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DOE OSTI2026

Observation of Charmonium Sequential Suppression in Heavy-Ion Collisions at the Relativistic Heavy Ion Collider

We report measurements of charmonium sequential suppression in Ru+Ru and Zr+Zr collisions at $\sqrt{s_{NN}}$ =200 GeV with the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The inclusive yield ratio of 𝜓⁡(2⁢S) to J/𝜓 as a function of transverse momentum is reported, along with the centrality dependence of the double ratio, defined as the 𝜓⁡(2⁢S) to J/𝜓 ratio in heavy-ion collisions relative to that in 𝑝 +𝑝 collisions. In the 0–80% centrality class, the double ratio is found to be 0.41±0.10 (stat)±0.03 (syst)±0.02 (ref), lower than unity with a significance of 5.6 standard deviations. This provides experimental evidence that 𝜓⁡(2⁢S) is significantly more suppressed than J/𝜓 in heavy-ion collisions at RHIC. This sequential suppression pattern seems to increase from peripheral to central collisions, but with no significant dependence on the transverse momentum.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Chemical and Structural Insights into Solid Electrolyte Interphase Evolution for Sodium Metal Electrodes

The solid electrolyte interphase (SEI) critically governs the reversibility of sodium metal batteries, through dynamically mediating ion transport and interfacial reactions. However, its kinetic evolution under operating conditions, and how it influences interfacial stability, remains poorly understood. Here, in this study, we reveal that the SEI undergoes coupled chemical and mechanical changes during sodium plating and stripping, leading to spatial and temporal heterogeneity that drives interfacial degradation. Synchrotron operando grazing-incidence wide-angle X-ray scattering and soft X-ray absorption spectroscopy capture the sequential formation and dissolution of inorganic SEI phases (NaF, NaH, NaOH, Na 2 PO 3 F), accompanied by depth-dependent alterations in organic SEI components. Mesoscale modeling connects this evolving SEI heterogeneity to localized current density fluctuations and stress accumulation at the Na interface, identifying pathways to electrically isolated sodium formation. These findings show that SEI instability fundamentally limits reversibility in sodium metal batteries, and that controlling SEI chemistry–mechanics coupling is essential to achieving its durability.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Flavour-dependent chemical freeze-out of light nuclei in relativistic heavy-ion collisions

We study the production of light nuclei in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7–200 GeV and Pb+Pb collisions at $\sqrt{s_{NN}}$ = 2.76 and 5.02 TeV within a flavour-dependent chemical freeze-out scenario, assuming different flavoured hadrons undergo separate chemical freeze-out. Using the Thermal-FIST package, thermal parameters extracted from fits to various sets of hadron yields, including and excluding light nuclei, are used to calculate the ratios of the yields of light nuclei, namely, d/p, $\overline{d}$/$\overline{p}$, t/p, t/d, 4 He/ 3 He, and 3 H Λ / 3 He. A comparison with experimental data from the STAR and ALICE collaborations shows that a sequential freeze-out scenario provides a better description of light nuclei yield ratios than the traditional single freeze-out approach. These results suggest the flavour-dependent chemical freeze-out for final state light nuclei production persists in heavy-ion collisions at both RHIC and LHC energies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Exact enforcement of temporal continuity in sequential physics-informed neural networks

The use of deep learning methods in scientific computing represents a potential paradigm shift in engineering problem solving. One of the most prominent developments is Physics-Informed Neural Networks (PINNs), in which neural networks are trained to satisfy partial differential equations (PDEs). While this method shows promise, the standard version has been shown to struggle in accurately predicting the dynamic behavior of time-dependent problems. To address this challenge, methods have been proposed that decompose the time domain into multiple segments, employing a distinct neural network in each segment and directly incorporating continuity between them in the loss function of the minimization problem. In this work we introduce a method to exactly enforce continuity between successive time segments via a solution ansatz. This hard constrained sequential PINN (HCS-PINN) method is simple to implement and eliminates the need for any loss terms associated with temporal continuity. The method is tested for a number of benchmark problems involving both linear and non-linear PDEs. Examples include various first order time dependent problems in which traditional PINNs struggle, namely advection, Allen–Cahn, and Korteweg–de Vries equations. Furthermore, second and third order time-dependent problems are demonstrated via wave and Jerky dynamics examples, respectively. Notably, the Jerky dynamics problem is chaotic, making the problem especially sensitive to temporal accuracy. Finally, the numerical experiments conducted with the proposed method demonstrated superior convergence and accuracy over both traditional PINNs and the soft-constrained counterparts.

