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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Revisiting Theoretical Tools and Approaches for the Valorization of Recalcitrant Lignocellulosic Biomass to Value-Added Chemicals

Biorefinery processes for converting lignocellulosic biomass to fuels and chemicals proceed via an integrated series of steps. Biomass is first pretreated and deconstructed using chemical catalysts and/or enzymes to liberate sugar monomers and lignin fragments. Deconstruction is followed by a conversion step in which engineered host organisms assimilate the released sugar monomers and lignin fragments, and produce value-added fuels and chemicals. Over the past couple of decades, a significant amount of work has been done to develop innovative biomass deconstruction and conversion processes that efficiently solubilize biomass, separate lignin from the biomass, maximize yields of bioavailable sugars and lignin fragments and convert the majority of these carbon sources into fuels, commodity chemicals, and materials. Herein, we advocate that advanced in silico approaches provide a theoretical framework for developing efficient processes for lignocellulosic biomass valorization and maximizing yields of sugars and lignin fragments during deconstruction and fuel and chemical titers during conversion. This manuscript surveys the latest developments in lignocellulosic biomass valorization with special attention given to highlighting computational approaches used in process optimization for lignocellulose pretreatment; enzyme engineering for enhanced saccharification and delignification; and prediction of the genome modification necessary for desired pathway fine-tuning to upgrade products from biomass deconstruction into value-added products. Physics-based modeling approaches such as density functional theory calculations and molecular dynamics simulations have been most impactful in studies aimed at exploring the molecular level details of solvent-biomass interactions, reaction mechanisms occurring in biomass-solvent systems, and the catalytic mechanisms and engineering of enzymes involved in biomass degradation. More recently, with ever increasing amounts of data from, for example, advanced mutli-omics experiments, machine learning approaches have begun to make important contributions in synthetic biology and optimization of metabolic pathways for production of biofuels and chemicals.

09 BIOMASS FUELS↗
DOE OSTI2022

An in silico method to assess antibody fragment polyreactivity

Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naïve synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally test our models’ performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the models allow us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its functional properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Conjugated Metal–Organic Macrocycles: Synthesis, Characterization, and Electrical Conductivity

The dimensional reduction of solids into smaller fragments provides a route to achieve new physical properties and gain deeper insight into the extended parent structures. Here, we report the synthesis of CuTOTP-OR (TOTP n – = 2,3,6,7-tetraoxidotriphenylene), a family of copper-based macrocycles that resemble truncated fragments of the conductive 2D metal–organic framework Cu 3 (HHTP) 2 (HHTP = 2,3,6,7,10,11-hexahydroxytriphenylene). The planar metal–organic macrocycles self-assemble into ordered nanotubes with internal diameters of ~2 nm and short interlayer distances of ~3.20 Å. Strong π–π stacking interactions between macrocycles facilitate out-of-plane charge transport, and pressed pellet conductivities as high as 2(1)×10 -3 S cm -1 are observed. Peripheral alkyl functionalization enhances solution processability and enables the fabrication of thin-film field-effect transistor devices. Ambipolar charge transport is observed, suggesting similar behavior may be operative in Cu 3 (HHTP) 2 . By coupling the attractive features of metal–organic frameworks with greater processability, these macrocycles enable facile device integration and a more nuanced understanding of out-of-plane charge transport in 2D conductive metal–organic frameworks.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Recommendations for Minimum Required Error Codes for Electric Vehicle Charging Infrastructure

