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At least 1,675 records · Page 93

Understanding Model Inadequacy in TRISO Nuclear Fuel Fission Products Release Models: Empirical and Mechanistic Approaches

The increasing use of tristructural isotropic (TRISO) particle fuel in both advanced and existing reactors necessitates a thorough evaluation of uncertainties and shortcomings in TRISO fission product release models. These inadequacies arise from the simplifications made in computational models compared to experimental data. Utilizing the BISON fuel performance code and experimental data from the Advanced Gas Reactor (AGR) program provides a unique chance to rigorously assess these inadequacies within a Bayesian uncertainty quantification (UQ) framework. This study contrasts the standard Bayesian framework with the Kennedy-O'Hagan (KOH) framework, which explicitly accounts for modeling inadequacies, in the context of UQ for TRISO silver release models. It examines both the traditional Arrhenius equation and a more advanced lower-length-scale (LLS)-informed model that incorporates microstructure information. The inverse UQ process applied to AGR-2 and AGR-3/4 datasets identified modeling inadequacy as the primary source of uncertainty, with experimental noise also being significant, while model parameter uncertainty was minimal. Both the Arrhenius and LLS-informed models showed similar levels of modeling inadequacy. For forward predictive UQ using the AGR-1 dataset, the KOH framework enhanced the accuracy and quality of quantified uncertainties by approximately 30% and 40%, respectively, compared to the standard Bayesian framework. This improvement was observed for both the Arrhenius and LLS-informed models. At the engineering scale, both models performed similarly, but the LLS-informed model outperformed the Arrhenius equation at the mesoscale. These findings underscore the importance of explicitly considering modeling inadequacy in the UQ process and highlight the need for ongoing refinement of physics-based models to address these shortcomings.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

High-Strength, High-Ductility, High Entropy Alloys with High-Efficiency Native Oxide Solar Absorbers for Concentrating Solar Power Systems

This EPSCoR Project has been investigating the synergy between the excellent high-temperature mechanical behavior of FeMnNiAlCr high entropy alloys (HEA) and the high solar absorptance of their native oxides for high efficiency concentrated solar thermal power (CSP) systems working at >700°C. While HEAs have attracted substantial interest in recent years, most investigations have focused on their applications as structural materials rather than functional materials. This EPSCoR project discovered that FeMnNiAlCr HEAs can potentially be applied synergistically as both a structural and functional material for high-efficiency concentrating solar thermal power (CSP) systems working at >700°C. The HEA itself would be used in high-temperature tubing to carry molten salts or supercritical CO 2 , while its surface oxide would act as a high-efficiency solar thermal absorber. With Fe and Mn being the major components in these HEAs (adding up to ~70 at.% of the alloy), these materials are much more cost-effective than the Ni-based superalloys currently being investigated for high-temperature CSP systems. Through this research, these Fe-Mn based HEAs have demonstrated yield strengths 2-3x greater than that of stainless steel at 700°C and a creep lifetime >800 h at 700ºC under a typical CSP tubing mechanical load of 35 MPa. Their Mn-rich surface oxides maintain a high optical-to-thermal conversion efficiency of ~87% under 1000x solar concentration ratio for 20 simulated day-night thermal cycles between 750ºC and room temperature. In preliminary corrosion studies, these HEAs have sustained immersion in unpurified bromide molten salts for 14 days at 750°C with <2% weight loss, in contrast to 70% weight loss from a 316 stainless steel reference. The simultaneous achievement of promising mechanical, optical, and thermochemical properties in this FeMnNiAlCr system opens the door to new applications of HEAs in solar energy harvesting. Partnerships with Ames Laboratory and Oak Ridge National Laboratory (ORNL) also advanced our understanding of the fundamental structure-property relationships through atomic scale material characterization and first-principles computational modeling. The key research results in this project can potentially be extended to other HEAs and their native oxides. In terms of applications, the proposed FeMnNiAlCr HEA/native oxide system could potentially exceed the mechanical and the optical performance of existing tubing and solar coating materials under EERE’s CSP program at lower cost, which also aligns well with the EPSCoR Science and Technology strategies of New Hampshire in boosting the deployment of renewable energy.

