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At least 1,153 records · Page 64

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Nonrad: Computing nonradiative capture coefficients from first principles

Point defects in semiconductor crystals provide a means for carriers to recombine nonradiatively. This recombination process impacts the performance of devices. We present the Nonrad code that implements the first-principles approach of Alkauskas et al. (2014) [8] for the evaluation of nonradiative capture coefficients based on a quantum-mechanical description of the capture process. An approach for evaluating electron-phonon coupling within the projector augmented wave formalism is presented. We also show that the common procedure of replacing Dirac delta functions with Gaussians can introduce errors into the resulting capture rate, and implement an alternative scheme to properly account for vibrational broadening. Finally, we assess the accuracy of using an analytic approximation to the Sommerfeld parameter by comparing with direct numerical evaluation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

EZ: An efficient, charge conserving current deposition algorithm for electromagnetic particle-in-cell simulations

Here, we present EZ, a novel current deposition algorithm for particle-in-cell (PIC) simulations. EZ calculates the current density on the electromagnetic grid due to macro-particle motion within a time step by solving the continuity equation of electrodynamics. Being a charge conserving hybridization of Esirkepov's method and ZigZag, we refer to it as “EZ” as shorthand for “Esirkepov meets ZigZag”. Simulations of a warm, relativistic plasma with PIConGPU show that EZ achieves the same level of charge conservation as the commonly used method by Esirkepov, yet reaches higher performance for macro-particle assignment-functions up to third-order. In addition to a detailed description of the functioning of EZ, reasons for the expected and observed performance increase are given, and guidelines for its implementation aiming at highest performance on GPUs are provided.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

ThinCurr: An open-source 3D thin-wall eddy current modeling code for the analysis of large-scale systems of conducting structures

In this paper we present a new thin-wall eddy current modeling code, ThinCurr, for studying inductively-coupled currents in 3D conducting structures -- with primary application focused on the interaction between currents flowing in coils, plasma, and conducting structures of magnetically-confined plasma devices. The code utilizes a boundary finite element method on an unstructured, triangular grid to accurately capture device structures. The new code, part of the broader Open FUSION Toolkit, is open-source and designed for ease of use without sacrificing capability and speed through a combination of Python, Fortran, and C/C++ components. Scalability to large models is enabled through use of hierarchical off-diagonal low-rank compression of the inductance matrix, which is otherwise dense. Ease of handling large models of complicated geometry is further supported by automatic determination of supplemental elements through a greedy homology approach. Here, a detailed description of the numerical methods of the code and verification of the implementation of those methods using cross-code comparisons against the VALEN code and Ansys commercial analysis software is shown.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Best of both worlds: Enforcing detailed balance in machine learning models of transition rates

The slow microstructural evolution of materials often plays a key role in determining material properties. When the unit steps of the evolution process are slow, direct simulation approaches such as molecular dynamics become prohibitive and Kinetic Monte-Carlo (kMC) algorithms, where the state-to-state evolution of the system is represented in terms of a continuous-time Markov chain, are instead frequently relied upon to efficiently predict long-time evolution. The accuracy of kMC simulations however relies on the complete and accurate knowledge of reaction pathways and corresponding kinetics. This requirement becomes extremely stringent in complex systems such as concentrated alloys where the astronomical number of local atomic configurations makes the a priori tabulation of all possible transitions impractical. Machine learning models of transition kinetics have been used to mitigate this problem by enabling the efficient on-the-fly prediction of kinetic parameters. While conventional KMC methods based on transition state theory naturally yield reversible dynamics that exactly obey the detailed balance criterion, providing strong guarantees on the properties of the stationary distribution, many recently-proposed ML-based approaches to barrier predictions provide no such guarantees. In this study, we derive conditions under which physics-informed ML architectures exactly enforce the detailed balance condition by construction, even when relying on non-extensive descriptions of states in terms of local environments around mobile defects. In conclusion, using the diffusion of a vacancy in a concentrated alloy as an example, we show that such ML architectures also exhibit superior performance in terms of prediction accuracy, demonstrating that the imposition of physical constraints can facilitate the accurate learning of barriers at no increase in computational cost.

