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235 results for “silica”
dataset for Fig 2 in NatComm "Localised structuring of metal-semiconductor cores in silica clad fibres using laser-driven thermal gradients"
<p>Infrared transmission of silicon core fiber through which gold has been laser-thermally moved to reystallize the material</p>
Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica: dataset
<p><strong>Abstract</strong>:<br> (from [1])</p> <blockquote> <p>The addition of fillers can significantly improve the mechanical behavior of polymers. The responsible mechanisms at the molecular level can be well assessed<br> by particle-based simulation techniques, such as molecular dynamics. However, the high computational cost of these simulations prevents the study of macroscopic<br> samples. Continuum-based approaches, particularly micromechanics, offer a more efficient alternative but require precise constitutive models for all<br> constituents, which are usually unavailable at these small length scales. In this contribution, we derive a molecular-dynamics-informed constitutive law by<br> employing a characterization strategy introduced in a previous publication. We choose silicon dioxide (silica) as an exemplary filler material used in polymer<br> composites and perform uniaxial and shear deformation tests with molecular dynamics. The material exhibits elastoplastic behavior with a pronounced anisotropy.<br> Based on the pseudo-experimental data, we calibrate an anisotropic elastic constitutive law and reproduce the material response for small strains accurately. <br> The study validates the characterization strategy that facilitates the calibration of constitutive laws from molecular dynamics simulations. Furthermore, the<br> obtained material model for coarse-grained silica forms the basis for future continuum-based investigations of polymer nanocomposites. In general, the presented<br> transition from a fine-scale particle model to a coarse and computationally efficient continuum description adds to the body of knowledge of molecular science<br> as well as the engineering community.<br> </p> </blockquote> <p><br> <strong>Contact</strong>:<br> Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><br> <strong>Software</strong>:<br> All simulations were performed with LAMMPS [3], version: 29 Oct 2020 / 20201029<br> Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11<br> Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p><strong>Installed packages:</strong><br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p><br> <strong>License:</strong><br> Creative Commons Attribution 4.0 International<br> <br> <strong>Context</strong>:<br> Data set supplementing journal paper:<br> [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080.</p> <p><br> This dataset contains the results presented in [1] and the necessary data to obtain those.</p> <p><br> <strong>Content</strong>:<br> The files to reproduce our simulations and their results are structured as follows:</p> <ul> <li>01_potentials<br> tabulated potentials calibrated via iterative Boltzmann inversion in [2] kindly provided by the Müller-Plathe group at Technische Universität Darmstadt <ul> <li>Angle_table<br> angular interactions</li> <li>Bond_table<br> bond interactions</li> <li>Nonbond_table<br> pair interactions</li> </ul> </li> <li>02_sample<br> Lammps data file (molecular style) of the investigated silica sample</li> <li>03_simulations<br> The condensed simulation directories with the naming convention given below are organized in the following subfolders: <ul> <li>01_time-proportional<br> time-proportional simulation data</li> <li>02_time-periodic<br> time-periodic simulation data</li> </ul> </li> </ul> <p>Each simulation directory contains:</p> <ul> <li>lammps input file (*.in) of the specific simulation</li> <li>input.prm: input parameters of the specific simulation (read by the input file)</li> <li>meta.info: meta data of the specific simulation