Search NASA⌕ Search

SEARCH · Search NASA

Results for “Kinetics Parameters”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Effective hopping in holographic Bose and Fermi-Hubbard models

In this paper, we analyze a proposed gravity dual to a SU(N) Bose-Hubbard model, as well as construct a holographic dual of a SU(N) Fermi-Hubbard model from D-branes in string theory. In both cases, the SU(N) is dynamical, i.e. the hopping degrees of freedom are strongly coupled to SU(N) gauge bosons which themselves are strongly interacting. The vacuum expectation value (VEV) of the hopping term (i.e. the hopping energy) is analyzed in the gravity dual as a function of the bulk mass of the field dual to the hopping term, as well as of the coupling constants of the model. The bulk mass controls the anomalous dimension (i.e. the critical exponent) of the hopping term in the SU(N) Bose-Hubbard model. We compare the hopping energy to the corresponding result in a numerical simulation of the ungauged SU(N) Bose-Hubbard model. We find agreement when the hopping parameter is smaller than the other couplings. Our analysis shows that the kinetic energy increases as the bulk mass increases, due to increased contributions from the IR. The holographic Bose-Hubbard model is then compared with the string theory construction of a SU(N) Fermi-Hubbard model. The string theory construction makes it possible to describe fluctuations around a half-filled state in the supergravity limit, which map to O(1) occupation number fluctuations in the Fermi-Hubbard model at half filling. Finally, the VEV of the Bose-Hubbard model is shown to agree with the one of the fermionic Hubbard model with the help of a two-site version of the Jordan-Wigner transformation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Effects of composition and crystallographic ordering on the ferromagnetic transition in Ni–Co–Mn–In magnetic shape memory alloys

In the present study, the effects of composition and the level of crystallographic ordering were studied in Ni 45 Co 5 Mn 50-X In X magnetic shape memory alloys with X = 13.55, 13.71 and 13.92, in order to reveal the relationship between the Curie temperature (T C ) of the austenite phase and the degree of L2 1 ordering. Various samples were prepared using secondary heat treatments at different temperatures below the order-disorder transition temperature and for different durations, after solution heat treatment, in order to control the degree of ordering. T C s were determined using calorimetric and magnetometry measurements. The relationship between the T C and the degree of order was used to predict the evolution of the order during selected heat treatments. It was shown that the T C of austenite exhibits a monotonous non-linear dependence on the fraction of the L2 1 phase. This relationship was corroborated via ab initio numerical calculations within a mean field approximation. Dependence of the crystallographic order parameter on temperature and duration of secondary heat treatments showed that ordering kinetics is fast, the total degree of order depends only on the annealing temperature and cooling rate for long enough treatments, and maximum achievable degree of order is limited by the low thermal energy available for atomic diffusion at low temperatures. Lastly, small changes in composition were observed to have a significant impact on martensitic transformation temperatures, and a significant but smaller effect on the ferromagnetic transition in these systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Numerical simulation of involute-plate research reactor flow behavior using RANS, LES and DNS

This paper investigates the flow behavior of involute-plate research reactors by performing Reynolds-Averaged Navier Stokes simulation (RANS), Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) of the channel flow between fuel plates. By modeling turbulence with different numerical approaches, this study provides data with three levels of fidelity. For the RANS simulation, three widely used turbulence models, i.e., k-ε, k-ω, Reynolds Stress Turbulence model (RST) are applied by using the commercial CFD code STAR-CCM +. For LES and DNS, the open-source CFD code, Nek5000, is used given its outstanding scalability on High Performance Computer (HPC) and high-order technique. The results from RANS simulations are compared with that from LES and DNS for benchmarking. Both macroscale parameters and turbulence statistics, such as velocity magnitude, lateral velocity and turbulence kinetic energy, are presented and analyzed. The results from RANS simulation achieve good agreement with LES and DNS on velocity and turbulence kinetic energy prediction. The RST turbulence model predicts the most similar flow pattern of lateral velocity as compared to LES and DNS. The Lambda-2 (λ2) criterion with a reasonable threshold is used to demonstrate the instantaneous vortices distribution in the involute channel from both LES and DNS calculation. The DNS simulation captures more detailed turbulence especially near the corner, which explains the discrepancy between LES and DNS results near the corner. The normalized RMS error are defined and calculated to assess the performance of those turbulence models. The RST model captures the anisotropic feature of turbulence, which enable it to outperform other turbulence models for predicting the flow behavior in an involute channel. Although some discrepancies are found between LES and DNS results in the corner, the overall deviations between LES and DNS are found to be small. In conclusion, given that the computational cost of DNS calculation is an order of magnitude higher, using LES data for benchmarking RANS model is a cost-effective approach.

