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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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At least 181 records · Page 10

Studying the Collective Functional Response of a Receptor in Alchemical Ligand Binding Free Energy Simulations with Accelerated Solvation Layer Dynamics

Ligand binding free energy simulations (LB-FES) that involve sampling of protein functional conformations have been longstanding challenges in research on molecular recognition. Particularly, modeling of the conformational transition pathway and design of the heuristic biasing mechanism are severe bottlenecks for the existing enhanced configurational sampling (ECS) methods. Inspired by the key role of hydration in regulating conformational dynamics of macromolecules, this report proposes a novel ECS approach that facilitates binding-associated structural dynamics by accelerated hydration transitions in combination with the lambda-exchange of free energy perturbation (FEP). Further, two challenging protein-ligand binding processes involving large configurational transitions of the receptor are studied, with hydration transitions at binding sites accelerated by Hamiltonian-simulated annealing of the hydration layer. Without the need for pathway analysis or ad hoc barrier flattening potential, LB-FES were performed with FEP/lambda-exchange molecular dynamics simulation at a minor overhead for annealing of the hydration layer. The LB-FES studies showed that the accelerated rehydration significantly enhances the collective conformational transitions of the receptor, and convergence of binding affinity calculations is obtained at a sweet-spot simulation time scale. Alchemical LB-FES with the proposed ECS strategy is free from the effort of trial and error for the setup and realizes efficient on-the-fly sampling for the collective functional response of the receptor and bound water and therefore presents a practical approach to high-throughput screening in drug discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Improving Simulations of Vegetation Dynamics over the Tibetan Plateau: Role of Atmospheric Forcing Data and Spatial Resolution

The efficacy of vegetation dynamics simulations in offline land surface models (LSMs) largely depends on the quality and spatial resolution of meteorological forcing data. In this study, the Princeton Global Meteorological Forcing Data (PMFD) and the high spatial resolution and upscaled China Meteorological Forcing Data (CMFD) were used to drive the Simplified Simple Biosphere model version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics (SSiB4/TRIFFID) and investigate how meteorological forcing datasets with different spatial resolutions affect simulations over the Tibetan Plateau (TP), a region with complex topography and sparse observations. By comparing the monthly Leaf Area Index (LAI) and Gross Primary Production (GPP) against observations, we found that SSiB4/TRIFFID driven by upscaled CMFD improved the performance in simulating the spatial distributions of LAI and GPP over the TP, reducing RMSEs by 24.3% and 20.5%, respectively. The multi-year averaged GPP decreased from 364.68 gC m –2 yr –1 to 241.21 gC m –2 yr –1 with the percentage bias dropping from 50.2% to –1.7%. When using the high spatial resolution CMFD, the RMSEs of the spatial distributions of LAI and GPP simulations were further reduced by 7.5% and 9.5%, respectively. This study highlights the importance of more realistic and high-resolution forcing data in simulating vegetation growth and carbon exchange between the atmosphere and biosphere over the TP.

54 ENVIRONMENTAL SCIENCES↗

Source Decomposition of Eddy-Covariance CO 2 Flux Measurements for Evaluating a High-Resolution Urban CO 2 Emissions Inventory

We present the comparison of source-partitioned CO2 flux measurements with a high-resolution urban CO 2 emissions inventory (Hestia). Tower-based measurements of CO and 14 C are used to partition net CO 2 flux measurements into fossil and biogenic components. A flux footprint model is used to quantify spatial variation in flux measurements. We compare the daily cycle and spatial structure of Hestia and eddy-covariance derived fossil fuel CO 2 emissions on a seasonal basis. Hestia inventory emissions exceed the eddy-covariance measured emissions by 0.36 µ mol m −2 s −1 (3.2%) in the cold season and 0.62 µ mol m −2 s −1 (9.1%) in the warm season. The daily cycle of fluxes in both products matches closely, with correlations in the hourly mean fluxes of 0.86 (cold season) and 0.93 (warm season). The spatially averaged fluxes also agree in each season and a persistent spatial pattern in the differences during both seasons that may suggest a bias related to residential heating emissions. In addition, in the cold season, the magnitudes of average daytime biological uptake and nighttime respiration at this flux site are approximately 15% and 27% of the mean fossil fuel CO 2 emissions over the same time period, contradicting common assumptions of no significant biological CO 2 exchange in northern cities during winter. This work demonstrates the effectiveness of using trace gas ratios to adapt eddy-covariance flux measurements in urban environments for disaggregating anthropogenic CO 2 emissions and urban ecosystem fluxes at high spatial and temporal resolution.

