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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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Hybrid additive featured plates used to form an overall microchannel heat exchanger and corresponding method of manufacture are disclosed. Various additive manufacturing (AM) techniques may be used to form walls defining microchannel features on a plate substrate. The manufacturing method is a hybrid process in that leverages both additive and conventional manufacturing techniques to minimize both cost and fabrication time.
Superconducting magnets are important pieces of technology in the world of particle accelerators, allowing researchers to study atomic and subatomic phenomena, among other things. In some instances, superconductors can lose this non-resistive property in a phenomenon known as quenching, which can cause damage to the magnets. This potential danger prompts the introduction of systems to predict when a quench is imminent; one such implementation is through the use of acoustic sensors that detect vibrations within the magnet. Within these acoustic sensor signals, significantly above-noise disturbances (referred to as ”events”) can be identified. Our research applies the statistical framework of a permutation test to features calculated from the power spectral density (PSD) to distinguish between events far from the quench at the end of the signal to events at the start of the signal. We found that dividing the PSD into frequency bands produced a feature capable of distinguishing between events early in the signal and late in the signal leading up the quench, providing a promising starting place for future quench prediction systems.
The prompt detection of explosions is a key element of the nuclear non-proliferation mission. With traditional sensors being limited in number and scale, smartphones as compact and economical multi-modal sensors are gaining traction and are being deployed. To address the flood of heterogeneous smartphone data, our team proposes a feature extraction using standardized constant-Q frequency bands across acoustic, barometric, and accelerometer data. The work presented in this poster utilizes three explosions collected on smartphones with Idaho National Lab. More explosions are planned in the near future at INL and NNSS. These extracted features will be used in machine learning methods in future iterations.
Three gaps impede the development of cost-effective and accurate data-driven building energy modeling/models (DBEM) for energy forecasting and predictive control strategies. Gap 1: data bias is common in building operation data, but this topic is hardly studied in DBEM; Gap 2: high data dimensionality is common in DBEM, but a systematic and scalable methodology is lacking to solve the problem; Gap 3: the interactions between data bias and high dimensionality have not been systematically studied for DBEM and predictive control in buildings. In this work, to address the three gaps mentioned above, we develop a framework that integrates active learning and feature selection for DBEM used for whole building data predictive control (or DPC, which is a branch of model predictive control). The framework provides a systematic methodology and automatic workflow that starts with raw data from building automation systems to the establishment of data-driven energy models for DPC controllers. The developed strategies and framework are evaluated in a virtual testbed based on EnergyPlus and BCVTB. Improved performance and reduced computational complexity are observed from the DBEM built with the developed framework, as well as the DPC controller based on that DBEM, indicating the effectiveness of the developed framework.
Split system air conditioners are widely used to cool residential buildings, because of their low cost and simplicity. However, their efficiency is impacted by installation faults, which include: improper refrigerant charge (undercharge or overcharge), improper evaporator airflow, liquid line restrictions (LL), and the presence of non-condensable gas (NC) in the refrigerant. No known previously published research has studied the effect of these four faults on a system equipped with a microtube condenser, which has smaller tube size than a traditional condenser and therefore holds less refrigerant charge, but has a different configuration than a microchannel. Furthermore, very few have studied the impacts of LL and NC. Herein this paper describes laboratory fault tests of a microtube-equipped system, compares the fault impacts with those of a traditional system, and considers the characteristic fault features. The tested system uses R-410A refrigerant and has a scroll compressor, a fixed orifice expansion device, and two fin-tube heat exchangers. The tests were carried out under steady operation with a range of fault intensities and operating conditions. The microtube system’s performance degradation from faults is similar to systems with traditional heat exchangers, despite the reduced capacity to hold refrigerant charge.
Real-time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery-powered devices. In this study, a methodology to predict the entire constant-current cycling curve with limited input information that can be collected in a short period of time is developed. A total of 10 066 charge curves of LiNiO 2 -based batteries at a constant C-rate are collected. With the combination of a feature extraction step and a multiple linear regression step, the method can accurately predict an entire battery charge curve with an error of < 2% using only 10% of the charge curve as the input information. The method is further validated across other battery chemistries (LiCoO 2 -based) using open-access datasets. The prediction error of the charge curves for the LiCoO 2 -based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications.