DNS↗
DOE OSTI2017

Reaction mechanism for oxygen evolution on RuO 2 , IrO 2 , and RuO 2 @IrO 2 core-shell nanocatalysts

Iridium dioxide, IrO 2 , is second to the most active RuO 2 catalyst for the oxygen evolution reaction (OER) in acid, and is used in proton exchange membrane water electrolyzers due to its high durability. In order to improve the activity of IrO 2 -based catalysts, we prepared RuO 2 @IrO 2 core-shell nanocatalysts using carbon-supported Ru as the template. At 1.48 V, the OER specific activity of RuO 2 @IrO 2 is threefold that of IrO 2 . While the activity volcano plots over wide range of materials have been reported, zooming into the top region to clarify the rate limiting steps of most active catalysts is important for further activity enhancement. Here, we verified theory-proposed sequential water dissociation pathway in which the O—O bond forms on a single metal site, not via coupling of two adsorbed intermediates, by fitting measured polarization curves using a kinetic equation with the free energies of adsorption and activation as the parameters. Consistent with theoretical calculations, we show that the OER activities of IrO 2 and RuO 2 @IrO 2 are limited by the formation of O adsorbed phase, while the OOH formation on the adsorbed O limits the reaction rate on RuO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Lamellae of α phase in a metastable β -Ti alloy studied by small-angle x-ray scattering

The shapes and orientations of lamellae of the hexagonal close-packed α phase in single crystals of the body-centred cubic β phase in a metastable β -Ti alloy (Timetal LCB) were investigated by synchrotron small-angle x-ray scattering. Several steps of thermal treatment with various annealing times and temperatures were employed. The measured scattering data were fitted to a model of ellipsoidal lamellae of random sizes, and the mean lamella shapes and orientations of their axes with respect to the β -Ti matrix were determined from the fit. The number and relative volume of the lamellae were estimated from the integrated scattered intensity. The elastic energy of the lamellae as well as the energy of defects at the α / β interface (misfit dislocations) were calculated from the parameters of the lamellae. In conclusion, the results provide information on the kinetics of the β $ \rightarrow$ α transformation in metastable β -Ti alloys and allow assessing the degree of thermodynamic equilibrium of the final α / β composite.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Conversion of Nuclear Waste into Nuclear Waste Glass: Experimental Investigation and Mathematical Modeling

The melter feed, slurry, or calcine charged on the top of a pool of molten glass forms a floating layer of reacting material called the cold cap. Between the cold-cap top, which is covered with boiling slurry, and its bottom, where bubbles separate it from molten glass, the temperature changes by up to 1000 K. The processes that occur over this temperature interval within the cold cap include liberation of gases, conduction and consumption of heat, dissolution of quartz particles, formation and dissolution of intermediate crystalline phases, and generation of foam and gas cavities. These processes have been investigated using thermal analyses, optical and electronic microscopies, x-ray diffraction, as well as other techniques. Properties of the reacting feed, such as heat conductivity and density, were measured as functions of temperature. Investigating the structure of quenched cold caps produced in a laboratory-scale melter complemented the crucible studies. The cold cap consists of two main layers. The top layer contains solid particles dissolving in the glass-forming melt and open pores through which gases are escaping. The bottom layer contains bubbly melt or foam where bubbles coalesce into larger cavities that move sideways and release the gas to the atmosphere. The feed-to-glass conversion became sufficiently understood for representing the cold-cap processes via mathematical models. These models, which comprise heat transfer, mass transfer, and reaction kinetics models, have been developed with the final goal to relate feed parameters to the rate of glass melting.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Development of a high temperature pyrolysis mechanism for cyclopentanone, a potential biofuel derived from biomass

