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Electrical Conductivity Testing Applied to the Assessment of Freshly Collected Kielmeyera coriacea Mart. Seeds: MyronLMeters.com
Tweet MyronLMeters.com brings you the latest in conductivity measurement research like the article below. Please click here for accurate, reliable, conductivity meters. Abstract Assessment of seed vigor has long been an important tool of seed quality control programs. The conductivity test is a promising method for assessment of seed vigor, but proper protocols for its […]
MyronLMeters.com brings you the latest in conductivity measurement research like the article below. Please click here for accurate, reliable, conductivity meters.
Assessment of seed vigor has long been an important tool of seed quality control programs. The conductivity test is a promising method for assessment of seed vigor, but proper protocols for its execution have yet to be established. The objective of this study was to assess the efficiency of electrical conductivity (EC) testing as a means of assessing the viability of freshly collected Kielmeyera Coriacea Mart. seeds. The test was performed on individual seeds rather than in a bulk configuration. Seeds were soaked for different periods (30 min, 90 min, 120 min., 180 min, and 240 min) at a constant temperature of 25°C. Conductivity was then measured with a benchtop EC meter.
Seeds are the primary factor of the seedling production process, despite their minor contribution to the end cost of each seedling. In order to estimate the success rate of seedling production, it is essential that seed characteristics such as vigor and germinability be known .
The importance of knowing the characteristics of Brazilian forest species to safer and more objective management of seedling production cannot be overstated. However, such studies are scarce, particularly in light of the vast number of species with this potential . Given the intensity of anthropogenic pressure and the importance of rehabilitating disrupted or degraded environments, in-depth research of forest species is warranted.
Routine methods used for determination of seed quality and viability include germination testing and the tetrazolium test. Methods such as measurement of soak solution pH, electrical conductivity, and potassium content of leachate, all based on the permeability of the cell membrane system, are increasingly being employed in the assessment of seed vigor, as they are reliable and fast and can thus speed the decision making process.
Electrical conductivity testing, as applied to forest seeds, has yet to be standardized. Studies conducted thus far have focused on assessment of seed soaking times, which may range from 4 to 48 hours. Even at 48 hours, the conductivity test is considered a rapid technique as compared to the germination test, which, despite its status as a widespread and firmly established method, can take anywhere from 30 to 360 days to yield results (depending on species), and is limited by factors such as dormant seeds.
The total concentration of electrolytes leached by seeds during soaking has long been assessed indirectly, mostly through the conductivity test, which takes advantage of the fact that inorganic ions make up a substantial portion of these electrolytes [3–5].
Rapid assessment of seed quality allows for preemptive decision-making during harvest, processing, sale and storage operations, thus optimizing use of financial resources throughout these processes.
K. coriacea Mart. is a species of the Clusiaceae (Guttiferae) family popularly known in Brazil as pau-santo (Portuguese for “holy wood”), due to its properties as a medicinal and melliferous plant and as a source of cork. In traditional Brazilian medicine, the leaves are used as an emollient and antitumor agent, and the resin as a tonic and in the treatment of toothache and various infections. The fruits are used in regional crafts and flower arrangements. Even if the dye is of the leaves and bark. The trunk provides cork .
K. coriaceae specimens grow to approximately 4 meters in height. The flowering period extends from January to April and the fruiting period from May to September, and seed collection can take place from September onwards. Leaves are alternate, simple, oval to elliptical, coriaceous, and clustered at the end of the branches, and feature highly visible, pink midribs. A white to off-white latex is secreted in small amounts upon removal of leaves. Flowers are white to pale pink in color, large, fragrant, with many yellow stamens and are borne in short clusters near the apex of the branches. Seedling production requires that seeds be sown shortly after collection.
In the fruit are found 60 to 80 seeds with anemochoric. The seed varies from round to oblong, winged at the ends, light brown color, has integument thin and fragile, with smooth texture, the sizes range from 4.3 to 5.6 cm long, 1.3 to 1.9 cm wide, and 0.2 to 0.5 centimeter thick. The individual weight of the seeds ranges from. 112 to.128 grams. Nursery radicle emission occurred at 7 days and the germination rate was 90%. Germination occurs within 7 to 10 days. The species is slow growing, both in the field and in a nursery setting .
The present study sought to assess the applicability of the conductivity test to freshly collected K. coriacea Mart. seeds by determining the optimal soak time for performance of the test and comparing results obtained with this method against those obtained by tetrazolium and germination testing of seeds from the same batch.
2. Materials and Methods
2.1. Seed Collection
Seeds were collected in the cerrado sensu stricto, in SCA (Clean Water Farm), area of study at the University of Brasília (UNB) in August 2010, matrixes marked with the aid of GPS, after the period of physiological maturation of the seeds. The collection of fruits was directly from the tree, with the help of trimmer, then the seeds were processed and stored in paper bags at room temperature in the laboratory.
2.2. Conductivity Test
The development of tests to evaluate the physiological quality of seeds, as well as the standardization of these is essential for the establishment of an efficient quality control . One of the main requirements for the seed vigor refers to obtain reliable results in a relatively short period of time, allowing the speed of decision making especially as regards the operations of collection, processing, and marketing . The literature indicates that rapid tests are most studied early events related to the deterioration of the sequence proposed by Delouche and Baskin  as the degradation of cell membranes and reduced activity, and biosynthetic respiratory . The measurement of electrical conductivity through the electrolyte amount released by soaking seeds in water has been applied by the individual method where each seed is a sample or more often, a sample of seed representative of a population (mass method). For this case, the results represent the average conductivity of a group of seeds, may a small amount of dead seeds affect the conductivity of a batch with many high-quality seed generating a read underestimated. To minimize this problem, we recommend choosing the seeds, excluding the damaged seeds.
