SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
N2H5NO3 crystallizes in the monoclinic P2_1/c space group. The structure is zero-dimensional and consists of four ammonia molecules, four ammonia molecules, and four nitric acid molecules.
The Area 3 Support Buildings site (A3SB) consists of two separate areas located on the north side of Beach Road in the northern portion of Kennedy Space Center, Florida (KSC), outside the secured perimeter of KSC. The A3SB areas are approximately 0.6 miles apart, and were developed as Shiffler's Grocery Store and Service Station (west site) and as a residence (east site) prior to acquisition by the National Aeronautics and Space Administration (NASA) in 1963. Both areas were used by the Bendix Company in support of NASA as chemical laboratories from 1963 through 1969. Both of the buildings were demolished by 1987. The west portion of the site was used by the US Fish & Wildlife Service (F&W) in support of the Merritt Island National Wildlife Refuge (MINWR) for parking at the entrance to the Hammock Trails from 1969 to the present. The east portion of the site was used for intermittent suspect materials staging in the early 1990s and is still used as an apiary location, but is otherwise no longer active. In support of the NASA HSWA permit requirements, this site was identified as Potential Release Location (PRL) 218 and a Solid Waste Management Unit (SWMU) Assessment (SA) was conducted in 2013. Confirmatory Sampling (CS) was recommended and approved by the KSC Remediation Team (KSCRT). Three locations of concern (LOCs) were identified and sampled at the site. The LOCs include two former chemical labs and a former suspect staging area. The CS was conducted in March of 2015 at three locations by means of Direct Push Technology (DPT) groundwater sampling and at one location for soil sampling. The samples were collected and analyzed in accordance with the approved CS Work Plan. There were no exceedances of criteria detected in any of the samples from the three LOCs. The results of this investigation indicate that past and/or present operations have not negatively impacted environmental media at the A3SB. Based upon no confirmed groundwater detections above GCTLs and no soil impacts above applicable screening criteria no further investigation is warranted at the A3SB. A No Further Action (NFA) status for all locations was approved by the KSCRT at the June 2015 meeting.
This dataset is a model archive of the paper Permafrost Thaw, Uneven Subsidence and Projected Drying of Ice-wedge Polygon Tundra (in prep) to support a modeling study investigating how projected increases in Arctic temperature and precipitation will jointly influence hydrologic conditions in ice-rich tundra landscapes. With this dataset, this study is to address the research question: Will Arctic tundra landscapes become wetter or drier with increasing precipitation and temperature in the future when thaw-induced ground subsidence and associated microtopographic evolution are represented? The simulations focus on ice-wedge polygon tundra, a widespread form of ice-rich permafrost terrain that is highly sensitive to thaw-driven landscape change. This dataset contains model input and output data for four study watersheds in Alaska: Anaktuvuk, Utqiagvik (formerly Barrow), Brooks Foothills, and Prudhoe Bay. Simulations were performed using the Advanced Terrestrial Simulator (ATS, v1.5), a physics-rich integrated surface–subsurface hydrologic model. For each watershed, ten modeling cases were performed representing two landscape evolution conditions (with subsidence and without subsidence) combined with five climate forcing scenarios derived from Shared Socioeconomic Pathways (SSP5, SSP5 with precipitation trend, SSP2, SSP2 with precipitation trend, and SSP2 with double precipitation trend). Particularly, for each watershed under the forcing SSP2 with precipitation trend, there are two additional simulations considering spatially heterogeneous subsidence distributions: one assumes randomly distributed scaling and the other includes elevation dependent distribution scaling. These simulations span 1980–2099 and include spin-up runs (1980–2009) followed by transient projections (2010–2099). To facilitate reproducibility of simulations, all datasets are organized by watershed. For each study watershed, the dataset contains: (1) Pre-partitioned mesh files for 32-core modeling (.par.32.XX), located in EACH_WATERSHED/mesh/basin; and also a non-partitioned mesh file (.exo) located in EACH_WATERSHED/mesh; (2) Climate forcings corresponding to the five SSP scenarios (.h5), located in EACH_WATERSHED/data; (3) Final states (.h5) from column spin-up modeling used to initialize historical watershed-scale spin-up runs from 1980 to 2009, located in EACH_WATERSHED/PreSpinupHistorical; (4) Final states (.h5) of historical watershed-scale spin-up runs from 1980 to 2009 used to initialize projection runs, located in EACH_WATERSHED/Spinup_daymetERA5; (5) ATS modeling input files (.xml), located in EACH_WATERSHED/EACH_SIMULATION_SCENARIO/inputfiles; (6) ATS modeling output files (.dat), located in in EACH_WATERSHED/EACH_SIMULATION_SCENARIO/combined_obs; (7) For the Brooks Foothills watershed, additional spatial model outputs are provided (.h5) for selected years (2033 and 2093) used to generate spatial figures in this study, located in Brooksfoothills/EACH_SIMULATION_SCENARIO/results-WITH/WITHOUT_SUBSIDENCE-year2033/2093. All data files with suffix .h5 can be accessible through Python h5py, and all data files with suffix of .dat can be imported by Python pandas. Mesh file with .exo can be visualized through Paraview or read by Python netCDF. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.
