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Basalt

Basalt is a common type of extrusive igneous rock formed from the rapid cooling of lava rich in magnesium and iron.
It is widely used in construction, landscaping, and as a raw material for various industrial processes.
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Most cited protocols related to «Basalt»

Modeling the Geological Carbon Cycle

Cited 11 times

The geological carbon cycle model builds on that described in Krissansen-Totton and Catling (36 (link)). Here, we summarize its key features, and additional details are provided in the SI Appendix. The Python source code is available on GitHub at github.com/joshuakt/early-earth-carbon-cycle.
We model the time evolution of the carbon cycle using two separate boxes representing the atmosphere–ocean system and the pore space in the seafloor (Fig. 1 and SI Appendix A). We track carbon and carbonate alkalinity fluxes into and between these boxes, and assume that the bulk ocean is in equilibrium with the atmosphere.
Many of the parameters in our model are uncertain, and so we adopt a range of values (SI Appendix, Table S1) based on spread in the literature rather than point estimates. Each parameter range was sampled uniformly, and the forward model was run 10,000 times to build distributions for model outputs such as pCO₂, pH, and temperature. Model outputs are compared with proxy data for pCO₂, temperature, and carbonate precipitation (SI Appendix D).
Continental silicate weathering is described by the following function:

F_{sil} = f_{b i o} f_{land} F_{sil}^{\mod} {(\frac{{pCO}_{2}}{{pCO}_{2}^{\mod}})}^{α} \exp (Δ T_{S} / T_{e})

Here,

f_{bio}

is the biological enhancement of weathering (see below),

f_{land}

is the continental land fraction relative to modern,

F_{sil}^{\mod}

is the modern continental silicate weathering flux (Tmol y⁻¹),

Δ T_{S} = T_{S} - T_{S}^{\mod}

is the difference in global mean surface temperature,

T_{S}

, relative to preindustrial modern,

T_{S}^{\mod}

. The exponent

α

is an empirical constant that determines the dependence of weathering on the partial pressure of carbon dioxide relative to modern,

{pCO}_{2} / {pCO}_{2}^{\mod}

. An e-folding temperature,

T_{e}

, defines the temperature dependence of weathering. A similar expression for carbonate weathering is described in SI Appendix A.
The land fraction,

f_{land}

, and biological modifier,

f_{b i o}

, account for the growth of continents and the biological enhancement of continental weathering, respectively. We adopt a broad range of continental growth curves that encompasses literature estimates (Fig. 2A and SI Appendix A). For our nominal model, we assume Archean land fraction was anywhere between 10% and 75% of modern land fraction (Fig. 2A), but we also consider a no-land Archean endmember (Fig. 2B).
To account for the possible biological enhancement of weathering in the Phanerozoic due to vascular land plants, lichens, bryophytes, and ectomycorrhizal fungi, we adopt a broad range of histories for the biological enhancement of weathering,

