Summary of vector, pathogen and disease symptoms of plants used to fit the apple proliferation (AP) joint model.
\r\n\tThe discussion will include between others topics: biodegradation processes of by-products coming from food-processing industries, tertiary treatment of wastewaters with microalgae coupled with the generation of potentially commercial interest compounds, biotechnological processes to improve lignocellulosic waste treatment or potential use of fungi in the degradation of environmental pollutants.
\r\n\tThis book intends to provide the reader with a comprehensive overview of the current state-of-the-art in biodegradation processes that contribute to a sustainable and circular bioeconomy giving added value compounds from waste and by-products, up to now hidden, and reducing waste generation at the same time.
Phytoplasma-induced diseases occur in a range of economically important crops and are therefore major threats in agriculture worldwide [1]. Phytoplasma are cell wall-less plant pathogenic bacteria vectored by insects belonging to the order Hemiptera [2].
From an ecological perspective, phytoplasma diseases are complex biological systems. Complexity is linked to many sources of uncertainty which in most cases are difficult to measure. Among others, these sources of uncertainty include vector-pathogen-plant interactions, but also the presence of unknown vectors (e.g., Reptalus panzeri was recently confirmed as a new vector in the grapevine phytoplasma disease “bois noir” [3]) or the time (i.e., latent period) between pathogen infection and symptom expression (in case of plants) or the ability to retransmit the pathogen (in case of vectors).
Besides complexity, the statistical treatment of the inherent dependencies of such biological systems represents another challenge in the modeling process. Traditional statistical methods [such as generalized linear models (GLM), generalized linear mixed models (GLMM)] could be used in a step-wise approach. In a first step, the vector-environment relationship is identified (vector model). Second, using the results of the vector model, the pathogen-environment relationship is established (pathogen model). Finally, the results of both previous models are used to fit the plant disease model. However, this approach does not consider the dependencies between the responses at the same time. In contrast, methods that allow for combined dependencies such as structural equation modeling lack the flexibility in model specification [4]. One solution is using Bayesian inference which allows to jointly estimate the model parameters and at the same time offers high flexibility in defining the model structure.
In this study, the phytoplasma disease apple proliferation (AP) was chosen as a modeling system. AP-specific disease symptoms on apple trees are the proliferation of auxiliary shots (formation of witches’ broom) and enlarged stipulae. AP nonspecific disease symptoms include early leaf reddening, small, taste- and colorless fruits, chlorosis and premature bud break. The causal AP agent is ‘Candidatus Phytoplasma mali’ [5]. In infected apple trees, the phytoplasma resides in phloem tubes and is transmitted by phloem-sucking insect vectors during feeding activity. In South Tyrol, Northern Italy, the most efficient AP vector is Cacopsylla picta (Hemiptera, Psyllidae) [6].
The aim of this chapter was to jointly infer the AP disease epidemiology in South Tyrol, Italy using Bayesian inference. Imperfect detection was accounted for in the vector and symptomatic plant models. The AP insect vector was modeled using an occupancy model. To account for detection bias during the vector sampling, information on sampling effort was used as a predictor in an additional Bernoulli process conditional on the AP vector’s true presence or absence. Based on molecular analyses of AP prevalences in apple trees, I estimated the proportion of latent infected trees to account for imperfect detection of truly AP phytoplasma-infected apple trees.
The AP vector C. picta and AP symptomatic apple trees were surveyed at 44 and 26 orchards in South Tyrol, Northern Italy, respectively. Prevalences of the AP phytoplasma ‘Ca. Phytoplasma mali’ within the vectors were available from 28 orchards (Figure 1). Insect vectors were caught using the “beating tray” method [7, 8]. Depending on orchard size, between 20 and 200 apple trees were randomly selected for vector sampling which was carried out in 2014. Collected vectors were identified according to Ossiannilsson [9]. Insect vectors were then molecularly analyzed for the presence of the pathogen ‘Ca. Phytoplasma mali’. Phytoplasma detection based on a SYBR green real-time PCR was carried out as described in [6]. AP infection status of apple trees was assessed by trained and experienced professionals using visual inspection. Apple cultivars were Golden Delicious and Gala. The monitoring of AP symptoms started in 2013 and each year new AP symptomatic trees were recorded. In most cases, disease symptoms appear 1 year following an infection with ‘Ca. Phytoplasma mali’ [10]. To take into account this latent period, monitoring data of AP symptoms from 2015 were used. Given the inspectors’ skills to detect AP symptoms and independent molecular analysis of AP symptomatic trees, the false-positive rate can be assumed to be approximately zero. Symptomatic means that at least one specific AP symptom or a combination of at least two unspecific AP symptoms was present.