run</li> <li>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li>thermo_out.Dat: raw output</li> <li>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</li> <li>thermo_out_STD.Dat: standard deviation of raw output</li> </ul> </li> </ul> <p><br> <strong>Naming convention</strong>:<br> Silica-[deformation]-[direction]_[deformation function]-[deformation magnitude]_[deformation rate]<br> ● [deformation]: uniaxial tension (UT), simple shear (SS)<br> ● [direction]: deformation carried out in X/Y/Z (UT) or XY/XZ/YZ (SS)<br> ● [deformation function]: time-proportional (strain), time-periodic (strain_ampl)<br> ● [deformation magnitude]: maximum strain (time-proportional), strain amplitude (time-periodic); unitless<br> ● [deformation rate]: rate-[strain rate] (only time-proportional): 0.001/ns-0.1/ns</p> <p><br> <strong>Output quantities</strong> (columns of *.Dat files):<br> ● Step: time step<br> ● Time: time in fs<br> ● TotEng: total energy in kcal/mol<br> ● PotEng: potential energy in kcal/mol<br> ● KinEng: kinetic energy in kcal/mol<br> ● E_pair: pair energy in kcal/mol<br> ● E_bond: bond energy in kcal/mol<br> ● E_angle: angle energy in kcal/mol<br> ● E_dihed: dihedral energy in kcal/mol<br> ● Temp: temperature in K<br> ● Press: hydrostatic pressure in atm<br> ● Pxx: xx component of pressure tensor in atm<br> ● Pyy: yy component of pressure tensor in atm<br> ● Pzz: zz component of pressure tensor in atm<br> ● Pxy: xy component of pressure tensor in atm<br> ● Pxz: xz component of pressure tensor in atm<br> ● Pyz: yz component of pressure tensor in atm<br> ● Volume: volume of simulation box in (Angstroms)^3<br> ● Lx: box length in x direction in Angstroms<br> ● Ly: box length in y direction in Angstroms<br> ● Lz: box length in z direction in Angstroms<br> ● Density: density in g/(cm^3)<br> ● c_RG: radius of gyration in Angstroms<br> ● c_RG[1]: squared radius of gyration tensor (xx component) in (Angstroms)^2<br> ● c_RG[2]: squared radius of gyration tensor (yy component) in (Angstroms)^2<br> ● c_RG[3]: squared radius of gyration tensor (zz component) in (Angstroms)^2<br> ● c_RG[4]: squared radius of gyration tensor (xy component) in (Angstroms)^2<br> ● c_RG[5]: squared radius of gyration tensor (xz component) in (Angstroms)^2<br> ● c_RG[6]: squared radius of gyration tensor (yz component) in (Angstroms)^2<br> ● c_bondave[1]: bond energy averaged over all atoms in kcal/mol<br> ● c_bondave[2]: bond distance averaged over all atoms in Angstroms<br> ● c_bondave[3]: squared bond distance averaged over all atoms in (Angstroms)^2<br> ● c_angleave[1]: angle energy averaged over all atoms in kcal/mol<br> ● c_angleave[2]: angle averaged over all atoms degree<br> ● c_angleave[3]: cosine of angle (unitless)<br> ● c_angleave[4]: squared cosine of angle (unitless)<br> ● c_MSD[1]: mean squared displacement x-direction in (Angstroms)^2<br> ● c_MSD[2]: mean squared displacement y-direction in (Angstroms)^2<br> ● c_MSD[3]: mean squared displacement z-direction in (Angstroms)^2<br> ● c_MSD[4]: total mean squared displacement in (Angstroms)^2<br> ● c_COM[1]: x coordinate of center of mass in Angstroms<br> ● c_COM[2]: y coordinate of center of mass in Angstroms<br> ● c_COM[3]: z coordinate of center of mass in Angstroms<br> ● v_strain_xx: xx component of engineering strain tensor (unitless) <br> ● v_strain_yy: yy component of engineering strain tensor (unitless) <br> ● v_strain_zz: zz component of engineering strain tensor (unitless) <br> ● v_vMisesequivstress: von Mises equivalent stress in MPa<br> ● v_Cauchy_xx: xx component of stress tensor in MPa <br> ● v_Cauchy_yy: yy component of stress tensor in MPa<br> ● v_Cauchy_zz: zz component of stress tensor in MPa<br> ● v_Cauchy_xy: xy component of stress tensor in MPa<br> ● v_Cauchy_xz: xz component of stress tensor in MPa<br> ● v_Cauchy_yz: yz component of stress tensor in MPa<br> ● v_strain_xy: xy component of engineering strain tensor (unitless) <br> ● v_strain_xz: xz component of engineering strain tensor (unitless) <br> ● v_strain_yz: yz component of engineering strain tensor (unitless) </p> <p><strong>References</strong>:<br> [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080.<br> [2] Ghanbari, A.; Ndoro, T. V. M.; Leroy, F.; Rahimi, M.; Böhm, M. C. & Müller-Plathe, F., “Interphase Structure in Silica-Polystyrene<br> Nanocomposites: A Coarse-Grained Molecular Dynamics Study”, Macromolecules, 2012, 45, 572-584.<br> [3] Plimpton, S., “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.</p> <p> </p>
