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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Sierra/SD – Verification Test Manual – 5.22

Verification and validation (V&V) of scientific computing programs are important at Sandia National Labs due to the expanding role of computational simulation in managing the United States nuclear stockpile. The complexities of structural response calculations used to analyze physical problems, the varieties of codes applied to the calculations, and the importance of accurate predictions when assessing field conditions demand confidence in the consistency and accuracy of computer codes. Confidence in the accuracy of the predictions arising from computer simulations must ultimately be gained through verification and validation. The Sierra salinas structural dynamics analysis code, Sierra/SD, is used at the DOE Laboratories, and in several DOD projects. The roles of Sierra/SD in the qualification of weapon systems and components for normal and hostile environments throughout the Stockpile-to-Target Sequence include to, • Redesign weapon components. • Certify weapon components and systems for target environments such as hypersonic vehicles. • Certify that components will survive the thermal mechanical shock loads associated with hostile environments. • Evaluate current stockpile issues, including issues associated with uncertainty quantification. • Address many other problems that are encountered in stockpile management. The Sierra/SD verification plan is described, and an evolving set of key verification tests are described in detail. The verification tests ensure the correctness of the mathematics and numerical algorithms associated with functionality describing engineering phenomena. Development is in accordance with a set of tailored Software Quality Engineering (SQE) practices. SQE practices guide the overall verification and validation effort.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Breaking the Million-Electron and 1 EFLOP/s Barriers: Biomolecular-Scale Ab Initio Molecular Dynamics Using MP2 Potentials

The accurate simulation of complex biochemical phenomena has historically been hampered by the computational requirements of high-fidelity molecular-modeling techniques. Quantum mechanical methods, such as ab initio wave-function (WF) theory, deliver the desired accuracy, but have impractical scaling for modeling biosystems with thousands of atoms. Combining molecular fragmentation with MP2 perturbation theory, this study presents an innovative approach that enables biomolecular-scale ab initio molecular dynamics (AIMD) simulations at WF theory level. Leveraging the resolution-of-the-identity approximation for Hartree-Fock and MP2 gradients, our approach eliminates computationally intensive four-center integrals and their gradients, while achieving near-peak performance on modern GPU architectures. The introduction of asynchronous time steps minimizes time step latency, overlapping computational phases and effectively mitigating load imbalances. Utilizing up to 9,400 nodes of Frontier and achieving 59% (1006.7 PFLOP/s) of its double-precision floating-point peak, our method enables us to break the million-electron and 1EFLOP/s barriers for AIMD simulations with quantum accuracy.

Kurzak, Jakub↗
DOE OSTI2021

Air Blast Mesh Sensitivity and Pressure Mapping Study

Nose cone structural and aerodynamic characteristics are essential for intelligent design of aircraft, spacecraft, and ballistic systems. Finite element analysis can be used to help understand the structural integrity and flight characteristics of different nose cones. A mesh sensitivity study was undertaken for a particular nose cone geometry that was used in tests at LANL facilities in order to confirm the integrity of the meshed geometry. A simple cone that best matched closed-form theoretical solutions was modeled, and received good correlation to the theory. Complexity was then added back to the nose cone. Parameters applied to the simple cone were then implemented in the nose cone geometry giving assurance of accuracy after the geometry was changed. Nose cone results averaged 6.3% error for radial displacement when compared with the theoretical. Hoop stress averaged 6.0% error and meridional stress averaged 5.7% error at the finest mesh level. Meshes showed signs of convergence when compared to all three theoretical solutions. Finally, pressure time-history data from LANL computational fluid dynamics simulations was applied to the surface of the final nose cone geometry. The pressure data was interpolated from pressure gauge locations onto nearby meshed elements, which allowed for FEA software to run simulations on the cone with the pressure data as a loading condition. The pressure mapping resulted in the ability to understand the nose cone’s rigid body motion that in turn can inform design of future nose cones.

