Monitoring of polymer melt processing

This article has been downloaded from IOPscience. Please scroll down to see the full text article.

2010 Meas. Sci. Technol. 21 062001

(http://iopscience.iop.org/0957-0233/21/6/062001)

Download details:

IP Address: 200.136.235.126

The article was downloaded on 10/02/2012 at 19:41

Please note that terms and conditions apply.

View the table of contents for this issue, or go to the journal homepage for more

Home Search Collections Journals About Contact us My IOPscience

IOP PUBLISHING MEASUREMENT SCIENCE AND TECHNOLOGY

Meas. Sci. Technol. 21 (2010) 062001 (19pp) doi:10.1088/0957-0233/21/6/062001

TOPICAL REVIEW

Monitoring of polymer melt processingIngo Alig1, Bernd Steinhoff and Dirk Lellinger

Deutsches Kunststoff-Institut, Schlossgartenstrasse 6, D-64289 Darmstadt, Germany

E-mail: ialig@dki.tu-darmstadt.de

Received 13 November 2009, in final form 15 February 2010Published 16 April 2010Online at stacks.iop.org/MST/21/062001

AbstractThe paper reviews the state-of-the-art of in-line and on-line monitoring during polymer meltprocessing by compounding, extrusion and injection moulding. Different spectroscopic andscattering techniques as well as conductivity and viscosity measurements are reviewed andcompared concerning their potential for different process applications. In addition toinformation on chemical composition and state of the process, the in situ detection ofmorphology, which is of specific interest for multiphase polymer systems such as polymercomposites and polymer blends, is described in detail. For these systems, the productproperties strongly depend on the phase or filler morphology created during processing.Examples for optical (UV/vis, NIR) and ultrasonic attenuation spectra recorded duringextrusion are given, which were found to be sensitive to the chemical composition as well as tosize and degree of dispersion of micro or nanofillers in the polymer matrix. By small-anglelight scattering experiments, process-induced structures were detected in blends ofincompatible polymers during compounding. Using conductivity measurements duringextrusion, the influence of processing conditions on the electrical conductivity of polymermelts with conductive fillers (carbon black or carbon nanotubes) was monitored.

Keywords: process monitoring, in-line, on-line, polymers, micro and nano composites, meltprocessing, extrusion, mould injection, UV–vis, NIR, ultrasonics, light scattering, dielectricspectroscopy

(Some figures in this article are in colour only in the electronic version)

1. Introduction

Over the last two decades economic, legislative andenvironmental demands have increased more and more.The same holds true for the need to ensure consistentquality of the final products. These issues are of specificrelevance for the pharmaceutical industry, where the so-called process analytical technology (PAT) has been developedto help companies improve conformity with manufacturingcompliance regulations. Similarly, for processing ofpolymers, requirements with respect to productivity andquality become more and more important [1] and canbe fulfilled by implementing PAT tools for real or near-real-time measurements of chemical composition and/orphysical properties as components of quality management

1 Author to whom any correspondence should be addressed.

systems. Such measurements yield data for control andoptimization of material properties and processing conditions.Furthermore, they provide the possibility of improving processunderstanding and allow generating input data for processsimulation or for validation of the simulation results.

PAT generally differentiates between in-line, on-line andoff-line measurements. In-line measurements are implementeddirectly within the processing line, resulting in very short(or even nonexistent) delays for sampling. However, in-linesensors may interfere with the main process and the sensorscan be influenced by the temperature or pressure. On-linetechniques require a sampling stream to be diverted fromthe process flow line and transferred to the measurementdevice. Thus, the latter is isolated from the main streamwhich simplifies maintenance work. On the other hand,delays might occur due to material storage in the bypass [1].

0957-0233/10/062001+19$30.00 1 © 2010 IOP Publishing Ltd Printed in the UK & the USA

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

Off-line analytics—which still dominates process analysis—is usually cost intensive and leads to large delays betweenthe occurrence of defects or process instabilities and theirdetection. A comprehensive discussion of the different processcontrol schemes can be found in [2].

Classical process control based on in-line measurementsof conventional parameters such as temperature and pressureis in many cases not sufficient to account for process-induced variations in material properties or to guaranteestable processing within a given process window. Thisis especially the case for complex polymer systems [3].Accordingly, polymer industry is more and more interestedin fast, reliable and robust in-line methods, providingdirect information on chemical composition or materialproperties. In general, polymer systems can be subdividedinto thermoplastic materials, elastomers and thermosets. Sincethe processing methods for all three classes are different,we focus in this report on methods for monitoring meltprocessing of thermoplastic polymers. Compounding offormulations by melt mixing using extruders is an importantstep in thermoplastic processing. This means that additives orfillers are incorporated into melts of the basis polymers. Inpolymer industries, a broad range of additive types, fulfillingdifferent requirements such as protection against UV, heat oroxygen, are used to tune the material properties for a givenapplication. Almost all commercial polymers contain—atleast in small amounts—additives ‘soluble’ in the polymer,or fillers. Fillers can consist of micro- or nano-sized particlesdispersed in the polymer matrix. Compounding is also usedto produce polymer blends of two or more polymers in orderto combine the properties of different polymers (or simply toreduce costs). Interesting properties which can be influencedby blending or adding modifiers or fillers are mechanical,thermal or electrical properties or melt flow characteristics.Since the final properties of blends or compounds containingfillers are largely determined by the morphology of the blenddomains or the quality of the filler dispersion, respectively,there is considerable demand for monitoring the morphologyduring compounding.

Due to the generally high viscosity of polymer melts,mass and thermal transport and thus mixing and possiblechemical reactions are strongly hindered during processingand must be forced by mechanical energy. Here extrudersare the established processing units. The melt leaving theextruder is quenched and the solid material thus obtained byvitrification or crystallization is pelletized. The pellets can beprocessed into final components by injection moulding. Inaddition to compounding, extruders are also used to producegoods such as tubes, hoses, window frames, plates and films.This report concentrates on compounding of thermoplastics,where the major part of process monitoring-oriented researchhas been done until now. Much less effort has been spent oninjection moulding or deep drawing.

The currently available in-line methods for compounding,extrusion and mould injection can be subdivided into threegroups: (i) methods which provide information on the stateof process (not topic of this report), (ii) methods which aremainly sensitive to the chemical composition and (iii) methods

which provide morphological information such as quality offiller dispersion, agglomeration or domain size and shape inpolymer blends. Excellent papers regarding in-line monitoringof chemical composition already exist in the literature and,therefore, are summarized only briefly in section 2. As yet,not much work related to obtaining morphological informationby in-line or on-line control (iii) exist. Therefore, section 3will expand recent research on morphology monitoring.

2. Monitoring techniques

2.1. Overview

Already in the first application of PAT to polymer processing,it has been noted that a simple transfer of laboratoryinstrumentation into the process is not sufficient for mostapplications. Implementation of process measurement devicesrequires in most cases reduction of data acquisition time, whilemaintaining a sufficient signal-to-noise ratio and resolutioneven in harsh production environments [4]. Furthermore,sensors for monitoring melt processing of thermoplasticpolymers will be operated at temperatures well above themelting or glass transition temperature of the polymer. Thisrequires sensors with long-term stability at temperaturesbetween 180 and 350 ◦C and the ability to withstandpressures up to 300 bar as well as corrosion and abrasionby fillers. Finally, the financial resources for processmonitoring (development, equipment, maintenance) in theindustry are limited due to the relatively low added value inclassical polymer production as compared to pharmaceuticalproducts.

A somewhat arbitrary overview of the methods thatin principle are suitable for in-line monitoring of polymerprocessing is shown in figure 1. Many of these methods weretested so far for monitoring of polymer melt compounding.As stated above, pressure and temperature are the classicalquantities for polymer process control. Furthermore, viscosityis measured for quality control of formulation batches in somecases on-line. For this purpose, commercially available slitdie rheometers are used.

In excellent papers of Coates et al ([3] and referencestherein), actual developments of in-line monitoring techniquesfor analysis of melt processing are described. Differenttechniques such as rheometric, optical, ultrasonic andelectrical methods have been used successfully to gaininformation about the physical properties and chemicalcomposition of polymer melts [5]. Therefore, in the followingonly a brief review of the methods for monitoring chemicalcomposition and material properties is given. The first part willfocus on extrusion monitoring. In the final part of this sectionsome developments for in-line control of injection mouldingare revised briefly. Actual examples for the scattering methodsin figure 1 will be given in section 3.

2.2. Extrusion monitoring: properties and chemicalcomposition

Beside optical (mainly NIR) spectroscopy, ultrasonicspectroscopy is one of the classical techniques for in-line

2

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

Figure 1. Methods for in-line monitoring of polymer processing.

control [3, 6–48]. The potential of the method is given by thedirect measurement of a mechanical property, the robustness ofthe sensor especially against abrasion and high pressure and therelatively low costs for the equipment. Since the beginning ofthe 1980s, ultrasonic measurements have been applied to manypolymeric systems during processing, including polymericfoam [20, 45] and thermoset processing [22, 24].

In-line measurement of the electrical conductivity orspecific resistivity is another technique which probes directlya physical property of practical relevance. For frequency-dependent measurements of the electrical conductivity ordielectric permittivity, the method is called dielectricrelaxation spectroscopy (DRS). Other common notations areimpedance or conductivity spectroscopy. In [40, 42, 43,49–64] applications of in-line conductivity measurements (orDRS) during polymer processing are described. Ac and dcconductivity or permittivity measurements have been appliedfor monitoring of conductive filler content (carbon black andcarbon nanotubes) and filler dispersion (including nanofiller).In figure 2, an example of our work on extrusion monitoring byin-line conductivity measurements is shown. The dependenceof the conductivity of a polycarbonate (PC) melt containingmulti-walled carbon nanotubes (MWCNT) [64] on processingconditions was studied during extrusion using the slit dieshown in figure 3. In this die, insulated electrodes in the plate–capacitor geometry are implemented. The die is attached tothe outlet of an extruder. During extrusion of the PC/MWNTmelt, the conductivity is very low, since the conductive fillernetwork is destroyed by the shear and elongational flow. Inthe quiescent melt, after the stopping of the extruder, theconductivity recovers by reformation of the conductive fillernetwork.

Optical spectroscopic techniques (Raman, IR, NIR, UV–visible or fluorescence) as analytical tools for chemicalquality control during melt processing are used to extractspecific molecular information or information on chemicalcomposition. These methods are interesting especially formonitoring of complex polymer systems and when reactionsoccur during processing [3].

Figure 2. In-line conductivity measurement of apolycarbonate/carbon nanotube composite during melt extrusion atdifferent melt temperatures (screw speed: 175 rpm) and in thequiescent melt after the extruder has stopped. Indication forshear-induced destruction and conductivity recovery [64].

Figure 3. The two half shells of a dielectric measurement die withrectangular electrodes insulated by a ceramic inlay [64]. Aftermounting, the two electrode areas are face to face.

