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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Results from the DUNE ND-LAr 2x2 Demonstrator Run 2

The Deep Underground Neutrino Experiment (DUNE) is a cutting-edge, long-baseline experiment under construction in the United States, based on large liquid-argon time projection chambers (LArTPCs). The DUNE Near Detector LArTPC (ND-LAr) will employ a novel modular architecture using a pixelated LArPix charge readout. To validate this design and characterize detector response, the 2×2 demonstrator—an array of eight optically isolated LArTPC modules—was deployed at Fermilab in 2024. During winter 2025, the 2x2 demonstrator was operated for a second data-taking campaign (Run 2) with the aim of studying the low-energy response of the detector. Run 2 focused on calibration and response studies using a suite of deployed radioactive sources. Gamma sources (²²Na, ⁶⁰Co, and ⁸⁸Y) were used to probe module-to-module performance variations, energy resolution, and calibrations. Neutron sources (AmBe and a pulsed neutron generator) enabled studies of neutron interactions in liquid argon, including inelastic scatters and neutron capture signals, relevant for low-energy backgrounds and detector modeling. In addition, the detector was doped with ²²⁰Rn, providing Bi–Po coincidence signals that allow precise calibration of the charge and scintillation response. This poster will present results from these calibration campaigns. These studies provide critical validation of the ND-LAr modular LArTPC concept and inform calibration and reconstruction strategies for the full DUNE near detector.

Mora-Lepin, Luis [Florida State U.] (ORCID:0000000↗
DOE OSTI2023

Comparing Measured Driver Behavior Distributions to Results from Car-Following Models using SUMO and Real-World Vehicle Trajectories from Radar: SUMO Default vs. Radar-Measured CF model Parameters

In this study, the physical principles governing car-following (CF) behavior and their impact on traffic flow at signalized intersections are investigated. High temporal-resolution radar data is used to provide valuable insights into actual CF behavior, including acceleration, deceleration, and time headway distribution. Demand-calibrated SUMO simulations are run using empirical CF parameter distributions, and three CF models are evaluated: IDM, EIDM, and Krauss. By emulating radar data in SUMO and processing simulated vehicle traces, discrepancies between empirical and simulated parameter distributions are identified. Further analysis includes comparisons with default SUMO CF model parameters. The findings reveal that measured accelerations differ from CF model parameter accelerations and using the empirical value ($\mu = 0.89m/s^2$) leads to unrealistic simulations that fail volume-based calibration. Default parameters for all three models reasonably approximate the mean and median of measured parameters, but fail to capture the true distribution shape, partly due to homogeneity when using default parameters. The results show that it is more effective to simulate with the default parameters provided by SUMO rather than using measurements of real-world distributions without additional calibration. Future work will investigate closing the loop between the measured real-world and SUMO distributions using traditional calibration tactics, as well as assess the impact of calibrated vs. default CF parameters on simulation outputs like fuel consumption.

Schrader, Max (ORCID:0000000336737672)↗
DOE OSTI2026

Building Energy Analysis of Manufactured and Multifamily Housing Types in Juneau, Alaska

This report details the results of building energy modeling analysis evaluating the potential energy savings, economic outcomes, and grid-level electricity reduction associated with cold climate air source heat pump (ccASHP) adoption across multifamily and manufactured housing (MMFH) building typologies in the City and Borough of Juneau (CBJ). Three building archetypes were evaluated: multifamily 4-plex apartments, multifamily 8-plex apartments, and manufactured housing units. Building energy models were developed using OpenStudio-HPXML and calibrated to actual utility consumption data and local meteorological data from the Juneau International Airport weather station using an automated calibration tool following the BPI-2400-S-2015 v.2 standard for model calibration. Occupant behaviors present the greatest variability in successful calibrations. Calibrated models were benchmarked against a baseline electric resistance heating condition, with the selected ccASHP modeled as the retrofit condition and typical meteorological year weather data for all results generation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Cosmological constraints from the Planck cluster catalogue with DES shear profiles and Chandra observations