f_{b i o}

(Fig. 2C). The lower end of this range is consistent with estimates of biotic enhancement of weathering from the literature (37 –39 (no links found)).
The dissolution of basalt in the seafloor is dependent on the spreading rate, pore-space pH, and pore-space temperature (SI Appendix A). This formulation is based on the validated parameterization in ref. 36 (link). Pore-space temperatures are a function of climate and geothermal heat flow. Empirical data and fully coupled global climate models reveal a linear relationship between deep ocean temperature and surface climate (36 (link)). Equations relating pore-space temperature, deep ocean temperature, and sediment thickness are provided in SI Appendix A.
Carbon leaves the atmosphere–ocean system through carbonate precipitation in the ocean and pore space of the oceanic crust. At each time step, the carbon abundances and alkalinities are used to calculate the carbon speciation, atmospheric pCO₂, and saturation state assuming chemical equilibrium. Saturation states are then used to calculate carbonate precipitation fluxes (SI Appendix A). We allow calcium (Ca) abundance to evolve with alkalinity, effectively assuming no processes are affecting Ca abundances other than carbonate and silicate weathering, seafloor dissolution, and carbonate precipitation. The consequences of this simplification are explored in the sensitivity analysis in SI Appendix C. We do not track organic carbon burial because organic burial only constitutes 10–30% of total carbon burial for the vast majority of Earth history (40 ), and so the inorganic carbon cycle is the primary control.
The treatment of tectonic and interior processes is important for specifying outgassing and subduction flux histories. We avoid tracking crustal and mantle reservoirs because explicitly parameterizing how outgassing fluxes relate to crustal production and reservoirs assumes modern-style plate tectonics has operated throughout Earth history (e.g., ref. 12 ) and might not be valid. Evidence exists for Archean subduction in eclogitic diamonds (41 (link)) and sulfur mass-independent fractionation in ocean island basalts ostensibly derived from recycled Archean crust (42 (link)). However, other tectonic modes have been proposed for the early Earth such as heat-pipe volcanism (43 (link)), delamination and shallow convection (44 (link)), or a stagnant lid regime (45 ).
Our generalized parameterizations for heat flow, spreading rates, and outgassing histories are described in SI Appendix A. Fig. 2D shows our assumed range of internal heat flow histories compared with estimates from the literature. Spreading rate is connected to crustal production via a power law, which spans endmember cases (SI Appendix A). These parameterizations provide an extremely broad range of heat flow, outgassing, and crustal production histories, and do not assume a fixed coupling between these variables.
We used a 1D radiative convective model (46 ) to create a grid of mean surface temperatures as a function of solar luminosity and pCO₂. The grid of temperature outputs was fitted with a 2D polynomial (SI Appendix E). We initially neglect other greenhouse gases besides CO₂ and H₂O, albedo changes, and assumed a constant total pressure over Earth history. However, later we consider these influences, such as including methane (CH₄) in the Precambrian. The evolution of solar luminosity is conventionally parameterized (47 ).
Our model has been demonstrated for the last 100 Ma against abundant proxy data (36 (link)) and it can broadly reproduce Sleep and Zahnle (12 ) if we replace our kinetic formulation of seafloor weathering with their simpler CO₂-dependent expression (SI Appendix B). Agreement with ref. 12 confirms that the omission of crustal and mantle reservoirs does not affect our conclusions.

Krissansen-Totton J., Arney G.N, & Catling D.C. (2018). Constraining the climate and ocean pH of the early Earth with a geological carbon cycle model. Proceedings of the National Academy of Sciences of the United States of America, 115(16), 4105-4110.

Publication 2018

Ion Torrent PGM Sequencing and Microbiome Analysis

Cited 9 times

After Ion Torrent PGM sequencing, the raw data was obtained in Fastq format. The pre-processing of the raw sequences, i.e., Fastq to Fasta file (sequence) conversion, qual (quality score), and data trimming were done by using MOTHUR version 1.34.3 (http://www.mothur.org/wiki) (Schloss et al., 2009 (link)) with the following conditions of minimum length: 130 bp, maximum length: 150 bp, maximum homopolymer: 5, maximum ambiguity: 0 and average quality score: 20. The trimmed good quality sequences were merged into a single FASTA file for further analysis in QIIME v1.7 (Quantitative Insights Into Microbial Ecology) (http://qiime.org/) (Caporaso et al., 2010 (link)). The sequences were aligned to bacterial 16S rRNA gene sequence, and clustering or OTU picking was performed by using a reference-based OTU picking approach with Silva database (Silva_111_release, March 2015) (Pruesse et al., 2007 (link)). The OTU picking was carried out using UCLUST method with a similarity threshold of 97% (Edgar, 2010 (link)). Taxonomic assignments were performed using RDP naïve Bayesian classifier (version rdp_classifier_2.10.1, released 29.10.2014) (Wang et al., 2007 (link)). Using QIIME, alpha diversity indices namely Chao1, ACE, Shannon, Simpsons' index, and Goods coverage were calculated. The sequences obtained from Ion Torrent sequencing are available at the NIH Sequence Read Archive (SRA) under the project accession number SRP059522.

Paul D., Kumbhare S.V., Mhatre S.S., Chowdhury S.P., Shetty S.A., Marathe N.P., Bhute S, & Shouche Y.S. (2016). Exploration of Microbial Diversity and Community Structure of Lonar Lake: The Only Hypersaline Meteorite Crater Lake within Basalt Rock. Frontiers in Microbiology, 6, 1553.