14 SOLAR ENERGY↗

Two-dimensional electronic Stark spectroscopy of a photosynthetic dimer

Stark spectroscopy, which measures changes in the linear absorption of a sample in the presence of an external DC electric field, is a powerful experimental tool for probing the existence of charge-transfer (CT) states in photosynthetic systems. CT states often have small transition dipole moments, making them insensitive to other spectroscopic methods, but are particularly sensitive to Stark spectroscopy due to their large permanent dipole moment. In a previous study, we demonstrated a new experimental method, two- dimensional electronic Stark spectroscopy (2DESS), which combines two-dimensional electronic spectroscopy (2DES) and Stark spec- troscopy. In order to understand how the presence of CT states manifest in 2DESS, here, we perform computational modeling and calculations of 2DESS as well as 2DES and Stark spectra, studying a photosynthetic dimer inspired by the photosystem II reaction center. Here, we identify specific cases where qualitatively different sets of system parameters produce similar Stark and 2DES spectra but significantly different 2DESS spectra, showing the potential for 2DESS to aid in identifying CT states and their coupling to excitonic states.

14 SOLAR ENERGY↗

Some numerical and physical aspects of unsteady Navier-Stokes computations over airfoils using dynamic meshes

An upwind-biased implicit approximate factorization algorithm is applied to several unsteady flows on dynamic meshes. The thin-layer form of the compressible Navier-Stokes equations is used to solve both laminar and turbulent flows over airfoils pitching about the quarter chord. Numerical aspects of the solutions are investigated, including grid and time step effects. Two methods for determining fluxes - flux-vector splitting and flux-difference splitting - are compared. Flux-difference splitting predicts results more accurately than flux-vector splitting on a coarse mesh, but both methods agree on a fine mesh. Physical aspects of the computations are also examined. An equilibrium turbulent boundary layer model computes generally better unsteady results in comparison with experiment than a nonequilibrium model for the transonic case analyzed. Also, the size and location of the primary shed vortex for an airfoil pitching up at a constant rate is calculated in good agreement with experiment for two pitch rates.

Rumsey, Christopher L.↗

Analysis of Arc Welding Process in Space

This work is motivated by NASA plans to conduct welding experiments on the ISS. It is expected that deployment of welding and additive manufacturing technologies in the space environment has the potential to revolutionise how orbiting platforms are designed, manufactured, and assembled. However, the structure, composition and quality of a weld is extremely dependent on the environment and can be difficult to control in space. Shielding gases would also be tough to manage as gases behave differently in zero gravity and airless environments. Additional points of concern are related to the spatter and sparks dynamics in space. Therefore, there is a need for a more basic understanding of welding processes by computational modelling. To provide such an insight we developed state of art models of the ARC torch, droplet detachment and transfer, and the melt pool build up using magnetohydrodynamics approximation and level set method for two-phase liquid metal/-gas flow modelling. Two finite element models were built using 2D axisymmetric geometry in COMSOL Multiphysics®: (i) stationary model of the ARC torch and (ii) dynamical model of droplet detachment and transfer. Both models demonstrate reasonable agreement with earlier experimental observations and high sensitivity to the temperature dependence of the thermophysical parameters on the system materials. The models were used to provide physical insight into ARC welding in various environments and geometries. It was shown, in particular, that there is a significant probability of gas bubbles trapping in the meltpool and a possibility of unbounded wondering of sparks in the welding chamber in zero gravity. In addition, we estimated metal evaporation rate that may be hazardous in the confined environment of the ISS. These issues raise concern of quality of the weld and safety for ISS applications.

