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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Bark morphological and chemical features are differentially correlated with disease resistance and yield in hybrid poplar taxa

In the southeastern United States, the establishment of short-rotation intensively cultured plantations of hybrid poplar has been hindered by its susceptibility to stem cankers. We evaluated the tradeoffs between biomass yield and disease tolerance in hybrid poplar genotypes belonging to P. deltoides × P. maximowiczii (DM), P. deltoides × P. nigra (DN), P. trichocarpa × P. maximowiczii (TM), and P. deltoides × P. deltoides (DD) taxa. We hypothesized that canker resistant genotypes will have thicker bark but bark thickness and biomass yield will be negatively correlated. After two growing seasons, the DD genotypes developed thicker bark compared to the genotypes of other taxa and bark thickness was not correlated with biomass yield in the DD genotypes (R 2 = 0.002). However, in the TM, DM, and DN genotypes, bark thickness was negatively correlated with biomass yield (R 2 = 0.33–0.77). Disease incidence studies revealed that the DM genotypes were most susceptible to canker whereas no disease was detected in DD genotypes. Furthermore, bark analysis conducted by Fourier transform infrared spectroscopy coupled with multivariate analysis showed that that DD genotypes to be chemically separate from the three hybrid genotypes and that bark chemistry was correlated with canker disease incidence. Taken together, these results reveal that it is possible to generate hybrid poplar genotypes with thicker bark, disease resistance, and higher biomass yields. This insight should guide further efforts to develop genetically improved hybrid poplar genotypes, both in terms of biomass yield and disease tolerance, for cultivation in the southeastern United States. Hybrid poplar cultivation in southeastern United States is hindered by its susceptibility to stem cankers. We evaluated tradeoffs between yield and canker disease resistance in various hybrid poplar genotypes. After two growing seasons, the DD genotypes showed disease resistance and developed thicker bark that was chemically distinct from the other genotypes. Bark thickness was not correlated with yield in the DD genotypes but was negatively correlated with yield in the other genotypes. These results will guide the development of hybrid poplar genotypes that are both disease resistant and high yielding for cultivation in the southeastern United States.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Soil biogeochemical properties and metrics of tree-mycorrhizal dominance for a 25-Ha forest in South Central Indiana, USA.

This data package contains a dataset used in the papers “Seeing the forest for all the trees: Mycorrhizal-associated nutrient economies are modulated by stem density and the synchrony between overstory and understory communities” and “Mycorrhizal associations of tree species influence soil nitrogen dynamics via effects on soil acid–base chemistry”. Four csv files are included along with a dataset. The dataset features chemical soil properties for a single sampling campaign within the 25 Ha Lilly-Dickey Woods Smithsonian Forest Global Earth Observatory (ForestGEO) plot in South Central Indiana, USA (ldw_dat_raw.csv). Also included are separate files focused on pH (pH_data.csv), carbon and nitrogen (CN_data.csv), and nitrification rates (Nitrification_data.csv). These variables are commonly associated with the tree-mycorrhizal dominance of forest stands. In these data subsets, each soil variable was matched to a 10 meter radius neighborhood wherein metrics of tree-mycorrhizal dominance (basal area, stem count, importance value, etc.) were calculated. Models between these soil variables and dominance metrics were used to investigate how different assessments of mycorrhizal associated nutrient economies (MANE) capture these relationships. This research was performed as a part of the Smithsonian ForestGEO project. This data package can be used to explore spatial variability in soil chemistry within a mature hardwood forest, or it can be combined with the included tree data, other fine-scale spatial information, or other tree inventory data for the site to evaluate how soil chemistry varies with tree community composition or edaphic or topographic properties.

