Disorder-induced expansion of silicate minerals arises from the breakage of weak topological constraints

Electron energy-loss near-edge structure (ELNES) is an effective probe of the local geometrical and electronic environment around particular atomic species in the solid state. Energy-loss spectra from several silicate minerals were mostly acquired using a VG HB501 STEM fitted with a parallel detector. Typically a collection angle of ≈8mrad was used, and an energy resolution of ≈0.5eV was achieved.Other authors have indicated that the ELNES of the Si L2,3-edge in α-quartz is dominated by the local environment of the silicon atom i.e. the SiO4 tetrahedron. On this basis, and from results on other minerals, the concept of a coordination fingerprint for certain atoms in minerals has been proposed. The concept is useful in some cases, illustrated here using results from a study of the Al2SiO5 polymorphs (Fig.l). The Al L2,3-edge of kyanite, which contains only 6-coordinate Al, is easily distinguished from andalusite (5- & 6-coordinate Al) and sillimanite (4- & 6-coordinate Al). At the Al K-edge even the latter two samples exhibit differences; with careful processing, the fingerprint for 4-, 5- and 6-coordinate aluminium may be obtained.

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USING SILICATE MINERALS AS MICROMODELS TO ACCESS THE RELATIONSHIP BETWEEN TRANSPORT AND REACTION AT THE PORE SCALE

10.1130/abs/2016am-282940 ◽

2016 ◽

Author(s):

Heewon Jung ◽

◽

Alexis K. Navarre-Sitchler ◽

Nathan Worts ◽

Erica Block ◽

...

Keyword(s):

Pore Scale ◽

Silicate Minerals ◽

The Relationship

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PLANT-DRIVEN ECTOMYCORRHIZAL WEATHERING OF SILICATE MINERALS

10.1130/abs/2018am-322506 ◽

2018 ◽

Author(s):

Zsuzsanna Balogh-Brunstad ◽

Keyword(s):

Silicate Minerals

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Trace Element Composition of Silicate Minerals in the Chondrules and Matrix of the Buschhof Meteorite

Geochemistry International ◽

10.1134/s001670292012006x ◽

2020 ◽

Vol 58 (12) ◽

pp. 1321-1330

Author(s):

K. G. Sukhanova ◽

S. G. Skublov ◽

O. L. Galankina ◽

E. V. Obolonskaya ◽

E. L. Kotova

Keyword(s):

Trace Element ◽

Trace Element Composition ◽

Element Composition ◽

Silicate Minerals

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Matching gradient descriptors with topological constraints to characterise the crowd dynamics

IET International Conference on Visual Information Engineering (VIE 2006) ◽

10.1049/cp:20060571 ◽

2006 ◽

Cited By ~ 3

Author(s):

B. Zhan ◽

P. Remagnino ◽

S. Velastin ◽

F. Bremond ◽

M. Thonnat

Keyword(s):

Topological Constraints ◽

Crowd Dynamics

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ModularBoost: an efficient network inference algorithm based on module decomposition

BMC Bioinformatics ◽

10.1186/s12859-021-04074-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Xinyu Li ◽

Wei Zhang ◽

Jianming Zhang ◽

Guang Li

Keyword(s):

Network Inference ◽

Detection Methods ◽

Inference Problem ◽

Topological Constraints ◽

Inference Algorithms ◽

Module Detection ◽

Series Expression ◽

Gene Modules ◽

Inference Methods ◽

Complicated Task

Abstract Background Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. Results ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. Conclusions As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

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Risk assessment of Ni, Cr, and Si release from alkaline minerals during enhanced weathering

Open Agriculture ◽

10.1515/opag-2020-0016 ◽

2020 ◽

Vol 5 (1) ◽

pp. 166-175

Author(s):

Fatima Haque ◽

Yi Wai Chiang ◽

Rafael M. Santos

Keyword(s):

Risk Assessment ◽

Environmental Protection Agency ◽

Hazard Index ◽

Carcinogenic Risk ◽

Contamination Level ◽

Potential Threat ◽

Silicate Minerals ◽

Guideline Value ◽

Quality Guideline ◽

Index Value

AbstractCalcium- and magnesium-rich alkaline silicate minerals, when applied to soil, can aid in carbon dioxide sequestration via enhanced weathering. The weathering of these silicate minerals is also associated with the release of heavy metals such as Ni and Cr, depending on the composition of the parent rock, and also labile Si. This paper critically analyses the risk associated with the release of Ni, Cr, and Si from alkaline silicate minerals as a result of enhanced weathering to evaluate its potential to be applied as a soil amendment. Based on the available data in the literature, this study evaluates the soil contamination level and quantifies the risk these elements pose to human health as well as the environment. To assess these potential threat levels, the geoaccumulation index was applied, along with the method recommended by the US Environmental Protection Agency for health risk assessment. The main findings of this study indicate the potential release of Ni, Cr, and Si to exceed the soil quality guideline value. The geochemical index suggests that the analyzed samples are in the class 0–3 and represents sites that lie between uncontaminated zones to highly contaminated zones. The hazard index value for Ni and Cr is greater than unity, which suggests that Ni and Cr release poses a non-carcinogenic risk. The probability of labile Si concentration in the soil to exceed the critical value is found to be 75%.

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