ARC welding↗

Thermoelectric coolers for high-power-density 3D electronics heat management

Future advancements in three-dimensional (3D) electronics require robust thermal management methodology. Thermoelectric coolers (TECs) are reliable and solid-state heat pumping devices with high cooling capacity that can meet the requirements of emerging 3D microelectronic devices. In this work, we first provide the design of TECs for electronics cooling using a computational model and then experimentally validate the main predictions. Key device parameters such as device thickness, leg density, and contact resistance were studied to understand their influence on the performance of TECs. Our results show that it is possible to achieve high cooling power density through optimization of TE leg height and packing density. Scaling of TECs is shown to provide ultra-high cooling power density.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Comparative structural and computational analysis supports eighteen cellulose synthases in the plant cellulose synthesis complex

A six-lobed membrane spanning cellulose synthesis complex (CSC) containing multiple cellulose synthase (CESA) glycosyltransferases mediates cellulose microfibril formation. The number of CESAs in the CSC has been debated for decades in light of changing estimates of the diameter of the smallest microfibril formed from the β-1,4 glucan chains synthesized by one CSC. We obtained more direct evidence through generating improved transmission electron microscopy (TEM) images and image averages of the rosette-type CSC, revealing the frequent triangularity and average cross-sectional area in the plasma membrane of its individual lobes. Trimeric oligomers of two alternative CESA computational models corresponded well with individual lobe geometry. A six-fold assembly of the trimeric computational oligomer had the lowest potential energy per monomer and was consistent with rosette CSC morphology. Negative stain TEM and image averaging showed the triangularity of a recombinant CESA cytosolic domain, consistent with previous modeling of its trimeric nature from small angle scattering (SAXS) data. Six trimeric SAXS models nearly filled the space below an average FF-TEM image of the rosette CSC. In conclusion, the multifaceted data support a rosette CSC with 18 CESAs that mediates the synthesis of a fundamental microfibril composed of 18 glucan chains.

59 BASIC BIOLOGICAL SCIENCES↗

Pressure flux-split viscous solutions for subsonic diffusers

A method for predicting flow in diffusers with inlet swirl has been developed. Solutions of the pressure flux-split Reduced Navier Stokes (RNS) equations are obtained for flows in axisymmetric diffusers. Viscous-Inviscid interactions and flow fields with toroidal recirculation regions are efficiently captured. The computational model is verified by comparision with experimental data and other computations. The extreme sensitivity to grid, turbulent closure model and inlet profiles is discussed.

Pordal, H. S.↗

Timescale Separation of Positive and Negative Signaling Creates History-Dependent Responses to IgE Receptor Stimulation

The high-affinity receptor for IgE expressed on the surface of mast cells and basophils interacts with antigens, via bound IgE antibody, and triggers secretion of inflammatory mediators that contribute to allergic reactions. To understand how past inputs (memory) influence future inflammatory responses in mast cells, a microfluidic device was used to precisely control exposure of cells to alternating stimulatory and non-stimulatory inputs. We determined that the response to subsequent stimulation depends on the interval of signaling quiescence. For shorter intervals of signaling quiescence, the second response is blunted relative to the first response, whereas longer intervals of quiescence induce an enhanced second response. Through an iterative process of computational modeling and experimental tests, we found that these memory-like phenomena arise from a confluence of rapid, short-lived positive signals driven by the protein tyrosine kinase Syk; slow, long-lived negative signals driven by the lipid phosphatase Ship1; and slower degradation of Ship1 co-factors. This work advances our understanding of mast cell signaling and represents a generalizable approach for investigating the dynamics of signaling systems.

59 BASIC BIOLOGICAL SCIENCES↗

RESOLVING THE ELECTROCHEMICAL EQUATIONS OF A SOLID OXIDE FUEL CELL FOR USE IN TRANSIENT SIMULATION AND INTEGRATION INTO CYBER-PHYSICAL SYSTEMS

