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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Optimizing dietary rumen-degradable starch to rumen-degradable protein ratio improves lactation performance and nitrogen utilization efficiency in mid-lactating Holstein dairy cows

The dietary rumen-degradable starch (RDS) to rumen-degradable protein (RDP) ratio, denoted as the RDS-to-RDP ratio (SPR), has been proven to enhance in vitro rumen fermentation. However, the effects of dietary SPR in vivo remain largely unexplored. This study was conducted to investigate the effect of dietary SPR on lactation performance, nutrient digestibility, rumen fermentation patterns, blood indicators, and nitrogen (N) partitioning in mid-lactating Holstein cows. Seventy-two Holstein dairy cows were randomly assigned to three groups (24 head/group), balanced for (mean ± standard deviation) days in milk (116 ± 21.5), parity (2.1 ± 0.8), milk production (42 ± 2.1 kg/d), and body weight (705 ± 52.5 kg). The cows were fed diets with low (2.1, control), medium (2.3), or high (2.5) SPR, formulated to be isoenergetic, isonitrogenous, and iso-starch. The study consisted of a one-week adaptation phase followed by an eight-week experimental period. The results indicated that the high SPR group had a lower dry matter intake compared to the other groups ( p < 0.05). A quadratic increase in milk yield and feed efficiency was observed with increasing dietary SPR ( p < 0.05), peaking in the medium SPR group. The medium SPR group exhibited a lower milk somatic cell count and a higher blood total antioxidant capacity compared to other groups ( p < 0.05). With increasing dietary SPR, there was a quadratic improvement ( p < 0.05) in the total tract apparent digestibility of crude protein, ether extract, starch, neutral detergent fiber, and acid detergent fiber. Although no treatment effect was observed in rumen pH, the rumen total volatile fatty acids concentration and microbial crude protein synthesis increased quadratically ( p < 0.05) as dietary SPR increased. The molar proportion of propionate linearly increased ( p = 0.01), while branched-chain volatile fatty acids linearly decreased ( p = 0.01) with increasing dietary SPR. The low SPR group (control) exhibited higher concentration of milk urea N, rumen ammonia N, and blood urea N than other groups ( p < 0.05). Despite a linear decrease ( p < 0.05) in the proportion of urinary N to N intake, increasing dietary SPR led to a quadratic increase ( p = 0.01) in N utilization efficiency and a quadratic decrease ( p < 0.05) in the proportion of fecal N to N intake. In conclusion, optimizing dietary SPR has the potential to enhance lactation performance and N utilization efficiency. Based on our findings, a medium dietary SPR (with SPR = 2.3) is recommended for mid-lactating Holstein dairy cows. Nevertheless, further research on rumen microbial composition and metabolites is warranted to elucidate the underlying mechanisms of the observed effects.

Chen, Panliang↗
DOE OSTI2023

The Amylolytic Regulator AmyR of Aspergillus niger Is Involved in Sucrose and Inulin Utilization in a Culture-Condition-Dependent Manner

Filamentous fungi degrade complex plant material to its monomeric building blocks, which have many biotechnological applications. Transcription factors play a key role in plant biomass degradation, but little is known about their interactions in the regulation of polysaccharide degradation. Here, we deepened the knowledge about the storage polysaccharide regulators AmyR and InuR in Aspergillus niger. AmyR controls starch degradation, while InuR is involved in sucrose and inulin utilization. In our study, the phenotypes of A. niger parental, ΔamyR, ΔinuR and ΔamyRΔinuR strains were assessed in both solid and liquid media containing sucrose or inulin as carbon source to evaluate the roles of AmyR and InuR and the effect of culture conditions on their functions. In correlation with previous studies, our data showed that AmyR has a minor contribution to sucrose and inulin utilization when InuR is active. In contrast, growth profiles and transcriptomic data showed that the deletion of amyR in the ΔinuR background strain resulted in more pronounced growth reduction on both substrates, mainly evidenced by data originating from solid cultures. Overall, our results show that submerged cultures do not always reflect the role of transcription factors in the natural growth condition, which is better represented on solid substrates. Importance: The type of growth has critical implications in enzyme production by filamentous fungi, a process that is controlled by transcription factors. Submerged cultures are the preferred setups in laboratory and industry and are often used for studying the physiology of fungi. In this study, we showed that the genetic response of A. niger to starch and inulin was highly affected by the culture condition, since the transcriptomic response obtained in a liquid environment did not fully match the behavior of the fungus in a solid environment. These results have direct implications in enzyme production and would help industry choose the best approaches to produce specific CAZymes for industrial purposes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Biocatalytic Membranes for Carbon Capture and Utilization

