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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 289 records · Page 16

Interplay between the non-resonant streaming instability and self-generated pressure anisotropies

The non-thermal particles escaping from collision-less shocks into the surrounding medium can trigger a non-resonant streaming instability that converts parts of their drift kinetic energy into large amplitude magnetic field perturbations, and promote the confinement and acceleration of high energy cosmic rays. We present simulations of the instability using an hybrid-Particle-in-Cell approach including Monte Carlo collisions, and demonstrate that the development of the non-resonant mode is associated with important ion pressure anisotropies in the background plasma. Depending on the initial conditions, the anisotropies may act on the instability by lowering its growth and trigger secondary micro-instabilities. Introducing collisions with neutrals yield a strong reduction of the magnetic field amplification as predicted by linear fluid theory. In contrast, Coulomb collisions in fully ionized plasmas are found to mitigate the self-generated pressure anisotropies and promote the growth of the magnetic field.

79 ASTRONOMY AND ASTROPHYSICS↗

A cryogenic muon tagging system based on kinetic inductance detectors for superconducting quantum processors

Ionizing radiation has emerged as a potential limiting factor for superconducting quantum processors, inducing quasiparticle bursts and correlated errors that challenge fault-tolerant operation. Atmospheric muons are particularly problematic due to their high energy and penetration power, making passive shielding ineffective. Therefore, monitoring the real-time muon flux is crucial to guide the development of alternative error-correction or mitigation strategies. We present the design, simulation, and first operation of a cryogenic muon-tagging system based on kinetic inductance detectors (KIDs), developed as a stand-alone cryogenic particle-tagging module for superconducting quantum processors. The system consists of two KIDs arranged in a vertical stack and operated at ∼20 mK. Monte Carlo simulations based on Geant4 guided the prototype design and provided reference expectations for muon-tagging efficiency and accidental coincidences due to ambient γ-rays. We observed a muon-induced coincidence rate among the top and bottom detectors of (192 ± 9) $\times\,10^{-3}$ events s$^{−1}$, in excellent agreement with the Monte Carlo prediction. The prototype achieves a muon-tagging efficiency of about 90% with negligible dead time. These results demonstrate the feasibility of operating a muon-tagging system at millikelvin temperatures and represent a key step toward the integration of cryogenic veto systems with multi-qubit chips to mitigate muon-induced errors.

Mariani, Ambra [INFN, Rome] (ORCID:000000028184857↗

Data-Driven Closures and Assimilation for Stiff Multiscale Random Dynamics

Here, we introduce a data-driven and physics-informed framework for propagating uncertainty in stiff, multiscale random ordinary differential equations (RODEs) driven by correlated (colored) noise. Unlike systems subjected to Gaussian white noise, a deterministic equation for the joint probability density function (PDF) of RODE state variables does not exist in closed form. Moreover, such an equation would require as many phase-space variables as there are states in the RODE system. To alleviate this curse of dimensionality, we instead derive exact, albeit unclosed, reduced-order PDF (RoPDF) equations for low-dimensional observables/quantities of interest. The unclosed terms take the form of state-dependent conditional expectations, which are directly estimated from data at sparse observation times. However, for systems exhibiting stiff, multiscale dynamics, data sparsity introduces regression discrepancies that compound during RoPDF evolution. This is overcome by introducing a kinetic-like defect term to the RoPDF equation, which is learned by assimilating in sparse, low-fidelity RoPDF estimates. Two assimilation methods are considered, namely nudging and deep neural networks, which are successfully tested against Monte Carlo simulations.

