The synthesis of low molecular weight (M
n
(NMR) < 7000 g/mol) lactic acid prepolymers
by condensation polymerization of l-lactic acid was
investigated. Besides the l-lactic acid
polymer,
hydroxyl- and carboxyl-terminated telechelic prepolymers were also
prepared by the addition of small
amounts of 1,4-butanediol and adipic acid, respectively. All
polymerizations were carried out in a melt
with tin octoate as the catalyst. The products were characterized
by differential scanning calorimetry,
gel permeation chromatography (GPC), IR, 1H-NMR, and
13C-NMR. According to NMR, the resulting
prepolymers contained less than 1 mol % of lactic acid monomer and
less than 4.1 mol % of lactide. End
group analysis of the polymers was carried out by comparing the NMR
spectra of different polymers.
According to NMR, the lactic acid can be copolymerized so that the
resulting prepolymer chains have
only one kind of end group, hydroxyl or carbonyl. The integrated
area of the identified end group peak
(hydroxyl or acid) was then used in molecular weight calculations.
In 13C-NMR studies, the molecular
weights were calculated by using the peaks in the methine area.
The molecular weights were also
calculated by using the peak integrals of 1H-NMR spectra of
different polymers. The calculated molecular
weights were systematically smaller than the molecular weights
determined by GPC, and on about the
same order as the molecular weights determined by titrimetric methods.
The number-average molecular
weights of prepared prepolymers determined by GPC varied from 2800 to
18 000 g/mol, depending on
the amount of difunctional substance added. The glass transition
temperatures varied from 16.7 to 46
°C.
In chemometric studies all predictor variables are usually collected in one data matrix X. This matrix is then analyzed by PLS regression or other methods. When data from several different sub-processes are collected in one matrix, there is a possibility that the effects of some sub-processes may vanish. If there is, for instance, mechanic data from one process and spectral data from another, the influence of the mechanic sub-process may not be detected. An application of multi-block (MB) methods, where the X-data are divided into several data blocks is presented in this study. By using MB methods the effect of a sub-process can be seen and an example with two blocks, near infra-red, NIR, and process data, is shown. The results show improvements in modelling task, when a MB-based approach is used. This way of working with data gives more information on the process than if all data are in one X-matrix. The procedure is demonstrated by an industrial continuous process, where knowledge about the sub-processes is available and X-matrix can be divided into blocks between process variables and NIR spectra.
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