IR spectroscopy (Fourier-transform infrared spectros-copy: FT-IR) has been used in analytical laboratories for

3

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

decades. It was the first optical spectroscopic methodthat was tested for controlling melt extrusion. Fritzand Ultsch [65] applied on-line FT-IR to monitor reactiveextrusion where polyethylene was grafted with organo-silanes. With a gear pump, a small fraction of themelt was drawn continuously to a process spectrometer[66, 67] equipped with a special transmittance measurementcell of adjustable path length [68]. Application of similar on-line instrumentation is described in [6, 69, 70]. In [70], in-lineFT-IR was tested also to monitor the copolymerization of thestyrene–maleic-anhydride model system with an alky-amineduring melt extrusion. A ZnSe-attenuated total reflection(ATR) probe was mounted in the crotch region between thescrews of a twin screw extruder.

Despite the fact that FT-IR delivers detailed vibrationalspectra which usually can be well correlated with the chemicalcomposition, the method has several drawbacks. Stronglyabsorbing melts require the path length to be as small as50 μm for FT-IR transmission measurement [66]. Thus, thesemeasurements are mainly restricted to on-line configurationsusing a bypass. A pronounced delay is caused by the timeit takes to replace the volume in the bypass and the narrowmeasurement cell by the viscous melt. In-line FT-IR by anATR probe likewise suffers the problem of high melt viscosity.The penetration depth of IR in the melt in an ATR measurementis only about 1.5 μm [70] and the exchange of this thin layerof high viscous material likewise takes time [70]. Last but notleast, optical fibre technology for the mid IR spectral range isstill very expensive and susceptible to damage.

Development of robust equipment for NIR, Raman, UV–vis and fluorescence spectroscopy has promoted the use ofthese methods to monitor polymer processes in an industrialenvironment. Progress was made in high-temperatureand high-pressure probes, robustly designed spectrometers(moving dispersive elements replaced by array detectors) andfibre optical technology [1, 3, 6]. Using fibres for UV–vis orNIR, the spectrometer can be placed dozens of metres awayfrom the melt processing units. Examples of high-temperatureand high-pressure probes for optical spectroscopy are shown infigure 4 together with an ultrasonic transducer. For extrusionmonitoring the probes are usually mounted on a measurementadaptor (see figure 4 also).

As with the dielectric die (figure 3), this measurementadaptor exhibits a rectangular melt channel and is attachedto the outlet of the extruder (figure 5). Probes for differentmethods are mounted along the melt channel for paralleloperation. Although such an assembly is not typicalfor industrial in-line application, it allows fast and easycomparison of the performance of different measurementtechniques and a selection of the most suitable one (orcombination) for a given monitoring problem. Probes of othermethods can be mounted as well.

Compared to IR spectra, the band structure of a NIRspectrum is less detailed. The overtone and combinationbands observed arise only from certain molecular groups(mainly C–H, O–H and N–H bonds), and the peaks showconsiderable overlap. Thus quantitative analysis is moredifficult. As an example, diffuse reflectance NIR spectra

Figure 4. Raman, NIR and ultrasonic (US) sensors. A measurementadaptor (slit die) is partially shown on the left side.

Figure 5. Scheme of a twin screw extruder (left) equipped with ameasurement slit die with different sensors (right). US: ultrasonicprobes. p, T: pressure and temperature.

Figure 6. Diffuse reflectance NIR spectra of POM modified withdifferent amounts of TPU (as indicated in the plot).

of polyoxymethylene (POM) melts modified with differentamounts of thermoplastic polyurethane (TPU) are shown infigure 6. The diffuse reflectance measurements were calibratedwith a ‘white standard’, a wavelength-independent strongdiffuse reflecting sample. Since the diffuse reflection of purePOM is much weaker compared to the white standard, theaverage absorbance increases with decreasing TPU content.Only the small peak at about 1490 nm gives a direct signatureof the increasing TPU content in the spectral shape.

4

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

Figure 7. Cross validation plot of the chemometric model fitted tothe diffuse reflectance NIR spectra shown in figure 6.

The information contained in such not well-pronouncedspectra can be exploited by multivariate methods(‘chemometrics’) [72, 73] yielding a calibration model. Thismodel is used to predict the chemical composition from ameasured spectrum. Such a calibration model was createdusing partial least-squares analysis (PLS) for the spectra shownin figure 6. Actually, for each composition ten spectra wererecorded. The 70 spectra in total form the so-called trainingset. A measure for the quality of a calibration model is obtainedby cross validation. The spectra belonging to the samples withone concentration, e.g. those with 5% TPU content (here tenspectra), are left out and the model is calculated with theremaining spectra. Next, the concentrations correspondingto the spectra not included in the model just produced arepredicted. This procedure is repeated for all samples, and thepredicted concentration is plotted as a function of the actualconcentration (‘cross validation plot’), figure 7.

The availability of fast computers has promoted the useof chemometrics. It has since been applied to almost any typeof spectra (see e.g. [43] and section 3.3 in this text). A numberof studies for extrusion monitoring have been presented in theliterature using NIR in transmission and reflectance [3, 6, 7,40, 42, 43, 46, 48, 71, 74–85], Raman spectroscopy [3, 6, 7,30, 42, 43, 46, 86–89], UV–visible spectroscopy [90–92] andfluorescence spectroscopy [3, 93–99]. Recently, we appliedmultivariate analysis also for prediction of the filler content byultrasonic attenuation spectroscopy [47].

In-line UV–vis spectroscopy can be useful to monitorprocess-induced degradation in polymer melts. An example isgiven in figure 8. An experimental setup similar to that shownin figure 5 was used to measure the UV–vis transmittance of apolylactide (PLA) melt as a function of processing conditions[92]. In figure 8 it can be seen that with increasing screw speed,the UV-absorption edge moves into the longer wavelengthregion (red shift). This mirrors increasing degradation. Theabsorption below 400 nm can be recognized visually asyellowing.

Figure 8. UV–vis transmission spectra of PLA as a function of thescrew speed.

2.3. Injection moulding

Ultrasonic techniques to monitor injection moulding aredescribed in [13, 100, 101]. Measurements of in-line nozzlemelt pressure and hydraulic injection pressure are discussedin [102]. Performing conductivity measurements duringinjection moulding is also rare. It is restricted to polymerscontaining conductive fillers such as carbon black (CB) orcarbon nanotubes (CNT) [103, 104]. Further examples arereported for in-line monitoring of the resin transfer mouldingprocess [105, 106]. An example [104] for a conductivitymeasurement during injection moulding is shown in figure 9.The in-line measured conductivity values (open squares infigure 9) for the polycarbonate/styrene-co-acrylnitrile blend(Bayblend R©) containing 4 wt% multi-walled carbon nanotubesare compared to the off-line measured values (open circles) fordifferent injection conditions. The mould, the plastic part andthe sensor are schematically shown in figure 10. For the in-linemeasured conductivity, the values recorded 9 s after the start ofinjection were taken. The good correlation between in-line andoff-line data demonstrates the potential of in-line conductivitymeasurements. The tremendous decrease of the conductivitywith increasing injection speed and decreasing temperaturecan be explained by the destruction of the conductive fillernetwork due to shear stress resulting from increasing shearrate or viscosity, respectively. The mould temperature seemsto have only minor influence.

Optical methods had also been tested for in-linemonitoring of injection moulding. Such work is describedin Bur et al [107] and Thomas and Bur [108–110]. In [107],a fibre optical sensor was attached to the mould cavity tomonitor the fluorescence intensity of polymers doped withan appropriate fluorescent dye during cooling and subsequentsolidification. The onset of solidification could be detectedand correlated with temperature. Transmittance measurementsin the mould are correlated with crystallization behaviourof polypropylene in [108, 109], while in [110] the productshrinkage is monitored in situ. In [111], transmittance NIR-spectroscopy with the probes mounted on the nozzle wassuccessfully tested to identify colour changes and moisturein the melt.

5

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

Figure 9. Influence of injection speed (left), melt temperature (middle) and mould temperature (right) on the in-line (squares) and off-line(circles) measured electrical conductivity of a polycarbonate/styrene-co-acrylnitrile blend containing 4 wt% MWCNT.

Figure 10. Scheme of the mould, the plastic part and of theconductivity sensor.

3. Morphology control

3.1. General considerations

In the context of polymer materials, the term morphologyis related to heterogeneous systems such as (i) compositeswith filler particles, (ii) immiscible polymer blends of two ormore components and (iii) semi-crystalline polymers. Sincethe focus of this report is melt processing, semi-crystallinemorphology, appearing during cooling, will be not considered.Classical polymer composites contain fillers such as chalk ortalcum with typical particle sizes in the micrometre regionand typical filler contents of about 40 wt% and more. Theparticles in so-called nanocomposites are of sub-micron sizefrom about 10 nm up to several hundred nanometres. Typicalfiller concentrations in nanocomposites are in the order of5 wt% or less. In binary polymer blends, the content of one ofthe components varies typically between 20 and 80 wt%. Inspecial applications, the content can be in the region of someper cent. The domain size in polymer blends ranges fromabout hundred nanometres to dozens of micrometres.

The final properties of polymer compounds such asmechanical modulus, stiffness, toughness or scratch resistanceare known to depend strongly on the degree of fillerdispersion, particle size and size distribution. For polymerblends, the relevant parameters are size and shape of the

domains. Therefore, ‘morphology control’ during polymermelt processing of compounds and polymer blends is highlydesired. This is especially the case for nanocomposites, whereeven small amounts of nanofillers can enhance the propertiestremendously. Therefore, optimum dispersion of the fillershas to be ensured.

Also related to ‘morphology’ is the detection of discreteimpurities in otherwise homogeneous polymers. Suchimpurities, which typically occur statistically, are referred to as‘spots’, ‘burners’ or ‘gel particles’. They can reduce the opticalquality of the final product or can act as ‘hot spots’ causinginitiation of defects. Their sizes vary typically from 0.1 to1 mm. Sometimes much smaller particles at the size of somemicrons or nanometres can also be relevant if their numberdensity is high enough so that the material becomes turbidor agglomerates are formed. Monitoring of those impuritiesduring extrusion is reviewed in section 3.5.

Classical ways of fast ‘morphology control’ are in-line, off-line or at-line turbidity measurements at a singlewavelength. This approach is well described in the literature(see e.g. references cited in section 3.2) and the equipment iscommercially available. In addition, it is well known that theanalysis of the angle-dependent scattering of electromagneticor acoustic waves can provide information on particle sizeand shape (‘form factor’) as well as particle arrangement(‘structure factor’). Thus, angle-dependent static and dynamiclight scattering experiments are well established in laboratoryanalytics for polymer solutions and colloidal systems [112].Angular-dependent static light scattering techniques are alsocommon for in-line, on-line or at-line particle sizing (‘laserdiffraction techniques’) of wet and dry production streamse.g. in pharmaceutical industry, cement production or metalpowder atomization. Their application to polymer meltprocessing is still limited. This might be due to the difficultiesof installation under the harsh conditions of melt processingand the extensive data analysis required. Furthermore,limitations arise because of too strong scattering of meltsof polymer blends or composites, often preventing reliabledata analysis because of strong multiple scattering. Insection 3.4, the state-of-the-art for in-line small-angle light

6

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

scattering during compounding is reviewed and an examplefor monitoring of extrusion of a binary melt is given.