Cyclopentanone (CPO) is a promising biofuel for spark-ignition engines due to its ring strain and high auto-ignition resistance. The Department of Energy has identified CPO as a potential biofuel candidate because of its unique chemical properties. Understanding CPO decomposition is crucial to building a high-temperature combustion model. In this study, we present a comprehensive kinetic model for high-temperature pyrolysis of CPO with verified results from high-pressure shock tube (HPST) measurements. The time histories of carbon monoxide (CO), ethylene (C2H4), and CPO absorbances over the temperature range of 1156-1416 K and 8.53-10.06 atm were measured. A corresponding kinetic model was generated using the Reaction Mechanism Generator (RMG) with the parameters of dominant pathways from either previous studies or quantum calculation within current work. The final model containing 821 species and 79,859 reactions exhibited a good agreement with the experimental results. In this study, the C2H4/CO ratio was used as an essential factor to validate the model and to prove that radical-involved bimolecular pathways were as significant as unimolecular decomposition of CPO. The rate of production (ROP) analysis showed that H radicals play major roles in both primary reactions and product pyrolysis. The insights from this work can be used to generate a better CPO combustion model and help evaluate CPO as a biofuel. Such understanding can also be helpful when proposing pyrolysis or combustion models for other biofuels.

cyclopentanone, reaction mechanism, pyrolysis, sho↗
DOE OSTI2023

Physics-informed neural network with transfer learning (TL-PINN) based on domain similarity measure for prediction of nuclear reactor transients

Nuclear reactor safety and efficiency can be enhanced through the development of accurate and fast methods for prediction of reactor transient (RT) states. Physics informed neural networks (PINNs) leverage deep learning methods to provide an alternative approach to RT modeling. Applications of PINNs in monitoring of RTs for operator support requires near real-time model performance. However, as with all machine learning models, development of a PINN involves time-consuming model training. Here, we show that a transfer learning (TL-PINN) approach achieves significant performance gain, as measured by reduction of the number of iterations for model training. Using point kinetic equations (PKEs) model with six neutron precursor groups, constructed with experimental parameters of the Purdue University Reactor One (PUR-1) research reactor, we generated different RTs with experimentally relevant range of variables. The RTs were characterized using Hausdorff and Fréchet distance. We have demonstrated that pre-training TL-PINN on one RT results in up to two orders of magnitude acceleration in prediction of a different RT. The mean error for conventional PINN and TL-PINN models prediction of neutron densities is smaller than 1%. We have developed a correlation between TL-PINN performance acceleration and similarity measure of RTs, which can be used as a guide for application of TL-PINNs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

A quantum quasi-harmonic nonlinear oscillator with an isotonic term

The properties of a nonlinear oscillator with an additional term k{sub g}/x², characterizing the isotonic oscillator, are studied. The nonlinearity affects to both the kinetic term and the potential and combines two nonlinearities associated to two parameters, κ and k{sub g}, in such a way that for κ = 0 all the characteristics of the standard isotonic system are recovered. The first part is devoted to the classical system and the second part to the quantum system. This is a problem of quantization of a system with position-dependent mass of the form m(x) = 1/(1 − κx²), with a κ-dependent non-polynomial rational potential and with an additional isotonic term. The Schrödinger equation is exactly solved and the (κ, k{sub g})-dependent wave functions and bound state energies are explicitly obtained for both κ < 0 and κ > 0.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Approximate models for the ion-kinetic regime in inertial-confinement-fusion capsule implosions