Bacteria rapidly adapt to environmental cues through morphological and ultrastructural changes that correlate with physiology and behavior. Cryogenic transmission electron microscopy (cryo‐TEM) can capture these phenotypic changes in near‐native, vitrified states, but manual analysis of low‐dose micrographs is labor intensive and limits throughput. Here, we present an end‐to‐end workflow that combines low‐dose cryo‐TEM imaging with a YOLOv11‐based instance‐segmentation model to automatically identify bacteria and quantify key structural features directly from the micrographs. This workflow enables (i) robust bacterial localization and counting from low‐magnification atlas/montage images, (ii) automated measurements of cell‐envelope (outer–inner membrane) thickness and anisotropy from higher‐magnification views, and (iii) detection and quantification of bacteria–flagella interactions, including overlap length and curvature metrics for interacting versus noninteracting flagella. Using Pantoea sp. YR343 grown under distinct media conditions, we show that the automated measurements agree with manual annotations while substantially reducing analysis time. Together, these tools provide a practical framework for scalable bacterial identification and quantitative phenotyping in low‐dose cryo‐TEM datasets and establish a foundation for extending cryo‐TEM image analysis toward higher‐throughput studies of microbial heterogeneity and biointerfaces.
Single-cell proteomics (scProteomics) promises to advance our understanding of cell functions within complex biological systems. However, a major challenge of current methods is their inability to identify and provide accurate quantitative information for low abundance proteins. Herein, we describe an ion mobility-enhanced mass spectrometry acquisition and peptide identification method, TIFF (Transferring Identification based on FAIMS Filtering), to improve the sensitivity and accuracy of label-free scProteomics. TIFF extends the ion accumulation times for peptide ions by filtering out singly charged ions. The peptide identities are assigned by a three-dimensional MS1 feature matching approach (retention time, accurate mass, and FAIMS compensation voltage). TIFF method enabled unbiased proteome analysis to a depth of >1,700 proteins in single HeLa cells with >1,100 proteins consistently identified. As a demonstration, we applied the TIFF method to obtain temporal proteome profiles of >150 single murine macrophage cells during lipopolysaccharide stimulation and identified time-dependent proteome changes.
Fully-connected deep neural networks with weights initialized from independent Gaussian distributions can be tuned to criticality, which prevents the exponential growth or decay of signals propagating through the network. However, such networks still exhibit fluctuations that grow linearly with the depth of the network, which may impair the training of networks with width comparable to depth. We show analytically that rectangular networks with tanh activations and weights initialized from the ensemble of orthogonal matrices have corresponding preactivation fluctuations which are independent of depth, to leading order in inverse width. Moreover, we demonstrate numerically that, at initialization, all correlators involving the neural tangent kernel (NTK) and its descendants at leading order in inverse width—which govern the evolution of observables during training—saturate at a depth of ~20, rather than growing without bound as in the case of Gaussian initializations. We speculate that this structure preserves finite-width feature learning while reducing overall noise, thus improving both generalization and training speed in deep networks with depth comparable to width. We provide some experimental justification by relating empirical measurements of the NTK to the superior performance of deep non-linear orthogonal networks trained under full-batch gradient descent on the MNIST and CIFAR-10 classification tasks.
In this contribution, we investigate why different dopant species and back-contact architectures lead to different sub-bandgap behaviors in CdSeTe solar cells. Through extraction of the absorptance from photoluminescence spectra, we parse the contributions from material properties and from cell optics. We show that, as expected, arsenic doping leads to an increase in sub-bandgap features over traditional copper doping, and that this is a material property of arsenic-doped CdSeTe. Conversely, the increase in sub-bandgap absorption and emission using alternative back contact architectures can be attributed to the cell optics, and more specifically to the increased reflectance of the back interface, leading to at least a doubling of the pathlength for sub-bandgap photons.
Arc GIS files of coal maps, traces and structural features for the Alabama Coalfields Project, Southern Appalachian Basin.
Aerosols and their interactions and influence on clouds are among the main sources of uncertainties in radiative direct and indirect forcing (IPCC 2013). Continuous height-resolved measurements of cloud and aerosol optical properties are needed to reduce these uncertainties. Here we describe the Raman Lidar Profiles – Feature detection and Extinction (RLPROF-FEX) Value-Added Product (VAP) derived using Raman lidar data at multiple U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility sites. RLPROF-FEX provides estimates of extinction, backscatter, and depolarization using the algorithm described by Thorsen et al. 2015 and Thorsen and Fu 2015. This document provides a description of the FEX algorithm, its input and output data, and related details about the Raman lidar (RL) system.
Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.