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Publication 2015

Genes, Bacterial RNA, Ribosomal, 16S

Characterizing Coral Reef Regimes in Hawaii

Cited 8 times

Existing data from underwater visual surveys of fish and benthic assemblages were collated from seven monitoring programs for the main Hawaiian Islands. Each dataset was transformed into a consistent format and checked for errors. Data were only included in the analysis if benthic and fish surveys were co-located at a unique latitude and longitude and were from forereef habitats at depths of 0 to 30 meters. We also tested for the sensitivity of results to including data from different depth zones between 0 and 30 meters (0–20 meters, and 5–20 meters), and found no effect (Supplementary Figure S1). The majority of surveys were between 10 and 20 meters, so these depth ranges were chosen as they allowed for testing sensitivity to inclusion of shallow depths (<5 meters) and deeper depths (>20 m). The majority of the data (98%) were from 2000–2013. A total of 3,345 unique sites, defined as a survey location with a unique latitude and longitude, were used in the analyses. To account for spatial autocorrelation, means were taken for surveys within a defined distance of 300 meters (Supplementary Figures S2 and S3), resulting in an overall sample size of 1,027. The mean was used for sites with data across multiple years. Data sources, survey methods, and other meta-data are provided in the Supplementary Material.
To account for differences in survey method, fish data were standardised using calibration factors to the NOAA Biogeography Program belt transect since that method had the greatest consistency with the majority of other programs^{34 (link)} (Supplementary Table S1). Calibration factors were developed using an automated software program that utilises general linear models and Monte Carlo simulations³⁵. Calibrations were calculated by species where possible based on the following decision rules: (1) a minimum of 10 paired observations were available within an island, (2) observations were not dominated by zeros – if they were, a delta model was run in which occurrences were modeled separately from non-occurrences, (3) residuals were normally distributed – if not, data were log-transformed and the model was rerun and checked again. If a species did not pass this series of rules, a calibration factor for each combination of family and trophic level (e.g. zooplanktivorous wrasses) was calculated. If a calibration factor could not be calculated at the family and trophic level, then a global calibration for the entire method was used. For all subsequent analyses, density estimates were based on calibrated densities of raw data. Benthic surveys were not calibrated as previous results found no large bias associated with percent cover among the methods used^{36 (link)} (Supplementary Table S1).
The biomass of individual fishes was estimated using the allometric length-weight conversion: W = aTL^b, where parameters a and b are species-specific constants, TL is total length (cm), and W is weight (g). Length-weight fitting parameters were obtained from a comprehensive assessment of Hawai‘i specific parameters (Donovan et al., unpublished data) and FishBase³⁷. Several fish species were removed from fish biomass calculations if aspects of their life history led to inaccurate counts with visual surveys, such as cryptic benthic species, nocturnal species, and pelagic schooling species. Likewise, manta rays were excluded, as their size is difficult to visually estimate and they have high biomass but are encountered infrequently. Additional methodology was developed for dealing with outliers in the fish data, accounting for extreme observations of schooling species. Extreme observations in the database were defined by calculating the upper 99.9% of all individual observations (e.g. one species, size and count on an individual transect), resulting in 26 observations out of over 0.5 million, comprised of 11 species. The distribution of individual counts in the entire database for those 11 species was then used to identify observations that fell above the 99.0% quantile of counts for each species individually. These observations were adjusted to the 99.0% quantile for analysis.
Fish and benthic assemblages were analyzed primarily at the level of functional groups. Benthic assemblages were broken into major functional groups including coral, macroalgae, turf algae, crustose-coralline algae, and other benthic cover (e.g. sponges, sand, basalt rock, recently dead coral). Other benthic cover was not broken down further due to limitations from incorporating data from different methods with different definitions for other benthic taxa. The fish assemblage was characterised into three trophic groups; herbivores, secondary consumers, and predators. Herbivores were further subdivided based on their feeding mode into browsers, grazers and scrapers following Edwards et al.^{38 (link)}, which have been suggested as important indicators of resilience on coral reefs (Supplementary Table S2)^{4 (link),29 (link),39 (link),40}. Browsers were defined as those herbivores that feed on macroalgae and associated epiphytic material and are important for reducing cover of competing macroalgae. Grazers are considered those fishes that feed largely on algal turfs, which can limit the establishment of macroalgae, and scrapers also feed on algal turfs but can remove the reef substratum, opening space for coral recruitment^{41 ,42}. Secondary consumers included corallivores, omnivores, invertivores, and planktivores. These groups were not further subdivided because they tend to have unstable biomass estimates (e.g., planktivores usually occur in large numbers with patchy distributions) so are not estimated well with transects, and thus may provide spurious results when considered independently. Predators were defined as large piscivorous species, such as sharks, jacks, and barracuda (Supplementary Table S2)^{43 (link)}. Additional functional groups that have been shown to relate to reef resilience, such as urchins and Acanthaster spp., could not be included because data were not available.