Interesting alternatives to angle-dependent scatteringmethods for morphology control are the detection ofabsorption spectra. For such measurements, commerciallyavailable process spectrometers and probes can be usedin simple transmission or reflection geometry. Ultrasonicspectroscopy is indeed already used for on-line particle sizing(e.g. on ore slurries [113]). Examples for the applicationof optical and ultrasonic spectroscopy to obtain informationon particle size and size distribution in composite polymermelts during extrusion are reported in sections 3.2 and 3.3,respectively. Optical as well as ultrasonic absorption spectrawere correlated with the size of filler particles obtained fromscanning electron microscopy (SEM) or x-ray micro-computertomography (μ-CT).

3.2. Morphology control by optical spectroscopy

3.2.1. Turbidity and spectroscopic measurements: previouswork. In [114], turbidity measurements at a singlewavelength were used to obtain information on particle sizeduring polymer latex production. For data analysis, themeasured turbidity was compared to simulated turbidity datausing Mie theory. Besides the measured turbidity, the numberdensity of particles is required. In [114], the latter was obtainedvia determining the mass fraction of latex particles in thereactor, and the density values of the latex and the dispersion.In [62], the turbidity was used to get a measure of the numberdensity of agglomerates remaining in a polymer melt aftercompounding of nanofillers. For the theoretical calculationof the turbidity, an average diameter of the agglomerates wasassumed. Both examples show the principal difficulty of theanalysis of single wavelength turbidity data. In addition tothe refractive index values for Mie calculations, the numberdensity of particles or, vice versa, the particle size as additionalsample information is required.

In the literature, NIR [115, 116], UV and NIR [117] andRaman spectroscopy [118] were tested for their ability to sizelatex particles in polymer dispersions. By use of chemometricanalysis, the recorded optical spectra were correlated with thesize of the latex particles obtained by dynamic light scatteringor scanning electron microscopy (SEM). Since the as-recordedspectra contain ‘chemical’ and ‘morphological information’,they are only indirectly related to the particle size. Therefore,even a slight change of the chemical composition (of matrixor filler) requires a new calibration of the chemometricmodel. Only the scattering background within the opticalspectra is directly related to morphology. In the case ofheterogeneous materials (e.g. compounds containing fillerparticles or phase domains in blends), the optical spectra(here UV/vis and NIR) contain a contribution due to lightscattering on the inhomogeneities. Therefore, the analysisof the shape of the so-called background scattering curvecan provide direct information on average particle size andnumber density. For quantitative analysis (see section 3.2.3),the background scattering curve has to be separated from the‘chemical information’ (i.e. specific absorption bands of the

components). This wavelength dependence of the scatteringcontribution has been used already in colloid science forprobing the particle size of colloids [119–121].

3.2.2. In-line experiments on polymer melts with nanofillers.As stated above, optimum dispersion of nanofillers isoften crucial to achieve the desired material propertiesof polymer nanocomposites. To get information on thedegree of dispersion of nanofillers during melt compounding,UV/vis and NIR-transmission spectroscopy were used onpolymer/clay nanocomposites. For these experiments, pairsof UV/vis and NIR probes (transmission mode) were attachedto a rectangular slit die. The height of the melt channel(i.e. sample thickness) was 4 mm [40, 122]. The clay(montmorillonite) consists of stacks of silicate platelets.The platelets are some nanometres thick and have lateraldimensions in the order of 100 nm. The dispersionmechanisms of the clay are ‘intercalation’ (polymer moleculesare arranged between the platelets of a stack) and ‘exfoliation’(random distribution of individualized platelets within thepolymer matrix) [123, 124]. Large agglomerates of clayparticles may exist as well. Since the wavelengths in theoptical (UV/vis/NIR) region are considerably larger than themean particle size of exfoliated clay, the spectra do not allowa direct differentiation between intercalation and exfoliation.However, the spectra can be used as an indirect probe for thenanoparticle dispersion, because the scattering contribution inthe optical range decreases with decreasing number and/orsize of remaining agglomerates: The smaller the average sizeof the remaining agglomerates (containing several plateletstacks), the more intercalation or exfoliation has occurred.Regarding the monitoring of agglomerates, this approach isanalogous to the work in [62].

Figure 11 shows as an example the in-line monitoredNIR absorption spectra of pure polystyrene (PS) melt togetherwith a typical spectrum of a PS melt containing 5 wt% ofclay. The polystyrene (PS168N) was a product of BASFSE (Germany) and the clay (Nanofil SE3010) was providedby Sud Chemie AG (Germany). The NIR spectrum of thepure polymer shows the specific absorption bands of PS.For the PS/clay composite, the PS bands are superimposedby an additional contribution from light scattering by theparticles (scattering background: bold line in figure 11). Inoptical spectroscopy, this scattering background is usuallyremoved to extract information on the chemical structureand/or composition from the absorption bands. On the otherhand, to obtain morphological information, the absorptionbands have to be ‘removed’ and the scattering backgroundhas to be analysed. The scattering background (differenceabsorbance spectrum: �A) can be extracted with adequateaccuracy by subtracting the spectrum of the pure polymer.Details for the data analysis are given in section 3.2.3.

The difference spectra �A from NIR and UV/visspectroscopy can be combined to form a single UV/vis/NIRdifference spectrum. Such spectra of a PS/clay compositewith 5 wt% clay as a function of the screw speed are shownin figure 12. It can be seen that the difference absorbance �A

decreases with increasing screw speed, indicating decreasing

7

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

Figure 11. NIR absorption spectra of PS and a PS/clay meltrecorded during extrusion at 210 ◦C (screw speed 450 rpm,throughput 5 kg h−1). The bold line (arrow) represents the scatteringbackground of the PS/clay nanocomposite calculated by subtractionof the spectrum of pure PS.

Figure 12. Combined UV/vis/NIR difference spectra (aftersubtraction of the PS spectrum) as a function of the screw speed fora PS/clay composite (5 wt% clay). The barrel temperature wasTB = 210 ◦C and the throughput was Q = 5 kg h−1.

scattering contribution. This qualitatively reflects the betterdispersion of the clay with increasing screw speed, but doesnot provide direct information on average particle size.

3.2.3. Extracting morphological information from opticalspectra. The following analysis of the optical spectra is basedon Lambert–Beer’s law. A linear mixing law is assumed forthe absorption coefficients of the polymer matrix αP (λ) andthe filler αF (λ). Furthermore, a linear superposition of thespecific absorption bands, the scattering background and theapparatus function is assumed. Consequently, the measured(apparent) absorption A1(λ) of a polymer/(nano)compositemelt can be described by

A1(λ) = lg(I0(λ)/It (λ))

= s(wP αP (λ) + wf αf (λ) + NCsc(λ)) + αa(λ) (1)

where I0(λ) and It (λ) are the incident and transmittedintensities. αa(λ) is the absorption (‘apparatus function’)caused by the probes, the optical fibres, etc, s is the thicknessof the polymer melt and wP and wf are the weight fractions ofpolymer and filler, respectively. αP (λ) and αF (λ) are relatedto the UV/vis electronic transitions and the NIR vibration

modes of polymer and filler, respectively. N is the numberdensity of the filler particles. Csc(λ) is the scattering crosssection of the particles in the melt. For a transmissionmeasurement, Csc(λ) is virtually an absorption coefficient,since the transmitted intensity is reduced due to the scattering.For the limiting case of a pure polymer melt, wP has a valueof 1 and all contributions from the filler are zero. Subtractionof the absorption spectrum of the pure polymer melt fromequation (1) yields the difference absorption spectrum �A(λ):

�A(λ) = s((wP − 1)αP (λ) + wf αf (λ) + NCsc(λ)). (2)

In figures 11 and 12, it can be seen that the differenceabsorption spectra are smoothly shaped without obviousreminiscences of band structure. Therefore, the contributionsof (wP − 1)αP (λ) and wf αf (λ) can be assumed to be verysmall. For the practical purpose of process monitoring,the first two terms in equation (2) can be neglected inmost cases and the difference spectrum reveals the scatteringbackground (3):

�A(λ) ∼= sNCsc(λ) = sτ (λ), (3)

where τ is the turbidity. This approach, however, is limited tofillers with low optical absorption.

For uniform spheres, the angular scattering properties andthus the scattering cross section Csc(λ), which is the integralof the angular scattering, can be calculated rigorously byMie theory [121, 126, 127]. Filler particles usually do notexhibit a uniform and well-defined shape. For the actualsystem, the agglomerates and the intercalated clay stacksare irregularly shaped with an aspect ratio close to 1, whileexfoliated clay consists of very thin platelets. Nonsphericityhas pronounced effects on the angular scattering but, as statedin [125], much less effect on the scattering cross section ofrandomly oriented asymmetric particles. Therefore, as a firstapproximation, the experimental results can be interpretedin terms of scattering of spherical particles with diameterscorresponding to the shape-averaged particle size. Theagglomerates, which dominate the scattering in the compositemelt, exhibit a shape close to spherical anyway, while theplatelets will hardly contribute to the scattering due to theirsmall thickness. Multiple scattering, a common problem forangular-dependent scattering in concentrated systems, is hereof minor importance, since transmission measurements areidentical to turbidity measurements at different wavelengths,which are almost insensitive to multiple scattering [128–130].

In order to illustrate the influence of particle size on theoptical spectra, scattering cross-section curves calculated byMie theory [121, 126, 127] are shown for spheres with radiiof 500 nm and 2 μm in figure 13. The smaller radius valuewas taken as representative for the shape-averaged particlesizes of exfoliated and intercalated clay and the latter for largeagglomerates. The wavelength ranges of the UV/vis andNIR spectrometer used are indicated. A computer programdescribed in [131] was used for the simulation. The inputparameters are radius and refractive index of the sphere aswell as the refractive index of the matrix. For the latter, thetypical value for the polymer matrix of 1.5 was assumed. Theclay is chemically similar to quartz, exhibiting a refractiveindex of 1.45. These values were taken for the calculations.

8

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

Figure 13. Scattering cross section Csc(λ) as a function of thewavelength. Theoretical calculation for spherical particles (Miescattering).

In figure 13, it can be seen that the scattering cross sectionincreases tremendously with particle size. The maximumvalue of the Csc(λ) curve is roughly proportional to the squareof the particle radius. Furthermore, with increasing particlesize, the position of the maximum of the scattering curve shiftsto larger wavelength values.