42 ENGINEERING↗

DOE OSTI2026

Temperature‐Dependent Crystallization in Two‐Step Perovskite Deposition Revealed by In Situ GIWAXS and Machine Learning‐Guided Analysis

The performance and stability of perovskite solar cells are strongly governed by the crystallization behavior of their active layer. In two-step sequential deposition, early-stage film formation plays a decisive role in determining final phase purity and device quality. Guided by a data-driven analysis of nearly 39 000 devices in the FAIR perovskite database, we identified solvent-mediated quenching and thermal processing as key variables affecting power conversion efficiency (PCE), particularly in two-step fabrication. Here, to investigate these effects in real time, we designed and implemented a custom-built, temperature-controlled spin-coating system, enabling precise thermal modulation during precursor deposition. Using this platform, we performed in situ GIWAXS measurements to study the crystallization dynamics of FA 0.5 MA 0.5 PbI 3 films over a temperature range of 30°C–90°C. Our results reveal a non-monotonic relationship between spin-coating temperature and α-phase formation, governed by the interplay between precursor interdiffusion, PbI 2 crystallinity, and δ-phase suppression. The custom thermal control enabled us to isolate and quantify these competing effects during the earliest stages of film formation, providing mechanistic insight into how spin-coating temperature governs both phase purity and kinetic pathways in two-step perovskite systems. Temperature-dependent SEM and photovoltaic device measurements further demonstrate that early-stage crystallization pathways directly translate into differences in morphology, charge-transport continuity, and device performance. These findings inform targeted strategies for optimizing deposition protocols to balance rapid nucleation, phase stability, and device performance.

Saadawy, Ahmed [King Fahd University of Petroleum ↗

DOE OSTI2024

Qudit Gate Decomposition Dependence for Lattice Gauge Theories

In this work, we investigate the effect of decomposition basis on primitive qudit gates on superconducting radio-frequency cavity-based quantum computers with applications to lattice gauge theory. Three approaches are tested: SNAP & Displacement gates, ECD & single-qubit rotations $R(\theta,\phi)$, and optimal pulse control. For all three decompositions, implementing the necessary sequence of rotations concurrently rather then sequentially can reduce the primitive gate run time. The number of blocks required for the faster ECD &$R_p(\theta)$ is found to scale $\mathcal{O}(d^2)$, while slower SNAP & Displacement set scales at worst $\mathcal{O}(d)$. For qudits with $d<10$, the resulting gate times for the decompositions is similar, but strongly-dependent on experimental design choices. Optimal control can outperforms both decompositions for small $d$ by a factor of 2-12 at the cost of higher classical resources. Lastly, we find that SNAP & Displacement are slightly more robust to a simplified noise model.

Kürkçüoglu, Doga Murat↗

DOE OSTI2024

Quantum Criticality Under Imperfect Teleportation

Entanglement, measurement, and classical communication together enable teleportation of quantum states between distant parties, in principle, with perfect fidelity. To what extent do correlations and entanglement of a many-body wave function transfer under teleportation protocols? We address this question for the case of an imperfectly teleported quantum critical wave function, focusing on the ground state of a critical Ising chain. We demonstrate that imperfections, e.g., in the entangling gate adopted for a given protocol, effectively manifest as weak measurements acting on the otherwise pristinely teleported critical state. Armed with this perspective, we leverage and further develop the theory of measurement-altered quantum criticality to quantify the resilience of critical-state teleportation. We identify classes of teleportation protocols for which imperfection (i) preserves both the universal long-range entanglement and correlations of the original quantum critical state, (ii) weakly modifies these quantities away from their universal values, and (iii) obliterates long-range entanglement altogether while preserving power-law correlations, albeit with a new set of exponents. We also show that mixed states describing the average over a series of sequential imperfect teleportation events retain pristine power-law correlations due to a “built-in” decoding algorithm, though their entanglement structure measured by the negativity depends on errors similarly to individual protocol runs. These results may allow one to design teleportation protocols that optimize against errors—highlighting a potential practical application of measurement-altered criticality. Published by the American Physical Society 2024

