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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Specialty chemicals production case study: Economic analysis of modular chemical process intensification versus conventional stick‐built approaches

Abstract The beneficial synergies of chemical process intensification and plant modularization present a unique step‐wise advancement opportunity for many chemical manufacturers, but economic case studies are needed to raise awareness of such opportunities. The primary objective of this case study is to better understand the business case economics of a specialty chemical plant using modular chemical process intensification (MCPI), by comparing it with that of a conventional stick‐built (CSB) plant that produces the same product at the same production capacity. When comparing MCPI against CSB approaches for a plant project strategy decision, analysts should thoroughly understand and model the differences and similarities in scope bases. The MCPI approach for this case study benefitted from dramatic reductions in capital expenditures (CAPEX). A sizeable reduction in plant spatial volume likely explains some of these reductions. Sizeable operational expenditure (OPEX) reductions with the MCPI plant appear to be associated with the reduced operator staffing required from converting a labor‐intensive batch process into an automated, continuous flow process. Traditional project investment economic measures strongly favored the MCPI case for the design, construction, and operation of the specialty chemical plant. The net present value for the MCPI case was nearly twice that for the CSB case over a ten‐year period, and the payback period for the CSB case was nearly five times longer than that of the MCPI case. The opinion‐based perspective of the study participant identified significant contributors to superior MCPI economic performance for this case study. With the two conditions of low MCPI CAPEX and high product profit margin, economic analysis indicates that incorporation of a backup MCPI train, for operational redundancy when downtime occurs, is a very beneficial strategy.

O'Connor, James T.↗
DOE OSTI2023

From Deposition to Encapsulation: Roll-to-roll manufacturing of organic light emitting devices for lighting (Final Report)

Organic light emitting devices (OLEDs) are promising solid state light sources due to their high efficiency, high color quality and flexible form factors. The key to enable low cost OLED lighting, is to rapidly fabricate thin film organic layers on a continuous flexible roll, called R2R processing. This project aims to investigate the feasibility of mass production of OLED lighting using the R2R process, from deposition to encapsulation. A high efficiency white OLED is fabricated on 10 cm-wide substrate rolls in a pilot R2R tool comprising of two different organic deposition: vacuum thermal evaporation (VTE) and organic vapor phase deposition (OVPD). A high quality encapsulation process to package OLEDs is demonstrated using an atomic layer deposition tool integrated to the R2R system without air exposure. The method to achieve ultrahigh deposition rates required by R2R processing is demonstrated by OVPD. Uniform organic semiconductor thin films grown by OVPD at rates as high as 50 Å/s are achieved. A comprehensive numerical model that is capable of simulating complex, multilayer WOLED structures is developed to provide an alternative to experimental iterations of OLED design and tests. A cost estimate on the R2R production of WOLEDs for lighting is developed. Assuming a WOLED luminance of 10 klm/m 2 , the cost of a WOLED light engine is anticipated to be $\$ 12.5$ /klm. With incremental reduction in material and driver costs and improved luminance, the cost of WOLED lighting can be reduced to $\$ 6.3$ /klm in the near term. These findings suggest OLED lighting can be volume manufactured by R2R vapor deposition methods with much reduced costs compared to current batch processing methods, potentially positioning WOLEDs for use in numerous premium lighting applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Evaluating Safeguards Benefits of Process Monitoring as compared with Nuclear Material Accountancy

This paper illustrates potential safeguards benefits that process monitoring (PM) may have as a diversion deterrent and as a complementary safeguards measure to nuclear material accountancy (NMA). This benefit is illustrated by quantifying the standard deviation associated with detecting a considered material diversion scenario using either an NMA-based method or a PM-based approach. To illustrate the benefits of PM for effective safeguards, we consider a reprocessing facility. We assume that the diversion of interest for detection manifests itself as a loss of Pu caused by abnormally operating a dissolver for an extended period to accomplish protracted diversion (or misdirection) of Pu to a retained (unconditioned) waste stream. For detecting the occurrence of this diversion (which involves anomalous operation of the dissolver), we consider two different data evaluation and integration (DEI) approaches, one based on NMA and the other based on PM. The approach based on PM does not directly do mass balance calculations, but rather monitors for the possible occurrence of anomaly patterns related to potential loss of nuclear material. It is thus assumed that the loss of a given mass amount of nuclear material can be directly associated with the execution of proliferation-driven activities that trigger the occurrence of an anomaly pattern consisting of series of events or signatures occurring at different unit operations and time instances. By effectively assessing these events over time and space, the PM-based DEI approach tries to infer whether this specific pattern of events has occurred and how many times within a given time period. To evaluate the goodness of PM, the 3 Sigma of the estimated mass loss is computed under both DEI approaches as function of the number of input batches processed. Simulation results are discussed.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2017