Assessment of Hydrophilicity/Hydrophobicity in Mesoporous Silica by combining Adsorption, Liquid Intrusion and solid-state NMR spectroscopy
<p>This data publication is based on the metadata and datasets underlying the manuscript "</p> <p><span>Assessment of Hydrophilicity/Hydrophobicity in Mesoporous Silica by Combining Adsorption, Liquid Intrusion, and Solid-State NMR Spectroscopy (</span>"https://doi.org/10.1021/acs.langmuir.3c03516")</p> <p>Included are the datasets used, raw and processed data of Adsorption measurements (Water, Ar 87K), Water Intrusion measurements, solid state MAS NMR measurements. and molecular dynamics simulations. </p>
Figs. 5-8 in Silica-scaled chrysophytes from Mt. Sinbul wetland in South Korea
Figs. 5-8. Silica-scaled chrysophytes from Mt. Sinbul wetland. 5-7. Mallomonas leboimei, 5. Whole cell of Mallomonas leboimei, 6. Showing the scales with very small or large dome, 7. Bristles, 8. M. metvienkoae. Scale bars = 5 (5 μm), 6-8 (1 μm).
Figs. 16-20 in Silica-scaled chrysophytes from Mt. Sinbul wetland in South Korea
Figs. 16-20. Mallomonas dimorphus sp. nov., 16. Apical scales with short papilla (arrow head) and longitudinal rib (arrow), 17. Body scales showing flat dome marked with irregularly ribs such as labyrinth, perforated base plate and small papillae (arrow) on the anterior submarginal rib, 18. Body scales showing hooded V-shaped rib, large meshes or pores and strutted posterior flange, 19, 20. Showing the small scales (arrows) with circular ribs and short, smooth needle like bristles. Scale bars = 1 μm.
NanoValid D.5.47 Annex 1: Inter-laboratory comparison on measurand particle size of ~15 nm Lys-SNPs-B1 Silica Nanoparticles Particles
<p>An inter-laboratory comparison on the particle size, expressed as mean diameter <em>d</em>, of nanoscaled SiO<sub>2</sub> (#14 BAM Silica, ~15 nm diameter (see D.5.41/5.42)) has been performed. The majority of participants used Dynamic Light Scattering (DLS). A few used Electron Microscopy as method (SEM, TEM, T-SEM). Following methods had been applied by only one participant: Small Angle X-ray Scattering, Analytical Ultracentrifugation, Atomic Force Microscopy and Atomizer with electric mobility spectrometer (SMPS).</p> <p>The Task 5.4 of NanoValid is designed to test, compare and validate current methods to measure and characterize physicochemical properties of selected engineered nanoparticles. This will be achieved by inter-laboratory comparisons. The measurand of one of these round robins is <em>Particle size/Particle size distribution.</em> The measurements are to be accompanied by estimates of the uncertainties at a confidence level of 95%, deduced from the standard uncertainties. Therefore an uncertainty budget comprising statistical (Type A) and systematic (Type B) errors has to be established and delivered for each measurand. The inter-laboratry comparison protocol comprises two Annexes addressing the establishment of uncertainty budgets following GUM. The final goal of the comparison is to identify those methods of measurement which have potential as reference methods in pc characterization of nanoparticles for the determination of a given measurand (Task 5.4 of the NanoValid Project).</p>
Figure An2. Distribution of mineral phosphorus (a), silica (b), nitrate (c) and nitrite nitrogen (d). in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure An2. Distribution of mineral phosphorus (a), silica (b), nitrate (c) and nitrite nitrogen (d).
