Structure correlation, chemical interpretation

Interpretive spectroscopy provides a basis for the establishment of cause-and-effect relationships between spectrometer response and the chemical properties of the samples. While many books available on NIR cover a range of applications and topics from a broad perspective, most of them barely touch on structure correlation and interpretation of spectra. The first, and arguably the only, book to tackle this intriguing and challenging area, Practical Guide to Interpretive Near-Infrared Spectroscopy presents the most detailed discussion of the subject to date. 

There is a long history of efforts to find simple and interpretable /i and fi functions for various activities and properties (29, 30). The quest for predictive QSAR models started with Hammett s pioneer work to correlate molecular structures with chemical reactivities (30-32). However, the widespread applications of modern predictive QSAR and QSPR actually started with the seminal work of Hansch and coworkers on pesticides (29, 33, 34) and the developments of various powerful analysis tools, such as PLS (partial least squares) and neural networks, for multivariate analysis have fueled these widespread applications. Nowadays, numerous publications on guidelines, workflows, and... 

In this chapter, the authors describe some recently completed work on the electrical properties of paper. Emphasis has been placed on two main areas. The first is the development of methods and tools with which conductivity measurements can be made readily. The second is the correlation of structure and chemical content with conductivity measurements on a variety of different types of paper. The paper samples which we have studied include those produced in our laboratory as well as those obtained from commercial sources. As pointed out earlier, paper is an extremely complex system from an electrical standpoint and therefore, it is a great advantage, in the interpretation of experimental results, to have a knowledge of the precise manner by which a paper is produced. 

The examples presented in this chapter also illustrate a development of the structure correlation method itself. Initially, it was applied to whatever representatives of a specific fragment happened to be available (Cd). Later, directed searches in the CSD [6] led to sometimes surprising new types of correlations (Sn). More recently, compounds have been synthesized in a planned way and their structures determined, so that a specific structural feature or reactivity problem could be studied (Si, B). We have also seen that the methods and interpretations of structure correlation can be applied to results of quantum-chemical calculations. Combinations of kinetic, mechanistic, and computational studies together with structure correlations, are just beginning to illuminate as yet poorly understood problems of chemical reactivity and selectivity, e.g. the factors differentiating between substitutions proceeding with retention or inversion at Si. 

The surface area, pore structure and chemical composition of the surface are important parameters of any support material or solid catalyst. Even when mechanistic interpretation is not a primary aim, many techniques are now applied in industrial laboratories to establish correlations between these parameters and the performance of specific catalysts. Such techniques may also be used on a routine basis to monitor the reproducibility of purchased materials and catalyst preparation methods. 

In conclusion, we suggest that when a new chemical or series of chemicals, such as the chlorinated dibenzofurans become the subject of environmental assessments it is important to obtain, correlate and interpret their physical-chemical property data using the approach suggested here. As more reliable experimental data become available, more refined property-structure relationships can bedeveloped including isomer differences, but a necessary first stage is to establish reliable initial estimates of three key solubilities . Much useful environmental fate information can be deduced from these data, indeed it is difficult to conceive how reliable environmental fate information can be obtained or interpreted without such data. 

FTIR, NMR, and EXAFS and ex situ methodologies such as electron microscopy (SEM and TEM) are also powerful and important tools in the investigation of the mechanisms by which materials form. Combination of experimental approaches not only facilitates their interpretation but also enables cross-correlation between experimental phenomena. This is especially important because SAXS provides information on reciprocal space. The estimation of the structure of a scatterer from its scattering profiles is called the inverse scattering problem, and this problem cannot be solved uniquely [1]. Scattering profiles are complicated further when polydispersity effects are operative, which is usually to some extent the case for sol-gel systems. In practice, the interpretation of SAXS patterns therefore depends heavily on the development of hypothetical structural models and on comparison of the simulated scattering profile, which can be calculated from a given structure, with the experimental profile. Hence, additional independent structural or chemical information may aid in the interpretation of SAXS profiles. 

Algorithms Infrared Data Correlations with Chemical Structure Infrared Spectra Interpretation by the Characteristic Frequency Approach Machine Learning Techniques in Chemistry Molecular Models Visualization Neural Networks in Chemistry NMR Data Correlation with Chemical Structure Partial Least Squares Projections to Latent Structures (PLS) in Chemistry Shape Analysis Spectroscopic Databases Spectroscopy Computational Methods Structure Determination by Computer-based Spectrum Interpretation Zeolites Applications of Computational Methods. 

Infrared Data Correlations with Chemical Structure Infrared Spectra Interpretation by the Characteristic Frequency Approach Neural Networks in Chemistry NMR Chemical Shift Computation Ab Initio NMR Chemical Shift... 

The theory of absolute reaction rates has also been applied to the prediction and interpretation of the rates of adsorption [42, 67]. The theory has proved useful in correlating and interpreting experimental data however, due to the difficulty in describing accurately the chemical structure and energetics of the activated complex on the surface, the prediction of the rates has not been very successful so far. 

With the present-day interest in correlating chemical structure with biological activity the quantitative structure-activity relationships (QSARs) is here presented under a plethora of novel, fiesh and fruitful picture of regression analysis aiming to closely approach the quantum interpretation... 

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