Perceived
Messaging Decision Making Social Networking Spindle Coding Consumer Loyalty The importance of polystyrene in handling and transportation of fragile equipment for safe delivery cannot be under stated. However; the post-usage has raised serious concern due to adverse effects caused by the litters on environment in blocking water-ways due to its weightlessness, release of oxides of carbon and resisting decomposition among others. This work was intended to model produced adhesive from polystyrene waste using Design Expert version 6.0.8 software and D-optimal mixture design for the responses analyses to obtain the best adhesive. Eight (8) experimental runs were generated for resin formulations with only 3 feasible, coded: R1, R4 and R7, based on 2 factorial design of experiment for resin formulations. Furthermore, 14 adhesive formulations were generated for each resin, coded: R1AD, R4AD and R7AD, that is, the formulated resin was combined with additives to produce adhesive using 3 factorial mixture designs and 4 responses, namely: viscosity, pH, percentage moisture content and percentage solid content. The responses were modeled using D-optimal mixture design: the viscosity response modeling was best fitted with quadratic model for R1AD produced adhesives, while R4AD and R7AD produced adhesives were fitted with Cubic model. The pH, percentage moisture content and percentage solid content responses were all fitted with cubic model based on the statistical and modeled data. The modeling solution was further optimized and validated for the three adhesive productions, the general selection of produced adhesive based on desirability factor and line with experimental analyses from the responses shows: R7AD2>R4AD1>R1AD1 produced adhesives in the order of fitness.
## 1. INTRODUCTIONPolystyrene waste disposal has been a subject of intense research due to its non-degradability. It is a synthetic aromatic polymer product produced from the styrene monomer during the alkylation of benzene reaction with ethylene to produce ethyl benzene [18], the styrene monomer which is covalently linked together with many monomers form polymer known as polystyrene with chemical formula [3] and structural formula presented in Figure (1) [38].
The global production of polystyrene is about 14 million tons annually with about 80% end-up in landfills and the remaining 20% in water ways [24]. It is the reliable modes of packaging, transportation and protection of sensitive devices, fragile equipment, electrical appliances and others for safe delivery. However, its post usage has been a global challenge as recycling is not economical due to its weightlessness and low cost [33], burying in the soil leads to prevention of water infiltration to ground [10] while burning releases carbon (IV) oxide aiding global warming [15], [29]. Some research works carried out on the recycling or reuse of polystyrene waste includes: formulation of adhesive for particleboard [1], [17] and binder to cushion the effect of formaldehyde emission in paint [4]. The growing concern to maximize wastes generated and conversion to usefulness is the drive for the concept of adhesive from polystyrene waste, which could offer opportunity to utilize in adhesive production for use in composite production such as “particleboard from lignocellulose waste material”. Adhesive is a substance that when applied to the surface of an object such that the two or more become bonded together by surface attachment [35], such bonding could be due to Van de Waals forces of chemical attraction [9]. Adhesive formulation is either solvent base or water-borne or 100% solid hot-melt [37]. In development of new product, scientific approach is mostly adopted to find the best formulation by modeling which is usually analyzed using various statistical tools to determine the fitness. Accordingly, adhesive formulation is by combination of different components such as: tackifyer for resin formulation [26], [39] and additives for stabilization of adhesive [17]. The choice of plasticizer additive depends on the type of polymer compatibility of the oil, availability and cost such as dibenzoate [5] while antioxidant additive prevents