Since the number density N of the agglomeratescontributing to scattering is usually not known, thescattering cross section of the particles Csc(λ) cannot becalculated from the measured difference absorption �A(λ)by equation (3). Therefore, no direct information onparticle size can be obtained by this approach. However,the factor N cancels out by introducing a normalizedscattering cross section Csc(λ)/〈Csc(λ)〉 for a wavelengthinterval from λmin to λmax, where 〈Csc(λ)〉 is definedby 〈Csc(λ)〉 = ∫ λmax

λminCsc(λ) dλ/(λmax − λmin). In the

experimental wavelength range, Csc(λ)/〈Csc(λ)〉 is equalto the measured normalized difference absorption spectrum�A(λ)/〈�A(λ)〉:

�A(λ)/〈�A(λ)〉λmin,λmax= �A(λ)λmin,λmax∫ λmax

λmin�A(λ) dλ/(λmax − λmin)

= Csc(λ)/〈Csc(λ)〉. (4)

Replacing �A(λ) in equation (4) with the simulated Csc(λ)curves from figure 13, the normalized scattering cross sectionfor spherical particles of 500 nm and 2 μm can be calculatedcorrespondingly. In order to compare the simulated curveswith the experimental spectra, in figure 14 Csc(λ)/〈Csc(λ)〉was calculated for the experimental available UV/vis/NIRwavelength range from λmin = 430 nm to λmax = 1625 nm.From the curves for the two particle sizes, it can be seen that thesmaller the (average) particle size, the more pronounced thevariation of Csc(λ)/〈Csc(λ)〉 with wavelength. This yields thepossibility of extracting information on both number density ofscatterers and mean particle size from the scattering spectrum,even for a limited wavelength range.

3.2.4. Correlation between processing conditions and fillerdispersion. The �A(λ)/〈�A(λ)〉 curves are calculated

Figure 14. Plot of the Csc(λ)/〈Csc(λ)〉 curves corresponding to thetheoretically obtained curves shown in figure 13 for the wavelengthrange of 430–1625 nm.

Figure 15. Plot of the experimentally obtained �A(λ)/〈�A(λ)〉curves for PS/clay, as calculated from the curves shown in figure 12using equation (4) for the wavelength range of 430–1625 nm. Barreltemperature and throughput are given in figure 12.

(equation (4)) from the combined UV/vis/NIR spectra shownin figure 12 and are plotted in figure 15. From comparisonwith the simulated curves in figure 14 it can be concludedthat a steeper decay of the curves in figure 15 indicates asmaller (averaged) particle size due to a better dispersion athigher screw speeds. This agrees well with x-ray μ-CT imagesfrom the extruded strands in figure 16, which shows that thenumber of huge agglomerates (size > 10 μm) decreases withincreasing screw speed. This results in decreased averagedparticle size.

A series of compounding experiments with differentpolymers (polyamide-6, PA6 and poly-butyleneterephthalate,PBT) and varying processing conditions (screw speed,throughput, melt temperature) confirm that the optical spectracorrelate quite well with the weight content wb (per cent ofclay net weight) of remaining huge agglomerates (radius >

10 μm) in the composite. The values for wb were obtainedby analysing μ-CT and optical microscopy images with theImageJ software (version 1.37v, http://rsb.info.nih.gov/ij/).The solid line in figure 17 represents an empirical relationship(quadratic polynomial) between wb and the value of�A(λ)/〈�A(λ)〉 (figure 15) at 450 nm.

At first glance, the good correlation between thecontent of agglomerates and normalized difference absorption

9

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

100 rpm 150 rpm

250 rpm 450 rpm

Figure 16. X-ray micro-computer tomography images of thePS/clay composites corresponding to the spectra shown infigure 12. The clay agglomerates attenuating the x-rays are mirroredby dark spots. The averaged ‘radius’ of the agglomerates is wellabove 10 μm.

Figure 17. Weight content of agglomerates wb (in percentage ofclay net weight) as a function of �A(λ)/〈�A(λ)〉 at 450 nm forclay composites of PS, PA6 and PBT. The numbers adjacent to thesymbols are the screw speeds in rpm. The solid line represents a fitwith a quadratic polynomial.

�A(λ)/〈�A(λ)〉 for different polymers as well as for quitedifferent processing conditions seems to be surprising. Areasonable explanation for the former is the similar refractiveindex for all polymer melts which leads to similar scatteringcontrast. Furthermore, the normalization procedure followingequation (3) seems to work rather well. Possibly, similarrelations can be found for other (nano) fillers which providea relative simple and fast monitoring of particle dispersionusing commercially available process spectrometers. It shouldbe noted that the approach is limited to composites with asufficient refractive index difference of the components. Asimilar approach was used before by Heller and Heller et al

[119, 120] for polymer latex dispersions, where the scatteringcurves were correlated with particle size by use of a log–logplot of the turbidity versus wavelength. Such a plot deliversfor at least a sub-interval of wavelengths a straight line. Theslope was correlated with theoretical calculations (Mie theory)for spherical particles.

3.3. Morphology control by ultrasonic spectroscopy

3.3.1. Sound scattering on fillers. In contrast tothe nanocomposites considered in the preceding section,traditional polymeric composites contain large amounts (e.g.40 wt% and higher) of micron-sized particles such as chalkor talcum. The particle size in those composites should bein the range of 1 to 10 μm and agglomeration has also to beprevented. Agglomerates with a size of dozens of micronsup to some 100 micrometres can reduce material properties.In such melts, the turbidity is usually too high for opticaltransmission measurements. Performing (diffuse) reflectionmeasurements is possible in principle, but the scatteringcontribution within the spectra (‘scattering background’) doesnot show a significant wavelength dependence, since at suchhigh concentrations of micron-sized particles strong multiplescattering occurs and smears out any wavelength dependence.

Due to the much larger wavelength of ultrasonic waves,the situation is different for ultrasonic spectroscopy, and itwas shown that this technique can be used to monitor thecontent and quality of dispersion of microfillers in polymermelts during extrusion. This has been tested for polypropylene(PP)/chalk composites using a pulse transmission setup(thickness of the melt channel 4 mm) during extrusion [47]. Insuch melts, the contribution of the polymer matrix to ultrasonicabsorption can practically be neglected, and, therefore, thesound attenuation in the PP/chalk melts is dominated byscattering of the ultrasonic waves on the filler particles. Thisis in analogy to the scattering contribution of the nanoparticlesin the optical absorption spectra shown in section 3.2. Also inanalogy to the Mie theory for scattering of optical waves byspherical particles, there exist similar formalisms for acoustics[132, 133]. In figure 18, the theoretically calculated ultrasonicattenuation for spherical chalk particles (with diameter of80, 100, 150 and 200 μm) is plotted as a function of thefrequency.

For ultrasonic wavelengths λ = c/f (c is the soundvelocity and f is the frequency) in the order of magnitudeof the particle diameter, a maximum appears in the ultrasonicattenuation spectra due to sound scattering. This is similarto the maximum in the scattering cross section of opticalspectra in figure 13. For particles with diameter wellbelow the wavelength λ0 of the central frequency of thetransducers (f0 = 5 MHz, λ0

∼= 200 μm) only a monotonicincrease of the attenuation with increasing frequency can beseen. Larger particles (d � 100 nm) cause a maximumwhich shifts to lower frequency with increasing particlesize. Therefore, measurements of the frequency-dependentultrasonic attenuation can be used for in-line monitoring ofparticle size and dispersion in the micrometre range. Ithas to be stated that ultrasonic in-line measurements are notlimited to optical transparent samples and that the experimentalassemblies are robust.

10

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

Figure 18. Simulated ultrasonic attenuation (α/f ) as a function ofthe frequency f for spherical particles immersed in a polypropylenemelt. The particle diameters are indicated in the plot. For densityand velocity of sound, typical values for chalk were used. Theexperimental frequency window (mainly restricted by the bandwidthof the ultrasonic transducers) is indicated by the shaded area.

Figure 19. Experimentally obtained ultrasonic attenuation (α) as afunction of the frequency for PP/chalk composites. The totalamount of chalk was 40 wt%, while the ratio of coarse/fine chalkwas varied.

3.3.2. Ultrasonic experiments on micro composite melts.As an example, results on PP/chalk composites containingmixtures of two chalk types were compounded on a twin screwextruder (Coperion Werner & Pfleiderer ZSK-30). The PP wasMoplen HP501H provided by LyondellBasell Industries. Thefirst chalk type was fine grained with an average diameterof 2 μm (Omyalite 50H, Omya GmbH, Germany), while thesecond one was coarse grained with a mean diameter of 85 μm(Omyacarb 130 AL, Omya GmbH, Germany). By mixing finegrained and coarse chalk types at a constant total filler contentin the PP, a ‘bimodal’ size distribution was created, whichcan be considered as a model system for a micro compositemelt with agglomerates (coarse chalk). The total amount ofchalk was kept constant at 40 wt%. The ultrasonic attenuationcurves (pulse transmission technique, 4 mm sample thickness)of the composite melt at 190 ◦C are shown in figure 19.

For the melts containing mainly fine chalk, the attenuationincreases monotonically with increasing frequency. Asystematic shift of the spectra to lower frequency occurs withincreasing amounts of coarse chalk, and at the high frequencylimit, a maximum becomes apparent, which is related to the

Figure 20. Cross validation plot of the chemometric analysis of thespectra shown in figure 19.

sound scattering by the coarse chalk fraction. The attenuationspectra were evaluated by chemometric analysis using PLS.Figure 20 shows the cross validation plot. The correlationcoefficient is 99%. It is therefore possible to use ultrasonicabsorption spectroscopy for inline monitoring of agglomeratesin composite melts with micron-sized fillers. The applicationof ultrasonic attenuation spectroscopy for monitoring of thefiller content has been shown elsewhere [47].

Ultrasonic measurements for the determination of fillerdispersion during extrusion of PP/chalk composites aredescribed in [31, 39] as well. In this work, the integral(not frequency-dependent) attenuation, sound velocity, meltpressure and temperature, throughput and chalk type werecorrelated with the degree of dispersion by a neural network.Since none of these parameters is so closely linked to particlesize (and therefore filler dispersion) as the shape of theultrasonic spectra, many more data sets to train the neuralnetwork are required compared to the number of ultrasonicattenuation spectra necessary to set up a chemometricmodel.

3.4. Morphology control by small-angle light scattering

3.4.1. State-of-the-art and experimental design. Thecharacteristic domain size of the two-phase morphology inimmiscible polymer blends to exhibit optimum mechanicalproperties (e.g. for impact modification) ranges from around100 nm up to 1 μm. This length scale can be probedby small-angle light scattering. Optimum coupling of thelight to the sample can be achieved when measuring inthe melt state. A first setup for on-line light scatteringexperiments during extrusion based on a slit die with sapphirewindows has been developed at NIST (National Instituteof Standards and Technology, USA). Using this setup, themelt morphology of polyethlene/polystyrene blends [134,135] and of liquid-crystalline polymer and polyethyleneterephthalate blends [136] was studied. The NIST designallows a variation of the channel depth to adjust thesample thickness according to the scattering properties ofthe melt, in order to avoid multiple scattering. Lightscattering experiments on polypropylene/polystyrene andblends of ethylene-butylacrylate copolymer with polyamide-6 are described in [137]. Instead of using a slit die with

11

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

Figure 21. Scheme of a small-angle light scattering setup for morphology monitoring during extrusion.

