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Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring

Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass nonintrusive load monitoring (NILM) approach that enables effective net-load monitoring capabilities at high-frequency with minimal additional equipment and cost. The proposed machine learning based solution provides accurate multiclass state predictions while operating at a faster timescale (able to provide a prediction for each 60- Hz ac cycle used in US power grid) without relying on event-detection techniques. We also introduce an innovative hybrid classification-regression method that allows for the prediction of not only load on/off states but also individual load operating power levels. A test bed with eight residential appliances is used for validating the NILM approach. Results show that the overall method has high accuracy, good scaling and generalization properties.

feature extraction↗

Novel Observing Strategies (NOS) and Earth System Digital Twins (ESDT) for Disaster Resilience

"Earth systems have now been observed continuously for more than 50 years, not only from space but also from airplanes, balloons and in-situ sensors. With the addition of commercial remote sensing providers, many more Internet-of-Things sensors and new NASA and international observatories being planned, these incredible amounts of data will soon be augmented by even larger amounts of diverse data and therefore will become more and more difficult to access, integrate, understand and utilize. At the same time, because of climate change and its impacts, in order to predict, manage, and mitigate the effects of extreme science events and disasters, the information produced by all of this data needs to be optimized, organized and analyzed in such a way that it can be utilized by traditional as well as many new non-traditional users. This calls for the development of observing systems that are agile, coordinated and responsive to events of interest, and for information systems to be dynamic, interactive and fast. With these objectives in mind, the Advanced Information Systems Technology (AIST) Program has been developing technologies that will allow for the development of Novel Observing Strategies (NOS) in a distributed, coordinated fashion (i.e., similar to an “Internet-of-Earth-Things”), as well as for the development of novel information systems or Earth System Digital Twins (ESDT) that will build a digital replica of the past and current states of the Earth systems, will derive forecasts of future states under nominal assumptions and will also offer the capability to investigate many hypothetical evolution scenarios under varying impact assumptions. Both NOS and ESDT will be facilitated by the unprecedented advances of Artificial Intelligence technologies, especially Machine Learning (ML), for extracting relevant information from these large amounts of data, for fusing and assimilating diverse data together and for running complex models faster. Together NOS and ESDT will help build the capabilities needed to anticipate, predict and mitigate the effects of future disasters."

Earth Science Remote Sensing; Information Systems↗

Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems

Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniques to build new temporal recurrent graph neural network models for fault diagnostics. The temporal recurrent graph neural network structures can extract the spatial-temporal features from data of voltage measurement units installed at the critical buses. From these features, fault event detection, fault type/phase classification, and fault location are performed. Compared with previous works, the proposed temporal recurrent graph neural networks provide a better generalization for fault diagnostics. Moreover, the proposed scheme retrieves the voltage signals instead of current signals so that there is no need to install relays at all lines of the distribution system. Therefore, the proposed scheme is generalizable and not limited by the number of relays installed. The effectiveness of the proposed method is comprehensively evaluated on the Potsdam microgrid and IEEE 123-node system in comparison with other neural network structures.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A Data Processing Pipeline To Extract A Knowledge Graph From Heterogeneous Data For Socio-technical Analysis Of Critical Infrastructure Influence

