Determination of n-paraffinic structure in petroleum fraction by infrared spectroscopy

Summary The characteristic strong absorption infrared band near 720–725 cm^–1 of an open chain methylene group of the type (CH₂) _n (n = 4 or more) has been used to estimate the number of n-paraffinic methylene groups in an average molecule of saturates, urea adductables and non-adductables of various gas-oil and vacuum gas oil fractions. The method was developed by computing the average absorptivity at 725 cm^–1 of several pure n-paraffins (C₁₄-C₃₂). Although the interference due to the aromatic C — H band at 740 cm^–1 disturbs the estimation of shorter chains, it is seen from the data on adductables and non-adductables that the estimation can be done in long chain paraffins even in the presence of aromatics.

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Bestimmung von n-Paraffinstrukturen in Erdölfraktionen mit Hilfe der Infrarot-Spektroskopie

Zusammenfassung Die charakteristische starke IR-Absorptionsbande der paraffinischen Methylengruppe des Typs (CH₂) _n (n = 4 oder mehr) bei 720–725cm^–1 wurde zur Bestimmung der Anzahl von Methylengruppen in einem durchschnittlichen Molekül von gesättigten Kohlenwasserstoffen, Harnstoffprodukten sowie Nicht-Addukten verschiedener Gasöl- und Vakuum-Gasöl-Fraktionen benutzt. Das Verfahren wurde durch Berechnung der durchschnittlichen Extinktion bei 725 cm^–1 von mehreren reinen n-Paraffmen (C₁₄-C₃₂) entwickelt. Wenn auch die Bestimmung kürzerer Ketten durch die aromatische C — H-Bande bei 740 cm^–1 gestört wird, so ergibt sich doch aus den Werten für Addukte und Nicht-Addukte, daß die Bestimmung bei langkettigen Paraffinen selbst in Gegenwart von Aromaten durchgeführt werden kann.

     

EEG Based Biometric Framework for Automatic Identity Verification

The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies–Bouldin index. For convenience and ease of cognitive processing, in the experiments, simple black and white drawings of common objects are used as visual stimuli. In the classification stage, the Elman neural network is employed to classify the generated EEG energy features. Simulations are conducted by using the hold-out classification strategy on an ensemble of 1,600 raw EEG signals, and 35 maximum informative channels achieved the maximum recognition rate of 98.56 ± 1.87%. Overall, this study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud.

First organotin complex of a phosphonic diamide RP(O)(NHR)2

Diorganotin dichloride-phosphonic diamide complex, [Ph₂SnCl₂(tBuP(O)(NHⁱPr)₂)₂] (1), is prepared by the addition of two equivalents of ^tBuP(O)(NHⁱPr)₂ to one equivalents of Ph₂SnCl₂ either in the presence or absence of triethylamine. Compound 1 is a rare example of an all-trans SnA₂B₂C₂ complex that contains H-bonded six-membered rings which are made up of six different main group elements.     

Rainbow Connection Number and Radius

The rainbow connection number, rc(G), of a connected graph G is the minimum number of colours needed to colour its edges, so that every pair of its vertices is connected by at least one path in which no two edges are coloured the same. In this note we show that for every bridgeless graph G with radius r, rc(G) ≤  r(r + 2). We demonstrate that this bound is the best possible for rc(G) as a function of r, not just for bridgeless graphs, but also for graphs of any stronger connectivity. It may be noted that for a general 1-connected graph G, rc(G) can be arbitrarily larger than its radius (K _1,n for instance). We further show that for every bridgeless graph G with radius r and chordality (size of a largest induced cycle) k, rc(G) ≤  rk. Hitherto, the only reported upper bound on the rainbow connection number of bridgeless graphs is 4n/5 ? 1, where n is order of the graph (Caro et al. in Electron J Comb 15(1):Research paper 57, 13, 2008). It is known that computing rc(G) is NP-Hard (Chakraborty and fischer in J Comb Optim 1–18, 2009). Here, we present a (r + 3)-factor approximation algorithm which runs in O(nm) time and a (d + 3)-factor approximation algorithm which runs in O(dm) time to rainbow colour any connected graph G on n vertices, with m edges, diameter d and radius r.     

Nuclearity Control in Molecular Copper Phosphates Derived from a Bulky Arylphosphate: Synthesis,Structural and Magnetic Studies

Starting from sterically encumbered 2,6-di-tert-butylphenyl phosphate (dtbppH₂) and co-ligand 3,5-dimethyl pyrazole (dmpz), it is possible to isolate either mono-, di- or tetranuclear copper phosphates by varying the copper source and making attendant changes in the reaction conditions. For example, reaction of copper nitrate with dtbppH₂ and dmpz at 60 °C leads to the isolation of the mononuclear copper phosphate [Cu(dtbppH)₂(dmpz)(MeOH)₂] ( 1 ) as the only product. However, the use of copper acetate in place of copper nitrate and conducting the reaction at the room temperature leads to the formation of both dinuclear [Cu(dtbpp)(dmpz)₂]₂ ( 2 ) and tetranuclear [Cu₂(dtbpp)(dmpz)₂(OAc)(MeO)]₂ ( 3 ) from the same reaction mixture. Compounds 2 and 3 could be isolated in pure form through fractional crystallization. Copper phosphates 1 – 3 have been characterized by both analytical and spectroscopic methods including EPR and magnetic measurements. The molecular structures of all three compounds were established through single crystal diffraction studies. Dc magnetic measurements indicate antiferromagnetic interactions between the metal centres in all the compounds.