Craig, Matthew [ORNL] (ORCID:0000000288907920)↗
DOE OSTI2021

Transamericium Radiation-Induced Redox Chemistry

The actinide series boasts many unique physical and chemical features worthy of study. However, the chemical influence of their inherent radiation field is often overlooked, especially as we begin to explore the late actinides (transamericium) in more detail than ever possible before. - Absorption of ionizing radiation induces the formation of a variety of transient and steady-state radicals, ions, and molecular degradation products. Many of these radiolysis products are strongly redox active and exhibit significant reactivity with a number of actinides, e.g., neptunium and americium. Here we present results from the first-ever time-resolved picosecond pulsed electron radiolysis measurements for the reaction of the late actinides (curium and californium) with reactive transients pertinent to actinide manipulations in aqueous solution, i.e., the hydrated electron (eaq–), the hydrogen atom (H•), and hydroxyl (•OH) and nitrate (•NO3) radicals.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Recent Advances in Radiation-Induced Actinide Redox Chemistry

The actinide series boasts many unique physical and chemical features worthy of both fundamental and applied study. However, the chemical influence of their inherent radiation field is often overlooked, especially as we begin to explore the late actinides in more detail than ever possible before. From the perspective of used nuclear fuel reprocessing, the absorption of ionizing radiation induces the formation of a variety of transient and steady-state excited states, radicals, ions, and molecular degradation products, many of which are highly redox active and can lead to significant changes in a reprocessing solvent system’s physical and chemical properties. For example, radiolysis of the actinides can drive steady-state redox distributions and the formation of non-traditional oxidation states which can complicate their separation and recovery from fission products. This scenario is further exacerbated when complexation is taken into account. Consequently, a molecular-level understanding of radiation effects on the actinides over multiple time, distance, and material domains is essential for supporting innovation in used nuclear fuel reprocessing technologies. Attaining this knowledge necessitates a firm grasp of actinide radiation chemistry to develop predictive, mechanistic, multiscale models to support engineering efforts. Presented here are several recent studies that highlight recent advances in actinide radiation chemistry, in particular, the effect of actinide complexation on ligand reactivity towards radiation-induced transients.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Characterizing major and trace element compositions in fallout melt glass from a near-surface nuclear test

We report the chemical and isotopic compositions of fallout melt glasses from nuclear tests contain a range of information constraining the physical conditions within the fireball and the mechanisms of fallout formation but historic studies tended to exclude the behavior of stable major and trace elements. Here, we present a large study specifically focused on major and trace element relationships within a population of macroscale fallout samples from a single event. We interpret these data to better constrain how fallout melt glass formation in near surface environments is influenced by that environment and demonstrate how major and trace element abundances can provide useful insights into chemical processes within the fireball. Data confirm that the uranium in the fallout glass population derives from two isotopically distinct endmembers: isotopically enriched uranium (presumably from the weapon), and natural composition uranium that may be a combination of anthropogenic and environmental materials from within the blast zone. The similarity between major and trace element concentrations in fallout and corresponding local soils from the event site confirm the local soils as the most probable source of entrained material into the fireball and the source of carrier material into which the bomb vapor was incorporated. The lack of correlation between major and trace element abundances with size indicates that volatility driven processes, such as condensation from the fireball, do not control the composition of macroscale fallout melt glass. Although the fallout has major and trace element chemical characteristics broadly similar to those of the local, associated soils, some systematic differences are observed between the two populations. Fallout melt glass is depleted in volatile elements such as K, Na, Tl and Pb, consistent with heating to temperatures above ~1000 °C for 3-10 s. This is supported by the results of laser heating experiments performed on rhyolitic soil at temperatures (1600-2200 °C) and timescales (1-120 s) that are broadly relevant to fallout formation conditions. Relative enrichments of metals such as Cu and Co do not correlate with the abundance of uranium, suggesting that fallout also records input of near field anthropogenic materials. Our observations suggest that major chemical features can be related to processing in the fireball and used to inform the thermal-chemical evolution of the system. Ultimately, these data are consistent with a fallout formation mechanism that involves rapid melting of surface materials to form carrier material melts with minor incorporation of bomb vapor and a degree of volumetric volatile loss due to heating.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Predicting path-dependent diffusion barrier spectra in vast compositional space of multi-principal element alloys via convolutional neural networks