OCPP protocol manages the interaction between the EVSE and its respective back-end communication network. It plays a pivotal role in both error reporting and troubleshooting, carried out primarily through the CSMS. OCPP defines both standard error codes and a flexible framework for creating and communicating custom error codes. The OCPI protocol orchestrates the communication between different backhaul communication networks, incorporating the exchange of error codes. These error codes are instrumental in pinpointing and rectifying issues that can surface prior to, during, or after charging operations, fortifying the reliability and resilience of the EV charging infrastructure. The flexibility offered by the OCPP and OCPI frameworks through the introduction of custom error codes also creates its own set of challenges. While the integration of custom error codes allows for enhanced granularity, it also introduces inconsistencies and fragmentation within the overarching diagnostic reporting system. To address the challenges with custom error codes this report proposes a set of Minimum Required Error Codes (MRECs) for streamlined error reporting, interpretability, and diagnostics. Recommendations in this report are based on independent analysis of custom error codes from multiple stakeholders within the EV charging ecosystem. For better error resolution, this report also assigns one or more entities responsible for the resolution of every mentioned error code. Finally, a functional classification for each mentioned error code is also identified to describe the nature of the error. In summary, the purpose of this document is to simplify the troubleshooting process and increase charging reliability for all EV users. This report serves as a recommendation for industry stakeholders, encouraging a unified methodology to define, transmit, and interpret common error codes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Discovery of unknown posttranslational modifications by top-down mass spectrometry

Post-translational modifications can be important biomarkers of function within biological systems. Top-down mass spectrometry is a valuable tool for the characterization of combinatorial modifications on proteins such as histones that have a range of complex modifications. In this chapter, we present a top-down liquid chromatography-mass spectrometry experimental and data analysis workflow for the identification of novel modifications on proteins. The data analysis workflow presented here is reliant on the combination of proteoform searching using TopPIC and data visualization and fragment ion spectral confirmation using LcMsSpectator, part of the Informed-Proteomics package of software.

Wilson, Jesse W.↗
DOE OSTI2020

Nucleation of soot: experimental assessment of the role of polycyclic aromatic hydrocarbon (PAH) dimers

The irreversible dimerization of polycyclic aromatic hydrocarbons (PAHs) – typically pyrene (C 16 H 10 ) dimerization – is widely used in combustion chemistry models to describe the soot particle inception step. This study concerns itself with the detection and identification of dimers of flame-synthesized PAH radicals and closed-shell molecules and an experimental assessment of the role of these PAH dimers for the nucleation of soot. To this end, flame-generated species were extracted from an inverse co-flow flame of ethylene at atmospheric pressure and immediately diluted with excess nitrogen before the mixture was analyzed using flame-sampling tandem mass spectrometry with collision-induced fragmentation. Signal at m/z = 404.157 (C 32 H 20 ) and m/z = 452.157 (C 36 H 20 ) were detected and identified as dimers of closed-shell C 16 H 10 and C 18 H 10 monomers, respectively. A complex between a C 13 H 9 radical and a C 24 H 12 closed-shell PAH was observed at m/z = 465.164 (C 37 H 21 ). However, a rigorous analysis of the flame-sampled mass spectra as a function of the dilution ratio, defined as the ratio of the flow rates of the diluent nitrogen to the sampled gases, indicates that the observed dimers are not flame-born, but are produced in the sampling line. In agreement with theoretical considerations, this paper provides experimental evidence that pyrene dimers cannot be a key intermediate in particle inception at elevated flame temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Single- and multi-photon-induced ultraviolet excitation and photodissociation of CH 3 I probed by coincident ion momentum imaging

The UV-induced photodissociation dynamics of iodomethane (CH 3 I) in its A-band are investigated by time-resolved coincident ion momentum imaging using strong-field ionization as a probe. The delay-dependent kinetic energy distribution of the photofragments resulting from double ionization of the molecule maps the cleavage of the carbon–iodine bond and shows how the existence of a potential well in the di-cationic potential energy surfaces shapes the observed distribution at small pump–probe delays. Furthermore, the competition between single- and multi-photon excitation and ionization of the molecule is studied as a function of the intensity of the UV-pump laser pulse. Two-photon excitation to Rydberg states is identified by tracking the transformation of the delay-dependent singly-charged iodomethane yield from a pure Gaussian distribution at low intensity to a Gaussian with an exponentially decaying tail at higher intensities. In conclusion, dissociative ionization induced by absorption of three UV photons is resolved as an additional delay-dependent feature in the kinetic energy of the fragment ions detected in coincidence.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Neural network emulation of spontaneous fission