eddy-covariance flux measurements↗

Measurement of the muon neutrino charged-current mesonless cross section in the NOvA near detector

NOvA is a long-baseline accelerator neutrino experiment at Fermilab. Its physics goals include precision neutrino oscillation measurements, neutrino interaction cross-section measurements and beyond Standard Model explorations. We present a measurement of muon neutrino charged-current cross section with zero mesons in the final state at the NOvA near detector. This measurement is performed as a function of the kinematics of the final state muon. Our chosen interaction channel is especially sensitive to quasielastic and meson exchange current interactions and it provides handles for constraining the cross section systematic uncertainties in oscillation analyses in present and future experiments. For particle identification, we use a convolutional neural network (CNN) trained on individual particles simulated in the NOvA Near detector. This allows us to select the desired signal while reducing the potential bias from neutrino interaction modeling. We study strategies for constraining the remaining charged-pion background via Michel electron information in a template fitting approach. The main experimental result is a two-dimensional differential cross section as a function of final-state muon kinetic energy and polar angle. The parameters of this measurement, including binning and unfolding, were optimized to reduce the expected systematic uncertainty in the total cross section. The final result shows good agreement with the main GENIE-based simulation framework that was independently fine-tuned in NOvA. We finally propose improvements and subsequent steps that build on this analysis and further dissect the final states of neutrino interactions. This work has been supported by US DOE grant DE-SC0015684.

Sánchez Falero, Sebastián Jesús [Iowa State U.]↗

Status of the muon neutrino charged-current zero mesons cross section measurement in the NOvA near detector

NOvA is a long-baseline accelerator neutrino experiment at Fermilab whose physics goals include precision neutrino oscillation as well as cross-section measurements. We present the status of the measurement of muon neutrino charged-current cross section with zero mesons in the final state at the NOvA near detector. This measurement will be made with respect to the kinematics of the final state particles. Our chosen interaction channel is especially sensitive to quasielastic and meson exchange current interactions and will provide a handle for constraining the cross section systematic uncertainties in oscillation analyses in current and future experiments. For particle identification, we use a convolutional neural network (CNN) trained on individual particles simulated in the NOvA near detector that allows us to select the desired signal while reducing the potential bias from neutrino interaction modeling. Charged pion background constraining is further improved via Michel electron tagging.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Analysis of the Latitudinal Variability of Tropospheric Ozone in the Arctic Using the Large Number of Aircraft and Ozonesonde Observations in Early Summer 2008

The goals of the paper are to: (1) present tropospheric ozone (O3) climatologies in summer 2008 based on a large amount of measurements, during the International Polar Year when the Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate Chemistry, Aerosols, and Transport (POLARCAT) campaigns were conducted (2) investigate the processes that determine O3 concentrations in two different regions (Canada and Greenland) that were thoroughly studied using measurements from 3 aircraft and 7 ozonesonde stations. This paper provides an integrated analysis of these observations and the discussion of the latitudinal and vertical variability of tropospheric ozone north of 55oN during this period is performed using a regional model (WFR-Chem). Ozone, CO and potential vorticity (PV) distributions are extracted from the simulation at the measurement locations. The model is able to reproduce the O3 latitudinal and vertical variability but a negative O3 bias of 6-15 ppbv is found in the free troposphere over 4 km, especially over Canada. Ozone average concentrations are of the order of 65 ppbv at altitudes above 4 km both over Canada and Greenland, while they are less than 50 ppbv in the lower troposphere. The relative influence of stratosphere-troposphere exchange (STE) and of ozone production related to the local biomass burning (BB) emissions is discussed using differences between average values of O3, CO and PV for Southern and Northern Canada or Greenland and two vertical ranges in the troposphere: 0-4 km and 4-8 km. For Canada, the model CO distribution and the weak correlation ( 30) of O3 and PV suggests that stratosphere-troposphere exchange (STE) is not the major contribution to average tropospheric ozone at latitudes less than 70oN, due to the fact that local biomass burning (BB) emissions were significant during the 2008 summer period. Conversely over Greenland, significant STE is found according to the better O3 versus PV correlation ( 40) and the higher 75th PV percentile. A weak negative latitudinal summer ozone gradient -6 to -8 ppbv is found over Canada in the mid troposphere between 4 and 8 km. This is attributed to an efficient O3 photochemical production due to the BB emissions at latitudes less than 65oN, while STE contribution is more homogeneous in the latitude range 55oN to 70oN. A positive ozone latitudinal gradient of 12 ppbv is observed in the same altitude range over Greenland not because of an increasing latitudinal influence of STE, but because of different long range transport from multiple mid-latitude sources (North America, Europe and even Asia for latitudes higher than 77oN).