Physics↗

DOE OSTI2025

Laser Powder Directed Energy Deposition of Steels for Nuclear Applications

This comprehensive investigation examines the structure–property relationships in two nuclear alloy systems—Alloy 709 (A709) austenitic stainless steel and Grade 92 (G-92) ferritic/martensitic (F/M) steel—manufactured via directed energy deposition (DED) for sodium-cooled fast reactor applications. This study establishes the fundamental mechanisms for controlling microstructures for optimizing the mechanical performance of additively manufactured nuclear materials through systematic heat treatment optimization and multiscale characterization. As-deposited A709 steel develops a complex multiscale strengthening architecture consisting of a fine cellular solidification structure with diameter of 2-3 µm within10–50 µm grains, elevated dislocation densities from rapid thermal cycling, and grain boundary precipitates that activate concurrent Hall–Petch, dislocation, and precipitation hardening mechanisms to achieve exceptional properties [yield strength (YS): 603 MPa, ultimate tensile strength (UTS): 844 MPa, Vickers hardness: 220 HV] that achieve a 44% superior strength compared to that of the wrought material. Heat treatments produce different results. Solution annealing (SA) dissolves the cellular structure and reduces the hardness to 190 HV. Precipitation treatment (PT) keeps the cellular structure but adds carbides, allowing the hardness to reach 205 HV. The best approach combines both treatments (SA+PT) and creates uniform precipitate distributions with M 23 C 6 carbides at the grain boundaries and MX carbonitrides in the matrix, achieving a hardness of 195 HV. However, directional differences persist, with a 12%–15% strength variation between orientations due to the inherited layered microstructural architecture that survives aggressive heat treatment. While tensile testing at 550°C demonstrates 40%–50% thermal softening with dynamic strain aging, DED A709 steel still maintains a 71% higher YS than that of the wrought material. Ion irradiation studies (100–400 dpa) of DED A709 steel reveal progressive radiation damage with increasing void density and radiation-induced segregation causing nickel enrichment and chromium depletion, which will ultimately compromise mechanical properties. As-deposited G-92 exhibits exceptional strength (UTS: 1650–1700 MPa, 430 HV) through a complex microstructure containing both ferrite and martensite phases, a high geometrically necessary dislocation (GND) density (17.04×10 14 /m 2 ), and fine carbides. Heat treatments create distinct changes. Normalizing produces fresh martensite with the highest hardness (460 HV) and an increased GND density (20.23×10 14 /m 2 ). Tempering develops dual precipitation systems and reduces the hardness to 290 HV. The optimal approach uses sequential normalizing plus tempering, achieving balanced properties with the lowest hardness (250 HV) and a reduced GND density (11.01×10 14 /m 2 ). A processing-dependent anisotropy is observed: horizontal specimens achieve superior ductile behavior, while vertical specimens exhibit brittle failure. A tempering heat treatment successfully mitigates this anisotropic behavior by transforming the hard martensitic as-deposited structure into tempered martensite enabling both horizontal and vertical specimens to exhibit similar stress–strain characteristics with visible necking behavior. Remarkably, testing at 550°C reveals a reversal in the anisotropy, where as-deposited specimens achieve near isotropy with superior thermal stability (a 15%–20% strength reduction), while tempered specimens develop an orientation dependence with a 25%–30% strength reduction. Both alloy systems demonstrate that DED processing creates specimens with a superior strength through refined microstructural features, though with distinct strengthening mechanisms—austenitic through cellular structures and precipitates versus F/M through phase transformations and precipitates. Heat treatment optimization requires alloy-specific approaches, with A709 benefiting from controlled precipitation while G-92 requires careful phase transformation control. The results show that DED manufacturing can produce nuclear materials with exceptional performance, but directional effects and temperature-dependent behavior must be carefully considered for reactor component design and qualification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Linking Manganese Fractions and Organic Carbon in Soils of Contrasting Land Use Systems

Manganese (Mn) is an essential micronutrient that influences carbon (C) cycling by binding or oxidizing soil organic matter. Mn fractions in soil and their plant availability depend largely on soil pH, which is commonly altered by agricultural practices. Fractions of Mn in soils range from readily available (e.g., bioavailable Mn, exchangeable Mn) to stabilized (e.g., Mn-oxide minerals, Mn contained in silicates). Furthermore, the distribution of soil Mn with depth was assessed in contrasting land use systems (organic agriculture, conventional agriculture, and unmanaged forest) using a sequential extraction method that targets Mn fractions ranging in bioavailability. Both agricultural sites had similar amounts of total Mn but had available Mn (1–7% of total Mn) lower than that of the unmanaged forested site (15% of total Mn). Manganese and organic C were generally positively correlated, but this relationship depended on soil depth, Mn fractions, and concentrations, while land management change had little influence.