Large-scale computations of fission properties are an important ingredient for nuclear reaction network calculations simulating rapid neutron-capture process (the 𝑟 process) nucleosynthesis. Due to the large number of fissioning nuclei potentially contributing to the 𝑟 process, a microscopic description of fission based on nuclear density functional theory (DFT) is computationally challenging. Here, we explore the use of neural networks (NNs) to construct DFT emulators capable of predicting potential energy surfaces and collective inertia tensors across the whole nuclear chart, starting from a minimal set of DFT calculations. We use constrained Hartree-Fock-Bogoliubov (HFB) calculations to predict the potential energy and collective inertia tensor in the axial quadrupole and octupole collective coordinates, for a set of nuclei in the 𝑟-process region. We then employ NNs to emulate the HFB energy and collective inertia tensor across the considered region of the nuclear chart. Least-action pathways characterizing spontaneous fission half-lives and fragment yields are then obtained by means of the nudged elastic band method. The potential energy predicted by NNs agrees with the DFT value to within a root-mean-square error of 500 keV, and the collective inertia components agree to within an order of magnitude. These results are largely independent of the NN architecture. The exit points on the outer turning line are found to be well emulated. For the spontaneous fission half-lives the NN emulation provides values that are found to agree with the DFT predictions within a factor of 10 3 across more than 70 orders of magnitude. Neural networks are able to emulate the potential energy and collective inertia well enough to reasonably predict physical observables. Future directions of study, such as the inclusion of additional collective degrees of freedom and active learning, will improve the predictive power of microscopic theory and further enable large-scale fission studies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

The [M 6 (S 2 C 2 Ph 2 ) 6 ] (M = Ni, Pd, Pt) Series: Multielectron Reservoirs That Sustain Ligand-Based Oxidations and Metal-Based Reductions

A complete [M 6 (S 2 C 2 R 2 ) 6 ] series (M = Ni (1), Pd (2), Pt (3); R = Ph), the rarest variety among homoleptic dithiolene transition-metal compounds, has been prepared by reaction between [M(S 2 C 2 Ph 2 ) 2 ] and a M 0 source. The platinum member of this set is the first of its type. Diffraction-quality crystals, grown with high reproducibility by evaporation from PhNO 2 solutions, reveal fully reduced [Ph 2 C 2 S 2 ] 2– dianions and an octahedral M 6 core that is reduced to C 2 symmetry by the fusion of a mononuclear D 2h [M(S 2 C 2 Ph 2 ) 2 ] fragment upon a C 4 -symmetric base. The [Ni 6 (S 2 C 2 Ph 2 ) 2 ] 1– monoanion, prepared by Cp* 2 Co reduction, shows only modest structural differences from its neutral counterpart. In CH 2 Cl 2 , 1 and 2 can undergo two reductions and an oxidation, while 3 sustains two reductions and two oxidations. In benzonitrile, 1 sustains three reversible oxidations at potentials that are shifted appreciably to less positive values. The cathodic processes are shown by density functional theory (DFT) calculations to involve an MO largely of metal–sulfur composition that has contributions throughout the C 4 -symmetric pentametallic base of the assembly, while the oxidations are largely ligand-based and confined to the monometallic [M(S 2 C 2 Ph 2 ) 2 ] cap. The absorption spectra are marked by multiple overlapping bands that produce a continuous, tapering absorption profile of unresolved shoulders and swells.

Ligands↗
DOE OSTI2025

Origin of Stabilization of Ligand-Centered Mixed Valence Ruthenium Azopyridine Complexes: DFT Insights for Neuromorphic Applications