The electrical conductivity is based on the principle that the deterioration process is the leaching of the cells of seeds soaked in water due to loss of integrity of cellular systems. Thus, low conductivity means a high-quality seed and high conductivity, that is, greater output seed leachate, suggests that less force .
The electrical conductivity is not yet widely used in Brazil, its use is restricted to activities related to research (Krzyzanowski et al., 1991). There are common jobs using this test to determine the physiological quality of tree seeds. However, it is a promising vigor test for possible standardization of the methodology, at least within a species. However, it is a promising vigor test for possible standardization of the methodology, at least within a species. However, there are factors which influence the conductivity values as the size, the initial water content, temperature and time of soaking, the number of seeds per sample, and genotype .
Five treatments were carried out to test the efficiency of the conductivity test as a means of evaluating the viability of freshly collected K. coriacea Mart. seeds.
Five runs of 20 seeds were tested for each treatment. Seeds were individually placed into containers holding 50 mL of distilled water and left to soak for 30, 90, 120, 180, and 240 minutes in a germination chamber set to a constant temperature of 25°C. The minimum time taken for the soaking of 30 minutes was adopted by the same authors and Amaral and peske , Fernandes et al. , and Matos  who concluded that the period of 30 minutes of soaking is more effective to estimate the germination of the seeds. After each period, the conductivity of the soak solution was immediately tested with a benchtop EC meter precise to +/−1% (Quimis). Readings were expressed as μS·cm−1/g−1 seed .
Data thus obtained were subjected to analysis of variance with partitioning into orthogonal polynomials for analysis of the effect of soaking times on electrical conductivity.
2.3. Tetrazolium Test
The tetrazolium test, also known as biochemical test for vitality, is a technique used to estimate the viability and seed germination. A fundamental condition for ensuring the efficiency of the test is the direct contact of the tetrazolium solution with the tissues of the seed to be tested. Due to the impermeability of the coats of most forest tree seeds, it is necessary to adopt a previous preparation of the seeds that were tested. This preparation is based on facilitating entry of the solution in the seed. Among the preparations that precede the test we have cutting the seed coat, seed coat removal, scarification by sandpaper scarification by soaking in hot water and water . In the previous preparation of the seeds, factors such as concentration of the solution or even the time of the staining solution can affect the efficiency of the test in the evaluation of seed quality. The time required for the development of appropriate color according to the Rules for Seed Analysis  varies depending on each species, can be between 30 and 240 minutes.
The tetrazolium test has been widely used in seeds of various species due to the speed and efficiency in the characterization of the viability and vigor, and the possibility of damage to the same distinction, assisting in the process of quality control from the steps of harvest storage (GRIS et al, 2007).
The tetrazolium test was also applied to freshly collected K. coriacea Mart. seeds, for a total of three runs and 20 seeds. Seeds were soaked in a 0.5% solution of 2,3,5-triphenyl-2H-tetrazolium for 24 hours in a germination chamber set to a constant temperature of 25°C. After each run, seeds were washed, bisected, and the half-containing the embryonic axis placed under a stereo viewer for examination of staining patterns .
2.4. Germination Test
The standard germination test is the official procedure to evaluate the ability of seeds to produce normal seedlings under favorable conditions in the field, but does not always reveal differences in quality and performance among seed lots, which can manifest in storage or in the field .
During the germination test optimum conditions are provided and controlled for seeds to encourage the resumption of metabolic activity which will result in the seedlings. The main objective of the germination test is the information about the quality of seeds, which is used in the identification of lots for storage and sowing .
Freshly collected K. coriacea Mart. seeds were placed in a germination chamber at a constant temperature of 25°C (Treatment 1) or an alternating temperature of 20–30°C (Treatment 2), on a standard cycle of 8 hours of light and 16 hours of dark. Each test consisted of five runs and was performed on 20 seeds.
Germination was defined as emergence of at least 2.0 mm of the primary root . Assessment was conducted daily, and emergence was observed between day 6 and day 7. At the end of the 14-day test period, the germination percentage was calculated on the basis of radicle emergence .
3.1. Conductivity Test
Different soaking times were not associated with any significant differences in conductivity results in K. coriacea Mart. seeds (Table 1).
Table 1: Conductivity ranges of freshly collected Kielmeyera coriacea Mart. seeds after soaking for different periods.
Seeds with a leachate conductivity range of 7–17.99 μS·cm·g were considered nonviable, confirming the hypothesis behind conductivity testing, which is the nonviable seeds that have higher soaking solution conductivity values (Table 2).
Table 2: Percentage of viable Kielmeyera coriacea Mart. seeds according to EC range.
Analysis of variance revealed a low coefficient of variation (20.26%), which suggests good experimental control (Table 3).
Table 3: Analysis of variance of various soaking times for electrical conductivity testing of Kielmeyera coriacea Mart. seeds.
After analysis of variance, the correlation between the soaking time and electrical conductivity variables was assessed. The cubic model yielded
which is indicative of a positive correlation between the study variables.
The following equation was obtained on the basis of the cubic model:
Analysis of a plot of the above function in the GeoGebra 2007 software package shows that variation in electrical conductivity as a function of soaking time is minor and approaches a constant, which is consistent with the study results, in which changes in soaking time had no influence on conductivity (Figure 1).
