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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 145 records · Page 8

Exploring Microphase Separation in Semi-Fluorinated Diblock Copolymers: A Combined Experimental and Modeling Investigation

We report the combined experimental and theoretical study of the bulk self-assembly behavior of polystyrene-blockpoly( 2,3,4,5,6-pentafluorostyrene) diblock copolymers. These block copolymers were designed to create highly antagonistic blocks (with a high Flory−Huggins interaction parameter, χ) with minimum disruption to the molecular construct (i.e., only replacing five hydrogen atoms with five fluorine atoms). A large library of diblock copolymers (41 samples) was synthesized by reversible addition− fragmentation chain transfer (RAFT) polymerization to map out a major portion of the phase space. All block copolymers exhibited narrow molecular weight distributions with dispersity (D) values between 1.07 and 1.32, and subsequent thermal annealing revealed phase separation into well-defined nanoscale morphologies depending on their molecular composition, as determined from small-angle X-ray scattering and transmission electron microscopy analyses, with an experimental phase diagram being constructed. The χ value at 25 °C for this block copolymer was estimated to be 0.2 using strong segregation theory, based on trends in phase-separated domain spacing and interfacial width. When applying theoretical approaches, the majority of the domain spacing data trends were captured by a coil−coil diblock copolymer model; however, a better fit to the data for samples with shorter fluorinated blocks was obtained with a rod−coil model, indicating that the chains in these fluorinated blocks likely have a higher inherent stiffness and were thus rod-like. This observation demonstrates that, due to the very high value of χ, a transition from coil−coil to rod−coil behavior can be obtained purely by reducing the length of the stiffer of the two blocks and without varying temperature or the chemical composition of the polymers. Here, this work showcases the presence of strong microphase separation within AB diblock copolymers despite the relatively similar chemical composition of the constituent “A” and “B” units, with a clear transition from rod−coil to coil−coil segregation behavior.

RAFT polymerization↗

Elimination LArTPC Simulation Uncertainty

Liquid Argon Time Projection Chambers (LArTPC) are essential for detecting muons and neutrinos by capturing electrons released during particle collisions, which drift toward wire planes under an electric field and induce currents measured to reconstruct particle paths. However, LArTPCs face challenges from effects such as electron-ion recombination, electron diffusion, and electron attenuation, complicating data simulation. The Short Baseline Neutrino (SNB) detector aims to measure neutrinos before oscillation occurs. To bridge the gap between simulation and actual data, we propose modifying the amplitude and width of signals on the TPC wires, addressing uncertainties by adjusting signal characteristics to better match observed data. A Gaussian fit to current waveforms produces hits with associated charge and width, and by comparing data and simulated values, discrepancies highlight areas where the model fails. Initial results indicate the current modification algorithm may increase divergence between simulation and data, necessitating further refinement. A discovered bug in the WireModMakeHist_plug.cpp file, which incorrectly computed simulation and data ratios, underscores the need for precise algorithm adjustments. Future work involves correcting code errors, fine-tuning the model, and conducting multiple simulation runs to enhance statistical confidence and reduce uncertainties, ultimately aiming for accurate LArTPC operation and reliable neutrino detection.

Mkrtchyan, Ka'ren↗

Toward a Machine Learning Approach to Interpreting X-ray Spectra of Trace Impurities by Converting XANES to EXAFS

The fact that the photoabsorption spectrum of a material contains information about the atomic structure, commonly understood in terms of multiple scattering theory, is the basis of the popular extended X-ray absorption spectroscopy (EXAFS) technique. How much of the same structural information is present in other complementary spectroscopic signals is not obvious. Here we use a machine learning approach to demonstrate that within theoretical models that accurately predict the EXAFS signal, the extended near-edge region does indeed contain the EXAFS-accessible structural information. We do this by exhibiting deep operator neural networks (DeepONets) that have learned the relationship between the extended and near edge portions of the X-ray absorption spectrum to predict the former from the latter. We find that we can accurately predict the EXAFS spectrum between 6 and 14 Å –1 from the first 6 Å –1 (≈100 eV) of the absorption spectrum of Cu 2 + substitutional defects in the Fe 3+ mineral hematite (α-Fe 2 O 3 ). This surprising finding implies that theoretical analyses of X-ray absorption spectra could be implemented that extract the same conclusions as high-quality EXAFS studies from spectra collected over a much smaller range of photon energies. This relaxes a host of experimental limitations related to the X-ray source and measurement sample, including collection time, minimum dopant concentration, source brilliance, and energy range. We describe the theoretical data sets and DeepONet construction and show that the resulting DeepONets produce EXAFS that recovers linear combination fits to experimental data with accuracy approaching the original ab initio calculations. We discuss the implications of our findings for minor constituent characterization and for understanding the information content of spectroscopic data more broadly, including how this approach might be applied to measured experimental spectra. In conclusion, to encourage similar efforts, the simulated X-ray spectra, machine learning, and fitting code are publicly available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Toward shell model interactions with credible uncertainties