climatologies↗

Effects of emitting surfaces and trapped ions on the sheath physics and current flow in multidimensional plasma systems

Recent one-dimensional simulations of planar sheaths with strong electron emission have shown that trapping of charge-exchange ions causes transitions from space-charge limited (SCL) to inverse sheaths. However, multidimensional emitting sheath phenomena with collisions remained unexplored, due in part to high computational cost. As such, we developed a novel continuum kinetic code to study the sheath physics, current flow and potential distributions in two-dimensional unmagnetized configurations with emitting surfaces. For small negatively biased thermionic cathodes in a plasma, the cathode sheath can exist in an equilibrium SCL state. The SCL sheath carries an immense density of trapped ions, neutralized by thermoelectrons, within the potential well of the virtual cathode. For further increases of emitted flux, the trapped ion cloud expands in space. The trapped ion space charge causes an increase of thermionic current far beyond the saturation limit predicted by conventional collisionless SCL sheath models without ion trapping. For sufficiently strong emission, the trapped ion cloud consumes the entire 2D plasma domain, forming a mode with globally confined ions and an inverse sheath at the cathode. In situations where the emitted flux is fixed and the bias is swept (e.g. emissive probe), the trapped ions cause a large thermionic current to escape for all biases below the plasma potential. Strong suppression of the thermionic emission, required for the probe to float, only occurs when the probe is above the plasma potential.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Machine learning and ligand binding predictions: A review of data, methods, and obstacles

We report that computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in exchange for time-intensive simulations. This paper reviews current trends in the use of machine learning for drug binding predictions, data sources to develop machine learning algorithms, and potential problems that may lead to overfitting and ungeneralizable models. A few popular datasets that can be used to develop virtual high-throughput screening models are characterized using spatial statistics to quantify potential biases. We can see from evaluating some common benchmarks that good performance correlates with models with high-predicted bias scores and models with low bias scores do not have much predictive power. A better understanding of the limits of available data sources and how to fix them will lead to more generalizable models that will lead to novel drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

Modeling Whitecaps on Global Scale

Whitecaps play an important role in the surface-atmosphere interactions across the ocean. They are directly linked to the energy dissipation rate during wave breaking and transfer of heat, momentum, and gas/aerosol exchange from the wind to the sea surface. Although the first models of W were dependent only on wind speeds, a large number of diverse models based on wind and sea state which include wave height, wave age, friction velocity, and stability effects have been proposed since then. However, it is recognized that most of the proposed W models have strong systematic (e.g., zonal bias) and random errors when compared against observations. This is partly due to the differences in environmental conditions, measurement techniques, and geographical locations among these studies. But, some of these biases are linked to the inability of the proposed models to capture the variability in W in certain wind/wave regimes. Despite the knowledge of existing biases, W residual relationships from the models with wind and wave fields remain highly uncertain, with residual trends varying between the published studies. Here, we take advantage of the availability of relatively dense observations of W from WindSat microwave satellite retrievals in combination with the University of Miami wave model which was recently incorporated within the NASA GMAO/GEOS system (GEOS-UMWM). We use Windsat W retrievals to assess and constrain the previously published W models and understand the relationships of residuals from models in different wind/wave regimes. We link these unexplained residual variations to additional factors such as swell index, drag coefficient etc and add information to the existing whitecap models. Since Windsat retrievals cover wide range of environmental conditions, it helps to reduce the uncertainties associated with differences in measurement techniques. Regression of wind-wave fields against all Windsat data points (CTL) results in larger residuals for lower wave age and W is overestimated upto ~4% for wave age < 10 and underestimated by upto ~2% as wave age increases. We attest to this bias by considering two approaches. One is to perform regression separately for different stages of wave development such as developing sea, fully developed, and wind sea regimes thereby understanding the sensitivity of regression coefficients to sea state (EXP1). Another is to derive coefficients of W models in EXP1 as a function of additional wind/wave factors such as swell index, drag coefficient, and mean squared slope, deriving more nonlinear W models (EXP2). EXP2 provides reduction in Root Mean Squared Error (RMSE) by 0.1-0.3%. Sea surface drag has a stronger relationship with regression coefficients compared to swell index.These additional factors provide improved parameterizations in different wind and wave age regimes, with smaller unexplained/residual variations in W that has been a major concern in the W community.