36 MATERIALS SCIENCE↗

DOE OSTI2025

CONFLUX: A standardized framework to calculate reactor antineutrino flux

Nuclear fission reactors are abundant sources of antineutrinos for neutrino physics experiments. The flux and spectrum of antineutrinos emitted by a reactor can indicate its activity and composition, suggesting potential applications of neutrino measurements beyond fundamental scientific studies that may be valuable to society. The utility of reactor antineutrinos for applications and fundamental science is dependent on the availability of precise predictions of these emissions. For example, in the last decade, disagreements between reactor antineutrino measurements and models have inspired revision of reactor antineutrino calculations and standard nuclear databases as well as searches for new fundamental particles not predicted by the Standard Model of particle physics. Past predictions and descriptions of the methods used to generate them are documented to varying degrees in the literature, with different modeling teams incorporating a range of methods, input data, and assumptions. The resulting difficulty in accessing or reproducing past models and reconciling results from differing approaches complicates the future study and application of reactor antineutrinos. The CONFLUX (Calculation Of Neutrino FLUX) software framework is a neutrino prediction tool built with the goal of simplifying, standardizing, and democratizing the process of reactor antineutrino flux calculations. CONFLUX includes three primary methods for calculating the antineutrino emissions of nuclear reactors or individual beta decays that incorporate common nuclear data and beta decay theory. The software is prepackaged with the current nuclear databases, including ENDF.B/VIII, JEFF-3.3, and ENSDF, and it includes the capability to predict time-dependent reactor emissions, adjust nuclear database or beta decay inputs/assumptions, and propagate related sources of uncertainty. Here, this paper describes the CONFLUX software structure, details the methods used for flux and spectrum calculations, and provides examples of potential use cases.

Zhang, Xianyi [Lawrence Livermore National Laborat↗

DOE OSTI2020

Revisiting the π → π* transition of the nitrite ion at the air/water interface: A combined experimental and theoretical study

Broadband deep ultraviolet electronic sum frequency generation spectroscopy allows measurement of electronic |χ (2) | 2 -spectra at aqueous interfaces, providing considerable improvement over deep ultraviolet electronic second harmonic generation spectroscopy that yields error-prone, pointwise spectra. Reexamination of the π → π* transition of nitrite at the air/water interface reveals that the interfacial |χ (2) | 2 -spectrum is strikingly similar to the bulk absorption spectrum. Molecular dynamics simulations and SOS-CIS(D 0 ) electronic structure calculations provide no evidence for the previously reported contact ion pair-induced red-shift of the π → π* transition at the air/water interface. Finally, our results thus revise the previous description for nitrite interfacial adsorption as a contact ion pair.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

A red algal polysaccharide influences the multicellular development of the choanoflagellate Salpingoeca rosetta

The choanoflagellate Salpingoeca rosetta exemplifies the capacity of marine microeukaryotes to integrate environmental cues into their life histories. Some of the cues that S. rosetta detects are lipids from their bacterial prey that induce the development of multicellular colonies called rosettes. In the aquatic environments where S. rosetta would encounter bacterial cues, microbial communities gain refuge and food from algae. For example, microbes in coastal environments benefit from macroalgae that annually release ∼10 14 g of carbon, primarily in the form of polysaccharides that comprise ∼80% of dissolved organic carbon in surface waters. Despite the ecological impact of algae and historical descriptions of choanoflagellates attached to algae, the influence of algae on choanoflagellate life history transitions has remained unexplored. Here, we report that porphyran, a polysaccharide produced by the red macroalga Porphyra umbilicalis, induces multicellular development in S. rosetta. We first noticed this response when S. rosetta formed multicellular rosette colonies while growing in media prepared from P. umbilicalis, even though rosette-inducing bacteria were absent. By biochemically purifying extracts of P. umbilicalis, we identified porphyran as the rosette-inducing factor. This response provides a biochemical insight that indicates algal polysaccharides may serve as cues to mediate associations between choanoflagellates and algae. Moreover, this observation complements evidence from environmental and geochemical studies that show the impact that algae have exerted on the ecology and evolution of eukaryotes, including a rise in algal productivity during the origin of animals, the closest living relatives of choanoflagellates.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Fuzzy Euclidean wormholes in the inflationary universe