Background: The nuclear shell model is a powerful framework for predicting nuclear structure observables, but relies on interaction matrix elements fit to experimental data as its inputs. Extending the shell model's applicability, particularly toward dripline nuclei, requires efficient fitting methods and credible uncertainty quantification. Traditional approaches face computational challenges and may underestimate uncertainties. Purpose: We develop and test a framework combining eigenvector continuation and Markov chain Monte Carlo to efficiently fit shell model interaction matrix elements and quantify their uncertainties. Methods: Eigenvector continuation is used to emulate shell model calculations, reducing computational costs. The emulator enables Markov chain Monte Carlo sampling to optimize interaction matrix elements and rigorously assess parametric uncertainties. Here, the framework is benchmarked using the USDB interaction in the 𝑠⁢𝑑 shell. Results: The emulator reproduces the USDB interaction with negligible error, validating its use in shell model fitting applications. However, we find that to obtain credible predictive intervals, the model defect of the shell model itself, rather than experimental or emulator error, must be taken into account in order to obtain credible uncertainties. Conclusions: The proposed framework provides an efficient and rigorous approach for fitting shell model interactions and quantifying uncertainties. Further, the normality assumption used in the past appears sufficient to describe the distribution of interaction matrix elements. However, it is crucial to account for model correlations to avoid underestimating uncertainties.

Nuclear forces↗

High-Temperature Fe-Based Fischer–Tropsch Synthesis: Experimentally Validated Kinetic Models Implemented at Pellet and Reactor Scales

Pellet- and reactor-scale models for Fischer–Tropsch synthesis (FTS) with a Fe–K/silica catalyst were developed to investigate the sensitivity of the hydrocarbon products and carbon dioxide selectivity to process conditions and feed composition at high temperature (350–400 °C), moderate pressure (1–10 bar), and a range of H 2 /CO ratios (3–1). The major objective of this paper is to develop, validate, and evaluate a high-temperature FTS model that is then used to assess the feasibility of process integration with syngas production. Since there is limited kinetic data available, in literature at these conditions, bench-scale reactor tests were conducted to obtain operational data for parameter fitting of kinetic expressions used in the model. This resulting kinetic model demonstrated agreement with the experimental data with an R 2 of 0.97 to the testing data set and, thus, was feasible to apply at pellet and reactor scales. Here, multiple pellet sizes were modeled to detail the role of transport limitations as the sphere’s diameter approached and exceeded 1 mm. Application of the reactor model indicated that hydrocarbon selectivity depended strongly on temperature, whereas the ratio of olefin to paraffin products decreased with increasing temperature, pressure, and H 2 /CO ratio. Product selectivity was not sensitive to the conversion of carbon monoxide. Furthermore, the roles of the pressure and H 2 /CO ratio were closely coupled. At a H 2 /CO ratio of 3, only slight variations in selectivity occurred over a pressure range of 1–20 bar, whereas at a ratio of 1, selectivity could vary by as much as 30% over the same pressure range. At pressures below 5 bar and temperatures above 350 °C, minimal selectivity to heavy hydrocarbons (C 12+ ) is obtained, and selectivity to midrange products (C 5–11 ) rapidly declined as pressure dropped below 5 bar, which indicated that an operational pressure of at least 5 bar is needed to achieve reasonable yields in this temperature range. These results, while tentative, provide guidelines for further experimentation and evaluation of integrated FTS processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Methods for Incorporating Model Uncertainty into Exoplanet Atmospheric Analysis