The code is written in Python and consists of the following pipeline that is implemented in Apache Airflow. This pipeline intends to understand the companies that are directly or indirectly involved with a type of critical infrastructure system at some point in that system's lifecycle. The pipeline takes a configuration file that specifies a list of initial companies to consider, a geographic region of interest, and a set of SEC form types as well as other data sources (e.g. CrunchBase) from which to extract entities and relations. There are four main components to this pipeline as currently implemented: Entity Extraction, Network Construction, Analysis, and Visualization. First, Entity Extraction, is implemented as the `topear-extract_organizations` Apache Airflow workflow. Given an initial query that specifies a geographic region of interest and a time interval, the software will extract CI facilities of interest and organizations that have a direct influence relationship to those facilities (e.g. ownership). During the course of the LDRD, we focused on Electric Vehicle charging stations and this information is available via the Department of Energy (DOE) database on fueling stations maintained by NREL. Within the context of the DOE CESER project, we have focused on Battery Energy Storage Systems (BESS). Second, the Network Extraction component will iteratively construct a social network graph given the set of organizations and people extracted in the previous step. Organizations (and eventually People if desired) are then fed as a query to the `topgear-construct_social_network` Apache Airflow workflow which given a set of initial companies and data sets (e.g. SEC EDGAR form types, OpenCorporates, Crunchbase). This Airflow workflow will iteratively query such data sources to discover relationships with new organizations and people. For example, this module can iteratively query SEC EDGAR for metadata that documents the number of each type of form for the given set of companies and their location. This forms metadata represents a catalog of data sources from SEC EDGAR for the extracted social network knowledge graph. The pipeline then downloads these forms from the website and saves them in a build directory for further processing. These documents are then parsed for entities and relations. Again, we note that in additional to SEC data sources, this step can also pull in information on organizations via API services such as CrunchBase and OpenCorporates or bulk data sources. At the end of this step, the resultant social network, the Critical Infrastructure network, and the edges that encode relationships between organizations and CI facilities, form the Adversarial Socio-Technical Network (ASTN) that informs the analysis. Third, the Analysis component processes these generated ASTN. Previously, that has included the ability to compare prevalence of different vendors for a given infrastructure component type across different regions as well as identify common public and private investors across those vendors. This was demonstrated for EV Charging Stations across several different metropolitan areas within an IEEE PES GridEdge publication. More recently, we have looked at ways to identify infrastructure owners and operators of BESS with the most nameplate capacity across different states as well as other indictors of risk resulting from changes in ownership over time. Finally, the Visualization component consists of an HTML/CSS/JS framework by which users can interact geospatial, operational, and organizational relationships across a given portfolio of Critical Infrastructure facilities. The objective is to provide a library of UI/UX modules that can be repurposed for stakeholder-specific dashboards. All of the modules are related via a common event model that enables UI actions in one view to percolate across the other views.

Weaver, Gabriel [Idaho National Laboratory (INL), ↗

Database tomography for commercial application

Database tomography is a method for extracting themes and their relationships from text. The algorithms, employed begin with word frequency and word proximity analysis and build upon these results. When the word 'database' is used, think of medical or police records, patents, journals, or papers, etc. (any text information that can be computer stored). Database tomography features a full text, user interactive technique enabling the user to identify areas of interest, establish relationships, and map trends for a deeper understanding of an area of interest. Database tomography concepts and applications have been reported in journals and presented at conferences. One important feature of the database tomography algorithm is that it can be used on a database of any size, and will facilitate the users ability to understand the volume of content therein. While employing the process to identify research opportunities it became obvious that this promising technology has potential applications for business, science, engineering, law, and academe. Examples include evaluating marketing trends, strategies, relationships and associations. Also, the database tomography process would be a powerful component in the area of competitive intelligence, national security intelligence and patent analysis. User interests and involvement cannot be overemphasized.

Kostoff, Ronald N.↗

Extracting text from real-world scenes

Many scenes contain significant textual information that can be extremely helpful for understanding and/or navigation. For example, text-based information can frequently be the primary cure used for navigating inside buildings. A subject might first read a marquee, then look for an appropriate hallway and walk along reading door signs and nameplates until the destination is found. Optical character recognition has been studied extensively in recent years, but has been applied almost exclusively to printed documents. As these techniques improve it becomes reasonable to ask whether they can be applied to an arbitrary scene in an attempt to extract text-based information. Before an automated system can be expected to navigate by reading signs, however, the text must first be segmented from the rest of the scene. This paper discusses the feasibility of extracting text from an arbitrary scene and using that information to guide the navigation of a mobile robot. Considered are some simple techniques for first locating text components and then tracking the individual characters to form words and phrases. Results for some sample images are also presented.

Bixler, J. Patrick↗

Development of coal-derived carbon-based structural unit as a potential new building material

Coal combustion for energy has not only wasted the hydrogen-rich volatile matters but also results in CO 2 emission causing serious environmental issues. An integrated coal pyrolysis and solvent extraction process under elevated temperatures in an inert atmosphere is implemented to convert as-mined coal from Wyoming Powder River Basin to functional carbon elements such as pyrolysis char (PC). This paper presents the development and fabrication of new carbon-based structural unit (CSU) using pulverized PC and two coal-derived pitches (mesophase pitch and tar pitch) to attain high compressive strength greater than 30 MPa, low thermal conductivity less than 0.30 W/m.K, and low density less than 1.25 g/cm 3 . CSUs have the potential to overcome the current challenges with cement and concrete through pressurized fabrication followed by carbonization. This study aims to develop CSU with nearly 100% coal-derived carbon material for structural application in buildings. The effects of pressing pressures, carbonization temperatures, and binder contents on the density and mechanical and thermal properties of CSU samples are investigated. The study concludes that mesophase pitch produces a better performance of CSU samples, a combination of 100 MPa pressing pressure and carbonization temperature of 900 ⁰C yields the highest compressive strength, and a 25% MP content is recommended for low-cost CSU fabrication. Furthermoer, CSU has a much lower density, lower thermal conductivity, higher compressive strength, and higher strength-to-density ratio than normal concrete.