oxidization, maintain stability and resist thermal degradation of adhesive when exposed to ultra-violet [8], it also hinders change in adhesive viscosity and char formation using typical antioxidant such as hindered amines [5], [36]. Modeling is a process of formulation using mathematical equations, showing the behavior that characterized the process by the response surface method [21], it is the essentials that can be experimentally verified in order to solve the problem or improve on the existing process condition [31], leading to establishment of the relationship between the response concern and the input. The variables are used to predict the responses that can be compared with experimental values [25] using mathematical model equation as presented in Eq. (1) [35]. Where Y= Predicted response, β Experimental design approach is extensively employed to optimize several processes in modeling to achieve a desired result, this includes: modeling in extraction of volatile compounds in turmeric leaves [40], response surface methodology as tool for heavy clay firing optimization process [7], optimization of alkaline protease production from Bacillus sp. using modeling [11] and D-optimal for optimization of extraction yield, viscosity and oil analysis [20]. Furthermore, design of experiment (DOE) approach was applied to understudy the behavior in formulation of adhesive with acceptable plasticizer based on the design and response variables [6], the effects of test variables in shear strength of adhesive produced and followed by analysis of data using one-way analysis of variance (ANOVA) to compare with regression [12], and D-Optimal mixture design in optimization of ternary matrix blends for controlled zero-order drug release from oral dosage [14]. Thermal mixture DOEs as an alternative method in optimizing the aqueous phase composition of a microemulsion was carried out [19], D-Optimal mixture design to compare with other experimental design to study logistic for regression analyses [22] and regression model to predict the California bearing ratio (CBR) values of black cotton soil for stabilization of cement [2]. Statistical and data analysis by different
models such as: linear model, 2FI model, quadratic model, cubic model [32], [41] and special cubic model [30] are usually generated by the Design Expert software for
determination of the best model. Each model analyzes the response interaction
between the manipulated variables and non-manipulated variables using the
statistical tools. Amongst these model techniques, D-optimal which is a design
for categorical factors minimizes errors associated with model coefficient that
could provide an efficient means to optimize processes depending on the choice
of factors for the formulation to provide desirable response.
Furthermore, other factors considered in suggesting the validity of the achieved
results by using ANOVA which describe the fitness of the model curves are the
predicted and adjusted coefficient of correlation (R ## 2. MATERIALS AND METHODSThe experimental procedure for material collection, pretreatment, washing and fragmentation method adopted was reported in a separate publication [17]. Design expert 6.0.8 version software was employed for the experimental design with the design variables: polystyrene waste in the range of 0.5≤PS≤0.75 and tackifyer in the range of 0.25≤TKF≤0.50 for resin formulation based on 2 factorial designs as presented in Table 1 and Eq. (2) for the component of resin formulation.
Where R = resin, PS = Polystyrene waste and TKF = Tackifyer. The design expert generated eight (8)
experimental runs for resin formulations (R) with duplication (R1, R2, R6, R8)
and had runs (R3, R5) not forming resin due to inadequacy of the ratios to
dissolve the polystyrene, thereby resulting into three feasible resin
formulations R1, R4 and R7. The feasible resin formulations produced was
further designed for adhesive formulation with added additives namely:
plasticizer and antioxidant being the variables for 3 factorial designs with 4
responses (viscosity, μ; proton of hydrogen, pH; percentage moisture
content, %MC and percentage solid content, %SC). Plasticizer was synthesized