Approved names are established for the six meteorites found in Castro County, Texas; the names and classes of the specimens, which are thought to represent six separate falls, are: (1) Nazareth(a), L6; (2) Nazareth(b), L6; (3) Nazareth(c), H5; (4) Nazareth(d), H5; (5) Nazareth(e), H6; and Nazareth(iron), IIIA. Attention is given to recovery details for the six and electron microprobe analysis results for olivine and low-Ca pyroxene grains in five of the Nazareth chondrites.
Two unique meteorites were identified by means of a mineralogical examination of the smaller-sized Yamato achondrites. Yamato-74130 is the most iron-rich ureilite with Na, Cr-rich augite instead of pigeonite. Yamato-74160 was extensively recrystallized, but the composition and proportion of olivine, orthopyroxene, augite, and plagioclase is consistent with LL7 chondrites. ALHA77005 is a unique achondrite with olivine, possibly three pyroxene assemblages, and maskelynite. These meteorites provide evidence that there may be other 'thermalized' asteroids than the howardite parent body. Detailed petrologic descriptions of the unique achondrites, recrystallized diogenite Yamato-74013, and the rapidly cooled eucrite Yamato-74450 with pyroxene phenocrysts are given. It is inferred from the bulk chemistry and the mineralogical reexamination of Yamato-75028 that it is composed of the H5-type clasts and chondrule-rich H(L)3-like matrix with the H5 fragments. A close relationship in the collisional evolution of some asteroids with these materials is inferred.
A search of active deflation basins near Jal, Lea County, New Mexico resulted in the discovery of two meteorites, Lea County 001 and 002. Lea County 001 has mean olivine and low-Ca pyroxene compositions of Fa(19) and Fs(17), respectively. These and all other mineralogical and petrological data collected indicate a classification of H5 for this stone. Lea County 002 has mean olivine and low-Ca pyroxene compositions of Fa(2) and Fs(4), and is unequilibrated. Although it is mineralogically most similar to Kakangari and chondritic clasts within Cumberland Falls, the high modal amount of forsterite makes Lea County a unique type 3 chondrite. Oxygen isotope data for Lea County 002 fall on an 0-16-mixing line through those of the enstatite meteorites and IAB irons, a feature shared by Kakangari.
The MAGIK Robotic analysis team provides kinematic feasibility assessments for the ISS and has produced an animation to evaluate Extravehicular Robotics (EVR) activities for the SpaceX (SpX)-29 flight. The animation also displays the current ISS configuration at the time of the flight. Flight SpX-29 includes a SpaceX Cargo Dragon vehicle docked to Node 2 Forward (N2F) International Docking Adapter (IDA) and consists of the delivery of AWE (Atmospheric Waves Experiment) and Integrated LCRD LEO User Modem Amplifier optical communications Terminal (ILLUMA-T). Robotic operations shown include the Special Purpose Dexterous Manipulator (SPDM) Trunk extraction of ILLUMA-T and maneuvering to the JEM RMS Handoff position, the SPDM Trunk extraction of AWE, maneuvering to Express Logistics Carrier (ELC) 1 Site 3 to remove Space Test Program - Houston 5 (STP-H5), and the installation of AWE to ELC-1 Site 3. There is no disposal payload for this flight.