Before analysis, all data were fourth root transformed and centered to meet the assumptions of linear models and all variables were standardised to the same scale. The fourth root transformation was chosen because it was strong enough to meet assumptions for all variables, such that a common transformation could be used for all 10 variables.
Regimes were identified using model-based clustering with the mclust package in R⁴⁴ with the 10 fish and benthic functional groups as inputs. The cluster analysis is based on a probability model where each cluster is a mixture of multivariate normal distributions composed of the densities of each component, and each observation is assigned to a cluster based on the probability of membership given the observation. The mclust function uses three strategies for defining clusters: 1) initialization of the model with model-based hierarchical clustering, 2) maximum likelihood estimation with the expectation-maximization algorithm, and 3) model selection and the number of clusters that are approximated with Bayes factors and Bayesian Information Criterion^{45 (link)} (Supplementary Figure S4). Uncertainty in a point’s assignment to a regime was obtained during the clustering process by subtracting the probability of the most likely regime for each observation from one⁴⁴.
The 10 multivariate benthic and fish functional groups were visualised with a non-metric multidimensional scaling plot using the metaMDS function in the vegan package in R⁴⁶. A Bray-Curtis distance matrix was created with 2-dimensions and a maximum of 50 random starts to search for a stable solution and avoid getting trapped in a local optima⁴⁷. Multivariate dispersion was also calculated for each regime and tested with an analysis of multivariate homogeneity of group dispersions with the betadispr function in the vegan package.
Coral species richness was examined across regimes with an Analysis of Variance, and contrasts and confidence intervals were calculated with Tukey’s honest significant differences where coral richness was the response variable and regimes were the explanatory variable. The community composition of corals was also examined by calculating the proportional cover of the four most abundant species within each regime, including Porites lobata, Pocillopora meandrina, Porites compressa, Montipora capitata. Coral species were also classified with a trait-based approach into competitive, stress-tolerant, generalist or weedy species following Darling et al.^{48 (link)}, with additional species specific information on bleaching tolerance, and were compared across regimes.
In the Hawaiian Islands, seascape variables such as depth, habitat complexity, and wave exposure have been shown to be important predictors of fish assemblages^{49 (link)–52 (link)}. As a result, spatial patterns across regimes were examined by comparing the proportion of each regime at each island, and by comparing the proportion of sites within a regime that were located on north, south, east, and west facing shores across islands. Additionally, depth and habitat complexity, measured as the maximum rate of change in seafloor slope (i.e. slope of slope), were calculated for each point from LiDAR derived bathymetry within a 60 m radius of each survey location^{53 (link)}, and were compared across regimes with an Analysis of Variance and post-hoc Tukey multiple comparisons with either depth or complexity as response variables and regimes as explanatory variables.
Temporal transitions between regimes were assessed by predicting the regime for each year at each site individually with the function predict.Mclust in R. Predictions were retained if there was at least a 95% probability of the regime prediction belonging to that regime, and only sites where predictions were available for at least three years between 2000 and 2016 were retained. We used an extended dataset for the transition analysis encompassing a greater number of years compared to the dataset used to characterise the regimes, which ended in 2013. The extended dataset was not used previously in characterizing the regimes in order to avoid confounding effects of coral bleaching events that occurred in 2014 and 2015, as widespread bleaching was unobserved in all previous study years. A total of 80 sites were included in the temporal data set, and patterns of regime transitions were compared by calculating the frequency of a given transition as a proportion of the total number of possible transitions (n = 261). We also tested the sensitivity of analysing data from all 80 sites compared with only analysing those with longer time series (>4 or 6 years) by calculating binomial confidence intervals for each transition in each case. These sites also tended to represent permanent monitoring stations, which allowed for testing for the sensitivity to using observations from locations that may shift spatially from year to year. Binomial confidence intervals for each transition in each case were produced with the binconf function in the Hmisc package in R⁵⁴ using the Wilson interval.
Finally, we tested the hypothesis that local and global human impacts will result in some transitions among regimes being more likely than others. Each observed transition was treated as a replicate, and the geographical position was used to obtain a value for a local impact represented as human population density within a 15 km radius, and a global impact represented as the degree heating weeks at the height of the 2015 bleaching event (Supplementary Material). The probability of a given transition was estimated with a Bayesian binomial model for each variable where there were at least four occurrences for a given transition as a function of either human population density or degree heating weeks. More details of the Bayesian binomial model are in the Supplementary material.
All analyses were conducted in the R environment for statistical computing version 3.3.0⁵⁵.