Raman, Aishwarya↗

Simulations of 7 Be and 10 Be with the GEOS-Chem global model v14.0.2 using state-of-the-art production rates

The cosmogenic radionuclides 7 Be and 10 Be are useful aerosol tracers for atmospheric transport studies. Combining 7 Be and 10 Be measurements with an atmospheric transport model can not only improve our understanding of the radionuclide transport and deposition processes but also provide an evaluation of the transport process in the model. To simulate these aerosol tracers, it is critical to evaluate the influence of radionuclides production uncertainties on simulations. Here we use the GEOS-Chem chemical transport model driven by the MERRA-2 reanalysis to simulate 7 Be and 10 Be with different production scenarios: the default production rate in GEOS-Chem based on an empirical approach (Lal and Peters, 1967; denoted as LP67), and two production rates from the CRAC:Be (Cosmic Ray Atmospheric Cascade: Beryllium; Poluianov et al., 2016) model considering only geomagnetic cut-off rigidities for a geocentric axial dipole (denoted as P16) or realistic spatial geomagnetic cut-off rigidity variations due to non-dipole moments of the geomagnetic field (denoted as P16spa). The model results are comprehensively evaluated with a large number of measurements including surface air concentrations and deposition fluxes. The model with the P16spa production can reproduce the absolute values and temporal variability of 7 Be and 10 Be surface concentrations and deposition fluxes on annual and sub-annual scales, as well as the vertical profiles of air concentrations. Simulations with the LP67 production tend to overestimate the absolute values of 7 Be and 10 Be concentrations. The P16 simulations suggest less than 10% differences compared to P16spa but tend to produce a significant positive bias (>20%) in the 7 Be deposition fluxes over East Asia. We find that the deposition fluxes are more sensitive to the production in the troposphere and downward transport from the stratosphere. Independent of the production models, surface air concentrations and deposition fluxes from all simulations show similar seasonal variations, suggesting a dominant meteorological influence. The model can also simulate reasonably the stratosphere-troposphere exchange process of 7 Be and 10 Be by producing stratospheric contribution and 10 Be/ 7 Be ratio values that agree with measurements. Finally, we illustrate the importance of including the time-varying solar modulation in the production calculation, which can significantly improve the agreement between model results and measurements, especially at mid- and high- latitudes. Reduced uncertainties in the production rates, as demonstrated in this study, improve the utility of 7 Be and 10 Be as aerosol tracers for evaluating and testing transport and scavenging processes in global models.

Minjie Zheng↗

Protoplast fusion in Bacillus species produces frequent, unbiased, genome-wide homologous recombination

Abstract In eukaryotes, fine-scale maps of meiotic recombination events have greatly advanced our understanding of the factors that affect genomic variation patterns and evolution of traits. However, in bacteria that lack natural systems for sexual reproduction, unbiased characterization of recombination landscapes has remained challenging due to variable rates of genetic exchange and influence of natural selection. Here, to overcome these limitations and to gain a genome-wide view on recombination, we crossed Bacillus strains with different genetic distances using protoplast fusion. The offspring displayed complex inheritance patterns with one of the parents consistently contributing the major part of the chromosome backbone and multiple unselected fragments originating from the second parent. Our results demonstrate that this bias was in part due to the action of restriction–modification systems, whereas genome features like GC content and local nucleotide identity did not affect distribution of recombination events around the chromosome. Furthermore, we found that recombination occurred uniformly across the genome without concentration into hotspots. Notably, our results show that species-level genetic distance did not affect genome-wide recombination. This study provides a new insight into the dynamics of recombination in bacteria and a platform for studying recombination patterns in diverse bacterial species.