Silica in Silico: a Molecular Dynamics Characterization of the Early Stages of Protein Embedding for Atom Probe Tomography
<p>The .zip archive contains the trajectories of all the simulations performed and analysed within the manuscript. The water molecules were removed for control systems.</p>
FIG. 3 in Silica-scaled chrysophytes from the Ukrainian Polissia
FIG. 3. — Mallomonas Perty taxa from the Ukrainian Polissia: A, Mallomonas matvienkoae Asmund & Kristiansen, SEM; B, Mallomonas ouradion K.Harris & D.E.Bradley, SEM; C, Mallomonas cf. pseudomatvienkoae B.Y.Jo, W.Shin, H.S.Kim, Siver & R.A.Andersen, SEM; D, Mallomonas paludosa Fott; E, F, Mallomonas papillosa K.Harris & D.E.Bradley emend. K.Harris, TEM (E) and SEM (F); G, Mallomonas pillula f. valdiviana Dürrschmidt, SEM; H, Mallomonas punctifera Korshikov, SEM; I, Mallomonas pugio D.E.Bradley, SEM; J, Mallomonas rasilis Dürrschmidt, SEM; K, L, Mallomonas schwemmlei Glenk emend. Glenk & Fott, body (K) and apical (L) scales, SEM; M, Mallomonas striata Asmund, SEM; N, Mallomonas teilingii W.Conrad, SEM; O, Mallomonas teres Nemcova & Kapustin, SEM; P, Mallomonas cf. tonsurata Teiling, SEM. Scale bars: A-F, I-M, O, P, 1 µm; G, 0.5 µm; H, N, 2 µm.
FIG. 1 in Silica-scaled chrysophytes from the Ukrainian Polissia
FIG. 1. — Chrysosphaerella Lauterborn, Paraphysomonas De Saedeleer emend. Scoble & Cavalier-Smith, Lepidochromonas Kristiansen and Spiniferomonas E.Takahashi species from the Ukrainian Polissia: A, Chrysosphaerella brevispina Korshikov, spine scale, TEM; B, Stomatocyst of Chrysosphaerella coronacircumspina Wujek & Kristiansen with broken spine scale and plate scales, SEM; C, Paraphysomonas acuminata Scoble & Cavalier-Smith, TEM; D, Paraphysomonas truncata (Preisig & D.J.Hibberd) Scoble & Cavalier-Smith, TEM; E, Paraphysomonas sp., scale with broken spine, SEM; F, Lepidochromonas sp., TEM; G, Spiniferomonas bourrellyi E.Takahashi, spine scale with plate scales attached to it, SEM; H, Spiniferomonas cf. trioralis E.Takahashi, spine scale, SEM; I, Chrysosphaerella longispina J.B.Petersen & J.B.Hansen, SEM. Scale bars: A, D, E, G, H, 1 µm; B, C, 2 µm; F, 0.5 µm; I, 5 µm.
FIG. 2 in Silica-scaled chrysophytes from the Ukrainian Polissia
FIG. 2. — Mallomonas Perty taxa from the Ukrainian Polissia: A, Mallomonas acaroides Perty emend. Iwanoff, SEM; B, Stomatocyst of Mallomonas cf. akrokomos Ruttner, SEM; C, Mallomonas annulata (D.E.Bradley) K.Harris, SEM; D, Mallomonas asmundiae (Wujek & van der Veer) K.H.Nicholls, SEM; E, F, Mallomonas calceolus D.E.Bradley, SEM; G, Mallomonas canina Kristiansen, SEM; H, Mallomonas caudata Iwanoff emend. Willi Krieger, SEM; I, Mallomonas corcontica (Kalina) L.Ş.Péterfi & Momeu, SEM; J, Mallomonas costata Dürrschmidt, SEM; K, Mallomonas crassisquama (Asmund) Fott, SEM; L, Mallomonas cratis K.Harris & D.E.Bradley, TEM; M, Mallomonas elongata Reverdin, SEM; N, Mallomonas heterospina J.W.G.Lund, SEM; O, Stomatocyst of Mallomonas mangofera var. foveata (Dürrschmidt) covered with scales, SEM. Scale bars: A, D-F, H-K, 1 µm; B, C, L-O, 2 µm; G, 0.5 µm.