A major challenge with complex cyber-physical systems stems from long model computational time that creates a mismatch between the model system and the physical system. The numerical modeling of solid oxide fuel cells (SOFCs) presents particular challenges due to the highly coupled nature of the underlying equations and the multiphysics needed to fully resolve their behavior during a transient event. To this end current approaches revolve around splitting the computational efforts into resolving temperature effects and resolving electrochemical effects. Current methods employed for the transient simulation of an SOFC for implementation in the Hybrid Performance (HyPer) facility cyberphysical plant at the National Energy Technology Laboratory reveal a distinct need for accelerated results with a high degree of stability. To this aim, an investigation into the computational time for the code reveals that the underlying electrochemical algorithm takes an order of magnitude more time than its thermal counterpart and has a tendency to vary in terms of iteration time and as such a rework of the underlying system is proposed. The primary method for accelerated electrochemical algorithm solutions is to employ higher order root finding recipes for the resolution of the highly coupled electrochemical equations. This is done with the intention to reduce the overall number of subiterations necessary for resolving voltage, current density, and species concentration, properties of the fuel cell that are all directly coupled and require nested iterative approaches. The overall objective of this approach is an order of magnitude reduction in calculation time without sacrificing stability and increasing accuracy. Specific approaches involve using both bounded and unbounded techniques, such as the False Position method and the Secant method (or if applicable Newton-Raphson) respectively, the drawbacks being slower convergence for False Position and instability for the Secant or Newton-Raphson methods. Current preliminary results on simplified versions of the parent functions involved for electrochemical calculations indicate a reduction in computational steps by a factor of two for the secant method and a factor of three for Newton-Raphson. When implemented into new modified electrochemical algorithms, the results indicate a possible order of magnitude reduction in calculation time.

Arias, Jesus↗

Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems

Abstract A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions. These closure models are common in many nonlinear spatiotemporal systems to account for losses due to reduced order representations, including many transport phenomena in fluids. Previous data-driven closure modeling efforts have mostly focused on supervised learning approaches using high fidelity simulation data. On the other hand, reinforcement learning (RL) is a powerful yet relatively uncharted method in spatiotemporally extended systems. In this study, we put forth a modular dynamic closure modeling and discovery framework to stabilize the Galerkin projection based reduced order models that may arise in many nonlinear spatiotemporal dynamical systems with quadratic nonlinearity. However, a key element in creating a robust RL agent is to introduce a feasible reward function, which can be constituted of any difference metrics between the RL model and high fidelity simulation data. First, we introduce a multi-modal RL to discover mode-dependant closure policies that utilize the high fidelity data in rewarding our RL agent. We then formulate a variational multiscale RL (VMRL) approach to discover closure models without requiring access to the high fidelity data in designing the reward function. Specifically, our chief innovation is to leverage variational multiscale formalism to quantify the difference between modal interactions in Galerkin systems. Our results in simulating the viscous Burgers equation indicate that the proposed VMRL method leads to robust and accurate closure parameterizations, and it may potentially be used to discover scale-aware closure models for complex dynamical systems.

97 MATHEMATICS AND COMPUTING↗

Data analysis and modeling pipelines for controlled networked social science experiments

There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.

97 MATHEMATICS AND COMPUTING↗

Parametric Study of a Mixer/Ejector Nozzle with Mixing Enhancement Devices

A numerical study employing a simplified model of the High Speed Civil Transport mixer/ejector nozzle has been conducted to investigate the effect of tabs (vortex generators) on the mixing process. More complete mixing of the primary and secondary flows within the confined ejector lowers peak exit velocity resulting in reduced jet noise. Tabs were modeled as vortex pairs and inserted into the computational model. The location, size, and number of tabs were varied and its effect on the mixing process is presented here both quantitatively and qualitatively. A baseline case (no tabs) along with six other cases involving two different vortex strengths at three different orientations have been computed and analyzed. The case with the highest vorticity (six vortices representing large tabs) gives the best mixing. It is shown that the influence of the vorticity acts primarily in the forward or middle portions of the duct, significantly alters the flow structure, and promotes some mixing in the lateral direction. Unmixed pockets were found at the top and bottom of the lobe, and more clever placement of tabs improved mixing in the vertical direction. The technique of replacing tabs with vortices shows promise as an efficient tool for quickly optimizing tab placement in lobed mixers.

DalBello, T.↗

Population balance modeling of polyurethane foam formation with pressure‐dependent growth kernel

Abstract Polyurethane foams are widely used materials often chosen for their useful characteristics such as low thermal conductivity, ease of application, and high strength‐to‐weight ratios. Computational models are needed to predict the dynamics of the flow and expansion, and the resulting material properties, to improve manufacturing processes. In this paper, a model for PMDI, a water‐blown polyurethane foam, is presented. By extending a kinetics‐based approach by adding bubble‐scale information via a population balance equation (PBE) using the quadrature method of moments, we can track bubble size distributions during foaming. We present results from a three‐dimensional computational fluid dynamics model using arbitrary Lagrangian–Eulerian interface tracking implemented in finite element software. The model compares favorably with experimental data, including dynamics, bubble distributions measured by both camera and diffusion wave spectroscopy, and post‐test bubble size from scanning electron microscopy and density measurements from x‐ray computed tomography.