Donovan M.K., Friedlander A.M., Lecky J., Jouffray J.B., Williams G.J., Wedding L.M., Crowder L.B., Erickson A.L., Graham N.A., Gove J.M., Kappel C.V., Karr K., Kittinger J.N., Norström A.V., Nyström M., Oleson K.L., Stamoulis K.A., White C., Williams I.D, & Selkoe K.A. (2018). Combining fish and benthic communities into multiple regimes reveals complex reef dynamics. Scientific Reports, 8, 16943.

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Publication 2018

Genetic Study of Podoconiosis in Ethiopia

Cited 7 times

Study participants were recruited from the Wolaita zone of southern Ethiopia from podoconiosis-affected families (DNA samples were collected from both parents and two affected siblings); unaffected persons were recruited to serve as controls. All participants were from the same broad geologic area covered by reddish-brown clay soils containing colloid-size particles derived from volcanic basalt rocks.¹³ The genomewide association study was conducted with the use of samples from one randomly selected affected sibling (of the two recruited) from each family and an unaffected, unrelated control for each case patient. The eligibility criteria for controls were the current absence of podoconiosis, the absence of a personal or family history of podoconiosis, age of at least 50 years (to minimize misclassification of potential case patients as controls), residence in the study area for at least 25 years, exposure to the same irritant clay soil as the case patients, and inconsistent use of shoes. Consequently, the controls were older than the case patients (average age, 62 years vs. 24 years). The family-based association test included the affected sibling who was not included in the genomewide association study as well as both parents from each family.

Ayele F.T., Adeyemo A., Finan C., Hailu E., Sinnott P., Burlinson N.D., Aseffa A., Rotimi C.N., Newport M.J, & Davey G. (2012). HLA Class II Locus and Susceptibility to Podoconiosis. The New England Journal of Medicine, 366(13), 1200-1208.

Publication 2012

basalt Clay Colloids Eligibility Determination Genome-Wide Association Study Irritants Parent Patients Podoconiosis

