Raman spectroscopic analysis of cyanobacterial colonization of hydromagnesite, a putative martian extremophile

Raman spectra of an extremophile cyanobacterial colony in hydromagnesite from Lake Salda in Turkey have revealed a biogeological modification which is manifest as aragonite in the stratum associated with the colony. The presence of key spectral biomarkers of organic protectant molecules such as β-carotene and scytonemin indicate that the survival strategy of the cyanobacteria is significantly one of UV-radiation protection. The terrestrial location of this extremophile is worthy of consideration further because of its possible putative link with the “White Rock” formations in Sabaea Terra and Juventae Chasma on Mars.     

RESOLUTION OF MULTICOMPONENT FLUORESCENT MIXTURES BY ANALYSIS OF THE EXCITATION-EMISSION-FREQUENCY ARRAY

Fluorescence lifetime provides a third independent dimension of information for the resolution of totalluminescence spectra of multicomponent mixtures.The incorporation of this parameter into theexcitation-emission matrix(EEM)by the phase modulation technique results in a three-dimensionalexcitation-emission-frequency array (EEFA).Multicomponent analysis based on the three-dimensionalEEFA brings a qualitative change for the resolved spectra,i.e.individual spectra can be uniquely resolved,which is impossible with any two-dimensional analysis.In this paper we present a method for analyzingthe EEFA.We show mathematically that with the three-dimensional analysis of the EEFA individualspectra and lifetimes can be obtained.Our algorithm is developed in mathematical detail and isdemonstrated by its application to a two-component mixture.     

Characterization of a chalcopyrite from Brazil by Mössbauer spectroscopy and other physicochemical techniques

We studied a chalcopyrite from a Cu ore deposit in Rio Grande do Sul, Brazil, by Mössbauer spectroscopy at room temperature and 110 K. Supporting methods to check for sample purity and to characterize further sample properties were slow-scanning X-ray powder diffraction and optical and microprobe analyses of polished sections of selected grains. Chemical analyses obtained using a scanning electron microscope equipped with an energy-dispersive X-ray spectrometer showed the sample to consist of homogeneous and essentially stoichiometric chalcopyrite (CuFeS₂). Mössbauer spectra taken at both the above temperatures consist of asymmetric magnetically ordered patterns with unequal intensities of the line pairs 1–6 and 2–5, pointing to the existence of non-equivalent or multiple Fe sites. Least-squares fittings evidenced that the resonance intensity ratio of subspectrum with lower quadrupole shift (indicative of a more symmetric environment) to that with higher shift is 69:31, at room temperature, and 68:32, at 110 K. Rietveld refinement of the XRD data indicates the existence of tetragonal [a = 0.52855(1) and c = 1.0412(1) nm] and cubic [a ₀ = 0.5273(2) nm] modifications in a proportion of 74:26, in good agreement with the Mössbauer data. The saturation magnetization of the sample was 32.7 J/(T kg), confirming the oxidation state of Fe as trivalent and pointing to little to no spin canting.     

Calibration of Infrared Absorption Coefficients for Structural Hydroxyl in Garnet by a Combined Study of Isotope Ratio Spectrometry and Vibrational Spectroscopy

An accurate determination of water content in garnet is critical to quantify the transport of water to the deep mantle by the subducted oceanic crust beyond the breakdown of hydrous phases. Fourier transform infrared spectroscopy (FTIR) is the most widely used approach to determine the species and contents of water in garnet. Accurate quantification of OH in garnet requires independent calibration using an external method, as OH absorbance is mineral and composition specific. To obtain the infrared absorption coefficients of structural hydroxyl in garnet, a combined study of spectrometric analyses by FTIR and a method combining a thermal conversion elemental analyser with isotope ratio mass spectrometry (TC/EA-MS) was carried out for fourteen gem-quality natural garnet crystals with variable compositions. The obtained molar absorption coefficients were 9322 ± 338 and 240 ± 26 l mol⁻¹ cm⁻² for grossular- and spessartine-rich garnet and pyrope-almandine garnet, respectively. These results are within the range of previous studies. A new molar absorption coefficient of 689 ± 177 l mol⁻¹ cm⁻² was obtained for pyrope-spessartine garnet. The large variation in the absorption coefficient indicates it is controlled by both garnet composition and OH-absorption bands. The obtained absorption coefficients are only appropriate for certain types of eclogitic garnet, and more studies should be carried out on eclogitic garnets.     

基于可见—近红外反射光谱的典型农田重金属污染风险分类研究

对于人为因素或自然因素造成的农田土壤重金属元素污染,需要进行大面积的土壤环境质量调查和分类管控,然而传统的采样测试方法存在工作量大、代价高等问题。可见—近红外（Vis-NIR）反射光谱是一种快速低成本获取土壤理化信息的手段。为研究Vis-NIR反射光谱预测模型划分土壤重金属污染风险类别的能力,文章以典型人为污染地区（浙江温岭）和典型地质高背景地区（广西横县）的390份农田土壤为样本,测定8种重金属元素（As、Cd、Cr、Cu、Hg、Ni、Pb和Zn）的含量和pH值,并测定土壤Vis-NIR光谱。使用偏最小二乘（PLS）和支持向量机（SVM）算法建立回归模型,对土壤重金属含量和pH值进行预测,并基于预测值进行土壤重金属污染风险分类。结果显示,温岭土壤主要污染元素Cd和Cu的光谱模型回归预测偏差（RPD）分别为1.23和1.19,预测机制与有机质有关。横县土壤主要污染元素As和Cd的RPD分别为1.98和1.93,预测机制与铁氧化物和粘土矿物有关。地质高背景土壤重金属与铁氧化物的正相关性普遍较强,使得光谱模型对重金属含量预测准确度较高。温岭和横县土壤pH值的光谱模型RPD分别为1.76和1.68。土壤重金属污染风险光谱分类的总体 准确度分别为75.0%~100%（温岭）和80.0%~100％（横县）。将Vis-NIR光谱与遥感技术相结合,对农田土壤重金属污染风险进行快速分类总体是可行的。     