Inkjet Printing of Single-Crystalline Bi 2 Te 3 Thermoelectric Nanowire Networks

Large-scale and low-cost fabrication techniques are needed to commercialize highly efficient, nanomaterial-based thermoelectric generators (TEGs) for use in small-scale, flexible applications (e.g., wearable energy harvesters). This study presents the first demonstration of inkjet-printed networks of phase-pure, single-crystalline Bi 2 Te 3 thermoelectric nanowires (BTNWs) that are amenable to large-scale production. The BTNWs are synthesized via chemical batch processing and formulated into a jettable ink that is printed onto glass substrates and subsequently annealed in nitrogen and forming gas environments. The inkjet-printed BTNWs annealed in forming gas provide the most favorable results with comparable thermoelectric performances to bulk Bi 2 Te 3 materials (Seebeck coefficient up to 140 µV K -1 ) while approximately utilizing only 1% to 3% of the telluride materials found in their bulk counterparts. Thus, these printed BTNWs help pave the way for the development of low-cost and scalable TEGs.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Equilibrium, Kinetic and Thermodynamic Studies Of Lead (II) Sorption on Hydrolyzed Starch Graft Copolymers

Hydrolyzed starch graft poly (acrylic acid) and starch graft poly(acrylonitrile) copolymers were used as a sorbents for the removal of Pb(II) from aqueous solution in batch process. The sorbents were characterized by scanning electron microscopy, Fourier transform infrared spectroscopy and thermogravimetric analysis. The effect of contact time, initial concentration and temperature were assessed to optimize the conditions for maximum sorption of the metal ions. The experimental data were analyzed by Langmuir, Freundlich, Temkin, Dubinin Radushkevich and Sips isotherm models. The Freundlich and the Sips isotherms confirmed the applicability of the models. The sorption capacity of the sorbent was found to be 118.61 mg/g for starch graft poly (acrylic) acid and 115.83 mg/g for starch graft poly (acrylonitrile). The Sorption kinetics was assessed by Lagergren pseudo first order, pseudo second order, Elovich equation, Intraparticle diffusion and the liquid film diffusion models. The experimental data fitted very well with the Elovich equation and the Intraparticle kinetic models. The thermodynamic analysis showed that the sorption was a spontaneous and endothermic process. The results indicated that starch graft copolymers can be used as an effective sorbents for Pb (II) removal from aqueous media.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Chitosan Functionalization with Amino Acids Yields to Higher Copper Ions Adsorption Capacity

Chitosan (Chi) beads were conjugated with three different amino acids [namely, glutamic acid (GLU), methionine (MET), and taurine (TAU)] aiming to increase the divalent copper ions uptake in aqueous media. Scanning Electron Microscopy evidenced the development of a large porous structure after amino acid functionalization, associated with the increase in a number of amino groups in the polymer backbone. X-Ray Photoelectron Spectroscopy and Fourier-Transform Infrared Spectra analyses were also employed to assess the conjugation of these three different amino acids in chitosan backbone. Adsorption experiments were conducted in a batch process, at 298 K, and kinetic data indicated a slightly better fitting for the pseudo-first-order model when compared to pseudo-second order. Intraparticle diffusion model suggested a three-step mechanism for Cu(II) adsorption kinetics, limited by the third step, the intraparticle diffusion. The isotherm data fitting to the traditional Langmuir and Freundlich models indicated a better fit for the former case. The amino acid conjugation resulted in the increase of the maximum adsorption capacity for Cu(II) from 1.30 mmol g{sup −1} prior to amino acid conjugation to values as high as 2.31 mmol g{sup −1}, 2.40 mmol g{sup −1} and 2.68 mmol g{sup −1} for Chi–TAU, Chi–GLU, and Chi–MET, respectively. These results are attributed to the introduction of additional amino groups and new carboxylate and amino acid residues into the chitosan backbone, which might also be explored for amino acid demanding applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Fed-batch production of deuterated protein in Escherichia coli for neutron scattering experimentation

Neutron scattering is a powerful technique for determining the structure and dynamics of biological materials in a variety of environmental conditions. A distinguishing property of the neutron is its sensitivity to detecting hydrogen and distinguishing it from its isotope deuterium. This enables unique types of experiments that take advantage of this differential sensitivity called isotopic contrast variation. Using this approach, the chemistry of the system is not changed, but the visibility of individual sample components can be tuned by varying the deuterium content of the system under investigation. Deuterated proteins are commonly produced in bacterial systems that are adapted to growth in D2O minimal media. To maximize the yield of deuterium-labeled protein and efficiently utilize D2O and occasionally the deuterated substrate, fed-batch processes are routinely used to maximize biomass production without compromising cell viability. A step-by-step procedure will be described along with a case study of the production of deuterated green fluorescent protein. Limitations of the process will also be discussed.