A key goal of exoplanet spectroscopy is to measure atmospheric properties, such as abundances of chemical species, in order to connect them to our understanding of atmospheric physics and planet formation. In this new era of high-quality JWST data, it is paramount that these measurement methods are robust. When comparing atmospheric models to observations, multiple candidate models may produce reasonable fits to the data. Typically, conclusions are reached by selecting the best-performing model according to some metric. This ignores model uncertainty in favor of specific model assumptions, potentially leading to measured atmospheric properties that are overconfident and/or incorrect. In this paper, we compare three ensemble methods for addressing model uncertainty by combining posterior distributions from multiple analyses: Bayesian model averaging, a variant of Bayesian model averaging using leave-one-out predictive densities, and stacking of predictive distributions. We demonstrate these methods by fitting the Hubble Space Telescope (HST) + Spitzer transmission spectrum of the hot Jupiter HD 209458b using models with different cloud and haze prescriptions. All of our ensemble methods lead to uncertainties on retrieved parameters that are larger but more realistic and consistent with physical and chemical expectations. Since they have not typically accounted for model uncertainty, uncertainties of retrieved parameters from HST spectra have likely been underreported. We recommend stacking as the most robust model combination method. Our methods can be used to combine results from independent retrieval codes and from different models within one code. They are also widely applicable to other exoplanet analysis processes, such as combining results from different data reductions.

79 ASTRONOMY AND ASTROPHYSICS↗

Robust Interpretation of Electrochemical Impedance Spectra Using Numerical Complex Analysis

Electrochemical Impedance Spectroscopy (EIS) is a noninvasive technique widely used for understanding charge transfer and charge transport processes in electrochemical systems and devices. Standard approaches for the interpretation of EIS data involve starting with a hypothetical circuit model for the physical processes in the device based on experience/intuition and then fitting the EIS data to this circuit model. This work explores a mathematical approach for extracting key characteristic features from EIS data by relying on fundamental principles of complex analysis. These characteristic features can suggest the presence of inductors and constant phase elements (nonideal capacitors) from impedance data and enable us to answer questions about the identifiability and nonuniqueness of equivalent circuit models. In certain scenarios such as models with only resistors and capacitors, we are able to enumerate all possible families of circuit models. Finally, we apply the mathematical framework presented here to real-world electrochemical systems and highlight results using impedance measurements from a lithium-ion battery coin cell.

charge transfer↗

Photometric redshifts and intrinsic alignments: Degeneracies and biases in the 3 × 2 pt analysis

We present a systematic study of cosmological parameter bias in weak lensing and large-scale structure analyses for upcoming imaging surveys induced by the interplay of intrinsic alignments (IA) and photometric redshift (photo-z) model misspecification error. We first examine the degeneracies between the parameters of the tidal alignment–tidal torquing (TATT) model for IA and of a photo-z model including a mean shift ( Δ z ¯ ) and variance ( σ z ) for each tomographic bin of lenses and sources, under a variety of underlying true IA behaviors. We identify strong degeneracies between: (1) the redshift scaling of the tidal alignment amplitude and the mean shift and variances of source bins, (2) the redshift scaling of the tidal torquing amplitude and the variance of the lowest- z source bin, and (3) the IA source density weighting and the mean shift and variance of several source bins. We then use this information to guide our exploration of the level of cosmological parameter bias which can be induced given incorrect modeling of IA, photo-z, or both. We find that marginalizing over all the parameters of TATT is generally sufficient to preclude cosmological parameter bias in the scenarios we consider. However, this does not necessarily mean that IA and photo-z parameters are themselves unbiased, nor does it mean that the best-fit model is a good fit to the data. We also find scenarios where the inferred parameters produce χ d . o . f . 2 values indicative of a good fit but cosmological parameter bias is significant, particularly when the IA source density weighting parameter is not marginalized over. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

A New Measurement of the Neutron Electric Form Factor with the Super Bigbite Spectrometer Apparatus