We present cosmological constraints from the Planck PSZ2 cosmological cluster sample, using weak-lensing shear profiles from Dark Energy Survey (DES) data and X-ray observations from the Chandra telescope for the mass calibration. We compute hydrostatic mass estimates for all clusters in the PSZ2 sample with a scaling relation between their Sunyaev-Zeldovich signal and X-ray derived hydrostatic mass, calibrated with the Chandra data. We introduce a method to correct these masses with a hydrostatic mass bias using shear profiles from wide-field galaxy surveys. We simultaneously fit the number counts of the PSZ2 sample and the mass calibration with the DES data, finding $Ω_\text{m}=0.312^{+0.018}_{-0.024}$, $σ_8=0.777\pm 0.024$, $S_8\equiv σ_8 \sqrt{Ω_\text{m} / 0.3}=0.791^{+0.023}_{-0.021}$, and $(1-b)=0.844^{+0.055}_{-0.062}$ for our baseline analysis when combined with BAO data. When considering a hydrostatic mass bias evolving with mass, we find $Ω_\text{m}=0.353^{+0.025}_{-0.031}$, $σ_8=0.751\pm 0.023$, and $S_8=0.814^{+0.019}_{-0.020}$. We verify the robustness of our results by exploring a variety of analysis settings, with a particular focus on the definition of the halo centre used for the extraction of shear profiles. We compare our results with a number of other analyses, in particular two recent analyses of cluster samples obtained from SPT and eROSITA data that share the same mass calibration data set. We find that our results are in overall agreement with most late-time probes, in very mild tension with CMB results (1.6$σ$), and in significant tension with results from eROSITA clusters (2.9$σ$). We confirm that our mass calibration is consistent with the eROSITA analysis by comparing masses for clusters present in both Planck and eROSITA samples, eliminating it as a potential cause of tension.

Aymerich, G. [Orsay, IAS; AIM, Saclay] (ORCID:0009↗
DOE OSTI2020

Measurement of space charge effects in the MicroBooNE LArTPC using cosmic muons

Large liquid argon time projection chambers (LArTPCs), especially those operating near the surface, are susceptible to space charge effects. In the context of LArTPCs, the space charge effect is the build-up of slow-moving positive ions in the detector primarily due to ionization from cosmic rays, leading to a distortion of the electric field within the detector. This effect leads to a displacement in the reconstructed position of signal ionization electrons in LArTPC detectors ("spatial distortions"), as well as to variations in the amount of electron-ion recombination experienced by ionization throughout the volume of the TPC. We present techniques that can be used to measure and correct for space charge effects in large LArTPCs by making use of cosmic muons, including the use of track pairs to unambiguously pin down spatial distortions in three dimensions. Finally, the performance of these calibration techniques are studied using both Monte Carlo simulation and MicroBooNE data, utilizing a UV laser system as a means to estimate the systematic bias associated with the calibration methodology.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Bayesian parameter estimation and evaluation of the K -ω shear stress transport model for plane impinging jets

Numerical simulations with semi-empirical turbulence models are commonly used to model impinging jets, often used for cooling solid surfaces. In this work, the constants in the k-ω shear stress transport model in ANSYS FLUENT are calibrated to experimental velocity and heat transfer data for a plane turbulent impinging air jet to determine if Kennedy-O'Hagan calibration (Kennedy and O'Hagan 2001 J. R. Stat. Soc. B 63 425–64) can improve predictions of near-surface velocities and surface Nusselt numbers for similar flows. Impinging jets have been proposed to cool the target plates of the divertor in future magnetic fusion energy reactors, where simulations are used to estimate divertor performance. The flat-plate divertor (Wang et al 2009 Fusion Sci. Technol .56 1023–7) uses a plane jet of helium issuing from a B = 0.5 mm slot to cool a surface with radius of curvature of 44 B at a distance 4 B from the slot. Predictions from the calibrated numerical model are compared with independent experimental data at different flow conditions, as well as surface temperature data for a flat plate divertor test section. The contribution of this work is evaluation of the accuracy of a calibrated turbulence model for modest extrapolations in flow geometry and flow conditions for a plane impinging jet.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Suppressing simulation bias in multi-modal data using transfer learning