Innovative carbon capture technologies that capture CO 2 from large point sources and directly from air are urgently needed to combat the climate crisis. Likewise, corresponding technologies are needed to convert this captured CO 2 into valuable chemical feedstocks and products that replace current fossil-based materials to close the loop in creating viable pathways for a renewable economy. Biocatalytic membranes that combine high reaction rates and enzyme selectivity with modularity, scalability, and membrane compactness show promise for both CO 2 capture and utilization. This review presents a systematic examination of technologies under development for CO 2 capture and utilization that employ both enzymes and membranes. CO 2 capture membranes are categorized by their mode of action as CO 2 separation membranes, including mixed matrix membranes (MMM) and liquid membranes (LM), or as CO 2 gas–liquid membrane contactors (GLMC). Because they selectively catalyze molecular reactions involving CO 2 , the two main classes of enzymes used for enhancing membrane function are carbonic anhydrase (CA) and formate dehydrogenase (FDH). Small organic molecules designed to mimic CA enzyme active sites are also being developed. CO 2 conversion membranes are described according to membrane functionality, the location of enzymes relative to the membrane, which includes different immobilization strategies, and regeneration methods for cofactors. Parameters crucial for the performance of these hybrid systems are discussed with tabulated examples. Progress and challenges are discussed, and perspectives on future research directions are provided.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Using Real-Time Electricity Prices to Leverage Electrical Energy Storage and Flexible Loads in a Smart Grid Environment Utilizing Machine Learning Techniques

With exposure to real-time market pricing structures, consumers would be incentivized to invest in electrical energy storage systems and smart predictive automation of their home energy systems. Smart home automation through optimizing HVAC (heating, ventilation, and air conditioning) temperature set points, along with distributed energy storage, could be utilized in the process of optimizing the operation of the electric grid. Using electricity prices as decision variables to leverage electrical energy storage and flexible loads can be a valuable tool to optimize the performance of the power grid and reduce electricity costs both on the supply and demand sides. Energy demand prediction is important for proper allocation and utilization of the available resources. Manipulating energy prices to leverage storage and flexible loads through these demand prediction models is a novel idea that needs to be studied. In this paper, different models for proactive prediction of the energy demand for an entire city using different machine learning techniques are presented and compared. The results of the machine learning techniques show that the proposed nonlinear autoregressive with exogenous inputs neural network model resulted in the most accurate predictions. These prediction models pave the way for the demand side to become an important asset for grid regulation by responding to variable price signals through battery energy storage and passive thermal energy storage using HVAC temperature set points.

25 ENERGY STORAGE↗
DOE OSTI2018

Soil Oxygen Limits Microbial Phosphorus Utilization in Humid Tropical Forest Soils

Soil phosphorus (P) availability is of special interest in many humid tropical forests, especially those on highly weathered, iron (Fe)- and aluminum (Al)-rich soils where P often limits net primary productivity. Phosphorus cycling is partly dependent on the ability of microbes to compete for P with Fe and Al minerals, which strongly bind P. Soil P availability is also indirectly affected by soil redox conditions due to its effects on microbial activity and reductive dissolution of Fe oxides that may weaken Fe-O-P sorption strength. Here, we explored P sorption, soil Fe (II) concentrations, soil CO2 production, organic and inorganic P pools, and microbial biomass P in tropical soils that typically experience frequent low redox (valley soils), or fluctuating redox conditions (slope soils). Soils from both topographic positions were pre-incubated under oxic or anoxic headspaces and then amended with a mixture of P (as orthophosphate) and carbon (C, as acetate, to maintain microbial activity) and incubated in the dark for 24 h. Phosphorus sorption to the mineral phase occurred on a time scale of seconds to minutes in valley and slope soils, reflecting strong abiotic P sorption capacity. Valley soils were characterized by inherently higher Fe(II) concentrations and lower respiration rates. Under anoxic headspaces, Fe(II) concentrations increased 3-to 5-fold in the both soils. Soil respiration and microbial P utilization declined significantly in both soils under anoxic conditions, regardless of Fe(II) concentrations. Microbial P concentrations were highest when slope soils were incubated under an oxic headspace, despite the high P sorption under these conditions. Our results suggest that microbial P utilization is indirectly limited by low O2 availability and that microbes are able to effectively compete with minerals for P under Fe-oxidizing conditions. These results emphasize the central role of soil microorganisms in regulating P availability, even in the presence of strong abiotic sorption capacity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Recent Progress on Surface Water Quality Models Utilizing Machine Learning Techniques

