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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Automated Laboratory Kilogram-Scale Graphene Production from Coal

The flash Joule heating (FJH) method converts many carbon feedstocks into graphene in milliseconds to seconds using an electrical pulse. This opens an opportunity for processing low or negative value resources, such as coal and plastic waste, into high value graphene. Here, in this work, a lab-scale automation FJH system that allows the synthesis of 1.1 kg of turbostratic flash graphene from coal-based metallurgical coke (MC) in 1.5 h is demonstrated. The process is based on the automated conversion of 5.7 g of MC per batch using an electrical pulse width modulation system to conduct the bottom-up upcycle of MC into flash graphene. This study then compare this method to two other scalable graphene synthesis techniques by both a life cycle assessment and a technoeconomic assessment.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Toolpath generation for automated wind turbine blade finishing operations

Incorporating automation into wind turbine blade manufacturing is important for reducing costs to meet current offshore wind energy production goals in the United States. This work proposes a process for automating three operations in wind blade manufacturing: trimming to remove flashing left over after bonding two blade skins together, grinding to produce a desired leading-edge shape, and sanding to prepare the blade for bonding overlamination or adding paint to the surface. The majority of this work focuses on the toolpath generation. The algorithms were tested on a 5-m blade section, and the results were analyzed in terms of operation speed and accuracy. Finally, future work is discussed to improve the performance of the system.

17 WIND ENERGY↗
DOE OSTI2017

A First-Order Estimate of Automated Mobility District Fuel Consumption and GHG Emission Impacts

A first of its kind, this study develops a framework to quantify the fuel consumption and greenhouse gas emission impacts of an Automated Small Vehicle Transit system on a campus area. The results show that the automated mobility district system has the potential to reduce transportation system fuel consumption and greenhouse gas emissions, but the benefits are largely dependent on the operation and ridership of the personal rapid transit system. Our study calls for more research to understand the energy and environmental benefits of such a system.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Relay Protection and Automation Systems Based on Programmable Logic Integrated Circuits

One of the most promising forms of developing the apparatus part of relay protection and automation devices is considered. The advantages of choosing programmable logic integrated circuits to obtain adaptive technological algorithms in power system protection and control systems are pointed out. The technical difficulties in the problems which today stand in the way of using relay protection and automation systems are indicated and a new technology for solving these problems is presented. Particular attention is devoted to the possibility of reconfiguring the logic of these devices, using programmable logic integrated circuits.

42 ENGINEERING↗
DOE OSTI2018

A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling

Precision medicine and the continuous analysis of “Big data” promises to improve patient outcomes dramatically in the near future. Very recently, healthcare facilities have started to explore automatic collection of patient-specific physiological data with the aim of reducing nursing workload and decreasing manual data entry errors. In addition to those purposes, continuous physiological data can be used for the early detection and prevention of common, and possibly fatal, diseases. For instance, poor patient outcomes from sepsis, a leading cause of mortality in healthcare facilities and a major driver of hospital costs in the USA, can be mitigated when detected early using screening tools that monitor the changing dynamics of physiological data. However, the potential cost of collecting continuous physiological data remains a barrier to the widespread adoption of automated high-frequency data collection systems. In this paper, we perform cost-benefit analysis (CBA) of machine learning applied to various types of acquisition systems (with different collection intervals) to determine if the benefits of such systems will outweigh their implementation costs. Although such systems can be used in the detection of various complications, in order to showcase the immediate benefits, we focus on the early detection of sepsis, one of the major challenges of hospital systems. We present a general approach to conduct such analysis for a wide range of hospitals and highlight its applicability using a case study for a small hospital with 150 beds and 3000 annual patients where the acquisition system would collect data at 1-min intervals. Finally, we discuss how the analysis may help guide incentives/policies with regard to adopting automated data acquisition systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Impact of cyberattacks on safety and stability of connected and automated vehicle platoons under lane changes

