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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Boosting the Oxygen Reduction Performance via Tuning the Synergy between Metal Core and Oxide Shell of Metal−Organic Frameworks‐Derived Co@CoO x

Abstract Co core@Co oxide shell (Co@CoO x ) catalysts represent a large family with promising oxygen reduction reaction (ORR) catalytic activity. However, inadequate understanding of Co@CoO x synergy prohibits further pursuit of catalytic performance enhancement. Herein, a Co zeolitic−imidazolate framework was converted into metallic Co, followed by controlled air treatment to form Co@CoO x . The composition and structure evolution as a function of air treatment temperature were studied thoroughly through conventional and synchrotron (both ex‐situ and in‐situ) characterizations, confirming the coexistence of CoO and Co 3 O 4 in the shell. The optimal catalyst showed an ORR half‐wave potential of 0.87 V (vs. RHE) in an alkaline half‐cell and delivered high discharge capacity in an aprotic Li−O 2 battery (7,124 mAh g Cat+C −1 ) and an aqueous Zn−air battery (694 mAh g Zn −1 ) with good performance retention after durability test. Modeling simulation and density functional theory calculation confirmed the charge donation from metal core to oxide shell and shed light on new insights of how metal@metal oxide synergy impacted the ORR via tuning the charge conductivity, oxygen affinity and intermediate transfer pathway. This work opens up a venue to boost ORR catalytic activity from an interfacial synergy perspective.

Wang, Hao↗
DOE OSTI2025

Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification" Willard et al. (2025).

This data release provides all data and code used in the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025)" to model stream temperature, evaluate, and assess results. The associated manuscript explores the effect of different ensemble construction techniques across different common machine learning (ML) architectures for predictions in unmonitored basins. Modeling was done using long short-term memory (LSTM), gated recurrent unit (GRU), temporal convolution network (TCN), and extreme gradient boosting (XGBoost) models, and stream site coverage spans 1362 locations across the conterminous United States. The ensemble construction techniques investigated include ensemble by random weight initialization, differing hyperparameters, different random subsets of training data, different subselections of input features, different architectures, and Monte Carlo Dropout. The data is organized into these items items:Code repository and data for the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025).Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code:- data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repositoryData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2025streamensembles,author = {Jared Willard and Charuleka Varadharajan},title = {Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification"},year = {2024},doi = {10.15485/2527393},publisher = {ESS-DIVE Repository},url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2527393}}MLA: Willard, Jared, et al. Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification". 2025. ESS-DIVE Repository, doi:10.15485/2448016.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A variational method for the sheath potential of hypersonic leading edges with space-charge limitations

Electron transpiration cooling for the leading edges (LE) of hypersonic aircraft utilizes thermionic emission; however, space-charge effects limit the electron emission rate, potentially diminishing the efficiency of this cooling mechanism. We develop a variational weak form of the Poisson equation that describes the sheath potential and then numerically solve it using the finite element method. This formulation has two main benefits: (1) the space-charge limit condition can be incorporated as a constraint and (2) it allows for the analysis of three-dimensional geometries with complex boundary conditions. We demonstrate that the current emitted from the surface of an LE is generally a small fraction of the Child–Langmuir limit due to space charge. We then propose several methods to enhance the emitted current from the surface and to boost the cooling effect of thermionic emission. These include increasing the plasma density, applying a negative surface potential, and using fringe fields under suitable geometric conditions. For a LaB6 emitting LE, the total emitted current is shown to be minimal and independent of the temperature of a surface with floating potential. However, when a negative potential is applied and the surface is heated, the emitted current follows the Richardson–Dushman relationship up to a critical temperature, beyond which it remains constant. At an applied surface potential of −5 V, the critical temperature is around 1700 K.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Anharmonic Cation–Anion Coupling Dynamics Assisted Lithium‐Ion Diffusion in Sulfide Solid Electrolytes