Matos  reported that a 30-minute soak was enough for assessment of Anadenanthera falcata, Copaifera langsdorffii, and Enterolobium contortisiliquum seeds by the soaking solution pH method—that is, the amount of matter leached after this period sufficed for measurement.
Although the principle of conductivity is the same used for the test pH of exudate, the soaking time needed to analyze the differential seeds through the conductivity may be explained by the fact that this technique is quantitative, while pH in the art exudate analyzes are qualitative. In other words to the technique of pH values of the exudate it is important to detect the acidity of imbibition while on the electrical conductivity we draw a comparison between the analyzed values to separate viable from nonviable samples. To determine a value of electrical conductivity as a reference to determine viable seeds are to be considered the values obtained for fresh seeds and seeds stored.
The thickness of the K. coriacea Mart. seed coat may also have affected the soaking procedure; this species has very thin seed coats, which makes soaking a very fast process.
These results are consistent with those reported by Rodrigues , who subjected stored K. coriaceaMart. seeds to the conductivity test and found that 90 minutes is an appropriate soaking time for analysis.
Therefore, it can be inferred that for seed Kielmeyera coriacea Mart. the soaking time of 90 minutes can be applied to obtain satisfactory results.
3.2. Tetrazolium Test
Table 4 shows the results of tetrazolium testing of K. coriacea Mart. seeds in our sample. The mean viability rate was 96.6%. The testing procedure was based on Brazilian Ministry of Agriculture recommendations .
The results of the tetrazolium test were quite similar to those obtained with the conductivity method, thus confirming the efficiency of the latter method as a means for assessing the viability of K. coriaceaMart. seeds.
3.3. Germination Test
The germination test results of freshly collected K. coriacea Mart. seeds are shown in Table 5. Regardless of temperature, both test batches exhibited good viability, and no seed dormancy was detected.
Radicle emergence was observed between day 7 and day 9 of the test, according to the analysis criteria proposed by Labouriau .
These findings are consistent with those of Melo et al.,  who reported high and relatively rapid germination rates for K. coriacea seeds kept at 25°C on paper towels, with emergence of a perfect radicle on the 7th day of assessment.
The electrical conductivity can be used as an indicator of seed viability and presents two advantages: to provide rapid and reliable results and the technique is not destructive and can use the seeds after the conductivity test, so they can be used to produce seedlings.
The present study showed that different soaking times had no effect on the results of conductivity testing of freshly collected K. coriacea Mart. seeds, suggesting that the amount of leached matter was never below the threshold required for adequate testing.
Electrical conductivity testing proved to be a feasible option for viability testing of K. coriacea Mart. seeds, as the results obtained with conductivity testing were confirmed by germination testing and by the tetrazolium test.
- J. M. M. Matos, Evaluation of pH test on exudate check feasibility of forest seeds, dissertation, University of Brasília, Brasília, Brazil, 2009.
- F. Poggiani, S. Bruni, and E. S. Q. Barbos, “Effect of shading on seedling growth of three species forest,” in National conference on native plants, vol. 2, pp. 564–569, Institute of Forestry, 1992.
- M. B. Mcdonald Jr. and D. O. Wilson, “ASA-610 ability to detect changes in soybean seed quality,” Journal of Seed Technology, vol. 5, no. 1, pp. 56–66, 1980.
- S. Matthews and A. Powell, “A eletrical conductivity test,” in Handbook of Vigor Test Methods, D. A. Perry, Ed., pp. 37–42, International Seed Testing Associaty, Zurich, Switzerland, 1981.
- J. Son Mark, W. R. Singh, A. D. C. Novembre, and H. M. C. P. Chamma, “Comparative studies to evaluate dem’etodos physiological quality of soybean seeds, with emphasis the electrical conductivity test,” Brazilian Journal of Agricultural Research, vol. 25, no. 12, pp. 1805–1815, 1990.
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- J. M. Felfili, C. W. Fagg, J. C. S. Silva, et al., Plants of the APA Gama Cabeça de Veado: Species, ecosystems and recovery, University of Brasilia, Brasília, Department of Engineering Forest, Brasília, Brazil, 2002.
- M. F. B. Muniz, et al., “Comparison of methods for evaluating the physiological and health quality of melon seeds,” Journal of Seeds, Pellets, vol. 26, no. 2, pp. 144–149, 2004.
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- A. S. Amaral and S. T. Peske, “Exudate pH to estimate, in 30 minutes seed viability of soybeans,”Journal of seeds, vol. 6, no. 3, pp. 85–92, 1984.
- E. J. Fernandes, R. Sader, and N. M. Carvalho, “seed viability beans (Phaseolus vulgaris L.) estimated by the pH of the exudate,” in Congress Brazil’s Seeds, Gramado, Brazil, 1987.
- F. C. Krzyzanowski and R. D. Vieira, “Electrical conductivity test,” in Seed Vigor: Concepts and Tests, F. C. Krzyzanowski, R. D. Vieira, and J. B. France Neto, Eds., pp. 4.1–4.26, Abrates, London, UK, 1999.
- Ministry of Agriculture, Livestock and Supply, Rule for seed testing, SNPA/DNPV/CLAV, Brasilia, Brazil, 1992.
- Ministry of Agriculture, Livestock and Supply, Rule for seed testing, SNPA/DNPV/CLAV, Brasilia, Brazil, 2009.