from benzoic acid and diethylene glycol to form diethylene glycol dibenzoate [6]. The antioxidant (diphenyl amine) was ground to powdered and sieved
with 400 µ - 1.8 mm aperture mesh without further modification. The design
expert generated fourteen (14) experimental runs (Tables 2, 3 and 4) with the
design variable ranges: resin 0.65≤R≤0.79, plasticizer 0.20≤PLZ≤0.34
and antioxidant 0.009≤AOX≤0.01 and the
component adhesive formulations (Eq. (3)). Where AD= adhesive, R = Resin, PLZ = plasticizer and AOX = antioxidant. ## 3. RESULTS AND DISCUSSIONThe experimental result for the produced
adhesives and the corresponding characterized responses is presented Tables 2–4
for the feasible resins formulated from R1, R4 and R7
respectively
*ND=Not detected by viscometer probably due to lack of fit
## 3.1. RESPONSE SURFACE OPTIMIZATION MODELING STATISTICAL AND DATA ANALYSESThe mixture design matrix for the
optimization modeling was based on the D-Optimal response specific for
categorical factors generated by the software which minimize errors associated
with the model coefficient throughout the modeling. In order
to determine the fitness and significance of the models, ANOVA and
sequential model sum of squares (SMSS) analysis was carried out (Tables of
values for ANOVA and SMSS are not shown). The ANOVA shows the effects of
individual responses and variables interaction on the D-optimal mixture and
while SMSS evaluated individual responses to suggest the best model for the
design. The F-value compares the variation of the differences in average
responses at the design points and its corresponding responses using any of the
models from replicated design point. The Prob>F values is the probability of
actualizing the F-value which should be less than 0.0500 for significant model
or greater than 0.0500 for insignificant model were used in selection of the
model [19]. The SMSS shows the individual model analysis by various
statistical data for adjusted and Predicted R ## 3.2. VISCOSITY RESPONSE MODELING FOR R1AD, R4AD AND R7AD PRODUCED ADHESIVESThe viscosity response ANOVA for R1AD, R4AD
and R7AD adhesives was evaluated and analyzed using F-value and Prob>F
values statistical tools. The ANOVA model F-values and Prob>F values for
R1AD, R4AD and R7AD adhesives shows significant models for the produced
adhesives, thus a model term is significant when the Prob>F value is less
than 0.0500 with high F-value leading to precision adequacy greater than 4 [16], [19]. Individual model for different coefficient for R1AD adhesive shows
that linear mixture AB model are significant model while AC and BC are not
significant models for Prob>F values were greater than 0.0500. The R4AD
adhesive ANOVA model shows that coefficients B and B2 are significant models
for Prob>F values are less than 0.0500 while coefficients C, C2 and BC are
not significant models as Prob>F values is greater than 0.0500. Similarly,
the R7AD adhesive ANOVA indicates model that coefficients B and B2 are
significant models with Prob>F values of less than 0.0500 and coefficients
C, C2 and BC are not significant models. The viscosity response was further
evaluated by SMSS for individual adhesive produced and analyzed using the
statistical data to suggest the best model that fits the design for validation
and selection. The best model was fitted according to high F-value, low
Prob>F value, low standard deviations and high R Where A = Resin (R), B = Plasticizer (PLZ) and C = Antioxidant (AOX). The diagnostic plots (not shown) for
viscosity R1AD response showed a linear plot suggesting the trend of the modeled data. The
viscosity R4AD response shows an over prediction of
values which occur above with cluster points appearing, an indication that the
model has problems resulting to high PRESS values and standard deviation from
the actual values [16], [30]. The viscosity R7AD response shows that point below was over
predicted resulting to deviation at 2999.00 (under predicted between 4373.23
and 5060.41 while over predicted between 5060.41 and 5747.55). These trends
have resulted into high deviation of the actual values from the predicted
values. However, the viscosity precision which is greater than 4 was adequate