59 BASIC BIOLOGICAL SCIENCES↗

Natural Air-Sea Flux of CO2 in Simulations of the NASA-GISS Climate Model: Sensitivity to the Physical Ocean Model Formulation

Results from twin control simulations of the preindustrial CO2 gas exchange (natural flux of CO2) between the ocean and the atmosphere are presented here using the NASA-GISS climate model, in which the same atmospheric component (modelE2) is coupled to two different ocean models, the Russell ocean model and HYCOM. Both incarnations of the GISS climate model are also coupled to the same ocean biogeochemistry module (NOBM) which estimates prognostic distributions for biotic and abiotic fields that influence the air-sea flux of CO2. Model intercomparison is carried out at equilibrium conditions and model differences are contrasted with biases from present day climatologies. Although the models agree on the spatial patterns of the air-sea flux of CO2, they disagree on the strength of the North Atlantic and Southern Ocean sinks mainly because of kinematic (winds) and chemistry (pCO2) differences rather than thermodynamic (SST) ones. Biology/chemistry dissimilarities in the models stem from the different parameterizations of advective and diffusive processes, such as overturning, mixing and horizontal tracer advection and to a lesser degree from parameterizations of biogeochemical processes such as gravitational settling and sinking. The global meridional overturning circulation illustrates much of the different behavior of the biological pump in the two models, together with differences in mixed layer depth which are responsible for different SST, DIC and nutrient distributions in the two models and consequently different atmospheric feedbacks (in the wind, net heat and freshwater fluxes into the ocean).

Gas exchange↗

Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China

Croplands play a critical role in regulating the energy and moisture exchanges between the land surface and atmosphere. However, the interactions between cropland and climate are usually poorly represented due to a lack of detailed representation in crop types and field management. Here, we coupled the Noah-MP-Crop model with the state-of-the-art Weather Research and Forecasting (WRF) model to explore and evaluate the crop growth dynamics in response to climate variations across Northeast China. The default parameters of the crop model were not exactly suitable for the agricultural ecosystems in Northeast China. The detailed cropland distribution, and crop phenology parameters including growing degree days (GDD) and planting (harvesting) date were first created using multi-source remote sensing products and reanalysis data, and was then successfully used to simulate the growth and yield for corn and soybean and associated energy exchanges. We also optimized and calibrated other crop parameters using the time-series of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface products. The modified crop model substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%.

54 ENVIRONMENTAL SCIENCES↗

Neutralization Short-Circuiting with Weak Electrolytes Erodes the Efficiency of Bipolar Membranes

Bipolar membranes (BPMs) are critical components of a variety of electrochemical energy technologies. Many electrochemical applications require the use of buffers to maintain stable, nonextreme pH environments, yet the impact of buffers or weak acids/bases on the electrochemical behavior of BPMs remains poorly understood. Here, our data for a cell containing weak electrolytes is consistent with internal pH gradients within the anion exchange membrane (AEM) or cation exchange membrane (CEM) component of the BPM that form via ionic short-circuiting processes at open-circuit. Short-circuiting results from the coupling of co-ion crossover and parasitic neutralization and leads to buffering of the bipolar interface. This phenomenon, which we term neutralization short-circuiting, serves to erode BPM efficiency by attenuating the open-circuit membrane voltage and introducing parasitic reverse bias currents associated with weak acid/base dissociation at the interface. These findings establish a mechanistic basis for the operation of BPM cells in the presence of weak acid/base electrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Status of the Muon Neutrino Charged-Current Mesonless Cross Section Measurement in the NOvA Near Detector

NOvA is a long-baseline accelerator neutrino experiment at Fermilab whose physics goals include precision neutrino oscillation as well as cross-section measurements. We present the status of the measurement of a muon neutrino charged-current cross section with zero mesons in the final state at the NOvA near detector. This measurement is being made with respect to the kinematics of the final state muon. The chosen interaction channel is especially sensitive to quasielastic and meson exchange current interactions and aims to provide experimental constraints for the development of models of neutrino interactions. It will also provide a handle for constraining cross section systematic uncertainties in oscillation analyses in current and future experiments. For particle identification, we use a convolutional neural network (CNN) trained on individual particles simulated in the NOvA near detector that allows us to select the desired signal while reducing the potential bias from neutrino interaction modeling. Charged pion background constraining is further improved via Michel electron tagging.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Status of the Muon Neutrino Charged-Current Zero Mesons Cross Section at the NOvA Near Detector