Sampling sites of vector, phytoplasma and plant disease symptoms of the apple proliferation epidemiology in South Tyrol, Northern Italy.
A summary of the final data set including the AP vector, the AP phytoplasma and AP symptoms of trees is provided in Table 1. Metric environmental predictors included elevation (m a.s.l.) and annual mean temperature (°C). Orchards were classified into integrated/not integrated management to account for different pest management strategies.
Min | Q1 | Median | Q3 | Max | Mean | sd | n | Missing | |
---|---|---|---|---|---|---|---|---|---|
Observed vector | 0 | 0 | 1 | 1 | 1 | 0.659 | 0.479 | 44 | 0 |
Vectors analyzed | 1 | 1 | 2 | 3.25 | 35 | 4.29 | 7.91 | 28 | 0 |
Vectors Inf | 0 | 0 | 0 | 1 | 8 | 0.929 | 1.98 | 28 | 0 |
Tree_total | 390 | 950 | 1171 | 1764 | 3065 | 1365 | 613 | 26 | 0 |
Tree_inf | 0 | 0 | 2.5 | 5 | 111 | 9.46 | 24.5 | 26 | 0 |
Summary of vector, pathogen and disease symptoms of plants used to fit the apple proliferation (AP) joint model.
Bayesian inference was used to jointly estimate the dependencies of all responses (AP vector, AP phytoplasma prevalences of the vector, AP symptoms of apple trees) and the environment. To fit the model, all environmental predictors (except vector and phytoplasma predictions) were scaled and centered (i.e., mean subtracted and divided by the standard deviation) to allow a faster convergence of the model fitting algorithm. To decide whether to account for unimodal response-curves, in a pre-step, I fitted multivariate GLMs including quadratic terms of elevation and temperature [11]. As the unimodal response curves were not found to be ecologically sensible, only linear relationships were considered in subsequent analysis. Generalized linear models were developed using a binomial error distribution and a logit link function (GLMM; [12, 13]). The GLMMs were then evaluated in a Bayesian framework. As prior distributions for the fixed effects, zero-centered normal distributions were used. Except for the intercept, priors were defined to be mildly informative which results in a shrinkage effect similar to a ridge-regression [14].
The vector data set, as is common for ecological data, contained many zero values due to the rarity and detectability of the species. To account for imperfect detection, I used a site-occupancy model [15, 16]. These models rely on the “closure assumption” stating that the occupancy state remains unchanged between survey times. The occupancy model combines (1) an ecological process and (2) an observation process. The ecological process of the true occupancy state z (which is a latent or unobserved variable) can be described using a Bernoulli distribution with the occupancy probability Ψ for each surveyed site (indexed with i):
In the observation process, real observations (detections/nondetections) for each survey time (indexed by j) follow a Bernoulli distribution conditional on the true occupancy state z:
where p is defined as the detection probability at site i and survey time j given the site was actually occupied. The detection probability was modeled using a logistic regression and sampling effort as explanatory variable. Sampling effort was defined as the number of sampled trees in proportion to the total number of surveyed trees for AP symptoms.
Field surveys on the prevalence of plant diseases caused by plant-pathogenic bacteria are often based on visual diagnosis of disease symptoms [17, 18, 19]. Given trained and experienced plant inspectors, the false-positive rate can be assumed to be close to zero. The false negative rate is also often considered very small because latent infections are mostly ignored. Based on molecular analyses latent infections for the AP disease were found to be 2.32 and 10.48% depending on age of the apple trees [20]. To account for imperfect detection caused by latent infections, an informative beta prior was used for the detection probability p with parameters a = 2 and b = 80. The specified prior distribution has a mean value of 0.02 and a 95% quantile of 0.06. Hence, in the observation process of the AP disease symptoms model, AP symptoms detections/nondetections were drawn from a Binomial distribution as follows:
where N is the total number of survey trees for each site.
MCMC sampling was carried out by the STAN software (RStan version 2.12.1), which uses the No-U-Turn sampler (NUTS) [21, 22]. Model specifications included three chains with 3000 iterations each and considered a chain to be converged when the potential scale reduction statistic, Ȓ < 1.05 [23]. To access model, fit posterior predictive checks were applied on each model separately using the DHARMa package [24]. The DHARMa package calculates scaled residuals (Bayesian p-values) by comparing observations simulated from the fitted model with observed values. All statistical analyses were carried out in the R statistical environment (version 3.2.2; [25]).
