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Renewable Energy 71 (2014) 473e479

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Renewable Energy

journal homepage: www.elsevier .com/locate/renene

Development of thermo-regulating polypropylene fibre containingmicroencapsulated phase change materials

Kashif Iqbal, Danmei Sun*

School of Textiles & Design, Heriot-Watt University, UK

a r t i c l e i n f o

Article history:Received 9 September 2013Accepted 30 May 2014Available online

Keywords:ExtrusionLatent heatMechanical propertiesPolypropylenePCMSEM

* Corresponding author. Tel.: þ44 1896892138.E-mail address: d.sun@hw.ac.uk (D. Sun).

http://dx.doi.org/10.1016/j.renene.2014.05.0630960-1481/© 2014 Elsevier Ltd. All rights reserved.

a b s t r a c t

Phase change materials are used for thermal management solution in textiles because of the automaticacclimatising properties of textiles. Most of the phase change materials used in textiles is usually foundin the range of 28e32 �C of their melting point. This paper reports a type of smart monofilament fibredevelopment incorporated with microencapsulated phase change material through melt spinning pro-cess. Up to 12% microcapsules are successfully incorporated into the polypropylene monofilamentshowing 9.2 J/g of latent heat. Some of the mechanical properties of the developed fibre are also studiedtogether with the surface morphology of monofilament. A statistical model is developed for latent heat,tenacity and modulus of monofilament fibre and is validated by experimental values. The fibre propertiespredicted by the developed models are agreed very well to the experiments results.

© 2014 Elsevier Ltd. All rights reserved.

1. Introduction

The search of new and renewable storage of energy which canbe converted conventionally into useful form is a present daychallenge for technologists. One of the emerging techniques forthe development of thermal energy storage system is the appli-cation of phase change materials [1]. Smart textile is an emergingarea in textile field which is becoming more significant by thedemand of society through consumer needs. Despite theincreasing impact of science and technology, smart textile de-mands the advancement through interdisciplinary support likefashion, design, engineering, technology, human and life sciences.In textile sector, smart textiles have its vast application in interiortextiles, technical textiles and clothing in which the last onecontains higher percentage in terms of usage of smart textiles [2].Phase change materials are kind of smart materials which wereused in clothing by US National Aeronautics and Space Adminis-tration (NASA) in 1980 [3], they were used to make thermo-regulated garment for space and to protect apparatus in spacewith drastic temperature change [4]. This technology was thentransferred to Outlast Technologies, based in Boulder, Coloradowho used PCM (phase change material) in textiles and fabriccoating [5].

When the temperature of environment or body increases,phase change materials absorb extra heat from environment orbody as latent heat and keep this energy stored. When thetemperature falls down outside the phase change materialenvironment, it releases stored energy to body and environmentkeeping the wearer in comfortable zone. Different naturallyoccurring phase change materials are discovered comprising ofinorganic and organic materials ranging their melting point fromsubzero to several hundred degrees Celsius. Phase change ma-terials are those materials which can change their state within acertain temperature range. Phase change material stores heat inliquefied PCM when rise in temperature occurs and thenbecomes solid PCM by releasing stored heat when temperaturefalls [6].

It is reported that incorporation of phase change materials intextiles will perform buffering effect keeping the skin tempera-ture constant against extreme weather hence prolongingthermal comfort for the wearer and is also claimed that usingphase change materials can decrease the fabric thicknessrequired to protect the human body from cold environment [7].Currently, phase change materials are being used in differenttextiles including bedding, apparel, footwear under the tradenames Outlast™ and ComforTemp®. Outlast Technologies hassucceeded in marketing viscose and PAN (polyacrylonitrile)containing MPCM (microencapsulated PCM) [9]. Paraffins areorganic phase change materials which absorb approximately200 kJ/kg of latent heat during phase change. This high amount

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of heat is released to surroundings during reverse cooling pro-cess called crystallisation. By incorporating phase change mate-rials to textiles, their heat storage capacity can be substantiallyenhanced [8].

Clothing containing microencapsulated PCM changes their statefrom solid to liquid by absorbing heat energy when worn in envi-ronment having temperature equal or greater than the MPCMmelting point giving cooling effect. Heat energy required to meltPCM may come from body or environment. If environment tem-perature gone equal or below the melting point of MPCM, theybecome solid releasing heat energy back and giving warming effect.Shim et al. claimed that MPCM acts as buffer which controls thethermal comfort for wearer by reducing the change in skin tem-perature [10].

There are many ways to make thermo-regulating textilescontaining phase change materials. Wet spun yarns are suc-cessfully launched in market while melt spun are still under in-vestigations. Since melt spinning process is the most convenientand commercially accepted method. This is also environmentallyfriendly and economical method because of no use of any solventas in wet spinning and simplest process. So researchers aremaking efforts to bring melt spun thermo-regulating yarns inmarket.

