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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Ligand field and intermolecular interactions tuning the magnetic properties of spin-crossover Fe(II) polymer with 4,4′-bipyridine

A new spin crossover coordination polymer (SCO-CPs) of Fe(II)-4,4′-bipyridine (4,4′-bipy) family: (Fe(4,4′-bipy){sub 2}(H{sub 2}O){sub 2})·(4,4′-bipy)· 8(H{sub 2}O)·2(ClO{sub 4}) (3), which displays half spin transitions between 100 and 300 K, has been synthesized and structurally characterized. Compound 3 featured with two-dimensional (2-D) grids connected by hydrogen bonds and π…π packing between one-dimensional (1-D) chains, the 2-D grids expand to three-dimensional (3-D) architecture supported by a “S-shaped holder” involving lattice 4-4′-bipy, water molecules and perchlorate anion. We compared 3 with the other two analogous complexes: ((Fe(4,4′-bipy) (H{sub 2}O){sub 2} (NCS){sub 2})·4,4′-bipy, 1 and (Fe(4,4′-bipy){sub 2}(NCS){sub 2})·mSolv, 2) through Hirshfeld surfaces analysis, which revealed that the low ligand field strength (NCS{sup −}) and lone-pair…H contacts contribute to the stabilization of HS (high-spin) state of the Fe(II) ion, while the high ligand field strength (4,4′-bipy) and strong intermolecular contacts (hydrogen bonds and π…π packing interactions) make for the LS (low-spin) state. - Highlights: ●A new member of Fe(||)-4,4′-bipy family has been prepared. ●It displays half spin transitions tuned by ligand field and intermolecular interactions. ●We have made a detailed comparison of this new member with two other analogous complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Electronic spectra of the tetraphenylcyclobutadienecyclopentadienylnickel(II) cation and radical

In this study, properties of the tetraphenylcyclobutadienecyclopentadienylnickel(II) cation 1 and its tetra-o-fluoro derivative 1a have been measured and calculated. The B3LYP/TZP optimized geometry of the free cation 1 agrees with a single-crystal X-ray diffraction structure except that in the crystal one of the phenyl substituents is strongly twisted to permit a close-packing interaction of two of its hydrogens with a nearby BF – 4 anion. The low-energy parts of the solution electronic absorption and magnetic circular dichroism (MCD) spectra of 1 and 1a have been interpreted by comparison with TD-DFT (B3LYP/TZP) results. Reduction or pulse radiolysis lead to a neutral 19-electron radical, whose visible absorption and MCD spectra have been recorded and interpreted as well. The reduction is facilitated by ~0.1 V upon going from 1 to 1a

14 SOLAR ENERGY↗
DOE OSTI2021

Additive Lithography–Organic Monolayer Patterning Coupled with an Area-Selective Deposition

The combination of area-selective deposition (ASD) with a patternable organic monolayer provides a versatile additive lithography platform, enabling the generation of a variety of nanoscale feature geometries. Stearate hydroxamic acid self-assembled monolayers (SAMs) were patterned with extreme ultraviolet (λ = 13.5 nm) or electron beam irradiation and developed with ASD to achieve line space patterns as small as 50 nm. Density functional theory was employed to aid in the synthesis of hydroxamic acid derivatives with optimized packing density to enhance the imaging contrast and improve dose sensitivity. Near-edge X-ray absorption fine structure spectroscopy and infrared spectroscopy reveal that the imaging mechanism is based on improved deposition inhibition provided by the cross-linking of the SAM to produce a more effective barrier during a subsequent deposition step. With patterned substrates composed of coplanar copper lines and silicon spacers, hydroxamic acids selectively formed monolayers on the metal portions and could undergo a pattern-wise exposure followed by ASD in the first combination of a patternable monolayer with ASD. This material system presents an additional capability compared to traditional ASD approaches that generally reflect a starting patterned surface. Finally, this bottoms-up additive approach to lithography may be a viable alternative to subtractive nanoscale feature generation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Investigation of electronic properties of the thienoacenes using first principles methods