Non-volatile resistive switching (NVRS) is a widely available effect in transitional metal oxides, colloquially known as memristors, and of broad interest for memory technology and neuromorphic computing. Until recently, NVRS was not known in other transitional metal dichalcogenides (TMDs), an important material class owing to their atomic thinness enabling the ultimate dimensional scaling. Here, various monolayer or few-layer 2D materials are presented in the conventional vertical structure that exhibit NVRS, including TMDs (MX 2 , M = transitional metal, e.g., Mo, W, Re, Sn, or Pt; X = chalcogen, e.g., S, Se, or Te), TMD heterostructure (WS2/MoS2), and an atomically thin insulator (h-BN). These results indicate the universality of the phenomenon in 2D non-conductive materials, and feature low switching voltage, large ON/OFF ratio, and forming-free characteristic. A dissociation–diffusion–adsorption model is proposed, attributing the enhanced conductance to metal atoms/ions adsorption into intrinsic vacancies, a conductive-point mechanism supported by first-principle calculations and scanning tunneling microscopy characterizations. Our results motivate further research in the understanding and applications of defects in 2D materials.
Abstract Carbon nitrides featuring three‐dimensional frameworks of CN 4 tetrahedra are one of the great aspirations of materials science, expected to have a hardness greater than or comparable to diamond. After more than three decades of efforts to synthesize them, no unambiguous evidence of their existence has been delivered. Here, the high‐pressure high‐temperature synthesis of three carbon–nitrogen compounds,tI14‐C 3 N 4 ,hP126‐C 3 N 4 , andtI24‐CN 2 , in laser‐heated diamond anvil cells, is reported. Their structures are solved and refined using synchrotron single‐crystal X‐ray diffraction. Physical properties investigations show that these strongly covalently bonded materials, ultra‐incompressible and superhard, also possess high energy density, piezoelectric, and photoluminescence properties. The novel carbon nitrides are unique among high‐pressure materials, as being produced above 100 GPa they are recoverable in air at ambient conditions.
In this study, ultra-low molecular weight disaccharide–polyolefin conjugates with cellobiose, lactose and maltose head groups and atactic polypropene tails, such as 1, undergo a series of irreversible thermotropic order–order transitions with increasing temperature to provide nanostructured phases in the sequence: lamellar (L), hexagonal perforated lamellar (HPL), double gyroid (DG) and hexagonal cylindrical (C). The DG phase displays exceptional stability at ambient temperature and features two interpenetrating sugar domain networks having a sub-2-nm strut width and a lattice parameter, a DG , of 13.1 nm. The unique stability of this DG phase extends further within ultrathin films all the way down to the two-dimensional limit of 15 nm in which film thickness, l, is now less than the surface-oriented unit cell height, h DG . In addition to raising the fundamental question of what minimally constitutes a Schoen triply periodic minimal surface and DG lattice, these results serve to establish the class of sugar–polyolefin conjugates as a new material platform for nanoscience and nanotechnology.
Our ability to study and valorize the lignin fraction of biomass is hampered by the fundamental and still unmet challenge of precisely quantifying native lignin's structural features. Here, we developed a rapid elevated-temperature 1 H– 13 C Heteronuclear Single-Quantum Coherence Zero (HSQC 0 ) NMR method that enables this precise quantification of native lignin structural characteristics even with whole plant cell wall (WPCW) NMR spectroscopy, overcoming fast spin relaxation in the gel phase. We also formulated a Gaussian fitting algorithm to perform automatic and reliable spectral integration. By combining HSQC 0 measurements with yield measurements following depolymerisation, we can confirm the combinatorial nature of radical coupling reactions during biosynthesis leading to a random sequential organization of linkages within a largely linear lignin chain. Such analyses illustrate how this analytical method can greatly facilitate the study of native lignin structure, which can then be used for fundamental studies or to understand lignin depolymerization methods like reductive catalytic fractionation or aldehyde-assisted fractionation.
Abstract Ultra‐low molecular weight disaccharide–polyolefin conjugates with cellobiose, lactose and maltose head groups and atactic polypropene tails, such as 1 , undergo a series of irreversible thermotropic order–order transitions with increasing temperature to provide nanostructured phases in the sequence: lamellar ( L ), hexagonal perforated lamellar ( HPL ), double gyroid ( DG ) and hexagonal cylindrical ( C ). The DG phase displays exceptional stability at ambient temperature and features two interpenetrating sugar domain networks having a sub‐2‐nm strut width and a lattice parameter, a DG , of 13.1 nm. The unique stability of this DG phase extends further within ultrathin films all the way down to the two‐dimensional limit of 15 nm in which film thickness, l , is now less than the surface‐oriented unit cell height, h DG . In addition to raising the fundamental question of what minimally constitutes a Schoen triply periodic minimal surface and DG lattice, these results serve to establish the class of sugar–polyolefin conjugates as a new material platform for nanoscience and nanotechnology.