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NASA NTRSDate not supplied

Mechanisms of Interfacial Mixing in the Blown Powder Deposition of Inconel 625 to Copper Alloy for Liquid Rocket Engine Applications

There is an increasing need for functional materials to support high heat flux applications and optimize weight by using bimetallic additive manufacturing (AM). While one promising fabrication technology is the use of blown powder directed energy deposition (DED) AM, challenges remain with bi-metallic combinations. Prominent among these challenges is the need to understand mixing of materials at the bimetallic interface for forming stronger, more durable and reliable joints to extend the versatility of AM into critical applications. One application is the manufacturing of combustion chambers with copper-alloy liners and Superalloy structural jackets. In this application, material combinations are selected for effective heat dissipation, using copper, and retention of strength at high temperatures using Inconel 625. Currently, the dominant problem with this configuration is the highly variable strength of the interface and its correlation with deposition parameters. In this study, samples were fabricated at several vendors with Inconel 625 deposited onto wrought C-18150 (Cu-Cr-Zr) to form a bimetallic interface. Characterization of the interfaces were achieved through optical and electron microscopy, mechanical testing of mini tensile specimens, and electron dispersion spectroscopy to understand diffusion and bulk mixing behaviors at the joint. A theoretical framework was constructed in the context of solidification and mixing mechanisms to explain the resulting differences in the interface. This qualitative approach utilized a fluid dynamics perspective to sum the order of magnitudes of the various acting forces on the weld pool during deposition to predict convection trends based off known parameters. The predicted trends for each vendor are compared to obtain a better understanding of how mixing in the molten pool varied based on deposition parameters.

Additive Manufacturing↗
NASA NTRS2026

Equivalent System Mass Comparison of ECLSS CO2 Removal Technologies

This project employs equivalent system mass (ESM) analyses to evaluate and compare state-of-the-art spacecraft CO2 removal systems. The ESM methodology converts sizing characteristics such as volume, power, and cooling requirements into a unified metric of mass, allowing for simple quantitative comparison of equipment impacts on a system level. Comparison of six high TRL technologies – the Carbon Dioxide Removal System (CDRS), Thermal Amine Scrubber (TAS), Carbon Dioxide Removal Assembly (CDRA), Four-Bed CO2 Scrubber (4BCO2), CO2 and Humidity Control Swing Bed (CHC), and the Carbon Dioxide Removal by Ionic Liquid Sorbent (CDRILS) – was achieved using an existing ESM spreadsheet tool that formerly ranked such technologies for consideration on Gateway. Fidelity of the tool was increased through revision with the most up-to-date sizing and performance data for each technology. ESM values were then estimated using NASA-supplied Lunar, Martian, and low-Earth orbit habitat infrastructure costs. This paper details the ESM approach used, lists major performance variables for each CO2 removal option, and summarizes conclusions of the technology comparison. The results are intended to act as a guide to assist programs in their selection of CO2 technologies best suited for a specific vehicle or mission.

Madeleine C Oliver↗
DOE OSTI2018

Development of Hot-Isostatic Pressing for Treatment of UK ILW Radioactive Wastes - 18280

