Search NASA⌕ Search

SEARCH · Search NASA

Results for “Landscape Characterization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

660 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Challenges at the Frontiers of Matter and Energy: Transformative Opportunities for Discovery Science

FIVE TRANSFORMATIVE OPPORTUNITIES FOR DISCOVERY SCIENCE As a result of this effort, it has become clear that the progress made to date on the five Grand Challenges has created a springboard for seizing five new Transformative Opportunities that have the potential to further transform key technologies involving matter and energy. These five new Transformative Opportunities and the evidence supporting them are discussed in this new report, “Challenges at the Frontiers of Matter and Energy: Transformative Opportunities for Discovery Science.” Mastering Hierarchical Architectures and Beyond-Equilibrium Matter Complex materials and chemical processes transmute matter and energy, for example from CO2 and water to chemical fuel in photosynthesis, from visible light to electricity in solar cells and from electricity to light in light emitting diodes (LEDs) Such functionality requires complex assemblies of heterogeneous materials in hierarchical architectures that display time-dependent away-from-equilibrium behaviors. Much of the foundation of our understanding of such transformations however, is based on monolithic single- phase materials operating at or near thermodynamic equilibrium. The emergent functionalities enabling next-generation disruptive energy technologies require mastering the design, synthesis, and control of complex hierarchical materials employing dynamic far-from-equilibrium behavior. A key guide in this pursuit is nature, for biological systems prove the power of hierarchical assembly and far- from-equilibrium behavior. The challenges here are many: a description of the functionality of hierarchical assemblies in terms of their constituent parts, a blueprint of atomic and molecular positions for each constituent part, and a synthesis strategy for (a) placing the atoms and molecules in the proper positions for the component parts and (b) arranging the component parts into the required hierarchical structure. Targeted functionality will open the door to significant advances in the harvesting, transforming (e.g., reducing CO2, splitting water, and fixing nitrogen), storing, and use of energy to create new materials, manufacturing processes, and technologies—the lifeblood of human societies and economic growth. Beyond Ideal Materials and Systems: Understanding the Critical Roles of Heterogeneity, Interfaces, and Disorder Real materials, both natural ones and those we engineer, are usually a complex mixture of compositional and structural heterogeneities, interfaces, and disorder across all spatial and temporal scales. It is the fluctuations and disorderly states of these heterogeneities and interfaces that often determine the system’s properties and functionality. Much of our fundamental scientific knowledge is based on “ideal” systems, meaning materials that are observed in “frozen” states or represented by spatially or temporally averaged states. Too often, this approach has yielded overly simplistic models that hide important nuances and do not capture the complex behaviors of materials under realistic conditions. These behaviors drive vital chemical transformations such as catalysis, which initiates most industrial manufacturing processes, and friction and corrosion, the parasitic effects of which cost the U.S. economy billions of dollars annually. Expanding our scientific knowledge from the relative simplicity of ideal, perfectly ordered, or structurally averaged materials to the true complexity of real-world heterogeneities, interfaces, and disorder should enable us to realize enormous benefits in the materials and chemical sciences, which translates to the energy sciences, including solar and nuclear power, hydraulic fracturing, power conversion, airframes, and batteries. Harnessing Coherence in Light and Matter Quantum coherence in light and matter is a measure of the extent to which a wave field vibrates in unison with itself at neighboring points in space and time. Although this phenomenon is expressed at the atomic and electronic scales, it can dominate the macroscopic properties of materials and chemical reactions such as superconductivity and efficient photosynthesis. In recent years, enormous progress has been made in recognizing, manipulating, and exploiting quantum coherence. This progress has already elucidated the role that symmetry plays in protecting coherence in key materials, taught us how to use light to manipulate atoms and molecules, and provided us with increasingly sophisticated techniques for controlling and probing the charges and spins of quantum coherent systems. With the arrival of new sources of coherent light and electron beams, thanks in large part to investments by the U.S. Department of Energy’s Office of Basic Energy Sciences (BES), there is now an opportunity to engineer coherence in heterostructures that incorporate multiple types of materials and to control complex, multistep chemical transformations. This approach will pave the way for quantum information processing and next-generation photovoltaic cells and sensors. Revolutionary Advances in Models, Mathematics, Algorithms, Data, and Computing Science today is benefiting from a convergence of theoretical, mathematical, computational, and experimental capabilities that put us on the brink of greatly accelerating our ability to predict, synthesize, and control new materials and chemical processes, and to understand the complexities of matter across a range of scales. Imagine being able to chart a path through a vast sea of possible new materials to find a select few with desired properties. Instead of the time-honored forward approach, in which materials with desired properties are found through either trial-and-error experiments or lucky accidents, we have the opportunity to inversely design and create new materials that possess the properties we desire. The traditional approach has allowed us to make only a tiny fraction of all the materials that are theoretically possible. The inverse design approach, through the harmonious convergence of theoretical, mathematical, computational, and experimental capabilities, could usher in a virtual cornucopia of new materials with functionalities far beyond what nature can provide. Similarly, enhanced mathematical and computational capabilities significantly enhance our ability to extract physical and chemical insights from vastly larger data streams gathered during multimodal and multidimensional experiments using advanced characterization facilities. Exploiting Transformative Advances in Imaging Capabilities across Multiple Scales Historically, improvements in imaging capabilities have always resulted in improved understanding of scientific phenomena. A prime challenge today is finding ways to reconstruct raw data, obtained by probing and mapping matter across multiple scales, into analyzable images. BES investments in new and improved imaging facilities, most notably synchrotron x-ray sources, free-electron lasers, electron microscopes, and neutron sources, have greatly advanced our powers of observation, as have substantial improvements in laboratory- scale technologies. Furthermore, BES is now planning or actively discussing exciting new capabilities. Taken together, these advances in imaging capabilities provide an opportunity to expand our ability to observe and study matter from the 3D spatial perspectives of today to true “4D” spatially and temporally resolved maps of dynamics that allow quantitative predictions of time-dependent material properties and chemical processes. The knowledge gained will impact data storage, catalyst design, drug delivery, structural materials, and medical implants, to name just a few key technologies. ENABLING SUCCESS Seizing each of these five Transformative Opportunities, as well as accelerating further progress on Grand Challenge research, will require specific, targeted investments from BES in the areas of synthesis, meaning the ability to make the materials and architectures that are envisioned; instrumentation and tools, a category that includes theory and computation; and human capital, the most important asset for advancing the Grand Challenges and Transformative Opportunities. While “Challenges at the Frontiers of Matter and Energy: Transformative Opportunities for Discovery Science” could be viewed as a sequel to the original Grand Challenges report, it breaks much new ground in its assessment of the scientific landscape today versus the scientific landscape just a few years ago. In the original Grand Challenges report, it was noted that if the five Grand Challenges were met, our ability to direct matter and energy would be measured only by the limits of human imagination. This new report shows that, prodded by those challenges, the scientific community is positioned today to seize new opportunities whose impacts promise to be transformative for science and society, as well as dramatically accelerate progress in the pursuit of the original Grand Challenges.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2012

