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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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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Molecules Functionalized with Cycling Centers for Quantum Information Science

The ability for a single molecule to produce enough laser-induced fluorescence to control and detect its quantum state is known as optical cycling. This property has been used to great effect in atoms, where the relatively small number of states promotes their ability to repeatedly fluoresce, for applications such as laser cooling, precision measurement, atomic clocks, and quantum information processing. The extension of this capability to molecular species, however, has been difficult, primarily due to their propensity to become vibrationally excited during illumination. This project brought together experts from atomic physics, physical chemistry, and computational quantum chemistry to develop means to create molecules capable of optical cycling. In particular, this program sought to produce an optical cycling center (OCC), a quantum functional group capable of chemical attachment to a wide variety of molecular hosts while retaining (and thereby furnishing to its host) the ability to optically cycle. Following our team’s theoretical identification of promising species, including alkaline-earth phenoxides, we then created these species and in many cases confirmed their optical cycle closure. We now not only know of the existence of a handful of large, molecular species that can be deployed for applications in quantum technologies and measurement, but have improved our knowledge of the molecular mechanisms leading to this discovery, which will enable generalization in the future. The extension of these ideas for informed chemical design of cycling molecules with bespoke properties is now being pursued by multiple groups.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Measurement of the 2 ν β β Decay Rate and Spectral Shape of Mo 100 from the CUPID-Mo Experiment

Neutrinoless double beta decay (0νββ) is a yet unobserved nuclear process that would demonstrate Lepton number violation, a clear evidence of beyond standard model physics. The process two neutrino double beta decay (2νββ) is allowed by the standard model and has been measured in numerous experiments. In this Letter, we report a measurement of 2νββ decay half-life of 100 Mo to the ground state of 100 Ru of [7.07±0.02(stat)±0.11(syst)]×10 18 yr by the CUPID-Mo experiment. With a relative precision of ±1.6% this is the most precise measurement to date of a 2νββ decay rate in 100 Mo. In addition, we constrain higher-order corrections to the spectral shape, which provides complementary nuclear structure information. We report a novel measurement of the shape factor ξ 3,1 =0.45±0.03(stat)±0.05(syst) based on a constraint on the ratio of higher-order terms from theory, which can be reliably calculated. This is compared to theoretical predictions for different nuclear models. Finally, we also extract the first value for the effective axial vector coupling constant obtained from a spectral shape study of 2νββ decay.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Inferring Quantum Network Topology Using Local Measurements

Statistical correlations that can be generated across the nodes in a quantum network depend crucially on its topology. However, this topological information might not be known a priori, or it may need to be verified. In this paper, we propose an efficient protocol for distinguishing and inferring the topology of a quantum network. We leverage entropic quantities-namely, the von Neumann entropy and the measured mutual information-as well as measurement covariance to uniquely characterize the topology. We show that the entropic quantities are sufficient to distinguish two networks that prepare GHZ states. Moreover, if qubit measurements are available, both entropic quantities and covariance can be used to infer the network topology without state-preparation assumptions. We show that the protocol can be entirely robust to noise and can be implemented via quantum variational optimization. Numerical experiments on both classical simulators and quantum hardware show that covariance is generally more reliable for accurately and efficiently inferring the topology, whereas entropy-based methods are often better at identifying the absence of entanglement in the low-shot regime.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Temperature of Suprathermal Electron Strahl at ACE as an Indicator of Solar Coronal Origin Temperature [Slides]

Determine the effectiveness of using electron strahl temperature to infer coronal source temperature. Suprathermal electron strahl temperature derived from measurements at 1 AU does retain information regarding the coronal source temperature from which it originates. This correlation is most strongly measured during the leading edges of coronal hole events but is generally also present in full event measurements. This correlation is also observed in CME events during a coronal hole.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Entanglement island, miracle operators and the firewall

In this paper, we obtain some general results on information retrieval from the black hole interior, based on the recent progress on quantum extremal surface formula and entanglement island. We study an AdS black hole coupled to a bath with generic dynamics, and ask whether it is possible to retrieve information about a small perturbation in the interior from the bath system. We show that the one-norm distance between two reduced states in a bath region A is equal to the same quantity in the bulk quantum field theory for region AI where I is the entanglement island of A. This is a straightforward generalization of bulk-boundary correspondence in AdS/CFT. However, we show that a contradiction arises if we apply this result to a special situation when the bath dynamics includes a unitary operation that carries a particular measurement to a region A and send the result to another region W. Physically, the contradiction arises between transferability of classical information during the measurement, and non-transferability of quantum information which determines the entanglement island. We propose that the resolution of the contradiction is to realize that the state reconstruction formula does not apply to the special situation involving interior-information-retrieving measurements. This implies that the assumption of smooth replica AdS geometry with boundary condition set by the flat space bath has to break down when the particular measurement operator is applied to the bath. Using replica trick, we introduce an explicitly construction of such operator, which we name as “miracle operators”. From this construction we see that the smooth replica geometry assumption breaks down because we have to introduce extra replica wormholes connecting with the “simulated blackholes” introduced by the miracle operator. We study the implication of miracle operators in understanding the firewall paradox.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Scintillation event imaging with a single photon avalanche diode camera