36 MATERIALS SCIENCE↗

Generalized Software Architecture Applied to the Continuous Lunar Water Separation Process and the Lunar Greenhouse Amplifier

This innovation provides the user with autonomous on-screen monitoring, embedded computations, and tabulated output for two new processes. The software was originally written for the Continuous Lunar Water Separation Process (CLWSP), but was found to be general enough to be applicable to the Lunar Greenhouse Amplifier (LGA) as well, with minor alterations. The resultant program should have general applicability to many laboratory processes (see figure). The objective for these programs was to create a software application that would provide both autonomous monitoring and data storage, along with manual manipulation. The software also allows operators the ability to input experimental changes and comments in real time without modifying the code itself. Common process elements, such as thermocouples, pressure transducers, and relative humidity sensors, are easily incorporated into the program in various configurations, along with specialized devices such as photodiode sensors. The goal of the CLWSP research project is to design, build, and test a new method to continuously separate, capture, and quantify water from a gas stream. The application is any In-Situ Resource Utilization (ISRU) process that desires to extract or produce water from lunar or planetary regolith. The present work is aimed at circumventing current problems and ultimately producing a system capable of continuous operation at moderate temperatures that can be scaled over a large capacity range depending on the ISRU process. The goal of the LGA research project is to design, build, and test a new type of greenhouse that could be used on the moon or Mars. The LGA uses super greenhouse gases (SGGs) to absorb long-wavelength radiation, thus creating a highly efficient greenhouse at a future lunar or Mars outpost. Silica-based glass, although highly efficient at trapping heat, is heavy, fragile, and not suitable for space greenhouse applications. Plastics are much lighter and resilient, but are not efficient for absorbing longwavelength infrared radiation and therefore will lose more heat to the environment compared to glass. The LGA unit uses a transparent polymer antechamber that surrounds part of the greenhouse and encases the SGGs, thereby minimizing infrared losses through the plastic windows. With ambient temperatures at the lunar poles at 50 C, the LGA should provide a substantial enhancement to currently conceived lunar greenhouses. Positive results obtained from this project could lead to a future large-scale system capable of running autonomously on the Moon, Mars, and beyond. The software for both applications needs to run the entire units and all subprocesses; however, throughout testing, many variables and parameters need to be changed as more is learned about the system operation. The software provides the versatility to permit the software operation to change as the user requirements evolve.

Perusich, Stephen↗

URBANopt™ District Energy Systems HVAC Measures (DES_HVAC) [SWR-21-30]

DES_HVAC is a repository of a Parametric Analysis Tool (PAT) project and for scripts related to modeling of different HVAC systems for buildings in a district served by a district thermal energy system. The Measures directory contains the new measures developed to extract data from the simulations. Update these measures and not the measures in the Test Suite measures directory. PAT should be used to update the measures in the Test Suite project. The Test directory contains a PAT project for testing the functionality of the measures. The PAT project is named HVAC_Test and can be opened in PAT > 3.x.

Allen, Amy↗

Power requirements for mining and microwave processing of extraterrestrial resources

Research in progress on the use of nuclear power in mining the Martian moons for volatiles and hydrocarbons as well as to power microwave generators for processing both lunar regolith and the spoils from the Martian mines into building materials is described. Previous work on mining the Martian moons developed estimates for the costs and power requirements for extracting and shipping any volatiles that might be found on Phobos and Diemos. The costs were compared to the cost of shipping the same volatiles from Earth and it was found that the mining operation would break even for launch costs from Earth as low as ninety dollars per pound (198 dollars/kg). Above this figure the mines would generate a profit. Based on the results of recent work on the microwave processing of lunar regolith it was decided to refine the concept description. Work under way and to be reported deals with a better definition of the power required for mine operations, nuclear propulsion, and microwave materials processing.