The RStan code for the joint model is available in Appendix A.
The marginal posterior distributions of the parameter of interest of the AP joint model are shown in Figure 2. For the AP vector C. picta, I found that the occurrence of the AP vector was positively correlated with elevation and temperature. The opposite was found for integrated pest management measures which negatively affected the vector occurrence probabilities. The sampling effort represented by the amount of sampled trees seemingly did not affect the detection of the AP vector. Unlike elevation, temperature and pest management, which did not affect the prevalences of the pathogen ‘Ca. Phytoplasma mali’ within the AP vector, an assumably positive relationship between AP vector and its phytoplasma infection rates is indicated in Figure 2. The 80% credible interval (CrI), however, also indicates that a high uncertainty is associated with the true value of this parameter.
Marginal posterior distributions of all environmental predictors for the vector, pathogen and disease symptoms of plants models. Black intervals denote the 95% credible intervals, and red bands indicate the 80% credible intervals. For each predictor, black bullets denote the medians of the three MCMC chains.
As the vector model, AP symptom occurrences on apple trees were likewise positively correlated with elevation and temperature and negatively with integrated pest management measures. Moreover, the model estimated a negative correlation between the AP vector and AP symptoms. No relationship between phytoplasma infection rates within the AP vector and AP symptoms was found.
Regarding the model performance, the potential scale reduction statistic, Ȓ, for each parameter was close to 1 (not shown). Hence, I found no indication of non-convergence of the three chains. Figure 3 shows the results of the residual diagnosis. The plots show no serious violations of distributional assumptions. To confirm the overall uniformity of the scaled residuals, I applied one-sample Kolmogorov–Smirnov tests, which were not significant for all three models.
Residual diagnosis for the vector (a), pathogen (b) and disease symptoms of plants (c) models. Residual diagnosis is based on scaled residuals. For each model, qq-plots are shown on the left, and scaled residuals versus predicted values are plotted on the right.
The modeling case study presented in this chapter illustrated the use of Bayesian inference to jointly investigate the influence of environment on the occurrence of the AP vector C. picta, the prevalences of the AP pathogen (‘Ca. Phytoplasma mali’) within the vector and the occurrence of AP disease symptoms on apple trees.
Using the 80% credible interval, I found that AP vector and AP symptoms on apple trees were positively associated with elevation and temperature and negatively with integrated pest management. While having similar ecological requirements, the joint model indicated a negative relationship between vector and symptoms. Elevation, temperature and integrated pest management did not affect AP phytoplasma prevalences within the vector. No correlation was found between prevalences of AP phytoplasma and symptoms.
‘Ca. Phytoplasma mali’-infection rates of C. picta in South Tyrol are usually higher than those of C. melanoneura, another AP vector (0.6% compared to 11.6%, [26]). Moreover, C. picta is assumed to be the more effective AP vector because it was shown to be able to vertically transmit the pathogen to its offspring [6]. Therefore, the finding that the vector is not correlated with AP symptoms is unexpected but agrees with the vector-symptomatic plant relationship of “bois noir,” a phytoplasma disease on grapevines [17]. The authors argued that this discrepancy may be explained by acknowledging the fact that vector’s presence alone is not responsible for disease occurrence rather it is important to define the pathogen prevalence in the vector population. In this study, however, no correlation was also found between pathogen predictions and AP symptoms. Intuitively, one would expect more AP symptoms given a high infection rate of the vectors but the marginal posterior distribution of the phytoplasma prevalence in the vector population is associated with a large credible interval and does not allow an interpretation of the true parameter value. The lack of positive correlation between infected vectors and AP symptoms hint at other infection sources not considered in this study. For example, recently, a phytoplasma transmission via root-bridges between apple trees was hypothesized [27].
Even though the joint model did not identify a clear correlation between the predictor variable integrated pest management and pathogen occurrences, overall, it seems that integrated pest management is an important environmental driver, negatively influencing vector, and disease symptom occurrences. But it is also possible that the AP responses are influenced by different management measures. For example, the presence/absence of the vectors may be influenced by application time, quantity and type of insecticides, while new disease incidences in plants may also relate to different levels in the effort of uprooting AP symptomatic trees, thereby eliminating sources of new vector infections or root transmissions to adjacent trees [28]. Hence, in a follow-up study, it would be worth to further investigate which specific management measure leads to a decrease in the responses to optimize insect pest management strategies.