Redox-driven conductance changes are critical processes in molecular- and coordination-complex-based memristive thin films and devices that are envisioned for neuromorphic technologies, but fundamental mechanisms of conductance switching are not fully understood. Here, we explore charge disproportionation (CD) processes in [Ru II L 2 ](PF 6 ) 2 molecular systems that intrinsically involve interfragment charge transfer (IFCT). Using a combination of ab initio molecular dynamics simulation (AIMD), time-dependent density functional theory (TD-DFT), and density functional theory (DFT) calculations, we investigate the electron transfer mechanisms and the roles of temperature and cell volumetric expansion in facilitating the counterion movements and electronic transitions required for low-cost IFCT and charge redistribution. A detailed analysis of the density of states and TD-DFT calculations highlights that unpaired electrons play a crucial role in low-energy transitions, with the azo (N=N) groups of the ligand serving as the primary sites for electronic transport between molecular fragments, further stabilizing the asymmetric state. Localization of added electrons on azo ligands occurs with negligible change at the Ru centers, supported by atomic volume expansions up to +4.74 bohr 3 , and goes along with a progressive reduction of the HOMO−LUMO gap across redox states, suggesting enhanced conductivity. The TD-DFT analysis reveals a dominant IFCT excitation at 2082.76 nm in the doubly reduced (22) state, while a stabilization energy of 1.20 eV of the asymmetric (13) state relative to the symmetric (22) state is predicted by constrained DFT. Periodic DFT and AIMD simulations emulating a molecular film show that the stabilization of the asymmetric state, relative to a symmetric one, translates in net charge separation values (order of ∼0.33 e) that are strongly linked to increased counterion mobility (average counterion displacements exceeding 0.7 Å per atom during CD events) and the involvement of azo groups in electron redistribution. These findings, which align with previously reported experimental and computational data, provide key insights into the IFCT mechanisms and electronic transport facilitated by azo groups, with important implications for redox-driven memristive and neuromorphic technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

$\mathrm {K_S}^{0}$- and (anti-)$\Lambda $-hadron correlations in $pp$ collisions at ${\sqrt{s}} = 13$ TeV

Two-particle Azimuthal correlations are measured with the ALICE apparatus in pp collisions at √s = 13 TeV to explore strangeness- and multiplicity-related effects in the fragmentation of jets and the transition regime between bulk and hard production, probed with the condition that a strange meson (KS o ) or baryon (Λ) with transverse momentum p T > 3 GeV/c is produced. Azimuthal correlations between kaons or Λ hyperons with other hadrons are presented at midrapidity for a broad range of the trigger (3 < $p$$^{trigg}_{T}$ < 20 GeV/c) and associated particle p T (1 GeV/c < $p$$^{assoc}_{T}$< $p$$^{trigg}_{T}$), for minimum-bias events and as a function of the event multiplicity. The near- and away-side peak yields are compared for the case of either K S o or Λ(Λ¯) being the trigger particle with that of inclusive hadrons (a sample dominated by pions). In addition, the measurements are compared with predictions from PYTHIA 8 and EPOS LHC event generators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Photochemistry of Hypervalent Iodoazide Derivatives

The photochemical properties and reaction mechanisms of a series of hypervalent iodoazide compounds (R–IN3) were investigated, with substituents (−CH 3 , −H, −CF 3 ) tuning the electronic density of the phenyl ring. With the help of UV irradiation, ultrafast time-resolved spectroscopy, and density functional theory (DFT) calculations, we elucidated the mechanisms of azide radical (N 3 • ) release and its subsequent reactivity. UV–vis spectroscopy reveals that the photoconversion rates follow the trend CH3 > H > CF3, aligning with DFT-calculated ΔG values for ring-opening transitions. Homolytic cleavage of the I–N bond is identified as the dominant pathway for N3• generation upon UV irradiation, occurring within 400 fs. The released azide radicals react with the solvent molecules, while the iodo radical R–I C • fragments undergo a thermodynamically uphill lactone ring-opening (RO) reaction and subsequent hydrogen atom abstraction from the solvent to form carboxylic acids R–I–COOH, as validated by NMR and IR spectroscopy. The study also highlights the role of substituents in influencing reaction kinetics and intermediate stability, with electron-donating groups accelerating the release of the N 3 • species. This work bridges experimental observations with computational predictions, offering a foundation for future advancements in azide-based reactions and materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Development of a Systematic and Extensible Force Field for Peptoids (STEPs)