Based on [1], XRFtomo brings together a variety of image processing tools used for preparing, processing and reconstructing 3D renditions of samples imaged using a synchrotron-based x-ray fluorescence (XRF) microprobe or nanoprobe at the Advanced Photon Source (APS). This software bridges the gap between data acquisition and visualization by providing tools to adjust for misalignment in the imaging process and tools for running reconstructions[2], all with the use of a GUI or command line. XRFtomo accepts .h5 files processed through MAPS[3] and it outputs reconstructed cross-sections, projections, sinograms, metadata, and a configuration file with last-used parameters for rapid processing the next time the program is launched. These images serve as inputs for other 3D visualization software for generating volumetric renderings and animations.
Dataset associated with "A sterile-neutrino search using data from the MicroBooNE liquid-argon time projection chamber performed in an undergraduate teaching laboratory" an educational paper to teach data analisys within particle physics for 3-4 yr physics major undergrads. Data format are csv, h5 and pkl
MIL 090657 meteorite (CR2.7) is one of the least altered primitive carbonaceous c hondrites [1]. This meteorite has amorphous silicates like GEMS (glass with embedded metal and sulfide), which are characteristically contained in cometary dust, in matrix [2,3] as with the Paris meteorite [4]. Three lithologies have been recognized; lithology-1 (L 1) dominated by submicron anhydrous silicates, lithology-2 (L2) by GEMS-like amorphous silicates and lithology-3 (L3) by phyllosilicates [2]. Organic materials are abundant in L 1 and L2 [2,3]. L 1 and L2 were further divided into sub-lithology respectively based on their textures and compositions [5]. These studies were performed by 2D SEM and TEM observations of sample surfaces and thin sections that are unable to reveal what constitute each lithology and how these lithologies are distributed and related to each other. This information will provide important insights into alteration and aggregation processes on asteroids and in the early solar nebula. In this study, MIL 090657 matrix was examined in 3D using two types of X-ray tomography; DET (dual-energy tomography) [6] and SIXM (scanning-imaging X-ray microscopy) [7]. Mineral phases can be discriminated based on absorption contrasts at two different X-ray energies in DET. In SIXM, materials composed of light elements such as water or organic materials can be identified based on phase and absorption contrasts. By combining these methods, we can discriminate not only organic materials from voids but also hydrous alteration products, such as hydrated silicates and carbonates, from anhydrous minerals [8]. In this study, we first observed cross sections of MIL 090657 matrix fragments C1 00 mm) in detail using FE-SEM/ EDS. Based on the results, three house-shaped samples (3 0 -50 mm) were extracted from L 1, L2 and their boundary (H1, H3 and H5, respectively) using FIB. 3D imaging of these samples were conducted at BL47XU of SPring-8, a synchrotron radiation facility, with -30-40 nm/ voxel and - 70-80 nm/ voxel at 7keV and 8keV in DET and -100 nm/ voxel at 8keV in SIXM. We found new lithologies that we named L4, L5 and L6 in H1 and H3 in addition to L 1 and L2. L4. L5 and L6 are mainly composed of probably phyllosilicates with different Fe contents. Sulfide and framboidal magnetite were recognized in L4. L5 includes magnetite and carbonate and L6 includes anhydrous silicates having cracks inside. L 1, L2, L4 and L5 are porous while few voids were observed in L6. L4 adjoins to L 1 with boundary, which is not very distinct. L2, L5 and L6 adjoin to each other, and the boundaries of L6 with L2 and L5 are clear. In H5, coarse mineral grains C5-1 0 mm) such as Fe-metal and enstatite are present in L 1 and L2. L 1-L2 boundary is not sharp in 3 D. In conclusion, we found a variety of lithologies by 3D observation for the first time, suggesting that t he MIL 0 90657 meteorite experienced complex alteration and aggregation histories. As L2 is dominated by amorphous silicates, which are extremely susceptible to aqueous alteration, t his is p resumed to be t he most primitive lithology. The contact between L2 and phyllosilicate-bearing lithologies (L5 and L6) with clear boundaries indicates that they were aggregated after aqueous alteration of L5 and L6. The indistinct boundary between L 1 and L2 is suggesting that these two lithologies might originally be the same aggregate composed of amorphous silicates and coarse mineral grains. L 1 might have experienced weak aqueous alteration followed by mild thermal alteration [2], while L2 did not undergo aqueous alteration.