Besides jointly estimating the disease system, Bayesian inference allows high flexibility in the model specifications. Models can be easily extended to include detection probabilities, overdispersion or zero-inflation [29, 30, 31]. The present joint model could be further extended by including AP symptoms detection probabilities depending on the cultivar and observed symptoms. The high flexibility is also important when data is collected for purposes different than statistical inference and prediction. For example, if vector data was collected to determine the first appearance of the vector in the orchard (to timely optimize the application of insecticides), vector prediction probabilities need to be constraint by probabilities of the true flight period of the pest insect.
Some parameter estimates in this study were associated with large credible intervals, meaning high uncertainty. One solution would be to use a higher number of observations, which is not always feasible in ecological studies. Another possibility is to include informative priors derived from the literature or previous analysis as illustrated for the informative beta prior to account for imperfect AP symptom detection due to latent infections. Priors play an essential role in every Bayesian analysis. For the environmental parameter estimates included in this chapter, no prior information from previous analysis was available. However, the identified relationships could be used to define prior distributions in future studies.
Finally, the results of a Bayesian inference (posterior distributions) can be summarized using, for example, credible intervals which allow an intuitive interpretation of the parameter estimates associated with well-defined uncertainties. Given chain convergence and successful posterior predictive checks, Bayesian credible intervals are also appropriate for small data sets [32]. This is especially true in observational studies on animal and plant populations where data collection is often time- and cost-consuming.
In summary, the results of the AP joint model suggested that the presence of the AP vector is not necessarily positively correlated with disease occurrence. Instead, other factors such as phytoplasma transmission via root-bridges or specific management strategies should be additionally considered in future studies. In case of the AP disease system, Bayesian inference allowed to jointly fit combined dependencies which are common to phytoplasma epidemiological diseases. Unlike maximum likelihood methods, posterior distributions for all quantities of interest are obtained which could be further summarized using credible intervals and allowed intuitive interpretation of the results. The provided example of a joint Bayesian modeling framework can be used as a basis to infer species-environment relationships of phytoplasma disease systems.
The work was performed as part of the project APPLClust and was funded by the Autonomous Province of Bozen/Bolzano (Italy) and the South Tyrolean Apple Consortium. The author would like to thank Stefanie Fischnaller, Martin Parth, Manuel Messner, Robert Stocker, Christine Kerschbamer and Katrin Janik for providing data on insect vectors, phytoplasma prevalences and occurrences of disease symptoms of apple trees.
Supplementary data associated with this chapter is available online:
The technique or process of using a material to reduce friction and wear between contact surfaces, which are in relative motion, is known as lubrication. It also helps in force transmission, foreign particle transportation and heat transfer. Lubricity is the property of a lubricant to reduce the friction. Lubricants are classified into solid lubricants (e.g.: graphite), semi-solid lubricants (e.g.: grease), liquid lubricants (e.g.: mineral oils based) and gaseous lubricants (e.g.: air). Liquid lubricants are further classified into fixed oil based, mineral oil based and synthetic oil based lubricants on basis of lubricant base stock [1].
\nEvidences for usage of lubricants for thousands of years have been found. Several methods were adopted by human race from time to time to solve the issues regarding friction and wear. Egyptians used animal fats in ball bearings for lubrication back in 1000 B.C. Oil-impregnated lumber were used to slide building stones in the time of the pyramids and on the axles of chariots dated to 1400 BC, calcium soaps have been found [2]. Romans used thrust bearing and lubricants having rapeseed and olive oil as well as animal fat as base back in 40 A.D. Mineral oil based lubricants were widely used since 1850, but they are non-biodegradable, fast depleting and also have adverse effects on the environment [3, 4]. Nowadays, synthetic oil is considered as a better alternative to these mineral oil based lubricants. However, these synthetic oil based lubricants are much expensive than the mineral oil based lubricants [5]. The improper after-use disposals of the available lubricants are creating severe environmental issues by polluting the water bodies [6]. Developing an efficient lubricant from a non-edible plant oil base stock is an effective solution to the above issues, as they are biodegradable, renewable and environment friendly [7]. Vegetable oil base stocks also possess high thermal stability, low volatility, good biodegradability, non-toxicity and good lubrication properties in comparison to mineral oil base stocks [8].
\nThe present article aims at developing a lubricant from a non-edible vegetable oil. In the current work, pongamia oil (PO) is selected as the base stock due to its high oleic acid content and non-edible nature [9, 10, 11, 12, 13, 14]. Formulated oil (FO) is developed by blending suitable additives in PO. Rheological, oxidative and tribological properties of PO and FO are evaluated and compared against the properties of a commercially available lubricant SAE20W40. Hence the primary objectives of the current work are to:
To develop a suitable bio-lubricant from a non-edible vegetable oil viz. PO.