42 ENGINEERING↗
DOE OSTI2020

Thermal Frequency Drift of 3D Printed Microwave Components

Fabrication of microwave slot array antennas and waveguide bandpass and notch filters using 3D printing has significant advantages in terms of speed and cost even for parts with high mechanical complexity. One disadvantage of Stereolithography (SLA) 3D printed, copper plated microwave components is that some SLA resins have a high Coefficient of Thermal Expansion (CTE), quoted in micrometers per meter per degree or 10 -6 per degree. Compared to typically used metals such as aluminum (CTE 24 × 10 -6 ·K -1 ) and copper (CTE 17 × 10 -6 ·K -1 ), SLA resin can have CTE above 100 × 10 -6 ·K -1 . Resonant structures experience significant frequency drift with temperature changes on the order of 10–50 °C. The issue of 3D printed microwave structures changing frequency characteristics significantly with temperature shift has not been addressed or reviewed in current literature. We measured and simulated the effect of temperature change on a slot array, cavity notch filters, and post loaded waveguide bandpass filters. We tested several types of SLA resin, different plating techniques, and also Direct Metal Laser Sintering (DMLS) and Binder Infusion metal 3D printing. Performance as a function of temperature is presented for these alternatives.

three-dimensional printing↗
DOE OSTI2022

Modeling Analog Tile-Based Accelerators Using SST

Analog computing has been widely proposed to improve the energy efficiency of multiple important workloads including neural network operations, and other linear algebra kernels. To properly evaluate analog computing and explore more complex workloads such as systems consisting of multiple analog data paths, system level simulations are required. Moreover, prior work on system architectures for analog computing often rely on custom simulators creating signficant additional design effort and complicating comparisons between different systems. To remedy these issues, this report describes the design and implementation of a flexible tile-based analog accelerator element for the Structural Simulation Toolkit (SST). The element focuses on heavily on the tile controller—an often neglected aspect of prior work—that is sufficiently versatile to simulate a wide range of different tile operations including neural network layers, signal processing kernels, and generic linear algebra operations without major constraints. The tile model also interoperates with existing SST memory and network models to reduce the overall development load and enable future simulation of heterogeneous systems with both conventional digital logic and analog compute tiles. Finally, both the tile and array models are designed to easily support future extensions as new analog operations and applications that can benefit from analog computing are developed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Lean-Stratified Combustion System with Miller Cycle for Downsized Boosted Application - Part 2

Automotive manufacturers relentlessly explore engine technology combinations to achieve reduced fuel consumption under continued regulatory, societal and economic pressures. For example, technologies enabling advanced combustion modes, increased expansion to effective compression ratio and reduced parasitics continue to be developed and integrated within conventional and hybrid propulsion strategies across the industry. Here, a high-efficiency gasoline engine capable for use in conventional or hybrid electric vehicle platforms is highly desirable. This paper is the second of two papers describing the multi-cylinder integration of a technology package combining lean-stratified combustion with Miller cycle for downsized boosted applications. The first paper describes the design, analysis and single-cylinder testing conducted to down-select the combustion system deployed to the multi-cylinder engine. This paper defines a light-duty engine package capable of achieving a 35% fuel economy improvement at US Tier 3 emission standards over a naturally aspirated stoichiometric baseline vehicle. A multi-mode combustion system was developed enabling highly efficient lean-stratified operation at light-load and stoichiometric Miller-cycle operation at mid- to high-loads. A central direct-injection four-valve layout was designed with high-tumble ports and a bowl-in-piston capable of stable operation under highly dilute mixtures when properly matched to fuel spray characteristics, injection strategy, and a high-energy ignition system. Boost system layout and base engine architecture were selected to minimize parasitics and maximize after treatment effectiveness. A multi-cylinder engine was developed and calibrated over a steady-state map. Results fed vehicle simulations over the EPA city and highway test cycles to predict the fuel economy improvement and tailpipe emissions potential. Fuel economy improvements over the baseline vehicle were projected at 42% and 39% for active-urea and passive-ammonia SCR after treatment systems respectively. Controls complexity to achieve regulatory compliance and the cost of the lean after treatment system were identified as the primary challenges to commercial viability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Engineering hybrid textile braids for tendon and ligament repair application