Biaxial Testing of Rat Bladder Tissue

Cited 7 times

Mechanical testing was performed using a custom biaxial system specifically designed for testing bladder tissue concurrent with imaging under a multiphoton microscope, Fig. 2. This design enabled imaging of collagen fibers in intact specimens without staining or fixation. In the biaxial system, displacement can be independently controlled by four actuators (Aerotech, Inc., linear actuator ANT-25LA) and force measurements are performed using load cells on two of the actuators (Transducer techniques, nonrepeatibility 0.05% of R.O., capacity 5 lbs), Fig. 2b. Tissue is mounted on the device using biorakes (World Precision Instruments, Inc.). The biaxial system includes a CCD camera and a 45 degree offset mirror to enable imaging of strain markers from beneath the mounted tissue, Fig. 2d,e. This imaging system enables MPM imaging at prescribed biaxial strains.
Prior to testing, the unloaded thickness t_o of each sample was measured in 5 positions using a 0-150mm digital caliper (Marathon watch company Ltd) and average Fiducial strain markers (Basalt microspheres, 425-500 μm, Whitehouse Scientific) were attached to the abluminal side of each sample for strain calculation. During testing, the square sample was first loaded lumen side up on the biorakes, Fig. 2b. Following Wognum et al. (2009) (link), a tare-load was applied to the sample after which it was preconditioned, then unloaded, then loaded to the tare-load, then mechanically tested. Five consecutive equibiaxial loading cycles to a stretch of 1.8 were used for preconditioning with a tare-load of 0.02N. The post-precondition tare-loaded state is used as reference configuration. The lumen surface was imaged under MPM at stepwise increases in strains, Fig. 2c, (Section 2.5). To avoid tissue damage while obtaining a large range of strain, loading was stopped at a strain where the collagen fibers were visibly straightened (recruited) defined as the maximum strain. Hence, an individual maximum strain was identified for each sample. After lumen side imaging, 5 equibiaxial loading cycles to the maximum strain were performed at a strain rate of 1% s⁻¹. The strain marker locations were recorded by a CCD camera using the 45 degree mirror block beneath the sample (Fig. 2d,e) and used to obtain the loading curves. The sample was then flipped and the imaging was repeated from the abluminal side, once again until collagen recruitment was observed. The components of the Green-Lagrange strains were calculated from the fiducial marker locations using a finite element interpolation method (Sacks 2000 ). Components of the Cauchy stress tensor were calculated using load measurements with estimates of current cross sectional area obtained from the strain measurements under the approximation of isochoric motion.
For the purpose of analyzing the loading regimes and motivated by prior studies of the bladder wall (Gloeckner et al. 2002 (link)), the rat bladder was modeled as an incompressible, hyperelastic, isotropic material with Cauchy stress tensor
where p is the Lagrange multiplier associated with incompressibility, I₁ is the first invariant of the left Cauchy-Green deformation tensor B, μ is the shear modulus and material constant γ controls the exponential dependence on I₁. An exponential dependence on stretch was proposed by Fung (1967) (link) and the form in (1) is commonly used for soft biological tissues. Data sets for the planar biaxial loading studies were combined for the longitudinal and circumferential directions and used to obtain the material constants in (1).

Cheng F., Birder L., Kullmann F.A., Hornsby J., Watton P., Watkins S.C., Thompson M, & Robertson A.M. (2017). Layer dependent role of collagen recruitment during loading of the rat bladder wall. Biomechanics and modeling in mechanobiology, 17(2), 403-417.

Publication 2017

basalt Biopharmaceuticals Cells Collagen Fiducial Markers Fingers Isochores Marathon composite resin Medical Devices Microscopy Microspheres Strains Tissues Transducers Urinary Bladder Vicia sativa

Most recents protocols related to «Basalt»

Characterizing Basalt Fiber Properties

The basalt fiber utilized in this study was sourced from Jiangsu Tianlong Basalt Continuous Fiber Co., Ltd. (Yizheng, China), and its technical properties are presented in Table 4.

Zhang Y., Zhang Y., Li B., Kang A, & Wang Y. (2024). Evaluation of Cracking Resistance of SMA-13 Hot Recycling Asphalt Mixtures Reinforced by Basalt Fiber. Materials, 17(8), 1762.

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Publication 2024

BASALT: Optimizing Metagenomic Assemblies

BASALT is a binning and post-binning bioinformatics tool that recovers, compares, and optimizes assembled genomes across series of assemblies generated from short-read, long-read, or hybrid platforms to produce high-quality MAGs. Although BASALT can function with only a single metagenomic dataset, the overall bin quality including MAG quality can be improved using multiple datasets and assemblies as inputs^{36 (link),83 (link)}. A set of nine programs, designed in-house, work in concert to carry out functions including Auto-binning, Bin selection, Best-bins grouping, Core Sequence Identification, Outlier Removal, Sequence Retrieval, Polishing, Restrained Overlap–Layout–Consensus (rOLC), and Reassembly (Fig. 1). These functions are packaged into four modules: Automated Binning, Bin Selection, Refinement, and Gap Filling.
BASALT is a command line software compiled in Python 3.0 scripts, with each of the above modules containing one or more algorithms/programs. As an automated tool running with a single command line interface, checkpoints in each BASALT module allow users to stop and restart at any checkpoint as needed. In addition, each module can be executed individually, enabling users to customize the preferences as appropriate for their specific dataset(s). Further details regarding the code and tutorials are available at Github (https://github.com/EMBL-PKU/BASALT).