To add suitable additives to PO and enhance its various properties.
To compare the FO with a commercially available lubricant viz. SAE20W40.
Rheological properties, oxidative stability and tribological properties of the PO with and without the addition of suitable additives are studied. Various PO blends with the additives are prepared using a magnetic stirrer. Rheological properties (dynamic viscosity, kinematic viscosity and viscosity index) of the oil blends are evaluated by using a rheometer (Anton Par MCR 102) having parallel plate geometry and redwood viscometer. Oxidation stability of the sample is determined using hot oil oxidation test (HOOT) in a dark oven. Tribological properties viz. wear scar diameter (WSD) and coefficient of friction (COF) are acquired with the help of a four-ball tester apparatus. Worn-out portions of the ball specimens are examined initially using an optical microscope and later by a scanning electron microscope (SEM). Chemical properties of the PO such as total acid number, total base number, iodine value, saponification value are also analyzed as per ASTM Standards (Table 1).
\nMaterials | \n|
---|---|
Category | \nItem | \n
Base oil | \nPongamia oil | \n
Commercial oil | \nSAE20W40 | \n
Viscosity improver additive | \nStyrene butadiene rubber (SBR) & ethylene-vinyl acetate (EVA) | \n
Antioxidant additive | \nTert-butylhydroquinone (TBHQ) | \n
Antiwear additive | \nSiO2 nanoparticle | \n
Instruments | \n|
Device | \nDescription | \n
Magnetic Stirrer with hot plate | \nUsed to blend additives in oil sample | \n
Rheometer (Anton Par MCR 102) | \nIt is a device used to measure the dynamic viscosity of oil samples | \n
Redwood viscometer | \nIt is a device used to measure the kinematic viscosity of oil samples | \n
Hot air oven | \nUsed for hot oil oxidation test of oil samples | \n
Four ball tester | \nDevice used to study the tribological properties of oil samples | \n
Thermo gravimetric analyzer (TGA) | \nIt is used to continuously measure mass while the temperature of a sample is changed over time | \n
Differential scanning calorimetry (DSC) | \nIt is used to measure the difference in the amount of heat required to increase the temperature of a sample and reference is measured as a function of temperature | \n
Materials and instruments used for the analysis.
\n
BLENDING OF POLYMERIC ADDITIVES TO PO: In the present study SBR and EVA were selected as the viscosity improver additives. The PO is blended with these polymer additives to enhance the rheological properties. The samples of 0.5, 1.5 and 2.5 SBR and EVA in PO are prepared separately with the help of a magnetic stirrer having hot plate.
BLENDING OF ANTIOXIDANT ADDITION TO PO: The PO is blended with TBHQ to enhance the oxidative stability. 0.5, 1.5 and 2.5 wt% of antioxidant TBHQ is blended with PO respectively. The blends are prepared using a magnetic stirrer.
BLENDING OF ANTIWEAR ADDITIVES TO PO: The PO is blended with nanoparticles of SiO2 (Particle Size: 15–40 nm) to improve its tribological properties viz. wear scar diameter and coefficient of friction. The blend is prepared by adding SiO2 in 0.6, 0.8 and 1.0 wt% respectively to PO using a magnetic stirrer.
\n
\nTOTAL ACID NUMBER (TAN): The TAN of the PO is obtained by titration method. It is a measure of acidity of the oil that is done by dissolving the PO in toluene and then titrating it against potassium hydroxide (KOH) using phenolphthalein as indicator. ASTM D664 standard was used for calculations.
\nTOTAL BASE NUMBER: The TBN of the PO is obtained by titration method. It is a measure of basicity of the oil done by dissolving PO in chlorobenzene and then titrating it against hydrochloric acid (HCl) using phenolphthalein as indicator. ASTM D2896 standard was used for calculations.
\nSAPONIFICATION VALUE: Saponification value denotes the number of milligrams of potassium hydroxide needed to saponify 1 gof fat according to the conditions specified. It is a calculation of the average molecular weight (or chain length) of all the fatty acids present. ASTM D5558-95 standard was used for calculations.
\nIODINE VALUE: The iodine value (or iodine adsorption value or iodine number or iodine index) is the mass of iodine in grams that is consumed by 100 g of a chemical substance. Iodine numbers are used to find the quantity of unsaturation in fatty acids. ASTM D1959 standard was used for calculations.