Raman Micro-Spectroscopic Study of Sulfate Ion in the System Na2SO4 – H2O

This work aims to quantify sulfate ion concentrations in the system Na₂SO₄-H₂O using Raman micro-spectroscopy.Raman spectra of sodium sulfate solutions with known concentrations were collected at ambient temperature（293 K） and in the 500 cm^₁-4000 cm^-1 spectral region.The results indicate that the intensity of the SO₄^2- band increases with increasing concentrations of sulfate ion.A linear correlation was found between the concentration of SO₄^2-（c） and parameter I₁,which represents the ratio of the area of the SO₄^2- band to that of the O-H stretching band of water（A_s/A_w）:I₁=-0.00102+0.01538 c.Furthermore,we deconvoluted the O-H stretching band of water(2800 cm^-1-3800 cm^-1) at 3232 and 3430 cm^-1 into two sub-Gaussian bands,and then defined Raman intensity of the two sub-bands as A_Bi(3232 cm^-1) and A_B2(3430 cm^-1),defined the full width of half maximum（FWHM） of the two sub-bands as W_B1(3232 cm^-1) and W_B2(3430 cm^-1).A linear correlation between the concentration of SO₄^2-（c） and parameter I₂,which represents the ratio of Raman intensity of SO₄^2-（A_s）(in 981 cm^-1) to(A_B1+A_B2),was also established:I₂=-0.0111+0.3653 c.However,no correlations were found between concentration of SO₄^2-（c） and FWHM ratios,which includes the ratio of FWHM of SO₄^2-（W_s） to W_B1 W_B2 and W_B1+B2(the sum of W_B1 and W_B2),suggesting that FWHM is not suitable for quantitative studies of sulfate solutions with Raman spectroscopy.A comparison of Raman spectroscopic studies of mixed Na₂SO₄ and NaCI solutions with a constant SO₄^2- concentration and variable CI^- concentrations suggest that the I\ parameter is affected by CI^-,whereas the I₂ parameter was not.Therefore,even if the solution is not purely Na₂SO₄-H₂O,SO₄^2- concentrations can still be calculated from the Raman spectra if the H₂O band is deconvoluted into two sub-bands,making this method potentially applicable to analysis of natural fluid inclusions.     

星地多源数据的区域土壤有机质数字制图

土壤有机质(SOM)是全球碳循环、土壤养分的重要组成部分,精确估算土壤有机质含量具有重要意义。本文以中国东北—华北平原为研究区,收集了1078个土壤样本,以遥感数据(MODIS,TRMM和STRM数据)与土壤地面光谱数据为预测因子,运用基于树形结构的数据挖掘技术构建土壤有机质-环境预测因子模型进行数字土壤制图。通过不同建模样本数建模精度比较,选择300个样本数时的模型为最优模型。建模结果表明土壤光谱和气候因子是研究区SOM变异的主控因子,生物因子次之,而地形因子影响最小。预测结果经检验,RMSE为7.25,R2为0.69,RPD为1.53制图结果与基于第二次全国土壤普查数据的土壤有机质地图具有相似的分布规律,呈现SOM自东北向西南递减的趋势。通过比较分析发现,经过20年左右的土地开发与利用,研究区低SOM和高SOM含量土壤面积减少,而中等SOM含量土壤面积增加。     

Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy

Spectroscopic techniques have become attractive to assess soil properties because they are fast, require little labor and may reduce the amount of laboratory waste produced when compared to conventional methods. Imaging spectroscopy (IS) can have further advantages compared to laboratory or field proximal spectroscopic approaches such as providing spatially continuous information with a high density. However, the accuracy of IS derived predictions decreases when the spectral mixture of soil with other targets occurs. This paper evaluates the use of spectral data obtained by an airborne hyperspectral sensor (ProSpecTIR-VS – Aisa dual sensor) for prediction of physical and chemical properties of Brazilian highly weathered soils (i.e., Oxisols). A methodology to assess the soil spectral mixture is adapted and a progressive spectral dataset selection procedure, based on bare soil fractional cover, is proposed and tested. Satisfactory performances are obtained specially for the quantification of clay, sand and CEC using airborne sensor data (R² of 0.77, 0.79 and 0.54; RPD of 2.14, 2.22 and 1.50, respectively), after spectral data selection is performed; although results obtained for laboratory data are more accurate (R² of 0.92, 0.85 and 0.75; RPD of 3.52, 2.62 and 2.04, for clay, sand and CEC, respectively). Most importantly, predictions based on airborne-derived spectra for which the bare soil fractional cover is not taken into account show considerable lower accuracy, for example for clay, sand and CEC (RPD of 1.52, 1.64 and 1.16, respectively). Therefore, hyperspectral remotely sensed data can be used to predict topsoil properties of highly weathered soils, although spectral mixture of bare soil with vegetation must be considered in order to achieve an improved prediction accuracy.