for the model acceptance [23]. ## 3.3. PH RESPONSE MODELING FOR R1AD, R4AD AND R7AD PRODUCED ADHESIVESThe pH values for the ANOVA modeling were
significant for all produced adhesives (R1AD, R4AD and R7AD), with high
F-values and Prob>F values less than 0.0500 for the model coefficients. This
implies that the pH of the produced adhesives was in conformity with the ANOVA
modeling which has statistical values of Prob>F values less than 0.0001 for
the model [23], [30]. The pH response by SMSS for all produced adhesives showed
agreement for the predicted and adjusted R
The pH of R1AD produced adhesive showed
cluster points between 3.10 and 3.90 which implies under prediction, low pH
value (acidic) which is not acceptable property of adhesive, thus highly acidic
will accelerates rapid weakening of substrate which determines the procuring of
adhesive in application [13], [28], furthermore, between 5.50 and 6.30 another cluster points occur
implying over prediction [34]. The pH
of R4AD produced adhesive shows no cluster points or any observed scattered
points above and below the curve; this implies that the predicted values were
adequately fitted resulting to low standard deviations from the actual
experimental values. All pH values from this model are greater than 4 implying
the adhesive is adequately fitted for all parameters [27]. Nevertheless, cubic model which
exhibits high standard values with lowest standard deviation of 0.012 and high
R ## 3.4. MOISTURE CONTENT RESPONSE MODELING FOR R1AD, R4AD AND R7AD PRODUCED ADHESIVESThe ANOVA model “F-values and Prob>F values for R1AD, R4AD and R7AD adhesives indicate significant models, with exception of individual model coefficient AB for R1AD adhesive which has Prob>F value 0.7729 greater than the standard value of 0.0500 [16]. However; other model coefficients for all produced adhesives were found significant for the moisture content response. Furthermore, it can be inferred from SMSS that R1AD, R4AD and R7AD produced adhesives statistical data satisfied cubic models indicating the significant effect of the independent variables on the responses. Equations (10-12) are the generated polynomial model equations for percentage moisture content. The corresponding diagnostic case plots (not shown) shows significant adequate relationship between the predicted models and actual experimental values for %MC R7AD response while R1AD and R4AD has cluster points at the middle implying slight deviation between the predicted and actual values. ## 3.5. SOLID CONTENT RESPONSE MODELING FOR R1AD, R4AD AND R7AD PRODUCED ADHESIVESThe percentage solid content response for
R1AD shows that the cubic model was statistically significant (Prob<0.0027),
although 2FI model was significant (Prob<0.0413) which indicates that the
model adequately describes R1AD adhesives; nevertheless, the model with lower
Prob>F values best describe the model. For the R4AD adhesive, both the
linear (Prob<0.026) and cubic model (Prob<0.0001) were statistically
significant and both predicted and actual R
The corresponding diagnostic cases plots
(not shown) from the model coefficients for predicted and actual experimental
values for R1AD shows scattered points above and below the diagonal line; when
scattered plots occur in a model, it shows over prediction or under prediction
of values and deviations [34]. For both R4AD and R7AD the points were linear
along the diagonal signifying agreement between the predicted and actual
experimental values, thus the predicted model obtained
using D-Optimal for mixture was significantly adequate for R4AD and R7AD
produced adhesives. ## 3.6. MODEL OPTIMIZATION SOLUTION AND VALIDATIONThe prediction of optimum from responses of the produced adhesives were evaluated using design expert optimization parameters based on the constraints conditions for the standard requirement of quality adhesives for particleboard production. Several optimization models were suggested for the analyses to determine the best adhesive and its validation for navigation. Table 4 presents the model solutions for each resin produced adhesives (R1AD, R4AD and R7AD) with the desirability values and the best model selected.
