Heat capacity and thermodynamic properties of N-(2-cyanoethyl) aniline (C9H10N2)

The low temperature heat capacities of N-(2-cyanoethyl)aniline were measured with an automated adiabatic calorimeter over the temperature range from 83 to 353 K. The temperature corresponding to the maximum value of the apparent heat capacity in the fusion interval, molar enthalpy and entropy of fusion of this compound were determined to be 323.33 +/- 0.13 K, 19.4 +/- 0.1 kJ mol(-1) and 60.1 +/- 0.1 J K-1 mol(-1), respectively. Using the fractional melting technique, the purity of the sample was determined to be 99.0 mol% and the melting temperature for the tested sample and the absolutely pure compound were determined to be 323.50 and 323.99 K, respectively. A solid-to-solid phase transition occurred at 310.63 +/- 0.15 K. The molar enthalpy and molar entropy of the transition were determined to be 980 +/- 5 J mol(-1) and 3.16 +/- 0.02 J K-1 mol(-1), respectively. The thermodynamic functions of the compound [H-T - H-298.15] and [S-T - S-298.(15)] were calculated based on the heat capacity measurements in the temperature range of 83-353 K with an interval of 5 K. (c) 2004 Elsevier B.V. All rights reserved.

Low temperature heat capacity of (S)-ibuprofen

Molar heat capacities of ( S)-ibuprofen were precisely measured with a small sample precision automated adiabatic calorimeter over the temperature range from 80 to 370 K. Experimental heat capacities were fitted into a polynomial equation of heat capacities ( C-p,C- m) with reduced temperature ( X), [ X = f(T)]. The polynomial equations for ( S)-ibuprofen were C-p,C- m(s) = - 39.483 X-4 - 66. 649 X-3 + 95. 196 X-2 + 210. 84 X + 172. 98 in solid state and C-p,C- m(L) = 7. 191X(3) + 4. 2774 X-2 + 56. 365 X + 498. 5 in liquid state. The thermodynamic functions relative to the reference temperature of 298. 15 K, H-T - H-298.15 and S-T - S-298.15, were derived for the( S)-ibuprofen. A fusion transition at T-m = (324. 15 +/- 0. 02) K was found from the C-p - T curve. The molar enthalpy and entropy of the fusion transition were determined to be (18. 05 +/- 0. 31) kJ.mol(-1) and (55. 71 +/- 0. 95) J.mol(-1).K-1, respectively. The purity of the ( S)-ibuprofen was determined to be 99. 44% on the basis of the heat capacity measurement. Finally, the heat capacities of ( S)-ibuprofen and racemic ibuprofen were compared.

Measurements of the heat capacity of an azeotropic mixture of water and n-butanol from 78 to 320 K

Molar heat capacities of n-butanol and the azeotropic mixture in the binary system [water (x=0.716) plus n-butanol (x=0.284)] were measured with an adiabatic calorimeter in a temperature range from 78 to 320 K. The functions of the heat capacity with respect to thermodynamic temperature were established for the azeotropic mixture. A glass transition was observed at (111.9 +/- 1.1) K. The phase transitions took place at (179.26 +/- 0.77) and (269.69 +/- 0.14) K corresponding to the solid-liquid phase transitions of. n-butanol and water, respectively. The phase-transition enthalpy and entropy of water were calculated. A thermodynamic function of excess molar heat capacity with respect to temperature was established, which took account of physical mixing, destructions of self-association and cross-association for n-butanol and water, respectively. The thermodynamic functions and the excess thermodynamic ones of the binary systems relative to 298.15 K were derived based on the relationships of the thermodynamic functions and the function of the measured heat capacity and the calculated excess heat capacity with respect to temperature.