“Reduced” (i.e., simplified or approximate) ion-kinetic (RIK) models in radiation-hydrodynamic simulations permit a useful description of inertial-confinement-fusion (ICF) implosions where kinetic deviations from hydrodynamic behavior are important. For implosions in or near the kinetic regime (i.e., when ion mean free paths are comparable to the capsule size), simulations using a RIK model give a detailed picture of the time- and space-dependent structure of imploding capsules, allow an assessment of the relative importance of various kinetic processes during the implosion, enable explanations of past and current observations, and permit predictions of the results of future experiments. The RIK simulation method described here uses moment-based reduced kinetic models for transport of mass, momentum, and energy by long-mean-free-path ions, a model for the decrease of fusion reactivity owing to the associated modification of the ion distribution function, and a model of hydrodynamic turbulent mixing. The transport models are based on local gradient-diffusion approximations for the transport of moments of the ion distribution functions, with coefficients to impose flux limiting or account for transport modification. After calibration against a reference set of ICF implosions spanning the hydrodynamic-to-kinetic transition, the method has useful, quantifiable predictive ability over a broad range of capsule parameter space. Calibrated RIK simulations show that an important contributor to ion species separation in ICF capsule implosions is the preferential flux of longer-mean-free-path species out of the fuel and into the shell, leaving the fuel relatively enriched in species with shorter mean free paths. Also, the transport of ion thermal energy is enhanced in the kinetic regime, causing the fuel region to have a more uniform, lower ion temperature, extending over a larger volume, than implied by clean simulations. We expect that the success of our simple approach will motivate continued theoretical research into the development of first-principles-based, comprehensive, self-consistent, yet useable models of kinetic multispecies ion behavior in ICF plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Experimental validation of a collision-radiation dataset for molecular hydrogen in plasmas

Quantitative spectroscopy of molecular hydrogen has generated substantial demand, leading to the accumulation of diverse elementary process data encompassing radiative transitions, electron-impact transitions, predissociations, and quenching. However, their rates currently available are still sparse, and there are inconsistencies among those proposed by different authors. In this study, we demonstrate an experimental validation of such a molecular dataset by composing a collisional-radiative model (CRM) for molecular hydrogen and comparing experimentally obtained vibronic populations across multiple levels. From the population kinetics of molecular hydrogen, the importance of each elementary process in various parameter space is studied. In low-density plasmas (electron density ne≲1017 m−3) the excitation rates from the ground states and radiative decay rates, both of which have been reported previously, determine the excited state population. The inconsistency in the excitation rates affects the population distribution the most significantly in this parameter space. However, in higher density plasmas (ne≳1018 m−3), the excitation rates from excited states become important, which have never been reported in the literature, and may need to be approximated in some way. In order to validate these molecular datasets and approximated rates, we carried out experimental observations for two different hydrogen plasmas; a low-density radio frequency heated plasma (ne≈1016 m−3) and the Large Helical Device (LHD) divertor plasma (ne≳1018 m−3). The visible emission lines from EF1Σg+, HH¯1Σg+, D1Πu±, GK1Σg+, I1Πg±, J1Δg±, h3Σg+, e3Σu+, d3Πu±,g3Σg+, i3Πg±, and j3Δg± states were observed simultaneously and their population distributions were obtained from their intensities. We compared the observed population distributions with the CRM prediction, in particular the CRM with the rates compiled by Janev et al., Miles et al., and those calculated with the molecular convergent close-coupling (MCCC) method. The MCCC prediction gives the best agreement with the experiment, particularly for the emission from the low-density plasma. However, the population distribution in the LHD divertor shows a worse agreement with the CRM than those from low-density plasma, indicating the necessity of the precise excitation rates from excited states. We also found that the rates for the electron attachment is inconsistent with experimental results. This requires further investigation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

Transport equations for the inflationary trispectrum

We use transport techniques to calculate the trispectrum produced in multiple-field inflationary models with canonical kinetic terms. Our method allows the time evolution of the local trispectrum parameters, τ{sub NL} and g{sub NL}, to be tracked throughout the inflationary phase. We illustrate our approach using examples. We give a simplified method to calculate the superhorizon part of the relation between field fluctuations on spatially flat hypersurfaces and the curvature perturbation on uniform density slices, ζ, and obtain its third-order part for the first time. We clarify how the 'backwards' formalism of Yokoyama et al. relates to our analysis and other recent work. We supply explicit formulae which enable each inflationary observable to be computed in any canonical model of interest, using a suitable first-order ODE solver.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Predicting the intensity mapping signal for multi-J CO lines

