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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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We report a 1310 nm heterogeneous quantum-dot distributed feedback laser on silicon with high efficiency and modulation capability and demonstrate isolator-free external modulation at 25 Gb/s using a metal-oxide semiconductor capacitor microring modulator.
This work reports on a high-efficiency InAs/GaAs distributed feedback quantum dot laser. The large optical wavelength detuning at room temperature between the lasing peak and the gain peak causes the static, dynamic, and nonlinear intrinsic properties to all improve with temperature, including the lasing efficiency, the modulation dynamics, the linewidth enhancement factor, and consequently the reflection insensitivity. Results reported show an optimum operating temperature at 75°C, highlighting the potential of the large optical mismatch assisted single-frequency laser for the development of uncooled and isolator-free high-speed photonic integrated circuits.
In photosynthetic organisms, feedback dissipation of excess absorbed light energy balances harvesting of light with metabolic energy consumption. This mechanism prevents photodamage caused by reactive oxygen species produced by the reaction of chlorophyll (Chl) triplet states with O2. Plants have been found to perform the heat dissipation in specific proteins, binding Chls and carotenoids (Cars), that belong to the Lhc family, while triggering of the process is performed by the PsbS subunit, needed for lumenal pH detection. PsbS is not found in algae, suggesting important differences in energy-dependent quenching (qE) machinery. Consistent with this suggestion, a different Lhc-like gene product, called LhcSR3 (formerly known as LI818) has been found to be essential for qE in Chlamydomonas reinhardtii. In this work, we report the production of two recombinant LhcSR isoforms from C. reinhardtii and their biochemical and spectroscopic characterization. We found the following: (i) LhcSR isoforms are Chl a/b– and xanthophyll-binding proteins, contrary to higher plant PsbS; (ii) the LhcSR3 isoform, accumulating in high light, is a strong quencher of Chl excited states, exhibiting a very fast fluorescence decay, with lifetimes below 100 ps, capable of dissipating excitation energy from neighbor antenna proteins; (iii) the LhcSR3 isoform is highly active in the transient formation of Car radical cation, a species proposed to act as a quencher in the heat dissipation process. Remarkably, the radical cation signal is detected at wavelengths corresponding to the Car lutein, rather than to zeaxanthin, implying that the latter, predominant in plants, is not essential; (iv) LhcSR3 is responsive to low pH, the trigger of non-photochemical quenching, since it binds the nonphotochemical quenching inhibitor dicyclohexylcarbodiimide, and increases its energy dissipation properties upon acidification. This is the first report of an isolated Lhc protein constitutively active in energy dissipation in its purified form, opening the way to detailed molecular analysis. Owing to its protonatable residues and constitutive excitation energy dissipation, this protein appears to merge both pH-sensing and energy-quenching functions, accomplished respectively by PsbS and monomeric Lhcb proteins in plants.
In United States, each year 650 children are diagnosed with neuroblastoma (NB) [1], an embryonal malignancy of sympathetic nervous system that is remarkable for its clinical heterogeneity [2]. Patients with the invasive/metastatic high-risk stage-IV NB showed refraction with all conventional therapeutic modalities and is associated with dismal prognosis [3]. High recurrence rate (20.2%) entailing substantial fractions of both local (17%-74% of patients) [4–8] and distant metastasis (46.8%) present considerable challenges for the clinical management of NB. With only 13 months since first diagnosis to recurrence, the survival ratio was 43% for local and 10% for systemic recurrences. Clinical and laboratory evidence suggests that several human cancers contain populations of rapidly proliferating clonogens that can have substantial impact on local control following chemoradiotherapy [9]. Tumor cell repopulation may arise from remnant cells of the original neoplasm that have escaped therapeutic intervention and later become visible at the original site. Radiotherapy (RT), now widely used for high-risk NB patients after chemotherapy, significantly improved the survival rate [10]. Patients, however, have been confronted with relapse and developed drug/radiation resistance, possibly through favoring alternative pathways. Therapeutic doses of radiation (IR) has been shown to activate various transcription factors including NFκB [11] and studies have suggested their influential role in tumorigenesis and progression [12]. Recently, we demonstrated the radiation triggered NFκB initiates TNFα cross signaling dependent maintenance of NFκB that in turn promotes survival advantage in both in vitro and in vivo NB models [13]. To that end, identifying ‘drug-deliverables’ that selectively disrupt IR-induced NFκB-TNFα feedback signaling and impedes NFκB maintenance could deter NFκB-dependent survival advantage and potentiate RT in NB cure.