Raymond S. Leonard↗

Summer 2015 Internship Abstract

Green fluorescent protein (GFP) visually shows the expression of proteins by fluorescing when exposed to certain wavelengths of light. The GFP in this experiment was used to identify cells actively releasing viruses. The experiment focused on the effect of microgravity on the GFP expression of Akata B-cells infected with Epstein Barr Virus (EBV). Two flasks were prepared with 30 million cells each and two bioreactors were prepared with 50 million cells each. All four cultures were incubated for 16 days and fed every four days. Cellometer readings were taken on the feeding days to find cell size, viability, and GFP expression. In addition, the cells were treated with Propodium monoazide (PMA) and run through real time PCR to determine viral load on the feeding days. On the International Space Station air samples are taken to analyze the bacterial and fungal organisms in the air. The Sartorius Portable Airport is being investigated for potential use on the ISS to analyze for viral content in the air. Multiple samples were taken around Johnson Space Center building 37 and in Clear Lake Pediatric Clinic. The filter used was the gelatin membrane filter and the DNA was extracted directly from the filter. The DNA was then run through real time PCR for Varicella Zoster Virus (VZV) and EBV as well as GAPDH to test for the presence of DNA. The results so far have shown low DNA yield and no positive results for VZV or EBV. Further inquiry involves accurately replicating an atmosphere with high viral load from saliva as would be found on the ISS to run the air sampler in. Another line of research is stress hormones that may be correlated to the reactivation of latent viruses. The stress hormones from saliva samples are analyzed rather than blood samples. The quantity found in saliva shows the quantity of the hormones actually attached to cells and causing a reaction, whereas in the blood the quantity of hormones is the total amount released to cause a reaction. The particular hormones tested for were cortisol, alpha-amylase, and DHEA. The DHEA was very high in the two control samples tested. Regularly, samples came into the lab from local clinics to be tested for various viruses. Saliva, blood, body scrapes, and tears were received from the clinics and then run for VZV, EBV, and Human Simplex Virus 1 (HSV-1) with the results then reported back to the clinician. Blood, saliva, and urine from astronauts were also tested for viruses and logged. In addition, several cell cultures were brought up and grown, including adherent Human Lung Fibroblast (HFL) cells infected with VZV, and Akata B-cells infected with EBV.

Smith, Courtney↗

Autonomous In-Situ Resources Prospector

This presentation will describe the concept of an autonomous, intelligent, rover-based rapid surveying system to identify and map several key lunar resources to optimize their ISRU (In Situ Resource Utilization) extraction potential. Prior to an extraction phase for any target resource, ground-based surveys are needed to provide confirmation of remote observation, to quantify and map their 3-D distribution, and to locate optimal extraction sites (e.g. ore bodies) with precision to maximize their economic benefit. The system will search for and quantify optimal minerals for oxygen production feedstock, water ice, and high glass-content regolith that can be used for building materials. These are targeted because of their utility and because they are, or are likely to be, variable in quantity over spatial scales accessible to a rover (i.e., few km). Oxygen has benefits for life support systems and as an oxidizer for propellants. Water is a key resource for sustainable exploration, with utility for life support, propellants, and other industrial processes. High glass-content regolith has utility as a feedstock for building materials as it readily sinters upon heating into a cohesive matrix more readily than other regolith materials or crystalline basalts. Lunar glasses are also a potential feedstock for oxygen production, as many are rich in iron and titanium oxides that are optimal for oxygen extraction. To accomplish this task, a system of sensors and decision-making algorithms for an autonomous prospecting rover is described. One set of sensors will be located in the wheel tread of the robotic search vehicle providing contact sensor data on regolith composition. Another set of instruments will be housed on the platform of the rover, including VIS-NIR imagers and spectrometers, both for far-field context and near-field characterization of the regolith in the immediate vicinity of the rover. Also included in the sensor suite are a neutron spectrometer, ground-penetrating radar, and an instrumented cone penetrometer for subsurface assessment. Output from these sensors will be evaluated autonomously in real-time by decision-making software to evaluate if any of the targeted resources has been detected, and if so, to quantify their abundance. Algorithms for optimizing the mapping strategy based on target resource abundance and distribution are also included in the autonomous software. This approach emphasizes on-the-fly survey measurements to enable efficient and rapid prospecting of large areas, which will improve the economics of ISRU system approaches. The mature technology will enable autonomous rovers to create in-situ resource maps of lunar or other planetary surfaces, which will facilitate human and robotic exploration.

Dissly, R. W.↗

A systems engineering framework for the optimization of food supply chains under circular economy considerations