Protons and neutrons, collectively known as nucleons, make up the nuclei at the core of atoms which form our world. The nucleon has been under intensive study for over 100 years, and yet we still do not fully understand the internal dynamics which govern properties like its spin or its mass-which contributes to almost all of the visible mass in the universe. These dynamics are governed by quantum chromodynamics(QCD), the predictions of which are experimentally tested at high energy accelerator facilities such as Jefferson Lab. TheGEN-II experiment (E12-09-016) is one such experiment. GEN-II is part of the Super Bigbite Spectrometer (SBS) experimental form factor pro gramme taking place in Hall A at Jefferson Lab, which aims to make precision measurements of the nucleon electromagnetic form factors(EMFFs)at record high values of squared four momentum transfer ¿2. EMFFs describe the electric and magnetic moment distributions within the nucleon. They can be measured through elastic electron scattering off the nucleon, and describe the recoil response of the target nucleon at a given energy scale. GEN-II is a double polarized semi-exclusive beam target asymmetry (BTA) experiment, seeking to measure the electric form factor of the neutron,¿¿ ¿,at three new values of squared four-momentum transfer¿2 =2.92,6.74and9.82GeV². The latter two points being at record high ¿2. The form factor is determined through measuring the BTA of quasielliptical scattering of a neutron from a polarized nuclear target. The experiment utilized the CEBAF accelerator to produce longitudinally polarized electrons up to ~85% polarization, which were scattered off neutrons within a novel polarized helium-3 (³He) target. This new polarized ³He target was employed by building on the technology of its precursors which existed in similar preceding experiments. This target was designed to operate at the high luminosities typical of Hall A, and reached a record breaking combination of polarization and beam intensity known as figure of merit, three times larger than those predecessors. The SBS collaboration designed and constructed two brand new high acceptance spectrometers for these experiments, an electron arm named Bigbite (BB) and a hadron arm named Super Bigbite. Both spectrometers featured a large acceptance EM dipole magnet, and complementary detector systems. The electron arm contained gaseous electronmultipliers(GEMs) which were used for high precision tracking of the scattered electrons, a heavy gas cherenkov (GRINCH) which was used for PID between electrons and pions, a plastic i ii scintillator timing hodoscope to provide high resolution timing of the start of events, and a pair of EM calorimeters (BBCal) which provided energy measurements of detected particles, and provided the experimental trigger. The hadron arm also contained a system of GEMs which will be utilized for future SBS experiments, and a hadron calorimeter designed to provide position, timing and energy measurements of the recoiling nucleon. The calibration of all detector subsystems, beam and target data is discussed, with a focus on novel timing calibrations to the hodoscope and hadron calorimeter. An analysis of selecting quasielliptical events and suppressing background contributions from a number of sources which contaminate the final event sample is given. The largest irreducible backgrounds are found to be from misidentified protons, timing accidentals and inelastic events. The physical asymmetry is measured and used to extract a value for the form factor ratio ¿¿ ¿/¿¿ ¿. High precision ¿2 data for ¿¿ ¿ is used to then extract ¿¿ ¿. This work finds at ¿2 = 2.92 GeV2 that ¿¿ ¿ = 0.0129+0.0019 -0.0020. This result is in statistical agreement with existing fits to world data, and predictions from the constituent quark model and Dyson–Schwinger equations, in this region of ¿2.

Penman, Gary [Univ. of Glasgow, Scotland (United K↗

Alpha-decay width of a near proton-threshold resonance in the 7 Li( α, α ) channel

We investigate the alpha-decay width of a near proton-threshold resonance in 11 B by the excitation functions of 7 Li(α, α) and 7 Li(α, α’) reactions. This resonance is an example of loosely bound atomic nuclei, understood as small open quantum systems, that exhibit properties significantly influenced by their coupling to the continuum. The present experiment focuses on the formation of a specific state in 11 B at excitation energy in the region near 11.4 MeV by bombarding a 7 Li target with a 4 He beam at energies ranging from 3.92 to 4.56 MeV in the laboratory frame. The study provides data complementary to previous observations of the proton emission in the β-decay of the neutron-rich halo nucleus 11 Be. An R-matrix fit to the data provides resonance energy and partial widths consistent with J π =1/2 + for a narrow near-threshold state in 11 B at 11.400(20) MeV, that is 171(20) keV above the proton emission threshold, with partial α-decay width Γ α =2.5$^{+3.5}_{–1.5}$ keV in elastic scattering and Γ α' =15.8$^{+1.9}_{–0.4}$ keV in inelastic scattering. These findings contribute to the understanding of the structure of this open quantum system resonance.