- N. M. Carvalho and J. Nakagawa, Seeds: Science, Technology and Production, FUNEP, Jaboticabal, Brazil, 2000.
- Pina-Rodrigues, et al., “Quality test,” in Germination from Basic to Applied, A. Ferreira and G. F. Borghetti, Eds., pp. 283–297, 2004.
- A. G. Ferreira and F. Borghetti, from basic to Germination applied, Artmed, Porto Alegre, Brazil, 2004.
- L. G. Labouriau, seed germination, OAS, Washington, DC, USA, 1983.
- L. L. Rodrigues, Study of imbibition time for application the method of electrical conductivity in the verification of the feasibility forest seeds stored, monograph, University of Brasília, Brasília, Brazil, 2010.
- J. T. Melo, J. F. Ribeiro, and V. L. G. F. Lima, “Germination of Seeds of some tree species native to the Cerrado,” Journal of Seeds, vol. 1, no. 2, pp. 8–12, 1979.
1Seed Technology Laboratory of Forestry, Department of Forestry, University of Brasilia, CP 04357, 70919970 Campus Asa Norte, DF, Brazil
2Department of Forestry, University of Brasilia, CP 04357, 70919970 Campus Asa Norte, DF, Brazil
Received 17 December 2011; Accepted 14 February 2012
Academic Editors: A. Berville, C. Gisbert, J. Hatfield, and Y. Ito
Copyright © 2012 Kennya Mara Oliveira Ramos et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
TweetHypothesis: Does the time of year affect the conductivity of stagnant water in a given location? Abstract: We decided to test the conductivity levels of the water at Flat Rock Brook. If the conductivity levels are higher, it might imply higher total dissolved solid levels. We would like to see if the conductivity level changes during seasons […]
Hypothesis: Does the time of year affect the conductivity of stagnant water in a given location? Abstract: We decided to test the conductivity levels of the water at Flat Rock Brook. If the conductivity levels are higher, it might imply higher total dissolved solid levels. We would like to see if the conductivity level changes during seasons with snowfall versus seasons without snowfall. Background:
- Independent Variable: Time of Year (Season)
- Independent Variable: Location
- Dependent Variable: Conductivity Level (mg/L TDS)
The purpose of the experiment is to test the change in conductivity level throughout the year (on a seasonal basis) in various locations. While doing this experiment, it is important to keep in mind these three things:
- How does conductivity vary at any of the given sites during a given season.(1)
- What human influence might have an impact on the conductivity of the water at any given part of the year.(1)
- Why might this change affect the ability of organisms to live in the given test sites.(1)
This is important to Flat Rock Brook because the data could be used to do several things. For example, the change in conductivity may change the organic life in the water, thus changing the ability to safely drink it. The change may impact the ability of organisms to grow in the water, and it may change the reactive nature of the water. Conductivity can be measured by the total dissolved solids in the water. Total Dissolves Solids include the number of mineral and salt impurities in the water. (1) Ultimately, the number of minerals and salts determines how many ions in mg/L. The impurities in the water can include runoff from roads, wastewater from industrial plants, and soils and rocks. (1) The amount of total dissolved solids in the water can have a physiological effect on plants and animals living in the ponds. (2) Conductivity can be used as a way of noting changes in water conditions over short periods of time. (2) Also, the level of total dissolved solvents in water can have an effect of the ability of habitat-forming plants to grow, thus disrupting the presence of certain species. (2) Materials:
- GPS Navigator by Magellan: We used the GPS as a way to mark off specific testing sites at McFadden’s Pond and Quarry Pond, in order to test in a precise and consistent location
- pH/Conductivity Probe: We used the pH/Conductivity Probe to test the level of conductivity in the various locations. The conductivity was measured in mg/L (TDS), and microsiemens (µS), but due to the difficult nature of working with microsiemens, we chose to work primarily with mg/L(TDS).
- Distilled Water: We used the distilled water to wash off the probe in between tests in order to maintain accurate readings without tainted results.
- Map of Flat Rock Brook: We used the map of Flat Rock Brook in order to find locations from which we could test conductivity levels of water.
- Vernier conductivity probe used with a Lab Pro interface: We used this for our May data in order to get a more accurate reading. By taking samples from Flat Rock Brook, we connected this probe to Logger Pro and recorded the conductivity which was also measured in TDS. NOTE: We used the conductivity data from these readings in our graphs and overall analysis because it provided a more accurate measurement
Methods: *Adapted from Electrical Conductivity Protocol Used by University Corporation for Atmospheric Research, Colorado State University, and NASA. (Water Temperature was not recorded.)
