Int J Plast Technol https://doi.org/10.1007/s12588-018-9203-1 RESEARCH ARTICLE
Investigation of parameters affecting the test results of oxidation induction time of polyolefins Pedram Malaekeh1
Received: 5 November 2016 / Accepted: 1 May 2018 © Central Institute of Plastics Engineering & Technology 2018
Abstract Oxidation induction time test is as an accelerated procedure and a qualitative evaluation performing to measure, thermal stability of polyolefins with using of Differential Scanning Calorimetry method. This can be interpreted as an indicator of the thermal stability of polyolefins, used in an oxidation environment. The results of test depend on various factors. Despite the widespread use of it in many different bases, studies about these factors and their impacts have not been significant. In ahead study, these factors were in two main groups, Ι-Sample conditions includes amount of the sample, the place of sample, sample geometry and ΙΙ-Test and instrument conditions includes heating rate, type of crucible, Oxygen gas flow rate, isothermal temperature and instrument temperature stabilization time. By choosing a High Density Polyethylene sample and conducted tests with preparation, together with statistical analysis by Yates algorithm and application of Minitab along with Design Expert software’s, the impact of above factors were investigated. Among the factors related to the conditions of the test sample, specimen geometry and location of its choice and among the ones related to test conditions, heating rate, isothermal temperature and instrument temperature stabilization time were identified important and with significant impacts. Keywords Oxidation induction time · Polyolefin · Oxidation · Thermal stability · Isothermal
* Pedram Malaekeh
[email protected] 1
Laboratory Unit, Mashhad Factory, Jahad Zamzam Plastic Industries, P.O. Box 91735‑464, Mashhad, Iran
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Introduction Oxidation Induction Time provides data on the oxidation stability of polymer substances. Since the life of plastic parts, thermal stability and their resistance to ultraviolet radiation during all operation time is determined according to the exposure to environmental factors such as temperature, oxygen, light, pollution and radiation, the provided data is important. Life extension and aging will bring the decline and drop of products physical properties and will lead to failure. Since the reaction between polymer and oxygen is exothermic, determining OIT by using Differential Scanning Calorimetry is an ideal solution for the study of this phenomenon [1]. In OIT test method, under an inert atmosphere, the sample is heated to a specified temperature in a crucible without any covers. After a short while, inert and ineffective gas will change to oxygen or pure air at isothermal conditions. The time duration between the movement of oxygen gas and the start of the oxidation reaction is defined as OIT [2]. OIT is an accelerated test which is used as a qualitative evaluation of oxidation stability of a substance. So in an oxidation environment, OIT can be utilized as an indicator of the thermal stability of a polyolefin. OIT test method has been standardized for polyolefin according to the ASTM D 3895 and DIN EN 728 [3]. OIT would be used to compare resistance of various plastics against process of degradation, identifying the effect of concentrations of antioxidants and also can be used to achieve the positive effects of antioxidants. According to the other resources and previous researches, some of the factors affecting the OIT test method are isothermal temperature, sample selection location, physical characteristics of sample and type of crucible [4]. Literature review shows that since 1987, a number of works has been done by some researchers such as Tyuleneva et al. [5] which examine the processes that occur in the induction time of oxidation in presence of inhibiting factors as well as examining the oxygen kinetics. In these studies and related works such as Shlyapnikov et al. [6], they investigate oxidation inhibitors parameters of a polyolefin by regarding the temperature dependence and durability of a stabilized polymer. In another research which was conducted by Tyuleneva and Shlyapnikov [7] the focus of the research was on explaining the induction period and mechanism of oxidation inhibiting. They have discussed the topics such as thermal stability of a polyolefin, set the time of failed samples and kinetics. Moreover antioxidant effect and inhibiting factors were discussed. At the same time, Latocha and Uhniat [8] also studied the kinetics of induction of stabilized Low Density Polyethylene oxidation with commercial antioxidants. More than two decades later, Celina [9] comprehensively reviewed the research done on the oxidation of polymeric substances and its relationship with raw materials performance and predicted the working life. By reviewing the resources and past experiences, one can say that totally all works that have done, examined the mechanisms of thermal oxidation and light oxidation. There are a few papers that don’t merely explored under OIT test
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conditions and review factors affecting the results. The except is what Rosa et al. [10] have done in 2000. They explored parameters which are involved in OIT oxidation induction time. Except that, no attempts have been made in this field. Rosa and colleagues have done their studies based on differential scanning calorimetry technique by considering the effective parameters on test results and preparing the sample.