Heat capacity and thermodynamic properties of myclobutanil

The low-temperature heat capacities of myclobutanil (C15H17CIN4) were precisely measured with an automated adiabatic calorimeter over the temperature range from 78 to 368 K. The sample was observed to melt at (348.800 +/- 0.06) K. The molar enthalpy and entropy of the melting as well as the chemical purity of the substance were determined to be Delta(fus)H(m) = (30931 +/- 11) J.mol(-1), Delta(fus)S(m) = (88.47 +/- 0.02) J.mol(-1).K-1 and 99.41%, respectively. Further research of the melting process for this compound was carried out by means of DSC technique. The result was in agreement with that obtained from the measurements of heat capacities.

Heat capacity and enthalpy of fusion of fenoxycarb

Fenoxycarb was synthesized and its heat capacities were precisely measured with an automated adiabatic calorimeter over the temperature range from 79 to 360 K. The sample was observed to melt at (326.31 +/- 0.14) K. The molar enthalpy and entropy of fusion as well as the chemical purity of the compound were determined to be (26.98 +/- 0.04) kJ-mol(-1), (82.69 +/- 0.09) J-K-1-mol(-1) and 99.53% +/- 0.01%, respectively. The thermodynamic functions relative to the reference temperature (298.15 K) were calculated based on the heat capacity measurements in the temperature range between 80 and 360 K. The extrapolated melting temperature for the absolutely pure compound obtained from fractional melting experiments was (326.62 +/- 0.06) K. Further research on the melting process of this compound was carried out by means of differential scanning calorimetry technique. The result was in agreement with that obtained from the measurements of heat capacities.

Enhancing the link between business and operations strategy.

This paper reprots on the use of banchmarking to improve the links between business and operations strategies. The use of benchmarking as a toll to facilitate improvements in these crucial links is examined. The existing literature on process benchmarking is used to from a structured questionnaire to apply to six case studies of major manuifacturing companies. Four of these case studies are presented in this paper to highlight the use of benchmarking in this application. Initial researh results are presented drawing upon the critical success factors indentified both in the literature and on the case results. Recommendations for further work are outlined

Benchmarking the business and operations strategy link.

This paper reports on the use of benchmarking to improve the links between business and operations strategies. The use of benchmarking as a tool to facilitate improvement in these crucial links is examined. The existing literature on process benchmarking is used to form a structured questionnaire to apply to six case studies of major maunfacturing companies. Four of these case studies are presented drawing upon the critical success factors identified both in the literature and on the case results. Recommendations for further work are outlined.

Semi-Supervised OWA Aggregation for Link-Based Similarity Evaluation and Alias Detection

T.Boongoen and Q. Shen. Semi-Supervised OWA Aggregation for Link-Based Similarity Evaluation and Alias Detection. Proceedings of the 18th International Conference on Fuzzy Systems (FUZZ-IEEE'09), pp. 288-293, 2009. Sponsorship: EPSRC

Adaptive capacity and learning to learn as leverage for social-ecological resilience

Ioan Fazey, John A. Fazey, Joern Fischer, Kate Sherren, John Warren, Reed F. Noss, Stephen R. Dovers (2007) Adaptive capacity and learning to learn as leverage for social?ecological resilience. Frontiers in Ecology and the Environment 5(7),375-380. RAE2008

Vibrato in singing voice: the link between source-filter and sinusoidal models

The application of inverse filtering techniques for high-quality singing voice analysis/synthesis is discussed. In the context of source-filter models, inverse filtering provides a noninvasive method to extract the voice source, and thus to study voice quality. Although this approach is widely used in speech synthesis, this is not the case in singing voice. Several studies have proved that inverse filtering techniques fail in the case of singing voice, the reasons being unclear. In order to shed light on this problem, we will consider here an additional feature of singing voice, not present in speech: the vibrato. Vibrato has been traditionally studied by sinusoidal modeling. As an alternative, we will introduce here a novel noninteractive source filter model that incorporates the mechanisms of vibrato generation. This model will also allow the comparison of the results produced by inverse filtering techniques and by sinusoidal modeling, as they apply to singing voice and not to speech. In this way, the limitations of these conventional techniques, described in previous literature, will be explained. Both synthetic signals and singer recordings are used to validate and compare the techniques presented in the paper.