Abstract Sulfide‐based lithium superionic conductors often show higher Li‐ion conductivity than other types of electrolyte materials. This work unveils a unique Li‐ion conductive behavior in these materials through the perspective of anharmonic coupling assisted Li‐ion diffusion. Li hopping events can happen simultaneously with various types of lattice dynamics, while only a statistically important synchronization of motions may indicate coupling. This method enables a direct evaluation of the coupling strength between these motions, which more fundamentally decides if a specific type of lattice motion is really anharmonically coupled to the Li hopping event and whether the coupling can facilitate the Li diffusion. By a new ab initio computational approach, this work unveils a unique phenomenon in prototype sulfide electrolytes in comparison with typical halide ones, that Li‐ion conduction can be boosted by the anharmonic coupling of low‐frequency Li phonon modes with high‐frequency anion stretching or flexing phonon modes, rather than the low‐frequency rotational modes. The coupling pushes Li ions toward the diffusion channels for reduced diffusion barriers. The result from the lower temperature range (≈0–300 K) of simulation can also be more relevant to the application of solid‐state batteries.

Chemistry↗
DOE OSTI2022

Advanced Thin Film Core Technology: CIGS Final Technical Report (FTR)

Cu(In,Ga)Se 2 (CIGS) thin-film photovoltaics are a high-efficiency and reliable technology. This project completed research in two important areas and was designed to work collaboratively with industrial partners. Task 1: Alkali Science focused on alkali post-deposition treatments (PDT). PDTs have been instrumental in the dramatic voltage improvements that have moved CIGS device efficiencies from 20% to 23.35% [1]. Based on a survey of CIGS companies at the beginning of the project, the single biggest breakthrough for the CIGS community would be a mechanistic understanding of the role of alkalis in the material system. Significant accomplishments of Task 1: Alkali Science: 1) KF post-deposition treatments were shown to improve lifetime, open-circuit voltage (VOC), and efficiency of industrial partner samples, even when done as a later step, separate from the original CIGS deposition. 2) KF boosted efficiency when incorporated at the end of the third stage of NREL CIGS growth. 3) XPS characterization of CIGS surfaces with and without PDTs led to a proposed mechanism whereby K drives structural transformation at 350 degrees C that is locked in at room temperature even after K is rinsed away. 4) Published recipes for KF and RbF PDTs. Literature to date did not provide enough detail to quickly reproduce experimental results. 5) Identified most important parameters (RbF cell temperature and lamp setpoint temperature) and set boundaries for successful RbF PDTs. The purpose of Task 2: Cell-level Reliability was to overcome the largest challenges to investor confidence and long product lifetime in CIGS-based photovoltaic products: metastability, shading-induced hot spots, and potential-induced degradation (PID). Key findings were made in each of these areas by studying CIGS reliability at the cell level: 1) Published NREL's cell-level reliability testing procedures along with challenges that were encountered while developing them. These were also distributed to the community through an MRS conference presentation. 2) Decreased metastability by adding a CdS hole-injection layer between the CIGS and Zn(O,S) in the device stack. It also improved device performance. Materials other than CdS can be used for the same purpose. 3) Reduced front-glass PID by replacing soda-lime glass with low-Na borosilicate glass. 4) Found that PID depends on leakage current and light/electrical bias. This will help labs avoid test-specific degradation. 5) Discovered that CIGS can suffer from two different types of PID with different mechanisms. Front is slower and leads to shunting ZnO. Back is faster and degrades the p-n junction. 6) Holding cells at open circuit slows PID compared to short circuit. This affects testing protocols for glass/glass modules.

14 SOLAR ENERGY↗
DOE OSTI2025

Data‐Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data

Understanding rare‐earth element (REE) mineralization mechanisms is essential for developing efficient separation strategies. Although the geochemical pathways that generate REE deposits are qualitatively known, quantitative links between specific conditions and mineralization outcomes remain limited. Herein, the repurpose laboratory REE hydrothermal synthesis data—originally collected for functional‐materials fabrication—as a surrogate for studying mineralization with data‐driven methods. The compiled 1,200+ hydrothermal reaction records and trained three machine‐learning models—K‐nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB)—to predict product elements and phases from precursors, additives, reaction conditions, and engineered features. Validation shows XGB achieves the highest accuracy. Feature importance indicates thermodynamic properties of cations and anions dominate model decisions. Correlations reveal positive relationships among precursor concentration, reaction time, pH, and temperature, consistent with classical crystallization behavior. XGB‐based regressors are built to predict crystallization temperature and pH from precursor/product attributes. Performance is strongest when similar training examples exist, while accuracy declines for underrepresented reactions, notably REE carbonates and heavy‐REE systems. Overall, the study shows that functional‐materials datasets can illuminate REE mineralization and provide priors for exploration and processing. Expanding datasets with less‐studied chemistries and conditions will improve generality and support deposit discovery and more efficient REE recovery.