Hot-Isostatic Pressing (HIPing) has been proposed as an improved method for the treatment of radioactive wastes in the UK. Sludges and slurries housed on the Sellafield site are a prime candidate for HIP treatment due to their large volumes and issues associated with the use of the baseline cementation strategy. Options have been developed by GeoRoc Ltd to co-process these wastes, including a detailed conceptual plant design. This paper will describe research and development aimed at demonstrating the viability of the concept presented in this design. This paper summarizes two studies performed to address the knowledge gaps identified for treatment of these wastes using HIP technology. The first study related to the poorly characterized nature of the wastes, which requires any treatment option to be flexible in terms of the chemical and physical properties of the waste feed. The second study involved demonstrating that HIPing can be effectively utilized at a scale applicable to treat a waste inventory estimated to be in excess of 5,000 m{sup 3}. The first section of this paper presents a summary of research carried out to determine where variation in the waste stream composition may affect the processing and wasteform produced when HIPing Magnox sludges. This study aimed to qualify where restrictions on the process envelope may exist due to poorly defined waste-stream characteristics. A baseline wasteform was selected with the aim of achieving significant waste volume reduction, while producing a physically and chemically stable wasteform. A glass-bonded magnesium-titanate/magnesium-silicate wasteform was selected as the preferred option for the Magnox sludge wastes. A series of studies investigated the densification and phase formation of waste-forms by altering key variables in the Magnox sludge simulant. To test the extremes of the processable waste loading, samples were produced and characterized at 100 wt% waste-loading for Magnox sludge, Site-Ion Exchange Plant (SIXEP) sand/clinoptilolite and a 50:50 mix of the two waste-streams. Further studies involved a) altering the waste loading to additive mass ratio (to simulate changes in the waste loading/sludge solids content), b) varying the particle size distribution from the datum to include only small or large particulates in the sludge feed; and c) introducing up to 20 wt% metallic Magnox alloy, organic species or carbonate materials (to determine how their inclusion may affect the processing and densification of the materials). A U{sub 3}O{sub 8} loaded sample was also produced using the baseline Magnox sludge formulation. All the tests performed were shown to produce monolithic, well densified waste-forms with substantial volume reductions. Minimal variations from the baseline were noted but the waste-forms produced with these deviations was determined to be of comparable quality to of the baseline product. U{sub 3}O{sub 8} was shown to behave in a similar fashion to the ZrO{sub 2} surrogate used to simulate it in the process envelope studies and was encapsulated in the magnesium titanate/magnesium silicate matrix. This work demonstrates that HIP technology can successfully produce waste-forms across the broad process envelope expected in the Magnox sludge inventory. It is also possible to produce monolithic waste-forms at 100 wt% waste loading from Magnox sludges, SIXEP sand clinoptilolite slurries or a co-processed mixture of the two. The second part of this paper summarizes a commercially relevant demonstration of HIP technology for the treatment of sludge and slurry wastes housed at the Sellafield site (UK). This study culminated in production of the largest HIPed wasteform successfully produced to date. This study was performed in a series of stages. Investigations into formulation and properties of waste-forms were performed at a lab scale to evaluate suitable wasteform systems and identify suitable combinations of additives and waste loadings. Scale up demonstrations were then performed in stages with custom-designed HIP canisters. Scale-up and characterization of key wasteform properties were successfully performed with initial canister volumes of 50 ml, 400 ml and 8 L. Suitable front-end unit operations to produce a granular, free-flowing calcined simulant were demonstrated at an engineering scale. The demonstration was successful in processing a HIP canister with an initial starting volume in excess of 100 L. A 75 % volume reduction from the raw sludge was achieved at scale while demonstrating that HIP is applicable at suitably large scale for immobilization of ILW waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

Background: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. Objective: We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA).Methods: Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. Results: We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. Conclusions: Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Oxaliplatin and irinotecan induce heterogenous changes in the EMT markers of metastasizing colorectal carcinoma cells

Highlights: • Significant variability and lower expression uniformity in lymph node metastatic cells. • Heterogeneity of cell response partially determined by cancer cell microenvironment. • Metastatic cells proved to be less sensitive to chemotherapy treatment compared to primary site cancer cells. • Irinotecan and oxaliplatin affected EMT markers in primary cell lines. In patients with advanced colorectal cancer (CRC), surgery is complemented with systemic therapy – chemotherapy and radiochemotherapy. Although the patients’ overall survival has been significantly improved, tumor resistance is still a frequent cause of chemotherapy failure. Several factors contribute to chemoresistance of tumor cells including changes related with epithelial-mesenchymal transition (EMT). The present study was designed to verify the presence of EMT markers in paired CRC primary cell lines obtained from primary tumor sites and lymph node metastases of three patients and to investigate the effect of irinotecan and oxaliplatin treatment on these markers as well. The samples of the higher stage of CRC and positive for angioinvasion were selected and qPCR, western blot analysis, migration assay, cytotoxicity testing was performed. Results confirmed the increased expression of several markers characteristic of EMT and invasiveness in lymph node metastatic cells, with a significant variability between individual samples. Irinotecan and oxaliplatin decreased migration activity of the cells and to the varying degree affected the expression of EMT and invasiveness markers. In conclusion, in CRC EMT is present in metastatic cells over a phenotypic continuum whose expression is altered heterogeneously upon irinotecan and oxaliplatin treatment.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2015

A Decision-Support Model for Selecting Additive Manufacturing Versus Subtractive Manufacturing Based on Energy Consumption