We present a novel approach to estimating the intensity mapping signal of any CO rotational line emitted during the Epoch of Reionization (EoR). Our approach is based on large velocity gradient (LVG) modeling, a radiative transfer modeling technique that generates the full CO spectral line energy distribution (SLED) for a specified gas kinetic temperature, volume density, velocity gradient, molecular abundance, and column density. These parameters, which drive the physics of CO transitions and ultimately dictate the shape and amplitude of the CO SLED, can be linked to the global properties of the host galaxy, mainly the star formation rate (SFR) and the SFR surface density. By further employing an empirically derived SFR−M relation for high redshift galaxies, we can express the LVG parameters, and thus the specific intensity of any CO rotational transition, as functions of the host halo mass M and redshift z. Integrating over the range of halo masses expected to host CO-luminous galaxies, we predict a mean CO(1-0) brightness temperature ranging from ∼ 0.6 μK at z = 6 to ∼ 0.03 μK at z = 10 with brightness temperature fluctuations of Δ{sub CO}{sup 2} ∼ 0.1 and 0.005 μK respectively, at k = 0.1 Mpc{sup −1}. In this model, the CO emission signal remains strong for higher rotational levels at z = 6, with ( T{sub CO} ) ∼ 0.3 and 0.05 μK for the CO J = 6arrow5 and CO J = 10arrow9 transitions respectively. Including the effects of CO photodissociation in these molecular clouds, especially at low metallicities, results in the overall reduction in the amplitude of the CO signal, with the low- and high-J lines weakening by 2–20% and 10–45%, respectively, over the redshift range 4 < z < 10.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The influence of the cloud virial parameter on the initial mass function

ABSTRACT Crucial for star formation is the interplay between gravity and turbulence. The observed cloud virial parameter, $\alpha _{\mathrm{vir}}$, which is the ratio of twice the turbulent kinetic energy to the gravitational energy, is found to vary significantly in different environments, where the scatter among individual star-forming clouds can exceed an order of magnitude. Therefore, a strong dependence of the initial mass function (IMF) on $\alpha _{\mathrm{vir}}$ may challenge the notion of a universal IMF. To determine the role of $\alpha _{\mathrm{vir}}$ on the IMF, we compare the star-particle mass functions obtained in high-resolution magnetohydrodynamical simulations including jet and heating feedback, with $\alpha _{\mathrm{vir}}=0.0625$, 0.125, and 0.5. We find that varying $\alpha _{\mathrm{vir}}$ from $\alpha _{\mathrm{vir}}\sim 0.5$ to $\alpha _{\mathrm{vir}}< 0.1$ shifts the peak of the IMF to lower masses by a factor of $\sim 2$ and increases the star formation rate by a similar factor. The dependence of the IMF and star formation rate on $\alpha _{\mathrm{vir}}$ is non-linear, with the dependence subsiding at $\alpha _{\mathrm{vir}}< 0.1$. Our study shows a systematic dependence of the IMF on $\alpha _{\mathrm{vir}}$. Yet, it may not be measurable easily in observations, considering the uncertainties, and the relatively weak dependence found in this study.

Mathew, Sajay Sunny (ORCID:0000000283818195)↗
DOE OSTI2011

Nuclear interference in dissociating of HD{sup +} in ultrashort laser fields

The dissociation dynamics of HD{sup +} molecules is studied theoretically by numerically solving the time-dependent Schroedinger equation in which the molecular vibrational and rotational degrees of freedom are included. Based on the Born-Oppenheimer approximation, the ground 1s{sigma}{sub g} state and the excited 2p{sigma}{sub u} state are taken into account, corresponding to two dissociative channels HD{sup +}{yields}D + H{sup +} and HD{sup +}{yields}H + D{sup +}, respectively. Two dissociative nuclear wave packets overlap and interfere after excited by two ultrashort laser pulses. The interference patterns can be controlled by varying the laser parameters and the dissociation probabilities are demonstrated for different laser fields. The kinetic energy-dependent distributions of the fragments are calculated using an asymptotic-flow expression in the momentum space. The branching ratio D{sup +}/(H{sup +} + D{sup +}), as a function of the delay time and the relative phase between two laser pulses, is also discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Development of a deep potential model for F and CF 2 etching of Si and SiO 2