Position and time measurements of scintillation events encode information about the radiation source. Single photon avalanche diode (SPAD) arrays offer multiple-megapixel spatial resolution and tens of picoseconds temporal resolution for detecting single photons. Current lensless designs for measuring scintillation events use sensors that are lower in spatial resolution. Camera-based designs use sensors that are lower in temporal resolution or readout rate and cannot image individual interactions. Here we propose to image scintillation events in a thick, monolithic scintillator using a high-resolution SPAD camera. We demonstrate that a commercial SPAD camera is able to gather sufficient signal to image individual scintillation events and observe 3D shifts in their spatial distribution. Simulations show that a SPAD camera can localize individual scintillation events in 3D. We report direct imaging of gamma-ray interactions in a scintillator with a SPAD camera. The proposed design may allow to measure complex signatures of individual particles interacting in the scintillator.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

CROCUS High-Frequency Measurements of CO₂, H₂O, Wind, and Temperature at University of Illinois Chicago

As of April 9, 2026: this dataset is currently being versioned to include data up to March 31, 2026. Once the versioning process is complete, new data files will be available for access. The dataset metadata will also be updated to reflect the data availability of the new data being versioned. Raw atmospheric measurements collected at the University of Illinois Chicago (UIC) as part of the Community Research on Climate and Urban Science (CROCUS) project. The data includes high-frequency measurements of carbon dioxide (CO₂) concentration, water vapor (H₂O) concentration, wind speed (U, V, W components), temperature, and atmospheric pressure. These raw data are recorded by the LI-7500DS Open Path CO₂/H₂O Analyzer and a sonic anemometer at a 10 Hz acquisition frequency, providing the necessary inputs for calculating fluxes of CO₂, H₂O, heat, and momentum. In addition to gas concentration measurements, the dataset includes diagnostic information from the instruments, including absorptance, sample and reference signals, and diagnostic values. The data were collected to study urban atmospheric conditions and contribute to flux calculations for urban climate research. These measurements form the basis for calculating 30-minute average fluxes of key atmospheric variables using the eddy covariance method. This dataset provides critical raw input data for researchers interested in atmospheric fluxes, urban air quality, and the interaction between the urban environment and atmospheric processes. The data is part of the U.S. Department of Energy’s Biological and Environmental Research (BER) program, under the CROCUS Urban Integrated Field Laboratory (UIFL) project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantum process tomography of a Mølmer-Sørensen gate via a global beam

Here, we present a framework for quantum process tomography of two-ion interactions that leverages modulations of the trapping potential and composite pulses from a global laser beam to achieve individual-ion addressing. Tomographic analysis of identity and delay processes reveals dominant error contributions from laser decoherence and slow qubit frequency drift during the tomography experiment. We use this framework on two co-trapped 40 Ca + ions to analyze both an optimized and an overpowered Mølmer–Sørensen gate and to compare the results of this analysis to a less informative Bell-state tomography measurement and to predictions based on a simplified noise model. These results show that the technique is effective for the characterization of two-ion quantum processes and for the extraction of meaningful information about the errors present in the system. The experimental convenience of this method will allow for more widespread use of process tomography for characterizing entangling gates in trapped-ion systems.

quantum computing↗
DOE OSTI2024

AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50

The AIACHNE (AI/ML Informed cAlifornium CHi Nuclear data Experiment) project aims at designing an experiment for the 252 Cf Prompt Fission Neutron Spectrum (PFNS) that explores systematic biases in an experimental database retrieved from the EXFOR databases. To that end, machine learning (ML) methods were applied to pint-point measurement features likely related to bia. From that information, we selected a feature that should be explored by the AIACHNE experiment. Measurement features are metadata encapsulating all pertinent information about the physical measurement and analysis techniques. Examples are, for instance, what neutron and fission detectors were used for the physical metadata, and what background reduction techniques were employed for analysis techniques. Such metadata were retrieved both from EXFOR entries as well as the literature of data sets described in detail in Ref. [2]. The prerequisite for applying machine learning techniques is casting the metadata into a format that can be parsed by the algorithm. This step might seem trivial but requires to find a unique language where metadata that carry the same physics meaning across several experiments must have the same identifier. One example is, for instance, the neutron detector. As seen in Figure 1, the machine learning code identified the use of 6 Li detectors as being related to bias in some datasets of the AIACHNE 252 Cf PFNS experimental database. In fact, here are several experiments that used neutron detectors containing 6Li in the database, for instance for the example below. EXFOR format has a unique keywords describing detectors such as “SCIN” or “GLASD”. One may think that these keywords are already sufficient descriptors for ML to uniquely find an issue. However, “SCIN” (used for [3, 4]) and “GLASD” (used for [5]) fail to inform the algorithm what is the active material in the detector. And, the key common issue leading to bias in 252 Cf related to neutron detectors is not whether it is a glass detector or a scintillator. No, the issue is that 6 Li was within both detector types and that even small mistakes in the detector response functions around approximately 200 keV are amplified by the 6 Li(n,α) resonance there leading to bias in data as highlighted in Fig. 1 and Ref. [1]. Hence, the features describing the neutron detector must call out the active material in the detector, rather than the existing EXFOR detector keyword, that the ML algorithm can find physically meaningful features related to bias. The AIACHNE team used a precursor of the WPEC (Working Party on International Nuclear Data Evaluation Co-operation) SG(Subgroup)-50 format to store the metadata for the ML analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