In the present work, an examination of the electronic properties of pentacene, dinaphtho[2,3-b:2',3'-f]thieno[3,2-b]thiophene (DNTT) and dianthra[2,3-b:2',3'-f]thieno[3,2-b]thiophene (DATT) using ab initio methodology is reported. According to the results, the herringbone packing and π-stack orientation play an important role in the hole mobilities for DNTT and DATT. Band structure of pentacene crystals is more anisotropic than DNTT and DATT and the highest values for the dispersion are found in the herrigbone plane. For the band transport regime, it is demonstrated that the DATT must have carrier mobilities larger than pentacene and smaller than that found in DNTT.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Synergistic effects of Al, Ga, and In doping on ZnO nanorod arrays grown via citrate-assisted hydrothermal technique for highly efficient and fast scintillator screens

To be used as efficient alpha particle scintillator in the fields of nuclear security, nuclear nonproliferation and high-energy physics, scintillator screens must have high light output and fast decay properties. While there has been a great deal of progress in scintillation efficiency, achieving fast decay time properties are still a challenge. In this work, the near band edge (NBE) UV luminescence and alpha particle induced scintillation properties of vertically aligned densely packed ZnO nanorods (NRs) doped with Al, Ga, and In have been thoroughly investigated. The high crystalline hexagonal wurtzite structure with a strong orientation through the c -axis plane (002) and aspect ratios in the range 13–22 have been observed for all ZnO NRs. Electron paramagnetic resonance (EPR) analysis exhibited paramagnetic signals at g ≈ 1.96 for all ZnO NRs. A cost effective green hydrothermal synthesis technique was employed to grow well-aligned NRs. Using citrate as an additive acting as a strong reducing agent in the solution during the crystal growth, defects on the surface are significantly suppressed, thereby enhancing the NBE UV emission. Significantly higher NBE UV emission was observed from the top surface of ZnO NRs in cathodoluminescence (CL) microscopy. Results show that citrate assisted donor doping of ZnO NRs not only reduces the defect emission and NBE self-absorption, but also induces fast decay time (~ 600–700 ps), which makes ZnO NRs a good candidate for fast alpha particle scintillator screens used in associated particle imaging for time and direction tagging of individual neutrons generated in D–T and D–D neutron generators.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Microstructure-Sensitive Uncertainty Quantification for Crystal Plasticity Finite Element Constitutive Models Using Stochastic Collocation Methods

Uncertainty quantification (UQ) plays a major role in verification and validation for computational engineering models and simulations, and establishes trust in the predictive capability of computational models. In the materials science and engineering context, where the process-structure-property-performance linkage is well known to be the only road mapping from manufacturing to engineering performance, numerous integrated computational materials engineering (ICME) models have been developed across a wide spectrum of length-scales and time-scales to relieve the burden of resource-intensive experiments. Within the structure-property linkage, crystal plasticity finite element method (CPFEM) models have been widely used since they are one of a few ICME toolboxes that allows numerical predictions, providing the bridge from microstructure to materials properties and performances. Several constitutive models have been proposed in the last few decades to capture the mechanics and plasticity behavior of materials. While some UQ studies have been performed, the robustness and uncertainty of these constitutive models have not been rigorously established. In this work, we apply a stochastic collocation (SC) method, which is mathematically rigorous and has been widely used in the field of UQ, to quantify the uncertainty of three most commonly used constitutive models in CPFEM, namely phenomenological models (with and without twinning), and dislocation-density-based constitutive models, for three different types of crystal structures, namely face-centered cubic (fcc) copper (Cu), body-centered cubic (bcc) tungsten (W), and hexagonal close packing (hcp) magnesium (Mg). Our numerical results not only quantify the uncertainty of these constitutive models in stress-strain curve, but also analyze the global sensitivity of the underlying constitutive parameters with respect to the initial yield behavior, which may be helpful for robust constitutive model calibration works in the future.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Autonomous Lunar Infrastructure Outfitting

The Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS) project at NASA Ames Research Center is developing autonomous infrastructure, instrumentation, and spacecraft assembly and manufacturing capabilities for next generation exploration and science missions, with a goal to change the cost scaling of these missions relative to mission size and duration. Using a building-block approach with a 'kit of parts' composed of ultra-light, high-performance mechanical metamaterials, simplified robots leverage the period environment to achieve high levels of autonomy and reliability for in-space and surface assembly of large-scale apertures, solar-arrays, towers, habitats, and other infrastructure. Robots and structure break down into a compact form factor for launch. By leveraging economies of scale and achieving high-packing ratios, ARMADAS technology can revolutionize space missions by breaking the tyranny of the launch shroud, decreasing development times, decreasing mission costs for transformative science capability, and providing a scalable and versatile space infrastructure strategy. An ecosystem of reconfigurable infrastructure modules can be reused, repaired, expanded reconfigured to meet emergency or unforeseen needs, and reduce spare parts.

Christine Gregg↗
DOE OSTI2025

Revealing structure and shaping priorities in plant and fungal cell wall architecture via solid-state NMR

Plant and fungal cell walls are essential for growth, adaptation, and survival, with their intricate architectures dictating both resistance to stress and susceptibility to antifungal or biomass-degrading strategies. Understanding how these walls are built, remodeled, and function at the molecular level is therefore central to both clinical and biotechnological applications. Solid-state nuclear magnetic resonance (ssNMR) has emerged as a uniquely powerful tool for this purpose, as it reveals the structure, dynamics, and interactions of intact biopolymers without disrupting their native organization. Using this approach, recent studies have shown how structural polymorphism, polymer-polymer interactions, and species-specific remodeling govern mechanical integrity, drug resistance, and stress adaptation. Applications highlighted here include lignin-carbohydrate packing during plant stem maturation, fungal wall reorganization under treatment by wall-targeting antifungals such as echinocandin and nikkomycin, and the functional diversity of glucans, chitins, and mannans. Together, these insights uncover conserved principles of polymer assembly across kingdoms while informing new opportunities for antifungal development and biomass utilization. Ongoing advances in sensitivity and resolution are expected to broaden the reach of ssNMR and further accelerate its role in linking structural heterogeneity to biosynthetic complexity and biological function.

Antifungal↗
DOE OSTI2022

Stable CsPbBr 3 Nanoclusters Feature a Disk-like Shape and a Distorted Orthorhombic Structure

CsPbBr 3 nanoclusters have been synthesized by several groups and mostly employed as single-source precursors for the synthesis of anisotropic perovskite nanostructures or perovskite-based heterostructures. Yet, a detailed characterization of such clusters is still lacking due to their high instability. In this work, we were able to stabilize CsPbBr 3 nanoclusters by carefully selecting ad hoc ligands (benzoic acid together with oleylamine) to passivate their surface. The clusters have a narrow absorption peak at 400 nm, a band-edge emission peaked at 410 nm at room temperature, and their composition is identified as CsPbBr 2.3 . Synchrotron X-ray pair distribution function measurements indicate that the clusters exhibit a disk-like shape with a thickness smaller than 2 nm and a diameter of 13 nm, and their crystal structure is a highly distorted orthorhombic CsPbBr 3 . Based on small- and wide-angle X-ray scattering analyses, the clusters tend to form a two-dimensional (2D) hexagonal packing with a short-range order and a lamellar packing with a long-range order.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Synthesis, structural characterization, Hirshfeld surface analysis and spectroscopic studies of cadmium (II) chloride complex with 4-hydroxy-1-methylpiperidine