feature importance analysis↗
DOE OSTI2020

Coupling High-Throughput Experiments and Regression Algorithms to Optimize PGM-Free ORR Electrocatalyst Synthesis

Over the past decades, significant improvement has been achieved in the performance of platinum group metal-free (PGM-free) materials as an alternative to Pt-based electrocatalysts for oxygen reduction reaction (ORR). However, further progress in ORR activity requires evaluation of precursors and synthesis approaches. In response to this challenge, we generated a first of its kind experimental data set of 36 samples using high-throughput synthesis and activity measurements. Several control parameters (e.g., Fe precursor identity, the precursor content, and pyrolysis temperature) were varied. We then developed several state-of-the-art machine learning (ML) based regression models to predict ORR activity, dependent on selected synthesis variables. Through an iterative algorithm, higher prediction accuracy (smaller root-mean-square error) was achieved. We identified that gradient boosting regression (GBR) and support vector regression (SVR), among several methods, work best for this data set. Aided by our ML-based surrogate models, we decided to alter catalyst synthesis conditions, which resulted in a 36% increase in measured ORR activity in comparison to the maximum ORR mass activity value of 21.9 A/g catalyst in the original data set. Overall, this combined experiment and machine learning approach represents a promising path forward toward developing highly efficient next-generation ORR electrocatalysts and, more generally, functional materials.

25 ENERGY STORAGE↗
DOE OSTI2025

Aligning thermal and current quenches with a high density low-Z injection

The conventional approach for thermal quench (TQ) mitigation in a tokamak disruption is through a high- Z impurity injection that radiates away the plasma’s thermal energy before it reaches the wall. The downside is a robust Ohmic-to-runaway current conversion due to the radiatively clamped low post-thermal-quench electron temperature. An alternative approach is to deploy a low- Z (either deuterium or hydrogen) injection that aims to slow down the TQ, and ideally aligns it with the current quench (CQ). This approach has been investigated here via 3D MHD simulations using the PIXIE3D code. By boosting the hydrogen density, a fusion-grade plasma is dilutionally cooled at approximately the original pressure. Energy loss to the wall is controlled by a Bohm outflow condition at the boundary where the magnetic field intercepts a thin plasma sheath at the wall, in addition to Bremsstrahlung bulk losses. Robust MHD instabilities proceed as usual, while the collisionality of the plasma has been greatly increased and parallel transport is now in the Braginskii regime. The main conclusion of this study is that the decreased transport loss along open field lines due to a sufficient low- Z injection slows down the TQ rate to the order of 20 ms, aligned with the CQ timescale for a 15 MA ITER plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Sulfur-Doped Carbon Support Boosts CO2RR Activity of Ag Electrocatalysts

For presentation at the 70th AVS International Symposium and Exhibition. In this work, we show that the activity of Ag electrocatalysts for electrochemical CO2 to CO conversion is improved when supported on sulfur-doped (S-doped) carbon materials. S-doped carbon support was created by treating the heavily sputtered, highly oriented pyrolytic graphite (HOPG) in H2S at elevated temperatures, as confirmed by the S 2p X-ray photoelectron spectroscopy (XPS) peak. Scanning tunneling microscopy (STM) images indicated that Ag nanoparticles supported on S-doped HOPG had similar size distributions as those supported on sulfur-free (S-free) HOPG. While both catalysts reached > 90% CO Faradaic efficiency (FECO) at E = -1.3 V vs. the reversible hydrogen electrode (RHE) in the CO2 reduction reaction (CO2RR), Ag catalysts supported on S-doped HOPG demonstrated 70% higher CO turnover frequency (TOFCO = 3.4 CO/atomAg/s) than those supported on S-free HOPG (TOFCO = 2.0 CO/atomAg/s). Preliminary calculations based on density functional theory (DFT) indicated a more favorable energetic pathway of CO2-to-CO at the C-S-Ag interface, tentatively consistent with experiments. These results hint at a new approach to design active and selective electrocatalysts for CO2 conversion.