Connected and automated vehicles (CAVs) offer a huge potential to improve the operations and safety of transportation systems. However, the use of smart devices and communications in CAVs introduce new risks. CAVs would leverage vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication, thus providing additional system access points compared to traditional systems. Automation makes these systems more vulnerable and increases the consequences of cyberattacks. This study utilizes an infrastructure-based communication platform consisting of cooperative adaptive cruise control and lane control advisories developed by the authors to perform cyber risk assessment of CAVs. The study emulates three types of cyberattacks (message falsification, dedicated denial of service, and spoofing attacks) in a representative traffic environment consisting of multiple CAV platoons and lane change events to analyze the safety and stability impacts of the cyberattacks. Simulation experiments using VISSIM reveals that traffic stream and CAV string is unstable under all three types of cyberattacks. The worst case is represented by the message falsification attack. Increases in volatility are observed over a no attack case, with variations increasing by an average of 43%–51% along with an increase of over 3000 crash conflicts. Similarly, lane change crash conflicts are observed to be more severe compared to rear end crash conflicts, showing a higher probability of severe injuries. Further, the case of slight cyberattack on a single CAV also creates significant disruption in the traffic stream. Analysis of variance (ANOVA) reveals the statistical significance of the results. Furthermore, these results pave the way for future design of secure systems from a monitoring perspective.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

In vitro continuous protein evolution empowered by machine learning and automation

Directed evolution has become one of the most successful and powerful tools for protein engineering. However, the efforts required for designing, constructing, and screening a large library of variants can be laborious, time-consuming, and costly. With the recent advent of machine learning (ML) in the directed evolution of proteins, researchers can now evaluate variants in silico and guide a more efficient directed evolution campaign. Furthermore, recent advancements in laboratory automation have enabled the rapid execution of long, complex experiments for high-throughput data acquisition in both industrial and academic settings, thus providing the means to collect a large quantity of data required to develop ML models for protein engineering. In this perspective, here we propose a closed-loop in vitro continuous protein evolution framework that leverages the best of both worlds, ML and automation, and provide a brief overview of the recent developments in the field.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

An automated cluster surface scanning method for exploring reaction paths on metal-cluster surfaces

Metal-cluster surfaces present a wide variety of unique coordination environments. This complexity makes it difficult to manually probe the surface reactivity of such clusters. Here, we present a simple and automated method to systematically discover reaction pathways on cluster surfaces, based on the automated cluster surface scanning (ACSS) technique for mapping out potential energy surfaces. We showcase our method on 55-atom icosahedral Cu and Ag clusters, where we determine the activation energies of four elementary steps common in heterogeneous catalysis – hydrogen recombination (H* + H* → H 2 * + *), oxygen recombination (O* + O* → O 2 * + *), water formation (OH* + H* → H 2 O* + *), and CO oxidation (CO* + O* → CO 2 * + *) – with density functional theory calculations (DFT-PBE + D3). We show that the ACSS method requires significantly less human effort than the established manually performed climbing-image nudged elastic band (MP + CI-NEB) technique and locates transition states with comparable accuracy (root-mean-squared error of 0.10 eV) and similar computational cost. Rigorous sampling of the potential energy surface with the ACSS method allows one to locate all lowest-energy reaction pathways obtained via the MP+CI-NEB approach, as well as alternative pathways that one may have missed with the MP+CI-NEB approach due to the many possible pathways available on these clusters. The accuracy and efficiency afforded by the ACSS method could enable high-throughput exploration of the diverse reactivity of metal clusters.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Automated fault detection of residential air-conditioning systems using thermostat drive cycles

Residential air conditioning equipment comprises a significant portion of the total energy consumption of a home. Unfortunately, air-conditioning systems can be susceptible to faulty operation either from installation errors or faults that accrue over the equipment’s lifetime. This paper presents a novel automated fault detection algorithm for residential air-conditioning systems that can alert the homeowner of the presence of these faults. The proposed algorithm utilizes only the home’s thermostat and outside air temperature to perform automated fault detection over the course of the equipment’s lifetime, including immediately after installation. The algorithm uses an extended Kalman filter approach to identify a three-resistor, two-capacitor (3R2C) electrical equivalent thermodynamic model. The identified 3R2C model is used to predict cooling times during a testing period comprising of a series of thermostat drive-cycle experiments. We tested the algorithm on an EnergyPlus™ model of a typical residential building in Orlando, Florida. Duct faults, indoor airflow faults, and refrigerant undercharge faults were introduced into the building model one at a time. The algorithm was able to accurately determine duct-leak faults, 40% airflow faults, 40% undercharge faults, and no-fault cases with an accuracy of 70%, 77%, 82%, and 87%, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Detection and Automated Scoring of Dicentric Chromosomes in Nonstimulated Lymphocyte Prematurely Condensed Chromosomes After Telomere and Centromere Staining