We lack fundamental information related to the location and timing of the aqueous alteration in the early solar system, and the detailed nature of the aqueous fluid itself [1,2,3]. Two thermally-metamorphosed ordinary chondrite regolith breccias (Monahans 1998, hereafter simply “Monahans” (H5) and Zag (H3-6)) contain fluid inclusion-bearing halite (NaCl) crystals [4,5]. The halites were dated by K-Ar, Rb-Sr and I-Xe systematics to be ~4.5 billion years old [4,6,7] and thus the trapped aqueous fluids are at least as ancient. Through previously-published O, H, Cr isotopic work we have established that the fluid-bearing halite originated in the outer solar system[8].
This dataset is a model archive of the paper A bespoke model of Arctic river basins based on hillslope delineation (in prep), which introduces a watershed decomposition and parameterization method for large scale permafrost hydrology simulation. With this dataset, this study aims to address the research question: whether a computationally efficient hillslope-based modeling framework can reliably simulate discharge at Arctic river-basin scales. This dataset contains model input and output data for five modeling scenarios at a study site located in the Sagavanirktok River basin. The five modeling scenarios include three modeling cases under temperate conditions using full 3D, decomposed 3D, and decomposed 2D modeling strategies; and two modeling cases under actual Arctic conditions with permafrost using full 3D and decomposed 2D modeling strategies. Simulations were performed using the Advanced Terrestrial Simulator (ATS, v1.6 for three temperate scenarios and v1.5 for two Arctic scenarios), a physics-rich integrated surface–subsurface hydrologic model with cryo-hydrology features. For the three temperate models, simulations were conducted for the period of 10/01/1993 - 09/30/2002; and for the two Arctic models, simulations were conducted for the period of 01/01/1994 - 12/31/2002. To facilitate reproducibility of simulations, all datasets are organized hierarchically. The dataset contains: (1) Mesh files (.exo) for full 3D model, decomposed 3D models, and decomposed 2D models, located in huc/190604020802_gauge15906000/mesh/. Mesh files can be visualized through Paraview or read by Python. (2) Climate forcings (.h5) for full 3D model and decomposed 3D/2D models are located in huc/190604020802_gauge15906000/daymet_onePiece/, and huc/190604020802_gauge15906000/vp_pr_revised_daymet_1980_2006_with_wind/ separately. Accessible by Python. (3) Raw measured gage discharge (.csv) from USGS, located in huc/190604020802_gauge15906000/gaged_basin15906000_discharge_usgs/. Accessible by Python. (4) Delineated subdomain raster (.tif) and shape files (.shp), and the final parameterized results (.npy) for decomposed models, located in huc/190604020802_gauge15906000/data_preprocessed-meshing. Accessible by Python. (5) Temperate models are located in nonpermaf_huc190604020802_gauge15906000/, which includes three cases: decomposed 2D models (inside model_0*-hillslope_*), decomposed 3D models (inside model_1*-subcatchment_*), and full 3D model (inside model_2*-onepiece_*). Two step spin-up results (checkpoint_final.h5) are located in model_*1-*_spinup_steadystate and model_*2-*_spinup_cycle, separately, which are used to initialize real transient models. The input files (.xml) and output results (.dat) of the real transient models are located in model_*3-*_transient/. Especially, for two example hillslope models (ID=-11 and 11), additional h5py files are included in model_03-hillslope_transient/hillslope-11/, model_03-hillslope_transient/hillslope11, model_13-subcatchment_transient/subcatchment-11/, model_13-subcatchment_transient/subcatchment/11, respectively, which are used to plot the saturation figure (Figure 5) in the manuscript. Accessible by Python. (6) Arctic models are located in huc190604020802_gauge15906000/, which includes two cases: decomposed 2D models (inside model_04-hillslope_transient), and full 3D model (inside model_05-onepiece_transient_mannp1_ra). Three step spin-up results (checkpoint_final.h5) are located in model_01-column_freezeup/, model_02-column_spinup/, model_03-hillslope_spinup/, respectively, which are used to initialize real 2D transient hillslope models. The input files (.xml) and output results (.dat) of transient 2D hillslope models are located in model_04-hillslope_transient/. The input files (.xml) and output results (.dat) of the full 3D transient model is located in model_05-onepiece_transient_mannp1_ra/. The full 3D transient model is initialized by model_02-column_spinup/. Accessible by Python. (7) The MOSART routed discharge results (.csv) under Arctic conditions is located in huc190604020802_gauge15906000/MOSART/. Accessible by Python. (8) All Python codes (.py) used to parameterize full 3D model to decomposed 2D models are located in script/. These codes fit with watershed workflow (a watershed delineation tool) v1.4 under the branch gaob/v1.4 from https://github.com/gaobhub/watershed-workflow.git.