FIG. 4 in Silica-scaled chrysophytes from the Ukrainian Polissia
FIG. 4. — Synura Ehrenb. taxa from the Ukrainian Polissia: A, Synura conopea Kynčlová & Škaloud, TEM; B, Synura echinulata Korshikov, SEM; C, Synura glabra, TEM; D, Synura heteropora Škaloud, Škaloudová & Procházková, TEM; E, Synura korshikovii Kapustin & Gusev, SEM; F, Synura macropora Škaloud & Kynčlová, TEM; G, Synura petersenii Korshikov, TEM; H, Synura petersenii f. columnata Siver, TEM; I, Synura sphagnicola (Korshikov) Korshikov, SEM; J, Synura spinosa f. spinosa Korshikov, SEM; K, Synura spinosa f. longispina J.B.Petersen & J.B.Hansen, TEM; L, Synura uvella Ehrenb. emend. Korshikov, SEM. Scale bars: A, C, D, F-H, K, 2 µm; B, E, I, J, L, 1 µm.
Equilibrium contact angle of acqueous glycerol on a silica-like surface from Molecular Dynamics
<p>This dataset contains the results of Molecular Dynamics simulations of quasi-2D water-glycerol liquid droplets, spreading on silica-like surfaces. The goal of the simulations is to quantify the equilibrium contact angle of said droplets.</p> <p>The pattern "Glycerol***" refers to the mass fraction of glycerol ("000": pure water, "100": pure glycerol). Each folder contains configuration files and compressed output molecular trajectories. The contact angle is computed from density maps binned on-the-fly using a customized Gromacs version (<a href="https://github.com/pjohansson/gromacs-flow-field">https://github.com/pjohansson/gromacs-flow-field</a>); frames are placed in a tarball ("flow-***p.tar.gz").</p> <p>The zipped folder 'scripts.zip' contains a self-contained library of functions to read density maps and a Jupyter notebook with an example of density reading and plotting.</p> <p>Simulations are performed with Gromacs. We refer to the code documentation for further information (<a href="https://manual.gromacs.org/">https://manual.gromacs.org/</a>).</p>
Simulated diagenesis of the iron-silica precipitates in banded iron formations: Data Repository
<p>This XRD dataset derives from experiments we performed bubbling 49 ppm O2 into simulated Archean seawater and then aging the produced precipitates at 25 degrees C, 80C, 150C, and 220C. Precipitate slurries were extracted from experimental samples and pipetted as 20 µL subsamples into Cole-Parmer Kapton tubes to keep anoxic during XRD analysis. Samples were sent to McMaster Analytical X-Ray Diffraction Facility (MAX) for XRD analysis using a Bruker D8 DISCOVER cobalt source tube (Co-XRD) with a DAVINCI.DESIGN diffractometer. More details on methods in associated article. Resultant XRD measurements of our samples yielded patterns showing increasing crystallinity with temperature. The bubbled experiment aged for 40 days at 25 °C produced a large and diffuse diffraction peak corresponding to the Kapton tube but no other sharp diffraction peaks, suggesting an amorphous to minimally crystalline product. A broad peak in the 25 °C precipitate, that persisted through the higher-temperature aging treatments, may correspond to ferrihydrite. The bubbled experiment aged at 80C contained diffraction peaks consistent with a serpentine group silicate and a spinel group oxide (like magnetite). After the 150 °C treatment, samples showed sharper peaks consistent with a serpentine group silicate and spinel group oxide. After 220 °C aging, the precipitates showed a continued narrowing of the diffraction peaks for a spinel group oxide, reflecting an increase in crystal size and/or crystallinity, but smaller and less sharp serpentine group peaks. </p>
Silica polymerization experimental data
<p>This dataset is accompanying the submission of the manuscript "Silica polymerization and nanocooloid nucleation and growth kinetics in aqueous solutions". It contains experimental data describing the concentration of aqueous monomeric silica as a function of time. The experimental conditions (temperature, pH, ionic strength) are also given. These data were used to fit the rate constants using the fourth-order rate kinetic expression described in the paper. Data are provided as an Excel file. </p>
Data from: Plant uptake offsets silica release from a large Arctic tundra wildfire
Open the record for dataset details and reuse information.