Ortiz, Weston↗

An efficient numerical model for predicting residual stress and strain in parts manufactured by laser powder bed fusion

Abstract Computational modeling of additively manufactured structures plays an increasingly important role in product design and optimization. For laser powder bed fusion processes, the accurate modeling of stress and distortion requires large amount of computational cost due to very localized heat input and evolving complex geometries. The current study takes advantage of a graphics processing unit accelerated explicit finite element analysis code and approximated heat conduction analysis to predict the macroscopic thermo-mechanical behavior in laser selective melting. Adjacent layers and tracks were lumped to reduce the number of time steps and elements in the finite element model. The effects of track and layer grouping on prediction accuracy and solution efficiency are investigated to provide a guidance for a cost-effective simulation. Thin-wall builds from Inconel alloy 625 (IN625) powders were simulated by applying the developed modeling approach to get the detailed residual stress and distortion at a computational speed 50 times higher than conventional approach. Under repeated heating and cooling cycles, a high tensile stress was produced near surfaces of a build due to a larger shrinkage on surface than that in central area. It is also shown that horizontal stresses concentrate near the root and top layers of the IN625 build. The predicted residual elastic strain distribution was validated by the experimental measurement using x-ray synchrotron diffraction.

36 MATERIALS SCIENCE↗

Atomic layer deposited aluminum nitride for interface protection of lithium germanium thiophosphate solid electrolytes

Lithium germanium thiophosphate (LGPS) is an attractive solid-state electrolyte material due to its exceptionally high ionic conductivity (~1.2 × 10 –2 S cm –1 ), comparable to many organic liquid electrolytes commonly used in batteries. Despite the high conductivity of LGPS, the susceptibility of LGPS to deleterious degradation reactions has impeded its commercial adoption into solid-state batteries. In particular, the poor voltage stability of LGPS with high-voltage cathode or lithium metal potentials often results in dramatically increasing cell impedance during galvanic cycling. Here, we use aluminum nitride (AlN) as a protection layer for Li metal anode, applied directly to the LGPS at 250 °C using plasma-enhanced atomic layer deposition, to enhance cell performance by preventing LGPS-Li metal reactions. We compare the surface chemistry and electrochemical cycling performance of atomic layer-deposited AlN grown using both plasma N 2 and NH 3 precursors. Galvanostatic cycling and electrochemical impedance spectroscopy show that AlN-coated LGPS cells perform better than bare LGPS cells in contact with Li metal anodes, with the AlN able to improve cycling longevity by over a factor of 3 in certain cases. Finally, we utilize x-ray photoelectron spectroscopy (XPS) line scans to highlight the slow room-temperature reactivity between AlN and evaporated lithium metal, and a computational model is built to aid further XPS analysis.

25 ENERGY STORAGE↗

Enabling large-scale viscoelastic calculations via neural network acceleration

One of the most significant challenges involved in efforts to understand the effects of repeated earthquake cycle activity is the computational costs of large-scale viscoelastic earthquake cycle models. Computationally intensive viscoelastic codes must be evaluated at thousands of times and locations, and as a result, studies tend to adopt a few fixed rheological structures and model geometries and examine the predicted time-dependent deformation over short (<10 years) time periods at a given depth after a large earthquake. Training a deep neural network to learn a computationally efficient representation of viscoelastic solutions, at any time, location, and for a large range of rheological structures, allows these calculations to be done quickly and reliably, with high spatial and temporal resolutions. We demonstrate that this machine learning approach accelerates viscoelastic calculations by more than 50,000%. Finally, this magnitude of acceleration will enable the modeling of geometrically complex faults over thousands of earthquake cycles across wider ranges of model parameters and at larger spatial and temporal scales than have been previously possible.

