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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Sensitivity of PBX 9502 to Drilling Operations

Polymer-bonded explosive (PBX) 9502 (95% TATB, 5% Kel-F 800 by weight) is dry-drilled on a CNC milling machine and its thermomechanical response to varying feed rates, drilling speeds, and peck depths with 4 mm and 5 mm diameter drill bits is investigated. The tested samples are affixed to a force sensor that enables recording temporally resolved cutting forces and torques, and a drill-embedded thermocouple yields local temperature data. From the data, an empirical relationship between temperature changes and feed per revolution is developed, which reveals reduced temperatures in higher feed per revolution regimes for PBX 9502. The observed relationship allows extrapolating to temperatures for other hole diameters, indicating increased temperature for smaller diameter drilling across the board. Additional testing was performed with PBX 9501 (95% HMX, 2.5% Estane®, 2.5% BDNPA/BDNPF by weight), albeit over a reduced parameter space, which revealed the opposite behavior for the feed per revolution temperature dependence. It is concluded that both PBX 9502 and PBX 9501 can be dry-drilled efficiently beyond the limits of presently applicable US-DOE standards, where cutting interface temperatures remain far below material critical temperatures. Finally, data reveals that coolant usage in the drilling process for these materials provides a wide safety margin.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Influence of printing parameters on the mechanical behavior of 3D-printed SS316L parts manufactured using laser hot wire directed energy deposition

Hybrid manufacturing combines the simultaneous benefits of additive manufacturing (complex geometries, part consolidation, and mass customization) with the advantages of subtractive manufacturing (superior surface finish and enhanced dimensional accuracies) by integrating a suite of complementary traditional processes into a base platform of additive manufacturing. The use of hybrid technology has grown in recent years given its capabilities on repairing metallic structures, producing parts with conformal cooling features, and manufacturing functionally graded products. These kinds of capabilities are of great interest to the medical implant, energy, automotive, maritime, and aerospace industry sectors, among many other fields. This work investigated the mechanical properties of stainless steel (SS) 316L as a function of different tool paths strategies using an integrated 5-axis CNC hybrid Mazak system with a laser hot wire deposition system (LHWDS). This study includes the evaluation of different printing parameters and their impact on the quality of the printed bead as well as the incorporation of a structure–property material relationship based on the mechanical performance of the manufactured coupons.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Ballistic-diffusive phonon transport in cellulose nanocrystals by ReaxFF molecular dynamics simulations

Cellulose nanocrystals (CNCs), a ubiquitous nano-sized natural biopolymer, have drawn considerable attention in recent years due to their excellent mechanical and chemical properties. However, thermal properties of CNCs, which are critical for their potential applications as structural or functional materials, are less investigated. In this work, their thermal properties were systematically studied by molecular dynamics (MD) simulations in terms of the effects of polymorphs, size, temperature, and strain. It was found that the thermal conductivities depend on the polymorphs of CNCs, but the thermal conductance of unit chain of each polymorph is similar. Moreover, the strong dependence of the thermal conductivities of CNCs on their length and cross-sectional area indicates the existence of remarkable ballistic-diffusive phonon transport. Single chain CNCs can achieve a thermal conductivity of ~6 W/(m∙K), implying their potential as high thermal conductive polymer. The mechanical strain can enhance the alignment of cellulose skeleton, which reduces phonon scattering and thus increases thermal conductivity. Possible underlying mechanism of the varying thermal conductivity was further discussed and attributed to the shift of phonon frequencies and varied degree of orientation. This study reveals the sophisticated ballistic-diffusive phonon transport in CNCs, and pave path to future design of CNC-based materials with desirable thermal properties.

42 ENGINEERING↗
DOE OSTI2025

Probing the Self-Assembly dynamics of cellulose nanocrystals by X-ray photon correlation spectroscopy