The current linear “take-make-waste-extractive” model leads to the depletion of natural resources and environmental degradation. Circular Economy (CE) aims to address these impacts by building supply chains that are restorative, regenerative, and environmentally benign. This can be achieved through the re-utilization of products and materials, the extensive usage of renewable energy sources, and ultimately by closing any open material loops. Such a transition towards environmental, economic and social advancements requires analytical tools for quantitative evaluation of the alternative pathways. Here, in this work, we present a novel CE system engineering framework and decision-making tool for the modeling and optimization of food supply chains. First, the alternative pathways for the production of the desired product and the valorization of wastes and by-products are identified. Then, a Resource-Task-Network representation that captures all these pathways is utilized, based on which a mixed-integer linear programming model is developed. This approach allows the holistic modeling and optimization of the entire food supply chain, taking into account any of its special characteristics, potential constraints as well as different objectives. Considering that typically CE introduces multiple, often conflicting objectives, we deploy here a multi-objective optimization strategy for trade-off analysis. A representative case study for the supply chain of coffee is discussed, illustrating the steps and the applicability of the framework. Single and multi-objective optimization formulations under five different coffee-product demand scenarios are presented. The production of instant coffee as the only final product is shown to be the least energy and environmental efficient scenario. On the contrary, the production solely of whole beans sets a hypothetical upper bound on the optimal energy and environmental utilization. In both problems presented, the amount of energy generated is significant due to the utilization of waste generated for the production of excess energy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Towards AI-assisted neutrino flavor theory design

Particle physics theories, such as those which explain neutrino flavor mixing, arise from a vast landscape of model-building possibilities. A model’s construction typically relies on the intuition of theorists. It also requires considerable effort to identify appropriate symmetry groups, assign field representations, and extract predictions for comparison with experimental data. We develop Autonomous Model Builder (AMBer), a framework in which a reinforcement learning agent interacts with a streamlined physics software pipeline to search these spaces efficiently. AMBer selects symmetry groups, particle content, and group representation assignments to construct models while minimizing the number of free parameters introduced. We validate our approach in well-studied regions of theory space and extend the exploration to a previously unexamined symmetry group. While demonstrated in the context of neutrino flavor theories, this approach of reinforcement learning with physics software feedback may be extended to other theoretical model-building problems in the future.

Baretz, Jason Benjamin↗

Mapping use cases and dataset needs for benchmarking buildings data

A perennial challenge in buildings research is the lack of high-quality datasets that can be relied upon for a wide array of tasks, including model calibration and improving energy efficiency and load flexibility. Instrumenting a building for data collection is resource intensive, so it is important to be methodical in the approach and ensure that resulting data are flexible and useful for a broad range of analyses. This study aims to fill the gaps in characterizing potential use cases for buildings datasets and mapping them to dataset needs using a well-defined data infrastructure. Here, we have developed a systematic mapping strategy between buildings dataset needs and use cases to help streamline the processes of efficiently targeting datasets, designing building sensing systems, and determining buildings research use cases. We selected 14 prospective use cases and 11 refined buildings data categories for developing the preliminary dataset-needs-to-use-cases mapping matrix (‘DN-UC mapping matrix’) with generic ‘Tags’—a detailed sub-level of data categories extracted by justifying the needs of an aspect of the datasets to use cases. We present two example applications of the developed mapping matrix to demonstrate use of the mapping matrix and its effectiveness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗

KG-Hub—building and exchanging biological knowledge graphs

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract–transform–load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial–environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.

59 BASIC BIOLOGICAL SCIENCES↗

Production of Solar Cells in Space from Non Specific Ores by Utilization of Electronically Enhanced Sputtering

An ideal method of construction in space would utilize some form of the Universal Differentiator and Universal Constructor as described by Von Neumann (1). The Universal Differentiator is an idealized non ore specific extractive device which is capable of breaking any ore into its constituent elements, and the Universal Constructor can utilize these elements to build any device with controllability to the nanometer scale. During the Human Exploration Initiative program in the early 1990s a conceptual study was done (2) to understand whether such devices were feasible with near term technology for the utilization of space resources and energy. A candidate system was proposed which would utilize electronically enhanced sputtering as the differentiator. Highly ionized ions would be accelerated to a kinetic energy at which the interaction between them and the lattice elections in the ore would be at a maximum. Experiments have shown that the maximum disintegration of raw material occurs at an ion kinetic energy of about 5 MeV, regardless of the composition and structure of the raw material. Devices that could produce charged ion beams in this energy range in space were being tested in the early 1990s. At this energy, for example an ion in a beam of fluorine ions yields about 8 uranium ions from uranium fluoride, 1,400 hydrogen and oxygen atoms from ice, or 7,000 atoms from sulfur dioxide ice. The ions from the disintegrated ore would then be driven by an electrical field into a discriminator in the form of a mass spectrometer, where the magnetic field would divert the ions into collectors for future use or used directly in molecular beam construction techniques. The process would require 10-7 Torr vacuum which would be available in space or on the moon. If the process were used to make thin film silicon solar cells (ignoring any energy inefficiency for beam production), then energy break even for solar cells in space would occur after 14 days.

Curreri, Peter A.↗