\nDYNAMIC VISCOSITY ANALYSIS: The dynamic viscosity is measured using Anton Par Rheometer MCR 102 in rotation mode having parallel plate geometry. Dynamic (absolute) viscosity is the tangential force per unit area required to move one horizontal plane with respect to another plane, at a unit velocity, while maintaining a unit distance apart in the fluid.
\nKINEMATIC VISCOSITY ANALYSIS: Kinematic viscosity is the ratio of absolute (or dynamic) viscosity to density. Force is not involved in this quantity. Kinematic viscosity can be found out by dividing the absolute viscosity of a fluid with the fluid mass density. The kinematic viscosity is measured using Redwood viscometer for PO from 40–100°C.
\nWEAR SCAR DIAMETER ANALYSIS: Four ball testing of the oil is done in a four ball tester apparatus, with the sample, given a load of 40 kg at 75°C. The ball is then analyzed using a scanning electron microscope and wear scar diameter is recorded.
\nCOEFFICIENT OF FRICTION ANALYSIS: Four ball testing of the oil is done in a four ball tester apparatus, with the sample, given a load of 40 kg at 75°C. Calculations were done as per ASTM D 5183-05 standard to test COF.
\nHOT OIL OXIDATION TEST: The quickened aging of vegetable oil and PO added with antioxidant are stimulated with HOOT. This is done to find the oxidation stability.
\nTHERMOGRAVIMETRIC ANALYSIS: The thermal stability of antioxidant selected and that of the formulated oil (FO) are evaluated using the thermo-analytical curves obtained from TGA, Q50 equipment, TA-Instruments.
A series of experiments have been conducted as per standards and results are taken as an average of 3 readings having standard deviation of the sample as the error bar. The experimental data are given in the graphs and tables. Table 2 represents the physicochemical properties of PO and compared with two widely studied bases stock viz. sesame oil and coconut oil.
\nSl. no | \nAnalysis | \nPongamia oil | \nSesame oil [9] | \nCoconut oil [9] | \n
---|---|---|---|---|
1 | \nIodine value (g I2/100 g) | \n88.18 ± 0.19 | \n105.1 | \n9 | \n
2 | \nSaponification value (mg KOH/g) | \n168.75 ± 3.72 | \n191 | \n261 | \n
3 | \nTotal acid number (mg KOH/g) | \n3.94 ± 0.14 | \n3.18 | \n0.56 | \n
4 | \nTotal base number (mg KOH/g) | \n0.37 ± 0.01 | \n0.41 | \n0.16 | \n
5 | \nDensity (g/cm3) | \n0.92 | \n0.9216 | \n0.92429 | \n
Physiochemical properties of PO, sesame oil, and coconut oil.
The density of PO is found to be 0.92 g/cm3 using a pycnometer, which is lower than that of water. It is found that the acid number of PO is slightly higher than that of the sesame oil and coconut oil. However, for lubricant TAN value should be low as possible [15]. PO has the least saponification value among the three. Low SV indicates long fatty acid chain, which helps in the formation of thick tribolayer [16]. Iodine value of a triglyceride indirectly shows the amount of unsaturation present in it [17]. From the iodine value, it is clear that unsaturation in PO lies between sesame oil and coconut oil. Thus, it is clear from the evaluation of physicochemical properties of PO that most of the chemical properties are well suited to the desirable properties of a base stock, which can lead to the development of an eco-friendly lubricant.
\nThe temperature below which the liquid loses its flow characteristics is known as pour point of a liquid [18]. Figure 1 represents the pour point of PO, which is evaluated from differential scanning calorimetry (DSC). The pour point of PO was found as 6.29°C. The pour point of PO is found to be lower than that of coconut oil [9] due to the presence of more unsaturated fatty acids. Pour point of the lubricants can be reduced using a suitable pour point depressant.
\nDifferential scanning calorimetry (DSC) curve of PO.
The thermal stability of PO is studied using TGA [19]. From the TGA results (Figure 2), the onset temperature of PO for thermal degradation of 98% is found out as 197.6°C. The weight percentage reduction 2% of PO was done by assuming the loss of moisture content and volatile components from it [19]. Thus, by observing the TGA, it is evident that PO is a well suited environment-friendly base stock for a wide range of temperatures. The thermal compared to SAE20W40 which have thermal degradation of at 204.39°C [9].
\nTGA of PO.
\nTable 3 shows the fatty acid profile of PO, compared with that of sesame oil and coconut oil. High amount of oleic acid present in the PO can improve the tribological properties [10]. A larger proportion of saturated fatty acids can adversely affect the pour point of the lubricants.