Three (3) solutions were found for R1AD
adhesive formulations from the optimization modeling numerical solutions with
AD1 composition R1, PLZ and AOX equal to 0.76, 0.23 and 0.01 respectively with
desirability factor of 0.664 was the best fitted formulation selected,
furthermore, four (4) solutions were found for R4AD adhesive formulations with
AD1 composition R4, PLZ and AOX equal to 0.68, 0.31 and 0.01 respectively with
desirability factor 0.737 was the best formulation selected and finally, three (3) solutions
were found for R7AD adhesive formulations with AD2 composition R7, PLZ and AOX equal to 0.79, 0.20 and 0.01 respectively with desirability
factor 0.736 was the best formulation selected but not AD1 with 0.858
desirability factor due to negativity value of pH and MC which is undefined in
modeling. Based on validation of optimization modeling solutions (Table 4), the general selection of adhesive based on desirability factor and in
line with experimental analyses from the responses: R7AD2>R1AD1>R4AD1. ## 4. CONCLUSIONIn this work, practical approaches based on the statistical analyses were used to develop the best formulation matrix and model for polystyrene waste adhesive production. Resins were formulated based on 2 factorial mixture DOE, followed by 3 factorial mixture DOE for adhesive formulation with 4 responses namely: viscosity, pH, percentage moisture content and percentage solid content using D-optimal mixture design for their analyses. The ANOVA models for produced adhesives (R1AD, R4AD and R7AD) all showed significant models as commonly used to describe other processes, however, the optimization formulation was predicted based on the polynomial model equations generated by design expert software and the use of statistically define model parameters for the selection of the best fitted model and its predictions which identified the best produced adhesive in descending order as: R7AD2>R1AD1>R4AD1. The use of D-optimal in analyzing the responses has also demonstrated the minimization of errors and suggesting the alternative design to the model having aliases which might result into errors in the modeling. ## SOURCES OF FUNDINGThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. ## CONFLICT OF INTERESTThe author have declared that no competing interests exist. ## ACKNOWLEDGMENTThe authors gratefully acknowledge the use of water and civil laboratory of Abubakar Tafawa Balewa University Bauchi and Nigerian Building and Road Research Institute, North Central Zonal Office Jos-Plateau State, Nigeria for using their facilities. ## REFERENCES
[1]
Abdulkareem, S. A., Raji, A. S and Adeniyi, A. G (2017). Development of
Particleboard from Waste Styrofoam and Sawdust. Nigerian Journal of
Technological Development, 14(1), 18 – 22. Doi: http://dx.doi.org/10.4314/njtd.v14i1.3.
[2]
Abdulkarim, I.I., Kundiri, A.M. and Abbagana,
M. (2019). Predicting CBR Values of Black Cotton Soil Stabilized with Cement
and Waste Glass Admixture Using Regression Model. American Journal of Traffic
and Transportation Engineering, 4(1), 31. doi:
10.11648/j.ajtte.20190401.15. [3] Ademović, Z., Suljagić,
J., Zulić, J. (2017). Influence of Physical
Properties on Thermal Conductivity of Polystyrene Insulation Materials
Contemporary Materials, 8(1), 42 – 47. Doi: 10.7251/COMEN1701042A.
[4]
Akinterinwa, A., Osemeahon, S. A., Nkafamiya,
I. I. and Dass, P. M. (2015). Formulation of Emulsion
Paint from a Copolymer Composite of Dimethylol
Urea/Polystyrene; Journal of Chemistry and Materials Research. 7(7), 20 –
26.www.iiste.org.
[5]
Arendt,
W. D., McBride, E. and Conner, M. M. (2014a). Use of Dibenzoate Plasticizers in Pressure-Sensitive Adhesives, Applications Chemist
Emerald Kalama Chemical LL. C Kalama Washington.
[6]
Arendt,
W. D., McBride, E. and Hanes, R. D. (2014b). New Dibenzoate Plasticizer Blends for PVC
Applications; Journal of Vinyl and Additive Technology, 20(3), 137 – 142.
Doi:10.1002/vnl.21343.
[7]
Arsenovic, M., Pezo, L. and Radojevic, Z.
(2012). Response surface method as a tool for heavy clay firing process
optimization: Roofing tiles. Processing and Application of Ceramics, 6(4),
209–214. doi: 10.2298/pac1204209a.