Design of experiments Considering the factors affecting the results of the test, the current research was conducted by running a series of tests planned in two main groups based on differential scanning calorimetry method. Group I: Typical conditions include the amount of sample, sample selection place, sample geometry, Group II: the test conditions and instruments that include heating rate, material of the crucible, the flow rate of oxygen gas, isothermal temperature and time of instrument’s stabilization temperature. Planning the tests and their results would help us to determine the best conditions of the test and select the most appropriate analysis method which will make the best reactions. In results evaluation phase, the impact of two or more quality and quantity factors should be assessed simultaneously in a single step, so it was appropriate to apply the Yates algorithm method. A number of tests design defined and limited in Minitab software. While investigating the test’s results, by utilize the Design Expert software, higher-grade equations and relations were ignored and the relations were simplified. By considering their nature, the factors were classified in two groups: qualitative and quantitative factors. These factors were examined in this study, includes the followings. They were selected by considering conditions presented in references [2, 3]. 1. 2. 3. 4. 5. 6. 7. 8.
The quantity of samples—3 and 17 mg Sample selection location—interior and exterior surfaces of a sample Sample geometry-disk shape and cubic shape The heating rate—10 and 40 °C/min Material of crucible—aluminum and steel The oxygen gas flow rate-of 20, 50 ml/min Isothermal temperature—200 and 210 °C Time duration of stabilization temperature of instrument—3 and 5 min
Materials and methods After reviewing the literature, High Density Polyethylene, (Borsafe HE3490-LS of Borouge petrochemical) was selected for this study and the values of OIT with calorimeter (Model DSC131 made by SETARAM Company) were calculated. Before doing the tests, the before mentioned calorimeter was calibrated by reference standards of Tin and Indium (which were provided by SETARAM company).
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Table 1 Investigated factors abbreviations A
The amount of sample
E
Type of crucible
B
Place of sample selection
F
Flow rate of oxygen gas
C
Sample geometry
G
Isothermal temperature
D
Heating rate
H
The temperature stabilization time
Table 2 Factors that impact the OIT test result with marks intended to them Amount of sample (A)
Geometry of sample (C)
Type of crucible (E)
Isothermal temperature (G)
3 mg (−)
Cubic (−)
Aluminum (−)
200 °C (−)
17 mg (+)
Disc shaped (+)
Steel (+)
210 °C (+)
Sample selection place (B)
Heating rate (D)
Oxygen gas flow rate (F)
The temperature stabilization time (H)
Exterior surface (−)
10 °C/min (−)
20 ml/min (−)
3 min (−)
Interior surface (+)
40 °C/min (+)
50 ml/min (+)
5 min (+)
Test procedure was as follows: samples from specific places were chosen and after preparation in the desired geometric shapes in accordance with weighing values, they were put into a crucible which was placed in a calorimeter. The device application is performed according to duration time of stabilization temperature and necessary heating rate. It should be continued until acquiring the desired isothermal temperature and the backing to the cold estate. During the test, the oxygen gas flow rate was entered the device in accordance with the predicted values. Calorimeter device plotted the enthalpy changes in terms of time at a specified temperature. As shown in Table 1, a symbol assigned to each of the considering factors. To implement the Yates algorithm method, we assigned (+) to the upper limit values of each influential factor in research and mark (−) was allocated to the lower limit amounts. Table 2 shows these points. Results and discussion At the beginning, before the start of the tests program, thermal analyzer which was used to check the correctness of applied calibration coefficients with reference standards, during two separate tests were checked to confirm. The results indicated the high accuracy and validity of them. Then, three primary OIT tests were conducted and their results were compared with the data of High Density Polyethylene sample. Programs set for doing these initial tests were in accordance with standard conditions. Results have both repeatable and reproducible properties. Thus, the selected sample and the
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applied instrument were recognized as appropriate ones. Experiments planning and number of the tests were in accordance with Table 3. The structure of states and possible interactions at up to 4° were as follows and state (I) represented the measurement of all of them.