Crops↗

DOE OSTI2025

A fast computational framework for the design of solvent-based plastic recycling processes

Multicomponent plastics cannot be processed using mechanical recycling technologies, hindering efforts to deal with plastic waste. Multicomponent plastics include multilayer plastic films, which are widely used for food and healthcare packaging. Multilayer films combine several layers (potentially dozens) of different polymers to protect products from external factors (e.g., oxygen, water, temperature, shock, and light). Solvent-based separation processes have emerged as a promising alternative to recycle these complex materials. For instance, the Solvent-Targeted Recovery and Precipitation (STRAP TM ) process uses sequential solvent washes to selectively dissolve and separate constituent polymers from multicomponent plastic waste, including films. STRAP TM process design (separation sequence, type of solvents, and operating conditions) changes significantly depending on the design of the multilayer plastic film (e.g., number, types, and proportions of polymers). The ability to quickly quantify the economic and environmental benefits of diverse STRAP TM process designs is essential to accelerate the development of sustainable recycling processes and more recyclable multilayer film products. In this work, we present a fast computational framework that integrates molecular-scale models, process modeling, and techno-economic and life cycle analysis to quickly evaluate STRAP TM designs. The computational framework is general and can be used to study the processing of complex multilayer plastic waste streams that contain many layers. Furthermore, we highlight the different uses of the framework via targeted case studies.

Computational framework↗

DOE OSTI2026

Exploring the Origins of Anti-Ambipolarity in BBL Polymer: Links to Redox Chemistry, Electronic Structure, and Structural Dynamics

We examine the intrinsic physical-chemical properties of the conjugated ladder-type polymer poly(benzimidazobenzophenanthroline) (BBL) in response to electron transfer. We aim at explaining the origin of the anti-ambipolar behavior behind the observed BBL nonlinear response associated with specific device architectures. To elucidate this point, we use theory and computation based on first principles, including density functional theory optimizations, ab initio molecular dynamics, time-dependent DFT, and Marcus-theory analysis. Our results reveal that this redox response is not simply monotonic but follows an alternating odd/even pattern in which gap narrowing and reopening occur sequentially before near-gapless behavior emerges at high charging. Converging theoretical evidence in this work demonstrates that bell shaped conductivity in BBL originates in its fundamental electronic structure and supramolecular organization.

FOS: Physical sciences↗

DOE OSTI2026

Estimating CO 2 fluxes through integrating spatial and temporal input layers via deep learning algorithms

Background Accurate estimation of net ecosystem exchange of CO 2 fluxes (Fc) is essential for understanding carbon cycle processes and assessing ecosystem carbon budgets. However, conventional modeling approaches often emphasize temporal dynamics while overlooking the pronounced spatial heterogeneity within the footprint of eddy covariance (EC) towers, potentially limiting predictive accuracy and interpretability of Fc estimates. To address this challenge, we developed a spatiotemporal model that integrates high-resolution footprint-weighted spatial information with sequential environmental drivers. Results The integrated model combines a deeper graph convolutional network to characterize fine-scale spatial variability within EC footprints and a gated recurrent unit network to capture temporal dependencies in biophysical conditions. Using multi-year flux tower observations, remote sensing vegetation indices and footprint modeling, we evaluate the proposed method across three land cover types. This spatiotemporal model consistently outperforms temporal-only and spatial-only baselines, achieving the highest overall accuracy (R 2 = 0.9569) and the lowest RMSE (1.8128 μmol m −2 s −1 ) and MAE (1.1939 μmol m −2 s −1 ). Performance gains are particularly evident in ecosystems with strong vegetation heterogeneity, where spatial structure substantially modulates Fc variability. Conclusions This study demonstrates the importance of joint modeling spatial heterogeneity and temporal dynamics for improving Fc estimation and provides a robust method for advancing footprint-based Fc estimates across diverse ecosystems, supporting refined assessments of terrestrial carbon fluxes, and enhancing scientific foundations for carbon studies.

CO2 flux estimate↗

DOE OSTI2025

Sequential Infiltration Synthesis of Bilayer Porous Alumina Nanostructures for Broad-Angle, Broadband Antireflective Coatings

Antireflective coatings (ARCs) are thin films engineered to reduce light reflections. Delivering broadband, wide-angle performance is essential for photovoltaics, imaging, and sensing, yet truly omnidirectional antireflection remains difficult due to angle-dependent optical paths and a narrow palette of suitable refractive indices. Here, in this study, we systematically investigate an emerging class of multilayer inorganic ARCs based on conformally coated nanoporous alumina templated by intrinsically microporous polymers (PIMs) and block copolymers (BCPs). We establish a design framework that maps thickness reflectance relationships to identify thickness pairs minimizing reflection across wavelength and incidence angle. We show that deliberately separating the local reflectance minima of the top and bottom layers in bilayer nanostructures broadens the antireflective bandwidth and angular range. We show that 235-nm single-side bilayer porous alumina nanostructures achieves under 2 % reflectance from 380–750 nm for incidence angles up to 45° with less than 0.6% reflectance for incidence angles under 20°. The approach is readily extensible to additional layers or materials with refractive indices tuned via templated nanoporosity and composition, enabling practical, etch-free ARC fabrication without HF or fluorinated precursors and advancing straightforward design of broadband, wide-angle (quasi-omnidirectional) ARCs for next-generation optical systems.