The chemical preparation, crystal structure, Hirshfeld surface analysis and spectroscopic characterization of the novel cadmium (II) 4-hydroxy-1-methylpiperidine complex, Cd{sub 4}Cl{sub 10}(C{sub 6}H{sub 14}NO){sub 2}·2H{sub 2}O, have been reported. The atomic arrangement can be described as built up by an anionic framework, formed by edge sharing CdCl{sub 6} and CdCl{sub 5}O octahedral linear chains spreading along the a-axis. These chains are interconnected by water molecules via O–H⋯Cl and O–H⋯O hydrogen bonds to form layers parallel to (011) plane. The organic cations are inserted between layers through C–H⋯Cl hydrogen bonds. Investigation of intermolecular interactions and crystal packing via Hirshfeld surface analysis reveals that the H{sub C}⋯Cl and H{sub C}⋯H{sub C} intermolecular interactions are the most abundant contacts of the organic cation in the crystal packing. The statistical analysis of crystal contacts reveals the driving forces in the packing formation. The {sup 13}C and {sup 15}N CP-MAS NMR spectra are in agreement with the X-ray structure. The vibrational absorption bands were identified by infrared spectroscopy. DFT calculations allowed the attribution of the NMR peaks and of the IR bands.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Finding new perovskite halides via machine learning

Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX 3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX 3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX 3 compositions with perovskite crystal structure.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Decoding Liquid Crystal Oligomer Phase Transitions: Toward Molecularly Engineered Shape Changing Materials

This work details an integrated investigation of liquid crystal (LC) oligomers that combines experiments and molecular dynamics simulations to obtain a detailed understanding of the molecular structure of LC oligomers and the mechanism underlying their phase transition temperatures. We synthesized and characterized a series of LC oligomers prepared from different lengths of methylene spacers in the reactive LC monomers and n-alkylamine chain extenders via the aza -Michael addition reaction. In parallel, we performed isothermal–isobaric ( NPT ) ensemble coarse-grained molecular dynamics (CG-MD) simulation of analogue mesogens that are connected to flexible spacers and extenders at varying temperatures, spacer lengths, and extender lengths. This approach allowed the effect of length in the flexible spacer as well as in the chain extender on the nematic–isotropic transition temperature ( T ni ) to be determined. The results showed that increasing the length of the extender decreases T ni for LC oligomers and amplifies the decrease of T ni in LC oligomers when the spacer length is short. We infer that the combination of spacer and extender changes the shape anisotropy of LC oligomers, changing the packing behavior of constituent mesogens, thus affecting their ability to transition from the isotropic to the nematic phase. Here, the detailed molecular structure–property relationships formulated enable prescribing design rules for LC oligomers geared toward molecularly engineered shape changing materials.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Disk Density Tuning of a Maximal Random Packing

We introduce an algorithmic framework for tuning the spatial density of disks in a maximal random packing, without changing the sizing function or radii of disks. Starting from any maximal random packing such as a Maximal Poisson-disk Sampling (MPS), we iteratively relocate, inject (add), or eject (remove) disks, using a set of three successively more-aggressive local operations. We may achieve a user-defined density, either more dense or more sparse, almost up to the theoretical structured limits. The tuned samples are conflict-free, retain coverage maximality, and, except in the extremes, retain the blue noise randomness properties of the input. We change the density of the packing one disk at a time, maintaining the minimum disk separation distance and the maximum domain coverage distance required of any maximal packing. These properties are local, and we can handle spatially-varying sizing functions. Using fewer points to satisfy a sizing function improves the efficiency of some applications. We apply the framework to improve the quality of meshes, removing non-obtuse angles; and to more accurately model fiber reinforced polymers for elastic and failure simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Structural evolution in the crystallization of rapid cooling silver melt