Surface waterbodies are heavily exposed to pollutants caused by natural disasters and human activities. Empowering sensor technologies in water quality monitoring, sufficient measurements have become available to develop machine learning (ML) models. Numerous ML models have quickly been adopted to predict water quality indicators in various surface waterbodies. This paper reviews 78 recent articles from 2022 to October 2024, categorizing water quality models utilizing ML into three groups: Point-to-Point (P2P), which estimates the current target value based on other measurements at the same time point; Sequence-to-Point (S2P), which utilizes previous time series data to predict the target value at one time point ahead; and Sequence-to-Sequence (S2S), which uses previous time series data to forecast sequential target values in the future. The ML models used in each group are classified and compared according to water quality indicators, data availability, and model performance. Widely used strategies for improving performance, including feature engineering, hyperparameter tuning, and transfer learning, are recognized and described to enhance model effectiveness. The interpretability limitations of ML applications are discussed. This review provides a perspective on emerging ML for surface water quality models.

machine learning (ML)↗
DOE OSTI2018

Assessment of Gamma-Ray-Spectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures

The analysis of measured data plays a significant role in enhancing nuclear nonproliferation mainly by inferring the presence of patterns associated with special nuclear materials. Among various types of measurements, gamma-ray spectra is the widest utilized type of data in nonproliferation applications. In this paper, a method that employs the fireworks algorithm (FWA) for analyzing gamma-ray spectra aiming at detecting gamma signatures is presented. In particular, FWA is utilized to fit a set of known signatures to a measured spectrum by optimizing an objective function, where non-zero coefficients express the detected signatures. FWA is tested on a set of experimentally obtained measurements optimizing various objective functions—MSE, RMSE, Theil-2, MAE, MAPE, MAP—with results exhibiting its potential in providing highly accurate and precise signature detection. Finally and furthermore, FWA is benchmarked against genetic algorithms and multiple linear regression, showing its superiority over those algorithms regarding precision with respect to MAE, MAPE, and MAP measures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Genetic engineering and improvement of a Zymomonas mobilis for arabinose utilization and its performance on pretreated corn stover hydrolyzate

In this paper, a glucose, xylose and arabinose utilizing Zymomonas mobilis strain was constructed by incorporating arabinose catabolic pathway genes, araBAD encoding L-ribulokinase, L-arabinose isomerase and L-ribulose-5-phosphate- 4-epimerase in a glucose, xylose co-fermenting host, 8b, using a transposition integration approach. Further improvement on this arabinose-capable integrant, 33C was achieved by applying a second transposition to create a genomic knockout (KO) mutant library. Using arabinose as a sole carbon source and a selection pressure, the KO library was subjected to a growth-enrichment process involving continuous sub-culturing for over 120 generations. Strain 13-1-17, isolated from such process demonstrated significant improvement in metabolizing arabinose in a dilute acid pretreated, saccharified corn stover slurry. Through Next Generation Sequencing (NGS) analysis, integration sites of the transposons were identified. Furthermore, multiple additional point mutations (SNPs: Single Nucleotide Polymorphisms) were discovered in 13-1-17, affecting genes araB and RpiB in the genome. Finally, we speculate that these mutations may have impacted the expression of the enzymes coded by these genes, ribulokinase and Ribose 5-P-isomerase, thus attributing to the improvement of the arabinose utilization.