This submission aimed to assess differences in short-term step warming manipulations and long-term chronic response to climate change in Alaskan ecosystems. Briefly, climate warming is occurring fastest at high latitudes. Based on short-term field experiments, this warming is projected to stimulate soil organic matter decomposition, and promote a positive feedback to climate change. We show here that the tightly coupled, nonlinear nature of high-latitude ecosystems implies that short-term (< 10 year) warming experiments produce emergent ecosystem carbon stock temperature sensitivities inconsistent with emergent multi-decadal responses. We first demonstrate that a well-tested mechanistic ecosystem model accurately represents observed carbon cycle and active layer depth responses to short-term summer warming in four diverse Alaskan sites. We then show that short-term warming manipulations do not capture the non-linear, long-term dynamics of vegetation, and thereby soil organic matter, that occur in response to thermal, hydrological, and nutrient transformations belowground. Our results demonstrate significant spatial heterogeneity in multi-decadal Arctic carbon cycle trajectories and argue for more mechanistic models to improve predictive capabilities.The model used in the current study is available publicly (https://github.com/jinyun1tang/ECOSYS), and the current submission contains the python/ matlab codes for analyzing output from the model (includng a readme file to explain the codes). The benchmark data, also enclosed, was collected from a range of published and publicly available sources (extracted using GRABIT: https://www.mathworks.com/matlabcentral/fileexchange/7173-grabit). These sources describe warming induced changes in tundra/ boreal ecosystems.
This data package is associated with the manuscript “Disturbance Triggers Non-Linear Microbe-Environment Feedbacks, in revision in Biogeosciences (Sengupta et al. and 2021;https://bg.copernicus.org/preprints/bg-2021-51/). The study used hyporheic zone sediments as a model system to provide an integrated view of how disturbance modulates linkages among microbial ecology, biogeochemistry, and organic matter thermodynamics. Laboratory experiments exposed hyporheic sediment to varying wetting/drying dynamics. Data types include dissolved oxygen rates used to derive respiration rates, Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) data used to derive thermodynamic properties of organic matter, and microbial community assembly metrics derived from amplicon-sequence data of putatively active (cDNA) and whole community (gDNA). The outcomes of the study are condensed into a broadly applicable conceptual model linking external forcing, internal dynamics, and history. This data package is comprised of a file-level metadata (FLMD) csv, metadata csv, and seven folders that contain csv files, R scripts, xml files, and associated documentation: (1) Rates, (2) bNTI, and (3) FTICR, (4) Statistics_Analyses, (5) Raw OTU Beta dispersion Analysis, (6) bMNTD Randomizations, and (7) Data Dictionaries. The FLMD file has a description of each file included in the data package.
The Electron–Ion Collider (EIC) achieves its design luminosity by cooling the ion beam with a high-current electron beam generated in an energy-recovery linac (ERL). The baseline ERL lattice employs a BNL five-cell cavity that is frequency-scaled to 197 MHz, 591 MHz, and 1.773 GHz, raising concerns about multibunch beam-breakup (BBU) instabilities. Threshold currents for each frequency option are established with two independent BBU tracking codes, providing cross-validated operating margins that guide cavity selection. To further increase the current limit, we incorporate a digital transverse feedback (FB) system that targets the dominant higher-order modes (HOMs). Simulations show the FB raises the BBU threshold by roughly an order of magnitude: power-spectral-density analysis of the beam centroid at the linac exit confirms strong suppression of the aliased HOM peak, although some spectral growth appears at secondary frequencies. This combined study quantifies baseline BBU limits, demonstrates effective active mitigation, and charts a practical path toward robust, high-current operation of the EIC ERL.
A focus group meeting is a very effective quality research approach to collect information on a specific project. Through focus group meetings at both Changchun and Ningbo in August 2010, the project team gained a more complete understandings of key stakeholders (such as their education level), their training needs and expectations, key factors influencing their decision making, and incurred implementation difficulties. In addition, the meeting helped the project team (especially PNNL) improve its understanding of the implementation status of building energy codes in other regions (such as small cities and counties neighboring to urban areas, small townships and rural areas distant from urban areas). The collected feedbacks will serve as important input not only for better design of training materials and the development of an on-line training website, but also for development of follow-up projects to promote building energy codes in China.
During this project the investigators implemented a fully parallel version of dual-grid approach in main frame code ICON, implemented a fully conservative first-order interpolation scheme for horizontal remapping, integrated UCLA-LES micro-scale model into ICON to run parallely in selected columns, and did cloud feedback studies on aqua-planet setup to evaluate the classical parameterization on a small domain. The micro-scale model may be run in parallel with the classical parameterization, or it may be run on a "physics grid" independent of the dynamics grid.