- Record water temperature
- Pour water sample into two containers (or measure in water body)
- Rinse electrodes with distilled water, blot dry
- Place meter in first container, 2-3 seconds
- Remove meter, shake gently, and place in second
container, 1 minute (Do not rinse with distilled water)
- Record value when stabilized
- Repeat measurement with new sample water, twice
- Average three measurements and check for accuracy
Original Protocol can be found at this link*: http://18.104.22.168/search?q=cache:tpTXJUjpiHgJ:globe.ucar.edu/trr-
Cleaning off the conductivity probe before testing the water. (Figure 2)
Testing the conductivity of the pond (Figure 3)
Results: Fall (November) : -Quarry Pond:
- .9 mg/L
-McFadden’s Pond (site A)*:
- 3.1 mg/L
-McFadden’s Pond (site B)*:
- 3.05 mg/L
Spring (May): (with ph/conductivity probe) -Quarry Pond:
- .83 mg/L
-McFadden’s Pond (Site A):
- 2.95 mg/L
-McFadden’s Pond (Site B):
- 2.02 mg/L
Spring (May): (LoggerPro Data) -Quarry Pond:
- .8 mg/L
-McFadden’s Pond (Site A):
- 3.1 mg/L
-McFadden’s Pond (Site B):
- 2.1 mg/L
Data Graph for Quarry Pond (Figure 4)
Data Graph for McFadden’s Pond Site A (Figure 5)
Data Graph for McFadden’s Pond Site B (Figure 6)
Data Graph for all three locations (Figure 7)
Discussion: Throughout our research, there was a general shift in the conductivity level in each site we tested. At Quarry Pond, the total dissolved solids reduced from .9 mg/L to .8 mg/L from November to May. This shift can be seen in the graph shown in Figure 4. McFadden’s Pond Site B also showed a substantial shift between the November and May readings, from 3.05 mg/L to 2.1 mg/L, as seen in Figure 6. Despite these significant changes, Site A at McFadden’s Pond did not change. This could potentially be due to its close proximity to moving water. A subtle, unnoticed under-current may have existed which may have caused the water to be mixed, and therefore diluted. The figures for this measurement can be seen in Figure 5.
The changes in conductivity at Quarry Pond may be the result of runoff from the parking lot and the roads in close proximity to it. Quarry Pond, unlike the other locations was close enough to a road that run-off affects the level of total dissolved solids. Although there was a significant change in conductivity between readings, the total dissolved solids were much lower than that of McFadden’s Pond. This may explain why the presence of algae was much higher in Quarry Pond than in McFadden’s Pond. McFadden’s Pond’s conductivity may have been higher due to a larger level of mineral deposits from soil runoff. One possible explanation for this shift in conductivity is the dilution of total dissolved solids in pond water due to rainfall and melting water from snow.
Conclusion: When comparing conductivity of water at a given point of time during the year, it is clear that there are noticeable differences. During the Fall and Winter, when there is more soil and road runoff, the conductivity level is higher. Conversely, during the spring, when there is more rainwater and melted snow and ice to dilute the ponds, the conductivity level drops. This would suggest that during fall and winter, the conditions of the pond are noticeably different. This suggests the possibility that there may be a shift in population from one group of organisms to another on a seasonal basis. Knowledge of these changes may help to explain why animals would migrate to a different habitat during different seasons. Because of the nature of soil runoff and road runoff, the level of Total Dissolved Solids in the water changes on a seasonal basis, and with that, the conductivity changes as well. In conclusion, conductivity does change over time of year in stagnant water, primarily because of external conditions such as runoff and wastewater.
(1)The GLOBE Program, “Electrical Conductivity Protocol.” Hydro-Electrical Conductivity. Ed. UCAR, Colorado State University, NASA.
We used the Power Point file linked to this page as our primary source of background information as well as a standard protocol for our field tests.
(2)Conductivity And Water Quality.
<[[http://kywater.org/ww/ramp/rmcond.htm%3C/span%3E%3Cspan|http://kywater.org/ww/ramp/rmcond.htm<span]] We used this website as our second source of data for finding out environmental impacts of change in conductivity and overall water quality. (Note: No Author, Publisher or Editor could be found for this web page.) __ *Site A is to the right of Mystery Bridge *Site B is to the left of Mystery Bridge *Note, this protocol was implemented both in the field and in a lab dependent on the time the data was collected *If the web page is difficult to view, a link to a .ppt file is available at the top of the page. The protocol can be found on slide #12.
Study authors: Margot Bennett and Rob Schwartz
Contributions to http://d-e-science11.wikispaces.com/ are licensed under a Creative Commons Attribution Share-Alike 3.0 License
Tweet Myron L Meters Myron L Meters sells the most accurate, reliable conductivity instruments in the water treatment industry. You can find some of our most popular meters here: http://www.myronlmeters.com/SearchResults.asp?Search=conductivity&x=-1345&y=-145 Introduction Along with the development of more and more complex integrated models for urban water systems the need of sufficient data bases grows as well. […]
Myron L Meters
Myron L Meters sells the most accurate, reliable conductivity instruments in the water treatment industry. You can find some of our most popular meters here:
Along with the development of more and more complex integrated models for urban water systems the need of sufficient data bases grows as well. It is even complicated to measure relevant parameters,e.g. dissolved nitrogen or COD, for their use in Waste Water Treatment Plants and sewer models to describe the influence of catchments to the receiving water.
This poster presents a method regarding the possibility of substituting an online ammonia measurement by conductivity measurements in the inflow of a Waste Water Treatment Plant . The aim was the description of the dynamics in wet weather flow through storm water events for modelling purposes.
The conductivity of an aqueous solution is the measure of its ability to conduct electricity. Responsible for that phenomenon are ions of dissolved salts. In natural and drinking water these are mainly carbonates, chlorides and sulphates of calcium, magnesium, sodium and potassium. Conducted experiences and measurements in combined sewers showed a relation between conductivity in Waste Water Treatment Plant inflow and the concentration of dissolved components, e.g. ammonia, in case of rainfall events. The data for different 3 Waste Water Treatment Plant are shown in Figure 1. Rainwater has nearly no ions that cause conductivity to be measured. Therefore, diluted wastewater flowing into the Waste Water Treatment Plant can be detected by a conductivity probe. The measure and quality of linear regression between ammonia concentration and conductivity can be found in Table 1 for all data from Figure 1.