The emergent multi-principal element alloys (MPEAs) provide a vast compositional space to search for novel materials for technological advances. How to screen promising compositions from such an ample design space for targeted properties is a grand challenge. In this report, we demonstrate the state-of-the-art deep learning technology—convolutional neural network (CNN)—in predicting path-dependent vacancy migration energy barrier spectra, which are critical to diffusion behavior and many high-temperature properties, in the hyperdimensional composition space of MPEAs. The developed CNN model, fully capturing local chemical features surrounding each vacancy, accurately and efficiently predicts migration energy barrier of MPEAs with different degrees of chemical short-range order and at any unseen compositions. By varying the size of the local region encapsulating vacancy in the CNN model, we reveal that the length scale influencing vacancy migration is surprisingly extensive, up to its six nearest neighboring shells. The efforts of the CNN model make it promising for developing a database of diffusion barriers for various MPEA systems, which would have profound implications for accelerating alloy screening and discovering new compositions with desirable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump

Cholestatic liver injury is frequently associated with drug inhibition of bile salt transporters, such as the bile salt export pump (BSEP). Reliable in silico models to predict BSEP inhibition directly from chemical structures would significantly reduce costs during drug discovery and could help avoid injury to patients. We report our development of classification and regression models for BSEP inhibition with substantially improved performance over previously published models. We assessed the performance effects of different methods of chemical featurization, data set partitioning, and class labeling and identified the methods producing models that generalized best to novel chemical entities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Application of machine learning to discover new intermetallic catalysts for the hydrogen evolution and the oxygen reduction reactions

The adsorption energies for hydrogen, oxygen, and hydroxyl were calculated by means of density functional theory on the lowest energy surface of 24 pure metals and 332 binary intermetallic compounds with stoichiometries AB, A 2 B, and A 3 B taking into account the effect of biaxial elastic strains. This information was used to train two random forest regression models, one for the hydrogen adsorption and another for the oxygen and hydroxyl adsorption, based on 9 descriptors that characterized the geometrical and chemical features of the adsorption site as well as the applied strain. All the descriptors for each compound in the models could be obtained from physico-chemical databases. The random forest models were used to predict the adsorption energy for hydrogen, oxygen, and hydroxyl of ≈2700 binary intermetallic compounds with stoichiometries AB, A 2 B, and A 3 B made of metallic elements, excluding those that were environmentally hazardous, radioactive, or toxic. This information was used to search for potential good catalysts for the HER and ORR from the criteria that their adsorption energy for H and O/OH, respectively, should be close to that of Pt. Further, this investigation shows that the suitably trained machine learning models can predict adsorption energies with an accuracy not far away from density functional theory calculations with minimum computational cost from descriptors that are readily available in physico-chemical databases for any compound. Moreover, the strategy presented in this paper can be easily extended to other compounds and catalytic reactions, and is expected to foster the use of ML methods in catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Raman spectroscopic investigation of selected natural uranyl sulfate minerals

Uranyl sulfates are important constituents of uranium ores and represent a significant fraction of U(VI) minerals discovered in recent years owing to their propensity to form in mine tailings and legacy sites related to uranium exploration. Recently, we surveyed all published Raman spectra for uranium minerals and found significantly less easily accessible data available for uranyl sulfates relative to other groups of uranium minerals (Spano et al. 2023). In that work, we described average spectra for groups of uranyl minerals to understand common vibrational spectroscopic features attributable to similarities in oxyanion chemistry among U(VI) minerals, but only data for three uranyl sulfate minerals were included in the study. The present work reports on Raman spectra collected for 18 additional uranyl sulfate minerals. In conclusion, to better understand underlying structural and chemical features that give rise to spectroscopic observables, we relate differences in structural topology, charge-balancing cations, and locality of origin to features observed in the Raman spectra of selected natural uranyl sulfates.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Current Density Regulated Atomic to Nanoscale Process on Li Deposition and Solid Electrolyte Interphase Revealed by Cryogenic Transmission Electron Microscopy