A new and unique basic plasma science laboratory device - the HelCat device (HELicon-CAThode) - has been constructed and is operating at the University of New Mexico. HelCat is a 4 m long, 0.5 m diameter device, with magnetic field up to 2.2 kG, that has two independent plasmas sources - an RF helicon source, and a thermionic cathode. These two sources, which can operate independently or simultaneously, are capable of producing plasmas with a wide range of parameters and turbulence characteristics, well suited to a variety of basic plasma physics experiments. An extensive set of plasma diagnostics is also operating. Experiments investigating the active feedback control of turbulent transport of particles and heat via electrode biasing to affect plasma ExB flows are underway, and ongoing.
The primary objective of the proposed research was to quantify and explain the effects of a sustained in situ 5°C soil temperature increase on net carbon (C) storage in a northeastern deciduous forest ecosystem. The research was done at an established soil warming experiment at the Harvard Forest in central Massachusetts – Barre Woods site established in 2001. In the field, a series of plant and soil measurements were made to quantify changes in C storage in the ecosystem and to provide insights into the possible relationships between C-storage changes and nitrogen (N) cycling changes in the warmed plots. Field measurements included: 1) annual woody increment; 2) litterfall; 3) carbon dioxide (CO 2 ) efflux from the soil surface; 4) root biomass and respiration; 5) microbial biomass; and 6) net N mineralization and net nitrification rates. This research was designed to increase our understanding of how global warming will affect the capacity of temperate forest ecosystems to store C. The work explored how soil warming changes the interactions between the C and N cycles, and how these changes affect land-atmosphere feedbacks. This core research question framed the project – What are the effects of a sustained in situ 5°C soil temperature increase on net carbon (C) storage in a northeastern deciduous forest ecosystem? A second critical question was addressed in this research – What are the effects of a sustained in situ 5°C soil temperature increase on nitrogen (N) cycling in a northeastern deciduous forest ecosystem?
Well-polarized positive surface muons are a tool to measure the magnetic properties of materials since the precession rate of the spin can be determined from the observation of the positron directions when the muons decay. For a dc beam an ideal µSR flux for surface µ + should be about 40 kHz/mm 2 . In this report we show how this flux could be achieved in a beam line using the AGS complex at BNL for a source of protons. We also determined that an orbit feedback system with a pair of thin silicon position monitors and kickers would miss the desired flux by at least an order of magnitude, even with perfect time resolution and no multiple scattering.
One of the largest sources of uncertainty in the projected global mean temperature response to anthropogenic carbon (C) emissions is the role of the land surface in contributing to or mediating the rise in atmospheric CO2 [IPCC, 2007]. Development of ecosystem processes in global land surface models (LSMs) is far from complete, with major components such as the nitrogen (N) cycle only recently being implemented in some LSMs [Thornton et al., 2007; Sokolov et al., 2008; Xu-Ri and Prentice, 2008; Fisher et al., 2010]. Inclusion of the N cycle can reduce terrestrial C uptake by up to ~50 Pg C·y-1 globally [Figure 1: Fisher et al., 2010]. However, even these early representations of the N cycle in LSMs are missing critical plant–soil interactions that have been shown empirically to exert a major influence over ecosystem C fluxes. Here we propose to use a newly developed plant N model by PI Fisher as a theoretical and analytical framework for integrating novel field-based methods and long-term flux observations to quantify the impacts of belowground processes on fluxes of C, water and energy, from ecosystem to regional scales. Although the model (Fixation & Uptake of Nitrogen: FUN) is one of the more advanced global plant N models available, the model is dependent on theoretical C flux curves optimizing plant C allocation to root exudates, symbiotic bacteria and fungi, and retranslocation enzymes that have not been confirmed or validated with measurements—primarily because the measurement techniques had not been available, until recently. Parallel to the model developments by Fisher et al. were developments in in situ direct measurement techniques by Phillips et al. [2008; 2011], who were able to measure C fluxes from roots to soil. Not only do these techniques have the capacity to confirm and/or modify the modeling components in the Fisher et al. plant N model, but they also provide insight into how belowground C supply from roots couples the C–N cycles at the micro-site and ecosystem scales. Collectively, the integration of measurements and modeling provides empirical grounding for theoretical process behavior. Three critical components will enable us to successfully carry out this research: I) new methodological approaches developed by co-I Phillips for in situ measurement of root exudates, and belowground C fluxes that allow for testing and refinement of FUN [Phillips et al., 2008; Phillips et al., 2011]; II) an innovative research design that involves data integration across an ecological gradient, including a DOE-supported AmeriFlux tower operated by co-I Dragoni, which is essential to determining the ecosystem flux impacts [Dragoni et al., 2011]; and, III) new results from co-I Evans showing unique spectral signatures of trees in association with ectomycorrhizae (ECM) and arbuscular mycorrhizae (AM) fungi from Landsat data using spectral mixture analysis (SMA), which is key to scaling up belowground processes to regional scales [Andersson et al., 2009]. Each component provides feedback to and is intimately linked with all other components; no part operates in isolation. Our team and approach combines expertise in modeling, remote sensing, ecosystem ecology, plant physiology, soil biogeochemistry, and micrometeorology to address these inherently interdisciplinary problems and questions. This proposal directly responds to the focused Science Area of the FOA: “The role of belowground processes and mechanisms across scales (e.g., soil carbon transformation, root dynamics, mycorrhizal interactions, and plant mediated (e.g., root exudates) biogeochemical transformations) associated with a changing climate.