[9]
Bastani, A., Militz, H., Adamopoulos,
S. and Rohumaa, A. (2016). Development of Bonding
Strength of Modified Birch Veneers during Adhesive Curing, Journal of Wood
Research. 61(2), 205 – 214. [10] Chanhoun, M., Padonou, S., Adjovi,
E. C., Olodo, E. and Doko,
V. (2018). Study of the Implementation of waste wood, Plastics and Polystyrene
for various Applications in Building Industry; Journal of Construction and
Building Materials. 167(1), 936 –
941.https://doi.org/10.1016/j.conbuildmat.2018.02.080. [11] Chauhan, B. and Gupta, R. (2004). Application of
statistical experimental design for optimization of alkaline protease production
from Bacillus sp. RGR-14. Process Biochemistry, 39(12), 2115–2122. doi: 10.1016/j.procbio.2003.11.002. [12] Derikvand, M. and Pangh, H. (2016). A Modified Method
for Shear Strength Measurement of Adhesive Bonds in Solid Wood, Peer-Review
Article; Adhesive Bond Strength, BioResources. 11(1),
354 – 364. [13] Elbadawi, M., Osman, Z., Paridah, T., Nasroun, T. and Kantiner, W.
(2015). Mechanical and Physical Properties of Particleboards made from
Ailanthus Wood and UF resin Fortified by Acacias Tannins Blend. Journal of
Materials and Environmental Sciences. 6(4), 1016 – 1021. [14] El-Malah, Y., Nazzal,
S. and Khanfar, N. M. (2006). D-Optimal Mixture
Design: Optimization of Ternary Matrix Blends for Controlled Zero-Order Drug
Release From Oral Dosage Forms, Drug Development and
Industrial Pharmacy, 32:1207–1218, DOI: 10.1080/03639040600685167. [15] Facts About Landfill and Styrofoam
(2018). https://Sciencing.com. [16] Gryze, Langhans and Vandebroek. (2007). Using the
correct Intervals for Prediction Actual on Tolerance Intervals of Ordinary
Least-squares Regression. Chemometrics and Intelligent Laboratory System.
87(2), 147 – 154. [17] Hamidu, L. A. J., Aroke U. O., Osha, O. A. and
Muhammad, I. M. (2019). Formulation and Characterization of Adhesive Produced
from Polystyrene Waste Using Response Surface Optimization;
Path of Science. 2019. 5(8), 2000 – 2009. DOI: 10.22178/pos.49-2; ISSN
2413-9009. [18] Haslego, C. (2010). Basics of Polystyrene Production, Chemical and Process
Engineering Resources, 2010. [19] Jeirani, Z., Jan, B. M., Ali, B. S., Noor, I. M., Hwa, S. C. and Saphanuchart, W. (2012). The Optimal Mixture Design of
Experiments: Alternative Method in Optimizing the Aqueous Phase Composition of
a Microemulsion, Chemometrics and Intelligent Laboratory Systems. 112 (2012)
1–7; DOI: 10.1016/j.chemolab.2011.10.008. [20]
Koocheki, A., Taherian, A.,
Razavi, S., and Bostan, A.
(2009). Response surface methodology for optimization of extraction yield,
viscosity, and hue and emulsion stability of mucilage extracted from Lepidium
Perfoliatum Seeds. Food Hydrocolloids, 23, 2369–2379. [21] Li, R., Guo, X., Ekevad, M., Marklund, B. and Cao, P. (2015).
“Investigation of Glue line Shear Strength of Pine Wood Bonded with PVAc by Response Surface Methodology,” Journal of BioResources. 10(3), 3831 – 3838. [22] Mancenido, M. V., Pan, R., Montgomery, D.C., Anderson-Cook, C. M. (2019).
Comparing D-optimal designs with common mixture experimental designs for
logistic regression, Chemometrics and Intelligent Laboratory Systems 187 (2019)
11–18, https://doi.org/10.1016/j.chemolab.2019.02.003. [24] Now and Forever: The Styrofoam Dilemna. (2016). Retrieved from Canada.com: http://www.canada.com/life/forever
Styrofoam dilemma/1522634/story.html. [25] Olubajo, O. O. and Makarfi, I. Y. (2019). Prediction
and Optimization of Sulphur Trioxide Yield from Calcination of Aluminium Sulfate Using Central Composite Design, Path of
Science. 5(10), 1001 – 1013. doi:10.22178/pos.51 – 2. [26] O’Brien, E. P., Germinario, L. T., Robe, G. R., Williams, T., Atkins, D. G., Moroney, D. A., Mark