Material and Methods
With this knowledge a simple regression-based inflow model for use in activated sludge modelling of Waste Water Treatment Plant was defined to use conductivity beside available composite samples as a measure for dynamics in ammonia concentration as one of the most dynamic measure.
Results and Discussion
For one of the considered Waste Water Treatment Plants (WWTP) the resulting quality for the inflow model is shown in Figure 2 for a time series of a week.
Furthermore, the inflow model was used as a source for a retention tank model at the inlet of another Waste Water Treatment Plant to describe the impact of different management strategies (storage or flow through) on receiving water and Waste Water Treatment Plant (Figure 3).
A long-term modelling of 9 storm water events was used to show the predictive capacity of the model. The regression parameters were fitted by an optimisation routine to get best fit for all concentrations (also for COD, not presented here). Figure 3 shows the fit for all events. A good prediction of dynamics and absolute values for ammonia can be seen.
The results of different Goodness-of-fit measures are summarized in Table 2 for both presented WWTP inflows. Especially the values for the modified Coefficient of Efficiency, as a well-known and used measure for model quality in hydrological sciences, show the degree of predicting of the used method and the usability of conductivity for description of influent dynamics to Waste Water Treatment Plant in storm water cases.
This simple and easy-to-use method is well suited for implementation in Waste Water Treatment Plant models to describe the inflow dynamics regarding a more realistic behavior e.g. for optimization of process control.
by Markus Ahnert*, Norbert Günther*, Volker Kuehn*, University of Dresden
Ahnert, M., Blumensaat, F., Langergraber, G., Alex, J., Woerner, D., Frehmann, T., Halft, N., Hobus, I., Plattes, M., Spering, V. und Winkler, S. (2007), Goodness-of-fit measures for numerical modelling in urban water management – a summary to support practical applications., paper presented at 10th IWA Specialised Conference on “Design, Operation and Economics of Large Wastewater Treatment Plants”, 9-13 September 2007, Vienna, Austria, 9-13 September 2007.
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TweetThe thermal conductivity enhancement of nanofluids Abstract Increasing interests have been paid to nanofluids because of the intriguing heat transfer enhancement performances presented by this kind of promising heat transfer media. We produced a series of nanofluids and measured their thermal conductivities. In this article, we discussed the measurements and the enhancements of the thermal […]
The thermal conductivity enhancement of nanofluids
Increasing interests have been paid to nanofluids because of the intriguing heat transfer enhancement performances presented by this kind of promising heat transfer media. We produced a series of nanofluids and measured their thermal conductivities. In this article, we discussed the measurements and the enhancements of the thermal conductivity of a variety of nanofluids. The base fluids used included those that are most employed heat transfer fluids, such as deionized water (DW), ethylene glycol (EG), glycerol, silicone oil, and the binary mixture of DW and EG. Various nanoparticles (NPs) involving Al2O3 NPs with different sizes, SiC NPs with different shapes, MgO NPs, ZnO NPs, SiO2 NPs, Fe3O4 NPs, TiO2 NPs, diamond NPs, and carbon nanotubes with different pretreatments were used as additives. Our findings demonstrated that the thermal conductivity enhancements of nanofluids could be influenced by multi-faceted factors including the volume fraction of the dispersed NPs, the tested temperature, the thermal conductivity of the base fluid, the size of the dispersed NPs, the pretreatment process, and the additives of the fluids. The thermal transport mechanisms in nanofluids were further discussed, and the promising approaches for optimizing the thermal conductivity of nanofluids have been proposed.
More efficient heat transfer systems are increasingly preferred because of the accelerating miniaturization, on the one hand, and the ever-increasing heat flux, on the other. In many industrial processes, including power generation, chemical processes, heating or cooling processes, and microelectronics, heat transfer fluids such as water, mineral oil, and ethylene glycol always play vital roles. The poor heat transfer properties of these common fluids compared to most solids is a primary obstacle to the high compactness and effectiveness of heat exchangers. An innovative way of improving the thermal conductivities of working media is to suspend ultrafine metallic or nonmetallic solid powders in traditional fluids since the thermal conductivities of most solid materials are higher than those of liquids. A novel kind of heat transfer enhancement fluid, the so-called nanofluid, has been proposed to meet the demands .
“Nanofluid” is an eye-catching word in the heat transfer community nowadays. The thermal properties, including thermal conductivity, viscosity, specific heat, convective heat transfer coefficient, and critical heat flux have been studied extensively. Several elaborate and comprehensive review articles and books have addressed thermal transport properties of nanofluids [1,3–6]. Among all these properties, thermal conductivity is the first referred one, and it is believed to be the most important parameter responsible for the enhanced heat transfer. Investigations on the thermal conductivity of nanofluids have been drawing the greatest attention of the researchers. A variety of physical and chemical factors, including the volume fraction, the size, the shape, and the species of the nanoparticles (NPs), pH value and temperature of the fluids, the Brownian motion of the NPs, and the aggregation of the NPs, have been proposed to play their respective roles on the heat transfer characteristics of nanofluids [7–19]. Extensive efforts have been made to improve the thermal conductivity of nanofluids [7–19] and to elucidate the thermal transport mechanisms in nanofluids [20–23].
