Corrected Values for Boiling Points and Enthalpies of Vaporization of Elements in Handbooks

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Corrected Values for Boiling Points and Enthalpies of Vaporization of Elements in Handbooks CORE Metadata, citation and similar papers at core.ac.uk 328 J. Chem. Eng. Data 2011, 56, 328–337 Provided by e-Prints Soton Corrected Values for Boiling Points and Enthalpies of Vaporization of Elements in Handbooks Yiming Zhang Ningbo Institute of Material Technology & Engineering, Chinese Academy of Sciences, No. 519 Zhuangshi Road, Zhenhai District, Ningbo, Zhejiang Province, P.R. China 315201, and Department of Materials, Queen Mary University of London, Mile End Road, London E1 4NS, United Kingdom Julian R. G. Evans Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, United Kingdom Shoufeng Yang* School of Engineering Sciences, University of Southampton, Southampton, SO17 1BJ, United Kingdom The scientific community relies upon the veracity of the scientific data in handbooks and databases. In a previous work, the authors developed a systematic, intelligent, and potentially automatic method to detect errors in such resources based on artificial neural networks (ANNs). This method revealed variations from (10 to 900) % in tables of property data for elements in the periodic table and pointed out the ones that are most probably correct. In this paper, we focus on the details of employing this method for analyzing the data of boiling points and enthalpies of vaporization recorded in different handbooks. The method points out the values that are likely to be correct. To verify the method employed, a detailed discussion of the data with reference to the original literature sources is given as well as factors that may affect the accuracy of the prediction. Introduction Experimental Details It is well-known that both the boiling point and the enthalpy Data Collection and Neural Network Construction. The data of vaporization are important thermodynamic properties that are are recorded from five different handbooks, including the required in product design and processes involving liquid and Chemistry Data Book (CDB),9 The Lange’s Handbook of vapor phase transitions such as distillation, vaporization, and Chemistry (LAG),10 The Elements (ELE),11 Table of Physical - drying1 3 and that, as a result, the quality and veracity of these and Chemical Constants (TPC),12 and CRC Handbook of data in handbooks are important for the academic and industrial Chemistry and Physics (CRC).13 The neural networks are scientific community.4 However, it is accepted that errors in constructed, trained, and simulated by employing MATLAB handbooks and databases are inescapable and are anticipated 7.4.0.287 (R2007a) software. A two-hidden layer network with 5-7 at rates of (1 to 5) %. tan-sigmoid transfer function in the first hidden layer and a linear This work challenges the inevitability of such high error rates. transfer function in the second hidden layer was established. A The values of boiling point and enthalpy of vaporization of the loop program was applied to redistribute the database to make elements were recorded for testing, because these data are the training set cover the problem domain.14,15 The detailed expected to be very reliable; only the elements with short half- process of data collection and neural network construction has lives were excluded. Tables 1 and 2 show the boiling point and been described in the authors’ related works.8 The ANN finds enthalpy of vaporization values of elements in the periodic table indirect relationships between data sets and then identifies values taken from five different handbooks. Considerable variation in which disobey the relationship. the values can be noticed, so it can be concluded that Stages of Error Correction. This includes four different inconsistencies are extant within these handbooks, and these stages. inconsistencies have persisted undetected into the 21st century. In the authors’ related work,8 we developed a systematic and Stage 1. During the exploration of the indirect relationship intelligent method based on artificial neural networks (ANNs) between boiling point and enthalpy of vaporization using data to detect wide levels of inconsistencies in