Deng, Xingyi↗
DOE OSTI2026

Evapotranspiration partitioning estimates from 8 methods from 47 NEON sites, 2019-2021

This dataset provides daily estimates of evapotranspiration (ET) and the transpiration-to-evapotranspiration ratio (T/ET) across 47 terrestrial National Ecological Observatory Network (NEON) sites spanning diverse environmental and biome conditions in the United States across three years of data (2019-2021). Daily ET is reported in both energy units (MJ m⁻² day⁻¹) and equivalent water depth (mm day⁻¹), assuming a constant latent heat of vaporization of 2.45 MJ/kg. The primary method uses a hybrid recurrent neural network–Penman–Monteith framework (RNN-PM), which integrates physically based surface energy balance constraints with data-driven learning to partition ET into transpiration and evaporation components. Model inputs include in situ meteorological observations (air temperature, vapor pressure deficit, wind speed, and radiation) combined with satellite-derived land surface temperature, leaf area index, and soil moisture. For benchmarking and uncertainty assessment, T/ET estimates from seven additional models are included: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Penman-Monteith (P-M), Two-Source Energy Balance (TSEB), Support Vector Regression (SVR), and Categorical Boosting (CatBoost), among others—spanning empirical, machine-learning, and process-based approaches (see methods section or linked publication for detailed descriptions). Data Package Contents: The dataset a csv files containing daily ET and T/ET estimates for each site and model, along with associated metadata files these variables. Data can be accessed using common spreadsheet software (e.g., Microsoft Excel, LibreOffice) or programming environments such as R or Python. Together, these data support cross-site comparisons of ecosystem water use, evaluation of ET partitioning methods, and development of improved land–atmosphere exchange models.

EARTH SCIENCE > ATMOSPHERE↗
DOE OSTI2026

Tailorable multiferroic tunnel junctions from all-van der Waals multilayer stacking

Multiferroic tunnel junctions (MFTJs) represent a class of multistate, non-volatile spintronic devices, in which electron tunnelling can be manipulated by switching long-range lattice and spin orders. In contrast to conventional oxide-based MFTJs, MFTJs constructed from two-dimensional van der Waals (vdW) crystals promise minimal defect concentration in the constituents and at interfaces, which may allow for probing intrinsic tunnelling physics and the development of high-performance devices. Here, in this study, we construct Fe 3 GeTe 2 /CuInP 2 S 6 /Fe 3 GeTe 2 all-vdW MFTJs by assembling multilayer flakes of ferromagnetic Fe 3 GeTe 2 electrodes and a ferroelectric CuInP 2 S 6 spacer. These MFTJs exhibit four non-volatile resistance states featuring sizable tunnelling magnetoresistance of ∼10 2 % and tunnelling electroresistance of ∼10 4 %. To tune the properties of the vdW MFTJ, we make use of the flexibility in material choice offered by vdW heterostructure devices; we use Fe 3 GeTe 2 /Fe 5 GeTe 2 asymmetric electrodes to boost the tunnelling electroresistance by 10 3 %, we integrate In 2 Se 3 as a ferroelectric with a smaller bandgap to enhance the ON-state current density by 10 4 % to 10 4 A cm −2 and we use Fe 3 GaTe 2 electrodes to demonstrate room temperature operation. Furthermore, when we combine the asymmetric ferromagnetic electrodes with the small-bandgap ferroelectric spacer to construct Fe 3 GeTe 2 /In 2 Se 3 /Fe 5 GeTe 2 MFTJs, we simultaneously realized tunnelling electroresistance of 10 6 % and an ON-state current density of 10 4 A cm −2 , both two orders of magnitude higher than the highest values achieved with conventional oxide-based MFTJs. In the future, our all-vdW MFTJs with the tailorability of all functional layers may make it possible to investigate fundamental aspects of interlayer tunnelling and enable the design of functional magnetoelectric nanodevices.

