Detection and classification of gaseous sulfur compounds by solid electrolyte cyclic voltammetry of cermet sensor array

Electrochemical sensors composed of a ceramic-metallic (cermet) solid electrolyte are used for the detection of gaseous sulfur compounds SO₂, H₂S, and CS₂ in a study involving 11 toxic industrial chemical (TIC) compounds. The study examines a sensor array containing four cermet sensors varying in electrode-electrolyte composition, designed to offer selectivity for multiple compounds. The sensors are driven by cyclic voltammetry to produce a current-voltage profile for each analyte. Raw voltammograms are processed by background subtraction of clean air, and the four sensor signals are concatenated to form one vector of points. The high-resolution signal is compressed by wavelet transformation and a probabilistic neural network is used for classification. In this study, training data from one sensor array was used to formulate models which were validated with data from a second sensor array. Of the 11 gases studied, 3 that contained sulfur produced the strongest responses and were successfully analyzed when the remaining compounds were treated as interferents. Analytes were measured from 10 to 200% of their threshold-limited value (TLV) according to the 8-h time weighted average (TWA) exposure limits defined by the National Institute of Occupational Safety and Health (NIOSH). True positive classification rates of 93.3, 96.7, and 76.7% for SO₂, H₂S, and CS₂, respectively, were achieved for prediction of one sensor unit when a second sensor was used for modeling. True positive rates of 83.3, 90.0, and 90.0% for SO₂, H₂S, and CS₂, respectively, were achieved for the second sensor unit when the first sensor unit was used for modeling. Most of the misclassifications were for low concentration levels (such 10-25% TLV) in which case the compound was classified as clean air. Between the two sensors, the false positive rates were 2.2% or lower for the three sulfur compounds, 0.9% or lower for the interferents (eight remaining analytes), and 5.8% or lower for clean air. The cermet sensor arrays used in this analysis are rugged, low cost, reusable, and show promise for multiple compound detection at parts-per-million (ppm) levels.     

HPTLC-Bestimmung von Paracetamol im Serum

Ohne Zusammenfassung

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Determination of paracetamol in serum by HPTLC

     

Magnetic behavior of the oxide spinels: Li0.5Fe2.5−2x Al x Cr x O4

In order to study the effect of substitution of Fe³⁺ by Al³⁺ and Cr³⁺ in Li_0.5Fe_2.5O₄ on its structural and magnetic properties, the spinel system Li_0.5Al _x Cr _x Fe_2.5?2x O₄ (x=0.0, 0.2, 0.4, 0.5, 0.6 and 0.8) has been characterized by X-ray diffraction, high field magnetization, low field ac susceptibility and ⁵⁷Fe Mossbauer spectroscopy. Contrary to the earlier reports, about 50% of Al³⁺ is found to occupy the tetrahedral sites. The system exhibits canted spin structure and a central paramagnetic doublet was found superimposed on magnetic sextet in the Mössbauer spectra (x>0.5).     

联苯胺分光光度法测定食盐中碘含量

基于在盐酸介质中,碘酸钾氧化联苯胺使其显色,提出了光度法测定食盐中微量碘的方法。在40℃的水浴中,当2 mol.L-1盐酸溶液用量为1.00 mL0、.42 g.L-1联苯胺溶液用量为0.80 mL时,方法的线性范围为100~1 400μg.L-1,最大吸收波长在425 nm处,表观摩尔吸光率为1.4×105L.mol-1.cm-1。用于测定加碘食盐中碘含量的测定,回收率在97.5%~101.2%之间,相对标准偏差(n=7)在0.9%~1.9%之间。     

Cutoff wavelength of single-mode fibers: Definition, measurement, and length and curvature dependence

The cutoff wavelength λ_c as defined by the CCITT can be defined equivalently as that wavelength for which the attenuation of the second-order LP₁₁ mode is 19.34 dB higher than the attenuation of the fundamental mode. This equivalent definition of λ_c does not depend on the length and curvature of the fiber. Both measuring methods for λ_c, the bending method and the multimode reference method, are analyzed and the measurement conditions that lead to the CCITT cutoff wavelength are indicated. The bend attenuation of the second-order mode of a typical matched-cladding, step-index fiber is calculated numerically, and methods are described to determine the length and curvature dependence of the cutoff wavelength from the bend loss data.