An understanding of plasma-surface interactions at increasingly smaller scales is invaluable for the development of novel technologies and processing techniques. Molecular dynamics (MD) simulations can provide insights into atomic-scale interactions, though they are restricted by the availability of interatomic potentials. Machine learning methods, such as Deep Potential Molecular Dynamics (DeepMD), provide a systematic framework for the development of accurate and flexible ab initio-based models. In this work, we develop DeepMD models for the ion-enhanced etching of Si and SiO 2 by F and CF 2 radicals. We employ an active learning process to expand the data set on which the model is trained and demonstrate its effect on the model accuracy. The DeepMD results are compared to data from classical MD simulations and experiments. Physical sputtering yields of SiO 2 by Ar + ions show good agreement with previous simulation results using conventional interatomic potentials, though the predicted depth profiles are different. Etching yields are calculated as a function of ion energy and neutral to ion flux ratio for the Ar + ion-enhanced etching of SiO 2 and Si by F atoms, as well as for etching of SiO 2 by CF 2 radicals, showing reasonable agreement with experimental data. Finally, an ion-enhanced surface kinetic model is fitted to the DeepMD etch yields, and the fitted parameters are compared to quantities computed directly from DeepMD simulations. This study illustrates how molecular dynamics simulations using machine learning potentials can provide an accurate model of etching processes relevant to device manufacturing.

Kounis-Melas, Andreas [Princeton Univ., NJ (United↗
DOE OSTI2016

Final Report, DOE Early Career Award: Predictive modeling of complex physical systems: new tools for statistical inference, uncertainty quantification, and experimental design

Predictive simulation of complex physical systems increasingly rests on the interplay of experimental observations with computational models. Key inputs, parameters, or structural aspects of models may be incomplete or unknown, and must be developed from indirect and limited observations. At the same time, quantified uncertainties are needed to qualify computational predictions in the support of design and decision-making. In this context, Bayesian statistics provides a foundation for inference from noisy and limited data, but at prohibitive computional expense. This project intends to make rigorous predictive modeling *feasible* in complex physical systems, via accelerated and scalable tools for uncertainty quantification, Bayesian inference, and experimental design. Specific objectives are as follows: 1. Develop adaptive posterior approximations and dimensionality reduction approaches for Bayesian inference in high-dimensional nonlinear systems. 2. Extend accelerated Bayesian methodologies to large-scale {\em sequential} data assimilation, fully treating nonlinear models and non-Gaussian state and parameter distributions. 3. Devise efficient surrogate-based methods for Bayesian model selection and the learning of model structure. 4. Develop scalable simulation/optimization approaches to nonlinear Bayesian experimental design, for both parameter inference and model selection. 5. Demonstrate these inferential tools on chemical kinetic models in reacting flow, constructing and refining thermochemical and electrochemical models from limited data. Demonstrate Bayesian filtering on canonical stochastic PDEs and in the dynamic estimation of inhomogeneous subsurface properties and flow fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Volumetric Additive Manufacturing of Polymer Structures by Holographically Projected Light Fields

As additive manufacturing technologies proliferate and mature, overcoming some of their process limitations becomes increasingly important for the continued expansion of practical applications. Two such limitations that arise from layer-based fabrication are slow speed and geometric constraints (which include poor surface quality and challenges fabricating span, cantilever, and overhang elements). Moving beyond point-by-point and layer-by-layer approaches, the ability to generate a complex 3D volume as a unit operation has the potential to overcome these limitations. Since holography has been extensively studied as a means for storage and retrieval of 3D geometrical information, this dissertation explores the use of holographically-shaped light fields for producing three-dimensional structures in a “volume at once” approach. Leveraging advances in spatial light modulator (SLM) technology, phase-controlled light fields are projected into photopolymer resin to cure a desired geometry. By overlapping multiple sub-regions of a single light field within the target volume, the successful fabrication of non-periodic complex 3D geometries is demonstrated by single exposures on timescales of seconds. This dissertation presents a complete prototype platform that makes this approach possible, comprising a suitable hardware configuration along with the computational algorithms necessary to calculate and optimize the required optical fields. A study of the photopolymerization kinetics is also carried out, to determine the boundaries of usable process parameters such as resin absorbance and available light intensity. The results indicate that low-absorbing resins containing ~0.1% photoinitiator, illuminated at modest powers (~10-100 mW) may be used to produce full 3D structures from 1-10 second exposures, with volume build rates exceeding 100 cm3/hr, without layering and with no need for a substrate or support material.

36 MATERIALS SCIENCE↗