Xie, Ti [University of Maryland, College Park, MD ↗
DOE OSTI2025

Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications

The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we leverage machine learning (ML) to predict hydrogen-to-metal (H/M) ratios and solution energy by incorporating thermodynamic parameters and local lattice distortion (LLD) as key features. Our best-performing ML model provides improvements to H/M ratios and solution energies over a broad class of medium-entripy alloys (easily extendable to multi-principal-element alloys), such as Ti–Nb-X (X = Mo, Cr, Hf, Ta, V, Zr) and Co–Ni-X (X = Al, Mg, V). Ti–Nb–Mo alloys reveal compositional effects in H-storage behavior, in particular Ti, Nb, and V enhance H-storage capacity, while Mo reduces H/M and hydrogen weight percent by 40–50 %. We attributed results in molybdenum-rich alloys to slow hydrogen kinetics, as validated by our pressure-composition-temperature (PCT) isotherm experiments on pure Ti and Ti 5 Mo 95 alloys. Density functional theory (DFT) and molecular dynamics (MD) simulations also confirm that Ti and Nb promote H diffusion, whereas Mo hinders it, highlighting the interplay between electronic structure, lattice distortions, and hydrogen uptake. Notably, our Gradient Boosting Regression model identifies LLD as a critical factor in H/M predictions. Here, to aid material selection, we present two periodic tables illustrating elemental effects on (a) H 2 wt% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance hydrogen solubility and storage.

08 HYDROGEN↗
DOE OSTI2025

Optimizing the critical temperature and superfluid density of a metal-superconductor bilayer

A promising path to realizing higher superconducting transition temperatures 𝑇 c is the strategic engineering of artificial heterostructures. For example, quantum materials could, in principle, be coupled with other materials to produce a more robust superconducting state. Here, in this work, we add numerical support to the hypothesis that a strongly interacting superconductor weakened by phase fluctuations can boost its 𝑇 c by hybridizing the system with a metal. Using determinant quantum Monte Carlo, we simulate a two-dimensional bilayer composed of an attractive Hubbard model and a metallic layer in two regimes of the interaction strength −|𝑈|. In the strongly interacting regime, we find that increasing the interlayer hybridization 𝑡 ⊥ results in a nonmonotonic enhancement of 𝑇 c , with an optimal value comparable to the maximum 𝑇 c observed in the single-layer attractive Hubbard model, confirming trends inferred from other approaches. In the intermediate coupling regime, when −|𝑈| is close to the value associated with the maximum 𝑇 c of the single-layer model, increasing 𝑡 ⊥ tends to decrease 𝑇 c , implying that the correlated layer was already optimally tuned. Importantly, we demonstrate that the mechanism behind these trends is related to enhancement in the superfluid stiffness, as was initially proposed by Kivelson [Phys. B: Condens. Matter 318, 61 (2002)].

Zhang, Yutan [Univ. of California, Davis, CA (Unit↗
DOE OSTI2020

Effect of water vapor on high-temperature oxidation of NiAl alloy

The high-temperature oxidation of NiAl is studied with dry oxygen and water vapor. The oxidation in H 2 O results in a thicker Al 2 O 3 oxide scale than that in O 2 . The oxide scale formed initially is a single layer of γ-Al 2 O 3 that subsequently transforms into a α-Al 2 O 3 /γ-Al 2 O 3 bilayer structure, in which the inner α-Al2O3 layer formed in H2O has a higher porosity than that in O 2 . Finally, further density functional theory calculations show that H protons derived from H 2 O molecules penetrate into the oxide lattice and boost the formation of lattice vacancies in both α-Al 2 O 3 and γ-Al 2 O 3 , thus enhancing the oxide scale growth.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Design of a Cryogenic Assembly for Comparing Qubit Infrared Packaging Inefficiencies

It is well known that everything emits infrared (IR) light in the form of thermal radiation. IR radiation on superconducting qubits carries enough energy to cause decoherence and so-called “quasiparticle poisoning.” Therefore, proper IR shielding is needed to keep the qubit in a high-coherence state. However, trying to extract IR-specific information from a qubit is difficult, so a microwave kinetic inductance detector (MKID) can be used instead. We show that the IR shielding inefficiencies of MKID packages can only be compared with other packaging inefficiencies. We create a cryogenic assembly CAD design, consisting of magnetic shield cans, a copper mounting plate and mounts, and an MKID enclosure, which thermalizes the assembly to dilution refrigerator temperatures. Phase 1 of this design involves using a narrowband IR laser source to test the IR shielding of packaging at the laser’s wavelength. When the best IR shielding packaging is determined, the superconducting qubit can replace the MKID, allowing the qubit to avoid decoherence due to IR radiation and boost its performance and coherence lifetime.