Peptoids (N-substituted glycines) are a class of biomimetic polymers that have attracted significant attention due to their accessible synthesis and enzymatic and thermal stability relative to their naturally occurring counterparts (polypeptides). While these polymers provide the promise of more robust functional materials via hierarchical approaches, they present a new challenge for computational structure prediction for material design. The reliability of calculations hinges on the accuracy of interactions represented in the force field used to model peptoids. For proteins, structure prediction based on sequence and de novo design has made dramatic progress in recent years; however, these models are not readily transferable for peptoids. Current efforts to develop and implement peptoid-specific force fields are spread out, leading to replicated efforts and a fragmented collection of parameterized sidechains. Here, we developed a peptoid-specific force field containing 70 different side chains, using GAFF2 as starting point. The new model is validated based on the generation of Ramachandran-like plots from DFT optimization compared against force field reproduced potential energy and free energy surfaces as well as the reproduction of equilibrium cis/trans values for some residues experimentally known to form helical structures. In conclusion, equilibrium cis/trans distributions (Kct) are estimated for all parameterized residues to identify which residues have an intrinsic propensity for cis or trans states in the monomeric state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Absolute dissociative electron attachment cross-section measurement of difluoromethane

Dissociative electron attachment (DEA) to difluoromethane (CH 2 F 2 ) has been studied in the electron energy range of 0 to 16 eV. Two resonant states around 2 and 11.4 eV leading to three different fragment anions (F - , CHF - , and F 2 - ) are observed. Ion-yield curves of the negative ions help us to locate the position of the resonant states. In the ion-yield curve of F - ions, one small hump near 9.8 eV followed by a peak around 15.2 eV is also observed. Absolute DEA cross sections of the F - ion is measured by using the well-known relative flow technique. Dissociation channels associated with each resonant states are identified by density-functional theory (DFT) calculations of the threshold energies. The theory matches quite well within the experimental uncertainty.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Amphiphilic Block Copolymers for Flocculation and Hydrophobization of Legacy Waste Suspensions in Flotation Driven Dewatering Operations - 20162

Flotation has been effectively demonstrated as a low footprint, high efficiency separation process when utilising simple anionic surface modifying agents such as sodium dodecyl sulphate (SDS). These agents increase hydrophobicity to remove suspended cationic surface charged particulates such as Mg(OH){sub 2} from waste suspensions similar to those at British nuclear fuel management sites- where corrosion processes have degraded fuel cladding. The technology is advantageous as it could effectively be retrofitted into existing waste management facilities at nuclear waste management sites such Sellafield and Hanford as the simplicity (no moving parts) and size of this technology coupled with low cost of construction could be rapidly deployed to aid in risk and hazard reduction decommissioning operations with minimum impact to secondary waste generation. Previously, sodium dodecyl sulphate has been deployed as a collector agent to increase the hydrophobicity of Mg(OH){sub 2} suspended particles, achieving a maximum particulate extraction percentage of 93%. To achieve further particulate extraction, flotation performance has been determined to be a function of the hydrodynamic limitations extracting fine material (>20 μm). Fine particulates have been shown in the minerals industry to have an inability to overcome slipstreams created from rising bubbles in flotation cells, likely due to the low mass and thus momentum of the colloidal material, preventing interaction between the hydrophobized particles and air-water interfaces (bubbles). To combat this mass transfer resistance poly(acrylic acid)-block-poly(n-butyl acrylate) amphiphilic diblock copolymers were synthesised via reversible addition-fragmentation chain-transfer (RAFT) polymerisation. The anticipated mode of action of these polymers involves the hydrophilic poly(acrylic acid) block polymer acting as a flocculation agent to increase the mass of the aggregates, and the hydrophobic poly(n-butyl acrylate) modifies the hydrophobicity of the resulting flocs. The concentration of this polymer (termed a macro-collector) was varied and the change in particle size distribution of the particulate suspension was analysed using static light scattering. The flotation performance was then characterised by several methods including percentage particulate recovery, volume reduction factor and residual bulk particulate concentration. This was then benchmarked against the traditional surfactant collector agent SDS. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

pvplr-python: Python package implementation of PVplr for Performance Loss Rate (PLR) analysis

Due to software fragmentation, PV system modeling teams can be limited to language specific packages, preventing cross-sectional analysis of different modeling techniques and workflows. To this end, PVplr, a popular PV performance modeling R software package, has been ported to the Python programming language. To verify and test the robustness of the port, NSRDB data has been used to simulated PV installations at native resolution (~2 million Sites), with a variety of degradation rates, degradation patterns, and modules. Performance Ratios were calculated using the ported functions from pvplr-python and compared against Rdtools YoY values. Due to the complicated nature of degradation, a new metric has been proposed to quantify the performance loss of a system. The cumulative production loss, is the total amount of energy lost due to the degrading performance of the system. Cumulative production loss alleviates the problems with fitting linear functions to non-linear degradation. Cumulative Production loss was shown to better estimate the total lost revenue for non-linear degradation patterns. $XbX + UTC$ was found to most accurately predict the total lost revenue in simulated systems.