The authors have carried out a series of studies on the heat transfer enhancement performance of nanofluids. A variety of nanofluids have been produced by the one- or two-step method. The base fluids used include deionized water (DW), ethylene glycol (EG), glycerol, silicone oil, and the binary mixture of DW and EG (DW-EG). Al2O3 NPs with different sizes, SiC NPs with different shapes, MgO NPs, ZnO NPs, SiO2 NPs, Fe3O4 NPs, TiO2 NPs, diamond NPs (DNPs), and carbon nanotubes (CNTs) with different pretreatments have been used as additives. The thermal conductivities of these nanofluids have been measured by transient hot wire (THW) method or short hot wire (SHW) technique. In this article, the experimental results that elucidate the influencing factors for thermal conductivity enhancement of nanofluids are presented. The thermal transport mechanisms in nanofluids and promising approaches for optimizing the thermal conductivity of nanofluids are further presented.
Preparation of nanofluids
Two techniques have been applied to prepare nanofluids in our studies: two- and one-step techniques. Most of the studied nanofluids were prepared by the two-step technique. During the procedure of two-step technique, the dispersed NPs were prepared by chemical or physical methods first, and then the NPs were added into a specified base fluid, with or without pretreatment and surfactant based on the need. In the preparation of nanofluids containing metallic NPs, one-step technique was employed.
The process was quite simple in the preparation of nanofluids containing oxide NPs like Al2O3, ZnO, MgO, TiO2, and SiO2 NPs. The NPs were obtained commercially and were dispersed into a base fluid in a mixing container. The NPs were deagglomerated by intensive ultrasonication after being mixed with the base fluid, and then the suspensions were homogenized by magnetic force agitation.
Two-step method was used to prepare graphene nanofluids. The first step was to prepare graphene nanosheets. Functionalized graphene was gained through a modified Hummers method as described elsewhere . Graphene nanosheets were obtained by exfoliation of graphite in anhydrous ethanol. The product was a loose brown powder, and it had good hydrophilic nature. The graphene nanosheets could be dispersed well in polar solvents, like DW and EG, without the use of surfactant. For liquid paraffin (LP)-based nanofluid, oleylamine was used as the surfactant. The fixed quality of graphene nanosheets with different volume fractions was dispersed in the base fluids.
Severe aggregation always takes place in the as-prepared CNTs (pristine CNTs: PCNTs) because of the non-reactive surfaces, intrinsic Von der Waals forces, and very large specific surface areas, and aspect ratios . In CNT nanofluid preparations, surfactant addition is an effective way to enhance the dispersibility of CNTs [26–28]. However, surfactant molecules attaching on the surfaces of CNTs may enlarge the thermal resistance between the CNTs and the base fluid , which limits the enhancement of the effective thermal conductivity. The steps involved in the preparation of surfactant-free CNT nanofluids include (1) disentangling the nanotube entanglement and introducing hydrophilic functional groups on the surfaces of the nanotubes by chemical treatments; (2) cutting the treated CNTs (TCNTs) to optimal length by ball milling; and (3) dispersing the treated and cut CNTs into base fluids. CNTs including single-walled CNTs (SWNTs), double-walled CNTs (DWNTs), and multi-walled CNTs (MWNTs) were obtained commercially. Two chemical routes for treating CNTs were used for this study. One is oxidation with concentrated acid, and the other is mechanochemical reaction with potassium hydroxide (KOH). The detailed treatment processes have been described elsewhere [8,30].
Phase transfer method was used to prepare stable kerosene-based Fe3O4 magnetic nanofluid. The first step is to synthesize Fe3O4 NPs in water by coprecipitation. Oleic acid was added to modify the NPs. When kerosene is added to the mixture with slow stirring, the phase transfer process took place spontaneously. There was a distinct phase interface between the aqueous and kerosene. After the removal of the aqueous phase using a pipette, the kerosene-based Fe3O4 nanofluid was obtained .
Nanofluids containing copper NPs were prepared using direct chemical reduction method. Stable nanofluids were obtained with the addition of poly(vinylpyrrolidone) (PVP). The diameters of copper NPs prepared by chemical reduction procedure are in the range of 5-10 nm, and copper NPs disperse well with no clear aggregation .
Surface modification is always used to enhance the dispersibility of NPs in the preparation of nanofluids. For example, diamond NPs (DNPs) were purified and surface modified by acid mixtures of perchloric acid, nitric acid and hydrochloric acid according to the literature  before being dispersed into the base fluids. SiC NPs were heated in air to remove the excess free carbon and their surfaces modified to enhance their dispersibility.
Consideration on the thermal conductivity measurement
Inconsistent experimental results and controversial arguments arise unceasingly from different groups conducting research on nanofluids, indicating the complexity of the thermal transport in nanofluids. Through an investigation, a large degree of randomness and scatter have been observed in the experimental data published in the open literature. Given the inconsistency in the data, it is impossible to develop a convincing and comprehensive physical-based model that can predict all the trends. To clarify the suspicion on the scattered and wide-ranging experimental results of the thermal conductivity obtained by different groups, it is preferred to screen the measurement technique and procedure to guarantee the accuracy of the obtained results.