G-LiHT: Goddard's LiDAR, Hyperspectral and Thermal Airborne Imager

Scientists at NASA's Goddard Space Flight Center have developed an ultra-portable, low-cost, multi-sensor remote sensing system for studying the form and function of terrestrial ecosystems. G-LiHT integrates two LIDARs, a 905 nanometer single beam profiler and 1550 nm scanner, with a narrowband (1.5 nanometers) VNIR imaging spectrometer and a broadband (8-14 micrometers) thermal imager. The small footprint (approximately 12 centimeters) LIDAR data and approximately 1 meter ground resolution imagery are advantageous for high resolution applications such as the delineation of canopy crowns, characterization of canopy gaps, and the identification of sparse, low-stature vegetation, which is difficult to detect from space-based instruments and large-footprint LiDAR. The hyperspectral and thermal imagery can be used to characterize species composition, variations in biophysical variables (e.g., photosynthetic pigments), surface temperature, and responses to environmental stressors (e.g., heat, moisture loss). Additionally, the combination of LIDAR optical, and thermal data from G-LiHT is being used to assess forest health by sensing differences in foliage density, photosynthetic pigments, and transpiration. Low operating costs (approximately $1 ha) have allowed us to evaluate seasonal differences in LiDAR, passive optical and thermal data, which provides insight into year-round observations from space. Canopy characteristics and tree allometry (e.g., crown height:width, canopy:ground reflectance) derived from G-LiHT data are being used to generate realistic scenes for radiative transfer models, which in turn are being used to improve instrument design and ensure continuity between LiDAR instruments. G-LiHT has been installed and tested in aircraft with fuselage viewports and in a custom wing-mounted pod that allows G-LiHT to be flown on any Cessna 206, a common aircraft in use throughout the world. G-LiHT is currently being used for forest biomass and growth estimation in the CONUS and Mexico in support of NASA's Carbon Monitoring System (CMS) and AMIGA-Carb (AMerican Icesat Glas Assessment of Carbon). For NASA's CMS, wall-to-wall G-LiHT data have been acquired over intensive study sites with historic LiDAR datasets, dense inventory data, stem maps and flux tower observations. For AMIGA-Carb, G-LiHT transects have been acquired over ICESat tracks and USDA-FS inventory plots throughout the CONUS, and similar data will be acquired in Mexico during 2013. This talk will highlight recent science results from continental-scale transects landscape-scale deployments of G-LiHT, as well as seasonal forest dynamics from repeat pass G-LiHT acquisitions.