Weiss, Kevin L.↗
DOE OSTI2026

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A distillation column for hydrogen isotope removal from liquid lithium

Recovery of tritium from plasma-facing components in fusion devices will be vital to future full-scale operation. Liquid, low-Z materials have demonstrated many inherent advantages over solid first wall materials. To this end, a thermal treatment method in the form of a distillation column for extraction of hydrogen isotopes from liquid lithium has been designed, developed, and constructed at the Center for Plasma-Material Interactions at the University of Illinois at Urbana-Champaign. Use of induction heating and lithium condensation stages are the two qualities that set this design apart from other thermal treatment systems. Induction heating capabilities were modeled using the COMSOL Multiphysics software, which were validated when commissioning the physical heater module. Proof-of-concept tests were performed in the prototype column, which were undertaken as batch processes to investigate the efficacy with which the column could remove hydrogen gas from lithium-rich and lithium hydride-rich samples. All of the tests reported used lithium hydride as a surrogate for lithium deuteride and lithium tritide. The design process and results from the initial tests will be discussed, along with the envisioned placement of this treatment scheme in a fully-functional lithium loop.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

mdtmFTP and its evaluation on ESNET SDN testbed

In this paper, to address the high-performance challenges of data transfer in the big data era, we are developing and implementing mdtmFTP: a high-performance data transfer tool for big data. mdtmFTP has four salient features. First, it adopts an I/O centric architecture to execute data transfer tasks. Second, it more efficiently utilizes the underlying multicore platform through optimized thread scheduling. Third, it implements a large virtual file mechanism to address the lots-of-small-files (LOSF) problem. In conclusion, mdtmFTP integrates multiple optimization mechanisms, including–zero copy, asynchronous I/O, pipelining, batch processing, and pre-allocated buffer pools–to enhance performance. mdtmFTP has been extensively tested and evaluated within the ESNET 100G testbed. Evaluations show that mdtmFTP can achieve higher performance than existing data transfer tools, such as GridFTP, FDT, and BBCP.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Staging of RF-accelerating Units in a MEMS-based Ion Accelerator

Multiple Electrostatic Quadrupole Array Linear Accelerators (MEQALACs) provide an opportunity to realize compact radio- frequency (RF) accelerator structures that can deliver very high beam currents. MEQALACs have been previously realized with acceleration gap distances and beam aperture sizes of the order of centimeters. Through advances in Micro-Electro-Mechanical Systems (MEMS) fabrication, MEQALACs can now be scaled down to the sub-millimeter regime and batch processed on wafer substrates. In this paper we show first results from using three RF stages in a compact MEMS-based ion accelerator. The results presented show proof-of-concept with accelerator structures formed from printed circuit boards using a 3 × 3 beamlet arrangement and noble gas ions at 10 keV. We present a simple model to describe the measured results. We also discuss some of the scaling behaviour of a compact MEQALAC. The MEMS-based approach enables a low-cost, highly versatile accelerator covering a wide range of currents (10 μA to 100 mA) and beam energies (100 keV to several MeV). Applications include ion-beam analysis, mass spectrometry, materials processing, and at very high beam powers, plasma heating.

43 PARTICLE ACCELERATORS↗
DOE OSTI2015

Direct Immersion Annealing of Thin Block Copolymer Films

We demonstrate ordering of thin block copolymer (BCP) films via direct immersion annealing (DIA) at enhanced rate leading to stable morphologies. The BCP films are immersed in carefully selected mixtures of good and marginal solvents that can impart enhanced polymer mobility, while inhibiting film dissolution. DIA is compatible with roll-to-roll assembly manufacturing and has distinct advantages over conventional thermal annealing and batch processing solvent-vapor annealing methods. We identify three solvent composition-dependent BCP film ordering regimes in DIA for the weakly interacting polystyrene–poly(methyl methacrylate) (PS–PMMA) system: rapid short-range order, optimal long-range order, and a film instability regime. Kinetic studies in the “optimal long-range order” processing regime as a function of temperature indicate a significant reduction of activation energy for BCP grain growth compared to oven annealing at conventional temperatures. An attractive feature of DIA is its robustness to ordering other BCP (e.g. PS-P2VP) and PS-PMMA systems exhibiting spherical, lamellar and cylindrical ordering.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning

Abstract Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions. Biomineralization in diatoms, unicellular algae that use silica to construct micron-scale cell walls with nanoscale features, is an attractive candidate for functional synthesis of materials for applications including photonics, sensing, filtration, and drug delivery. Therefore, controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field. In this work, we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning. An artificial neural network (NN) was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein (Thaps3_21880), resulting in 94% detection accuracy. Class activation maps visualized physical changes that allowed the NNs to separate diatom strains, subsequently establishing a specific gene that controls pores. A further NN was created to batch process image data, automatically recognize pores, and extract pore-related parameters. Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool, called CrossVis, and allowed to directly link changes in morphological diatom phenotype of pore size and distribution with changes in the genotype.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Continuous, One-pot Synthesis and Post-Synthetic Modification of NanoMOFs Using Droplet Nanoreactors

Here, metal-organic frameworks (MOFs); also known as porous coordination polymers (PCP) are a class of porous crystalline materials constructed by connecting metal clusters via organic linkers. The possibility of functionalization leads to virtually infinite MOF designs using generic modular methods. Functionalized MOFs can exhibit interesting physical and chemical properties including accelerated adsorption kinetics and catalysis. Although there are discrete methods to synthesize well-defined nanoscale MOFs, rapid and flexible methods are not available for continuous, one-pot synthesis and post synthesis modification (functionalization) of MOFs. Here, we show a continuous, scalable nanodroplet-based microfluidic route that not only facilitates the synthesis of MOFs at nanoscale, but also offers flexibility for direct functionalization with desired functional groups (e.g., -NH 2 , -COCH 3 , fluorescein isothiocyanate; FITC). In addition, the presented route of continuous manufacturing of functionalized MOFs takes significantly less time compared to state-of-the-art batch methods currently available (1 hr vs. several days). We envisage our approach to be a breakthrough method for synthesizing complex functionalized nanomaterials (metal, metal oxides, quantum dots and MOFs) that are not accessible by direct batch processing, and expand the range of a new class of functionalized MOF-based functional nanomaterials.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Multiphase separation of copper nanowires

Here, this communication reports a new method to purify copper nanowires with nearly 100% yield from undesired copper nanoparticle side-products formed during batch processes of copper nanowire synthesis. Also, this simple separation method can yield large quantities of long, uniform, high-purity copper nanowires to meet the requirements of nanoelectronics applications as well as provide an avenue for purifying copper nanowires in the industrial scale synthesis of copper nanowires, a key step for commercialization and application of nanowires.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Ultrafast flow chemistry for the acid-catalyzed conversion of fructose

Biomass conversion to chemicals and fuels has been epitomized by batch processing and long reaction times that limit the economic viability of future biorefineries and prevent modular manufacturing near the source-production site that is necessary due to the biomass’ large water content. Herein we introduce a continuous flow microreactor to enable process intensification and one thousand-fold device miniaturization for remote chemical processing. We demonstrate the approach in HCl-catalyzed fructose dehydration in water. We characterize and quantify the mixing using laser induced fluorescence (LIF) of fluorescent dyes and particle image velocimetry (PIV) of fluorescent microspheres. The estimated mixing times ranged from 0.03 to 4.8 s for residence times from 1 to 120 s, respectively. Moreover, a curved channel geometry induces secondary (Dean) vortices that produce a three-fold increase in mixing by diffusion through advection along the cross-sectional direction of the channel, creating dissipative chaotic mixing under laminar flow. We then obtain for the first time isothermal kinetics of fructose dehydration at short contact times and high temperatures and compare to a previously published, hybrid ab initio and data-driven kinetic model. Rigorous model optimization is carried out and an optimal HMF yield of 54% is attained in a single aqueous phase. High temperatures and low pH improve the reactor throughput by increasing the HMF space-time yield. We demonstrate the highest ever productivity (HMF yield per unit time). Importantly, this can be achieved at ~100% fructose conversion, eliminating the need for recycling to improve economics. Furthermore, optimization of the reactor dimensions and the specific energy dissipation rate results in ~60% pressure-drop related energy savings. We describe a methodology to scale-up microreactors to millimeter size and increase productivity 16-fold under optimal conditions. The energy efficiency and cost analysis for HMF production from corn stover of a typical corn farm indicate an optimal strategy for numbering-up millimeter size reactors and, importantly, they underscore the potential feasibility of producing bioproducts in a distributed manner.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