In this paper, we investigate complex-valued Euclidean wormholes in the Starobinsky inflation. Due to the properties of the concave inflation potential, the classicality condition at both ends of the wormhole can be satisfied, as long as the initial condition of the inflation field is such that it is located sufficiently close to the hilltop. Furthermore, we compare the probabilities of classicalized wormholes with the Hartle–Hawking compact instantons and conclude that the Euclidean wormholes are probabilistically preferred than compact instantons, if the inflation lasts more than 50 e-foldings. Our result assumes that the Euclidean path integral is the correct effective description of quantum gravity. This opens a new window for various future investigations that can be either confirmed or refuted by future experiments.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics.

The standard model of cosmology has provided a good phenomenological description of a wide range of observations both at astrophysical and cosmological scales for several decades. This concordance model is constructed by a universal cosmological constant and supported by a matter sector described by the standard model of particle physics and a cold dark matter contribution, as well as very early-time inflationary physics, and underpinned by gravitation through general relativity. There have always been open questions about the soundness of the foundations of the standard model. However, recent years have shown that there may also be questions from the observational sector with the emergence of differences between certain cosmological probes. In this White Paper, we identify the key objectives that need to be addressed over the coming decade together with the core science projects that aim to meet these challenges. These discordances primarily rest on the divergence in the measurement of core cosmological parameters with varying levels of statistical confidence. These possible statistical tensions may be partially accounted for by systematics in various measurements or cosmological probes but there is also a growing indication of potential new physics beyond the standard model. After reviewing the principal probes used in the measurement of cosmological parameters, as well as potential systematics, we discuss the most promising array of potential new physics that may be observable in upcoming surveys. We also discuss the growing set of novel data analysis approaches that go beyond traditional methods to test physical models. These new methods will become increasingly important in the coming years as the volume of survey data continues to increase, and as the degeneracy between predictions of different physical models grows. There are several perspectives on the divergences between the values of cosmological parameters, such as the model-independent probes in the late Universe and model-dependent measurements in the early Universe, which we cover at length. The White Paper closes with a number of recommendations for the community to focus on for the upcoming decade of observational cosmology, statistical data analysis, and fundamental physics developments.

Dienes, Keith [Univ. of Arizona, Tucson, AZ (Unite↗

DOE OSTI2026

The high explosives & affected targets (HEAT) dataset

Artificial Intelligence (AI) surrogate models offer a computationally efficient alternative to full-physics simulations, yet no existing datasets are publicly available for training, testing, and validation of machine learning models of the dynamics of high-explosive driven shocks through multiple materials. Shock propagation through materials is a computationally challenging problem because simulations must include material-specific equations of state (EOS) along with descriptions of other physical processes such as plastic deformation, phase change, damage processes, fluid instabilities, and multi-material interactions. Shocks are typically initiated by high-velocity impacts or explosive loading. The latter case necessitates the addition of models of reactive materials to represent high-explosive (HE) detonation. Here, to address the lack of an expansive dataset for multi-material shock propagation in the AI/ML community, we present the High-Explosives and Affected Targets (HEAT) Dataset. HEAT is a physics-rich collection of two-dimensional, cylindrically symmetric, simulations generated using an Eulerian, multi-material, shock-propagation code developed at Los Alamos National Laboratory. The dataset includes two partitions: (1) the expanding shock-cylinder (CYL) simulations, Figs. 1, and (2) the Perturbed Layered Interface (PLI) simulations, Fig. 2. Entries in both partitions consist of time series of arrays of thermodynamic fields (pressure, density, and temperature), kinematic fields (position and velocity), and additional fields that depend on thermodynamic and/or kinematic fields (e.g., material stress). Materials in the CYL partition include solids (aluminium, copper, depleted uranium, stainless steel, tantalum, and a generic polymer), a liquid (water), gases (air, nitrogen), and a generic detonating material (high explosive, HE). The PLI partition spans a highly varying geometry but consists of fixed materials across entries: Copper, aluminium, stainless steel, generic polymer, and generic HE. HEAT captures critical phenomena such as momentum transfer, shock propagation, plastic deformation, and thermal effects, making HEAT a valuable benchmark for development of AI/ML emulation of multi-material shock propagation.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Striving to translate shale physics across ten orders of magnitude: What have we learned?