Figure 22. Light scattering pattern (left column) and SEM images at different positions (see figure 23) within the extruded strand of aPP/POE blend for different throughputs (2.5 and 20 kg h−1). The mixing ratio of PP to PEO was 75/25 wt%, the screw speed was set to400 rpm and the barrel temperature was 230 ◦C. The bar in the upper-left SEM image corresponds to 20 μm and in the other images to5 μm.

sapphire windows, the melt flows through a central bore ofa glass ball with a diameter of 10 mm. The diameter of thebore was 0.8 mm. This design was intended to also explorescattering in the wide-angle region. In [138], an experimentalsetup to monitor phase formation during blending in a mixer,based on detection of the back-scattered light, is described. Itis assumed that monitoring of back scattering overcomes theproblems with multiple scattering.

Figure 21 shows schematically the design of a lightscattering setup developed in our group [139, 140]. Atemperature-controlled measurement slit die is attached to anextruder, and sapphire windows are mounted face to face atthe melt channel so that the melt can be crossed by a laserbeam. Typically, a helium–neon laser (wavelength: 632.8 nm)is used. The incident laser light is scattered by the phasedomains in the melt, and the scattering cone is captured by adiffusive screen. The scattering pattern is projected onto thechip of a CCD camera. Alternatively, the diffusive screen canbe replaced by a special lens system imaging the scatteringpattern directly to the CCD chip, without an intermediatediffusive screen [141]. Due to geometrical restriction, anangular range between ∼1 and 30◦ can be detected.

3.4.2. In-line light scattering on polymer blends (example).In figure 22, the light scattering patterns and correspondingimages from scanning electron microscopy (SEM) are shownfor a blend of polypropylene (PP) with a polyolefine elastomer(POE) for throughputs of 2.5 and 20 kg h−1. The mixing ratioof PP and PEO was 75/25 wt%, the screw speed was set to400 rpm and the barrel temperature was 230 ◦C. For PP MoplenHP501H from LyondellBasell Industries and for POE Exact0201 from EXXON Mobil were used.

At a throughput of 2.5 kg h−1, the scattering pattern(figure 22, first image in top row) exhibits superposition ofa pronounced needle and a circular pattern. This ‘combined’pattern mirrors the coexistence of elongated (‘needle’) andglobular domains (‘circular pattern’) in the melt. Due to theflow profile in the slit die being perpendicular to the laser beam,the light scattering pattern is a superposition of morphologiesresulting from different shear conditions. The correspondingmorphologies are shown in SEM images (figure 22, top row)taken at different positions of the extruded melt strand. Theanalysed image plane and the positions of the SEM images arevisualized in figure 23. Samples for the images were preparedby cold fracture at liquid nitrogen temperatures.

12

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

Figure 23. Visualization of the plane within the melt strand analysed by SEM (left). The right image defines the positions where thesamples for the SEM images in figure 22 were taken. In the right image, the parabolic flow profile and the domain types formed by differentshear rates are schematically shown.

In the ‘boundary’ and ‘intermediate’ regions, elongatedstructures can clearly be seen in the SEM images, while inthe ‘centre’ the domains are globular. Such domain types areexpected to be formed by a parabolic flow, which is sketched infigure 23 as well. At a throughput of 20 kg h−1, the scatteringpattern (figure 22, first image in the lower row) is almostcircular with a faint reminiscence of the needle. This meansthat the domains are mainly globular. The SEM images supportthis. In the ‘centre’ and the ‘intermediate’ region only globulardomains can be seen, while in the boundary region some faintneedles are left. It is assumed that at the higher throughput(20 kg h−1 in the described experiment), elongated structuresare no longer stable. They are broken up by the increasedshear forces leading to the formation of small droplets. Onlyin a boundary region, sticking to the wall some elongatedstructures remain.

3.4.3. Applications and limitations. As shown in theliterature and in our experiments, small-angle light scatteringcan be used to monitor the morphology of polymer blends. Theangular-resolved two-dimensional detection of the scatteredlight allows recognizing non-radial symmetrical scattering (seefigure 22) from elongated blend structures. This is interestingfor pilot-plant experiments on melt processing of blends aswell as for monitoring purposes of production processes. Inmany cases, it is possible to extract information on domainsize and shape from the scattering image.

However, the refractive index of the blend componentsshould not be much different. Otherwise, the blend willbecome turbid and multiple scattering will occur. For blendswhere the refractive index of the components differs greatly,light scattering can still be possible for very low contents ofone of the components or at low sample thickness. Since thevalue of the refractive index is based on the chemical structure,similar refractive index values are obtained for chemicallysimilar blend components. This is the case in PP/POE blends(the example shown here) or in blends of polystyrene withpolycarbonate.

3.5. Monitoring of spots and gel particles

Monitoring of spots, burners and gel particles can be doneon extruded flat films (solid polymer) or in the melt. For the

detection of such particles, in crystallized or vitrified materials,in-line imaging by commercial instruments and software isestablished. The running flat film is, for instance, filmedby a CCD camera and the images are immediately analysedby special pattern-recognition software. In cases where thepolymer melt is not processed to a flat film, sometimes a flatfilm extrusion is installed in a bypass to monitor spots.

The detection of spots via imaging of the melt was testedby Balke and Ing [142, 143]. The melt was pumped througha slit die equipped with windows and monitored in brightfield. Detecting gel particles was hardly possible, since thedifference in refractive index is small.

Another approach is the so-called micro-photometricmethod [144–146], which is commercially available.Depending on configuration of the instrument, the transmittedand/or back-scattered light is measured in-line. The diameterof the scattering volume is 30 μm. When the scatteringvolume is crossed by a particle, the transmitted light is reducedand/or the intensity in the back-scattering detector increases.The small scattering volume was chosen to suppress multiplescattering in the case of higher particle concentration.

We have recently applied our small-angle light scatteringsetup in combination with the ultrasonic transmissiontechnique described in sections 3.3 and 3.4 for in-line detectionof spots and gel particles. Figure 24 shows the fluctuationsof the scattered light intensity (left) and the amplitude of theultrasonic attenuation (right) as a function of the processingtime for a thermoplastic elastomer melt during extrusion. Forthe scattered light the circularly integrated scattering intensityfor a scatting angle of 3◦ was taken. At process time zero, asmall amount of material containing spots (diameter about 0.7mm) was added. After the residence time, the melt with thespots arrives at the respective detectors. This is indicatedby the vertical lines in figure 24. The slight differencein time is due to the different positions of ultrasonic andlight scattering detection along the melt stream. The shortspikes in the scattered light intensity and ultrasonic absorptionindicate particles with higher optical or acoustical scatteringcontrast in the initially optical clear melt. On comparisonof the right and left graphs in figure 24, good correlationbetween light and ultrasonic scattering events is indicated.It is obvious that ultrasound detects a larger number ofsuch events, which can be explained by the larger scattering

13

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

Figure 24. Fluctuations of the scattered light intensity (left) and the ultrasonic attenuation of the transmitted signal (right) as a function ofthe processing time for a thermoplastic elastomer during extrusion at 190 ◦C.

volume for sound transmission (8 mm diameter transducers)compared to light scattering (diameter about 4 mm of thelaser beam). Both methods, ultrasonic measurements andlight scattering, are sensitive to spots. In other experiments,we found that ultrasonic spectroscopy is able to detect gelparticles as well. This can be explained by the different originfor scattering contrast: refractive index for light scattering anddensity and/or modulus difference for ultrasound. Since theultrasonic pulse transmission technique is not limited to opticaltransparent samples and due to the lower price of the setup,the method is expected to have a high potential for detectionof spots, burners and gel particles in polymer melts.

4. Concluding remarks

The aim of this report was to review the state-of-the-art ofin-line, on-line and at-line process monitoring during polymermelt processing. In addition to optical spectroscopy, usuallyapplied for monitoring chemical composition or reactions(e.g. for reactive extrusion), we concentrated on ‘morphologymonitoring’ by scattering methods using electromagnetic orultrasonic waves. Examples for the application of angle-dependent scattering and frequency or wavelength-dependenttransmission experiments are given. The sensitivity for agiven composite or a blend was found to depend on theratio of the wavelength to the main domain or particle sizeas well as the scattering contrast. The optical methods aresensitive to particles in the range of hundreds of nanometres,whereas ultrasonic absorption measurements are useful formicro composites. The latter can also be used for turbidsamples. In addition to morphology control, light and soundscattering can be applied to detection of spots, burners orgel particles. The detection of fluctuations of the measuredproperties can be as well used for monitoring of the processstability, e.g. along the screw of an extruder [32, 34]. Inaddition to optical and acoustic spectroscopy, x-rays usingsynchrotron sources or terahertz scattering have been usedfor morphology control. Although these methods provideinteresting information, they are still limited to pilot plants orbasic experiments and were not considered here. The sameis true for in-line solid-state NMR, where the problems forhigh-temperature applications are not yet solved.

In addition to ‘chemical’ and ‘morphology’ control, in-line measurements of macroscopic properties were discussed.Examples for monitoring of electrical conductivity duringextrusion and injection moulding are given for conductivenanocomposites.

In summary, one can state that a huge number ofexperimental methods have been adapted, modified and testedin the last two decades for in-line or on-line monitoring ofpolymer melt processing. Although all this knowledge and anincreasing number of process probes and commercial setupsare available, the implementation of in-line control for polymermelt processing was slower than expected at the beginningof this development. This is mainly due to the low addedvalue of plastics products and the dominance of small- andmedium-sized companies with frequently changing products.Another reason is the need for specific qualified personnel.However, industrial application of extrusion monitoring forpharmaceutical products or high price polymers (e.g. meltglues) is known. Based on the accumulated knowledge andthe commercially available instrumentation, the breakthroughpredicted by ‘road maps’ on process control can be expectedin the next few years.

For polymer processing, much work has been doneon extrusion monitoring of thermoplastics. Since the finalproperty of the product appears in the solid state (glassyor semi-crystalline) of the polymer, there is an increasingneed for in-line monitoring during injection moulding orflat film extrusion. The same is the case for monitoring ofprocess stability along the production line in order to guaranteeconstant quality and processing reliability.

Besides the thermoplastics considered in this report,thermosets are another important class of polymeric materialswith actually growing importance, for example as matrices forfibre composites in aerospace applications. There is also anincreasing need for in-line monitoring of thermoset processing.Monitoring of curing reaction is described in [147–150] andreferences therein. For instance, in-line DRS was actuallyapplied for in situ monitoring of morphology changes duringcuring of thermoset/thermoplastic blends [148, 150]. Themicrostructural evolution and the development of internalstress of an epoxy system were observed in situ by DRS,ultrasonic and refractive index measurements [149]. Solution

14

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

[151] and bulk polymerization [152]—which are analogousto thermoset curing—were monitored successfully via in-lineDRS as well.

