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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 37 records · Page 2

Self–Sorting of 10–µm–Long Single–Walled Carbon Nanotubes in Aqueous Solution

Single-walled carbon nanotubes (SWCNTs) are a class of 1D nanomaterials that exhibit extraordinary electrical and optical properties. However, many of the fundamental studies and practical applications are stymied by sample polydispersity. SWCNTs are synthesized in bulk with broad structural (chirality) and geometrical (length and diameter) distributions; problematically, all known post-synthetic sorting methods rely on ultrasonication, which cuts SWCNTs into short segments (typically <1 μm). Here it is demonstrated that ultralong (>10 μm) SWCNTs can be efficiently separated from the shorter ones through a solution-phase “self-sorting.” It is shown that thin film transistors fabricated from long semiconducting SWCNTs exhibit a carrier mobility as high as ≈90 cm 2 V –1 s –1 , which is ≈10 times higher than the shorter counterparts and well exceeding other known materials such as organic semiconducting polymers (<1 cm 2 V –1 s –1 ), amorphous silicon (≈1 cm 2 V –1 s –1 ), and nanocrystalline silicon (≈50 cm 2 V –1 s –1 ). Mechanistic studies suggest that this self-sorting relies on the equilibrium between isotropic and liquid crystalline SWCNT phases, which is driven by the solution phase behavior above the cloud point of the isotropic phase, and inversely scales with the SWCNT length. Furthermore, this length-dependent self-sorting technique opens a path to attain the long- sought ultralong, electronically pure carbon nanotube materials through scalable solution processing.

36 MATERIALS SCIENCE↗

A Fundamental Frequency Sorting Algorithm for Capacitor Voltage Balance of Modular Multilevel Converter With Low-Frequency Carrier Phase Shift Modulation

This paper proposes a fundamental frequency sorting algorithm for balancing the floating capacitors in modular multilevel converters with low-frequency carrier phase shift modulation. The relationship between the driving pulses and submodules (SMs) is rebuilt in every fundamental period by sorting the voltage increments and present voltages. Besides, an improved and simplified strategy is proposed to predict the charging abilities of the driving pulses, so as to avoid sorting the voltage increments. Based on the proposed method, the switching frequency of each power device and the carrier frequency are identical, which reduces the operational losses for applications regarding high power. At the same time, the sorting frequency is as low as the fundamental frequency, which alleviates a large amount of computational cost, and the necessity to measure arm currents is omitted. In this case, the arm current sensors are eliminated, and simplified communication among the central controller and the local ones is set up. Finally, a three-phase 1-MVA simulation platform with 120 SMs and a down-scaled 6-kVA experimental prototype with 48 SMs are constructed to validate the proposed approach.

42 ENGINEERING↗

The Power of Exclusion using Automated Osteometric Sorting: Pair‐Matching,

Abstract This study compares the original pair‐matching osteometric sorting model ( J Forensic Sci 2003 ;48:717) against two new models providing validation and performance testing across three samples. The samples include the Forensic Data Bank, USS Oklahoma , and the osteometric sorting reference used within the Defense POW / MIA Accounting Agency. A computer science solution to generating dynamic statistical models across a commingled assemblage is presented. The issue of normality is investigated showing the relative robustness against non‐normality and a data transformation to control for normality. A case study is provided showing the relative exclusion power of all three models from an active commingled case within the Defense POW / MIA Accounting Agency. In total, 14,357,220 osteometric t ‐tests were conducted. The results indicate that osteometric sorting performs as expected despite reference samples deviating from normality. The two new models outperform the original, and one of those is recommended to supersede the original for future osteometric sorting work.

Lynch, Jeffrey James↗

Multiple stage sorting

A material sorting system sorts materials utilizing multiple stages of classification and sorting, including a vision system that implements a machine learning system in order to identify or classify each of the materials, and Laser Induced Breakdown Spectroscopy to perform a subsequent classification and sorting of the remaining materials.