58 GEOSCIENCES↗

Polynuclear Aromatic Hydrocarbons with Curved Surfaces: Buckyballs

The discovery of a new allotropic form of elemental carbon – the fullerenes – and subsequently other novel forms of elemental carbon with pyramidalized surfaces, most notably single-walled and multi-walled carbon nanotubes, introduced a novel structural motif to the polycyclic aromatic hydrocarbons (PAHs) with nonplanar surfaces. Our research program supported by BES DOE grant DE-FG02-04ER15514 has dealt with the synthesis, structural studies, and chemistry of the novel curved-surface PAHs with carbon frameworks structurally related to fullerenes. They are referred to as “buckybowls”. We prepared several new buckybowls and, even more importantly, developed the efficient, gram-scale synthetic methodologies for the preparation of small buckybowls, most notably corannulene (C20H10) and its derivatives. In addition, the employment of the corannulene-based synthons previously developed in our laboratory led to a number of highly nonplanar molecular architectures with two or more corannulene subunits with a potential for the applications as novel materials in separation sciences, nanoelectronics, photovoltaics and catalysis. In collaboration with Professor Angelici (Iowa State) we prepared and characterized several transition metal complexes of corannulene, providing the first structural characterization of η6 metal complexes of buckybowls by a single crystal X-ray diffraction. In addition to the definitive structural characterization of the complexes we demonstrated that the (η6-C6Me6)Ru2+ unit in some relatively stable complexes activate the corannulene ligand to react with proper nucleophiles suggesting that such complexex may be used in catalysis. (Section C). We have explored the efficiency of the dispersion-based interactions of curved-surface conjugated carbon networks by high-level computational models. We showed that the curvature of such networks does not reduce the van der Waals attractions as compared to the planar systems of similar size. We than concentrated on the design, synthesis and testing of the previously unknown molecular receptors with corannulene pincers which are capable of forming the strong ball-and-socket inclusion complexes with fullerenes in solution and in the solid state. Buckycatcher I (a molecular receptor with two corannulene pincers preorganized on a tetrabensocyclooctatetraene tether) inclusion complexes with fullerenes provided the first experimental evidence for the importance of concave-convex π – π interactions in the supramolecular chemistry of fullerene carbon cages with buckybowls. C60@Buckycatcher I has become the prototypical supramolecular system formed by the relatively weak (on the atom-to-atom bases) and has been extensively used by the computational community to test the quality of the theoretical models. In addition, Buckycatcher I shows an exceptional ability to adopt other guest molecules and several its inclusion complexes and/or solvates have been structurally characterized. These unusual structures show the potential of the buckycatchers to be tested as novel organic materials. “Intelligent design” of molecular receptors with corannulene receptors using computational approach allowing for a priori prediction of their binding potentials led to the preparation of other buckycatchers. Both bi- and tridentate corannulene pincers were tested and two of the bidentate receptors (Buckycatchers II and III) exhibited the outstanding affinity toward fullerenes, significantly exciding Buckycatcher I performance. Careful tailoring of the tethers resulted in the optimization of binding energies of the receptors with guest carbon cages and in the reduction of the entropy/solvation penalties. Practical preparation of dicorannulenopentacene opens a new avenue for the synthesis of a pool of bis-corannulene receptors possessing polar groups on their dibenzobarrelene tether. The polar “anchors” can be used to attach these efficient fullerene receptors to solid supports in order to modify their surfaces with the potential applications in separation sciences, catalysis and photovoltaic materials. additionally, the presence of such groups will improve the solubility of the receptors in polar solvents, expanding the scope of their supramolecular chemistry in solutions. Rigorous studies of thermodynamics of host-guest association of molecular clips and tweezers in organic solvents with C60/C70 inclusion complexes with Buckycatcher I as a model system have provided a full set of thermodynamical data including ΔG, ΔH, and –TΔS, for the formation of the inclusion complexes over a range of temperatures and in several solvents. Both 1H NMR and Isothermal Calorimetry (ITC) titrations provide virtually identical association constants, confirming the reliability of the results. WE have shown that thermodynamics of the host-guest complex formation is more complicated than anticipated and proposed by the existing computational models. These data are of premium importance for the testing of the computational solvation models and for a deeper understanding of the supramolecular interactions in organic solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