I = ABCF = ABDG = BCDEH = CDFG = ADEFH = ACEGH = BEFGH I + ABCF + ABDG + CDFG A + BCF + BDG + CEGH + DEFH B + ACF + ADG + CDEH + EFGH C + ABF + DFG + AEGH + BDEH D + ABG + CFG + AEFH + BCEH E + ACGH + ADFH + BCDH + BFGH F + ABC + CDG + ADEH + BEGH G + ABD + CDF + ACEH + BEFH H + ACEG + ADEF + BCDE + BEFG
Table 3 Experiments planning Number of experiment
A
B
C
D
E
F
G
H
Number of experiment
A
B
C
D
E
F
G
H
1
−
−
−
−
−
−
−
+
17
−
−
−
−
+
−
−
−
2
+
−
−
−
−
+
+
+
18
+
−
−
−
+
+
+
−
3
−
+
−
−
−
+
+
−
19
−
+
−
−
+
+
+
+
4
+
+
−
−
−
−
−
−
20
+
+
−
−
+
−
−
+
5
−
−
+
−
−
+
−
−
21
−
−
+
−
+
+
−
+
6
+
−
+
−
−
−
+
−
22
+
−
+
−
+
−
+
+
7
−
+
+
−
−
−
+
+
23
−
+
+
−
+
−
+
−
8
+
+
+
−
−
+
−
+
24
+
+
+
−
+
+
−
−
9
−
−
−
+
−
−
+
−
25
−
−
−
+
+
−
+
+
10
+
−
−
+
−
+
−
−
26
+
−
−
+
+
+
−
+
11
−
+
−
+
−
+
−
+
27
−
+
−
+
+
+
−
−
12
+
+
−
+
−
−
+
+
28
+
+
−
+
+
−
+
−
13
−
−
+
+
−
+
+
+
29
−
−
+
+
+
+
+
−
14
+
−
+
+
−
−
−
+
30
+
−
+
+
+
−
−
−
15
−
+
+
+
−
−
−
−
31
−
+
+
+
+
−
−
+
16
+
+
+
+
−
+
+
−
32
+
+
+
+
+
+
+
+
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AB + CF + DG AC + BF + EGH + ADFG + BCDG AD + BG + EFH + ACFG + BCDF AE + CGH + DFH + BCEF + BDEG AF + BC + DEH + ACDG + BDFG AG + BD + CEH + ACDF + BCFG AH + CEG + DEF + BCFH + BDGH BE + CDH + FGH + ACEF + ADEG BH + CDE + EFG + ACFH + ADGH CD + FG + BEH + ABCG + ABDF CE + AGH + BDH + ABEF + DEFG CG + DF + AEH + ABCD + ABFG CH + AEG + BDE + ABFH + DFGH DE + AFH + BCH + ABEG + CEFG DH + AEF + BCE + ABGH + CFGH EF + ADH + BGH + ABCE + CDEG EG + ACH + BFH + ABDE + CDEF EH + ACG + ADF + BCD + BFG FH + ADE + BEG + ABCH + CDGH GH + ACE + BEF + ABDH + CDFH ABE + CEF + DEG + ACDH + AFGH + BCGH + BDFH ABH + CFH + DGH + ACDE + AEFG + BCEG + BDEF ACD + AFG + BCG + BDF + ABEH + CEFH + DEGH
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Each phase was repeated three times after the tests were done, and the mean values and standard deviations of tests were calculated at 95% confidence level. The values are represented in Table 4. By Statistical analysis of the results, all possible modes of interaction and reactions of factors were studied. Table 5 shows the levels of interactions and reactions between factors which are obtained by Yates algorithm. The possibility of occurring deviation was related with mutual impacts and interactions that are obtained through Yates algorithm. Figure 1 shows the results. As the figure clearly showed, much of the obtained data from influences of interaction of factors had a linear sequence. They were consistent with the calculated model of data resulted from performed tests on samples. Impacts of corresponding