Purpose: To combine telomere and centromere (TC) staining of premature chromosome condensation (PCC) fusions to identify dicentrics, centric rings, and acentric chromosomes, making possible the realization of a dose–response curve and automation of the process. Methods and Materials: Blood samples from healthy donors were exposed to {sup 60}Co irradiation at varying doses up to 8 Gy, followed by a repair period of 8 hours. Premature chromosome condensation fusions were carried out, and TC staining using peptide nucleic acid probes was performed. Chromosomal aberration (CA) scoring was carried out manually and automatically using PCC-TCScore software, developed in our laboratory. Results: We successfully optimized the hybridization conditions and image capture parameters, to increase the sensitivity and effectiveness of CA scoring. Dicentrics, centric rings, and acentric chromosomes were rapidly and accurately detected, leading to a linear-quadratic dose–response curve by manual scoring at up to 8 Gy. Using PCC-TCScore software for automatic scoring, we were able to detect 95% of dicentrics and centric rings. Conclusion: The introduction of TC staining to the PCC fusion technique has made possible the rapid scoring of unstable CAs, including dicentrics, with a level of accuracy and ease not previously possible. This new approach can be used for biological dosimetry in radiation emergency medicine, where the rapid and accurate detection of dicentrics is a high priority using automated scoring. Because there is no culture time, this new approach can also be used for the follow-up of patients treated by genotoxic therapy, creating the possibility to perform the estimation of induced chromosomal aberrations immediately after the blood draw.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

An automated micro-separation system for the chromatographic removal of uranium matrix for trace element analysis by ICP-OES

An automated, miniaturized, off-line separation technique is presented here using an Elemental Scientific Inc. microFAST MC system with UTEVA resin to extract the uranium matrix from its trace element impurities in aqueous media. The collected fractions were analyzed for ~30 trace elements using inductively coupled plasma – optical emission spectroscopy. Ten replicate samples were processed with a single column resulting in precision ranging from 3.3% to 6.2% relative standard deviation with regards to the trace element recoveries. Accuracy, with respect to trace element concentrations in the U 3 O 8 Certified Reference Material 124-1, resulted in an average of 13.9% relative deviation while accuracy to the Canadian U3O8 reference material, CUP-2, resulted in an average relative deviation of 8.6%. In conclusion, the total separation time of this automated process was reduced to ~30 min per sample while employing a 0.5 mL UTEVA chromatographic resin bed and 2.5 mg of uranium.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Data-driven optimal charging decision making for connected and automated electric vehicles: A personal usage scenario

This study introduces an optimal charging decision making framework for connected and automated electric vehicles under a personal usage scenario. This framework aims to provide charging strategies, i.e. the choice of charging station and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. A data-driven method is introduced to establish a stochastic energy consumption prediction model with consideration of realistic uncertainties. This is performed by analyzing a large scale electric vehicle data set. A real-time updating method is designed to construct this prediction model from new consecutive data points in an adaptive way for real-world applications. Based on this energy cost prediction framework from real electric vehicle data, multistage optimal charging decision making models are introduced, including a deterministic model for average outcome decision making and a robust model for safest charging strategies. A dynamic programming algorithm is proposed to find the optimal charging strategies. Detailed simulations and case studies demonstrate the performance of the proposed algorithms to find optimal charging strategies. They also show the potential capability of connected and automated electric vehicles to reduce the range anxiety and charging infrastructure dependency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography–Mass Spectrometry for High-Throughput Single-Cell Proteomics

Single-cell proteomics can provide critical biological insight into the cellular heterogeneity that is masked by bulk-scale analysis. Here, we have developed a nanoPOTS (nanodroplet processing in one pot for trace samples) platform and demonstrated its broad applicability for single-cell proteomics. However, because of nanoliter-scale sample volumes, the nanoPOTS platform is not compatible with automated LC-MS systems, which significantly limits sample throughput and robustness. To address this challenge, we have developed a nanoPOTS autosampler allowing fully automated sample injection from nanowells to LC-MS systems. We also developed a sample drying, extraction, and loading workflow to enable reproducible and reliable sample injection. The sequential analysis of 20 samples containing 10 ng tryptic peptides demonstrated high reproducibility with correlation coefficients of >0.995 between any two samples. The nanoPOTS autosampler can provide analysis throughput of 9.6, 16, and 24 single cells per day using 120, 60, and 30 min LC gradients, respectively. As a demonstration for single-cell proteomics, the autosampler was first applied to profiling protein expression in single MCF10A cells using a label-free approach. At a throughput of 24 single cells per day, an average of 256 proteins was identified from each cell and the number was increased to 731 when the Match Between Runs algorithm of MaxQuant was used. Using a multiplexed isobaric labeling approach (TMT-11plex), ~77 single cells could be analyzed per day. We analyzed 152 cells from three acute myeloid leukemia cell lines, resulting in a total of 2558 identified proteins with 1465 proteins quantifiable (70% valid values) across the 152 cells. These data showed quantitative single-cell proteomics can cluster cells to distinct groups and reveal functionally distinct differences.