97 MATHEMATICS AND COMPUTING↗

Acceleration mechanisms of energetic ion debris in laser-driven tin plasma EUV sources

Laser-driven tin plasmas are driving new-generation nanolithography as sources of extreme ultraviolet (EUV) radiation centered at 13.5 nm. A major challenge facing industrial EUV source development is predicting energetic ion debris produced during the plasma expansion that may damage the sensitive EUV channeling multilayer optics. Gaining a detailed understanding of the plasma dynamics and ion acceleration mechanisms in these sources could provide critical insights for designing debris mitigation strategies in future high-power EUV sources. Here, we develop a fully kinetic model of tin-EUV sources using one-dimensional particle-in-cell simulations to study ion debris acceleration, which will be valuable for cross-validation of radiation-hydrodynamic simulations. An inverse-bremsstrahlung heating operator is used to model the interaction of a tin target with an Nd:YAG laser, and thermal conduction is included through a Monte Carlo Coulomb collision operator. While the large-scale evolution is in reasonable agreement with analogous hydrodynamic simulations, the significant timescale for collisional equilibration between electrons and ions allows for the development of prominent two-temperature features. A collimated flow of energetic ions is produced with a spectrum that is significantly enhanced at high energies compared to fluid simulations. The dominant acceleration mechanism is found to be a large-scale electric field supported mainly by the electron pressure gradient, which is enhanced in the kinetic simulations due to the increased electron temperature. We discuss the implications of these results for future modeling of tin-EUV sources and the development of debris mitigation schemes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Thermophysical Properties of Liquid Tritium: A Path Integral Monte Carlo Study

Here, we present worm-algorithm, path integral Monte Carlo simulations of bulk liquid tritium. The simulations are benchmarked against empirically known thermophysical properties of liquid deuterium and liquid tritium. Results for the pair correlation function, chemical potential, isothermal compressibility, isochoric heat capacity, and single-particle momentum distributions are reported. Given the benchmark comparisons, our predictions of liquid tritium properties are expected to be accurate to within a few percent. Our simulations unambiguously demonstrate the significance of nuclear quantum effects to the properties of liquid tritium. In particular, under saturated vapor pressure, the average molecular kinetic energy of the liquid is found to be more than 60% higher than the value expected from the classical equipartition theorem.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Contributions to the mechanistic understanding of the microstructural evolution in irradiated U-Mo dispersion fuel

Here, advanced microstructural characterization techniques, such as scanning electron microscopy (SEM) and scanning transmission electron microscopy - energy dispersive x-ray spectroscopy (STEM-EDS), were used to interpret the fuel microstructure evolution and fission products behavior in U-Mo dispersion fuel irradiated in the Advanced Test Reactor (ATR) as part of the European Mini-Plate Irradiation Experiment (EMPIrE) test. The larger as-fabricated fuel grain size achieved by heat-treating the U-Mo powder resulted in slower high burnup structure (HBS) development and reduced fission gas porosity. Slower HBS kinetics was observed at the fuel kernels’ periphery, which contained smaller and less fission gas bubbles at all fission densities (FDs) investigated and was attributed to a locally reduced damage density and fission products concentration, as corroborated with Monte Carlo simulations. The non-refined grains at the fuel kernel periphery hosted a perfectly ordered fission Gas Bubble Superlattice (GBS) up to 6.3 × 10 21 fissions/cm 3 . Nano-scale STEM-EDS analysis presented in this study provided useful information on the GBS characteristic morphology and evolution in U-Mo fuel. The concentration of fission gas in the GBS progressively increased with FD, pointing to an evolution of the nanobubble pressure status with irradiation. A possible connection between the GBS collapse and HBS onset is proposed for which there exists a threshold in the misorientation of the refined sub-grains above which the GBS stability during irradiation is no longer preserved, resulting in the GBS collapse.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

A Cryogenic Muon Tagging System Integrated with a Superconducting Qubit Device for Radiation-Induced Error Mitigation