handbooks and so from CDB (shown in Table 3) by an ANN, best linear fit stop their transmission to future researches and documents. In equations with a regression coefficient of R ) 0.973 and 0.972 this paper, we are going to show the details of application of were found (as shown in Figure 1). This result prompts us to this method in the case of correcting values of the boiling point raise the correlation hypothesis that the correlation applies to and enthalpy of vaporization for the elements. all of the elements. * Authors to whom correspondence should be addressed. E-mail: s.yang@ Stage 2. In this stage, a conformity criterion is selected. On soton.ac.uk. Tel.: 0044-23-8059-8697. Fax: 0044-23 8059 3016. the basis of the inconsistency within different handbooks, 10.1021/je1011086 2011 American Chemical Society Published on Web 01/11/2011 Journal of Chemical & Engineering Data, Vol. 56, No. 2, 2011 329 Table 1. List of Boiling Points Divided by Kelvin from Five Handbooksa element CDB LAGb ELE TPC CRC (max/min - 1) · 100 % Agc 2483 2483 2485 2433 2435 2.14 % Alc 2743 2743 2740 2793 2792 1.93 % Ar 87.15 87.15 87.29 87.29 87.3 0.17 % As (gray)d 886.2 886.2 889 883.2 876.2 1.46 % Au 3243 3243 3080 3123 3129 5.29 % Bae 1913 1913 1910 2173 2170 13.80 % Bee 2750 2750 3243f 2743 2744 18.20 % Bc 4203 4203 3931 4273 4273 8.70 % Bic 1833 1833 1883 1833 1837 2.73 % Br 331.7 332 331.9 332.1 332 0.12 % C (graphite)e 5103 5103 5100d - 4098d 24.50 % Ca 1760 1760 1757 1757 1757 0.17 % Cd 1038 1038 1038 1043 1040 0.48 % Cec 3743 3743 3699 3693 3716 1.35 % Cl 238.5 238.5 239.2 239.2 239.1 0.29 % Co 3173 3173 3143 3203 3200 1.91 % Cr 2755 2755 2945 2943 2944 6.90 % Cs 963.2 963.2 951.6 943.2 944.2 2.12 % Cu 2868 2868 2840 2833 2835 1.24 % Dy 2873 2873 2835 2833 2840 1.41 % Er 3173 3173 3136 3133 3141 1.28 % Eu 1713 1713 1870 1873 1802 9.34 % F 85.15 85.15 85.01 85.05 85.03 0.16 % Fe 3273 3273 3023 3133 3134 8.27 % Ga 2673 2673 2676 2473 2477 8.21 % Gd 3273 3273 3539 3533 3546 8.34 % Ge 3103 3103 3103 3103 3106 0.10 % H 21.15 21.15 20.28 20.28 20.28 4.29 % He 4.15 4.15 4.216 4.37 4.22 5.30 % Hfg 5673 5673 5470 4873 4876 16.40 % Hg 630.2 630.2 629.7 629.8 629.9 0.08 % Hoc 2873 2873 2968 2973 2973 3.48 % c I(I2) 457.2 457.2 457.5 457.2 457.6 0.09 % In 2273 2273 2353 2343 2345 3.52 % Irg 5573 5573 4403 4703 4701 26.60 % K 1047 1047 1047 1033 1032 1.45 % Krc 121.2 121.2 120.9 120 119.9 1.08 % La 3743 3743 3730 3733 3737 0.35 % Li 1603 1603 1620 1613 1615 1.06 % Lu 3603 3603 3668 3663 3675 2.00 % Mg 1383 1383 1363 1363 1363 1.47 % Mn 2373 2373 2235 2333 2334 6.17 % Mog 5833 5833 4885 4913 4912 19.40 % N 77.15 77.15 77.4 77.35 77.36 0.32 % Nac 1163 1163 1156 1153 1156 0.87 % Nbe 3573 3573 5015 4973 5017 40.40 % Nd 3303 3303 3341 3343 3347 1.33 % Ne 27.15 27.15 27.1 27.07 27.07 0.30 % Nib 3003 3003 3005 3263 3186 8.66 % O 90.15 90.15 90.19 90.19 90.2 0.06 % Osc 5273 5273 5300 5273 5285 0.51 % P (white)c 553.2 553.2 553 550.2 553.7 0.64 % Pb 2017 2017 2013 2023 2022 0.50 % Pde 4253 4253 3413 3233 3236 31.60 % Pre 3403 3403 3785 3783 3793 11.50 % Ptb,h 4803 4803 4100 4093 4098 17.40 % Rb 961.2 961.2 961 963.2 961.2 0.23 % Rec 5903 5903 5900 5873 5869 0.58 % Rhe 4773 4773 4000 3973 3968 20.30 % Ruh 5173 5173 4173 4423 4423 24.00 % S (monoclinic)c 718.2 718.2 717.8 717.8 717.8 0.06 % Sbg 1653 1653 1908 1860 1860 15.40 % Sc 3003 3003 3104 3103 3109 3.53 % Sec 958.2 958.2 958.1 958.2 958.2 0.01 % Sig 2633 2633 2628 3533 3538 34.60 % Smc 2173 2173 2064 2063 2067 5.33 % Sne 2543 2543 2543 2893 2875 13.80 % Sr 1653 1653 1657 1653 1655 0.24 % Ta 5693 5693 5698 5833 5731 2.46 % Tbg 3073 3073 3396 3493 3503 14.00 % Tec 1263 1263 1263 1263 1261 0.16 % Ti 3533 3533 3560 3563 3560 0.85 % Tl 1733 1733 1730 1743 1746 0.92 % Tme 2003 2003 2220 2223 2223 11.00 % Ve 3273 3273 3650 3673 3680 12.40 % W 6203 6203 5930 5823 5828 6.53 % 330 Journal of Chemical & Engineering Data, Vol. 56, No. 2, 2011 Table 1. Continued element CDB LAGb ELE TPC CRC (max/min - 1) · 100 % Xe 165.2 165.2 166.1 165.1 165 0.67 % Ybe 1703 1703 1466 1473 1469 16.20 % Ye 3203 3203 3611 3613 3618 13.00 % Zn 1180 1180 1180 1183 1180 0.25 % Zrg 3853 3853 4650 4673 4682 21.50 % a Acronyms as in text. This table does not have exclusions based on judgement.
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