11B↗

Scaling the SciDAC QuantOm Workflow

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by combining the input from theoretical and experimental nuclear physics into a single, composable workflow. The optimization itself (I.e. fitting the experimental data with theoretical predictions) is carried out by a machine / deep learning algorithm. The size of the acquired DIS data as well as the complexity of the workflow itself require that the analysis is performed across multiple GPUs on high performance computing systems, such as Polaris at Argonne National Laboratory. This presentation discusses the novelties and challenges that came along with parallelizing this workflow. Recent results are compared to common distributed training techniques.

Lersch, Daniel↗

Ab initio informed 20 Ne(p, pα) 16 O reaction elucidates the emergence of alpha clustering from chiral potentials

We report on the first ab initio informed α knock-out reaction in the intermediate-mass region, with the aim to probe the underlying chiral potential and its impact on the emergence of alpha clustering in this mass region. The theoretical predictions of the α+ 16 O clustering in the 20 Ne ground state, based on the ab initio symmetry-adapted no-core shell model with continuum, yield a triple differential cross section for 20 Ne(p, pα) 16 O that is in a remarkable agreement with the data. This allows us to examine predictions of surface and in-medium α-cluster features that emerge from the underlying realistic nucleon-nucleon interaction with no parameters fitted to nuclear data beyond the two-body system, and to compare these to the successful antisymmetrized molecular dynamics approach.

Sargsyan, G. H. [Michigan State Univ., East Lansin↗

Granular Temperature Modeling with Large-Scale CFD-DEM Data

The granular temperature is a measure of solids-phase fluctuating kinetic energy in particle-laden gas-solid flows. This report focuses on reduced-order models of granular temperature in triply-periodic cluster induced turbulent flow. The granular temperature data was generated by post-processing large-scale CFD-DEM simulations that were carried out as part of an ALCC awarded study. The algebraic model of Tang et al, originally fit to DNS data, was found to be amenable to the present data. The results of several regression analyses are presented building on the original expression. This work was performed in part through the National Nuclear Security Administration (NNSA) Minority Serving Institutions Internship Program (MSIIP).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Forward Modeling of 3-D Ion Properties in Jupiter’s Magnetosphere Using Juno/JADE-I Data

The Jovian Auroral Distributions Experiment Ion sensor (JADE-I) on NASA’s Juno mission provides in-situ measurements of ions from 0.1 to 46.2 keV/q inside Jupiter’s magnetosphere. JADE-I is used to study the plasma with two types of datasets from the same measurement: Time-of-flight (TOF) and SPECIES. The TOF dataset provides mass-per-charge measurements with a range of 1–64 amu/q but oversamples particles over 6π steradian viewing per spacecraft spin and has little directional information. On the other hand, the SPECIES dataset can provide a good measurement of the flow direction but does not provide mass-per-charge information due to the telemetry limit. In this study, we developed a 2-step forward modeling method that combines the advantages and avoids the disadvantages of TOF and SPECIES data to derive the 3-D properties of heavy ions. Assuming that the ion velocity distribution can be described with the kappa distribution, we first perform the forward model fit of the TOF data to calculate the relative abundance of heavy ion species. Then we fix the relative abundance and perform the second forward model fit on the SPECIES data. Here, using this method, we obtain the densities of different heavy ions, the shared temperature and kappa value, and the 3-D flow velocity vector. Some data examples of the equatorial plasma disk before Perijove 24 are included to demonstrate the method. Plasma properties can then be mapped to explore spatial and temporal variabilities in Jupiter’s magnetosphere.