Current density has been perceived to play a critical rule for controlling Li deposition morphology and solid electrolyte interphase (SEI). However, the atomic level mechanism of current density on Li deposition and the SEI remains unclear. In this work, based on cryogenic transmission electron microscopy imaging combined with energy dispersive X-ray spectroscopy and electron energy loss spectroscopy electronic structure analyses, we reveal the atomic level correlation of Li deposition morphology and SEI with current density. We discover that increasing current density leads to increased overpotential for Li nucleation and growth, leading to the transition from growth-limited to nucleation-limited mode for Li dendrite. Independence of current density, the electrochemically deposited Li metal (EDLi) exhibits crystalline whisker-like morphology. The SEI formed at low current density (0.1 mA cm -2 ) is monolithic amorphous; while, a current density of above 2 mA cm -2 leads to a mosaic structured SEI, featuring an amorphous matrix with Li 2 O and LiF dispersoids, and the thickness of the SEI increases with the increase of current density. Uniquely, the Li 2 O particles is spatially located at the top surface of the SEI, while LiF is spatially adjacent to the Li-SEI interface. These results highlight the possible tuning of crucial structural and chemical features of EDLi and SEI through altering deposit conditions and consequently direct correlation with electrochemical performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Analysis of Orthogonal Efflux and Permeation Properties of Compounds Leads to the Discovery of New Efflux Pump Inhibitors

Optimization of compound permeation into Gram-negative bacteria is one of the most challenging tasks in the development of antibacterial agents. Two permeability barriers–the passive diffusion barrier of the outer membrane (OM) and active drug efflux–act synergistically to protect cells from the antibacterial action of compounds. In Escherichia coli (E. coli) and relatives, these two barriers sieve compounds based on different physicochemical properties that are defined by their interactions with OM porins and efflux pumps, respectively. In this study, we critically tested the hypothesis that the best substrates and inhibitors of efflux pumps are compounds that can effectively permeate the OM and are available at relatively high concentrations in the periplasm. For this purpose, we filtered a large subset of the ZINC15 database of commercially available compounds for compounds containing a primary amine, a chemical feature known to facilitate the uptake through E. coli general porins. The assembled library was screened by ensemble docking to AcrA, the periplasmic component of the AcrAB-TolC efflux pump, followed by experimental testing of the top predicted binders for antibacterial activities, efflux recognition, and inhibition. In this work, we found that the filtered primary amine library is a rich source of compounds with efflux-inhibiting activities and identified efflux pump inhibitors with novel chemical scaffolds effective against E. coli AcrAB-TolC and efflux pumps of multidrug-resistant clinical isolates of Acinetobacter baumannii. However, primary amines are not required for the recognition of compounds by efflux pumps and their efflux-inhibitory activities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Exploring the Effects of Node Topology, Connectivity, and Metal Identity on the Binding of Nerve Agents and Their Hydrolysis Products in Metal–Organic Frameworks

Recent studies have shown that metal–organic frameworks (MOFs) built from hexanuclear M(IV) oxide cluster nodes are effective catalysts for nerve agent hydrolysis, where the properties of the active sites on the nodes can strongly influence the reaction energetics. The connectivity and metal identity of these M6 nodes can be easily tuned, offering extensive opportunities for computational screening to predict promising new materials. Thus, we used density functional theory (DFT) to examine the effects of node topology, connectivity, and metal identity on the binding energies of multiple nerve agents and their corresponding hydrolysis products. By computing an optimization metric based on the relative binding strengths of key hydrolysis reaction species (water, agent, and bidentate-bound products), we predicted optimal M6 nodes for hydrolyzing specific nerve agent and simulant molecules, where our results are in qualitative agreement with observed experimental trends. This analysis highlighted the notion that no single metal or node topology is optimal for all possible organophosphates, suggesting that MOFs should be selected based on the agent of interest. Using the large amount of data generated from our DFT calculations, we then derived quantitative structure–activity relationship (QSAR) models to help explain the complex trends observed in the binding energies. Through linear regression, we identified the most important descriptors for describing the binding of nerve agents and their hydrolysis products to M6 nodes. These results suggested that both molecular and node properties, including both structural and chemical features, collectively contribute to the binding energetics. By performing a thorough statistical analysis, we showed that our QSAR models are capable of making quantitatively accurate binding energy predictions for nerve agents and their hydrolysis products in a wide variety of M(IV)-MOFs. In conclusion, the insights gained herein can be used to guide future experiments for the synthesis of MOFs with enhanced catalytic activity for organophosphate hydrolysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Internal calibration of transient kinetic data via machine learning