El valor de los metadatos para las estaciones de recuperación de recursos del agua

Spanish: Los metadatos hacen referencia a información descriptiva (como ubicación del sensor, unidad de medida, rango de medida, fecha de calibración, fecha de limpieza, si ocurrió algún evento como episodio de lluvia/fallo operativo/vertido tóxico . . .) que es esencial para convertir los grandes volúmenes de datos que se recogen actualmente en las instalaciones de tratamiento de agua y que están sin procesar en información y recursos útiles. Con el avance de la digitalización en el sector del agua, es fundamental evitar los cementerios de datos y, por otro lado, utilizar los datos almacenados para resolver problemas actuales y futuros. Este artículo se centra en el papel crucial que tienen los metadatos para responder a desafíos futuros y posiblemente impredecibles. El objetivo de este documento es presentar el ‘reto de los metadatos’ y destacar la necesidad de tener en cuenta los metadatos cuando se recoge información como parte de las buenas prácticas de digitalización. English: Metadata refers to descriptive information (such as sensor location, measurement unit, measurement range, calibration date, cleaning date, if any event occurred such as rain event/operating failure/toxic spill, . . .) essential to convert large volumes of raw data that are currently collected at water treatment facilities into useful information and resources. With the advance of digitalization in the water sector, it is fundamental to avoid data graveyards and, on the other hand, using collected data to address current and future problems. This paper focuses on the crucial role that metadata has in responding to future and possibly unpredictable challenges. The aim of this document is to present the ‘metadata challenge’ and to highlight the need to consider metadata when collecting information as part of good digitalization practices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Adaptive estimation of quantum observables

The accurate estimation of quantum observables is a critical task in science. With progress on the hardware, measuring a quantum system will become increasingly demanding, particularly for variational protocols that require extensive sampling. Here, we introduce a measurement scheme that adaptively modifies the estimator based on previously obtained data. Our algorithm, which we call AEQuO, continuously monitors both the estimated average and the associated error of the considered observable, and determines the next measurement step based on this information. We allow both for overlap and non-bitwise commutation relations in the subsets of Pauli operators that are simultaneously probed, thereby maximizing the amount of gathered information. AEQuO comes in two variants: a greedy bucket-filling algorithm with good performance for small problem instances, and a machine learning-based algorithm with more favorable scaling for larger instances. The measurement configuration determined by these subroutines is further post-processed in order to lower the error on the estimator. We test our protocol on chemistry Hamiltonians, for which AEQuO provides error estimates that improve on all state-of-the-art methods based on various grouping techniques or randomized measurements, thus greatly lowering the toll of measurements in current and future quantum applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Non-intrusive data authentication systems and methods

Systems and methods are disclosed that create a separate digital and cryptographically secure signal “branch” to a secondary observer from a primary signal path of an information system. The information system may be a measurement system. The secure signal branch is creating without interfering with the primary signal path. In such a manner, the secondary observer may authenticate reported data while not interfering with the signal of the information system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50

The AIACHNE (AI/ML Informed cAlifornium CHi Nuclear data Experiment) project aims at designing an experiment for the 252 Cf Prompt Fission Neutron Spectrum (PFNS) that explores systematic biases in an experimental database retrieved from the EXFOR databases. To that end, machine learning (ML) methods were applied to pint-point measurement features likely related to bias. From that information, we selected a feature that should be explored by the AIACHNE experiment. Measurement features are metadata encapsulating all pertinent information about the physical measurement and analysis techniques. Examples are, for instance, what neutron and fission detectors were used for the physical metadata, and what background reduction techniques were employed for analysis techniques. Such metadata were retrieved both from EXFOR entries as well as the literature of data sets described in detail in Reference 2 (at the end of the article).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Performance analysis and comparison of data-driven models for predicting indoor temperature in multi-zone commercial buildings