A. and Peters, M. A. (2007).
Fundamentals of Hot-Melt Pressure-Sensitive Adhesive Tapes: The Effect
of Tackifier Aromaticity, Journal of Adhesion Science Technology, 21(7), 637 –
661. [27] Pan, Z., Zheng, Y., Zhang, R. and Jenkins B. M. (2007). Physical Properties of Thin Particleboard
from Saline Eucalyptus, Industrial Crop Products, 26(2007), 185 – 194.doi:
10.1016/j.indcrop.2007.03.006. [28] Pan, Z., Cathcart, A. and Wang, D (2005). Thermal and chemical treatments to improve
adhesive property of rice bran, Industrial Crops and Products, An International
Journal, 22(2005), 233 – 240.doi: 10.1016/j.indcrop.2005.01.003. [29] Polystyrene Fact Sheet (November, 2017). www.cleanup.org.au Retrieved 03/07/2019. [30] Richard Buxton (2007).
Mathematical Learning Support Center, Design Expert 7 Tutorial, 1 –10. [31] Roffel, B. and Betlem, B. (2006). Process Dynamics
and Control Modeling for Control and Prediction, Copyright © 2006 John Wiley
& Sons Ltd The Atrium, Southern Gate, Chichester,
West Sussex PO19 8SQ, England Telephone (+44) 1243 779777. [32] Sarteshnizi, R. A., Hosseini, H., Bondarianzadeh, D., Colmenero, F. J., Khaksar, R. (2015). Optimization of
prebiotic sausage formulation: Effect of using b-glucan and resistant starch by
D-optimal mixture design approach, LWT - Food Science and Technology 62 (2015)
704 – 710. http://dx.doi.org/10.1016/j.lwt.2014.05.014. [33] Sekharan, R. V., Abraham, B. T. and Thachil, E. T.
(2012). Utilization of waste expanded polystyrene: Blends with silica-filled
natural rubber, Journal of Materials and Design 40(1) 221–228. Doi:http://dx.doi.org/10.1016/j.matdes.2012.03042. [35] Silva, F. B. M., Vianna, R. F. and
Neubert, E. I. (2014). Study of Adhesion Properties of Natural Rubber - Based
Pressure Sensitive Adhesive with Variation of Tackifier Resin and Plasticizers
Agents, Area tematica: Engenharia de Materials e Nanotecnologia; 1(2014), 1 – 8. [37] Strickland, Barbara (2013).
Introduction to Adhesives UPACO Division of Worthen Industries, April, 2013. [38] Vodnik, V.V., Dzunuzovic, E.S. and Dzunuzovic, J.V. (2014). SYNTHESIS AND CHARACTERIZATION OF POLYSTYRENE
BASED NANOCOMPOSITES In: Polystyrene: Synthesis, Characteristics and
Applications, Editor: Cole Lynwood, Nova Science Publishers, Inc. [39] Xenidou, M (2005). ASC Hot Melt Short Course, Senior Associate Henkel
(formerly National Starch and Chemical Company), 10 Finerne
Avenue Bridgewater, NJ, October 9, 2005. [40] Zaibunnisa, A. H., Norashikin, S., Mamot,
S. and Osman, H. (2009). An experimental design approach for the extraction of
volatile compounds from turmeric leaves (Curcuma domestica) using pressurised liquid extraction (PLE). LWT - Food Science and
Technology, 42(1), 233–238. Doi: 10.1016/j.lwt.2008.03.015. [41] Zen, N. I. M., Gani, S. S. A., Shamsudin, R. and Masoumi, H. R. F. (2015). The Use of D-Optimal Mixture
Design in Optimizing Development of Okara Tablet
Formulation as a Dietary Supplement, The Scientific World Journal,
http://dx.doi.org/10.1155/2015/684319
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