The temporal analysis of products (TAP) reactor provides a vast amount of transient kinetic information that may be used to describe a variety of chemical features including residence time distributions, kinetic coefficients, number of active sites, reaction mechanism, etc. However, as with any measurement device, the TAP reactor signal is convoluted with noise and drift is common. In order to reduce the uncertainty of the kinetic measurement and any derived parameters or mechanisms, proper preprocessing must be performed prior to any advanced type of analysis. This preprocessing includes baseline correction, i.e., a shift in the voltage response, and calibration, i.e., a scaling of the flux response based on prior experiments. The traditional methodology of preprocessing requires significant user discretion and reliance on separate calibration experiments that may drift over time. Herein we use machine learning techniques combined with physical constraints to understand the noise and drift that is being generated within and between experiments for enhancement of the chemical kinetic signal. As such, the proposed methodology demonstrates clear benefits over the traditional preprocessing approach by eliminating the need for separate calibration experiments or heuristic input from the user.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Multiscale Characterization of Electrode-Induced Degradation in Perovskite Solar Cells

The stability of metal-halide-perovskite (MHP) solar cells must be understood and improved for the commercial viability of MHP technologies. Here, we apply multiscale characterization methods to study degradation modes, specifically electrode corrosion, for p-i-n MHP partial device stacks and full devices that are stored in the dark under an inert atmosphere. Our multiscale characterization approaches include full-device electro-optical performance using current-voltage (JV) curves and spatial imaging with electroluminescence (EL) and photoluminescence (PL). We further correlate interface properties using cross-sectional Kelvin probe force microscopy, which maps the nanoscale electric field properties, and electron microscopy, which demonstrates structural and chemical features. Devices stored as a full device stack degrade primarily by metal (Ag) electrode diffusion into the absorber, with formation of AgI byproducts and Ag accumulation near the indium tin oxide (ITO) contact. This causes decomposition of the perovskite absorber domains, loss of the potential drop at the electron transport layer (ETL)/perovskite interface near the metal contact, and increased equivalent resistance at the perovskite/hole transport layer (HTL) interface near the ITO contact. The devices stored without metal show a different degradation pathway dominated by corrosion of the ITO, creating voids at the ITO electrode surface with diffusion of In and Sn into the absorber. We conclude that metal electrode-induced degradation is the most severe degradation pathway under dark storage, but that ITO corrosion and absorber instability must also be mitigated. We further demonstrate mitigation of these degradation pathways by changes to the device stack, including a SnO x blocking layer at the ETL side and replacing ITO with FTO at the HTL side. These results provide a useful demonstration of specific dark degradation pathways at each electrode interface, as well as a unique multiscale example that links degradation of chemical, structural, and electrical interface properties to the full-device electro-optical characteristics.

14 SOLAR ENERGY↗
DOE OSTI2021

Structure-guided microbial targeting of antistaphylococcal prodrugs

Carboxy ester prodrugs are widely employed to increase oral absorption and potency of phosphonate antibiotics. Prodrugging can mask problematic chemical features that prevent cellular uptake and may enable tissue-specific compound delivery. However, many carboxy ester promoieties are rapidly hydrolyzed by serum esterases, limiting their therapeutic potential. While carboxy ester-based prodrug targeting is feasible, it has seen limited use in microbes as microbial esterase-specific promoieties have not been described. Here we identify the bacterial esterases, GloB and FrmB, that activate carboxy ester prodrugs in Staphylococcus aureus. Additionally, we determine the substrate specificities for FrmB and GloB and demonstrate the structural basis of these preferences. Finally, we establish the carboxy ester substrate specificities of human and mouse sera, ultimately identifying several promoieties likely to be serum esterase-resistant and microbially labile. These studies will enable structure-guided design of antistaphylococcal promoieties and expand the range of molecules to target staphylococcal pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Electrochemical Oxidation of HMF via Hydrogen Atom Transfer and Hydride Transfer on NiOOH and the Impact of NiOOH Composition