Abstract Challenges involving tendon and ligament repair have motivated the investigation of new strategies to improve clinical outcomes. These have been mainly based on polymer constructs, which may be non‐biodegradable or biodegradable. The former typically fails due to lack of device integration and the latter demands a complex balance between biodegradability and tissue ingrowth, often failing due to insufficient mechanical properties. This work presents the development of hybrid braids based on polyethylene terephthalate (PET) and polylactic acid (PLA) yarns. A textile technique was used to fabricate braids based on 16 multifilament yarns of varying PLA/PET composition. The composition was varied to maximize biodegradability while ensuring mechanical performance. The braids' morphology, physical and mechanical properties were characterized. As production parameters and architecture were maintained, the braids exhibited similar porosity and wicking ability. The breaking force and stiffness decreased significantly as the number of PLA yarns increased, although strain levels remained constant. Braids containing 10 and 12 PET yarns (out of the total 16 yarns) demonstrated good creep and force‐relaxation behavior, as well as resistance to cyclic loading. These compositions were selected for future work, to be assembled into more elaborate structures to mimic the fibrous organization and tensile properties of different tendons/ligaments.

Peixoto, Tânia↗
DOE OSTI2024

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗
DOE OSTI2024

IDAES-PSE 2.7.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.7.0 Release Highlights New features: AutoScaler and CustomScalerBase classes: Such tools are the core of the new scaling framework being implemented in IDAES. Wider adoption of scaling tools among users will result in quicker and more robust model solutions. Scaler for equilibrium reactor and saponification properties: These scaler models are examples to follow for how to use the new scaling tools. ONNX Surrogate support from Optimization & Machine Learning Toolkit (OMLT): ONNX is an open standard format to save and load ML/AI models that is widely supported by all major frameworks. This capability makes it easier for IDAES users to create surrogate models and use them without having to support each framework individually. 1D Membrane Model for CO2 Capture and Utilization: Supports ongoing efforts for modeling and optimizing polymer membrane processes for CO2 capture and conversion into formic acid. StreamScaler unit model: Unrelated to the CustomScalerBase, this unit model allows a stream’s extensive variables to be scaled by a fixed factor. This allows streams being processed by multiple units in parallel to be scaled down to unit scale and scaled back up to process scale. Bug fixes or improvements: Scaling, EoS, Diagnostics tool, Modular Properties, tests & documentation Deprecations: Old Cubic EoS

AS↗
DOE OSTI2021

Bioinspired energy absorbing material designs using additive manufacturing

Nature provides many biological materials and structures with exceptional energy absorption capabilities. Few, relatively simple molecular building blocks (e.g., calcium carbonate), which have unremarkable intrinsic mechanical properties individually, are used to produce biopolymer-bioceramic composites with unique hierarchical architectures, thus producing biomaterial-architectures with extraordinary mechanical properties. Several biomaterials have inspired the design and manufacture of novel material architectures to address various engineering problems requiring high energy absorption capabilities. For example, the microarchitecture of seashell nacre has inspired multi-material 3D printed architectures that outperform the energy absorption capabilities of monolithic materials. Using the hierarchical architectural features of biological materials, iterative design approaches using simulation and experimentation are advancing the field of bioinspired material design. However, bioinspired architectures are still challenging to manufacture because of the size scale and architectural hierarchical complexity. Notwithstanding, additive manufacturing technologies are advancing rapidly, continually providing researchers improved abilities to fabricate sophisticated bioinspired, hierarchical designs using multiple materials. This review describes the use of additive manufacturing for producing innovative synthetic materials specifically for energy absorption applications inspired by nacre, conch shell, shrimp shell, horns, hooves, and beetle wings. Potential applications include athletic prosthetics, protective head gear, and automobile crush zones.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Instrumentation development and testing of a wind turbine blade for sub-scale wake studies