Shales will play an important role in the successful transition of energy from fossil-based resources to renewables in the coming decades. Aside from being a significant source of low-carbon intensity fuels, like natural gas, they also serve as geologic seals of subsurface formations that may be used to isolate nuclear waste, sequester CO 2 , or store intermittent energy (e.g., solar hydrogen). Despite their importance, shales pose significant engineering and environmental challenges due to their nanoporous structure and extreme heterogeneity that spans at least ~10 orders of magnitude in spatial scale. Two challenges inhibit a system-level understanding: (1) the physics of fluid flow and phase behavior in shales are poorly understood due to the dominant molecular interactions between minerals and fluids under confinement, and (2) the apparent lack of scale separation that prevents a reliable (closed) description of the physics at any single scale of observation. In this review, we focus on the latter issue and discuss scale translation, which in its broadest sense is transforming data or simulations from one spatiotemporal scale to another. While effective scale translation is not exclusive to shales, but all geologic porous media, the need for it is especially acute in shales given their high degree of heterogeneity. Classical theories like homogenization, while indispensable, fail when scales are not separated. Other methods, like numerical upscaling, scale-translate in only one direction: small to large, but not the reverse, called downscaling. However, the confluence of advances in three areas are bringing challenging problems such as shales within reach: increased computational power and scalable algorithms; high-resolution imaging and multi-modal data acquisition; and machine learning to process massive amounts of data. While these advances equip geoscientists with a wide array of experimental and computational tools, no individual tool can probe the entire gamut of heterogeneity in shales. Their effective use, therefore, requires an ability to bridge between various data types obtained at different scales. The aim of this review is to present a coherent account of computational and experimental methods that may be used to achieve just that, i.e., to perform scale translation. We provide a broader definition of scale translation, one that transcends classical homogenization and upscaling methods, but is consistent with them and accommodates notions like downscaling and data translation. After a brief introduction to homogenization, we review hybrid methods, numerical upscaling and its recent extensions, multiscale computing, high-resolution imaging, and machine learning. We place particular emphasis on multiscale computing and propose an algorithmic framework to bridge between the pore (micro) and Darcy (macro) scales. Throughout the paper, we draw comparisons between the various methods and highlight their (often hidden) similarities, differences, benefits, and pitfalls. We finally conclude with two case studies on shales that exemplify some of the methods presented.

58 GEOSCIENCES↗

DOE OSTI2023

Pore-scale simulation of multiphase flow and reactive transport processes involved in geologic carbon sequestration

Multiphase flow and reactive transport are two essential physicochemical processes that govern the effectiveness of geological carbon sequestration (GCS). The interaction and feedback among different phases and components during intricate physicochemical processes hold great significance in understanding CO 2 sequestration. Pore-scale simulations can account for multiphase flow and reactive transport processes in porous media and obtain spatial distributions of parameters (density, velocity, concentration, etc.) in the pore space as well as their temporal evolutions. This proves especially valuable considering that experiments can be hindered by constraints in spatial and temporal resolution. The comprehensive insights garnered from pore-scale research can be leveraged for continuum modeling using the representative elementary volume (REV) concept. In this contribution, four sequential mechanisms of CO 2 -brine-rock interaction in three zones delineated by CO 2 saturation are elaborated to elucidate complicated physicochemical processes involved in GCS, which are followed by general descriptions of mathematical equations and pore-scale numerical methods. In addition, as interested and commonly encountered processes, leakage risks during GCS and CO 2 -enhanced oil recovery (CO 2 -EOR) processes are presented. The existing challenges and future directions are discussed for both the performance of the pore-scale models and the current gaps in the field of GCS. Importantly, we expect that this review will prove beneficial for researchers interested in pore-scale simulations, GCS, and related disciplines.