Charge-stabilized colloidal cellulose nanocrystals (CNCs) can self-assemble into higher-ordered chiral nematic structures by varying the volume fraction. The assembly process exhibits distinct dynamics during the isotropic to liquid crystal phase transition, which can be elucidated using X-ray photon correlation spectroscopy (XPCS). Anionic CNCs were dispersed in propylene glycol (PG) and water spanning a range of volume fractions, encompassing several phase transitions. Coupled with traditional characterization techniques, XPCS was conducted to monitor the dynamic evolution of the different phases. Additionally, simulated XPCS results were obtained using colloidal rods and compared with the experimental data, offering additional insights into the dynamic behavior of the system. The results indicate that the particle dynamics of CNCs undergo a stepped decay in three stages during the self-assembly process in PG, coinciding with the observed phases. The phase transitions are associated with a total drop of Brownian diffusion rates by four orders of magnitude, a decrease of more than a thousand times slower than expected in an ideal system of repulsive Brownian rods. Given the similarity in the phase behaviors in CNCs dispersed in PG and in water, we hypothesize that these dynamic behaviors can be extrapolated to other polar solvent environments. Importantly, these findings represent the direct measurement of CNC dynamics using XPCS, underscoring the feasibility of directly assessing the dynamic behavior of other rod-like colloidal suspensions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Achieving clean production with nanostructured coated milling tools dry machining low carbon steel

The advancement of clean production using nanostructured materials in subtractive manufacturing processes has focused on replacing the use of liquid lubrication with solid lubricants coated to conventional cutting tools machining low carbon steel (~ 0.2 wt. % carbon). However, little is known about the wear mechanisms that dominate such tools when coated with functional graded nanostructured coatings. The present study follows an international standard for characterizing such wear (ISO 8688) but uses advanced measurement techniques such as x-ray fluorescence technology to measure the diffusion of chemical species from tool to workpiece and vice versa, that contributes to tool wear with the view to enhance the principles of cleaner production. The results show that when commercially-available tools coated with the appropriate functionally graded industrial coating, diffusion does not take place and creates an effective chemical barrier during machining. The experimental methods used in the study include machining workpieces using a CNC milling machine, tachometer for measuring spindle speed, dynamometer for measuring cutting forces, infra-red camera for measuring cutting temperatures, chemical species diffusion wear using x-ray florescence detector, and an optical microscope for measuring tool wear. Coupled with the physical measurement of wear, it is concluded that mechanical wear dominates the milling of a low carbon steel and that thermal properties of the coatings do not correlate with such wear. It is also concluded that flank wear as a function of volume of workpiece removed and machining duration (machining index) is a sound method for assessing the transition of the stages of wear during clean machining operations. The present study not only has implication for the design of better cutting tools, but also advances dry machining processes that eliminate the use of liquid lubricants making subtractive production processes cleaner. The main conclusions drawn from this study show that coated tools have a longer life under dry conditions compared to uncoated tools and that there is no correlation between coating properties and tool wear. In conclusion, diffusion of chemical species into the cut chips did not occur because of the lack of thermally induced chemical wear of the cutting tool and that wear is caused by the gradual erosion of the flank face due to the chip abrading the cutting tool.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. In this report a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.

42 ENGINEERING↗
DOE OSTI2023

Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.

42 ENGINEERING↗
DOE OSTI2023

Reprint of: Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Autonomous aerial flight path inspection using advanced manufacturing techniques

Robotic systems have shown capabilities to perform inspection tasks in dangerous and difficult-to-access environments, such as those found in different components of power plants. However, most of the current robotic inspection technology is designed for specific components. Aerial robots, commonly termed as Drones, have raised an option to inspect a wider range of structural components. Nevertheless, current aerial inspecting technology still relies on a human pilot with limited line of sight, field of view and a reduced perception as the drone flies away, which prevents performing close-quarter inspection in intricate, structurally complex and GPS-denied environments. This work introduces offline inspection path generation methods based on robotics-integrated to manufacturing techniques. One method uses computer-aided manufacturing (CAM) techniques and the other an additive manufacturing (AM) approach to generate the flight path. That is to say, the drone would fly along the path described by a 3D (Three Dimensional) printer’s extruder or a CNC (Computer Numerical Control) machining tool, enabling to fly very close the structure even in physical structures with a complex geometry. Once the trajectories are generated, they are introduced for its validation in the Gazebo robotics simulator. Simulation results demonstrate the proper performance of the method and confirm that this approach can be used for close inspection of structural components.