Qiu Z., Yuan L., Lian C.A., Lin B., Chen J., Mu R., Qiao X., Zhang L., Xu Z., Fan L., Zhang Y., Wang S., Li J., Cao H., Li B., Chen B., Song C., Liu Y., Shi L., Tian Y., Ni J., Zhang T., Zhou J., Zhuang W.Q, & Yu K. (2024). BASALT refines binning from metagenomic data and increases resolution of genome-resolved metagenomic analysis. Nature Communications, 15, 2179.

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Publication 2024

Basalt Glass Fiber Synthesis and Characterization

Based on the basalt information collected from various lunar missions as cited in references [25 ,26 (link),27 (link),28 (link),29 (link),30 (link),31 (link)], and to enhance the experimental study of the effects of several specific oxides on the glass network structure, this work has eliminated the uncertainties introduced by trace elements. The compositions of the simulated mare basalt samples prepared are shown in Table 1, with all the materials being of analytical-grade purity (Sinopharm, Beijing, China). The raw materials are mixed evenly and heated to 1500 °C in a high-temperature atmosphere furnace for 2 h. Subsequently, the mixture is poured into water to obtain quenched material, dried, and finally, as shown in the Figure 1, melted and drawn into basalt fibers in a platinum crucible single-filament furnace at atmospheric pressure and 1350 °C.

Liu J., Luo L., Xu J., Zhu X., Shi G, & Wang Q. (2024). The Impact of the Composition on the Properties of Simulated Lunar Mare Basalt Fibers. Materials, 17(9), 2043.

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Publication 2024

Geological Sampling of Paraná Flood Basalts

Geological sampling in central Brazil was conducted to investigate and collect (see location in Fig. 1B) the representative rocks of the Paraná continental flood basalts. The basalts studied for the CO₂ mineralization experiment ware collected at the northern limit of the Paraná continental flood basalts (Fig. 1A,B)^{20 (link),26 (link)–28 (link)}. The basalt outcrops display massive layers (Fig. 3A), usually interbedded with layers exhibiting high vesicular content (Fig. 3B). The massive basalt layers display pairs of NE and NW vertical and horizontal fractures (Fig. 3C). The mineral assemblage of the basalt layers includes plagioclase (45–55%), clinopyroxene (15–25%), olivine (2–5%) and Fe–Ti oxides (5–15%) as magnetite and ilmenite (Fig. 3D–F). The basalt samples used in the experiment do not have any veins or discrete carbonate crystals. Plagioclase with 12–16 wt.% CaO and clinopyroxene with 21–23 wt.% CaO (10–12 wt.% MgO) are the major calcium-bearing minerals (7.2–10.8 wt.% CaO in whole rock composition)^{20 (link),28 (link)}. Since these minerals represent 60–80% of the reactive surface of basalt (Fig. 3D), the studied crystalline basalts have a high potential for dissolution and release of Ca²⁺.Figure 3

Field and petrographic features of studied basalts. (A–C) Basalt layers features in the Jataí region, central Brazil. (D–F) Detailed mineral textures and modal proportion of the basalt mineral assemblage.

Ferreira A., Santos R.V., de Almeida T.S., Camargo M.A., Filho J.A., Miranda C.R., dos Passos S.D., Baptista A.D., Tassinari C.C., Rubio V.A, & Capistrano G.G. (2024). Unraveling the rapid CO2 mineralization experiment using the Paraná flood basalts of South America. Scientific Reports, 14, 8116.

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Publication 2024

Basalt Fiber Reinforced Asphalt Mixtures

Basalt fiber asphalt mixtures are mostly used in the upper and middle surface layers of roads, therefore, the type of asphalt mixtures used in this study was AC-13 dense grading asphalt mixtures, as shown in Fig 1. The asphalt used No. 70 base asphalt produced in Tianjin. The coarse and fine aggregates and mineral powder come from limestone produced in Tongzhou. Basalt fiber chose Changsha North America floating company produced 9mm short cut basalt fiber, its length to diameter ratio of 560. In this study, the fiber content was selected as 0%, 0.1%, 0.2% and 0.3% (the fiber content accounted for the mass fraction of the asphalt mixture), and for the convenience of the study, they were numbered as BF-0, BF-1, BF-2 and BF-3, respectively. The basalt fiber asphalt mixture is prepared by dry mixing method to ensure that the basalt fiber is fully dispersed in the asphalt mixtures.