Acknowledgments

Part of the presented work was funded by theBundesministerium fur Wirtschaft und Arbeit via theArbeitsgemeinschaft industrieller Forschungsgesellschaften(AiF Project Nos. 10943, 11961, 73Z, 122Z, 190 ZBG,14452N, 14359N, 14454N, 15098N, 15988N and 16256N).Further part was funded by the German Federal Ministryof Education and Research (BMBF) within the frameworkconcept ‘Research for Tomorrow’s Production’ (project no02PU2394 and 02PU2361). We would like to thankTorsten Finnberg for carefully revising the manuscript. Wethank Gunter Vulpius, Harald Dorr, Norbert Schuchmannand Shilpa Khare (DKI Darmstadt) for their help inperforming the extrusion and injection moulding experiments.We also thank Dr Dieter Fischer (Leibniz-Institut furPolymerforschung Dresden eV), Dr Petra Potschke (Leibniz-Institut fur Polymerforschung Dresden eV), Dr Hans Kothe,Felix Simon, Alexander Ohneiser, Martin Engel, SergejDudkin, Kai Wassum, Dr Yong Wang, Dr Yaming Wangand Dr Donghua Xu (DKI Darmstadt) for their valuableexperimental contributions and helpful discussions. Finally,we would like to thank all our industrial partners, in particularKathrin Lehmann (Evonik Goldschmidt GmbH), Dr DieterLilge (LyondellBasell Industries), Dr Richard Lamour (BayerAG), Jurgen Ramthun (Bayer AG), Dr Michael Bierdel(Bayer Technology Services GmbH), Dr Reiner Rudolf(Bayer Technology Services GmbH), Dr Helmut Meyer(Bayer Material Sciences GmbH), Professor Christian Kohlert(Klockner Pentaplast), Peter Lehnhoff (Brabender GmbH &Co. KG), Uwe Kirschner (Sentronic GmbH) and SteffenPiecha (tec5 AG).

References

[1] Dumoulin M M, Gendron R and Cole K C 1996 Techniquesfor real-time monitoring of polymer processing TrendsPolym. Sci. 4 109–15

[2] Callis J B, Illman D L and Kowalski B R 1987 Processanalytical chemistry Anal. Chem. 59 624A–37A

[3] Barnes S E, Sibley M G, Edwards H G M and Coates P D2007 Trans. Inst. Meas. Control 29 453–65

[4] Maestro J 2000 Sampling and engineering considerationsSpectroscopy in Process Analysis ed J M Chalmers(Sheffield, UK: Sheffield Academic Press) pp 284–335

[5] Coates P D 2001 Polymer processing: inprocessmeasurements Encyclopedia of Materials; Science andTechnology (New York: Pergamon)

[6] Coates P D, Barnes S E, Sibley M G, Brown E C,Edwards H G M and Scowen I J 2003 In-processvibrational spectroscopy and ultrasound measurements inpolymer melt extrusion Polymer 44 5937–49

[7] Barnes S E, Brown E C, Sibley M G, Edwards H G M,Scowen I J and Coates P D 2005 Vibrational spectroscopicand ultrasound analysis for in-process characterization ofhigh-density polyethylene/polypropylene blends duringmelt extrusion Appl. Spectrosc. 59 611–9

[8] Bridge B and Cheng K H 1987 On-line ultrasonic monitoringof the extrusion of CaCO3-filled polypropylene J. Mater.Sci. Lett. 6 219–22

[9] Gendron R, Tatibouet J, Guevremont J, Dumoulin M Mand Piche L 1995 Ultrasonic behaviour of polymer blendsPolym. Eng. Sci. 35 79–91

[10] Gendron R, Piche L, Hamel A, Dumoulin M M andTatibouet J 1997 Ultrasonic characterization of extrusioninstabilities SPE ANTEC Conf. Proc. pp 2254–8

[11] Jen C-K, Chen J-Y, Hoa S V, Nguyen K T, Legoux J-Gand Herbert H 1997 Clad buffer rods for in situ processmonitoring Proc. IEEE Ultrasonics Symp. pp 801–6

[12] Shen J, Edwards R, Thomas C L and Bur A J 1998 Ultrasonicmelt temperature measurement during extrusion SPEANTEC Conf. Proc. pp 2076–9

[13] Brown E C, Olley P, Collins T L D and Coates P D 1999Ultrasonic investigation of process variation during singlescrew extrusion and injection moulding: real timedetection of temperature profile and material degradationSPE ANTEC Conf. Proc. pp 1137–41

[14] Brown E C, Collins T L D, Dawson A J, Olley P andCoates P D 1999 Ultrasound: a virtual instrumentapproach for monitoring of polymer melt variablesJ. Reinf. Plast. Compos. 18 331–8

[15] Alig I, Lellinger D, Lamour R and Ramthun J 2000Continuous monitoring of quality parameters—inlinemonitoring in the extrusion process using ultrasonicsKunststoffe–Plast. Eur. 90 96

[16] Alig I and Lellinger D 2000 Ultrasonic methods forcharacterizing polymeric materials Chem. Innov. 30 13–8

[17] Franca D R, Jen C-K, Nguyen K T and Gendron R 2000Ultrasonic in-line monitoring of polymer extrusion Polym.Eng. Sci. 40 82–94

[18] Balasubramaniam K and Boudreaux G 2000 Ultrasonicsensors for high temperature molten processes Proc. 15thWCNDT (Italy) http://www.ndt.net/article/wcndt00/papers/idn523/idn523.htm

[19] Jen C-K, Legoux J-G and Parent L 2000 Experimentalevaluation of clad metallic buffer rods for high temperatureultrasonic measurements NDT&E Int. 33 145–53

[20] Sahnoune A, Tatibouet J, Gendron R, Hamel A and Piche L2001 Application of ultrasonic sensors in the study ofphysical foaming agents for foam extrusion J. Cell. Plast.37 429–54

[21] Smith G D, Brown E C and Coates P D 2001 Sensitivity ofultrasonic velocity and attenuation measurements totemperature, pressure and filler concentration formagnesium hydroxide filled LDPE and HDPE PolymerProcess Engineering ’01 pp 267–74

[22] Doring J and Stark W 2001 Produktionskontrolle mitUltraschall Kunststoffe 91 83

[23] Tatibouet J and Huneault M A 2002 In-line ultrasonicmonitoring of filler dispersion during extrusion Int. Polym.Process. 17 49–52

[24] Stark W, Doring J, Kelm J and Fritsch S 2002 Kontrolle derKautschukvulkanisation im Werkzeug mit UltraschallDGZfP-Jahrestagung (Berlin: Deutsche Gesellschaft furZerstorungsfreie Prufung) http://www.ndt.net/article/dgzfp02/papers/p21/p21.htm (in German)

[25] Abu-Zahra N H and Seth A 2002 In-process density controlof extruded foam PVC using wavelet packet analysis ofultrasound waves Mechatronics 12 1083–95

[26] Sun Z, Tatibouet J and Jen C-K 2002 Application ofultrasound in the determination of fundamental extrusionperformance: monitoring of single-screw compoundingextrusion Proc. 18th Ann. Meeting of the PolymerProcessing Society

[27] Tatibouet J, Gendron R, Hamel A and Sahnoune A 2002Effect of different nucleating agents on the degassing

15

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

conditions as measured by ultrasonic sensors J. Cell. Plast.38 203–18

[28] Jen C-K, Sun Z, Kobayashi M, Sayer M and Shih C-K 2002Application of ultrasound in the determination offundamental extrusion performance: barrel and screwwear measurement SPE ANTEC Conf. Proc. (CD-ROM)

[29] Sun Z, Jen C-K, Shih C-K and Denelsbeck D A 2003Application of ultrasound in the determination offundamental extrusion performance: residence timedistribution measurement Polym. Eng. Sci. 43 102–11

[30] Barnes S E, Sibley M G, Brown E C, Edwards H G M,Scowen I J, Coates P D and Baker W 2003 Real timemonitoring of ethylene vinyl acetate extrusion usingprocess spectroscopy Proc. of Polymer ProcessEngineering—PPE ’03: Enhanced Polymer Processing(Bradford: University of Bradford) pp 342–58

[31] Sun Z, Anghel C V and Tatibouet J 2003 Application ofultrasound and neural networks in the determination offiller concentration and dispersion during polymerextrusion process SPE ANTEC Conf. Proc.pp 3331–5

[32] Alig I, Lellinger D and Wassum K 2003 Residence timemeasurements and detection of melt heterogeneitiesKunststoffe-Plast. Eur. 93 53

[33] Alig I, Lellinger D and Wassum K 2003 Temperaturefluctuations in the processing unit Kunststoffe-Plast. Eur.93 62

[34] Abu-Zahra N H 2004 Measuring melt density in polymerextrusion processes using shear ultrasound waves Int. J.Adv. Manufact. Technol. 24 661–6

[35] Tanoue S, Utracki L, Garcia-Rejon A, Tatibouet J, Cole K Cand Kamal M R 2004 Melt compounding of differentgrades of polystyrene with organoclay: Part 1.Compounding and characterization Polym. Eng. Sci.44 1046–59

[36] Jen C K, Zun Z G and Kobayashi M 2005 Real-timemonitoring of barrel thickness and barrel/screw separationusing ultrasound Meas. Sci Technol. 16 842–50

[37] Wang D and Min K 2005 In-line monitoring and analysis ofpolymer melting behavior in an intermeshingcounter-rotating twin-screw extruder by ultrasound wavesPolym. Eng. Sci. 45 998–1010

[38] Tatibouet J and Gendron R 2005 Heterogeneous nucleation infoams as assessed by in-line ultrasonic measurementsJ. Cell. Plast. 41 57–72

[39] Sun Z G, Jen C K, Yan J and Chen M Y 2005 Application ofultrasound and neuronal networks in the determination offiller dispersion during polymer extrusion processesPolym. Eng. Sci. 45 764–72

[40] Steinhoff B, Lellinger D, Potschke P and Alig I 2005 In-linemonitoring of the dispersion of nanofillers in polymercomposites using vibration spectroscopy, ultrasonicmeasurements, in-line rheology and dielectricspectroscopy PPS-21—Proc. 21st Ann. Meeting of thePolymer Processing Society CD-ROM, SL13.10

[41] Fischer D, Alig I and Hutschenreuter J 2005 In-linemonitoring of natural fiber reinforced composites andnanocomposites during extrusion by near infrared andultrasonic spectroscopy PPS-21—Proc. 21st Ann. Meetingof the Polymer Processing Society CD-ROM, P13.3