Wind turbine blade aerodynamics and the resulting wake flow are complex, and wind tunnel testing of these flows can provide critical insight. The data from such tests are also valuable for validation of numerical models. For experiments using sub-scale turbines to be useful, the blade aerodynamics and wakes must exhibit the important features observed in utility-scale turbine flows. In this work, ~1 m blades designed to produce physics relevant to larger scale turbines were manufactured and affixed to an existing turbine. One of the blades was instrumented for blade surface pressure measurements. Time-dependent surface pressure measurements coupled with instantaneous measurements of the inflow with Cobra probes and the wake with particle image velocimetry allowed for characterization of the inflow, blade flow, and near wake. The results demonstrate that the instrumentation was effective in characterizing the blade loading and the flow field. For the one test case discussed in this paper, the measurements of inflow, blade loading, and wake properties facilitate understanding of the wake's behavior.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Direct Phase-Angle Detection for Three-Phase Inverters in Asymmetrical Power Grids

This paper introduces a signal reformation based direct phase-angle detection (DPD-SR) technique for three-phase inverters supporting asymmetrical grids. Asymmetries in three-phase systems may happen because of unbalanced three-phase loads, which can lead to the voltage asymmetry at the terminals of grid-tied inverters. The proposed DPD-SR technique can detect the voltage phase-angle under asymmetrical conditions. The DPD-SR technique also offers precise and rapid phase-angle detection via signal reformation and trigonometric functions. In essence, the phase-angle detection is directly derived from the trigonometric properties of the line-line voltages, while signal reformation is implemented to handle asymmetrical voltages. The proposed method measures two line-line voltages and detects the phase-angle of a three-phase system under both symmetrical and asymmetrical conditions. Unlike classical phase-locked loop (PLL) techniques, DPD-SR does not need any closed-loop PI controller, which significantly reduces the complexity and the delay in detecting the phase-angle. In this paper, the efficacy of the proposed technique is examined in comparison with the state-of-the-art PLLs under asymmetrical conditions via a set of laboratory experiments. Here, the performance of the phase-angle detection is also tested as part of an inverter feeding an asymmetrical grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Supramolecular assembly of polycation/mRNA nanoparticles and in vivo monocyte programming

Size-dependent phagocytosis is a well-characterized phenomenon in monocytes and macrophages. However, this size effect for preferential gene delivery to these important cell targets has not been fully exploited because commonly adopted stabilization methods for electrostatically complexed nucleic acid nanoparticles, such as PEGylation and charge repulsion, typically arrest the vehicle size below 200 nm. Here, we bridge the technical gap in scalable synthesis of larger submicron gene delivery vehicles by electrostatic self-assembly of charged nanoparticles, facilitated by a polymer structurally designed to modulate internanoparticle Coulombic and van der Waals forces. Specifically, our strategy permits controlled assembly of small poly(β-amino ester)/messenger ribonucleic acid (mRNA) nanoparticles into particles with a size that is kinetically tunable between 200 and 1,000 nm with high colloidal stability in physiological media. We found that assembled particles with an average size of 400 nm safely and most efficiently transfect monocytes following intravenous administration and mediate their differentiation into macrophages in the periphery. When a CpG adjuvant is co-loaded into the particles with an antigen mRNA, the monocytes differentiate into inflammatory dendritic cells and prime adaptive anticancer immunity in the tumor-draining lymph node. This platform technology offers a unique ligand-independent, particle-size-mediated strategy for preferential mRNA delivery and enables therapeutic paradigmsviamonocyte programming.

Science & Technology - Other Topics↗
DOE OSTI2021

Automatic identification and quantification of dense microcracks in high-performance fiber-reinforced cementitious composites through deep learning-based computer vision

