm.

Hybrid manufacturing↗
DOE OSTI2024

Effect of crystallite size on lithium storage performance of high entropy oxide (Cr 0.2 Mn 0.2 Co 0.2 Ni 0.2 Zn 0.2 ) 3 O 4 nanoparticles

High-entropy oxides (HEOs), known for their high theoretical capacity and structural stability, are considered promising anode materials for next-generation lithium-ion batteries (LIBs). In this research, we synthesized a novel spinel-type HEO, (Cr 0.2 Mn 0.2 Co 0.2 Ni 0.2 Zn 0.2 ) 3 O 4 , using a solution combustion method. By adjusting the quantity of the combustion agent, we produced samples with varying crystallite sizes. The crystallite size of the HEOs initially enlarges with an increased combustion agent, then diminishes. The enhancement of crystallite size correlates with improved electrochemical performance for lithium storage. Notably, the (Cr 0.2 Mn 0.2 Co 0.2 Ni 0.2 Zn 0.2 ) 3 O 4 nanoparticles, with the largest crystallite size of 36.3 nm, demonstrated a reversible capacity of 343 mA h g -1 after 100 cycles at 100 mA g -1 , a capacity retention to 319 mA h g -1 after 1000 cycles at 1 A g -1 , and a commendable rate capability of 260 mA h g -1 at 2 A g -1 . Furthermore, this study underscores the pivotal role of crystallite size in LIB performance and presents a viable strategy to enhance the lithium storage capabilities of HEOs and other metal oxides.

25 ENERGY STORAGE↗
DOE OSTI2016

SU-F-T-476: Performance of the AS1200 EPID for Periodic Photon Quality Assurance

Purpose: To assess the dosimetric performance of a new amorphous silicon flat-panel electronic portal imaging device (EPID) suitable for high-intensity, flattening-filter-free delivery mode. Methods: An EPID-based QA suite was created with automation to periodically monitor photon central-axis output and two-dimensional beam profile constancy as a function of gantry angle and dose-rate. A Varian TrueBeamTM linear accelerator installed with Developer Mode was used to customize and deliver XML script routines for the QA suite using the dosimetry mode image acquisition for an aS1200 EPID. Automatic post-processing software was developed to analyze the resulting DICOM images. Results: The EPID was used to monitor photon beam output constancy (central-axis), flatness, and symmetry over a period of 10 months for four photon beam energies (6x, 15x, 6xFFF, and 10xFFF). EPID results were consistent to those measured with a standard daily QA check device. At the four cardinal gantry angles, the standard deviation of the EPID central-axis output was <0.5%. Likewise, EPID measurements were independent for the wide range of dose rates (including up to 2400 mu/min for 10xFFF) studied with a standard deviation of <0.8% relative to the nominal dose rate for each energy. Also, profile constancy and field size measurements showed good agreement with the reference acquisition of 0° gantry angle and nominal dose rate. XML script files were also tested for MU linearity and picket-fence delivery. Using Developer Mode, the test suite was delivered in <60 minutes for all 4 photon energies with 4 dose rates per energy and 5 picket-fence acquisitions. Conclusion: Dosimetry image acquisition using a new EPID was found to be accurate for standard and high-intensity photon beams over a broad range of dose rates over 10 months. Developer Mode provided an efficient platform to customize the EPID acquisitions by using custom script files which significantly reduced the time. This work was funded in part by Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A novel hardmask-to-substrate pattern transfer method for creating 3D, multi-level, hierarchical, high aspect-ratio structures for applications in microfluidics and cooling technologies

Abstract This letter solves a major hurdle that mars photolithography-based fabrication of micro-mesoscale structures in silicon. Conventional photolithography is usually performed on smooth, flat wafer surfaces to lay a 2D design and subsequently etch it to create single-level features. It is, however, unable to process non-flat surfaces or already etched wafers and create more than one level in the structure. In this study, we have described a novel cleanroom-based process flow that allows for easy creation of such multi-level, hierarchical 3D structures in a substrate. This is achieved by introducing an ultra-thin sacrificial silicon dioxide hardmask layer on the substrate which is first 3D patterned via multiple rounds of lithography. This 3D pattern is then scaled vertically by a factor of 200–300 and transferred to the substrate underneath via a single shot deep etching step. The proposed method is also easily characterizable—using features of different topographies and dimensions, the etch rates and selectivities were quantified; this characterization information was later used while fabricating specific target structures. Furthermore, this study comprehensively compares the novel pattern transfer technique to already existing methods of creating multi-level structures, like grayscale lithography and chip stacking. The proposed process was found to be cheaper, faster, and easier to standardize compared to other methods—this made the overall process more reliable and repeatable. We hope it will encourage more research into hybrid structures that hold the key to dramatic performance improvements in several micro-mesoscale devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Reducing false positives of microcalcification detection systems by removal of breast arterial calcifications