[42] Alig I, Fischer D, Lellinger D and Steinhoff B 2005Combination of NIR, Raman, ultrasonic and dielectricspectroscopy for in-line monitoring of the extrusionprocess PPS-21—Proc. 21st Ann. Meeting of the PolymerProcessing Society CD-ROM, KL13.1

[43] Alig I, Fischer D, Lellinger D and Steinhoff B 2005Combination of NIR, Raman, ultrasonic and dielectricspectroscopy for in-line monitoring of the extrusionprocess Macromol. Symp. 230 51–8

[44] Lee S M, Park J C, Ahn Y J and Lee J W 2005 In-linemeasurements of residence time distribution in twin-screwextruder using non-destructive ultrasound Korea-Aust.Rheol. J. 17 87

[45] Oehler H, Xu J, Lellinger D and Alig I 2005 Monitoring offoam formation by an ultrasonic reflection techniquePPS-21—Proc. 21st Ann. Meeting of the PolymerProcessing Society CD-ROM, SL 15.9

[46] Alig I, Lellinger D, Steinhoff B and Fischer D 2006Kombinierte Messung—NIR-, Raman undUltraschall-Spektroskopie wahrend der extrusionGIT Labor-Fachzeitschrift 11 1017–20

[47] Alig I, Lellinger D, Steinhoff B and Fischer D 2006 Fillerdispersion: ultrasonic spectroscopy for in-line monitoringKunststoffe Int. 5 60–5

[48] Fischer D, Sahre K, Abdelrhim M, Voit B, Sadhu V B,Pionteck J, Komber H and Hutschenreuter J 2006 Processmonitoring of polymers by in-line ATR-IR, NIR andRaman spectroscopy and ultrasonic measurementsC. R. Chim. 9 1419

[49] Boersma A and van Turnhout J 1999 Dielectric on-linespectroscopy during extrusion of polymer blends Polymer40 5023

[50] McBrearty M, Bur A J and Perusich S 1999 In-line dielectricsensor for chemical composition measurements in moltenpolymers SPE ANTEC Conf. Proc. pp 2580–4

[51] McBrearty M, Bur A J and Roth S C 2000 Real-timedielectric measurements during extrusion of filledpolymers SPE ANTEC Conf. Proc. pp 287–91

[52] Bur A J, Roth S C and McBrearty M 2002 In-line dielectricmonitoring during extrusion of filled polymers Rev. Sci.Instrum. 73 2097–102

[53] Bur A J, Roth S C and McBrearty M 2003 Monitoringpolymer/clay nanocomposite compounding using adielectric slit die SPE ANTEC Conf. Proc. pp 3326–30

[54] Le H H, Ilisch S, Jakob B and Radusch H-J 2003 OnlineCharakterisierung der Fullstoffdispersion rußgefullterKautschuke wahrend des Mischprozesses unter Nutzungder elektrischen Leitfahigkeit Kautschuk GummiKunststoffe 56 388–95

[55] Le H H, Ilisch S, Jakob B and Radusch H-J 2004Rußdispersion in Kautschukmischungen in Anwesenheitvon Weichmacherolen- Nutzung der online gemessenenelektrischen Leitfahigkeit zur Charakterisierungrheologischer und thermodynamischer Effekte KautschukGummi Kunststoffe 57 355–62

[56] Bur A J, Roth S C and Lee Y-H 2004 A dielectric slit die forin-line monitoring of polymer compounding Rev. Sci.Instrum. 75 1103–10

[57] Davis R D, Gilman J W, Bur A J, McBrearty M, Start P Rand Lee Y-H 2004 Dielectric spectroscopy duringextrusion processing of polyamide-6 nanocomposites: ahigh throughput method Polym. Mater.: Sci. Eng.90 715

[58] Davis R D, Bur A J, McBrearty M, Lee Y-H, Gilman J Wand Start P R 2004 Dielectric spectroscopy duringextrusion processing of polymer nanocomposites: a highthroughput processing/characterization method tomeasure layered silicate content and exfoliation Polymer45 6487–93

[59] Abu-Zahra N H 2004 In-line monitoring of PVC meltviscosity using dielectric measurements Int. Polym.Process. 19 16–20

[60] Davis R D, Bur A J, McBrearty M, Lee Y-H, Gilman J Wand Start P R 2004 Dielectric spectroscopy duringextrusion processing of polymer nanocomposites: a highthroughput processing/characterization method tomeasure layered silicate content and exfoliation Polymer45 6487–93

16

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

[61] Noda N, Lee Y-H, Bur A J, Prabhu V M, Snyder C R,Roth S C and McBrearty M 2005 Dielectric properties ofnylon 6/clay nanocomposites from on-line processmonitoring and off-line measurements Polymer46 7201–17

[62] Bur A J, Lee Y-H, Roth S C and Start P R 2005 Measuringthe extent of exfoliation in polymer/clay nanocompositesusing real-time process monitoring methods Polymer46 10908–18

[63] Alig I, Lellinger D, Dudkin S M and Potschke P 2007Conductivity spectroscopy on melt processedpolypropylene—multiwalled carbon nanotube composites:recovery after shear and crystallization Polymer48 1020–29

[64] Alig I, Lellinger D, Engel M, Skipa T and Potschke P 2008Destruction and formation of conductive carbon nanotubenetwork in polymer melts: in-line experiments Polymer49 1902–9

[65] Fritz H-G and Ultsch S 1989 Sensorentwicklung undAutomatisierungstendenzen bei derKunststoffaufbereitung Kunststoffe 79 785–90 (in German)

[66] Stengler R. and Weis G 1989 FTIR-Messungen anKunststoffschmelzen zur On-line QualitatsprufungKunststoffe 79 955–60 (in German)

[67] Stohrer B, Kurrer H, Stengler R and Weis G 1989On-line-Qualitatssicherung bei der Silanvernetzung vonPE Kunststoffe 79 1165–7 (in German)

[68] Harvey R J 1986 Apparatus for on-line spectrophotometricchemical analysis in moving process stream US Patent4717827

[69] Fidler R A, Rowe D and Weis G 1991 On-line infraredprocess control of molten polymers via a high pressure,high temperature flow cell SPE ANTEC Conf. Proc.pp 850–5

[70] Jakisch L, Fischer D, Stephan M and Eichhorn K-J 1995In-line-analytik von polymerreaktionen Kunststoffe 851338–44 (in German)

[71] Xanthos M and Patel S H 1995 Recent developments inin-line spectroscopy/microscopy for monitoring extrusionprocesses Adv. Polym. Technol. 14 151–7

[72] Martens H and Naes T 1989 Multivariate Calibration(Chichester: Wiley)

[73] Henrion R and Henrion G 1994 Multivariate Datenanalyse(Berlin: Springer)

[74] Sohl C H 1991 Just in time compounding using feedbackcontrol from near infrared melt composition sensor SPEANTEC Conf. Proc. pp 846–9

[75] Hansen M G and Khettry A 1994 In-line monitoring ofmolten polymers: near infrared spectroscopy, robustprobes, and rapid data analysis Polym. Eng. Sci.34 1758–66

[76] Batra J, Khettry A and Hansen M G 1994 In-line monitoringof titanium-dioxide content in poly(ethylene-terephthalate)extrusion Polym. Eng. Sci. 34 1767–72

[77] Fischer D and Eichhorn K-J 1998 Process analysis ofpolymers by NIR spectroscopy ANALUSIS 26 M58–61

[78] Fischer D, Bayer T, Eichhorn K J and Otto M 1997 In-lineprocess monitoring on polymer melts byNIR-spectroscopy Fresenius J. Anal. Chem. 359 74–7

[79] Bart J C J 2000 Near-infrared spectroscopic product andprocess control SPE ANTEC Conf. Proc. pp 2074–8

[80] Siesler H W 2002 Application to industrial process controlNear Infrared Spectroscopy; Principles, Instruments,Applications ed H W Siesler, Y Ozaki, S Kawata andH M Heise (Weinheim: Wiley-VCH)

[81] Khettry A and Hansen M G 1996 Real-time analysis ofethylene vinyl acetate random copolymers using nearinfrared spectroscopy during extrusion Polym. Eng. Sci.36 1232–43

[82] Rohe T, Becker W, Kolle S, Eisenreich N and Eyerer P 1997Spectroscopy for in-line monitoring of polymer extrusionprocesses Talanta 50 283–90

[83] Reshadat R, Desa S, Joseph S, Mehra M, Stoev N andBalke S T 1999 In-line near-infrared monitoring ofpolymer processing: Part I. Process/monitor interfacedevelopment Appl. Spectrosc. 53 1412–8

[84] Becker W and Eisenreich N 2005 Measurement of theIrganox content in polypropylene polymers duringextrusion J. Near Infrared Spectrosc. 13 147–54

[85] Barres C, Bounor-Legare V, Melis F and Michel A 2006In-line near infrared monitoring of esterification of amolten ethylene-vinyl alcohol copolymer in a twin screwextruder Polym. Eng. Sci. 46 1613–24

[86] Deshpande B J, Dhamdhere M S, Li J G and Hansen M G1998 In-line fiber-optic Raman spectroscopy:simultaneous monitoring of composition and rheologicalproperties in poly(ethylene vinyl acetate) copolymers SPEANTEC Conf. Proc. pp 1672–5

[87] Chalmers J M (ed) 2000 Raman spectroscopy in chemicalprocess analysis Spectroscopy in Process Analysis(Sheffield, UK: Sheffield Academic Press)

[88] Dhamdhere M, Deshpande B, Patil P and Hansen M G 2000In line monitoring of poly(ethylene vinyl acetate)extrusion process using fiber optic Raman spectroscopySPE ANTEC Conf. Proc. pp 1742–5

[89] Slater J, Tedesco J M, Fairchild R and Lewis I R 2001 Ramanspectroscopy and its adaptation to the industrialenvironment Handbook of Raman Spectroscopy: From theResearch Laboratory to the Process Line ed I R Lewis andH G M Edwards (New York: Dekker) pp 41–144

[90] Miller K L, Foulk S J and Spatafore R 1993 On-linemonitoring of polymer melts using UV fibre opticspectroscopy Proc. SPIE 2069 85–94

[91] Lutzen J, Van Veen K and Balke S T 2002 In-line monitoringof polymer additives during extrusion using a UVspectrometer SPE ANTEC Conf. Proc. (CD-ROM)

[92] Wang Y, Steinhoff B, Brinkmann C and Alig I 2008 In-linemonitoring of the thermal degradation of poly(L-lacticacid) during melt extrusion by UV-Vis spectroscopyPolymer 49 1257–65

[93] Bar A J and Gallant F M 1991 Fluorescence monitoring oftwin screw extrusion Polym. Eng. Sci. 31 1365–71

[94] Bur A J, Roth S C and Thomas C L 2000 Fluorescenceanisotropy sensor and its application to polymer processingand characterisation Rev. Sci. Instrum. 71 1516–23

[95] Bur A J, Vangel M G and Roth S C 2001 Fluorescence basedtemperature measurements and applications to real-timepolymer processing Polym. Eng. Sci. 41 1380–9