interactions with linear points can be considered ineffective, because they have an equal amount with degree of measurement error of conducted analysis. Therefore only 5 factors of B, C, D, G, and H were important and effective. Hence, again Yates Algorithm was calculated by these factors to obtain the necessary information for Yates reverse algorithm that was necessary at this stage. Table 6 shows the data. The relation between corresponding model and the above mentioned table data are described in the algebraic Eq. 1. Y = 23.1241 − 0.5978 (ΔB) + 0.1891 (ΔC) + 2.7191 (ΔD) − 1.0828 (ΔG) − 4.5072 (ΔH)
(1)
Table 4 Calculated time during the performed OIT tests, the mean values and standard deviation in each phase Test no.
OIT (min)
Mean values
Standard deviation
Test no.
OIT (min)
Mean values
Standard deviation
1
4.60
4.60 ± 0.52
0.52
17
26.12
26.12 ± 0.25
0.25
2
9.12
9.12 ± 0.31
0.31
18
20.44
20.44 ± 0.25
0.25
3
10.31
10.31 ± 0.07
0.07
19
7.78
7.78 ± 0.20
0.20
4
18.63
18.63 ± 0.49
0.49
20
45.85
45.85 ± 0.19
0.19
5
53.16
53.16 ± 0.05
0.05
21
11.90
11.90 ± 0.20
0.20
6
53.21
53.21 ± 0.22
0.22
22
6.78
6.78 ± 0.19
0.19
7
10.72
10.72 ± 0.17
0.17
23
25.69
25.69 ± 0.33
0.33
8
12.36
12.36 ± 0.18
0.18
24
9.81
9.81 ± 0.33
0.33
9
13.94
13.94 ± 0.30
0.30
25
15.89
15.89 ± 0.24
0.24
10
37.03
37.03 ± 0.17
0.17
26
7.49
7.49 ± 0.40
0.40
11
60.23
60.23 ± 0.14
0.14
27
32.90
32.90 ± 0.7
0.37
12
5.89
5.89 ± 0.52
0.52
28
50.74
50.74 ± 0.29
0.29
13
40.11
40.11 ± 0.02
0.02
29
38.89
38.89 ± 0.27
0.27
14
21.99
21.99 ± 0.18
0.18
30
18.88
18.88 ± 0.51
0.51
15
3.96
3.96 ± 0.38
0.38
31
22.40
22.40 ± 0.28
0.28
16
28.39
28.39 ± 2.05
2.05
32
14.76
14.76 ± 0.33
0.33
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Table 5 Interactions values and interrelations between factors based on Yates algorithm Interactions
The estimated effects
Interactions
The estimated effects
I
23.1241
BE
A
− 0.5384
BH
4.4797
B
− 0.5978
CD
− 2.3597
C
0.1891
CE
− 3.8203
D
2.7191
CG
5.0884
E
− 0.8541
CH
− 1.1784
F
1.5434
DE
0.2547
G
− 1.0828
DH
2.2591
H
− 4.5072
EF
− 5.8172
AB
1.3159
EG
1.4341
AC
− 2.0022
EH
− 1.1566
AD
− 2.1584
FH
0.3084
AE
0.1122
GH
− 3.6528
AF
− 6.7041
ABE
3.1591
AG
2.1634
ABH
− 1.0128
AH
− 2.5484
ACD
2.0316
4.5691
Fig. 1 Probability values (percent) versus the amount of modified impacts (standardized effect)
The residual values that were the difference between the predicted values and the values resulted from the tests were obtained through Yates reverse algorithm. Table 7 shows these amounts for thirty two performed tests. The relationship between residual values and predicted values are shown in Fig. 2.