Kumar, Suraj↗
DOE OSTI2023

Elucidating Biomass-Derived Pyrolytic Lignin Structures from Demethylation Reactions through Density Functional Theory Calculations

Pyrolytic lignin is a fraction of pyrolysis oil that contains a wide range of phenolic compounds that can be used as intermediates to produce fuels and chemicals. However, the characteristics of the raw lignin structure make it difficult to establish a pyrolysis mechanism and determine pyrolytic lignin structures. Herein this study proposes dimer, trimer, and tetramer structures based on their relative thermodynamic stability for a hardwood lignin model in pyrolysis. Different configurations of oligomers were evaluated by varying the positions of the guaiacyl (G) and syringyl (S) units and the bonds βO4 and β5 in the hardwood model lignin through electronic structure calculations. The homolytic cleavage of βO4 bonds is assumed to occur and generate two free radical fragments. These can stabilize by taking hydrogen radicals that may be in solution during the intermediate liquid (pathway 1) formation before the thermal ejection. An alternative pathway (pathway 2) could occur when the radicals use intramolecular hydrogen, turning themselves into stable products. Subsequently, a demethylation reaction can take place, thus generating a methane molecule and new oligomeric lignin-derived molecules. The most probable resulting structures were studied. We used FTIR and NMR spectra of selected model compounds to evaluate our calculation approach. Thermophysical properties were calculated using group contribution methods. The results give insights into the lignin oligomer structures and how these molecules are formed. They also provide helpful information for the design of pyrolysis oil separation and upgrading equipment.

09 BIOMASS FUELS↗
DOE OSTI2023

Description of the multinucleon transfer mechanism for Ca 48 + Pu 244 and Kr 86 + Pt 198 reactions in a quantal transport approach

Multinucleon transfer (MNT) reactions involving heavy projectile and target combinations stand as a promising method for synthesizing new neutron-rich exotic nuclei, which may not be possible using hot or cold fusion reactions or fragmentation. Exploring the mechanisms behind MNT reactions is essential and it requires a comprehensive theoretical framework that can explain the physical observables in these reactions. This work aims to show that the quantal diffusion approach based on the stochastic mean-field (SMF) theory is capable of explaining the reaction dynamics observed in MNT reactions. Primary product mass distributions in 48 Ca + 244 Pu reaction at E c.m. = 203.2 MeV and 86 Kr + 198 Pt reaction at E c.m. = 324.2 MeV are calculated and compared with the available experimental data. In this work, we utilize the time-dependent Hartree-Fock (TDHF) calculations to analyze the mean-field reaction dynamics computationally in the reactions 48 Ca + 244 Pu and 86 Kr + 198 Pt for a broad range of initial angular momenta. Quantal transport description based on the SMF approach is used to calculate quantal diffusion coefficients and mass variances in 48 Ca + 244 Pu and 86 Kr + 198 Pt systems. The primary products arising from quasifission reactions are described by joint probability distribution in the SMF approach and those arising from fusion-fission are estimated by using the statistical deexcitation code gemini + +. Mean values of charge and mass numbers, scattering angles of the primary reaction products, and the total kinetic energies after the collision are calculated within the TDHF framework for a broad range of initial angular momenta. Throughout all the collisions, drift toward the mass symmetry and large mass dispersion associated with this drift are observed. Here, the calculated primary fragment and mass distributions using the SMF approach successfully explain experimental observations for the 48 Ca + 244 Pu and 86 Kr + 198 Pt systems. The primary mass distributions, mean values of binary products, and mass dispersions are determined and results are compared with the available experimental data. The observed agreement between the experimental data and SMF results highlights the effectiveness of the quantal diffusion mechanism based on the SMF approach, which does not include any adjustable parameters other than standard parameters of Skyrme energy density functional.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