Mailliard, Robert J. [Fermilab] (ORCID:00090000277↗
DOE OSTI2026

Design of a Cryogenic Assembly for Comparing Qubit Infrared Packaging Inefficiencies

It is well known that everything emits infrared (IR) light in the form of thermal radiation. IR radiation on superconducting qubits carries enough energy to cause decoherence and so-called “quasiparticle poisoning.” Therefore, proper IR shielding is needed to keep the qubit in a high-coherence state. However, trying to extract IR-specific information from a qubit is difficult, so a microwave kinetic inductance detector (MKID) can be used instead. We show that the IR shielding inefficiencies of MKID packages can only be compared with other packaging inefficiencies. We create a cryogenic assembly CAD design, consisting of magnetic shield cans, a copper mounting plate and mounts, and an MKID enclosure, which thermalizes the assembly to dilution refrigerator temperatures. Phase 1 of this design involves using a narrowband IR laser source to test the IR shielding of packaging at the laser’s wavelength. When the best IR shielding packaging is determined, the superconducting qubit can replace the MKID, allowing the qubit to avoid decoherence due to IR radiation and boost its performance and coherence lifetime.

Mailliard, Robert J. [Fermilab] (ORCID:00090000277↗
DOE OSTI2026

Design of a Cryogenic Assembly for Comparing Qubit Infrared Packaging Inefficiencies

It is well known that everything emits infrared (IR) light in the form of thermal radiation. IR radiation on superconducting qubits carries enough energy to cause decoherence and so-called “quasiparticle poisoning.” Therefore, proper IR shielding is needed to keep the qubit in a high coherence state. However, trying to extract IR-specific information from a qubit is difficult, so a microwave kinetic inductance detector (MKID) can be used instead. We show that the IR shielding inefficiencies of MKID packages can only be compared with other packaging inefficiencies. We create a cryogenic assembly CAD design, consisting of magnetic shield cans, a copper mounting plate and mounts, and an MKID enclosure, which thermalizes the assembly to dilution refrigerator temperatures. Phase 1 of this design involves using a narrowband IR laser source to test the IR shielding of packaging at the laser’s wavelength. When the best IR shielding packaging is determined, the superconducting qubit can replace the MKID, allowing the qubit to avoid decoherence due to IR radiation and boost its performance and coherence lifetime.

Mailliard, Robert J. [Fermilab] (ORCID:00090000277↗
DOE OSTI2026

Design of a Cryogenic Assembly for Comparing Qubit Infrared Packaging Inefficiencies

It is well known that everything emits infrared (IR) light in the form of thermal radiation. IR radiation on superconducting qubits carries enough energy to cause decoherence and so-called “quasiparticle poisoning.” Therefore, proper IR shielding is needed to keep the qubit in a high-coherence state. However, trying to extract IR-specific information from a qubit is difficult, so a microwave kinetic inductance detector (MKID) can be used instead. We show that the IR shielding inefficiencies of MKID packages can only be compared with other packaging inefficiencies. We create a cryogenic assembly CAD design, consisting of magnetic shield cans, a copper mounting plate and mounts, and an MKID enclosure, which thermalizes the assembly to dilution refrigerator temperatures. Phase 1 of this design involves using a narrowband IR laser source to test the IR shielding of packaging at the laser’s wavelength. When the best IR shielding packaging is determined, the superconducting qubit can replace the MKID, allowing the qubit to avoid decoherence due to IR radiation and boost its performance and coherence lifetime.

Mailliard, Robert J. [Fermilab] (ORCID:00090000277↗