Superconducting qubits are highly sensitive to ionizing radiation, which can induce correlated errors and limit scalable fault-tolerant quantum computing. In particular, cosmic-ray muons can deposit energy in the substrate, generating phonon bursts that break Cooper pairs and produce quasiparticles, leading to correlated decoherence events across multiple qubits. We present the development of a cryogenic muon tagging system based on Kinetic Inductance Detectors (KIDs) and its integration with superconducting quantum hardware. Originally developed within the ACE-SuperQ project and validated as a standalone detector, the system demonstrated a muon tagging efficiency of approximately 90% and excellent agreement with Monte Carlo simulations. Building on this validation, the tagging system has been integrated with a multi-qubit superconducting chip operated in a dilution refrigerator. The detector configuration consists of a multi-layer KID stack arranged above and below the quantum device, enabling time-coincident identification of muon-induced events within the same cryogenic environment. The integrated setup has been successfully commissioned, enabling simultaneous operation of the qubit chip and the muon tagging system. A first measurement campaign has been carried out, and preliminary data show time-correlated events between the muon tagging detectors and the qubit readout. A quantitative analysis of radiation-induced effects on qubit performance is currently ongoing. This work represents a step toward the implementation of event-level radiation tagging as a tool for characterizing and potentially mitigating correlated errors in superconducting quantum processors, while establishing a modular platform for future studies at the interface between particle physics and quantum information science.

Roy, Tanay [Fermilab] (ORCID:000000019442862X)↗

X-ray induced electron and ion fragmentation dynamics in IBr

Characterization of the inner-shell decay processes in molecules containing heavy elements is key to understanding x-ray damage of molecules and materials and for medical applications with Auger-electron-emitting radionuclides. The 1s hole states of heavy atoms can be produced by absorption of tunable x rays and the resulting vacancy decays characterized by recording emitted photons, electrons, and ions. The 1s hole states in heavy elements have large x-ray fluorescence yields that transfer the hole to intermediate electron shells that then decay by sequential Auger-electron transitions that increase the ion’s charge state until the final state is reached. In molecules, the charge is spread across the atomic sites, resulting in dissociation to energetic atomic ions. We have used x-ray/ion coincidence spectroscopy to measure charge states and energies of Iq+ and Brq′+ atomic ions following 1s ionization at the I and Br K-edges of IBr. We present the charge states and kinetic energies of the two correlated fragment ions associated with core-excited states produced during the various steps of the cascades. Here, to understand the dynamics leading to the ion data, we develop a computational model that combines Monte-Carlo/Molecular-Dynamics (MC/MD) simulations with a classical over-the-barrier model to track inner-shell cascades and redistribution of electrons in valence orbitals and nuclear motion of fragments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

LigninGraphs: lignin structure determination with multiscale graph modeling

Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Potts Model parameter study of particle size, Monte Carlo temperature, and “Particle-Assisted Abnormal Grain Growth”

A Potts Model was proposed to account for temperature and particle heterogeneity-dependent Zener Pinning. This was accomplished by relating the random fluctuations of grain boundary position allowed by the Potts Model switching probability at finite simulation temperature to experimentally observed grain growth stagnation behavior. We assume that these fluctuations arise from random fluctuations in the thermodynamic and kinetic properties of the grain boundaries and/or interfaces as they change with temperature. As an application of this model, the grain growth kinetics of U-10 wt. % Mo nuclear fuels were simulated with the input of microstructural images during different heat treatment processes. Simulated average grain growth behavior is in good agreement with experiments.

Frazier, William E.↗

Accurate Kohn-Sham auxiliary system from the ground-state density of solids

The Kohn-Sham (KS) system is an auxiliary system whose effective potential is unknown in most cases. It is in principle determined by the ground-state density and it has been found numerically for some low-dimensional systems by inverting the KS equations starting from a given accurate density. For solids, only approximate results are available. In this work, we determine accurate exchange-correlation (xc) potentials for Si and NaCl using the ground-state densities obtained from auxiliary field quantum Monte Carlo calculations. We show that these xc potentials can be rationalized as an ensemble of a few local functions of the density, whose form depends on the specific environment and can be well characterized by the gradient of the density and the local kinetic energy density. Further, the KS band structure can be obtained with high accuracy. The true KS band gap turns out to be larger than the prediction of the local density approximation, but significantly smaller than the measurable photoemission gap, which confirms previous estimates. Finally, our findings show that the conjecture that very different xc potentials can lead to very similar densities and other KS observables is true also in solids, which questions the meaning of details of the potentials and, at the same time, confirms the stability of the KS system.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

