Polymers chemometrics

NMR spectroscopy is one of the most widely used analytical tools for the study of molecular structure and dynamics. Spin relaxation and diffusion have been used to characterize protein dynamics [1, 2], polymer systems[3, 4], porous media [5-8], and heterogeneous fluids such as crude oils [9-12]. There has been a growing body of work to extend NMR to other areas of applications, such as material science [13] and the petroleum industry [11, 14—16]. NMR and MRI have been used extensively for research in food science and in production quality control [17-20]. For example, NMR is used to determine moisture content and solid fat fraction [20]. Multi-component analysis techniques, such as chemometrics as used by Brown et al. [21], are often employed to distinguish the components, e.g., oil and water. 

If we consider only a few of the general requirements for the ideal polymer/additive analysis techniques (e.g. no matrix interferences, quantitative), then it is obvious that the choice is much restricted. Elements of the ideal method might include LD and MS, with reference to CRMs. Laser desorption and REMPI-MS are moving closest to direct selective sampling tandem mass spectrometry is supreme in identification. Direct-probe MS may yield accurate masses and concentrations of the components contained in the polymeric material. Selective sample preparation, efficient separation, selective detection, mass spectrometry and chemometric deconvolution techniques are complementary rather than competitive techniques. For elemental analysis, LA-ICP-ToFMS scores high. 

COlfen H (2007) Bio-inspired Mineralization Using Hydrophilic Polymers. 271 1-77 Collin J-P, Heitz V, Sauvage J-P (2005) Transition-Metal-Complexed Catenanes and Rotax-anes in Motion Towards Molecular Machines. 262 29-62 Collins BE, Wright AT, Anslyn EV (2007) Combining Molecular Recognition, Optical Detection, and Chemometric Analysis. 277 181-218 Collyer SD, see Davis F (2005) 255 97-124 Commeyras A, see Pascal R (2005) 259 69-122 Coquerel G (2007) Preferential Crystallization. 269 1-51 Correia JDG, see Santos I (2005) 252 45-84 Costanzo G, see Saladino R (2005) 259 29-68 Cotarca L, see Zonta C (2007) 275 131-161 Credi A, see Balzani V (2005) 262 1-27 Crestini C, see Saladino R (2005) 259 29-68... 

Under eonstant experimental conditions, the number of Raman seattered photons is proportional to analyte eoneentration. Quantitative methods can be developed with simple peak height measurements [1]. Just as with infrared calibrations, multiple components in eomplex mixtures ean be quantified if a distinet wavelength for each component can be identified. When isolated bands are not readily apparent, advaneed multivariate statistical tools (chemometrics) like partial least squares (PLS) ean help. These work by identifying all of the wavelengths correlated to, or systematically changing with, the eoneentration of a eomponent [2], Raman speetra also can be correlated to other properties, sueh as stress in semieonduetors, polymer erystal-linity, and particle size, because these parameters are refleeted in the loeal moleeular environment. 

T. Salomonsen, H.M. Jensen, D. Stenbaek and S.B. Engelsen, Chemometric prediction of alginate monomer compositions A comparative spectroscopic study using IR, Raman, NIR, and NMR, Carbohydr. Polym., 72, 730-739 (2008). 

Another aspect of polymer informatics, beyond the representation and registration of polymer information and data, is the conversion of data into knowledge and thus into the power to make decisions. To this end, the same tools, which are common in small molecule informatics, have also been used to study polymer data. The work that has been reported so far subdivides into two categories, namely classification and chemometrics problems and property prediction. 

All of these studies suffer from the fact that they were carried out on relatively small datasets of more or less homogeneous polymers and are generally not well validated. As such, they indicate that there may be useful chemometric methods here, but there is considerable scope for further studies on much larger and heterogeneous sample sets to demonstrate general applicability and usefulness. 

Rosengren AM, Karlsson JG, Andersson PO, Nicholls lA. Chemometric models of template— molecularly imprinted polymer binding. Anal Chem 2005 77 5700-5705. 

Balke, S. T, "Quantitative Column Liquid Chromatography - A Survey of Chemometric Methods," Elsevier, N.Y. (1984). Provder, T., Ed., "Size Exclusion Chromatograpy -Methodology and Characterization of Polymers and Related Materials", ACS SYMPOSIUM SERIES, No. 245, American Chemical Society, Washington, D.C. (1984). 

