Methane-Derived Carbonates in a Native Sulfur Deposit: Stable Isotope and Trace Element Discriminations Related to the Transformation of Aragonite to Calcite

Abstract

Stable isotope (¹³C, ¹⁸O, ³⁴S) and trace element (Sr²⁺, Mg²⁺, Mn²⁺, Ba²⁺, Na⁺) investigations of elemental sulfur, primary calcites and mixtures of aragonite with secondary, post-aragonitic calcite from sulfur-bearing limestones have provided new insights into the geochemistry of the mineral forming environment of the native sulfur deposit at Machów (SE-Poland). The carbon isotopic composition of carbonates (δ¹³C = ?41 to ?47‰ vs. PDB) associated with native sulfur (δ³⁴S = + 10 to + 15‰ vs. V-CDT) relates their formation to the microbiological anaerobic oxidation of methane and the reduction of sulfate derived from Miocene gypsum. From a comparison with experimentally derived fractionation factors the element ratios of the aqueous fluids responsible for carbonate formation are estimated. In agreement with field and laboratory observations, ratios near seawater composition are obtained for primary aragonite, whereas the fluids were relatively enriched in dissolved calcium during the formation of primary and secondary calcites. Based on the oxygen isotope composition of the carbonates (δ¹⁸O = ?3.9 to ?5.9‰ vs. PDB) and a secondary SrSO₄ (δ¹⁸O = + 20‰ vs. SMOW; δ³⁴S = + 59‰ vs. V-CDT), maximum formation temperatures of 35°C (carbonates) and 47°C (celestite) are obtained, in agreement with estimates for West Ukraine sulfur ores. The sulfur isotopic composition of elemental sulfur associated with carbonates points to intense microbial reduction of sulfate derived from Miocene gypsum (δ³⁴S ≈ + 23‰) prior to the re-oxidation of dissolved reduced sulfur species.     

A Review of Signal Enhancement and Noise Reduction Techniques for Tunable Diode Laser Absorption Spectroscopy

Abstract: Tunable diode laser absorption spectroscopy (TDLAS), as a noninvasive spectroscopic method, permits high-resolution, high-sensitivity, fast, in situ absorption measurements of atomic and molecular species and narrow spectral features in gaseous, solid, and liquid phases. Advances in new diode laser sources and laser spectroscopic techniques generally have triggered an increasing application of TDLAS in various disciplines (for example, atmospheric environmental monitoring, chemical analysis, industrial process control, medical diagnostics and combustion monitoring, etc.) over the last four decades. This article reviews some important developments in TDLAS, from its basic principles as a spectroscopic tool to the demonstration of gas absorption measurements, emphasizing signal enhancement and noise reduction techniques developed for improving current TDLAS performance.     

Analyzing radiation absorption difference of dental substance by using Dual CT

The purpose of this study was to evaluate the changes of noise and computer tomography (CT) number in each dental substance, by using the metal artefact reduction algorithm; we used dual CT for this study. For the study, we produced resin, titanium, gypsum, and wax that are widely used by dentists. In addition, we made nickel to increase the artefact. While making the study materials, we made sure that there is no difficulty when inserting the substances inside phantom. In order to study, we scanned before and after using the metal artefact reduction algorithm. We conducted an average analysis of CT number and noise, before and after using the metal artefact reduction algorithm. As a result, there was no difference in CT number and noise before and after using the metal artefact reduction algorithm. However, when it comes to the noise value in each substance, wax's noise value was the lowest whereas titanium's noise value was the highest, after applying the metal artefact reduction algorithm. In nickel, CT number and noise value from artefact area showed a decreased noise value when applying the metal artefact reduction algorithm. In conclusion, we assumed that we could increase the effectiveness of CT examination by applying dual energy's metal artefact reduction algorithm.     