The Development of Kinetic and Radiation Hydrodynamics Modeling of Thermonuclear Burn Propagation in Isochoric p - 11 B Through the Support of the INFUSE Program

The report summarizes DOE INFUSE-supported work between HB11 Energy and the University of Rochester’s TriForce Institute to improve computational modeling of advanced fusion fuels, especially proton–boron-11 (p- 11 B). The project extended the TriForce particle-in-cell/Monte Carlo collision code to include physics needed for dense, high-temperature p- 11 B burn studies, including p- 11 B fusion reactions, three-alpha-particle reaction products, relativistic Coulomb collisions, large-angle nuclear scattering, bremsstrahlung radiation, inverse bremsstrahlung absorption, and photon transport. The upgraded models were verified against focused physics tests and against known deuterium–tritium burn behavior. The study then used one-dimensional spherical simulations to estimate the conditions required for thermonuclear burn propagation in isochoric p- 11 B fuel. The calculations found that burn propagation is possible in the model, but only under very extreme hot-spot conditions, such as about 7000 g/cm 3 at 500 keV or 9000 g/cm 3 at 300 keV for a 20-micron hot spot. These conditions are much more demanding than current demonstrated inertial confinement fusion hot spots. The report concludes that the INFUSE collaboration successfully advanced kinetic and radiation modeling capabilities for p- 11 B fusion and provided useful estimates of ignition requirements. However, the simulated fuel gains remain below what would be needed for practical inertial fusion energy, and further work is needed to reconcile differences among kinetic, radiation-hydrodynamic, and analytic models and to identify more achievable target designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

A general mechanism for gel layer formation on borosilicate glass under aqueous corrosion

Mineral and glass dissolution is a scientific topic deeply investigated but incompletely understood and of a great interest for the geochemical and materials science communities. If the interfacial dissolution/reprecipitation mechanism seems to be applicable to most of silicate minerals, the debate remains open concerning glass. Here we studied two model glasses, a ternary borosilicate (CJ1) and the same glass doped with 4.1 mol% of Al2O3 (CJ2). The two glasses were altered at 90°C, pH 9, and in conditions far and close to saturation with respect to amorphous silica, to determine the initial and residual rates. Moreover, a specific experiment was conducted for a short duration with a solution highly enriched with 18O and 29Si isotopes to understand how passivating gels form. SEM, TEM and ToF-SIMS characterization, along with Monte Carlo simulations were used to understand the rate limiting reactions at play and infer the role of Al. We show that Al yields a slower matrix dissolution in dilute conditions. However, it slows down he formation and the maturation of the passivating gel and favors alteration by partial hydrolysis of Si and Al entities followed by in-situ reorganization/relaxation into a porous network. Unexpectedly, CJ1 experienced both interfacial dissolution/reprecipitation and partial hydrolysis followed by in situ reorganization of the silicate network during the course of a single experiment. This study offers a unified concept that can pave the way for the future development of a predictive kinetic model based on a detailed description of bond breaking and bond forming as a function of glass composition and alteration conditions.

Gin, Stephane↗

Kinetic Plasma Simulation in the MOOSE Framework: Verification of Electrostatic Particle In Cell Capabilities

In magnetic confinement nuclear fusion reactors, the interaction between the plasma edge and plasma facing components is extremely important. At the plasma edge, a kinetic representation such as particle-in-cell (rather than a fluid representation) is required to accurately capture the plasma behavior. General purpose particle-in-cell plasma simulation capabilities have been developed in the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. This new capability is a part of the development of a new MOOSE-based framework for modeling plasma facing components, the Fusion ENergy Integrated multiphys-X (FENIX) framework. In this work, the verification of foundational particle-in-cell capabilities in FENIX is presented. This new plasma simulation capability has three main components: moving particles in discrete steps on the finite element mesh, mapping charge density from the particle's location to the finite element mesh, and solving for the electrostatic potential based on the charge density mapped from particles to the mesh. In this paper, simple verification problems demonstrating each of these new capabilities are presented, and future work includes electromagnetic capabilities and Monte Carlo collisions with neutral gas particles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