Bitterness often associated with whole wheat products may be related to phenolics in the bran. Cyclodextrins (CDs) are known to form inclusion complexes. The objective was to form inclusion complexes between β-CD and wheat phenolics. Pure phenolic acids (trans-ferulic acid (FA), caffeic acid (CA), and p-coumaric acid (CO)) and phenolic acids from wheat bran were used to investigate complex formation potential. Complexes were characterized by spectroscopy techniques, and a computational and molecular modeling study was carried out. The relative amount of complex formation between β-CD and wheat bran extract was CA > CO > FA. The phenolic compounds formed inclusion complexes with β-CDs by non-covalent bonds. The quantum-mechanical calculations supported the experimental results. The most stable complex was CO/β-CD complex. The ΔH value for CO/β-CD complex was -11.72 kcal/mol and was about 3 kcal/mol more stable than the other complexes. The QSPR model showed good correlation between binding energy and 1H NMR shift for the H5 signal. This research shows that phenolics and β-CD inclusion complexes could be utilized to improve the perception of whole meal food products since inclusion complexes have the potential to mask the bitter flavor and enhance the stability of the phenolics in wheat bran.
We describe a powerful and generalized parameter sweep tool in this report that was originally developed to analyze the performance of existing and novel water treatment models being developed in WaterTAP. Since WaterTAP is built upon IDAES and Pyomo, the parameter sweep tool can be used to systematically explore and debug the behavior of most Pyomo and IDAES numerical models. In order to enable meaningful analyses, the parameter sweep tool has been designed with the following features: 1) Model flexibility: The parameter sweep tool does not enforce any restrictions on the types of models that can be used with it. As long as a Pyomo model can be solved and the parameter is active and mutable, the tool only needs functions that describe how to run the model, the sweep parameters, and the output quantities of interest. 2) Flexible sampling: The parameter sweep tool has inbuilt functions to generate samples from a random distribution or a multidimensional Euclidean space. Furthermore, the users have to ability to supply samples generated from a tool of their choice. 3) Multiple sweep types: A user can choose from one of 3 types of parameter sweeps depending on their needs. 4) Detailed outputs: Outputs generated by the parameter sweep tool can be stored in detailed H5 file or user-friendly CSV files for post processing. 5) Parallel computing: The parameter sweep supports shared and distributed memory parallel computing to enable the use of high performance computers (HPC) for large-scale analyses. 6) Modular: The parameter sweep tool is self-contained and can easily be integrated within an outer-loop analysis or as desired by the user. 7) Ease of use: The tool is well documented and a simple sweep can be easily executed by following the online documentation in a few lines of code. We demonstrate the use of the parameter sweep tool on a simple water treatment system from the WaterTAP repository and show its parallel scaling performance on an Apple laptop and NREL's Eagle HPC. The parameter sweep tool is actively being used with models currently being developed within WaterTAP and we expect its use to grow beyond it to other IDAES and Pyomo models.
This dataset includes Rayleigh Frequency Shift (RFS) Distributed Strain Sensing (DSS) cumulative strain change and change rate data. The data was acquired during the stimulation of Utah FORGE Well 16A(78)-32 in April 2024 via fiber installed in Well 16B(78)-32. The fiber optic data was acquired using Neubrex SR7000 RFS DSS Distributed Strain sensing instruments and is saved here in the format of HDF5 files (.h5 extension). The spatial sampling on the full wellbore profiles is 0.20 centimeters. The data is the far field strain change response from a baseline profile made down the 16B well on April 3, 2024, so each strain value represents the strain change or strain change rate at each depth relative to the baseline reference profile. The data arrays for each type share the same dimensions (number of channels and time stamps).
