Yaser Beyad

Reactions of CO2 with Aqueous Piperazine Solutions: Formation and Decomposition of Mono- and Dicarbamic Acids/Carbamates of Piperazine at 25.0 °C

Piperazine (PZ) is widely recognized as a promising solvent for postcombustion capture (PCC) of carbon dioxide (CO(2)). In view of the highly conflicting data describing the kinetic reactions of CO(2)(aq) in piperazine solutions, the present study focuses on the identification of the chemical mechanism, specifically the kinetic pathways for CO(2)(aq) in piperazine solutions that form the mono- and dicarbamates, using the analysis of stopped-flow spectrophotometric kinetic measurements and (1)H NMR spectroscopic data at 25.0 °C. The complete set of rate and equilibrium constants for the kinetic pathways, including estimations for the protonation constants of the suite of piperazine carbamates/carbamic acids, is reported here using an extended kinetic model which incorporates all possible reactions for CO(2)(aq) in piperazine solutions. From the kinetic data determined in the present study, the reaction of CO(2)(aq) with free PZ was found to be the dominant reactive pathway. The superior reactivity of piperazine is confirmed in the kinetic rate constant determined for the formation of piperazine monocarbamic acid (k(7) = 2.43(3) × 10(4) M(-1) s(-1)), which is within the wide range of published values, making it one of the faster reacting amines. The corresponding equilibrium constant for the formation of the monocarbamic acid, K(7), markedly exceeds that of other monoamines. Kinetic and equilibrium constants for the remaining pathways indicate a minor contribution to the overall kinetics at high pH; however, these pathways may become more significant at higher CO(2) loadings and lower pH values where the concentrations of the reactive species are correspondingly higher.

Chemometrics and data fitting

There are several definitions for the term ‘chemometrics’; a possible compromise between them could be that chemometrics is the science (or the art) of extracting useful information from chemical data. Traditional chemometrics methods that fit the definition include PCR/PLS (Principal Component Regression/Partial Least Squares), multivariate calibration, cluster analysis, soft modelling and many others. The argument brought forward in this editorial is the following: the most important data analysis tool for chemists in general is data fitting usually by linear or nonlinear least-squares fitting. In chemometrics literature, it is often called hard modelling. Clearly and according to the definition earlier, data fitting is chemometrics. However, this analysis technique is not seen as chemometrics by most chemists who use it. As we will demonstrate, the most widespread chemometrics method appears not to be part of chemometrics! Note that in the following, we will use the notation chemometrics for the traditional interpretation of the expression. In order to support this claim, we undertook a limited literature review, searching the frequency of expressions that indicate that chemometrics methods were used and expressions that indicate that data fitting was used in the reported research. After a few trials, we chose the keywords chemometrics and data fitting and determined the frequency they were used over the last 20 years in some characteristic journals of the chemical literature. The selection of journals that represent the chemometrics literature included Analytical Chemistry, Analytica Chimica Acta, Chemometrics and Intelligent Laboratory Systems and, of course, the Journal of Chemometrics; the representatives for general chemistry were the Journal of the American Chemical Society and Angewandte Chemie. The results are represented graphically in Figure 1. In the dedicated chemometrics journals, the expression chemometrics is clearly more relevant, and the expression is about three times more common. In the analytical journals, the ratio is about 1 with a slight emphasis on data fitting. In the general chemistry literature, the ratio is between 100 and 300 in favour of data fitting. The difference in the absolute occurrences of the two keywords in the figure does not appear to be dramatic with 20,000 occurrences of data fitting and 9300 occurrences of chemometrics in the selected journals. However, it is important to realise that the chemometrics literature is almost completely represented by the four journals investigated here while the general chemistry literature is dramatically larger than the two journals used in this investigation. Thus, it is safe to assume that the ratio of the occurrences of the two keywords in the complete chemical literature is approximately the same as in the two general journals chosen here. One could endlessly argue about the details of this investigation; for example, are the journals and keywords representative of the different analysis methods discussed here? Clearly, different choices would result in different outcomes, but these outcomes would not be dramatically different. Thus, we can confidently conclude that in chemistry, data fitting is at least 100 times more commonly used and thus more important than chemometrics methods. Chemometrics methods are of course well established in the chemometrics literature, and they are very actively and successfully used in many analytical chemistry applications. Many chemists outside these circles have hardly heard of chemometrics or see in it some mysterious methodology that cannot be trusted and should better be left alone. Does it matter? In some ways of course not, as long as the chemometrics methods are successfully used by those who understand them. However, it cannot harm the cause of chemometrics if all data analysis methods that result in useful information are counted as chemometrics. Such a development would increase the recognition of the importance of advanced analysis methods, and it would increase the success rate for relevant grant applications (despite many applications, the main author of this letter never received a government grant for a chemometrics project; it appeared to be impossible for the committee to have three reviewers that could appreciate the potential of the project, and chemometrics was often seen as too exotic to possibly be useful). Of course, wider recognition would also help in increasing the citation index of the chemometrics journals. Again, do these things matter? As we all know, they do.

Multivariate linear regression with missing values

This contribution presents and discusses an efficient algorithm for multivariate linear regression analysis of data sets with missing values. The algorithm is based on the insight that multivariate linear regression can be formulated as a set of individual univariate linear regressions. All available information is used and the calculations are explicit. The only restriction is that the independent variable matrix has to be non-singular. There is no need for imputation of interpolated or otherwise guessed values which require subsequent iterative refinement.