Cook, Bruce↗
DOE OSTI2021

Photosynthetic adaptation to polar life: Energy balance, photoprotection and genetic redundancy

The persistent low temperature that characterize polar habitats combined with the requirement for light for all photoautotrophs creates a conundrum. The absorption of too much light at low temperature can cause an energy imbalance that decreases photosynthetic performance that has a negative impact on growth and can affect long-term survival. The goal of this review is to survey the mechanism(s) by which polar photoautotrophs maintain cellular energy balance, that is, photostasis to overcome the potential for cellular energy imbalance in their low temperature environments. Photopsychrophiles are photosynthetic organisms that are obligately adapted to low temperature (0⁰- 15 °C) but usually die at higher temperatures (≥20 °C). In contrast, photopsychrotolerant species can usually tolerate and survive a broad range of temperatures (5⁰- 40 °C). First, we summarize the basic concepts of excess excitation energy, energy balance, photoprotection and photostasis and their importance to survival in polar habitats. Second, we compare the photoprotective mechanisms that underlie photostasis and survival in aquatic cyanobacteria and green algae as well as terrestrial Antarctic and Arctic plants. We show that polar photopsychrophilic and photopsychrotolerant organisms attain energy balance at low temperature either through a regulated reduction in the efficiency of light absorption or through enhanced capacity to consume photosynthetic electrons by the induction of O 2 as an alternative electron acceptor. Finally, we compare the published genomes of three photopsychrophilic and one photopsychrotolerant alga with five mesophilic green algae including the model green alga, Chlamydomonas reinhardtii. We relate our genomic analyses to photoprotective mechanisms that contribute to the potential attainment of photostasis. Finally, we discuss how the observed genomic redundancy in photopsychrophilic genomes may confer energy balance, photoprotection and resilience to their harsh polar environment. Primary production in aquatic, Antarctic and Arctic environments is dependent on diverse algal and cyanobacterial communities. Although mosses and lichens dominate the Antarctic terrestrial landscape, only two extant angiosperms exist in the Antarctic. Furthermore, the identification of a single ‘molecular key’ to unravel adaptation of photopsychrophily and photopsychrotolerance remains elusive. Since these photoautotrophs represent excellent biomarkers to assess the impact of global warming on polar ecosystems, increased study of these polar photoautotrophs remains essential.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Completeness and coverage of open-access freshwater fish distribution data in the United States

Aim: Primary biodiversity data constitute observations of particular species at given points in space and time. Open-access databases provide unprecedented access to these data, but their utility depends on how complete species inventories are at a given surveying resolution and how uniformly distributed surveys are in space and time. Our aim was to characterize the completeness of freshwater fish data across spatial scales and among habitat types (lotic stream reaches and lentic waterbodies), and assess coverage bias in space and time. Location: Freshwater habitats of the contiguous United States. Methods: We compiled fish occurrence records from the Global Biodiversity Information Facility (GBIF), the Multi-state Aquatic Resources Information Server (MARIS), and three federally-administered surveys (EMAP, NAQWA, NRS). Next, we evaluated survey completeness across seven spatial resolutions representing nested stream catchments, and separately for lotic and lentic habitats. Lastly, we tested for bias in survey coverage along environmental and temporal gradients for each habitat type. Results: A total of 39,855,157 records representing 1925 species were available from 1800 to 2014. The percent of catchments surveyed increased as spatial resolution decreased while the density of occurrence records within surveyed catchments decreased. Species richness also increased with catchment size consistent with putative species–area relationships. Occurrence records were distributed among 72,114 lotic reaches and 3,325 lentic waterbodies throughout the contiguous US. Seven percent (N=5,055) of surveyed lotic reaches and 23% (N=771) of surveyed lentic waterbodies were well-surveyed based on the most conservative thresholds for two completeness indices. Most well-surveyed lotic reaches (95%) and undammed lentic waterbodies (97%) were completed since 1980, and most were surveyed during only one 20-year time interval (83% and 56% for lotic and lentic waterbodies, respectively). Coverage analyses indicated that contemporary and future climates corresponding to regions along the west coast and gulf coast are the most under-represented for both lotic and lentic habitats. Small lentic waterbodies (surface area < 0.1 km2) are also under-represented. Main Conclusions: Contemporary surveys of lotic reaches and, to a lesser extent, lentic waterbodies are plentiful and generally representative of climatic and landscape characteristics. Data compilation efforts should focus on adding historical data to public repositories. Clustering of well-surveyed waterbodies in MARIS-participating states highlights the value of state-agency data and demonstrates the need for dissemination of these data by more state agencies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