COMPUTATIONAL MODELING OF IGNITION AND PREMIXED FLAME PROPAGATION INITIATED BY A PRE-CHAMBER TURBULENT JET

Addressing the pressing need for reduced carbon emissions, Turbulent Jet Ignition (TJI) emerges as a promising technology for ultra-lean combustion, offering enhanced thermal efficiencies and minimized cyclic variability in spark-ignited engines. To facilitate rapid testing and integration of this technology, a robust computational modeling framework is crucial. This study delves into the predictive capabilities of computational models for main-chamber ignition and premixed flame propagation using a single-cycle TJI rig measured by Biswas et al. (Applied Thermal Engineering, vol 106, 2016). Employing an open-source compressible flow simulation solver with Large Eddy Simulation (LES) for turbulence modeling, the investigation integrates the conventional Laminar Finite Rate Chemistry (LFRC) model alongside the transported Probability Density Method (PDF) for turbulence-chemistry interaction. A fully-consistent Eulerian Monte-Carlo Fields (EMCF) method is utilized to approximate the transported PDF, while Interaction by Exchange with Mean is employed to close micro-mixing terms in stochastic differential equations. A reduced chemical reaction mechanism with 21 species and 84 reactions (DRM-19) is used for solving chemical kinetics, and a double Gaussian energy deposition model is used to approximate the spark ignition in the pre-chamber. An unstructured O-grid mesh with 0.3 million cells in the prechamber and 1 million cells in the main chamber is employed. Results are divided into two phases: pre-chamber initialization and full TJI simulations. Validation of the predicted pre-chamber flame propagation and the lean ignition in the main-chamber is carried out by using available experimental data. Under quiescent conditions, both the LFRC and transported PDF methods largely underestimate the flame speed and subsequent pressure growth in the pre-chamber. A linear momentum forcing technique is applied to investigate the impact of initial turbulence in the pre-chamber, demonstrating a notable influence on flame propagation. Fine-tuning of the forcing coefficient reproduces the sudden pressure growth observed in the experiment. The experimentally validated pre-chamber simulation serves as the initial condition for the full TJI simulations. It is found that the LFRC model fails to predict lean-ignition in the main-chamber, resulting in a misfiring event. Incorporation of turbulence-chemistry interaction using the transported PDF method substantially improves the prediction of the ignition event in the main-chamber, achieving fair qualitative agreement and quantitative validation of combustion parameters within ±10% of the reported experimental data. The rich simulation results consisting of a full set of statistical description of the thermo-chemical states enable us to gain deep insights into the ignition mechanisms in the main chamber, which is limited when done experimentally. A novel dual ignition phenomenon is revealed in the TJI rig for the first time. Initially, a primary ignition kernel is formed at a downstream location which eventually detaches from the main jet. As the jet momentum decreases, a secondary ignition event follows, this time at a more upstream location which eventually combines with the primary ignition kernel to form a single connected flame front. Investigation of these ignition sequences in chemical composition space reveal distinct differences between the two. The primary ignition event in the main-chamber is followed by a large concentration of active radicals from the pre-chamber jet, accelerating the chain-branching steps, characterizing what has been referred to as flame ignition. In contrast, the secondary ignition occurs in the absence of active radicals in the pre-chamber jet, hence characterized as jet ignition. Further analysis of the effect of pre-chamber jet characteristics on lean ignition in the main-chamber is conducted by setting up cases with different initial pressure ratios (por) between the two chambers, a non-dimensional parameter, ranging from 1.2 to 3.2. As the initial pressure ratio increases, jet momentum increases, with dual ignition observed in cases above por= 2.2. Case with por= 3.2 lead to misfiring. The effect of ignition sequence on global combustion characteristics of TJI is analyzed. Dual ignition events lead to non-monotonicity in combustion characteristics such as global reaction progress variable, flame penetration, and global heat release rate. In dual ignition events, although the rate of fuel consumption and global heat release rate is initially lower, the secondary ignition leads to a sudden increase in flame surface area, resulting in a sudden jump and promoting the overall performance of the TJI system.