Zag (H3-6) and Monahans (1998) (H5) are regolith breccias that contain 4.5 GY old halite crystals which in turn contain abundant inclusions of aqueous fluids, solids and organics [1-4]. We have previously proposed that these halites originated on a hydro-volcanically-active C-class asteroid, probably Ceres [3-7]. We have begun a detailed analysis of the included solids and organics and are re-examining the related carbonaceous (C)) chondrite clast we previously reported in Zag [5-7]. These new investigations will potentially reveal the mineralogy of asteroid Ceres. We report here on potentially identical C chondrite clasts in the H chondrite regolith breccias Tsukuba (H5-6) and Carancas (H4-5). The clast in Tsukuba was known before [8], but the Carancas clast is newly recognized.
Two new fragments of the Almahata Sitta meteorite and a sample of sand from the related strewn field in the Nubian Desert, Sudan, were analyzed for two to six carbon aliphatic primary amino acids by ultrahigh performance liquid chromatography with UV-fluorescence detection and time-of-flight mass spectrometry (LC-FT/ToF-MS). The distribution of amino acids in fragment #25, an H5 ordinary chondrite, and fragment #27, a polymict ureilite, were compared with results from the previously analyzed fragment #4, also a polymict ureilite. All three meteorite fragments contain 180-270 parts-per-billion (ppb) of amino acids, roughly 1000-fold lower than the total amino acid abundance of the Murchison carbonaceous chondrite. All of the Almahata Sitta fragments analyzed have amino acid distributions that differ from the Nubian Desert sand, which primarily contains L-alpha-amino acids. In addition, the meteorites contain several amino acids that were not detected in the sand, indicating that many of the amino acids are extraterrestrial in origin. Despite their petrological differences, meteorite fragments #25 and #27 contain similar amino acid compositions; however, the distribution of amino acids in fragment #27 was distinct from those in fragment #4, even though both arc polymict ureilites from the same parent body. Unlike in CM2 and CR2/3 meteorites, there are low relative abundances of alpha-amino acids in the Almahata Sitta meteorite fragments, which suggest that Strecker-type chemistry was not a significant amino acid formation mechanism. Given the high temperatures that asteroid 2008 TC3 appears to have experienced and lack of evidence for aqueous alteration on the asteroid, it is possible that the extraterrestrial amino acids detected in Almahata Sitta were formed by Fischer-Tropsch/Haber-Bosch type gas-grain reactions at elevated temperatures.
Isopropyl 3-deoxy-α-D-ribo-hexopyranoside (isopropyl 3-deoxy-α-D-glucopyranoside), C 9 H 18 O 5 , (I), crystallizes from a methanol–ethyl acetate solvent mixture at room temperature in a 4C1 chair conformation that is slightly distorted towards the C5 S C1 twist-boat form. A comparison of the structural parameters in (I), methyl α-D-glucopyranoside, (II), α-D-glucopyranosyl-(1→4)-D-glucitol (maltitol), (III), and 3-deoxy-α-D-ribo-hexopyranose (3-deoxy-α-D-glucopyranose), (IV), shows that most endocyclic and exocyclic bond lengths, valence bond angles and torsion angles in the aldohexopyranosyl rings are more affected by anomeric configuration, aglycone structure and/or the conformation of exocyclic substituents, such as hydroxymethyl groups, than by monodeoxygenation at C3. The structural effects observed in the crystal structures of (I)–(IV) were confirmed though density functional theory (DFT) calculations in computed structures (I) c –(IV) c . Exocyclic hydroxymethyl groups adopt the gauche–gauche (gg) conformation (H5 anti to O6) in (I) and (III), and the gauche–trans (gt) conformation (C4 anti to O6) in (II) and (IV). The O-glycoside linkage conformations in (I) and (III) resemble those observed in disaccharides containing β-(1→4) linkages.