58 GEOSCIENCES↗

DOE OSTI2021

Effect of Cu on the passivity of Ti–xCu (x = 0, 3 and 5 wt%) alloy in phosphate-buffered saline solution within the framework of PDM-II

The effect of copper content on the passivity of Ti–xCu alloys in a phosphate-buffered saline electrolyte (pH 7.4), in the steady-state condition, was examined using potentiostatic polarization, electrochemical impedance spectroscopy, and Mott-Schottky analysis. The study demonstrates that the oxide layer forms on the Ti–xCu alloys surface have n-type semiconducting character, and the steady-state thickness of the oxide layer is observed linearly and depends on the applied potential. The study observations are in line with the predictions of the Point Defect Model (PDM-II), which provides a physiochemically realistic description of the oxide layer formation on Ti–xCu alloys. The observations reveal that the Cu additions result in a decrease in the charge transfer resistance and capacitances associated with the hydroxide and barrier layer, an increase in the donor density, and reduce the electrochemical resistance of the Ti–xCu alloys. The study also exhibits that the decrease in the steady-state current density after the initial addition of 3 wt% of Cu is attributed to the progressive substitution of copper on the cation sublattice of the film resulting in enhanced electrostatic interaction between the immobilized copper $Cu^{x'}_{Ti}$, and the mobile cation interstitials $Cu^{+}_{i}$, which carries the excess current over and that conveyed by oxygen vacancies $V^{••}_o$ in the barrier layer. Moreover, it is also observed that over a hundred-year implant period, about 0.006 cm (0.06 mm) of Ti–3Cu alloy is predicted to lose due to steady-state corrosion in the PBS solution under the given set of conditions, it is further seen that, as the Cu amount increased from 3 wt% to 5 wt% in the Ti–xCu alloys, the steady-state corrosion rate decreases.

Electrochemical Impedance Spectroscopy↗

DOE OSTI2022

Niobium addition improves the corrosion resistance of TiHfZrNb x high-entropy alloys in Hanks’ solution

High-strength TiHfZrNb x high-entropy alloys could become the ideal materials for small-diameter endovascular stents once their corrosion resistance in the physiological milieu is confirmed. Here, this work aims at evaluating the corrosion resistance of a TiHfZrNb x high-entropy-alloys family in Hanks’ solution at 37 °C and revealing the influence of niobium on the mechanisms of dissolution and passivation. The alloys were subjected to a series of static and dynamic electrochemical tests and surface characterization, employing the static/dynamic/cyclic polarizations, impedance spectroscopy, XPS, AFM, and SEM. Results confirm a higher corrosion resistance of the alloys compared to that of CP-Ti and Ti6Al4V. The addition of niobium considerably improves the microstructural homogeneity that ensures a low dissolution rate and a greater resistance of the film due to the lower concentration of point defects. The passive film behaves as an n-type semiconductor and is composed of a mixture of TiO 2 , Nb 2 O 5 , ZrO 2 , and HfO 2 oxides, with the presence of metallic hydroxides on the outermost layer. A detailed description of the niobium influence on the mechanisms of the dissolution and passivation is presented in this work.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A first and second law analysis of a thermoresponsive polymer desiccant dehumidification and cooling cycle

Herein, we present a theoretical description for a new desiccant air conditioning cycle that uses thermoresponsive polymers instead of traditional desiccants. We use a combined first and second law analysis to demonstrate that this new cycle has three major advantages relative to the traditional case: (i) it can regenerate at lower temperatures, (ii) it can harvest liquid water and (iii) it has significantly higher coefficients of performance (COPs). For example, this new cycle can achieve a COP of 5.1 when regenerated at 95 degrees C, whereas the traditional desiccant cycle is limited to a COP of ~ 1. The fundamental origins of these advantages can be traced to the method of regeneration. The traditional desiccant cycle regenerates by flowing hot air over the desiccant, which provides a medium for gaseous water desorption. However, this also generates entropy and places a minimum temperature constraint on the hot air. In contrast, the thermoresponsive polymer cycle regenerates through a polymer phase transition. The polymer absorbs water vapor in humid air, and then it expels liquid water when raised above its transition temperature. This regeneration method generates liquid water that can be harvested and relaxes constraints on entropy generation and minimum temperature. The minimum regeneration temperature of the thermoresponsive cycle is only limited by the transition temperature of the polymer, which can be tuned through materials science. Due to its liquid water harvesting capability, the new cycle potentially eliminates water consumption when used with evaporative cooling, or it can be directly used for atmospheric water harvesting.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Data mining of plug-in electric vehicles charging behavior using supply-side data