42 ENGINEERING↗

Determination of the characteristic magnetic pre-sheath length at divertor surfaces using micro-engineered targets on DiMES at DIII-D

The magnetic pre-sheath (MPS) width, L MPS , is a critical parameter to define the sheath potential, which controls the ion trajectory of low-Z species (D, T, He, and C), as well as the prompt re-deposition of high-Z species. In this work, to determine L MPS , we fabricated micro-trenches (30×30×4 µm) via focused ion beam (FIB) milling on a silicon surface and exposed them to L-mode deuterium plasmas in DIII-D via the Divertor Material Evaluation System (DiMES) removable sample exposure probe. The areal distribution of impurity depositions, mainly consisting of carbon, was measured by energy-dispersive X-ray spectroscopy (EDS) to reveal the deuterium ion shadowing effect on the trench floors. The carbon deposition profiles showed that the erosion was maximized for the azimuthal direction of φ = -40° (referenced to the toroidal magnetic field direction) as well as the polar angle of θ = 80°. A Monte Carlo equation-of-motion model, based on a collisionless MPS, was used to calculate the azimuthal and polar deuterium ion angle distributions (IADs) for a range of L MPS = k × ρ i , where ρ i is the ion gyro radius and k = 0.5-4. Then, gross erosion profiles were calculated by a Monte Carlo micro-patterning and roughness (MPR) code for ion sputtering using as input the calculated azimuthal and polar IADs for each value of k . Good agreement with the experimental C deposition profiles was obtained for the case k = 2.5-3.5. This result is consistent with a previous kinetic modeling prediction of k ~ 3, as well as previous analytical investigations that predicted the L MPS to be several ion gyro radii. A validation of theoretical sheath models supports its applicability to ITER and pilot plant divertors to successfully predict plasma-materials interactions.

Erosion↗

Measurement of temperature of a dusty plasma from its configuration

A new concept called “configurational temperature” is introduced in the context of dusty plasma, where the temperature of the dust particles submerged in the plasma can be measured directly from the positional information of the individual dust particles and the interaction potential between the dust grains. This method does not require the velocity information of individual particles, which is a key parameter to measure the dust temperature in the conventional method. The technique is initially tested using two-dimensional (2D) OpenMP parallel molecular dynamics and Monte Carlo simulation and then compared with the temperature evaluated from experimental data. The experiment have been carried out in the Dusty Plasma Experimental (DPEx) device, where a 2D stationary plasma crystal of melamine formaldehyde particles is formed in the cathode sheath of a DC glow discharge argon plasma. The kinetic temperature of the dust is calculated using the standard particle image velocimetry technique at different pressures. An extended simulation result for the three-dimensional case is also presented, which can be employed for the temperature measurement of a three-dimensional dust crystal in laboratory devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Path sampling of recurrent neural networks by incorporating known physics

Recurrent neural networks have seen widespread use in modeling dynamical systems in varied domains such as weather prediction, text prediction and several others. Often one wishes to supplement the experimentally observed dynamics with prior knowledge or intuition about the system. While the recurrent nature of these networks allows them to model arbitrarily long memories in the time series used in training, it makes it harder to impose prior knowledge or intuition through generic constraints. In this work, we present a path sampling approach based on principle of Maximum Caliber that allows us to include generic thermodynamic or kinetic constraints into recurrent neural networks. We show the method here for a widely used type of recurrent neural network known as long short-term memory network in the context of supplementing time series collected from different application domains. These include classical Molecular Dynamics of a protein and Monte Carlo simulations of an open quantum system continuously losing photons to the environment and displaying Rabi oscillations. Our method can be easily generalized to other generative artificial intelligence models and to generic time series in different areas of physical and social sciences, where one wishes to supplement limited data with intuition or theory based corrections.

59 BASIC BIOLOGICAL SCIENCES↗