42 ENGINEERING↗
DOE OSTI2026

Correlated Ion Transport Governed by Dynamic Local Structure in High Concentration and Localized High Concentration Electrolytes

Understanding the dynamics of cluster formation and network percolation provides the mechanistic link between microscopic solvation structure and transport in concentrated electrolytes, including localized high-concentration electrolytes (LHCEs). Although recent studies have shown LHCEs to form micelle-like aggregates at specific compositions, a quantitative understanding of how solvation structures and transport properties depend on salt–solvent–diluent ratios remains limited. Here, we integrate molecular dynamics with Onsager transport analyses to chart the evolution of solvation microstructure and associated ionic transport in LiFSI/DMC/TTE, achieving good agreement with experimental conductivities across composition. We show that micelle formation arises from a dynamic instability of the cation–anion network, and that network percolation is the primary determinant of conductivity. Two compositional thresholds emerge: A critical network concentration (CNC) and a critical micelle concentration (CMC) that delineate transitions from extended percolating networks to micelle-like clusters and then to fragments. These structural transitions rationalize the nonintuitive conductivity decrease at intermediate dilution despite monotonically decreasing viscosity and provide composition-level design rules for LHCEs.

Mohanakrishnan, Rohith Srinivaas [University of Ca↗
DOE OSTI2024

One-Component Nanocomposite Membranes from Polymer Grafted Cellulose Nanocrystals

Membrane filtration is an important industrial purification process used to access clean and potable water. The fabrication of the membranes used in these purification applications often involves expensive and energy-intensive processes that have a large negative impact on the environment. Sustainable alternatives with a high water flux and strong rejection performance are needed to purify water. Here, the focus of this work is to investigate the use of polymer-grafted cellulose nanocrystals (CNCs) in membrane applications. The impact of the polymer grafting density and polymer conformation was investigated and it is shown that by increasing the grafting density of PEG such that it adopts a semidilute polymer brush conformation, the water flux through the membranes could be increased from 3.5 to 2900 L h –1 m –2 for CNC membranes without and with grafted PEG, respectively. These membranes also exhibited rejection performances with molecular weight cutoffs between 62 and 100 kDa for all polymer-grafted samples, consistent with the ultrafiltration regime. Thus, the design of these one-component composite materials can enhance the water permeability of ultrafiltration membranes while maintaining effective selectivity.

cellulose nanocrystals↗
DOE OSTI2021

In Vitro and in Vivo Analyses of the Effects of Source, Length, and Charge on the Cytotoxicity and Immunocompatibility of Cellulose Nanocrystals

Cellulose nanocrystals (CNCs) are an emergent, sustainable nanomaterial which are bio-sourced, abundant, and biodegradable. On account of their high aspect ratio, low density, and mechanical rigidity, they have been employed in numerous areas of biomedical research including as reinforcing materials for bone or tissue scaffolds or as carriers in drug delivery systems. Given the promise of these materials for such use, characterizing and understanding their interactions with biological systems is an important step to prevent toxicity or inflammation. Reported herein are studies aimed at exploring the in-vitro and in-vivo effects that source, length, and charge of the CNCs have on cytotoxicity and immune response. In this study, CNCs from four different biosources (cotton, wood, Miscanthus x. Giganteus, and sea tunicate) were prepared and functionalized with positive or negative charges to obtain a small library of CNCs with a range of dimensions and surface charge. A method to remove endotoxic or other impurities on the CNC surface leftover from the isolation process was developed, and the biocompatibility of the CNCs was subsequently assayed in-vitro and in-vivo. After subcutaneous injection, it was found that unfunctionalized (uncharged) CNCs form aggregates at the site of injection, inducing splenomegaly and neutrophil infiltration, while charged CNCs having surface carboxylates, sulfate half-esters, or primary amines were biologically inert. No effect of particle source or length was observed in the in-vitro and in-vivo studies conducted. The lack of in-vitro or in-vivo immune response toward charged CNCs in these experiments supports their use in future biological studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Cellulose Nanocrystals’ Role in Critical Mineral Beneficiation: Dual Aggregate-Dispersant Behavior Supports Environmentally Benign Nickel Processing