This paper presents a simple computational model for determining whether additive manufacturing or subtractive manufacturing is more energy efficient for production of a given metallic part. The key discriminating variable is the fraction of the bounding envelope that contains material – i.e. the volume fraction of solid material. For both the additive process and the subtractive process, the total energy associated with the production of a part is defined in terms of the volume fraction of that part. The critical volume fraction is that for which the energy consumed by subtractive manufacturing equals the energy consumed by additive manufacturing. For volume fractions less than the critical value, additive manufacturing is more energy efficient. For volume fractions greater than the critical value, subtractive manufacturing is more efficient. The model considers the entire manufacturing lifecycle – from production and transport of feedstock material through processing to return of post-production scrap for recycling. Energy consumed by processing equipment while idle is also accounted for in the model. Although the individual energy components in the model are identified and accounted for in the expressions for additive and subtractive manufacturing, values for many of these components may not be currently available. Energy values for some materials’ production and subtractive and additive manufacturing processes can be found in the literature. However, since many of these data are reported for a very specific application, it may be difficult, if not impossible, to reliably apply these data to new process-material manufacturing scenarios since, very often, insufficient information is provided to enable extrapolation to broader use. Consequently, this paper also highlights the need to develop improved knowledge of the energy embodied in each phase of the manufacturing process. To be most valuable, users of the model should determine the energy consumed by their manufacturing process equipment on the basis of energy-per-unit-volume of production for each material of interest – considering both alloy composition and form. Energy consumed during machine idle per unit time should also be determined by the user then scaled to specific processing scenarios. Energy required to generate feedstock material (billet, plate, bar, wire, powder) must be obtained from suppliers.

J K Watson↗
NASA NTRS1990

Study of a cesium plasma as a selective emitter for thermophotovoltaic applications

This experimental study evaluates the potential of a cesium plasma as an emitter for a thermophotovoltaic (TPV) energy conversion system. A cesium plasma, as a result of the ground-state transitions of its single outer-shell electron, produces large amounts of radiation in the 850-890-nm wavelength region. This would provide excellent coupling to silicon, gallium arsenide, and indium phosphide photovoltaic cells. Measurements of the radiative efficiency, the sum of the power at the 852 and 894 nm wavelengths relative to the total emitted power, were made and correlated to the plasma operating variables. It was determined that, for atomic density in the range (3-6) x 10 exp 21/cu cm and electron temperature in the range 2000-3000 K, radiative efficiencies in excess of 70 percent are attainable. This would indicate that a cesium plasma with its selective emission characteristics and low electron operating temperatures of 2000-3000 K would be an excellent candidate as an emitter in a TPV system.

Lowe, R.↗
DOE OSTI2023

The need for ion-exchange membranes with high charge densities

Ion-exchange membranes (IEMs) are essential for controlling ion transport in electrochemical membrane-based technologies for water purification, energy generation, and energy storage. In general, IEMs must exhibit high ionic conductivity and selectivity, both of which are significantly influenced by the charge and water content of the hydrated membrane. In this perspective, we compiled the charge and water contents of approximately 1,000 IEMs reported over the past several decades to assess their range and variability, as well as to reveal gaps and set clear targets for future membrane design. We compared the charge and water content of negatively and positively charged membranes, as well as membranes developed for desalination and energy applications. The majority of IEMs, regardless of structure or intended application, have volumetric charge densities below 3 mol/L[membrane]. This narrow range of charge densities limits the performance of IEMs and contributes to the observed tradeoff between ion throughput and selectivity. It is critical to develop IEMs with higher charge densities to overcome this performance limitation. Further, we highlighted several design strategies that yielded IEMs with fixed charge densities higher than 3 mol/L[membrane]. These strategies provide valuable insights and can guide the design of next-generation IEMs with high charge densities.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Characterization of Covetic Materials for Process Control. Final CRADA report