A high-throughput workflow to analyze sequence-conformation relationships and explore hydrophobic patterning in disordered peptoids

Understanding how a macromolecule’s primary sequence governs its conformational landscape is crucial for elucidating its function, yet these design principles are still emerging for macromolecules with intrinsic disorder. Herein, we introduce a high-throughput workflow that implements a practical colorimetric conformational assay, introduces a semi-automated sequencing protocol using matrix-assisted laser desorption/ionization and tandem mass spectrometry (MALDI-MS/MS), and develops a generalizable sequence-structure algorithm. Using a model system of 20mer peptidomimetics containing polar glycine and hydrophobic N-butylglycine residues, we identified nine classifications of conformational disorder and isolated 122 unique sequences across varied compositions and conformations. Conformational distributions of three compositionally identical library sequences were corroborated through atomistic simulations and ion mobility spectrometry coupled with liquid chromatography. A data-driven strategy was developed using existing sequence variables and data-derived “motifs” to inform a machine-learning algorithm toward conformation prediction. Here, this multifaceted approach enhances our understanding of sequence-conformation relationships and offers a powerful tool for accelerating the discovery of materials with conformational control.

data-driven analysis↗
NASA NTRS2017

Monitoring Land Surface Albedo and Vegetation Dynamics Using High Spatial and Temporal Resolution Synthetic Time Series from Landsat and the MODIS BRDF/NBAR/Albedo Product

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warmingcooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500-meter Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF (Bidirectional Reflectance Distribution Function) / NBAR (Nadir BRDF-Adjusted Reflectance) / albedo products and 30-meter Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDFAlbedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30-meter Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30-meter albedos for the intervening daily time steps in this study. These enhanced daily 30-meter spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of 0.006. These synthetic time series provide much greater spatial detail than the 500 meter gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 kilometers by 14 kilometers) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF-Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30-meter resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.

Vegetation Index↗
DOE OSTI2023

PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection (Final Report)

The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic, recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability”. More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture”. Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multi scale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator. The promise of advances in DL is apparent in the realm of human health and medicine. DL models have been validated for evaluating a variety of clinical threats to human health in a range of contexts, including infection and cancer, and they demonstrated improved performance in predicting stroke relative to human neurologists in some categories of data. Continuously evolving advances in AI/ML are expected to support more efficient evaluation of raw sequence, spectroscopy, and spectrometry data. For instance, recent advances and deployment of large language models (LLM) such as Generative Pre training Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) have already motivated application of these models for biological function prediction. As frameworks such as LLMs become larger and more complex in their representations, their capacity to serve as pre-trained models that can be fine-tuned for biological/biodetection purposes will similarly be amplified. While existing and emerging AI/ML have found broad applicability and use cases in the clinical sciences, development for environmental evaluation and biodetection has been limited. Functionalizing such capabilities for this purpose requires an understanding of the existing technical landscape and how the respective tools and algorithms are currently being employed. This landscape awareness then allows an assessment of the current practical capabilities of existing models and the anticipated requirements and development efforts that will be needed to adapt available algorithms for biodetection applications relevant to DHS. Leveraging expertise in biodetection, ML, and operational biodetection, the effort described in this report is comprised of a systematic landscape assessment (Subtask 2.1), comparative evaluation (Subtask 2.2), and formulation of a value proposition (Subtask 2.3) for the prospect of ML-enabled, agnostic biodetection from raw, or minimally-processed, datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions

Ecohydrology combines empiricism, data analytics, and the integration of models to characterize linkages between ecological and hydrological processes. A challenge for practitioners is determining which models best generalizes heterogeneity in hydrological behaviour, including water fluxes across spatial and temporal scales, integrating environmental and socio–economic activities to determine best watershed management practices and data requirements. We conducted a literature review and synthesis of hydrologic, hydraulic, water quality, and ecological models designed for solving interdisciplinary questions. We reviewed 1,275 papers and identified 178 models that have the capacity to answer an array of research questions about ecohydrology or ecohydraulics. Of these models, 43 were commonly applied due to their versatility, accessibility, user–friendliness, and excellent user–support. Forty–one of 43 reviewed models were linked to at least 1 other model especially: Water Quality Analysis Simulation Program (linked to 21 other models), Soil and Water Assessment Tool (19), and Hydrologic Engineering Center's River Analysis System (15). However, model integration was still relatively infrequent. There was substantial variation in model applications, possibly an artefact of the regional focus of research questions, simplicity of use, quality of user–support efforts, or a limited understanding of model applicability. Simply increasing the interoperability of model platforms, transformation of models to user–friendly forms, increasing user–support, defining the reliability and risk associated with model results, and increasing awareness of model applicability may promote increased use of models across subdisciplines. Furthermore, the current availability of models allows an array of interdisciplinary questions to be addressed, and model choice relates to several factors including research objective, model complexity, ability to link to other models, and interface choice.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

Lidar Altimeter Measurements of Canopy Structure: Methods and Validation for Closed Canopy, Broadleaf Forests

Lidar altimeter observations of vegetated landscapes provide a time-resolved measure of laser pulse backscatter energy from canopy surfaces and the underlying ground. Airborne lidar altimeter data was acquired using the Scanning Lidar Imager of Canopies by Echo Recovery (SLICER) for a successional sequence of four, closed-canopy, deciduous forest stands in eastern Maryland. The four stands were selected so as to include a range of canopy structures of importance to forest ecosystem function, including variation in the height and roughness of the outer-most canopy surface and the vertical organization of canopy stories and gaps. The character of the SLICER backscatter signal is described and a method is developed that accounts for occlusion of the laser energy by canopy surfaces, transforming the backscatter signal to a canopy height profile (CHP) that quantitatively represents the relative vertical distribution of canopy surface area. The transformation applies an increased weighting to the backscatter amplitude as a function of closure through the canopy and assumes a horizontally random distribution of the canopy components. SLICER CHPs, averaged over areas of overlap where lidar ground tracks intersect, are shown to be highly reproducible. CHP transects across the four stands reveal spatial variations in vegetation, at the scale of the individual 10 m diameter laser footprints, within and between stands. Averaged SLICER CHPs are compared to analogous height profile results derived from ground-based sightings to plant intercepts measured on plots within the four stands. Tbe plots were located on the segments of the lidar ground tracks from which averaged SLICER CHPs were derived, and the ground observations were acquired within two weeks of the SLICER data acquisition to minimize temporal change. The differences in canopy structure between the four stands is similarly described by the SLICER and ground-based CHP results, however a Chi-square test of similarity documents differences that are statistically significant. The differences are discussed in terms of measurement properties that define the smoothness of the resulting CHPs and Lidar Altimeter Measurements of Canopy Structure - Harding et al. canopy properties that may vertically bias the CHP representations of canopy structure. The statistical differences are most likely due to the more noisy character of the ground-based CHPs, especially high in the canopy where ground-based sightings are rare resulting in an underestimate of canopy surface area and height, and to departures from the assumption of horizontal randomness which bias the CHPs toward the observer (upward for SLICER and downward for ground-based CHPs). The results demonstrate that the SLICER observations reliably provide a measure of canopy structure that reveals ecologically interesting structural variations such as those characterizing a successional sequence of closed-canopy, broadleaf forest stands.