NOvA is a long-baseline accelerator neutrino experiment at Fermilab whose physics goals include precision neutrino oscillation as well as cross-section measurements. We present the status of the measurement of a muon neutrino charged-current cross section with zero mesons in the final state at the NOvA near detector. This measurement is being made with respect to the kinematics of the final state muon. The chosen interaction channel is especially sensitive to quasielastic and meson exchange current interactions and aims to provide experimental constraints for the development of models of neutrino interactions. It will also provide a handle for constraining cross section systematic uncertainties in oscillation analyses in current and future experiments. For particle identification, we use a convolutional neural network (CNN) trained on individual particles simulated in the NOvA near detector that allows us to select the desired signal while reducing the potential bias from neutrino interaction modeling. Charged pion background constraining is further improved via Michel electron tagging.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Dynamic global vegetation models underestimate net CO 2 flux mean and inter-annual variability in dryland ecosystems

Despite their sparse vegetation, dryland regions exert a huge influence over global biogeochemical cycles because they cover more than 40% of the world surface (Schimel 2010 Science 327 418–9). It is thought that drylands dominate the inter-annual variability (IAV) and long-term trend in the global carbon (C) cycle (Poulter et al 2014 Nature 509 600–3, Ahlstrom et al 2015 Science 348 895–9, Zhang et al 2018 Glob. Change Biol. 24 3954–68). Projections of the global land C sink therefore rely on accurate representation of dryland C cycle processes; however, the dynamic global vegetation models (DGVMs) used in future projections have rarely been evaluated against dryland C flux data. Here, we carried out an evaluation of 14 DGVMs (TRENDY v7) against net ecosystem exchange (NEE) data from 12 dryland flux sites in the southwestern US encompassing a range of ecosystem types (forests, shrub- and grasslands). We find that all the models underestimate both mean annual C uptake/release as well as the magnitude of NEE IAV, suggesting that improvements in representing dryland regions may improve global C cycle projections. Across all models, the sensitivity and timing of ecosystem C uptake to plant available moisture was at fault. Spring biases in gross primary production (GPP) dominate the underestimate of mean annual NEE, whereas models' lack of GPP response to water availability in both spring and summer monsoon are responsible for inability to capture NEE IAV. Errors in GPP moisture sensitivity at high elevation forested sites were more prominent during the spring, while errors at the low elevation shrub and grass-dominated sites were more important during the monsoon. We propose a range of hypotheses for why model GPP does not respond sufficiently to changing water availability that can serve as a guide for future dryland DGVM developments. Our analysis suggests that improvements in modeling C cycle processes across more than a quarter of the Earth's land surface could be achieved by addressing the moisture sensitivity of dryland C uptake.

54 ENVIRONMENTAL SCIENCES↗

Direct determination of cellulosic glucan content in starch-containing samples

A simple and highly selective analytical procedure is presented for the determination of cellulosic glucan content in samples that contain both cellulose and starch. This method eliminates the unacceptably large compounding errors of current two-measurement methods. If both starch and cellulose are present before analytical hydrolysis, both will be hydrolyzed to glucose causing bias and inaccuracy in the method. To prevent this interference, the removal of starch prior to cellulosic quantification is crucial. The method presented here is a concise in-series procedure with minimal measurements, eliminating large compounding errors. Sample preparation consists of a starch extraction employing enzymatic hydrolysis followed by a simple filtration and wash. The samples are then subjected to a two-stage acid hydrolysis. The concentration of glucose is determined by ion exchange high-performance liquid chromatography with a Pb 2+ column and a refractive index detector. The cellulosic glucan content is calculated based on the initial dry weight of the starting material. Data for the native biomass materials studied show excellent reproducibility, with coefficients of variance of 3.0% or less associated with the method. This selectivity for cellulosic glucan by the procedure was validated with several analytical techniques such as liquid chromatography coupled with mass spectrometry (LC–MS), Raman spectroscopy, and nuclear magnetic resonance.

09 BIOMASS FUELS↗