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Int J Plast Technol Table 6 Factors used in the Yates reverse algorithm Interactions
Estimated effects
I
23.1241
B
− 0.5978
C
0.1891
D
2.7191
G
− 1.0828
H
− 4.5072
A, E, F, AB, AC, AD, AE, AF, AG, AH, BE, BH, CD, CE, CG, CH, DE, DH, EF, EG, EH, FH, GH, ABE, ABH, ACD
0
Figure 2 represents model efficiency in which random distribution of points shows the residual amounts in range of − 6.20 to 6.20 min. Figure 3 shows the probable values associated with residual values. Linear distribution of points indicates that analytical errors do not obey a specific rule. It can be occur due to statistical errors, the test samples, calibration, method and other items. Otherwise constant residual values could be seen for different tests possibilities.
Conclusion Applying Yates algorithm method along with results analysis by Minitab and Design Expert software showed that the following factors had the greatest impact on the results of the OIT test. Place of sample selection B; sample geometry C; heating rate D; isothermal temperature G and the temperature stabilization time H. The model that was acquired by the Yates reverse algorithm was as follows: Y = 23.1241 − 0.5978(ΔB) + 0.1891(ΔC) + 2.7191(ΔD) − 1.0828(ΔG) − 4.5072(ΔH)
Regardless of algebraic symbols, it shows that B factor is 0.59 times bigger than C factor. Also C factor is 0.41 times larger than D factor. And also factor D is 2.3 times of G factor. G is 1.22 times of H factor. The results showed that the sample amount in case of the amount in the range of relevant standards and even out of it does not have a significant impact on test result. The materials of crucibles had much higher melting range than of test samples; so they had no effect on them and their results. Copper crucibles utilization and its alloy did not examined because of its catalytic effect and accelerate oxidation time. Thermal stability of the test samples according to the results of statistical analysis and also the use of dry oxygen gas of high purity grade 5 (99.999%) in the implementation of the program was not a function of oxygen gas flow rate. According to research at the time of oxidation induction- among the other factors that their significant impact were detected- location of sample selection from internal or external surfaces was important due to their exposure to oxidation. In
13
13
AC
AD
AE
AF
AG
AH
13
14
15
16
AB
10
11
H
9
12
F
G
E
6
7
D
5
8
B
C
A
2
3
I
1
4
Interaction
Test no.
28.39
3.96
21.99
40.11
5.89
60.23
37.03
13.94
12.36
10.72
53.21
53.16
18.63
10.31
9.12
4.60
Test values
28.50
3.85
15.79
46.31
12.09
54.03
36.92
14.05
18.56
4.52
53.10
53.27
18.74
10.20
2.92
10.80
Predicted values
Table 7 Residual values obtained from the model
− 0.11
0.11
6.20
− 6.20
− 6.20
6.20
0.11
− 0.11
− 6.20
6.20
0.11
− 0.11
− 0.11
0.11
6.20
− 6.20
Residual values
32
31
30
29
28
27
26
25
24
23
22
21
20
19
18
17
Test no.