\nConstituent | \nPongamia oil [10] | \nSesame oil [9] | \nCoconut oil [9] | \n
---|---|---|---|
Oleic acid | \n62.98% | \n42% | \n5% | \n
Linoleic acid | \n16.84% | \n38% | \n1% | \n
Palmetic acid | \n9.1% | \n13% | \n7.5% | \n
Fatty acid profile of PO and sesame oil.
It is clear from Figures 3 and 4 that the dynamic viscosities of PO at various temperature range is inferior w.r.t commercially available SAE 20W40 (Figures 3 and 4). Hence, the dynamic viscosity of PO is enhanced using different weight percentages of EVA and SBR polymers. The variation of viscosity is studied from 25–100°C [20, 21, 22].
\nDynamic viscosity v/s temperature curve for PO with EVA compared against SAE20W40.
Dynamic viscosity v/s temperature curve for PO with SBR compared against SAE20W40.
From Figure 3, it is observed that the plain PO is having the lowest viscosity both at lower as well as higher temperatures. The viscosity is found to be improving on addition of EVA in different weight percentages to PO. PO with 2.5% w.t. EVA has shown the highest change in viscosity both at lower as well as higher temperatures.
\nFrom Figure 4, it is found that SBR tends to show better improvements in viscosity than EVA at different weight percentages. The curve of SBR at 2.5% w.t. in PO is found to be close enough to reference oil SAE20W40. However, from the results obtained from Figures 3 and 4, at equal weight percentages of the additives PO + SBR combinations have shown higher viscosity enhancements. Hence, SBR has been selected as the viscosity enhancer in the current study.
\nHigh oxidation stability of TBHQ is because of the longer alkyl chains present in it [23]. Oxidation stability of the oil samples are estimated by measuring the change in viscosity at 40°C after hot oil oxidation test (HOOT) from the fresh sample without subjected to HOOT [24]. Change in dynamic viscosity of neat PO, PO oil samples blended with different weight percentages of TBHQ and SAE20W40 at 40°C are calculated and plotted as shown in Figure 5. From the analysis of different oil samples blended with different weight percentages of TBHQ , PO + 2.5% TBHQ showed the least change in viscosity. It is observed that for the weight percentage of TBHQ beyond 2.5%, it is difficult to dissolve in PO. The change in viscosity at 40°C of PO + 2.5% TBHQ is found to be very lesser than neat PO comparable with that of SAE20W40. Thus PO + 2.5% TBHQ have excellent oxidation stability.
\nChange in dynamic viscosities of oil samples at 40°C after HOOT.
Tribological properties were evaluated with the help of a four ball tester equipment [25]. Coefficient of friction (COF) and wear scar diameter (WSD) are the parameters used to evaluate tribological properties. From Figure 6, it is noted that with the addition of nanoparticles, the WSD is reduced [26, 27, 28, 29]. The WSD is found to be decreased by 18.31% with the blending of 0.85% weight percentage of SiO2. The small size of SiO2 makes improvement in the tribological properties by reducing friction and wear by rolling, mending, and protective film formation [30].
\nVariation in WSD with the blending of different weight percentages of SiO2 with PO.
From Figure 7, it is noted that with the addition of nanoparticles, the COF is increasing for PO blends with SiO2. However, in comparison with SAE20W40, the COF value of PO blends is considerably low.
\nVariation in COF with the blending of different weight percentages of SiO2 with PO.
Based on the tests conducted, the formulated oil (FO) is PO + 2.5 wt% SBR+ 2.5 wt% TBHQ +0.85 wt% SiO2.
\n\nFigure 8 represents the dynamic viscosity v/s temperature curve of the formulated oil and was found to be comparable with that of reference oil SAE 20W40.
\nDynamic viscosity v/s temperature curve comparison of formulated oil and SAE 20W40.
The evaluated properties and results of FO compared with SAE20W40 is as shown in Table 4. The COF of FO is found to be comparatively lower than that of SAE20W40. This implies that the frictional effect produced by FO will be much lower than the latter. The viscosity index of the FO shows much improved results than that of SAE20W40. This ensures the use of FO at wide range of temperatures. The WSD of the FO is much closer to that of SAE20W40. Therefore, the FO has almost similar anti-wear effects as that of SAE20W40. Increase in dynamic viscosity after HOOT at 40°C indicates that the oxidation stability of the FO is superior that PO and is comparable with that of SAE20W40.
\nProperties | \nFO | \nSAE20W40 [9] | \n
---|---|---|
COF | \n0.092 | \n0.107 | \n
Wear scar diameter | \n0.48 mm | \n0.47 mm | \n
Viscosity index | \n227.4 | \n135.57 | \n
Increase in dynamic viscosity after HOOT at 40°C | \n5.4 cP | \n3 cP | \n
Evaluated properties and results of the FO compared with SAE20W40.