In 1988, Bryant and Colvin succeeded in incorporating cap-sules containing eicosane as phase change material into viscoserayon or acrylic by wet and dry spinning method which gavethermo-regulating characteristics when subjected to heat andcold. So viscose rayon successfully came into the market con-taining leak resistant capsules [11]. Outlast Technologiesassigned many projects to researchers to get enhanced withreversible thermal properties. Hartmann et al. published patentin 2010 describing manufacturing of viscose rayon containingmicroencapsulated phase change materials which was incontinuation of his previous work. The latent heat mentioned inhis patent was from 1 J/g to 20 J/g according to the differentembodiments [8].

Magill et al. [12] prepared a multi-component containingphase change material by keeping the PCM in core and any ther-moplastic or elastic in the outer sheath using polyester, nylon, andmany other polymers. They claimed that material contains 6.9 J/gand 8.4 J/g of latent heat in different embodiments. Hagstrom fromSwerea IVF Sweden prepared core/sheath melt spun PA6 and PETcontaining n-octadecane as phase change material in core. Ac-cording to Hagstrom the fibre gave 80 J/g of latent heat against 70%of PCM in core [13].

This research has been focused on melt spinning of poly-propylene containing microencapsulated phase change materials.The latent heat of the developed fibres, together with some of themechanical properties is studied. The surface morphology ofmonofilament is also studied through SEM analysis.

2. Experimental

2.1. Materials

Microencapsulated phase change materials were supplied byAmerican company Microteklab. Capsules contain n-octadecane asphase change material in core and Melamine Formaldehyde asshell. The capsules were in the form of dry powder and claimed tobe extremely stable and have less than 1% leakage when heated to250 �C.

The melting point of n-octadecane was 28 �C having enthalpy offusion 180e190 J/g (claimed by company) and determined as160e180 J/g (confirmed by DSC).

Raw polypropylene granules were supplied by BasellPolyolefins.

2.2. Methodology

For incorporation of MPCM into polypropylene, Benchtopextruder made by Extrusion System Limited was used. The Mono-filaments polypropylene was made with different percentage ofMPCM by varying the processing parameters. The most importantparameters observed were temperature, metering speed and theextruder speed. They had to be set differently with different per-centages of MPCM. The filament was collected by a winder throughwater bath. After spinning, the filaments were drawn on drawingmachine to enhance the strength and get the appropriate finenessof the fibre. MPCMwere mixed with PP granules from 2% to 12% onthe weight of polymer in a container and well shaken manually toget homogenous mixing of capsules with granules.

The micrographs of the filaments were obtained by using SEM(Scanning Electron Microscopy) Hitachi S-4300 model.

The latent heat of filament containing MPCM was obtained us-ing Mettler DSC (differential Scanning Calorimetry) 12E.

The modulus and tenacity of filament were determined ac-cording to the British Standard BS-EN-ISO 2062:2009 using InstronTester 3345 series.

3. Results and analysis

3.1. SEM observation

Scanning electron microscopy images show the presence ofcapsules in MPCM incorporated polypropylene filament. Fig. 1clearly indicates the difference between filaments with andwithout MPCMs. The images of cross sections of fibres with andwithout MPCM were taken by SEM are shown in Fig. 1(a) and (b)respectively. The fibres in Fig. 1(a) show rough surface and manyparticles presented in the surface of the fibres, indicating thepresence of MPCM in comparison to the fibre without MPCM inFig. 1(b) having very smooth surface.

In Fig. 2 below, the images have been taken at higher magnifi-cation to actually show the presence of PCM microcapsules. Itshows from the cross section of the fibre that the PCM capsules arein both mono and aggregated forms within the fibre.

3.2. Latent heat

DSC results are shown in Fig. 3. The graph shows the latentheat against different percentages of MPCM. The latent heat isdefined by area under the peak of the curve. The higher thepercentage of MPCM, the larger the area under the curve indi-cating more latent heat stored in the material. PP monofilamentwithout MPCM does not indicate any peak throughout the tem-perature range. For those fibres with MPCM, the peak of curveincreases with the increase of the percentage of MPCM in thefibre, indicating increase in latent heat by increasing the amountof MPCM.

3.3. Yarn modulus and tenacity

Fig. 4 shows the load and extension relationship of a yarn testedby Instron tensile testing equipment following the yarn tensiletesting standard.

The modulus and tenacity of yarn indicate the stiffness andstrength of yarn. Modulus is the property of a material represen-tative of its resistance to deformation. In tensile testing themodulus is expressed as the ratio of tenacity to strain, while the

Fig. 1. SEM images (a) with MPCM (b) without MPCM.