Abstract Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

StarDICE III: characterization of the photometric instrument with a collimated beam projector

The measurement of Type Ia supernovae magnitudes provides cosmological distances, which constrain dark energy parameters. Current and upcoming large photometric surveys require improved photometric calibration precision to reduce systematic uncertainties in cosmological constraints. The StarDICE experiment aims to establish accurate broad-band flux references for these surveys, targeting sub-percent precision in magnitude measurements. Achieving this requires precise filter bandpass measurements for both StarDICE and survey instruments with sub-nanometre accuracy. To this end, we developed the Collimated Beam Projector (CBP), an optical device for calibrating the throughput of astronomical telescopes and their filters. The CBP uses a tunable laser source and a reversed telescope to emit a parallel monochromatic light beam, continuously monitored in flux and wavelength. The CBP output flux is measured with a large-area photodiode calibrated relative to a NIST photodiode. Using CBP measurements, we derive the StarDICE telescope throughput and filter transmissions, anchoring them to NIST’s absolute calibration. After analysing systematic uncertainties, we achieved sub-nanometre accuracy for filter central wavelengths, measured filter transmission with ~0.5 per cent precision per 1 nm bin, and detected out-of-band leakages at a relative level of 10 –4 ⁠. Furthermore, we synthesized equivalent transmission for full pupil illumination from four sampled positions in the StarDICE telescope mirror, with ~0.2 nm accuracy for central wavelengths and 7 mmag for broad-band fluxes. This demonstrates our ability to characterize telescope throughput down to the millimagnitude, paving the way for future developments, such as the Rubin-CBP for measuring the LSST at Vera Rubin Observatory, and a portable CBP version for in-situ transmission monitoring.

Calibration↗
DOE OSTI2025

In-orbit operation of Resolve Filter Wheel and modulated X-ray source

The Resolve soft X-ray spectrometer is a high spectral resolution microcalorimeter spectrometer for the X-ray Imaging and Spectroscopy Mission. In the beam of Resolve, there is a filter wheel containing X-ray filters. In the beam, there is also an active calibration source, the modulated X-ray source (MXS), which can provide pulsed X-rays to facilitate gain calibration. The filter wheel consists of six filter positions. Two open positions, one 55 Fe source to aid in spectrometer characterization during the commissioning phase, and three transmission filters: a neutral density filter, an optical blocking filter, and a beryllium filter. The X-ray intensity, pulse period, and pulse separation of an MXS are highly configurable. Furthermore, the switch-on time is synchronized with the spacecraft’s internal clock to give accurate start and end times of the pulses. One of the issues raised during ground testing was the susceptibility of an MXS at high voltage to ambient light. Although measures were taken to mitigate the light leak, the efficacy of those measures must be verified in orbit. Along with an overview of issues raised during ground testing, we will discuss the calibration source and the filter performance in-flight and compare with the transmission curves present in the Resolve calibration database.

X-ray Imaging and Spectroscopy Mission/Resolve↗
DOE OSTI2022

Automatic Qubit Characterization and Gate Optimization with QubiC

As the size and complexity of a quantum computer increases, quantum bit (qubit) characterization and gate optimization become complex and time-consuming tasks. Current calibration techniques require complicated and verbose measurements to tune up qubits and gates, which cannot easily expand to the large-scale quantum systems. We develop a concise and automatic calibration protocol to characterize qubits and optimize gates using QubiC, which is an open source FPGA (field-programmable gate array) based control and measurement system for superconducting quantum information processors. We propose multi-dimensional loss-based optimization of single-qubit gates and full XY-plane measurement method for the two-qubit CNOT gate calibration. We demonstrate the QubiC automatic calibration protocols are capable of delivering high-fidelity gates on the state-of-the-art transmon-type processor operating at the Advanced Quantum Testbed at Lawrence Berkeley National Laboratory. Finally, the single-qubit and two-qubit Clifford gate infidelities measured by randomized benchmarking are of 4.9(1.1) × 10 -4 and 1.4(3) × 10 -2 , respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Solar Radiation Research Laboratory (SRRL) Final Report: Fiscal Years 2019-2021