Highlights: • A method is presented to detect, locate, quantify, and visualize dense microcracks in HPFRCC. • Quantification of dense microcracks is realized using deep learning method for the first time. • The presented method uses deep learning models that are trained using a realistic dataset size. • The presented method has a high computation efficiency for identifying and quantifying cracks. • The presented method provides crack width with errors up to 50 μm and a R{sup 2} value of 0.984. High-performance fiber-reinforced cementitious composites (HPFRCCs) feature high mechanical strengths, crack resistance, and durability. Under excessive loading, HPFRCCs demonstrate dense microcracks that are difficult to identify using existing methods. This study presents a computer vision method for identification, quantification, and visualization of microcracks in HPFRCCs based on deep learning. The presented method integrates multiple deep learning models and computer vision techniques in a hierarchical architecture. The crack pattern (e.g., number, width, and spacing of cracks) are automatically determined from pictures without human intervention. This study shows that the presented method achieves an accuracy of 0.992 for crack detection and an accuracy finer than 50 μm (R{sup 2} > 0.984) for quantification of crack width when deep learning models are trained using only 200 pictures of HPFRCCs and 200 pictures of conventional concrete with incorporation of data augmentation. The presented method is expected to be also applicable to other materials featuring complex cracks.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Volatility Basis Set Distributions and Viscosity of Organic Aerosol Mixtures: Insights from Chemical Characterization Using Temperature-Programmed Desorption–Direct Analysis in Real-Time High-Resolution Mass Spectrometry

Quantitative assessment of gas-particle partitioning of individual components within complex atmospheric organic aerosols (OA) mixtures is critical for predicting and comprehending the formation and evolution of OA particles in the atmosphere. This investigation leverages previously documented data obtained through a temperature programmed desorption - direct analysis in real time – high resolution mass spectrometry (TPD-DART-HRMS) platform. This methodology facilitates the bottom-up construction of volatility basis set (VBS) distributions for constituents found in three biogenic secondary organic aerosol (SOA) mixtures produced through the ozonolysis of a-pinene, limonene, and ocimene. The apparent enthalpies (ΔH*, kJ mol -1 ) and saturated vapor mass concentrations (C T *, µg∙m -3 ) of individual SOA components, determined as a function of temperature (T, K), facilitated an assessment of changes in VBS distributions and gas-particle partitioning with respect to T and atmospheric total organic mass loadings (tOM, µg∙m -3 ). Further, the VBS distributions reveal distinct differences in volatilities among monomers, dimers, and trimers, enabling their categorization into separate volatility bins. At the ambient temperature of T = 298 K, only monomers efficiently partition between gas and particle phases across a broad range of atmospherically relevant total organic mass loadings (tOM) values of 1–100 µg∙m -3 . Partitioning of dimers and trimers becomes notable only at T > 360 K and T > 420 K, respectively. The viscosity of SOA mixtures is assessed using a bottom-up calculation approach, incorporating the input of elemental formulas, ΔH*, C T *, and particle-phase mass fractions of the SOA components. Through this approach, we are able to accurately estimate the variations in SOA viscosity that result from the evaporation of its components. These variations are, in turn, influenced by atmospherically relevant changes in tOM and T. Comparison of the calculated SOA viscosity and diffusivity values with literature reported experimental results shows close agreement, thereby validating the employed calculation approach. These findings underscore the significant potential for TPD-DART-HRMS measurements in enabling the untargeted analysis of organic molecules within OA mixtures. This approach facilitates quantitative assessment of their gas-particle partitioning and allows for the estimation of their viscosity and condensed-phase diffusion, thereby contributing valuable insights to atmospheric models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Local Structural Coherence and Interfacial Charge Transfer in Cu 2 ⁢S/Mo⁢S 2 Heterostructure

Precise control over electronic coupling at nanoscale interfaces is critical for designing materials with tunable charge-transfer behavior and catalytic function. Heterostructures with locally coherent interfaces provide a platform for interrogating interfacial charge redistribution in coupled material systems. Here, we report Cu 2 ⁢S/Mo⁢S 2 heterostructures exhibiting nanoscale crystallographic alignment, which are synthesized through a rapid thermal transformation pathway. We employed electrochemical reduction reactions to probe interfacial charge transfer, revealing shifts in product distribution attributable to modified interfacial energetics, even in the absence of optimized catalytic performance. The observed formate Faradaic efficiency suggests that interfacial electronic modulation in the heterostructure shifts product selectivity towards formate, highlighting how interface-driven electronic modulation can direct reaction pathways and influence product selectivity. Optimizing catalyst loading, architecture, and reactor configuration will be critical for future improvement. Structural and compositional integrity were confirmed through powder x-ray diffraction, x-ray photoelectron spectroscopy, and high-resolution transmission electron microscopy. Electron transfer between Cu 2 ⁢S and Mo⁢S 2 domains was further evaluated by electrochemical impedance spectroscopy, while selected-area electron diffraction revealed local crystallographic alignment consistent with a local epitaxial relationship at the Cu 2 S/Mo⁢S 2 heterointerface. To illustrate the broader applicability of this approach, a Zn⁢S/Mo⁢S 2 heterostructure was also synthesized using the same microwave strategy, confirming the generalizability of interfacial engineering principles across metal sulfide-Mo⁢S 2 systems. Collectively, these findings demonstrate that controlled local epitaxial alignment serves as an effective design principle for tuning interfacial energetics and catalytic reactivity in complex heterostructure materials.