Harding, D. J.↗
DOE OSTI2018

Magnify: A Final Technical Report of the American Energy and Manufacturing Competitiveness Partnership

The energy landscape has undergone profound transformation, with dramatic shifts having an impact on U.S. productivity, global investment, manufacturing operations, and job creation. The sense of urgency for a tighter linkage between clean technologies, energy and advanced manufacturing has only grown. Prior to 2009, the tone of the nation’s energy conversation was centered on how to deal with long-standing energy security challenges and scarcity. Today, the tone is focused on seizing emerging energy growth opportunities to transform America’s industrial base and job creation outlook—centering on energy abundance and strength. In this context, the Council on Competitiveness and the Department of Energy’s Office of Energy Efficiency & Renewable Energy (EERE) teamed in the American Energy & Manufacturing Competitiveness (AEMC) Partnership to tackle two major goals via a multi-year partnership. The AEMC Partnership identified means to: • Increase U.S. competitiveness in the production of clean energy products • Increase U.S. manufacturing competitiveness across the board by increasing energy productivity The AEMC Partnership has engaged hundreds of leaders from industry, academia, labor and government in a series of 9 regional, progressive dialogues; original research; and 4 national summits. The AEMC dialogues and summits spanned the United States—taking place in our nation’s greatest manufacturing, research, technology and innovation hotspots. The goals of the AEMC Partnership have been straightforward: • State and define key barriers, challenges, and problems in U.S. competitiveness in manufacturing of clean energy products, energy efficiency products, and advanced manufacturing products. • Dive deeply into these problems and generate policies, solutions, concepts and models where the U.S. public and private sectors can work together to solve these problems. • Catalyze policy solutions—including models for public-private partnership (PPP) pilots—to increase competitive manufacturing of clean energy and energy efficiency products in the USA. • Elevate and increase awareness of the importance and benefits of competitive clean energy manufacturing. • Understand how energy game-changers, like breakthrough technologies, impact U.S. clean energy and energy efficient manufacturing. The Council on Competitiveness worked with its stakeholder network to generate potential PPP concepts and proposals to advance the goals of the AEMC Partnership. Magnify outlines 2 PPP concepts—honed by dialogues, conversations, interviews and research—that could be carried out by EERE and/or the Council to increase the competitive production of clean energy products, energy efficient products, and advanced manufacturing in the USA. Magnify’s 2 PPP concepts aim to bridge very specific gaps in the nation’s innovation ecosystem: • Clean Energy Materials Accelerator: This PPP concept focuses on reducing the risks associated with deploying newly developed materials in commercial products and processes by creating a platform to identify and address common challenges; increasing access to existing materials qualification and characterization tools; and creating standards for advanced materials with leaders in industry, academic, government, and other organizations. Why accelerate materials production? As the AEMC Partnership Dialogue and supporting research from the public and private sectors have documented, countries that lead in making next-generation materials will gain significant competitive advantage by unleashing a new wave of manufacturing innovation. • Manufacturing and Energy Technology Accelerator: This PPP concept is a new, physical and virtual collaborative resource platform designed to connect the nation’s world-class innovation institutions—SMEs, large multinational companies, universities, national laboratories, etc.—to facilitate the transition of cutting-edge clean energy technologies into products, processes, or services that are manufactured in the United States. Why should public and private sector leaders in innovation partner to co-create a scale-up platform? The United States is already a mecca for the world’s greatest minds in science and technology—drawn to our shores by world-class universities and opportunities to work with global leaders in innovation. Unfortunately, when it comes time to bring their ideas to market, technologists and entrepreneurs often choose, or are forced, to locate manufacturing overseas. The United States must regain its position in the world as a national scale-up platform for next-generation technologies. A thorough explanation of these PPP concepts and the rationale behind these recommendations is provided in Part 3 of Magnify. Magnify is an important step on the critical journey to define barriers, challenges and problems in the manufacturing of clean energy products and energy efficient products—and further honing concepts for scalable, public-private partnerships—to increase the competitive manufacturing of clean energy and energy efficient products, and the energy productivity throughout the U.S. manufactur¬ing sector. The rest of the world is waking up to the opportunities associated with a strategic focus on manufacturing and energy competitiveness. Global competition is on the rise, and the stakes are high for the United States to act now, to act decisively, and to leverage inherent strengths to ensure a more prosperous, competitive future for decades to come.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Artificially Structured Semiconductors to Model Novel Quantum Phenomena