The Solar Radiation Research Laboratory (SRRL) at the National Renewable Energy Laboratory (NREL) is a world-leading solar calibration and measurement facility and maintains and disseminates the World Radiation Reference (essentially the W/m 2 ) for the United States, which is essential for traceable and accurate measurements of solar radiation at all solar generation facilities. SRRL operates two International Organization for Standardization (ISO)/International Electrotechincal Commission (IEC) 17025 calibration facilities that provide unique, high-quality calibrations to NREL and other U.S. Department of Energy laboratories. The Baseline Measurement System at SRRL provides a high-quality record of solar irradiance and surface meteorological conditions. SRRL capabilities are used to develop: improved methods for the calibration of solar radiometers; new standards through the ISO, the IEC, and ASTM International; models; advanced instrumentation and methods for operating solar measurement stations. The SRRL data sets are also critical for the validation of new models and data sets, such as the National Solar Radiation Database (NSRDB).

14 SOLAR ENERGY↗
DOE OSTI2026

Solar Radiation Research Laboratory (SRRL) Core Project Final Report: Fiscal Years 2022-2024

The Solar Radiation Research Laboratory (SRRL) at the National Laboratory of the Rockies (NLR) is a world-leading solar calibration and measurement facility and maintains and disseminates the World Radiation Reference (essentially the W/m2) for the United States that is essential for traceable and accurate measurements of solar radiation at all solar generation facilities. SRRL operates two calibration facilities that meet International Standards Organization-17025 (ISO-17025) standards and provide unique high-quality calibrations to NREL and other U.S. Department of Energy laboratories. The Baseline Measurement System (BMS) at SRRL provides a high-quality record of solar irradiance and surface meteorological conditions. SRRL capabilities are used to develop (1) improved methods for the calibration of solar radiometers; (2) new standards through the ISO, the International Electrotechnical Commission (IEC), and the American Standards for Testing of Materials (ASTM) International; (c) solar radiation and meteorological models; and (d) advanced instrumentation and methods for operating solar measurement stations. The SRRL datasets are also critical for the validation of new models and datasets, such as the National Solar Radiation Database (NSRDB). The research and development of solar radiation measurement systems and resource modeling techniques are essential for advancing the scientific basis for producing reliable resource data. Specifically, the spatial, temporal, and spectral (wavelength dependency) characteristics of the solar resource are required in several different time frames for various project phases.

14 SOLAR ENERGY↗
DOE OSTI2020

Mathematical Underpinnings of the Multiwavelength Structure of the Tip of the Red Giant Branch

We consider the application of the tip of the red giant branch (TRGB) in the optical and in the near-infrared for the determination of distances to nearby galaxies. We analyze ACS VI (F555W and F814W) data and self-consistently cross-calibrate WFC3-IR JH (F110W and F120W) data using an absolute magnitude calibration of M {sub I} = −4.05 mag as determined in the Large Magellanic Cloud using detached eclipsing binary star geometric parallaxes. We demonstrate how the optical and near-infrared calibrations of the TRGB method are mathematically self-consistent, and illustrate the mathematical basis and relations among these multiwavelength calibrations. We go on to present a method for determining the reddening, extinction, and the true modulus to the host galaxy using multiwavelength data. The power of the method is that with high-precision data, the reddening can be determined using the TRGB stars themselves, and decreases the systematic (albeit generally small) uncertainty in distance due to reddening for these halo stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Dependence of Cosmological Constraints on Gray Photometric Zero-point Uncertainties of Supernova Surveys