79 ASTRONOMY AND ASTROPHYSICS↗

Measurements of polarization and spin correlation and observation of entanglement in top quark pairs using lepton + jets events from proton-proton collisions at $\sqrt{𝑠}$ = 13 TeV

Measurements of the polarization and spin correlation in top quark pairs ($t\bar{t}$) are presented using events with a single electron or muon and jets in the final state. The measurements are based on proton-proton collision data from the LHC at $\sqrt{𝑠}$ =13 TeV collected by the CMS experiment, corresponding to an integrated luminosity of 138 fb −1 . All coefficients of the polarization vectors and the spin correlation matrix are extracted simultaneously by performing a binned likelihood fit to the data. The measurement is performed inclusively and in bins of additional observables, such as the mass of the $t\bar{t}$ system and the top quark scattering angle in the $t\bar{t}$ rest frame. The measured polarization and spin correlation are in agreement with the standard model. From the measured spin correlation, conclusions on the $t\bar{t}$ spin entanglement are drawn by applying the Peres-Horodecki criterion. The standard model predicts entangled spins for $t\bar{t}$ states at the production threshold and at high masses of the $t\bar{t}$ system. Entanglement is observed for the first time in events at high $t\bar{t}$ mass, where a large fraction of the $t\bar{t}$ decays are spacelike separated, with an expected and observed significance of above 5 standard deviations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

A Markov chain Monte Carlo (MCMC) Bayesian inference approach to analyze apparent activation barriers and reaction orders from microreactor data

Statistical analysis of steady-state catalytic kinetic data is often limited by data sparsity due to the slow pace at which the data is collected. Data sparsity and limitations in statistical analysis make it difficult to differentiate between mechanistic models and catalytic sites. A Bayesian inference tool is reported for catalysis researchers to estimate error in the determination of reaction orders from steady state microreactor data. The benefits of a Bayesian inference approach are discussed, as an alternative to the more common frequentist approach. The approach incorporates prior knowledge of the system and the data collected to form an error estimate on reaction orders. We investigated the effects of three distinct data treatments—individual fitting of trials, pooled analysis, and constrained regression methods—on the precision and uncertainty of reaction order determinations. To assess the robustness of our findings, we conducted sensitivity analyses to evaluate the influence of Bayesian parameters on uncertainty estimation. Additionally, we utilized synthetic data to illustrate how data quality impacts the precision of uncertainty assessments. We show Bayesian analysis can obtain a more precise estimation of error with a sparse data set than a frequentist analysis. Finally, this work provides strong evidence that the adoption of Bayesian analysis of kinetic data may help researchers make more precise arguments as to the strength of their evidence for a particular mechanistic hypothesis, or in comparing across different catalysts.

42 ENGINEERING↗

Nonmonotonic-potential description of polarization effects, fusion, and nuclear rainbows in elastic scattering of 6 Li + 12 C at 4.5–600 MeV

The experimental differential cross-section (CS) and analyzing power (AP) data of the 6 Li + 12 C elastic scattering over a wide laboratory energy scale (4.5MeV≤𝐸 lab ≤600MeV) are analyzed within the framework of the optical model (OPM) using nonmonotonic (NM) nucleus–nucleus potentials. The real part of the NM potentials is derived from the Pauli-embodied energy density-functional (EDF) formalism with the sudden approximation. The real part of the noncentral spin-orbit and tensor terms, as well as the imaginary parts, are treated phenomenologically. The effect of the radius of sensitivity on the CS and AP data is found to be more important at lower energies. The diffractive and refractive scattering with Airy structures in the whole angular region of the elastic scattering across the studied energy range is successfully described within the OPM using the NM 6 Li + 12 C potential. The near- and far-side (N and F) decomposition of the total elastic-scattering amplitudes has also been studied using our NM potentials. The evolution of the Airy minima in the angular distributions, coupled with the fitting of the AP data, provides an accurate description of Airy minima of different orders. The OPM calculations with the NM potentials describe exceptionally well the CS, vector analyzing power (VAP), and tensor analyzing power data at 𝐸 lab =9.0,19.24,20,30, and 50MeV. In agreement with our past successful descriptions of CS and the opposite signs of the VAP data for the 6 Li and 7 Li elastic scattering using NM potentials in OPM, the present results appear to provide a better fit, so far, than those obtained from the coupled-channels method. The fusion cross sections of 6 Li + 12 C have been predicted in the energy range (4.5MeV≤𝐸 lab ≤20MeV), fitting the experimental data well in the range 𝐸 lab =2.97–11.77MeV. The EDF potential without any energy dependence and renormalization is also found to describe satisfactorily the experimental CS and AP data at energies up to several hundreds of MeV.

6 ≤ A ≤ 19↗