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

Exploring Domain-Wall Pinning in Ferroelectrics via Automated High-Throughput Atomic Force Microscopy

Domain-wall dynamics in ferroelectric materials are strongly position-dependent, since each polar interface is locked into a unique local microstructure. This necessitates spatially resolved studies of wall pinning using scanning-probe microscopy techniques. The pinning centers and pre-existing domain walls are usually sparse within the image plane, precluding the use of dense hyperspectral imaging modes and requiring time-consuming human experimentation. Here, a large-area epitaxial PbTiO 3 film on cubic KTaO 3 was investigated to quantify the electric-field-driven dynamics of the polar–strain domain structures using ML-controlled automated piezoresponse force microscopy. Analysis of 1500 switching events reveals that domain-wall displacement depends not only on field parameters but also on the local ferroelectric–ferroelastic configuration. For example, twin boundaries in polydomains regions, like a 1 – /c+ ∥ a 2 – /c – , stay pinned up to a certain level of bias magnitude and change only marginally as the bias increases from 20 to 30 V, whereas single-variant boundaries, like the a 2 + /c + ∥ a 2 – /c – stack, are already activated at 20 V. These statistics on the possible ferroelectric and ferroelastic wall orientations, together with the automated high-throughput AFM workflow, can be distilled into a predictive map that links domain configurations to pulse parameters. Here, this microstructure-specific rule set forms the foundation for the design of ferroelectric memories.

automated scanning probe microscopy↗
DOE OSTI2024

ATLAS-MAP: An Automated Test Station for Gated Electronic Transport Measurements

The diversification of electronic materials in devices provides a strong incentive for methods to rapidly correlate device performance with fabrication decisions. In this work, we present a low-cost automated test station for gated electronic transport measurements of field-effect transistors. Utilizing open-source PyMeasure libraries for transparent instrument control, the “ATLAS-MAP” system serves as a customizable interface between sourcemeters and samples under test and is programmed to conduct transfer curve and van der Pauw methods with static and sweeping gate voltages. Zinc oxide transistors of variable thickness (5, 10, and 20 nm) and channel size (50 μm to 3 mm, of equal length and width) were fabricated to validate the design. Standardization of testing procedures and raw data formatting enabled automated data analysis. A detailed list of parts and code files for the system are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Probing Electron Beam Induced Transformations on a Single-Defect Level via Automated Scanning Transmission Electron Microscopy

A robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, in this study, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects in graphene. The ELIT-based approach facilitates direct on-the-fly analysis of the STEM data and engenders real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Continuous Polymer Synthesis and Manufacturing of Polyurethane Elastomers Enabled by Automation

Connecting polymer synthesis and processing is an important challenge for streamlining the manufacturing of polymeric materials. In this work, the automated synthesis of acrylate-capped polyurethane oligomers is integrated with vat photopolymerization 3D printing. This strategy enabled the rapid manufacturing of a library of polyurethane-based elastomeric materials with differentiated thermal and mechanical properties. The automated semi-continuous batch synthesis approach proved enabling for resins with otherwise short shelf lives because the intimate connection between synthesis, formulation, and processing. Structure-property studies demonstrated the ability to tune properties through systematic alteration of crosslink density and chemical composition.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning

Supramolecular polymer blends (SPBs) offer tunable morphologies that dictate their macroscopic properties, yet their rational design is limited by the absence of predictive structure−morphology models. Here, we introduce a data-driven highthroughput workflow that integrates modular polymer synthesis, robotic formulation, automated morphology characterization, and machine learning (ML) for accelerated SPB discovery. Using a plug-and-play synthetic strategy, 33 hydrogen-bonding endfunctional homopolymers were prepared and orthogonally combined to generate 260 SPBs in 1 day. A fully automated atomic force microscopy (AFM) pipeline enabled systematic imaging, producing 2340 morphology data sets with minimal human intervention. Domain spacings were extracted through complementary imageprocessing methods and used to train ML models. A support vector regression (SVR) model accurately predicted target phase-separation sizes (50, 100, and 150 nm), which were experimentally validated. This work demonstrates the power of coupling high-throughput experimentation with ML to accelerate morphology discovery and provides one of the first large-scale experimental data sets for supramolecular polymer systems.

ML-guided polymer design↗