[96] Bur A J, Vangel M G and Roth S C 2002 Temperaturedependence of fluorescent probes for applications topolymer materials processing Appl. Spectrosc.56 174–81

[97] Bur A J, Roth S C, Lee Y H, Noda N and McBrearty M 2004In-line monitoring of dielectric and fluorescencespectroscopy during polymer/filter compounding Plast.Rubber Compos. 33 5–10

[98] Maupin P H, Gilman J W, Harris R H, Bellayer S, Bur A J,Roth S C, Murariu M, Morgan A B and Harris J D 2004Optical probes for monitoring intercalation and exfoliationin melt-processed polymer nanocomposites Macromol.Rapid Commun. 25 788–92

[99] Bur A J, Roth S C, Start P R, Lee Y H and Maupin P H 2007Monitoring clay exfoliation during polymer/claycompounding using fluorescence spectroscopy Trans. Inst.Meas. Control. 29 403–16

[100] Brown E C, Dawson A J and Coates P D 2002 Ultrasonicmonitoring of nozzle and cavity during injection mouldingJ. Reinf. Plast. Compos. 21 441–50

17

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

[101] Fronimidis D, Edge K A, Stelson K A, Mileham A Rand Sokola M A 2004 Use of non-invasive measurementmethods to aid the control of part quality in the injectionmoulding process Proc. Inst. Mech. Eng. B 218 1195–201

[102] Speight R G, Coates P D, Hull J B and Peters C 1997 In-lineprocess monitoring for injection moulding control Proc.Inst. Mech. Eng. E 211 115–28

[103] Gonnet J-M, Guillet J, Ravyre C, Assezat G, Fulchiron Rand Seytre G 2002 In-line monitoring of the injectionmolding process by dielectric spectroscopy Polym. Eng.Sci. 42 1171–80

[104] Lellinger D, Xu D, Ohneiser A, Skipa T and Alig I 2008Influence of the injection moulding conditions on thein-line measured electrical conductivity ofpolymer–carbon nanotube composites Phys. Status Solidib 245 2268–71

[105] Kranbuehl D E, Eichinger D, Hamilton T and Clark R 1991In situ monitoring of the resin transfer moldingimpregnation and cure process Polym. Eng. Sci. 31 56–60

[106] Kranbuehl D E, Kingsley P, Hart S, Hasko G, Dexter Band Loos A C 1994 In-situ sensor monitoring andintelligent control of the resin transfer molding processPolym. Compos. 15 299–305

[107] Bur A J, Wang F W, Thomas C L and Rose J L 1994 In-lineoptical monitoring of polymer injection-moulding Polym.Eng. Sci. 34 671–9

[108] Thomas C L and Bur A J 1998 Optical monitoring ofpolypropylene injection molding J. Reinf. Plast. Compos.17 1382–90

[109] Thomas C L and Bur A J 1999 Optical monitoring ofpolypropylene injection molding Polym. Eng. Sci.39 1291–302

[110] Thomas C L and Bur A J 1999 In-situ monitoring of productshrinkage during injection molding using an optical sensorPolym. Eng. Sci. 39 1619–27

[111] Dumitrescu O R, Baker D C, Foster G M and Evans K E2005 Near infrared spectroscopy for in-line monitoringduring injection moulding Polym. Test. 24 367–75

[112] Schartl W 2007 Light scattering from polymer solutions andnanoparticle dispersions Series: Springer Laboratory(Berlin: Springer)

[113] Pankewitz A and Behrens C 2009 High resolution on-lineparticle size analysis on highly concentrated ore slurriesAufbereitungs Technik 50 4–17

[114] Gossen P D and MacGregor J F 1993 On-line particlediameter for poly(vinyl acetate) latex using specificturbidity method J. Coll. Interface Sci. 160 24–38

[115] Santos A F, Lima E L and Pinto J C 1998 In-line evaluationof average particle size in styrene suspensionpolymerizations using near-infrared spectroscopy J. Appl.Polym. Sci. 70 1737–45

[116] Reis M M, Araujo P H H, Sayer C and Giudici R 2003Correlation between polymer particle size and in-situ NIRspectra Macromol. Rapid Commun. 24 620–4

[117] Gossen P D, MacGregor J F and Pelton R H 1993Composition and particle diameter for styrene/methylmethacrylate copolymer latex using UV and NIRspectroscopy Appl. Spectrosc. 47 1852–70

[118] Ito K, Kato T and Ona T 2002 Non-destructive method for thequantification of the average particle diameter of latex aswater-based emulsions by near-infrared Fourier transformRaman spectroscopy J. Raman Spectrosc. 33 466–70

[119] Heller W, Bhatnagar H L and Nakagaki M 1962 Theoreticalinvestigations on the light scattering of spheres: XIII. The‘wavelength exponent’ of differential turbidity spectraJ. Chem. Phys. 36 1163–70

[120] Heller W 1964 Theoretical investigations on the lightscattering of spheres. XV. The wavelength exponents atsmall α values J. Chem. Phys. 40 2700–5

[121] Kerker M 1969 The Scattering of Light and OtherElectromagnetic Radiation (London: Academic)

[122] Alig I 2007 Inline-monitoring der Dispersion vonNanoadditiven Deutsches Kunststoff-Institut DarmstadtJahresbericht 2007 pp 53–6 (in German)

[123] Ray S S and Okamoto S 2003 Polymer/layered silicatenanocomposites: a review from preparation to processingProg. Polym. Sci. 28 1539–641

[124] Schonefeld S and Lechner F 2003 Economic compounding:nanocomposites Kunststoffe-Plast. Eur. 93 28

[125] Mishchenko M I, Hovenier J W and Travis L D 2000 LightScattering by Nonspherical Particles (London: Academic)p 58

[126] van de Hulst H C 1981 Light Scattering by Small Particles(New York: Dover)

[127] Born M and Wolf E 1970 Principles of Optics 4th edn(Oxford: Pergamon)

[128] Penders M H G M and Vrij A 1990 A turbidity study oncolloidal silica particles in concentrated suspensions usingthe polydisperse adhesive hard sphere model J. Chem.Phys. 93 3704–11

[129] Apfel U, Grunder R and Ballauff M 1994 A turbidity study ofparticle interaction in latex suspensions Colloid. Polym.Sci. 272 820–9

[130] Apfel U, Horner K D and Ballauff M 1995 Precise analysisof the turbidity spectra of a concentrated latex Langmuir11 3401–7

[131] Barber P W and Hill S C 1990 Light Scattering by Particles:Computational Methods (Singapore: World Scientific)

[132] Waterman P C and Truell R 1961 Multiple scattering ofwaves J. Math. Phys. 2 512–37

[133] Alig I and Lellinger D 1992 Frequency dependence ofultrasonic velocity and attenuation in two-phasecomposite systems with spherical scatterers J. Appl. Phys.12 5565–70

[134] Li S, Migler K B, Hobbie E K, Kramer H, Han C C andAmis E J 1997 Light-scattering photometer with opticalmicroscope for the inline study of polymer extrusionJ. Polym. Sci. B 35 2935–43

[135] Migler K B, Hobbie E K and Qiao F 1999 In-line study ofdroplet deformation in polymer blends in channel flowPolym. Eng. Sci. 39 2282–91

[136] Qiao F, Migler K B and Han C C 1999 In-line investigationof shear-induced morphology transition in liquidcrystalline polymer/PET blends SPE ANTEC Conf. Proc.pp 3911–5

[137] Schlatter G, Serra C, Bouquey M, Muller R and Terrisse J2002 Online light scattering measurements: a method toassess morphology development of polymer blends in atwin-screw extruder Polym. Eng. Sci. 42 1965–75

[138] Yang Y-P, Xiao Z-G, Jiang X and Sheng J 2006 Lightscattering studies on phase behavior of PP/PcBR blendsduring melt-mixing J. Macromol. Sci. B 45 1083–98

[139] Alig I 2001 Nachweis von Morphologieanderungen imExtruder Deutsches Kunststoff-Institut DarmstadtJahresbericht 2001 pp 40–5 (in German)

[140] Lellinger D, Steinhoff B and Alig I 2005 In-line monitoringof the morphology of polymer blends during extrusionusing small angle light scattering PPS-21 Conf. Proc.P13.5, 19-23

[141] Alig I 2008 In-line Monitoring der Morphologie von BlendsDeutsches Kunststoff-Institut Darmstadt Jahresbericht2008 pp 59–63 (in German)

[142] Ing L D, Balke S T, Stoev N, Tamber H and Planeta M 2001In-line monitoring of particles in polymer melt duringextrusion using a scanning particle monitor SPE ANTECConf. Proc. pp 3046–50

[143] Ing L D, Balke S T and Brewster N V 2002 In-linemeasurement of dispersed phase properties using the

18

Meas. Sci. Technol. 21 (2010) 062001 Topical Review

scanning particle monitor SPE ANTEC Conf. Proc.pp 3416–20

[144] Stephan M, Große S, Blankenschein U and Stintz M 2005Partikel-monitoring am extruder Kunststoffe 9 160–3(in German)

[145] Stephan M, Große S, Stintz M, Blankschein U and RudolphA 2004 Microphotometric detection ofparticles/inhomogeneities in flowing polymer meltsMacromol. Symp. 217 343–56

[146] Stephan M, Große S, Stintz M and Blankenschein U 2006Real time detection of particulate heterogeneities inpolymer extrusion process using microphometricmeasuring method Plast. Rubber Compos. 35 432–8

[147] Kranbuehl D, Hood D, Wang Y, Boiteux G, Stephan F,Mathieu C, Seytre G, Loos A and McRae D 1997 In situmonitoring of polymer processing properties Polym. Adv.Technol. 8 93–9

[148] Poncet S, Boiteux G, Pascault J P, Sautereau H, Seytre G,Rogozinski J and Kranbuehl D 1999 Monitoring phase

separation and reaction advancement in situ inthermoplastic/epoxy blends Polymer 40 6811–20

[149] Chailleux E, Salvia M, Jaffrezic-Renault N, Jayet Y,Mazzouz A and Seytre G 2000 In situ multidetection:application for composite cure monitoring Proc. SPIE4073 290–6

[150] Kranbuehl D, Rogozinski J, Warner J, Best B, Pascault J P,Sautereau H, Boiteux G and Seytre G 2003 Dielectricin situ sensor monitoring of phase separation and changesin the state of each phase Macromol. Symp. 198 377–87

[151] Cherfi A, Santos A F, Pinto J C, Seytre G, Boiteux G,McKenna T F and Fevotte G 2003 Application of dielectricanalysis to the measurement of conversion during batchsolution copolymerization Chem. Eng. Process.42 121–8

[152] Hakme C, Stevenson I, Maazouz A, Cassagnau P, Boiteux Gand Seytre G 2007 In situ monitoring of cyclic butyleneterephthalate polymerization by dielectric sensingJ. Non-Cryst. Solids 353 4362–5

19