Building thermal models, which characterize the properties of a building’s envelope and thermal mass, are essential for accurate indoor temperature and cooling/heating demand prediction. Because of their flexibility and ease of use, data-driven models are increasingly used. Here, this study compared and analyzed the performance of gray-box (resistance-capacitance) and black-box (recurrent neural network) models for predicting indoor air temperature in a real multi-zone commercial building. The developed resistance-capacitance model served as a benchmark model for which full sets of temporal data and building information were used as inputs. The recurrent neural network models were trained and tested assuming various available types and amounts of temporal data and known building physical information to investigate the effects of data and information availability. Feature importance analysis was conducted to select the key variables for different prediction targets under different scenarios. This research provides guidance in selecting an appropriate building thermal response modeling method based on the measured data availability, building physical information, and application.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Deforestation reshapes land-surface energy-flux partitioning

Land-use and land-cover change significantly modify local land-surface characteristics and water/energy exchanges, which can lead to atmospheric circulation and regional climate changes. In particular, deforestation accounts for a large portion of global land-use changes, which transforms forests into other land cover types, such as croplands and grazing lands. Many previous efforts have focused on observing and modeling land–atmosphere–water/energy fluxes to investigate land–atmosphere coupling induced by deforestation. However, interpreting land–atmosphere–water/energy-flux responses to deforestation is often complicated by the concurrent impacts from shifts in land-surface properties versus background atmospheric forcings. In this study, we used 29 paired FLUXNET sites, to improve understanding of how deforested land surfaces drive changes in surface-energy-flux partitioning. Each paired sites included an intact forested and non-forested site that had similar background climate. We employed transfer entropy, a method based on information theory, to diagnose directional controls between coupling variables, and identify nonlinear cause–effect relationships. Transfer entropy is a powerful tool to detective causal relationships in nonlinear and asynchronous systems. The paired eddy covariance flux measurements showed consistent and strong information flows from vegetation activity (gross primary productivity (GPP)) and physical climate (e.g. shortwave radiation, air temperature) to evaporative fraction (EF) over both non-forested and forested land surfaces. More importantly, the information transfers from radiation, precipitation, and GPP to EF were significantly reduced at non-forested sites, compared to forested sites. We then applied these observationally constrained metrics as benchmarks to evaluate the Energy Exascale Earth System Model (E3SM) land model (ELM). ELM predicted vegetation controls on EF relatively well, but underpredicted climate factors on EF, indicating model deficiencies in describing the relationships between atmospheric state and surface fluxes. Moreover, changes in controls on surface energy flux partitioning due to deforestation were not detected in the model. We highlight the need for benchmarking model simulated surface-energy fluxes and the corresponding causal relationships against those of observations, to improve our understanding of model predictability on how deforestation reshapes land surface energy fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Uncovering Hidden Entanglement in Twin Beams

Proper characterization of quantum correlations in multimode optical quantum states is critical for applications in quantum information science. However, standard entanglement measurements can lead to incomplete state reconstruction and characterization. Here, we implement a resonator-based detection system that reveals entanglement between sideband modes of twin beams, achieving full tomography and retrieving often ignored quantum correlations. Unlike standard spectral measurements such as homodyne detection, resonator detection can independently address the sidebands of each beam, thereby accessing these hidden correlations. Additionally, we show how phase shifts between the carrier and the sideband modes of the involved fields redistribute information and modify the observation of entanglement for different witnesses. The ability of the resonant detection to independently address sideband modes of entangled states can contribute to enhancing the capacity for secure communication and quantum networking protocols.

Rincon Celis, Raul [University of Sao Paulo, Brazi↗
DOE OSTI2022

A cosine-based correlation information entropy approach for building automatic fault detection baseline construction

Building automatic fault detection and diagnosis (AFDD) technologies have shown great potential for energy savings. To enable AFDD, a baseline depicting the normal operation mode is needed to detect whether the building operation deviates from normality. Existing research using physics-based knowledge and models for AFDD has mainly taken a trial-and-error approach to determine if a given baseline is sufficient via empirical experiments. A mechanism to support decisions such as how many samples and what samples should be included in the baseline is currently lacking. In this study, a data-driven method for AFDD baseline construction based on information entropy is developed. The entropy is derived based on cosine similarity among typical building automation system measurements in conjunction with outdoor weather information. The performance of the proposed method is evaluated using real building data. Evaluation results indicate that the fault detection strategy adopting the proposed method has similar or better accuracy in detecting faults compared to the same fault detection strategy using the baseline construction method from the literature. Additionally, the use of entropy enables the proposed method to automatically construct and assess the baseline consisting of information-rich samples.

42 ENGINEERING↗