The project was aimed on formulating a bio-lubricant from a non-edible vegetable oil as base stock. In addition to non-edible nature, pongamia oil (PO) was chosen as the base oil due to its high oleic acid content. SBR, TBHQ and SiO2 nanoparticle additives are added to PO for improving its viscosity, oxidation stability and antiwear properties respectively. The final base oil and additive combination of the formulated oil (FO) is PO + 2.5 wt% SBR + 2.5 wt% TBHQ +0.85 wt% SiO2. Rheological studies show that the performance of the FO is identical to the dynamic viscosity trend of SAE20W40 and possess a superior viscosity index than SAE 20 W40. HOOT indicates that the oxidation stability of FO is much closer to that of SAE20W40. Tribological studies indicate that FO possess identical WSD and lower COF than SAE20W40.
\nPongamia (milletia) is a genus of legume plant family. It is distributed in tropical and subtropical regions of the world. It is easily cultivable [31] and can grow in semi-arid conditions [32]. The cultivation of these trees has increased due to its recent interests in the field of bio-fuels and lubricants. This project is based on the enhancement of pongamia oil (PO) to a bio-lubricant, the cultivation of these trees will be promoted to a great extend thereby improving the agricultural sector.
\nThese Terms and Conditions outline the rules and regulations pertaining to the use of IntechOpen’s website www.intechopen.com and all the subdomains owned by IntechOpen located at 5 Princes Gate Court, London, SW7 2QJ, United Kingdom.
',metaTitle:"Terms and Conditions",metaDescription:"These terms and conditions outline the rules and regulations for the use of IntechOpen Website at https://intechopen.com and all its subdomains owned by Intech Limited located at 7th floor, 10 Lower Thames Street, London, EC3R 6AF, UK.",metaKeywords:null,canonicalURL:"/page/terms-and-conditions",contentRaw:'[{"type":"htmlEditorComponent","content":"By accessing the website at www.intechopen.com you are agreeing to be bound by these Terms of Service, all applicable laws and regulations, and agree that you are responsible for compliance with any applicable local laws. Use and/or access to this site is based on full agreement and compliance of these Terms. All materials contained on this website are protected by applicable copyright and trademark laws.
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\n\nThe following terminology applies to these Terms and Conditions, Privacy Statement, Disclaimer Notice, and any or all Agreements:
\n\n“Client”, “Customer”, “You” and “Your” refers to you, the person accessing this website and accepting the Company’s Terms and Conditions;
\n\n“The Company”, “Ourselves”, “We”, “Our” and “Us”, refers to our Company, IntechOpen;
\n\n“Party”, “Parties”, or “Us”, refers to both the Client and ourselves, or either the Client or ourselves.
\n\nAll Terms refer to the offer, acceptance, and consideration of payment necessary to provide assistance to the Client in the most appropriate manner, whether by formal meetings of a fixed duration, or by any other agreed means, for the express purpose of meeting the Client’s needs in respect of provision of the Company’s stated services/products, and in accordance with, and subject to, the prevailing laws of the United Kingdom.
\n\nAny use of the above terminology, or other words in the singular, plural, capitalization and/or he/she or they, are taken as interchangeable.
\n\nUnless otherwise stated, IntechOpen and/or its licensors own the intellectual property rights for all materials on www.intechopen.com. All intellectual property rights are reserved. You may view, download, share, link and print pages from www.intechopen.com for your own personal use, subject to the restrictions set out in these Terms and Conditions.
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\n\nIn no circumstances shall IntechOpen or its suppliers be liable for any damages (including, without limitation, damages for loss of data or profit, or due to business interruption) arising out of the use, or inability to use, the materials on IntechOpen's websites, even if IntechOpen or an IntechOpen authorized representative has been notified orally or in writing of the possibility of such damage. Some jurisdictions do not allow limitations on implied warranties, or limitations of liability for consequential or incidental damages; consequently, these limitations may not apply to you.
\n\nIntechopen.com website content and services are provided on an "AS IS" and an "AS AVAILABLE" basis. Material appearing on www.intechopen.com could include minor technical, typographical, or photographic errors. IntechOpen may make changes to any material contained on its website at any time without notice.
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\n\nThese Terms and Conditions are governed by and construed in accordance with the laws of the United Kingdom and you irrevocably submit to the exclusive jurisdiction of the courts in London, United Kingdom.
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