Abstract A great deal of attention has been directed toward studying the electrochemical oxidation of 5‐hydroxymethylfurfural (HMF), a molecule that can be obtained from biomass‐derived cellulose and hemicellulose, to 2,5‐furandicarboxylic acid (FDCA), a molecule that can replace the petroleum‐derived terephthalic acid in the production of widely used polymers such as polyethylene terephthalate. NiOOH is one of the best and most well studied electrocatalysts for achieving this transformation; however, the mechanism by which it does so is still poorly understood. This study quantitatively examines how two different dehydrogenation mechanisms on NiOOH impact the oxidation of HMF and its oxidation intermediates on the way to FDCA. The first mechanism is a well‐established indirect oxidation mechanism featuring chemical hydrogen atom transfer to Ni 3+ sites while the second mechanism is a newly discovered potential‐dependent (PD) oxidation mechanism involving electrochemically induced hydride transfer to Ni 4+ sites. The composition of NiOOH was also tuned to shift the potential of the Ni(OH) 2 /NiOOH redox couple and to investigate how this affects the rates of indirect and PD oxidation as well as intermediate accumulation during a constant potential electrolysis. The new insights gained by this study will allow for the rational design of more efficient electrochemical dehydrogenation catalysts.

2,5-furandicarboxylic acid↗
DOE OSTI2020

Isolation and characterization of lignocellulosic nanofibers from four kinds of organosolv-fractionated lignocellulosic materials

The objective of this research was to investigate the presence of residual lignin in cellulose fibers on the efficiency and energy consumption during nanofibers processing. Four kinds of lignin-containing cellulose nanofibers (LCNFs) from switchgrass, yellow poplar, hybrid poplar, and pine, respectively, were isolated via organosolv fractionation coupling mechanical grinding. Nanofibrils were observed after organosolv fractionation. The details of their morphological features, chemical structures, water retention value (WRV), and thermal degradation characteristics were revealed and compared. Mean diameters of nanofibers separated from switchgrass, yellow poplar, hybrid poplar, and pine were 27.9 nm, 25.4 nm, 24.6 nm, and 21.5 nm, respectively. The presence of lignin for the four types of LCNFs led to a decrease in energy consumption and an increase in WRV, among which pine nanofibers show the best with an average energy consumption of 0.511 kWh/kg and a WRV of 537%. It was also demonstrated that increasing lignin content for LCNFs could contribute to the sample’s thermal stability. In conclusion, exact benefits of residual lignin for nanofiber will facilitate its preparation process and extend its application.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Uptake of aqueous heavy metal ions (Co2+, Cu2+ and Zn2+) by calcium-aluminium-silicate-hydrate gels

Highlights: • C-A-S-H gels containing metal ions (Co{sup 2+}, Cu{sup 2+} or Zn{sup 2+}) were precipitated by a sol-gel technique. • Uptake mechanism and immobilization capacity of divalent metal ions by C-A-S-H are elucidated. • Up to ~55–60 mol% of Zn can be incorporated in defective tobermorite-type C-A-S-H. • Metal uptake by C-A-S-H is controlled mainly by isomorphous substitution for Ca ions. • Crystal-chemical features of C-A-S-H gels and implications for practical application are discussed. Calcium-aluminium-silicate-hydrate (C-A-S-H) gels containing heavy metal ions (Me: Co{sup 2+}, Cu{sup 2+} or Zn{sup 2+}) were precipitated at different Me/Si molar ratios to study the uptake mechanism and immobilization capacity. Aqueous solution chemistry and solid-phase characterization using XRD, FTIR, ESEM-EDX, TEM-HAADF and TG-DSC methods reveal threshold values for ion substitution of ~55–60 mol% for Zn, ~30–40 mol% for Co and ~25–30 mol% for Cu in defective tobermorite-type C-A-S-H with (Ca + Me)/(Si + Al) molar ratios from 0.86 to 1.04. At higher aqueous Me concentrations, Me (chloride) hydroxides start to co-precipitate. The uptake mechanism of Me ions by C-A-S-H is based on (i) isomorphous substitution of Me for Ca in the CaO layer, (ii) ion exchange in the interlayer, (iii) tetrahedral substitution (Zn) within the dreierketten chains and (iv) surface adsorption onto the C-A-S-H gel structure. Systematics in crystal-chemistry of Me-bearing C-A-S-H and implications for application and performance in natural and man-made settings are discussed.

36 MATERIALS SCIENCE↗