Spectrophotometry, 42 Absorbance, 42 Infrared, 44 Luminescence, 45 Raman, 48 Fiber Optics, 50 Refractive Index, 52 Piezoelectric Mass Sensors, 53 New Chemistry, 54 Immunochemistry, 54 Polymers and New Materials, 56 Recognition Chemistry, 57 Chromatography and Electrophoresis, 61 Flow Injection Analysis and Continuous Flow Analysis, 63 Robotics, 65 Chemometrics, 68 Communications, 70... 

Often, relationships between measured process parameters and desired product attributes are not directly measurable, but must rather be inferred from measurements that are made. This is the case with several spectroscopic measurements including that of octane number or polymer viscosity by NIR. When this is the case, these latent properties can be related to the spectroscopic measurement by using chemometric tools such as PLS and PCA. The property of interest can be inferred through a defined mathematical relation.39 Latent variables allow a multidimensional data set to be reduced to a data set of fewer variables which describe the majority of the variance related to the property of interest. This data compression using the most relevant data also removes the irrelevant or noisy data from the model used to measure properties. Latent variables are used to extract features from data, and can result in better accuracy of measurement and a reduced measurement time.4... 

The feasibility of diffuse reflectance NIR, Fourier transform mid-IR and FT-Raman spectroscopy in combination with multivariate data analysis for in/ on-line compositional analysis of binary polymer blends found in household and industrial recyclates has been reported [121, 122]. In addition, a thorough chemometric analysis of the Raman spectral data was performed. 

The application of chemometrics in near-infrared spectroscopy is finding widespread use in many different industries for monitoring the identity and quality of raw materials and finished products in the food and agricultural industry [46], polymer, pharmaceutical, and organic chemical manufacturing industries [18, 47],... 

There are many other practical examples of using chemometric methods to make Raman spectroscopy into a real-time monitoring method (14-18). One such example is the use of Raman for process control in the curing of polymers (18). The Raman spectra of a commercial cyanate ester resin,... 

Steinke et al.156 have used the chemometrics approach to investigate the effect of variables such as type and quantity of monomers, cross-linker, porogens, initiator, type of initiation (UV or thermal), polymerization pressure, temperature, reaction time and reaction vial dimensions have on the properties of synthesized polymers. 

Some of the earliest chemometric NMR analysis of materials involved the PCA of organic peat using C CPMAS NMR spectra,and the analysis of H and C NMR relaxation parameters in coal. Chemometrics and C CPMAS has also been applied to classify soil organic matter. Clayden and co-workers have used FA of the time domain F NMR signal in poly(tetrafluoroethane), PTFE polymers as a method to measure crystallinity. It was demonstrated that the dominant principal component did not correlate well, but that the scores of second and third principal component show an excellent correlation with the PTFE crystallinity. PLS methods have also been used to determine... 

Basically, the book can be subdivided into three parts. In the first part the fundamentals of the instrumentation for infrared and Raman imaging and mapping and an overview on the chemometric tools for image analysis are covered in two introductory chapters. The second part comprises the chapters 3-9 and describes a wide variety of applications ranging from biomedical via food and agriculture to polymers and pharmaceuticals. Some historical insights are given as well. In the third part the chapters 10-15 cover special methodical developments and their utiHty in specific fields. 

Samples of each polymer were equilibrated at different relative humidities by storage over saturated salt solutions in desiccators. The equilibrated samples were then examined using FT Raman spectroscopy and differential scanning calorimetry (DSC). Gravimetry was used to assess the water vapor sorption profile. Chemometric analysis of spectroscopic data was performed using a commercial software package. Unscrambler (Camo.). 

This chapter discussed several aspects of DNA analysis by CE and microchip technologies using polymer solutions as the sieving matrix. Analysis of DNA is a multivariate system by nature and both the separation and the result of the separation are suitable to a large number of chemometric tools. 

Conducting polymer sensors can be operated either to quantitatively measure the concentration of a target vapor species or to qualitatively analyze a complex mixture of vapors. For single vapors, the detection limits can be in the low-ppm region. Exposure to a mixture of vapors results in a unique pattern of responses, which is usually deciphered using standard chemometric techniques. The pattern can be used like a fingerprint to identify certain products, or to establish the quality of foodstuffs, wines, perfumes, etc. The electronic nose has similar components as the natural nose this is illustrated in Figure 1.15. 

Another use of Raman spectroscopy for quantitative analysis is the determination of percent crystallinity in polymers. Both the frequency and intensity of peaks can shift on going from the amorphous to the semicrystalline state for polymers. The percent crystallinity can be calculated with the help of chemometrics software. 

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