97 MATHEMATICS AND COMPUTING↗

Sorting between metal alloys

A material sorting system sorts materials utilizing an x-ray fluorescence and/or a vision system that implements a machine learning system in order to identify or classify each of the materials, which are then sorted into separate groups based on such an identification or classification determining that the materials are composed of either wrought aluminum, extruded aluminum, or cast aluminum. The system is capable of sorting between cast aluminum alloys and also between wrought aluminum alloys.

Kumar, Nalin↗

Sorting rare earth magnets motors for recycling without opening the motors

Abstract A novel approach for sorting electric motors without dismantling them is reported in this article. This approach increases the likelihood that a motor selected from a mixture for recycling would contain rare earth elements (REEs), which are pivotal for numerous advanced green technologies. The challenge of inadvertently dismantling motors without REEs during recycling is addressed by this proposed innovative method. It relies on cogging interactions and power density for sorting motors with REEs. The process uses an algorithm introduced in this work to sort motors in two steps by (a) distinguishing induction motors from permanent magnet motors, and (b) distinguishing motors with critical REEs (e.g., Nd, Pr, Dy, Tb) from ferrite-based motors, all without opening the motors. The former was accomplished with 100% accuracy while the latter was accomplished with ~ 78% accuracy. Such high accuracy improves the recycling efficiency of critical REEs from motors, alleviating concern about supply disruption and supporting resource conservation and sustainability.

36 MATERIALS SCIENCE↗

Mammalian Chromosome Analysis and Sorting by Flow Cytometry

The analysis of chromosomes by flow cytometry is termed flow cytogenetics, and it involves the analysis and sorting of single mitotic chromosomes in suspension. The study of flow karyograms provides insight into chromosome number and structure to provide information on chromosomal DNA content and can enable the detection of deletions, translocations, or any forms of aneuploidy. Beyond its clinical applications, flow cytogenetics greatly contributed to the Human Genome Project through the ability to sort pure populations of chromosomes for gene mapping, cloning, and the construction of DNA libraries. Maximizing the potential of these important applications of flow cytogenetics relies on precise instrument setup and optimal sample processing, both of which impact the accuracy and quality of the data that are generated. This article is a compilation of the existing protocols that describe the stepwise methodology of accumulating, isolating, and staining metaphase chromosomes to prepare single-chromosome suspensions for flow cytometric analysis and sorting. Although the chromosome preparation protocols have remained largely unchanged, cytometer technology has advanced dramatically since these protocols were originally developed. Advances in cytometry technologies offer new and exciting approaches for understanding and monitoring chromosomal aberrations, but the hallmark of these protocols remains their simplicity in methodologies and reagent requirements and the accuracy of data resolvable to every chromosome of the cell.

59 BASIC BIOLOGICAL SCIENCES↗

Impact of automated battery sorting for mineral recovery from lithium-ion battery recycling in the United States

The United States has identified several lithium-ion battery materials as critical for reaching the national emission reduction targets that have been set in accordance with the 2015 Paris Agreement. However, with few natural resources available domestically, there is a rapidly growing focus on the development of a domestic recycling industry to recover these materials from end-of-life batteries. Here, we use the Lithium-Ion Battery Resources Assessment (LIBRA) system dynamics model to evaluate the impact of automated battery sorting technology in terms of the shares of cobalt and nickel that are recovered through recycling. Findings show that automated sorting has clear benefits over manual sorting methods by helping recyclers selectively process high-cobalt batteries. By maximizing cobalt recovery, recycling becomes more profitable and drives greater investment in recycling capacity, resulting in a higher share of nickel and cobalt recovered from EOL batteries over time.