Fig. 2. Monofilament with MPCMs.

Fig. 3. DSC graph showing latent heat of fibres with MPCM.

Fig. 4. Load vs. extension of PP fibre.

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tenacity is the breaking force divided by the linear density of un-strainedmaterial. The yarn in Instron Tensile Tester is held betweenjaws to pull it by applying load until it breaks. So the softwareconnected to the machines automatically calculates the tenacityand modulus of yarn by dividing the load with yarn Tex (lineardensity).

Tenacity ðcN=TexÞ ¼load at breakðin N or cNÞ=Texðlinear densityÞ (1)

Modulus ðcN=TexÞ ¼ stressðtenacityÞ=strainðno unitÞ (2)

3.4. Response surface analysis

Response surface analysis is used to better understand therelation between the input and output variables and the effect ofone variable on others. The design of experiment was not made inRSM because of some limitations in combinations of all the pro-cessing parameters. The possible combinations by RSM were notdesirable and impossible to run on Benchtop extruder. The otherreason is each percentage of MPCM requires specific processingparameters to obtain the fibre and cannot be determined byrandom combination of processing parameters. Therefore the inputprocessing variables and output properties were manually feed forthe study of the statistical model.

Table 1Coefficient of determination of model showing p-values.

Model predicted coefficient of regression

Response Latent heat Modulus Tenacity

Factors PCM Tex PCM Tex Tex * Tex PCM Tex Tex * Tex

p-Value 0.000 0.152 0.001 0.000 0.014 0.006 0.000 0.005R-Sq 99.58% 97.02% 96.36%R-Sq (adj) 99.43% 95.95% 95.06%R-Sq (pred) 98.69% 93.60% 83.56%

Table 2ANOVA table showing significance of model terms.

Response

Latent heat Modulus Tenacity

Linear p ¼ 0.000 p ¼ 0.000 p ¼ 0.000Square p ¼ 0.820 p ¼ 0.044 p ¼ 0.010Interaction p ¼ 0.557 p ¼ 0.025 p ¼ 0.273

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The statistical significance of model was determined by analysisof variance (ANOVA). R2 and adjusted R2 represent the proportionof variation in the response that could be explained by the model(R2 is also called the coefficient of determination). The predicted R2

indicates howwell themodel can be used for prediction. Anymodelterms having p-value less than a-value (a ¼ 0.05) indicate theirstatistical significance at 95% confidence interval.

3.4.1. Analysis of latent heat, tenacity and modulus of thermo-regulating

Table 1 shows the coefficient of determination in terms of p-value. The p-value provides a way of testing the relationship be-tween the predictor and the responsewith respect to a pre-selecteda-value. If p-value is less than the a-value (0.05 at 95% confidenceinterval), it means that the specific parameter has statistically sig-nificant effect on model. The p-value for latent heat, modulus andtenacity is presented in Table 1. The p-value of PCM and Tex (Tex is aterm used to indicate the linear mass density of yarn. It is defined asmass in grams per 1000 m.) for latent heat is 0.000 and 0.152

Fig. 5. Residual plots f

respectively which means that the percentage of PCM has signifi-cant effect on latent heat while Tex does not have significant effecton the response latent heat. This can be explained that latent heat isthermal energy storage which can only depend upon the amount ofphase change materials incorporated in the material. For modulusand tenacity, the p-values show both PCM and Tex are significantparameters. By increasing Tex increases the orientation of respon-sible for strength and hence increases the modulus and tenacity.PCM can hinder the orientation of fibre which in turn affects themodulus and tenacity of the fibre. The R-Sq, R-Sq (adj) and R-Sq(pred) show that data can be well predictable by the models, sug-gested by Minitab.

The linear, squared and interaction effects indicate the order ofa term in a model. A linear effect is a first-order term; a squaredand interaction effects are second-order terms. If the responsedirectly depends on the factors then the effect is defined as linear.The squared terms are used to evaluate whether or not there iscurvature (quadratic) in the response surface while interactioneffect is the situation in which the effects of one factor aredifferent at different levels of another factor. The analysis ofvariance shows that latent heat has significant linear effect whilemodulus has all linear, square and interaction effect statisticallysignificant but in case of tenacity, the linear and square effect aresignificant (Table 2).

3.4.2. Residual plotsResiduals generally explain the difference between observed

values in the time series and fitted values. The residual plots ofthe fibre properties are shown in Figs. 5e7. For latent heat,modulus and tenacity, the residuals generally appear to follow astraight line. There does not exist any evidence of non-normality,skew and outliers. In versus fitted and versus order graphs, theresiduals appear to be randomly distributed and scattered aboutzero for latent heat, modulus and tenacity, showing no evidencefor non-constant variance, missing terms and correlation of errorterms.