Abstract Type Ia supernova (SN) measurements of the Hubble constant, H 0 ; cosmic mass density, Ω M ; and dark energy equation-of-state parameter, w , rely on heterogeneous SN surveys across three decades of observation. These distinct surveys may have undiagnosed, relative photometric zero-point errors. We determine the sensitivities of the SH0ES+Pantheon+ cosmological constraints to unknown gray systematics in the photometric zero-point calibration between the 19 surveys that comprise the Pantheon+ SN compendium. Varying the surveys’ gray zero-points simultaneously with cosmological parameters, we determine that the SH0ES+Pantheon+ measurement of H 0 is robust against gray intersurvey photometric miscalibration. Specifically, uncalibrated intersurvey systematics could represent a source of uncertainty no larger than 0.39 km s −1 Mpc −1 for H 0 . This modest increase in H 0 uncertainty could not account for the 7 km s −1 Mpc −1 “Hubble tension” between the SH0ES measurement of H 0 and the Planck ΛCDM-based inference of H 0 . However, the SH0ES+Pantheon+ best-fit values of Ω M and w are not robust against gray zero-point error, slipping by up to 0.16 and −0.63. Because measurements of Ω M and w depend on intrasurvey cross-band calibration, the hypothetical gray miscalibration underestimates the sensitivity of these measurements to zero-point miscalibration. Because the Pantheon+ compendium contains many surveys that share low- z Hubble flow (HF) and Cepheid-paired SNe, intersurvey photometric calibration errors do not significantly impede the joint use of SH0ES and Pantheon+ to measure H 0 to 1% accuracy. However, H 0 constraints that rely on one HF survey but numerous galactic distance calibration surveys are susceptible to intersurvey photometric miscalibration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

AMS / BARC Joint Survey Addendum Technical Report

This report details the data analysis results from data collected during the September-October 2019 joint survey series between the Department of Energy (DOE) National Nuclear Security Administration (NNSA) Aerial Measuring System (AMS) asset, and the Government of India’s Department of Atomic Energy (DAE) Bhabha Atomic Research Centre (BARC). This joint survey series took place at the Nevada National Security Site (NNSS), areas of public land in Southern Nevada, and at the DOE Office of Legacy Management (LM) Large Area Calibration Pads (LACP) at the Grand Junction Regional Airport in Grand Junction, CO. The data detailed in this report were gathered at the LACP. More details regarding the setup of these surveys and personnel involvement can be found in the AMS / BARC Joint Survey Summary Report. The data from the LACP collected by AMS are consistent with past data collection campaigns at the LACP. The AMS Bell 412 (B412) helicopter, equipped with both the standard AMS thallium-doped sodium iodide (NaI(Tl)) 2"x4"x16" 12-detector system and a subset of the BARC Aerial Gamma Spectroscopy System (AGSS) consisting of two independent 3"x3"cylindrical NaI(Tl) detectors, measured each of the five calibration pads for a minimum of 10 minutes. After the helicopter measurements, a pressurized ion chamber (PIC) was used to directly measure radiation exposure rates on each of the pads for a period 5 minutes. The geophysical calibration procedure published by the International Atomic Energy Agency (IAEA) (Erdi-Krausz et al. 2003) was used to determine the spectral stripping sensitivity matrix for naturally occurring radiological material (NORM). The sensitivity matrix is consistent with the AMS long term average. Additionally, the PIC data was used to calibrate the AMS system for exposure rate. The results of the exposure rate analysis are also consistent with AMS long-term averages.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Rapid Inverse Parameter Inference Using Physics-Informed Neural Network