25 ENERGY STORAGE↗

Development of a new approach based on midwave infrared spectroscopy for post-consumer black plastic waste sorting in the recycling industry

Highlights: • MWIR reflectance contact measurements were performed on post-consumer waste. • MWIR absorptions are found to be potentially suitable for black plastic resin classification. • MWIR is also potentially suitable for separation of coloured plastic and wood based products. • Atmospheric transmittance simulations suggest feasibility of stand-off measurements. - Abstract: Waste sorting is key to the process of waste recycling. Exact identification of plastic resin and wood products using Near Infrared (NIR, 1–1.7 µm) sensing is currently in use. Yet, dark targets characterized by low reflectance, such as black plastics, are hard to identify by this method. Following the recent success of Midwave Infrared (MWIR, 3–12 µm) measurements to identify coloured plastic polymers, the aim of this study was to assess whether this technique is applicable to sorting black plastic polymers and wood products. We performed infrared reflectance contact measurements of 234 plastic samples and 29 samples of wood and paper products. Plastic samples included black, coloured and transparent Polyethylene Terephthalate (PET), Polyethylene (PE), Polyvinyl Chloride (PVC), Polypropylene (PP), Polylactic acid (PLA) and Polystyrene (PS). The spectral signatures of the black and coloured plastic samples were compared with clear plastic samples and signatures documented in the literature to identify the polymer spectral features in the presence of coloured material. This information was used to determine the spectral bands that best suit the sorting of black plastic polymers. The main NIR-MWIR absorption features of wood, cardboard and paper were identified as well according to the spectral measurements. Good agreement was found between our measurements and the absorption features documented in the literature. The new approach using MWIR spectral features appears to be useful for black plastics as it overcomes some of the limitations in the NIR region to identify them. The main limitation of this technique for industrial applications is the trade-off between the signal-to-noise ratio of the sensor operating in standoff mode and the speed at which waste is moved under the sensor. This limitation can be resolved by reducing the system’s spectral resolution to 16 cm{sup −1}, which allows for faster spectra acquisition while maintaining a reasonable signal-to-noise ratio.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Self-Sorting vs Coassembly in Peptide Amphiphile Supramolecular Nanostructures

The functionality of supramolecular nanostructures can be expanded if systems containing multiple components are designed to either self-sort or mix into coassemblies. This is critical to gain the ability to craft self-assembling materials that integrate functions, and our understanding of this process is in its early stages. Here, in this work, we have utilized three different peptide amphiphiles with the capacity to form β-sheets within supramolecular nanostructures and found binary systems that self-sort and others that form coassemblies. This was measured using atomic force microscopy to reveal the nanoscale morphology of assemblies and confocal laser scanning microscopy to determine the distribution of fluorescently labeled monomers. We discovered that PA assemblies with opposite supramolecular chirality self-sorted into chemically distinct nanostructures. In contrast, the PA molecules that formed a mixture of right-handed, left-handed, and flat nanostructures on their own were able to coassemble with the other PA molecules. We attribute this phenomenon to the energy barrier associated with changing the handedness of a β-sheet twist in a coassembly of two different PA molecules. This observation could be useful for designing biomolecular nanostructures with dual bioactivity or interpenetrating networks of PA supramolecular assemblies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Raman-based sorting of microbial cells to link functions to their genes

In our recent work, we developed an optofluidic platform that allows a direct link to be made between the phenotypes (functions) and the genotypes (genes) of microbial cells within natural communities. By combining stable isotope probing, optical tweezers, Raman microspectroscopy, and microfluidics, the platform performs automated Raman-based sorting of taxa from within a complex community in terms of their functional properties. In comparison with manual sorting approaches, our method provides high throughput (up to 500 cells per hour) and very high sorting accuracy (98.3 ± 1.7%), and significantly reduces the human labour required. The system provides an efficient manner to untangle the contributions of individual members within environmental and host-associated microbiomes. In this News and Thoughts, we provide an overview of our platform, describe potential applications, suggest ways in which the system could be improved, and discuss future directions in which Raman-based analysis of microbial populations might be developed.

59 BASIC BIOLOGICAL SCIENCES↗

Parallel sorting algorithm classification: is manual instrumentation necessary?

Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.

Algorithm Classification↗

Anti-Hermitian photodetector facilitating efficient subwavelength photon sorting

The ability to split an incident light beam into separate wavelength bands is central to a diverse set of optical applications, including imaging, biosensing, communication, photocatalysis, and photovoltaics. Entirely new opportunities are currently emerging with the recently demonstrated possibility to spectrally split light at a subwavelength scale with optical antennas. Unfortunately, such small structures offer limited spectral control and are hard to exploit in optoelectronic devices. Here, we overcome both challenges and demonstrate how within a single-layer metafilm one can laterally sort photons of different wavelengths below the free-space diffraction limit and extract a useful photocurrent. This chipscale demonstration of anti-Hermitian coupling between resonant photodetector elements also facilitates near-unity photon-sorting efficiencies, near-unity absorption, and a narrow spectral response (~30 nm) for the different wavelength channels. This work opens up entirely new design paradigms for image sensors and energy harvesting systems in which the active elements both sort and detect photons.

36 MATERIALS SCIENCE↗

Sorting process of nanoparticles and applications of same

A sensing platform includes a substrate, an array of wells formed in the substrate, and sorted nanoparticles filling in the array of wells such that each well contains a single sorted nanoparticle, wherein the sorted nanoparticles are in a cluster state.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Sorting cast and wrought aluminum

A material sorting system sorts materials utilizing a vision system that implements a machine learning system in order to identify or classify each of the materials, which are then sorted into separate groups based on such an identification or classification determining that the materials are composed of either wrought aluminum or cast aluminum.

36 MATERIALS SCIENCE↗

River Organic Carbon Fluxes Modulated by Hydrodynamic Sorting of Particulate Organic Matter

Rivers regulate the global carbon cycle by transferring particulate organic carbon (POC) from terrestrial landscapes to marine sedimentary basins, but the processes controlling the amount and composition of fluvially exported POC are poorly understood. We propose that hydrodynamic sorting processes modify POC fluxes during fluvial transit. We test this hypothesis by studying POC transported along a ~1,200 km reach of the Rio Bermejo, Argentina. Nanoscale secondary ion mass spectrometry revealed that POC was either fine, mineral-associated organic matter, or coarse discrete organic particles. Mineral-associated POC is more resistant to oxidation and has a lower particle settling velocity than discrete POC. Consequently, hydraulic sorting and downstream fining amplify the proportion of fine, mineral-associated POC from ~55% to ~78% over 1,220 km of downstream transit. This suggests that mineral-associated POC has a greater probability of export and preservation in marine basins than plant detritus, which may be oxidized to CO 2 during transit.

58 GEOSCIENCES↗

Delamination-informed lifecycle decisions: A dielectric and machine learning framework for composite sorting and recycling

Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.

dielectric variables↗

Evaluation of sustainable waste management: An analysis of techno-economic and life cycle assessments of municipal solid waste sorting and decontamination

This study evaluates the economic and environmental feasibility of Municipal Solid Waste (MSW) sorting and decontamination technologies across urban, suburban, and rural areas. Using Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA), the research assesses cost-effectiveness and environmental impacts, with a focus on cost variability analyzed through Monte Carlo simulations. Findings indicate significant cost differences based on population density: rural areas incur high costs up to $$764/ton due to low waste volumes and limited infrastructure, whereas suburban and urban areas have more feasible costs ranging from $36.3 to $142.5/ton. Environmental impacts also vary, with greenhouse gas emissions at 171 kg CO 2 eq/ton for copy paper and 118.6 kg CO 2 eq/ton for plastics. PM2.5 levels are 9.1 g/ton for copy paper and 6.3 g/ton for plastics, with sorting lines being the main contributors. Monte Carlo simulations reveal a 50% probability of costs being below $$102.26/ton for copy paper and $115.8/ton for plastics in suburban settings. Further, the study underscores the importance of customized waste management strategies to improve economic viability and sustainability based on local conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