Purpose: In the past decades, computer-aided detection (CADe) systems have been developed to aid screening radiologists in the detection of malignant microcalcifications. These systems are useful to avoid perceptual oversights and can increase the radiologists’ detection rate. However, due to the high number of false positives marked by these CADe systems, they are not yet suitable as an independent reader. Breast arterial calcifications (BACs) are one of the most frequent false positives marked by CADe systems. In this study, a method is proposed for the elimination of BACs as positive findings. Removal of these false positives will increase the performance of the CADe system in finding malignant microcalcifications. Methods: A multistage method is proposed for the removal of BAC findings. The first stage consists of a microcalcification candidate selection, segmentation and grouping of the microcalcifications, and classification to remove obvious false positives. In the second stage, a case-based selection is applied where cases are selected which contain BACs. In the final stage, BACs are removed from the selected cases. The BACs removal stage consists of a GentleBoost classifier trained on microcalcification features describing their shape, topology, and texture. Additionally, novel features are introduced to discriminate BACs from other positive findings. Results: The CADe system was evaluated with and without BACs removal. Here, both systems were applied on a validation set containing 1088 cases of which 95 cases contained malignant microcalcifications. After bootstrapping, free-response receiver operating characteristics and receiver operating characteristics analyses were carried out. Performance between the two systems was compared at 0.98 and 0.95 specificity. At a specificity of 0.98, the sensitivity increased from 37% to 52% and the sensitivity increased from 62% up to 76% at a specificity of 0.95. Partial areas under the curve in the specificity range of 0.8–1.0 were significantly different between the system without BACs removal and the system with BACs removal, 0.129 ± 0.009 versus 0.144 ± 0.008 (p<0.05), respectively. Additionally, the sensitivity at one false positive per 50 cases and one false positive per 25 cases increased as well, 37% versus 51% (p<0.05) and 58% versus 67% (p<0.05) sensitivity, respectively. Additionally, the CADe system with BACs removal reduces the number of false positives per case by 29% on average. The same sensitivity at one false positive per 50 cases in the CADe system without BACs removal can be achieved at one false positive per 80 cases in the CADe system with BACs removal. Conclusions: By using dedicated algorithms to detect and remove breast arterial calcifications, the performance of CADe systems can be improved, in particular, at false positive rates representative for operating points used in screening.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

A simple novel device for air sampling by electrokinetic capture

A variety of different sampling devices are currently available to acquire air samples for the study of the microbiome of the air. All have a degree of technical complexity that limits deployment. Here, we evaluate the use of a novel device, which has no technical complexity and is easily deployable. An air-cleaning device powered by electrokinetic propulsion has been adapted to provide a universal method for collecting samples of the aerobiome. Plasma-induced charge in aerosol particles causes propulsion to and capture on a counter-electrode. The flow of ions creates net bulk airflow, with no moving parts. A device and electrode assembly have been re-designed from air-cleaning technology to provide an average air flow of 120 lpm. This compares favorably with current air sampling devices based on physical air pumping. Capture efficiency was determined by comparison with a 0.4 μm polycarbonate reference filter, using fluorescent latex particles in a controlled environment chamber. Performance was compared with the same reference filter method in field studies in three different environments. For 23 common fungal species by quantitative polymerase chain reaction (qPCR), there was 100 % sensitivity and apparent specificity of 87%, with the reference filter taken as “gold standard.” Further, bacterial analysis of 16S RNA by amplicon sequencing showed equivalent community structure captured by the electrokinetic device and the reference filter. Unlike other current air sampling methods, capture of particles is determined by charge and so is not controlled by particle mass. We analyzed particle sizes captured from air, without regard to specific analyte by atomic force microscopy: particles at least as low as 100 nM could be captured from ambient air. This work introduces a very simple plug-and-play device that can sample air at a high-volume flow rate with no moving parts and collect particles down to the sub-micron range. In conclusion, the performance of the device is substantially equivalent to capture by pumping through a filter for microbiome analysis by quantitative PCR and amplicon sequencing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Spatiotemporal Fractionation Schemes for Irradiating Large Cerebral Arteriovenous Malformations