antireflective coatings↗

DOE OSTI2026

Understanding Solvent-Induced Glass Transition in Polymer Thin Films Using Absorption–Desorption Isotherms

The fundamental thermodynamic and mechanical underpinnings of polymer thin films exposed to solvent vapor are critical for the development of advanced nanolithography and high-performance coatings. This work investigates the solvent− polymer interactions of glassy thin films by using the solvent absorption−desorption isotherms. An analogous relationship to the Flory−Fox equation was observed between solvent−induced glass transition, swelling, Flory−Huggins interaction parameter, and molecular weight. Isothermal swelling measurements revealed that the glass transition trends are more robust in the absorption curve compared to desorption, contrary to previous reports. Excess osmotic pressure analysis of the isotherm provides a measure of the degree of physical aging in thin films annealed below the glass transition. This is further validated in the ordering of block copolymer (BCP) films annealed at low solvent activity. In agreement with the thermal analysis, free-surface plasticization effects become the most prominent below 100 nm. However, solvent annealing is largely dependent on solvent mass transport, as made evident by the strong dependence on solvent viscosity. From these observations, four general types of isotherms are identified that graphically capture distinct solvent−polymer interaction regimes. More broadly, these results inform solvent vapor annealing-induced self-assembly, sequential infiltration synthesis, membrane-based separations, adsorptive processes, and swelling-based responsive materials design.

Hendeniya, Nayanathara [Iowa State Univ., Ames, IA↗

DOE OSTI2026

New correlated numerical methods for attosecond molecular single and double ionization

This project resulted in the development of ASTRA, a new molecular ionization code capable of describing single and double ionization processes in polyatomic molecules with correlated electrons and time-dependent fields. Key achievements include: (i) the development of the PRISM hybrid-integral library enabling core-ionization and high-angular-momentum convergence; (ii) implementation of time-dependent Schrödinger equation solvers for pump–probe spectroscopies; (iii) validation against experimental and theoretical benchmarks for atoms and molecules; and (iv) extension to two-photon double ionization using the finite-pulse virtual-sequential model. These developments establish ASTRA as a versatile platform for attosecond molecular dynamics.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Modeling of Inductive Constant Power Load for Electromagnetic-Transient Simulations-Part II

This paper improves the dynamic constant power (CP) load model that was published in Part I, which is appropriate for electromagnetic-transient (EMT). The improved model conserves all features of its predecessor. For instance, it maintains a fixed power consumption (both active and reactive parts) and a fixed power factor for loads that are predominantly inductive. Furthermore, as the proposed model is a time-dependent system, it is applicable to both sinusoidal and non-sinusoidal case studies. However, the previous model cannot be easily integrated with numeric solvers because it simulated load data over one cycle all together, not sequentially in a time-step manner, due to the limitation involved with the power factor. The improved version, on the contrary, allows the load to be simulated at every time step, which would facilitate its integration with numeric solvers. The model's validity is confirmed by comparing its response with data that is synthesized from constant impedance load, and the result is satisfactory.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Flow-dependent tagging of 214 Pb decays in the LZ dark matter detector

The LUX-ZEPLIN (LZ) experiment is searching for dark matter interactions in a liquid xenon time projection chamber (LXe-TPC). This article demonstrates how control of the flow state in the LXe-TPC enables the identification of pairs of sequential alpha-decays, which are used to map fluid flow and ion drift in the liquid target. The resulting transport model is used to tag 214 Pb~ beta-decays, a leading background to dark matter signals in LZ. Temporally evolving volume selections, at a cost of 9.0% of exposure, target the decay of each 214 Pb~ atom up to 81 minutes after production, resulting in (63±6 (s⁢t⁢a⁢t) ±7 (s⁢y⁢s) )% identification of 214 Pb~decays to ground state. We also demonstrate how flow-based tagging techniques enable a novel calibration side band that is concurrent with science data. Lastly we report updated estimates of radon-chain charge branching fractions in liquid xenon, finding branching to 218 Po + at 0.49±0.01, 214 Pb + at 0.48±0.12, and 214 Bi + at 0.74±0.05, with a mean charged ion lifetime in the LZ TPC of 49±4 min.

Lorenzon, Wolfgang [University of Michigan, Ann Ar↗