Environmentally unsustainable and toxic chemical flocculants and dispersants from nickel (Ni) processing contribute to industrial effluents that greatly impact biodiversity and aquatic life. Despite the industry’s efforts to reduce its ecological footprint—primarily due to the lack of commercially available biodegradable, environmentally benign, and non-toxic reagents—these reagents continue to harm natural ecosystems. Chemicals used during the processing operations often target a specific unit operation that negatively affects downstream operations. In this study, we discover the concentration-dependent behavior of cellulose nanocrystals (CNCs) as a dispersant and flocculant, avoiding the use of various harmful chemicals in the nickel processing stages. Electrophoretic, optical brightfield microscopy, and quartz crystal microbalance-dissipation studies detected charge neutralization behavior by renewable and biodegradable CNCs, which can greatly benefit Ni processing operations. Microflotation experiments demonstrated that CNCs enhanced Ni recovery from 62 to 77 wt.% and concentrate grade from 15 to 20 wt.%. Settling and turbidity studies demonstrated the dual flocculant-dispersant behavior of CNCs resulting from the alignment of CNC fibers along the octahedral brucite basal and amphoteric edge plane of serpentine. CNCs can be used as dispersants during froth flotation to improve Ni beneficiation and as post-processing flocculants for tailings management and dewatering, which is one of the major environmental and social concerns facing the mining and mineral processing industry. This study paves the way for CNCs to be used as flocculants and dispersants in a range of industries from healthcare to pharmaceuticals to semiconductor devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Natural nanoparticle complexes at water-water interfaces

Aqueous two-phase systems (ATPSs) with multifunctional attributes have significant promise as biomimetic materials, but current approaches do not harness their full potential. Here, we show that ATPSs can be stabilized to form microcapsules by the interfacial assembly of chitin nanofibers (ChNF) and rod-like cellulose nanocrystals (CNC). The high structural stability of the ChNF/CNC complexes integrates permeability and transport across the membrane at the liquid/liquid interface. Driven by density and osmotic stress gradients, the microcapsules show switchable motility, including cyclic meniscus-climbing and subsurface transport. These observations demonstrate a self-regulating system with potential for cargo transfer, cell biomimicry, as well as ATPSs-based microreactors and microrobots.

Bioinspired materials↗
DOE OSTI2021

Shear-free mixing to achieve accurate temporospatial nanoscale kinetics through scanning-SAXS: ion-induced phase transition of dispersed cellulose nanocrystals

Time-resolved in situ characterization of well-defined mixing processes using small-angle X-ray scattering (SAXS) is usually challenging, especially if the process involves changes of material viscoelasticity. In specific, it can be difficult to create a continuous mixing experiment without shearing the material of interest; a desirable situation since shear flow both affects nanoscale structures and flow stability as well as resulting in unreliable time-resolved data. Here, we demonstrate a flow-focusing mixing device for in situ nanostructural characterization using scanning-SAXS. Given the interfacial tension and viscosity ratio between core and sheath fluids, the core material confined by sheath flows is completely detached from the walls and forms a zero-shear plug flow at the channel center, allowing for a trivial conversion of spatial coordinates to mixing times. With this technique, the time-resolved gel formation of dispersed cellulose nanocrystals (CNCs) was studied by mixing with a sodium chloride solution. It is observed how locally ordered regions, so called tactoids, are disrupted when the added monovalent ions affect the electrostatic interactions, which in turn leads to a loss of CNC alignment through enhanced rotary diffusion. The demonstrated flow-focusing scanning-SAXS technique can be used to unveil important kinetics during structural formation of nanocellulosic materials. However, the same technique is also applicable in many soft matter systems to provide new insights into the nanoscale dynamics during mixing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Environmentally sustainable lithium-ion battery cathode binders based on cellulose nanocrystals

Aqueous binders as environmentally sustainable alternatives to conventional polyvinylidene difluoride (PVDF) binders have not yet been successful for cathodes in lithium-ion batteries (LIBs). Here, carboxylic acid functionalized cellulose nanocrystals (CNC-COOHs) have been obtained from Miscanthus × giganteus (M×G) biomass and evaluated as aqueous binders for LIB cathodes.

Cellulose nanocrystal↗
DOE OSTI2024

Fabrication and Testing of a Stainless Steel Quartz Coaxial Cable Structure for High Temperature Measurement

This paper presents a stainless-steel quartz coaxial cable structure for distributed sensing of high temperatures. The coaxial cable sensor uses stainless steel as the inner and outer conductors and quartz as the insulator in order to achieve mechanical strength and temperature resilience. Girdle-shaped reflectors are fabricated on the quartz tubing by CNC grinding and CO 2 laser resurfacing. The sensor operates by measuring the temperature dependent change in the effective electromagnetic length between the two adjacent reflectors on the quartz tubing. A retaining collar is fabricated at the tip of the quartz tubing by 3D printing and laser resurfacing to secure the connection between the sensor and the signal cable. The prototype sensor was tested for high temperature measurements up to 600°C, and the performance characteristics of the sensor such as sensitivity, resolution uncertainty, repeatability, and stability were estimated based on the test results.

Jiao, Xinyu↗