Huang Q., Kang X., Chen P., Zhang Z., Yan E.H., Zang Z, & Yan H. (2024). Characterization of viscoelastic behavior of basalt fiber asphalt mixtures based on discrete and continuous spectrum models. PLOS ONE, 19(2), e0296087.

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Publication 2024

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Pulverisette 7 by Fritsch

Sourced in Germany

The Pulverisette 7 is a laboratory mill designed for fine grinding and homogenization of small sample quantities. It features a planetary ball mill system that uses grinding jars and balls to reduce the particle size of various materials.

Ultra plus by Zeiss

Sourced in Germany, United States, United Kingdom, Japan

The Ultra Plus is a high-performance laboratory equipment designed for precise analytical measurements. It features advanced optics and advanced sensor technology to deliver accurate and reliable results. The core function of the Ultra Plus is to provide precise data analysis and measurements for various scientific applications.

Nova nanosem 450 by Thermo Fisher Scientific

Sourced in United States, Netherlands, Czechia, Singapore, Japan, Germany, United Kingdom, Italy, China

The Nova NanoSEM 450 is a high-resolution scanning electron microscope (SEM) designed for advanced materials analysis. It features a field emission electron gun, multiple detection modes, and a range of imaging and analytical capabilities.

Jsn5510lv by JEOL

Sourced in Japan

The JEOL JSN5510LV is a scanning electron microscope (SEM) designed for high-resolution imaging and analysis of a wide range of samples. It features a tungsten filament electron source, a high-performance vacuum system, and advanced imaging capabilities. The JSN5510LV is capable of producing high-quality, high-magnification images with a resolution of up to 3.0 nm.

Aerosil 200 fumed silica by Evonik

Sourced in Germany

Aerosil 200 is a fumed silica product manufactured by Evonik. It is a fine, white, and fluffy powder composed of amorphous silicon dioxide particles. Aerosil 200 is characterized by a high specific surface area and is commonly used as a thickening, anti-caking, and reinforcing agent in various applications.

What are the common applications of Basalt?

Basalt is a versatile material with a wide range of applications. It is commonly used in construction for building foundations, roads, and landscaping projects due to its durability and strength. Basalt is also a raw material for various industrial processes, such as the production of fiberglass, mineral wool, and abrasives. Additionally, it is utilized in the manufacturing of basalt fiber reinforced polymer (BFRP) composites, which are used in applications like wind turbine blades, automotive parts, and sports equipment.

How can PubCompare.ai help with my Basalt research?

PubCompare.ai's AI-powered platform can streamline your Basalt-related research by helping you identify the best protocols from literature, preprints, and patents. The platform's intelligent comparisons can ensure reproducibility and accuracy, allowing you to experieince the future of scientific discovery. With PubCompare.ai, you can efficiently screen protocol literature, leverage AI to pinpoint critical insights, and identify the most effective Basalt protocols for your specific research goals. This enables you to choose the best option and ensure the highest level of reproducibility and accuracy in your work.

What are the different types or variations of Basalt?

Basalt can exist in various forms and compositions. Some common types include: 1. Alkali Basalt: Richer in sodium and potassium, often found in oceanic islands and rift zones. 2. Tholeiitic Basalt: More common type, lower in alkali content and higher in iron and magnesium, typical of mid-ocean ridges. 3. Porphyritic Basalt: Contains larger, more visible crystals in a finer-grained matrix. 4. Vesicular Basalt: Contains small cavities or holes, formed by gas bubbles trapped during the cooling process. These different varieties of Basalt can have slight variations in their physical and chemical properties, which can influence their suitability for specific applications.

What are some common challenges in using Basalt?

While Basalt is a versatile and widely-used material, there can be some challenges associated with its use: 1. Variability in quality and composition: Basalt deposits can vary in their mineral content and physical properties, which can impact its performance in certain applications. 2. Processing and handling: Basalt can be difficult to cut, crush, or shape due to its hardness and brittle nature, requiring specialized equipment and techniques. 3. Cost considerations: Depending on the source and processing requirements, Basalt can sometimes be more expensive than alternative materials like concrete or steel. 4. Environmental impact: The extraction and processing of Basalt can have environmental implications, such as dust pollution and energy consumption, which need to be carefully managed.

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