09 BIOMASS FUELS↗
DOE OSTI2019

Process for Study of Micro-pilot Diesel-NG Dual Fuel Combustion in a Constant Volume Combustion Vessel Utilizing the Premixed Pre-burn Procedure

A constant volume spray and combustion vessel utilizing the pre-burn mixture procedure to generate pressure, temperature, and composition characteristic of near top dead center (TDC) conditions in compression ignition (CI) engines was modified here with post pre-burn gas induction to incorporate premixed methane gas prior to diesel injection to simulate processes in dual fuel engines. Two variants of the methane induction system were developed and studied. The first used a high-flow modified direct injection injector and the second utilized auxiliary ports in the vessel that are used for normal intake and exhaust events. Flow, mixing, and limitations of the induction systems were studied. As a finding of this study, the high-flow modified direct injection injector was selected because of its controlled actuation and rapid closure. Further studies of the induction system post pre-burn were conducted to determine the temperature limit of the methane auto-ignition. It was found that for sufficient induction and mixing time determined from experimental observations and CFD modeling studies, a maximum core temperature of 750 K at the time of micro-pilot diesel injection can be achieved. Although lower than TDC temperatures in diesel CI engines, this temperature is sufficient for studying dual fuel injection and auto-ignition with high cetane fuels. Results from this work confirm the feasibility of dual fuel combustion using the proposed CV process and provide constraints for further micro-pilot diesel-NG dual fuel combustion studies.

03 NATURAL GAS↗
DOE OSTI2021

Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error

Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting. Many machine learning methods utilize exogenous variables as input features, but there remains the question of which atmospheric variables are most beneficial for forecasting, especially in handling non-linearities that lead to forecasting error. This question is addressed via creation of a hybrid model that utilizes an autoregressive integrated moving-average (ARIMA) model to make an initial wind speed forecast followed by a random forest model that attempts to predict the ARIMA forecasting error using knowledge of exogenous atmospheric variables. Variables conveying information about atmospheric stability and turbulence as well as inertial forcing are found to be useful in dealing with non-linear error prediction. Streamwise wind speed, time of day, turbulence intensity, turbulent heat flux, vertical velocity, and wind direction are found to be particularly useful when used in unison for hourly and 3 h timescales. The prediction accuracy of the developed ARIMA–random forest hybrid model is compared to that of the persistence and bias-corrected ARIMA models. The ARIMA–random forest model is shown to improve upon the latter commonly employed modeling methods, reducing hourly forecasting error by up to 5 % below that of the bias-corrected ARIMA model and achieving an R 2 value of 0.84 with true wind speed.

17 WIND ENERGY↗
DOE OSTI2017

Protecting drinking water utilities from cyberthreats

Cyber-security challenges have the potential for becoming one of the defining issues of our time. Cyber-attacks have become an ever-increasing threat and the United States (US) Federal Bureau of Investigation (FBI) now ranks cyber-crime as one of its most important law enforcement activities. In addition to the general problems associated with cyber-crime, critical infrastructure (CI) related to energy production, manufacturing, water supply and other systems have come under attack. For example, drinking water utilities are increasingly incorporating computer technology into their routine operations and are therefore increasingly vulnerable to cyber- threats. Systems control and data acquisition (SCADA) systems used to manage automated physical processes essential to water treatment and distribution systems have become standard in medium to large drinking water utilities and in many small water systems. However, even with the application of standard information technology cybersecurity best practices these types of systems have proven to be vulnerable to cyber-attacks. In 2015, the US Department of Homeland Security (DHS) responded to 25 cybersecurity incidents in the Water Sector and to 46 incidents in the Energy Sector. Comparatively, between 2014 and 2015, the reported number of Water Sector incidents actually increased by 78.6% (from 14 to 25). The DHS is in a collaborative partnership with the US Environmental Protection Agency to ensure cybersecurity in the Water Sector. As a result of this partnership a number of guidance documents and techniques have been developed to counter cyber-attacks and minimize cyber vulnerability. These approaches are documented along with a summary of common vulnerabilities. However, a new approach which has great promise in protecting drinking water systems against hacking and cyber-attacks, based on the concept of unidirectional gateways, is presented and discussed.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Africa Battery Energy Storage Systems (BESS) Capacity Building Utility-Scale Storage: BESS Valuation, Tariffs, and Remuneration [Slides]

Utility-scale Battery Energy Storage Systems (BESS) are key to enhancing grid reliability, integrating renewable energy, and providing operational flexibility. Designing effective valuation, remuneration, and tariff frameworks is essential to ensure both system benefits and financial viability for developers. This presentation outlines a structured methodology for evaluating BESS projects, covering policy and legal considerations, cost and revenue analysis, benchmarking, financial sensitivity, and risk assessment, while ensuring alignment with public interest. It also explores valuation of multiple storage services - bulk energy, ancillary services, and infrastructure support - and monetization strategies through capacity payments, energy tariffs, tolling, arbitrage, and non-wires alternative payments. Technical factors, including round-trip efficiency, degradation, and storage duration, are integrated into financial and operational modeling to quantify both system-wide and project-level benefits. Through case studies and simulation-based approaches, this framework provides regulators, utilities, and developers with practical guidance for tariff design, payment structures, and investment decisions, maximizing the economic and societal value of BESS deployment.