GDC Industries LLC has been performing process research on covetic materials since its formation in February 2015. With assistance from DOE and the Air Force via SBIR projects, GDC focused on the enhancement of aluminum alloys to covetics to replicate a previously reported electrical conductivity of about 67% IACS (International Annealed Copper Standard). GDC has established a joint development agreement with General Cable Corporation, wherein General Cable agreed to perform numerous extrusions of as cast covetics and subsequent tensile and electrical conductivity tests on the resultant wire. Covetics conversion was proven but the significant improvement is yet to be replicated. To control the experiments towards enhanced process control, a series of designed experiments was created by an expert in process control from the automotive industry. Six process variables were identified and a designed experiment matrix requiring 18 independent conversions was created at GDC. The result of those designed experiments has now allowed GDC to focus on two process variables, namely, residence time and DC current in the conversions with the other four variable held constant. While this work is useful in replicating the earlier enhancements, GDC feels it will not be sufficient for the necessary process knowhow for scale up required by industry as GDC still does not have a direct correlation of the process variables to the actual carbon in the finished covetics. Since 2015, GDC has been aware of the capabilities existing at Argonne National Laboratory for performing carbon analysis that exceeds the precision commonly available to Industry. GDC and ANL submitted a proposal in response to the DOE-EERE – Small Business Vouchers Pliot Program announcement and the proposal was selected for funding. GDC will melt-process covetic aluminum samples following their design of experiment criteria and have the as-casted samples extruded by their partner, General Cable. Argonne will measure the conductivity, analyze carbon content, perform X-ray diffraction, and examine the microstructural features of the samples. Careful process variable measurements will be recorded by each party such that correlations can be developed indicating process parameters that yield the best carbon concentration in covetics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2019

Scalable Preconditioning of Block-Structured Linear Algebra Systems using ADMM

In this paper, we study the solution of block-structured linear algebra systems arising in optimization by using iterative solution techniques. These systems are the core computational bottleneck of many problems of interest such as parameter estimation, optimal control, network optimization, and stochastic programming. Our approach uses a Krylov solver (GMRES) that is preconditioned with an alternating method of multipliers (ADMM). We show that this ADMM-GMRES approach overcomes well-known scalability issues of Schur complement decomposition in problems that exhibit a high degree of coupling. The effectiveness of the approach is demonstrated using linear systems that arise in stochastic optimal power flow problems and that contain up to 2 million total variables and 4,000 coupling variables. We find that ADMM-GMRES is nearly an order of magnitude faster than Schur complement decomposition. Moreover, we demonstrate that the approach is robust to the selection of the augmented Lagrangian penalty parameter, which is a key advantage over the direct use of ADMM.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Nonlinear solution of classical three-wave interaction via finite-dimensional quantum model

The quantum three-wave interaction, the lowest-order nonlinear interaction in plasma physics, describes energy–momentum transfer between three resonant waves in the quantum regime. We describe how it may also act as a finite-degree-of-freedom approximation to the classical three-wave interaction in certain circumstances. By promoting the field variables to operators, we quantize the classical system, show that the quantum system has more free parameters than the classical system and explain how these parameters may be selected to optimize either initial or long-term correspondence. We then numerically compare the long-time quantum–classical correspondence far from the fixed point dynamics. We discuss the Poincaré recurrence of the system and the mitigation of quantum scrambling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1980

Advanced propulsion system for hybrid vehicles

A number of hybrid propulsion systems were evaluated for application in several different vehicle sizes. A conceptual design was prepared for the most promising configuration. Various system configurations were parametrically evaluated and compared, design tradeoffs performed, and a conceptual design produced. Fifteen vehicle/propulsion systems concepts were parametrically evaluated to select two systems and one vehicle for detailed design tradeoff studies. A single hybrid propulsion system concept and vehicle (five passenger family sedan)were selected for optimization based on the results of the tradeoff studies. The final propulsion system consists of a 65 kW spark-ignition heat engine, a mechanical continuously variable traction transmission, a 20 kW permanent magnet axial-gap traction motor, a variable frequency inverter, a 386 kg lead-acid improved state-of-the-art battery, and a transaxle. The system was configured with a parallel power path between the heat engine and battery. It has two automatic operational modes: electric mode and heat engine mode. Power is always shared between the heat engine and battery during acceleration periods. In both modes, regenerative braking energy is absorbed by the battery.