Rational Design and Application of Covalent Organic Frameworks for Solar Fuel Production

Harnessing solar energy and converting it into renewable fuels by chemical processes, such as water splitting and carbon dioxide (CO₂) reduction, is a highly promising yet challenging strategy to mitigate the effects arising from the global energy crisis and serious environmental concerns. In recent years, covalent organic framework (COF)-based materials have gained substantial research interest because of their diversified architecture, tunable composition, large surface area, and high thermal and chemical stability. Their tunable band structure and significant light absorption with higher charge separation efficiency of photoinduced carriers make them suitable candidates for photocatalytic applications in hydrogen (H₂) generation, CO₂ conversion, and various organic transformation reactions. In this article, we describe the recent progress in the topology design and synthesis method of COF-based nanomaterials by elucidating the structure-property correlations for photocatalytic hydrogen generation and CO₂ reduction applications. The effect of using various kinds of 2D and 3D COFs and strategies to control the morphology and enhance the photocatalytic activity is also summarized. Finally, the key challenges and perspectives in the field are highlighted for the future development of highly efficient COF-based photocatalysts.     

A Lower Bound on the Second Fiber Coefficient of the Fiber Cones

Let (R, 𝔪) be a Cohen–Macaulay local ring of dimension d > 0, I an 𝔪-primary ideal of R and K an ideal containing I. When depth G(I) ≥ d ? 1 and r(I | K) < ∞, we present a lower bound on the second fiber coefficient of the fiber cones, and also provide a characterization, in terms of f ₂(I, K), of the condition depth F _K (I) ≥ d ? 1.     

Compression of Multivariate Discrete Measures and Applications

In this article, we discuss two methods for the compression of multivariate discrete measures, with applications to node reduction in numerical cubature and least-squares approximation. The methods are implemented in the Matlab computing environment, in dimension two.     

Penalized Orthogonal Iteration for Sparse Estimation of Generalized Eigenvalue Problem

We propose a new algorithm for sparse estimation of eigenvectors in generalized eigenvalue problems (GEPs). The GEP arises in a number of modern data-analytic situations and statistical methods, including principal component analysis (PCA), multiclass linear discriminant analysis (LDA), canonical correlation analysis (CCA), sufficient dimension reduction (SDR), and invariant co-ordinate selection. We propose to modify the standard generalized orthogonal iteration with a sparsity-inducing penalty for the eigenvectors. To achieve this goal, we generalize the equation-solving step of orthogonal iteration to a penalized convex optimization problem. The resulting algorithm, called penalized orthogonal iteration, provides accurate estimation of the true eigenspace, when it is sparse. Also proposed is a computationally more efficient alternative, which works well for PCA and LDA problems. Numerical studies reveal that the proposed algorithms are competitive, and that our tuning procedure works well. We demonstrate applications of the proposed algorithm to obtain sparse estimates for PCA, multiclass LDA, CCA, and SDR. Supplementary materials for this article are available online.     

Sliced Coordinate Analysis for Effective Dimension Reduction and Nonlinear Extensions

Sliced inverse regression (SIR) is an important method for reducing the dimensionality of input variables. Its goal is to estimate the effective dimension reduction directions. In classification settings, SIR is closely related to Fisher discriminant analysis. Motivated by reproducing kernel theory, we propose a notion of nonlinear effective dimension reduction and develop a nonlinear extension of SIR called kernel SIR (KSIR). Both SIR and KSIR are based on principal component analysis. Alternatively, based on principal coordinate analysis, we propose the dual versions of SIR and KSIR, which we refer to as sliced coordinate analysis (SCA) and kernel sliced coordinate analysis (KSCA), respectively. In the classification setting, we also call them discriminant coordinate analysis and kernel discriminant coordinate analysis. The computational complexities of SIR and KSIR rely on the dimensionality of the input vector and the number of input vectors, respectively, while those of SCA and KSCA both rely on the number of slices in the output. Thus, SCA and KSCA are very efficient dimension reduction methods.     

A note: Every homogeneous difference equation of degree one admits a reduction in order

Every difference equation x _n+1 = f _n (x _n ,x _n ? 1,…,x _n ? k ) of order k+1 with each mapping f _n being homogeneous of degree 1 on a group G is shown to be equivalent to a system consisting of an equation of order k and a linear equation of order 1.