As Li-ion batteries become more essential in today's economy, tools need to be developed to accurately and rapidly diagnose a battery's internal state-of-health. Using a Li-ion battery's (high-rate) voltage response, it is proposed to determine a battery's internal state through Bayesian calibration. However, Bayesian calibration is notoriously slow and requires thousands of model runs. To accelerate parameter inference using Bayesian calibration, a surrogate model is developed to replace the underlying physics-based Li-ion model. Developing a surrogate model for rapid Bayesian calibration analysis is discussed for both the single particle model (SPM) and the pseudo two-dimensional (P2D) model. Surrogate models are constructed using physics-informed neural networks (PINNs) that encode the influence of internal properties on observed voltage responses. In practice, a neural network can be trained by: 1) using simulation results of the physics-based model (i.e., a data-loss approach); 2) using the residuals of the governing equations themselves (i.e., a physics-loss approach); or 3) using a combination of simulation results and governing equation residuals. In the present work, PINNs are developed using a variety of training losses and neural network architectures. In this analysis, it is shown that a PINN surrogate model can be reliably trained with only physics-informed loss. However, using a coupled data-informed and physics-loss approach produced the most accurate PINNs.

Bayesian calibration↗
DOE OSTI2022

Quantifying subsurface parameter and transport uncertainty using surrogate modelling and environmental tracers

Here, we combine physics-based groundwater reactive transport modelling with machine-learning techniques to quantify hydrogeological model and solute transport predictive uncertainties. We train an artificial neural network (ANN) on a dataset of groundwater hydraulic heads and 3 H concentrations generated using a high-fidelity groundwater reactive transport model. Using the trained ANN as a surrogate model to reproduce the input–output response of the high-fidelity reactive transport model, we quantify the posterior distributions of hydrogeological parameters and hydraulic forcing conditions using Markov chain Monte Carlo calibration against field observations of groundwater hydraulic heads and 3 H concentrations. We demonstrate the methodology with a model application that predicts Chlorofluorocarbon-12 (CFC-12) solute transport at a contaminated field site in Wyoming, United States. Our results show that including 3 H observations in the calibration dataset reduced the uncertainty in the estimated permeability field and infiltration rates, compared to calibration against hydraulic heads alone. However, predictive uncertainty quantification shows that CFC-12 transport predictions conditioned to the parameter posterior distributions cannot reproduce the field measurements. We found that calibrating the model to hydraulic head and 3 H observations results in groundwater mean ages that are too large to explain the observed CFC-12 concentrations. The coupling of the physics-based reactive transport model with the machine-learning surrogate model allows us to efficiently quantify model parameter and predictive uncertainties, which is typically computationally intractable using reactive transport models alone.

58 GEOSCIENCES↗
DOE OSTI2024

Establishing performance metrics for quantitative non-targeted analysis: a demonstration using per- and polyfluoroalkyl substances

Abstract Non-targeted analysis (NTA) is an increasingly popular technique for characterizing undefined chemical analytes. Generating quantitative NTA (qNTA) concentration estimates requires the use of training data from calibration “surrogates,” which can yield diminished predictive performance relative to targeted analysis. To evaluate performance differences between targeted and qNTA approaches, we defined new metrics that convey predictive accuracy, uncertainty (using 95% inverse confidence intervals), and reliability (the extent to which confidence intervals contain true values). We calculated and examined these newly defined metrics across five quantitative approaches applied to a mixture of 29 per- and polyfluoroalkyl substances (PFAS). The quantitative approaches spanned a traditional targeted design using chemical-specific calibration curves to a generalizable qNTA design using bootstrap-sampled calibration values from “global” chemical surrogates. As expected, the targeted approaches performed best, with major benefits realized from matched calibration curves and internal standard correction. In comparison to the benchmark targeted approach, the most generalizable qNTA approach (using “global” surrogates) showed a decrease in accuracy by a factor of ~4, an increase in uncertainty by a factor of ~1000, and a decrease in reliability by ~5%, on average. Using “expert-selected” surrogates ( n = 3) instead of “global” surrogates ( n = 25) for qNTA yielded improvements in predictive accuracy (by ~1.5×) and uncertainty (by ~70×) but at the cost of further-reduced reliability (by ~5%). Overall, our results illustrate the utility of qNTA approaches for a subclass of emerging contaminants and present a framework on which to develop new approaches for more complex use cases. Graphical Abstract

Pu, Shirley (ORCID:0000000201223797)↗