Norrup, L. V.↗
NASA NTRS1987

A piloted simulation of helicopter air combat to investigate effects of variations in selected performance and control response characteristics

A piloted simulation study investigating handling qualities and flight characteristics required for helicopter air to air combat is presented. The Helicopter Air Combat system was used to investigate this role for Army rotorcraft. Experimental variables were the maneuver envelope size (load factor and sideslip), directional axis handling qualities, and pitch and roll control-response type. Over 450 simulated, low altitude, one-on-one engagements were conducted. Results from the experiment indicate that a well damped directional response, low sideforce caused by sideslip, and some effective dihedral are all desirable for weapon system performance, good handling qualities, and low pilot workload. An angular rate command system was favored over the attitude type pitch and roll response for most applications, and an enhanced maneuver envelope size over that of current generation aircraft was found to be advantageous. Pilot technique, background, and experience are additional factors which had a significant effect on performance in the air combat tasks investigated. The implication of these results on design requirements for future helicopters is presented.

Lewis, Michael S.↗
NASA NTRS1987

Digital robust active control law synthesis for large order systems using constrained optimization

This paper presents a direct digital control law synthesis procedure for a large order, sampled data, linear feedback system using constrained optimization techniques to meet multiple design requirements. A linear quadratic Gaussian type cost function is minimized while satisfying a set of constraints on the design loads and responses. General expressions for gradients of the cost function and constraints, with respect to the digital control law design variables are derived analytically and computed by solving a set of discrete Liapunov equations. The designer can choose the structure of the control law and the design variables, hence a stable classical control law as well as an estimator-based full or reduced order control law can be used as an initial starting point. Selected design responses can be treated as constraints instead of lumping them into the cost function. This feature can be used to modify a control law, to meet individual root mean square response limitations as well as minimum single value restrictions. Low order, robust digital control laws were synthesized for gust load alleviation of a flexible remotely piloted drone aircraft.

Mukhopadhyay, Vivek↗
NASA NTRS1991

An efficient method for estimating neighboring steady-state numerical solutions to the Euler equations

The paper concentrates on a linear approximation method for predicting the changes occurring in steady-state numerical solutions of the Euler equations as a consequence of small changes in the independent variables which control the problem. The importance of proper boundary-condition treatment and other issues concerning the problem are covered along with the importance of proper algorithm selection for a fully supersonic inviscid flow. The method is applied to a subsonic nozzle involving variation of the pressure on the outflow boundary and to a supersonic inlet involving variation of the inflow Mach number. In the subsonic test case, the comparisons between the predicted and conventional numerical solutions are shown to be good, while in the supersonic test case, the agreement between the approximation method and conventional numerical solution starts out well but rapidly degenerates at some point in the flowfield as the perturbation of the boundary conditions is increased.

Taylor, Arthur C., III↗
NASA NTRS1991

Advanced thrust chamber technology for in-space propulsion system

Two new concepts, the afterburning technique and the spray optimization method for the purpose of upgrading the combustion efficiency and C efficiency, are examined to assess the technical merits and hurdles that need to be resolved. Details of combustion and aerodynamic processes of monomethylhydrazine and nitrogen tetroxides in a toroidal shaped combustion chamber of a variable thrust engine, devised with a wall impingement cooling system and the secondary injection of an oxidizer from the chamber wall for an afterburning, are investigated for the selected secondary injection conditions. With an appropriate injection condition, the combustion efficiency was found to be almost 23 percent higher than that of an engine without afterburning.

Chiu, H. H.↗
NASA NTRS1992

Diploptene: an indicator of terrigenous organic carbon in Washington coastal sediments

The pentacyclic triterpene 17 beta(H),21 beta(H)-hop-22(29)-ene (diploptene) occurs in sediments throughout the Columbia River drainage basin and off the southern coast of Washington state in concentrations comparable to long-chain plantwax n-alkanes. The same relationship is evident for diploptene and long-chain n-alkanes in soils from the Willamette Valley. Microorganisms indigenous to soils and soil erosion are indicated as the biological source and physical process, respectively, for diploptene in coastal sediments. Similarity between the stable carbon isotopic composition (delta 13CPDB) of diploptene isolated from soil in the Willamette Valley (-31.2 +/- 0.3%) and from sediments deposited throughout the Washington coastal environment (-31.2 +/- 0.5%) supports this argument. Values of delta for diploptene in river sediments are variable and 8-17% lighter, indicating that an additional biological source such as methane-oxidizing bacteria makes a significant contribution to the diploptene record in river sediments. Selective biodegradation resulting from a difference in the physicochemical association within eroded particles can explain the absence of the more-13C-depleted form of diploptene in Washington coastal sediments, but this mechanism remains unproven.

Non-NASA Center↗