Dissolved silica time series for Beaverdam Reservoir, Carvins Cove Reservoir, Claytor Lake, Falling Creek Reservoir, Gatewood Reservoir, Smith Mountain Lake, and Spring Hollow Reservoir in southwestern Virginia, USA during 2014
Water column dissolved silica (SiO2) was analyzed during 2014 in seven freshwater reservoirs in southwestern Virginia (VA), USA. These reservoirs are: Beaverdam Reservoir (Vinton, VA), Carvins Cove Reservoir (Roanoke, VA), Claytor Lake (Pulaski, VA), Falling Creek Reservoir (Vinton, VA), Gatewood Reservoir (Pulaski, VA), Smith Mountain Lake (Bedford, VA), and Spring Hollow Reservoir (Salem, VA). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia; Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia; and Smith Mountain Lake is jointly treated by Bedford Regional Water Authority and Western Virginia Water Authority as a drinking water source for Franklin County, Virginia. Claytor Lake is utilized for hydroelectric power generation by Appalachian Power Company. The dataset consists of depth profiles of dissolved silica samples generally measured at the deepest site of each reservoir adjacent to the dam and an inflow stream into Falling Creek Reservoir. The water column samples were collected approximately fortnightly from April-June, weekly from June-July and sporadically from July-October at Beaverdam Reservoir; weekly from April-July and fortnightly from July-November at Carvins Cove Reservoir; sporadically from April-August at Claytor Lake; weekly from April-November at Falling Creek Reservoir; fortnightly from April-October at Gatewood Reservoir; fortnightly from May-November at Spring Hollow Reservoir; and fortnightly from May-October at Smith Mountain Lake.
Temperature Activates Contact Aging in Silica Nanocontacts
<p>Data presented in the CECAM workshop: Emergence of surface and interface structure from friction, fracture and deformation ( link: cecam.org/index.php/workshop-details/215). </p> <p>Here we unravel the temperature dependence of both static friction and contact stiffness on a silica contact. Further in-depth details are provided in the associated peer-reviewed freely available publication (https://doi.org/10.1103/PhysRevX.9.041045).</p> <p> </p>
Dataset from: Forces between silica particles in isopropanol solutions of 1:1 electrolytes
<p>The dataset from the publication: "Forces between silica particles in isopropanol solutions of 1:1 electrolytes"</p> <p>DOI: <a href="https://doi.org/10.1103/PhysRevResearch.2.023315">10.1103/PhysRevResearch.2.023315</a></p> <p>Files contain x and y data for the figures in the publication in the ASCII format.</p>
Data from: Biogenic silica accumulation varies across tussock tundra plant functional type
1. Silica (SiO2) accumulation by terrestrial vegetation is an important component of the biological silica cycle because it improves overall plant fitness and influences export rates of silica from terrestrial to marine systems. However, most research on silica in plants has focused on agricultural and forested ecosystems, and knowledge of terrestrial silica cycling in the Arctic, as well as the potential impacts of climate change on the silica cycle is severely lacking. 2. We quantified biogenic silica (BSi) accumulation in above and belowground portions of three moist acidic tundra (MAT) sites spanning a 300 km latitudinal gradient in central and northern Alaska, USA. We also examined plant silica accumulation across three main tundra types found in the Arctic (MAT, moist non-acidic tundra (MNT), and wet sedge tundra (WST)). 3. BSi concentrations in live Eriophorum vaginatum, a tussock-forming sedge that is the foundation species of tussock tundra, were not significantly (p<0.05) different across the three main sites. Concentrations of BSi in live aboveground tissue were highest in the graminoid species (0.55 ± 0.07 % BSi in sedges from WST, and 0.27 ± 0.01% in E. vaginatum across the three MAT sites). Both inter-tussock tundra species and shrubs contained substantially lower BSi concentrations than E. vaginatum. 4. Our results have implications for how shifts in vegetation cover associated with climatic warming may alter silica storage in tussock tundra vegetation. Our calculations suggest that shrub expansion via warming will increase BSi storage in Arctic land plants due to the higher biomass associated with shrub tundra, whereas conversion of tussock tundra to WST via permafrost thaw would produce the opposite effect in the terrestrial plant BSi pool. Such changes in the size of the terrestrial vegetation silica reservoir could have direct consequences for the rates and timing of silica delivery to receiving waters in the Arctic.
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