ACD
ABH
ABE
GH
FH
EH
EG
EF
DH
DE
CH
CG
CE
CD
BH
BE
Interaction
14.76
22.40
18.88
38.89
50.74
32.90
7.49
15.89
9.81
25.69
6.78
11.90
45.85
7.78
20.44
26.12
Test values
10.58
26.58
21.02
36.75
48.60
35.04
11.67
11.71
7.67
27.83
10.96
7.72
41.67
11.96
22.58
23.98
Predicted values
4.18
− 4.18
− 2.14
2.14
2.14
− 2.14
− 4.18
4.18
2.14
− 2.14
− 4.18
4.18
4.18
− 4.18
− 2.14
2.14
Residual values
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Fig. 2 Residue values versus predicted values
Fig. 3 Probability values (percent) versus the residual values
addition, in terms of surface to volume ratio geometry of test sample was very important. Disc shaped test samples had more surface contact with the bottom of the crucible and more efficient heat transfer, so they provide more time and reproducibility potential that has a positive correlation with sample thickness. Chopped and powdery like samples were not studied due to non-reproducible results, nonidentical and non-stable morphology. Moreover normal and fast speed heating rates were effective in speed of sample reaction with oxygen. This point was under control, so that heterogeneous and dispersed results didn’t occur. A critical parameter in determining the OIT was isothermal temperature. By increasing the isothermal temperature, thermal stability time of the sample will decline and in the less time OIT will occur. Repeatability and separate ability of test results in high temperature were reported poor. The temperature stabilization time of the instrument was a new issue which was studied and had important impact on the results. Therefore the more stabilization time had a significant impact on renewability and repeatability of results.
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The results compared with results of previous research done by Rosa et al. [10]. There were common issues and also common points in this study and theirs, and this confirm the validity of the results. The model output was obtained by comparing the experimental planning results with gained model and review maximum residual value that had 1 to 10 ratio by considering the total time taken for the tests. Random distribution of residual values against predicted values were shown. We also compared the model with the results of Minitab and Design Expert so that the validity of the study could be confirmed. Acknowledgements Thereby I appreciate the scientific support of Mr. Peyman Malaekeh. He helped me with performing statistical analysis and applying Design Expert software.
References 1. Ye P, Tan BC (2014) How to optimize OIT tests, thermal analysis, application note. PerkinElmer Inc, Waltham 2. ASTM-D-3895-14 (2014) Standard test method for oxidative induction time of polyolefin by differential scanning calorimetry. American Society for Testing and Materials, West Conshohocken, pp 1–8 3. DIN-EN-728-97 (1997) Determination of oxidation induction time-polyolefin pipes and fittings— plastics piping and ducting systems. European Comity for Standardization, Brussels, Belgium, pp 1–12 4. Ehrenstein GW, Riedel G, Trawiel P (2004) Thermal analysis of plastics. Hanser Gardner, Munich 5. Tyuleneva NK, Iring M, Kiryushkin SG, Tudesh F, Shlyapnikov YA (1987) Process occurring in the period of induction of inhibited oxidation of polyethylene. Polym Sci 29(11):2511–2518. https://doi. org/10.1016/0032-3950(87)90224-3 6. Shlyapnikov YA, Marin AP, Kiryushkin SG (1987) Temperature dependence of the inhibited oxidation parameters of polyethylene. Polym Degrad Stab 17:265–272. https://doi.org/10.1016/01413910(87)90086-3 7. Shlyapnikov YA, Tyuleneva NK (1997) Inhibited oxidation of polyethylene: anatomy of induction period. Polym Degrad Stab 56:311–315. https://doi.org/10.1016/S0141-3910(96)00211-X 8. Latocha C, Uhniat M (1992) The kinetics of oxidative induction of LDPE stabilized with commercial antioxidants. Polym Degrad Stab 35:17–22. https://doi.org/10.1016/0141-3910(92)90130-W 9. Celina MC (2013) Review of polymer oxidation and its relationship with materials performance and lifetime prediction. Polym Degrad Stab 98:2419–2429. https://doi.org/10.1016/j.polymdegradstab .2013.06.024 10. Rosa DS, Sarti J, Mei LHI, Filho MM, Silveira S (2000) A study of parameters interfering in oxidative induction time (OIT) results obtained by differential scanning calorimetry in polyolefin. Polym Test 19:523–531. https://doi.org/10.1016/S0142-9418(99)00022-7
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