Purpose: To optimally exploit fractionation effects in the context of radiosurgery treatments of large cerebral arteriovenous malformations (AVMs). In current practice, fractionated treatments divide the dose evenly into several fractions, which generally leads to low obliteration rates. In this work, we investigate the potential benefit of delivering distinct dose distributions in different fractions. Methods and Materials: Five patients with large cerebral AVMs were reviewed and replanned for intensity modulated arc therapy delivered with conventional photon beams. Treatment plans allowing for different dose distributions in all fractions were obtained by performing treatment plan optimization based on the cumulative biologically effective dose delivered at the end of treatment. Results: We show that distinct treatment plans can be designed for different fractions, such that high single-fraction doses are delivered to complementary parts of the AVM. All plans create a similar dose bath in the surrounding normal brain and thereby exploit the fractionation effect. This partial hypofractionation in the AVM along with fractionation in normal brain achieves a net improvement of the therapeutic ratio. We show that a biological dose reduction of approximately 10% in the healthy brain can be achieved compared with reference treatment schedules that deliver the same dose distribution in all fractions. Conclusions: Boosting complementary parts of the target volume in different fractions may provide a therapeutic advantage in fractionated radiosurgery treatments of large cerebral AVMs. The strategy allows for a mean dose reduction in normal brain that may be valuable for a patient population with an otherwise normal life expectancy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

In-line three-dimensional holography of nanocrystalline objects at atomic resolution

We report that resolution and sensitivity of the latest generation aberration-corrected transmission electron microscopes allow the vast majority of single atoms to be imaged with sub-Ångstrom resolution and their locations determined in an image plane with a precision that exceeds the 1.9-pm wavelength of 300 kV electrons. Such unprecedented performance allows expansion of electron microscopic investigations with atomic resolution into the third dimension. Here we show a general tomographic method to recover the three-dimensional shape of a crystalline particle from high-resolution images of a single projection without the need for sample rotation. The method is compatible with low dose rate electron microscopy, which improves on signal quality, while minimizing electron beam-induced structure modifications even for small particles or surfaces. Lastly, we apply it to germanium, gold and magnesium oxide particles, and achieve a depth resolution of 1–2 Å, which is smaller than inter-atomic distances.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering

Abstract Modern scientific instruments are acquiring data at ever-increasing rates, leading to an exponential increase in the size of data sets. Taking full advantage of these acquisition rates will require corresponding advancements in the speed and efficiency of data analytics and experimental control. A significant step forward would come from automatic decision-making methods that enable scientific instruments to autonomously explore scientific problems—that is, to intelligently explore parameter spaces without human intervention, selecting high-value measurements to perform based on the continually growing experimental data set. Here, we develop such an autonomous decision-making algorithm that is physics-agnostic, generalizable, and operates in an abstract multi-dimensional parameter space. Our approach relies on constructing a surrogate model that fits and interpolates the available experimental data, and is continuously refined as more data is gathered. The distribution and correlation of the data is used to generate a corresponding uncertainty across the surrogate model. By suggesting follow-up measurements in regions of greatest uncertainty, the algorithm maximally increases knowledge with each added measurement. This procedure is applied repeatedly, with the algorithm iteratively reducing model error and thus efficiently sampling the parameter space with each new measurement that it requests. We validate the method using synthetic data, demonstrating that it converges to faithful replica of test functions more rapidly than competing methods, and demonstrate the viability of the approach in an experimental context by using it to direct autonomous small-angle (SAXS) and grazing-incidence small-angle (GISAXS) x-ray scattering experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Sparse optimization of two-dimensional terahertz spectroscopy

Two-dimensional terahertz spectroscopy (2DTS) is a low-frequency analog of two-dimensional optical spectroscopy that is rapidly maturing as a probe of a wide variety of condensed matter systems. However, a persistent problem with 2DTS is the long experimental acquisition times, which prevent its broader adoption. A potential solution, requiring no increase in experimental complexity, is signal reconstruction via compressive sensing. In this work, we apply the sparse exponential mode analysis (SEMA) technique to 2DTS of a cuprate superconductor. We benchmark the performance of the algorithm in reconstructing terahertz nonlinearities and find that SEMA reproduces the asymmetric photon echo line shapes at sampling rates as low as 10%, reaching the reconstruction noise floor at sampling rates beyond 20%–30%. The success of SEMA in reproducing such subtle, asymmetric line shapes confirms compressive sensing as a general method to accelerate 2DTS and multidimensional spectroscopies more broadly.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