Award Period: September 1st, 2013 through February 15th, 2017 Submitted to the USDOE Office of Basic Energy Sciences By Aron Pinczuk and Shalom J. Wind Department of Applied Physics and Applied Mathematics Columbia University New York, NY 10027 January 2017 Award # DE-SC0010695 ABSTRACT Research in this project seeks to design, create and study a class of tunable artificial quantum structures in order to extend the range and scope of new and exciting physical phenomena and to explore the potential for new applications. Advanced nanofabrication was used to create an external potential landscape that acts as a lattice of confinement sites for electrons (and/or holes) in a two-dimensional electron gas in a high perfection semiconductor in such a manner that quantum interactions between different sites dictate the significant physics. Our current focus is on ‘artificial graphene’ (AG) in which a set of quantum dots (or sites) are patterned in a honeycomb lattice. The combination of leading edge nanofabrication with ultra-pure semiconductor materials in this project extends the frontier for small period, low-disorder AG systems, enabling the exploration of graphene physics in a semiconductor platform. TECHNICAL DESCRIPTION Contemporary condensed matter science has entered an era of discovery of new low-dimensional materials, such as graphene and other atomically thin materials, that exhibit exciting new physical phenomena that were previously inaccessible. Concurrent with the discovery and development of these new materials are impressive advancements in nanofabrication, which offer an ever-expanding toolbox for creating a myriad of high quality patterns at nanoscale dimensions. This project started about four years ago. Among its major achievements are the realizations of very small period artificial lattices with honeycomb topology in GaAs quantum wells. In our most recent work the periods of the ‘artificial graphene’ (AG) lattices extend down to 40 nm. These small periods are about three times smaller than previously reported in GaAs quantum wells. This milestone establishes a new state-of-the-art in fields of research and nanofabrication. In experiments using optical scattering methods we uncovered evidence that free electrons in the small period AG lattices display novel features that arise from the symmetry of the honeycomb lattice. These achievements create semiconductor platforms for explorations of novel states and effects that offer opportunities to create quasiparticles with tunable character. The quest for the discovery of novel quantum physics by nanofabrication of ‘artificial structures’ in semiconductor quantum structures overlaps with the development of quantum simulators. Nanopatterns were created at Columbia University by the group of co-PI Shalom Wind using a 100keV e-beam nanolithography instrument (along with associated processing) that is part of the Columbia Nano Initiative. Optical experiments were carried out in the group of PI Aron Pinczuk. GaAs/AlGaAs quantum wells(QWs) of world-class perfection that serve as electron hosts are the starting material grown by molecular beam epitaxy (MBE) by our partners Dr. Loren Pfeiffer (Princeton Univ.) and Prof. Michael Manfra (Purdue Univ.). The inductively coupled plasma reactive ion etching (ICP-RIE) was carried out at the PRISM Micro/Nano Fabrication Laboratory of Princeton University. Dr. Vittorio Pellegrini (Istituto Italiano di Tecnologia, Genoa, Italy) has contributed critical insight on this research. Two graduate students in this project Sheng Wang and Diego Scarabelli, graduated in the summer/fall of 2016. Dr. Yuliya Kuznetsova has been a postdoc in the group. The current work is led by Dr. Lingjie Du, a postdoctoral scientist that joined the group of the PI on October 15th, 2016. Since the start of this project we have focused primarily on developing protocols towards the fabrication of the artificial lattices and in the implementation of characterization methods to identify the new electron states that are created. A major milestone has been the realization of the Dirac cones that emerge in honeycomb lattices. In the next phases of this project, and as we achieve milestones, we wish to expand our experimental platform so that we will be better positioned to access emergent novel quantum behavior and phases of electrons in artificial lattices designed and fabricated with ‘exquisite’ precision. The AG lattices with greatly reduced disorder from nano-processing that we have already created demonstrate that we are poised for the next stage exploration of graphene physics and applications in semiconductor quantum structures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Giant Electrocaloric Effect in Ferroelectrics with Tailored Polaw-Nanostructures