25 ENERGY STORAGE↗
NASA NTRS1973

Design and evaluation of an optical fine-pointing control system for telescopes utilizing a digital star sensor

One of the most significant problems associated with the development of large orbiting astronomical telescopes is that of maintaining the very precise pointing accuracy required. A proposed solution to this problem utilizes dual-level pointing control. The primary control system maintains the telescope structure attitude stabilized within the field of view to the desired accuracy. In order to demonstrate the feasibility of optically stabilizing the star images to the desired accuracy a regulating system has been designed and evaluated. The control system utilizes a digital star sensor and an optical star image motion compensator, both of which have been developed for this application. These components have been analyzed mathematically, analytical models have been developed, and hardware has been built and tested.

Ostroff, A. J.↗
NASA NTRS1973

SIRU utilization. Volume 2: Software description and program documentation

A complete description of the additional analysis, development and evaluation provided for the SIRU system as identified in the requirements for the SIRU utilization program is presented. The SIRU configuration is a modular inertial subsystem with hardware and software features that achieve fault tolerant operational capabilities. The SIRU redundant hardware design is formulated about a six gyro and six accelerometer instrument module package. The modules are mounted in this package so that their measurement input axes form a unique symmetrical pattern that corresponds to the array of perpendiculars to the faces of a regular dodecahedron. This six axes array provides redundant independent sensing and the symmetry enables the formulation of an optimal software redundant data processing structure with self-contained fault detection and isolation (FDI) capabilities. Documentation of the additional software and software modifications required to implement the utilization capabilities includes assembly listings and flow charts

Oehrle, J.↗
NASA NTRS1974

A study to develop improved spacecraft show survey methods using Skylab/EREP data: Demonstration of the utility of the S190 and S192 data

The author has identified the following significant results. This interim report provides a demonstration of the utility of spacecraft acquired Skylab S190A and S190B photography and S192 imagery for mapping areal extent of snow cover in western United States test site areas. The data sample is from the SL-2 mission flown in June 1973. Results of the investigation indicate that areal snow cover extent can be mapped more accurately from the S190A and S190B photography than from any other spacecraft system, including ERTS. The results of a qualitative analysis of the S192 imagery indicate considerable potential for the utility of multispectral snow cover analysis; the potential for distinguishing snow from clouds automatically is particularly significant.

Barnes, J. C.↗
NASA NTRS1974

Social cost considerations and legal constraints in implementing modular integrated utility systems

Social costs associated with the design, demonstration, and implementation of the Modular Integrated Utility System are considered including the social climate of communities, leadership patterns, conflicts and cleavages, specific developmental values, MIUS utility goal assessment, and the suitability of certian alternative options for use in a program of implementation. General considerations are discussed in the field of socio-technological planning. These include guidelines for understanding the conflict and diversity; some relevant goal choices and ideas useful to planners of the MIUS facility.

Lede, N. W.↗
NASA NTRS1974

The MIST /MIUS Integration and Subsystems Test/ laboratory - A testbed for the MIUS /Modular Integrated Utility System/ program

The MIUS (Modular Integrated Utility System) concept is to be an energy-conserving, economically feasible, integrated community utility system to provide five necessary services: electricity generation, space heating and air conditioning, solid waste processing, liquid waste processing, and residential water purification. The MIST (MIUS Integration and Subsystem Test) integrated system testbed constructed at the Johnson Space Center in Houston includes subsystems for power generation, heating, ventilation, and air conditioning (HVAC), wastewater management, solid waste management, and control and monitoring. The key design issues under study include thermal integration and distribution techniques, thermal storage, integration of subsystems controls and displays, incinerator performance, effluent characteristics, and odor control.

Beckham, W. S., Jr.↗