carbon capture & utilization↗
DOE OSTI2024

Compiling Quantum Circuits for Dynamically Field-Programmable Neutral Atoms Array Processors

Dynamically field-programmable qubit arrays (DPQA) have recently emerged as a promising platform for quantum information processing. In DPQA, atomic qubits are selectively loaded into arrays of optical traps that can be reconfigured during the computation itself. Leveraging qubit transport and parallel, entangling quantum operations, different pairs of qubits, even those initially far away, can be entangled at different stages of the quantum program execution. Such reconfigurability and non-local connectivity present new challenges for compilation, especially in the layout synthesis step which places and routes the qubits and schedules the gates. In this paper, we consider a DPQA architecture that contains multiple arrays and supports 2D array movements, representing cutting-edge experimental platforms. Within this architecture, we discretize the state space and formulate layout synthesis as a satisfiability modulo theories problem, which can be solved by existing solvers optimally in terms of circuit depth. For a set of benchmark circuits generated by random graphs with complex connectivities, our compiler OLSQ-DPQA reduces the number of two-qubit entangling gates on small problem instances by 1.7x compared to optimal compilation results on a fixed planar architecture. To further improve scalability and practicality of the method, we introduce a greedy heuristic inspired by the iterative peeling approach in classical integrated circuit routing. Using a hybrid approach that combined the greedy and optimal methods, we demonstrate that our DPQA-based compiled circuits feature reduced scaling overhead compared to a grid fixed architecture, resulting in 5.1X less two-qubit gates for 90 qubit quantum circuits. These methods enable programmable, complex quantum circuits with neutral atom quantum computers, as well as informing both future compilers and future hardware choices.

Physics↗
DOE OSTI2020

Decoupling copolymer, lipid and carbon nanotube interactions in hybrid, biomimetic vesicles

Bilayer vesicles that mimic a real biological cell can be tailored to carry out a specific function by manipulating the molecular composition of the amphiphiles. These bio-inspired and bio-mimetic structures are increasingly being employed for a number of applications from drug delivery to water purification and beyond. Complex hybrid bilayers are the key building blocks for fully synthetic vesicles that can mimic biological cell membranes, which often contain a wide variety of molecular species. While the assembly and morpholgy of pure phospholid bilayer vesicles is well understood, the functionality and structure dramaticlly changes when copolymer and/or carbon nanotube porins (CNTP) are added. The aim of this study is to understand how the collective molecular interactions within hybrid vesicles affect their nanoscale structure and properties. In situ small and wide angle X-ray scattering (SAXS/WAXS) and molecular dynamics simulations (MD) are used to investigate the morphological effect of molecular interactions between polybutadiene polyethylene oxide, lipids and carbon nanotubes (CNT) within the hybrid vesicle bilayer. Within the lipid/copolymer system, the hybrid bilayer morphology transitions from phase separated lipid and compressed copolymer at low copolymer loadings to a mixed bilayer where opposing lipids are mostly separated from the inner region. This transition begins between 60 wt% and 70 wt%, with full homogenization observed by 80 wt% copolymer. The incorporation of CNT into the hybrid vesicles increases the bilayer thickness and enhances the bilayer symmetry. Analysis of the WAXS and MD indicate that the CNT–dioleoyl interactions are much stronger than the CNT–polybutadiene.

59 BASIC BIOLOGICAL SCIENCES↗