Electrocaloric effect (ECE) is the temperature and/or entropy change in a dielectric material caused by an electric field induced polarization change. Although ECE has been studied since 1930s, the very small ECE observed in earlier studies in bulk materials before 2007 makes it not attractive for practical cooling applications. The objectives of this DOE program are to carry out a systematical scientific research on the entropy change and ECE in polar-dielectrics, especially ferroelectrics based on several fundamental hypotheses and to search for answers on a few scientific questions. Especially, this research program developed a series of polar-dielectric materials with controlled nano- and meso-structures and carried out studies on how these structures affect the polar-ordering, correlations, energy landscapes, and consequently the entropy states at different phases and ECE. The key hypotheses of the program include: (i) Whether a large ECE can be obtained near the ferroelectric-paraelectric (FE-PE) transition in properly designed ferroelectrics which possess large polarization P and large ß (the coefficient in the thermodynamic Landau theory where the Gibbs free energy G = G = G 0 + ½ a P 2 +1/4 b P 4 + 1/6 c P 6 – EP, and a = ß (T-T c ), where b,c,ß and Tc are constants)? (ii) What determines/determine ß? Whether a ferroelectric material with built-in disorders, which disrupt the polar-correlations and enabling a large number of local polar-states, such as a properly designed ferroelectric relaxor, can achieve a large ECE? (iii) How to design a ferroelectric material which has flat energy landscape so that the energy barriers for switching among different phases are vanishingly small? What are the necessary conditions to maximize the number of coexisting phases? (iv) How to design ferroelectric materials with a large tunable dielectric response? That is, at zero electric field, the material possesses very low polar-correlation and hence a very small dielectric constant, under the application of electric field, the material develops long range polar-correlation and hence a high dielectric response. Studying and developing these materials will deepen our understanding on the polarization responses in strongly coupled materials and the roles of molecular and nano, meso-, and micro-scale defects and structures on the polarization responses. On the application front, besides ECE, these dielectrics will also have great impact on micro-electronics and communications. (v) The multi-field effect, besides the electric, elastic and even magnetic effects, could be made use of to tune the energy landscape of polar-materials and hence enhance the ECE. Hence the question is what are the suitable material systems to develop and maximize the multi-field effects? (vi) Besides solid dielectric, liquid dielectrics with properly designed molecular structures and dipolar coupling can also exhibit a large ECE near the dipolar order-disorder transition. The study of order-disorder transition and their influence on entropy change and ECE will provide additional avenue to study dielectrics and understand relationship between the polar-ordering and dipolar entropy in dielectrics. (vii) Besides the regular ECE in which applying an electric field will induce dipolar ordering, there are dielectric material systems which can exhibit negative ECE in which the applied field will reduce the dipolar ordering and anomalous ECE in which applying an electric field pulse will generate cooling only. The question is how to control and balance the nano- and meso-scale polar coupling in dielectrics to achieve such effects? ECE in dielectrics provides an interesting and effective avenue to probe the polar-correlation in dielectrics. Thus the study of ECE in polar-dielectrics, besides the application values, will also deepen our understanding of strongly coupled materials systems, phase transitions, and materials systems with nano- and meso-scale disorders. Through the efforts of this DoE program, we have developed understandings for many questions and materials approaches for many hypotheses listed above. The major accomplishments include: (i) The first one to show that a giant ECE can be obtained in bulk materials of ferroelectric P(VDF-TrFE) copolymer, which has a large ß coefficient and high polarization, near FE-PE transition.[1,3,12] (ii) The first who developed the theoretical analyses on the upper bound of dipolar entropy change in polar-materials and the general approach to maximize the coexisting phases with vanishingly small switching fields among the coexisting phases[10,23] Experimental results confirm these theoretical predictions.[24] (iii) The first to show that the relaxor ferroelectrics, due to built-in defects structures at nano- and meso scale, exhibit a giant ECE over a broad temperature range.[1,3,7,14] (iv) The first to show that a large ECE can be obtained near order-disorder transition in dielectric fluids such as liquid crystals with large dielectric anisotropy. Also the study developed a general approach for developing dielectric fluids to achieve a large electric field induced entropy change.[26] (v) We are starting to explore the multi-field effect (multiferroic effect) in nanocomposites in which there exist large dielectric contrasts between the matrix and nanofilelrs and showed that a significantly enhanced ECE compared with polymer matrix.[36] (vi) By facially tuning the nano- and meso-scale dipolar coupling, we are the first to show that an anomalous ECE can be obtained in a relaxor/normal ferroelectric blend.[39] (vii) Introduced and demonstrated that the internal bias field approach can be effective in enhancing the EC response at low electric field. The result is significant since for practical applications, a low applied field is highly desired. (viii) A high sensitivity ECE characterization system has been developed. This program has made major contributions to the advancement of the EC materials and understandings of EC phenomena. To reflect the advancement in the EC materials development and scientific understandings on ECE through in this time period (from Sept. 1, 2007 to May 2015), this final report is written based on the reports complied each year through the program. Some early works on the ECE which were obtained using the indirect method are not included in this report.

36 MATERIALS SCIENCE↗