3.4.3. Contour plotsFig. 8 shows that maximum latent heat can be obtained by

incorporating high amount of MPCM while Tex does not have anyeffect on latent heat. Fig. 9 shows that modulus depends on bothPCM percentage and Tex and both factor values should be kept

or latent heat J/g.

Fig. 7. Residual plots for modulus cN/Tex.

Fig. 6. Residual plots for tenacity cN/Tex.

Fig. 8. Contour plot of latent heat J/g vs. PCM%, Tex. Fig. 9. Contour plot of modulus cN/Tex vs. PCM%, Tex.

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Fig. 10. Contour plot of tenacity cN/Tex vs. PCM%, Tex.

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minimum to get high modulus. For high tenacity, the Tex valueshould be low and MPCM does not affect a lot as can be seen fromFig. 10.

3.5. Statistical model for latent heat, modulus and tenacity

Equations (3)e(5) are statistical models for latent heat, modulusand tenacity. They are created according to the results from ex-periments. For simplification, X ¼ MPCM%, Y ¼ Tex

L:H: ðJ=gÞ ¼ 0:279703þ 0:832129X � 0:0219239Y

� 0:00244207X2 þ 9:74289E� 05Y2

þ 0:000513071XY (3)

Modulus ðcN=TexÞ ¼ 1064:43� 17:7730X � 14:7934Y

� 0:296939X2 þ 0:0524250Y2

þ 0:216460XY (4)

Tenacity ðcN=TexÞ ¼ 90:4268þ 1:78041X � 1:32445Y

� 0:113053X2 þ 0:00599395Y2

� 0:00961094XY (5)

The above three equations can be used to predict the latent heat,modulus and tenacity of any filament yarn to be made. They canalso be used to predict the percentage of phase change microcap-sules needed for a filament yarn with specific Tex with desiredlatent heat.

4. Model validation

Model validation was made by comparisons between the datacollected from practical tests and predicted data obtained by the

Table 3Validation of model.

Factors Latent heat (J/g)

Set PCM% Tex Obtained Predicted

1 7 73 5.2 5.1652 11 52 8.48 8.5545

three equations (3)e(5). The models were tested using datacollected from practical tests to the two new fibres with 7% and 11%MPCM which were not made previously for model generation. Theresponse from model and experiments against input variables arecompared and shown in Table 3. It shows that there is 0.6% and 0.8%difference between models predicted and tested results for latentheat against set 1 and 2 respectively. For set 1, the difference be-tween obtained and predicted values of tenacity and modulus are1.28% and 4.5%. For set 2, the modulus and tenacity difference isfound to be 8.3% and 2.3%, respectively.

It shows that there is 99.4% and 99.2% agreement between theresults from developed models and experiments for latent heatagainst set 1 and 2 respectively. For set 1, the agreement betweenexperimental and model predicted values of tenacity andmodulus are 98.72% and 95.5% respectively. For set 2, theagreement for the modulus and tenacity between the modelpredicted and experimental results are 91.7% and 97.7%, respec-tively. It is believed that the models can be used for propertyprediction of fibres.

5. Applications of PCM incorporated textiles

The fibres incorporated with MPCM can be used in smarttextiles providing thermal storage and comfort against extremeweather. This fibre can be used in clothing for old people athome, and is able to keep them in comfortable microenviron-ment without extra heating required. Similarly fibre containingMPCM can also be used in upholstery and curtains which cantake energy from the sun, stores it in the form of latent heat andrelease the energy later on while the room temperature dropsdown to certain level to keep the room temperature relativelystable.

The PCM encapsulated fibres can be used in smart textileshaving heat sensor where these fibres can provide a source of heat.They can be used in automobile interiors and seat covers due tohigh heat storage capability reducing the cost of extra equipmentrequired for heating interiors.

6. Conclusion

The main objective of the research was to build thermo-regulating melt spun fibre incorporated with microencapsulatedphase change materials. The following conclusion can be made:

� The SEM and DSC results confirm the presence of MPCMs inmonofilament fibre. The maximum latent heat was obtained as9.2 J/g against 12% of MPCM contained in the PP fibre.

� Statistical models are successfully developed and can beused to predict the latent heat, modulus and tenacity of anyMPCM incorporated PP fibre. The response is up to 98.69%,93.60% and 83.56% for latent heat, modulus and tenacity,respectively.

� The models are further tested by comparing predicted andexperimental results of new fibres, they are highly correlated.

Modulus (cN/Tex) Tenacity (cN/Tex)

Obtained Predicted Obtained Predicted

246.81 235.53 28.05 27.69359.44 329.29 37.31 38.17

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Acknowledgements

This research project has been funded by Heriot Watt Universitywith the collaboration of CDI Theme.

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