This chapter describes the Sandia National Laboratories (SNL) processes for feedback and improvement of radiological operations, including triennial self-assessment (TSA) and radiological process improvement reports (RPIR). This chapter applies to all radiological activities performed by Members of the Workforce (MOW). Certain operations are outside the scope of this manual and do not require self-assessments or use of the RPIR process. See the "Introduction" for exemptions to the requirements of this manual.
Modeling of orbit feedback with realistic parameters of elements (including 360 microseconds delay) showed feasibility of robust solution with good dynamic response.
Peatlands store one-third of soil C in terrestrial ecosystems and have persisted through changing climate over millennia from the arctic to the tropics. Approximately one-third of peat stores are found in subtropical and tropical peatlands (STPs) formed from high-lignin woody biomass. In this project, our questions are: 1) why do these non-sphagnum peatlands (STPs) accumulate C under warmer-drier climates and 2) how might insights coming from studying control mechanisms in STPs improve the management and conservation of the vast C stores in boreal peatlands subject to increasing climate forcing. We hypothesized that a dual control or “latch mechanism” reduces decomposition in shrub/tree communities in STPs due to both (1) higher production of polyphenol and aromatic compounds in STPs than found in northern Sphagnum/Carex communities and (2) the buildup of recalcitrant organic matter produced by light fire-drought-warming-adapted communities, together leading to a reduction in the microbial decay rate of peat. After three-years of intensive biological and chemical analysis in a series of field and microcosm experiments along our north to south bog gradient from Minnesota to Peru, we show how previously unrecognized biotic factors, particularly dynamic interlinked above- and belowground attributes control C sequestration in peatlands. Our key findings include (1) phenolics-bridged plant-microbe symbioses, principally slow-growing microbes dominated in higher phenolic wooded STPs, preserving C in peatlands under climate change, 2) phenolics are the overarching factor controlling the relative abundance of slow-and fast-growing microbes, the slow-growing microbes in STPs metabolize C slowly and are inherently resistant to disturbance, 3) global data analysis shows that soil respiration does not increase exponentially from boreal to tropical peatlands, suggesting that slow-growing microbes may have become dominant in most non-boreal peatlands, 4) peat chemistry analysis from over 2000 samples show that across peatlands both from the arctic to tropics and from high to low elevation peatlands recalcitrance increases as aromatic content increases, and 5) peat affected by low-severity wildfires displays a similar pattern of higher aromatic content. Our findings all demonstrate links between peat recalcitrance and increased content of phenolics and other aromatic compounds in plants. Thus, linked plant–microbe symbiotic traits are a key to understanding ecological resilience and resistance developed in peatlands under disturbance. New trait-based approaches that can better link above-and below ground processes are needed to advance both the accuracy and precision of current abiotic-factor-based Earth system models in predicting future soil C responses to climate-change feedbacks.
The University of Miami (Subcontractor)’s Research Scientist James Benedict (with oversight by PI Amy Clement) was tasked with producing global climate model simulations as part of the Cloud-Feedback Model Intercomparison Project (CFMIP) Tier 2 protocol, managing the output of these simulations, providing assistance (as requested) to staff at Lawrence Livermore National Lab (LLNL) regarding model output and setup, and meeting with LLNL scientists and team members to coordinate work on the project. The simulations represent an important contribution to the CFMIP data repository and will advance understanding of a wide range of critical cloud, circulation, and precipitation responses to climate change. The Subcontractor completed all proposed simulations, formatted the model output to be compliant with CFMIP protocols, and published the model data to the CFMIP repository. When requested, the Subcontractor provided assistance to LLNL for model output and configuration queries. Benedict and/or Clement also met in person or virtually 1-2 times per year with project scientists to coordinate work and review results.
Focal Areas: The paper aims to improve predictability of the coastal carbon cycle through improved model representation and quantification of the terrestrial feedbacks to aquatic ecosystems. The paper covers two focus areas, 1. Improved predictive modeling of terrestrial and aquatic biogeochemistry through ML-enabled surrogate models, and 2. Employing machine learning to integrate multimodal data sets collected across terrestrial and aquatic ecosystems at high spatial and temporal resolution.