The structural evolution in a rapid cooling process of silver melt has been investigated at different scales by adopting several analysis methods. The results testify Ostwald’s rule of stages and Frank conjecture upon icosahedron with many specific details. In particular, the cluster-scale analysis by a recent developed method called LSCA (the Largest Standard Cluster Analysis) clarified the complex structural evolution occurred in crystallization: different kinds of local clusters (such as ico-like (ico is the abbreviation of icosahedron), ico-bcc like (bcc, body-centred cubic), bcc, bcc-like structures) in turn have their maximal numbers as temperature decreases. And in a rather wide temperature range the icosahedral short-range order (ISRO) demonstrates a saturated stage (where the amount of ico-like structures keeps stable) that breeds metastable bcc clusters. As the precursor of crystallization, after reaching the maximal number bcc clusters finally decrease, resulting in the final solid being a mixture mainly composed of fcc/hcp (face-centred cubic and hexagonal-closed packed) clusters and to a less degree, bcc clusters. This detailed geometric picture for crystallization of liquid metal is believed to be useful to improve the fundamental understanding of liquid–solid phase transition. - Highlights: • A comprehensive structural analysis is conducted focusing on crystallization. • The involved atoms in our analysis are more than 90% for all samples concerned. • A series of distinct intermediate states are found in crystallization of silver melt. • A novelty icosahedron-saturated state breeds the metastable bcc state.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Characterizing the Three-Dimensional Structure of Block Copolymers via Sequential Infiltration Synthesis and Scanning Transmission Electron Tomography

Understanding and controlling the three-dimensional structure of block copolymer (BCP) thin films is critical for utilizing these materials for sub-20 nm nanopatterning in semiconductor devices, as well as in membranes and solar cell applications. Combining an atomic layer deposition (ALD) based technique for enhancing the contrast of BCPs in transmission electron microscopy (TEM) together with scanning TEM (STEM) tomography reveals and characterizes the three-dimensional structures of poly(styrene-block-methyl methacrylate) (PS-b-PMMA) thin films with great clarity. Sequential infiltration synthesis (SIS), a block-selective technique for growing inorganic materials in BCPs films in ALD, and an emerging tool for enhancing the etch contrast of BCPs, was harnessed to significantly enhance the high-angle scattering from the polar domains of BCP films in the TEM. The power of combining SIS and STEM tomography for three dimensional (3D) characterization of BCPs films was demonstrated with the following cases: self-assembled cylindrical, lamellar, and spherical PS-PMMA thin films. In all cases, STEM tomography has revealed 3D structures that were hidden underneath the surface, including: 1) the 3D structure of defects in cylindrical and lamellar phases, 2) non-perpendicular 3D surface of grain boundaries in the cylindrical phase, and 3) the 3D arrangement of spheres in body centered cubic (BCC) and hexagonal closed pack (HCP) morphologies in the spherical phase. The 3D data of the spherical morphologies was compared to coarse-grained simulations and assisted in validating the simulations’ parameters. STEM tomography of SIS-treated BCP films enables the characterization of the exact structure used for pattern transfer, and can lead to better understating of the physics which is utilized in BCP lithography.

Block copolymers↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2019

Two-dimensional polythiophene homopolymer as promising hole transport material for high-performance perovskite solar cells

A polythiophene homopolymer with conjugated side chains (2D-PT) was applied as a hole transport material (HTM) of perovskite solar cells (PSCs). The all-conjugated two-dimensional structure effectively increases the ionization potential, realizing a high open-circuit voltage near 1 V for the 2D-PT-based PSC. Very interestingly, the two-dimensional wide-angle X-ray scattering measurements show that the 2D-PT molecules are self-assembled into ordered structure with a face-on dominant orientation and exhibit a hole mobility, which is ~1.6 times faster than that of poly(3-hexylthiophene) (P3HT). Consequently, the power conversion efficiency of PSC is noticeably increased by ~30% as 2D-PT replaces P3HT as HTM. Moreover, the densely packed 2D-PT slows down the moisture induced degradation of perovskite crystals, leading to a better environmental stability. As a result, this work demonstrates that two-dimensional non-donor-acceptor structural conjugated polymers can be utilized as promising HTM of PSCs.

36 MATERIALS SCIENCE↗