This paper aims to better understand the charging patterns of plug-in electric vehicles (PEVs) and identify factors that may significantly impact PEVs’ charging behavior. We collected 189,864 supply-side charging session data over 13 months from 821 charging stations in Illinois from ChargePoint. Through descriptive and regression analyses, we characterize the distributions of key charging behavior indicators, including charging location, dwell time, and battery start state of charge (SOC), and quantify the impacts of closely related factors on these charging behaviors. In this work, we find that: (1) PEVs are more likely to charge in the morning at multifamily commercial locations with a lower start SOC compared with single family residential locations; (2) Weekday and morning sessions are more likely to utilize workplace charging and have shorter dwell time compared with weekend and afternoon sessions; (3) Single family residential area and locations with Levels 1/2 chargers have a higher start SOC and longer dwell time compared with other locations and DC fast chargers (DCFCs). These findings provide policy insights to identify potential time and locations to incentivize PEVs for grid services, as well as identify critical location categories for further charging infrastructure investment to better reduce range anxiety and promote PEV adoption.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Networks of electrochemical oxidation of common lithium-ion Battery solvents revealed by NMR spectroscopy

Raising the upper cutoff voltage of lithium-ion batteries (LIBs) to increase energy density often exceeds the electrolyte’s anodic stability limit, accelerating degradation and creating a major durability tradeoff. Designing electrolytes that can sustain long-term high-voltage cycling requires a clearer understanding of the fundamental mechanisms occurring when commercial carbonate solvents oxidize. Here, to this end, simplified single-salt, single-solvent formulations of LiClO 4 and LiPF 6 in dimethyl carbonate (DMC), ethylene carbonate (EC), or ethyl methyl carbonate (EMC) were anodically electrolyzed on inert electrodes and monitored for extended periods of time using 1 H, 13 C, 19 F, and 35 Cl nuclear magnetic resonance (NMR) spectroscopy. The controlled environment of the experiments, coupled to the unique sensitivity of NMR, unveiled novel metastable intermediates and the formation of branching networks of products with temporal evolution. Oxidation of the pristine solvent primarily proceeds through a radical pathway that also produces highly reactive protons but faces competition from a second pathway involving a radical carbocation intermediate. In all cases, the intermediates follow a variety of downstream pathways that can intersect with each other. The concomitant network of reactions represents a significant increase in complexity compared to common descriptions in the literature, yet, critically, it helps explain the wide range of products typically identified in electrolyte oxidation in complete cells. The results highlight the need for refocusing fundamental research on anodic stability to analysis of the hierarchy of reaction networks to better inform efforts to mitigate the detrimental effects on battery performance, including prevention and harvesting of proton and radical products.

Electrolytes↗

DOE OSTI2021

How can biosphere models simulate enough vegetation biomass in the mountains of the western United States? Implications of meteorological forcing

Most carbon stocks and fluxes in the western United States are found in mountainous terrain, where observations and modeling are difficult. Terrestrial biosphere models generally underestimate above-ground biomass (AGB) over this region. In this report, we identify methods to reduce this underestimation by focusing upon 1) biases in meteorological datasets, 2) model representation of water stress, and 3) spatial resolution. We adopted the widely-used Community Land Model version 4.5 (CLM 4.5) with six different meteorological datasets and found a 6-fold variation in simulated AGB across Utah/Colorado. Simulations underestimated AGB because of warm and dry biases within the meteorological datasets that reduced water availability and restricted plant growth. To eliminate the AGB underestimation we adopted a meteorological dataset designed for complex terrain (gridMET), combined with a representation of plant hydraulic stress (CLM 5.0). Conversely, changes in spatial resolution (meteorological variables and land surface description) had negligible impact on simulated AGB.

54 ENVIRONMENTAL SCIENCES↗