Several researchers observed the “time-dependent characteristic” of thermal conductivity [34–36], that is to say, thermal conductivity was the highest right after nanofluid preparation, and then it decreased considerably with elapsed time. We believe that the “time-dependent characteristic” does not represent the essence of thermal conduction capability of nanofluids. The following two factors may account for this phenomenon. The first one is the motion of the remained particle caused by the agitation during the nanofluid preparation. To make a nanofluid homogeneous and long-term stable, it is always subjected to intensive agitation including magnetic stirring and sonication to destroy the aggregation of the suspended NPs. In very short time after nanofluid preparation, the NPs still keep moving in the base fluid (different from Brownian motion). The motion of the remained particle would cause convection and enhance the energy transport in the nanofluids. Second, when a nanofluid is subjected to long-time sonication, its temperature would be increased. The temperature goes down gradually to the surrounding temperature (thermal conductivity measurement temperature). In very short time after the sonication stops, the process has been remaining. Although the temperature decrease is not severe, the thermal conductivity obtained is very sensitive to the temperature decrease when the transient hot-wire technique is used to measured the thermal conductivity. In our measurements, this phenomenon would be observed. When measuring the thermal conductivity at an unequilibrium state, it was found that the measured data might be very different for a nanofluid even at a specific temperature (see 25°C) if the process to reach this temperature is different. If the temperature is increasing, then the datum obtained of the thermal conductivity would be lower than the true value. While the temperature is decreasing, the datum obtained of the thermal conductivity would be higher than the true value. Therefore, keeping a nanofluid stable and initial equilibrium is very important to obtain accurate thermal conductivity data in measurements.
A transient short hot-wire method was used to measure the thermal conductivities of the base fluids (k0) and the nanofluids (k). The detailed measurement principle, procedure, and error analysis have been described in . In our measurements, a platinum wire with a diameter of 50 μm was used for the hot wire, and it served both as a heating unit and as an electrical resistance thermometer. The platinum wire was coated with an insulation layer of 7-μm thickness. Initially the platinum wire immersed in media was kept at equilibrium with the surroundings. When a regulation voltage was supplied to initiate the measurement, the electrical resistance of the wire changed proportionally with the rise in temperature. The thermal conductivity was calculated from the slope of the rise in the wire’s temperature against the logarithmic time interval. The uncertainty of this measurement is estimated to be within ± 1.0%. A temperature-controlled bath was used to maintain different temperatures of the nanofluids. Instead of monitoring the temperature of the bath, a thermocouple was positioned inside the sample to monitor the temperature on the spot. When the temperature of the sample reached a steady value, the authors waited for further 20 min to make sure that the initial state is at equilibrium. At every tested temperature, measurements were made three times and the average values were taken as the final results. A 20-min interval was needed between two successive measurements. After the above-mentioned careful check on the measurement condition and procedure, the authors could gain confidence on the experimental results.
Influencing factors of thermal conductivity enhancement
In the experiment of the study, it was found that the thermal conductivity enhancements of nanofluids might be influenced by multi-faceted factors including the volume fraction of the dispersed NPs, the tested temperature, the thermal conductivity of the base fluid, the size of the dispersed NPs, the pretreatment process, and the additives of the fluids. The effects of these factors are presented in this section.
The idea of nanofluid application originated from the fact that the thermal conductivity of a solid is much higher than that of a liquid. For example, the thermal conductivity of the most used conventional heat transfer fluid, water, is about 0.6 W/m · K at room temperature, while that of copper is higher than 400 W/m · K. Therefore, particle loading would be the chief factor that influences the thermal transport in nanofluids. As expected, the thermal conductivities of the nanofluids have been increased over that of the base fluid with the addition of a small amount of NPs. Figure 1 shows the enhanced thermal conductivity ratios of the nanofluids with NPs at different volume fractions [7,8,38–42]. (k – k0)/k0 and φ refer to the thermal conductivity enhancement ratio of nanofluids and the volume fraction of NPs, respectively, in this article. Figure1a presents oxide nanofluids, while Figure 1b presents nonoxide nanofluids. The results show that all the nanofluids have noticeable higher thermal conductivities than the base fluid without NPs. In general, the thermal conductivity enhancement increases monotonously with the volume fraction. For the graphene nanofluid with a volume fraction of 0.05, the thermal conductivity can be enhanced by more than 60.0%. There is an approximate linear relationship between the thermal conductivity enhancement ratios and the volume fraction of graphene nanosheets. The nanofluids containing graphene nanosheets show larger thermal conductivity enhancement than those containing oxide NPs. It demonstrates that graphene nanosheet is a good additive to enhance the thermal conductivity of base fluid. However, the enhancement ratios of nanofluids containing graphene nanosheets are less than those of CNTs with the same loading. Many factors have direct influence on the thermal conductivity of the nanofluid. One of the important factors is the crystal structure of the inclusion in the nanofluid. Graphene is a one-atom-thick planar sheet of sp2-bonded carbon atoms that are densely packed in a honeycomb crystal lattice. The perfect structure of graphene is damaged when graphite is chemically oxidized by treatment with strong oxidants. There is no doubt that the high thermal conductivity is diminished by defects, and the defects have direct influence on the heat transport along the 2-D structure.
Figure 1. Thermal conductivity enhancement ratios of the nanofluids as a function of nanoparticle loading. (a) Oxide nanofluids: MgO-EG ; Al2O3-EG ; ZnO-EG ; (b) Nonoxide nanofluids: CNT-EG ; DNP-EG ; Graphene-EG ; Cu-EG .
Some studies have demonstrated that the temperature has a great effect on the enhancement of the thermal conductivity for nanofluids. However, there is considerable disagreement in the literature with respect to the temperature dependence of their thermal conductivity. For example, Das et al. reported strong temperature-depended thermal conductivity for water-based Al2O3 and CuO nanofluids . The thermal conductivity enhancements of nanofluids containing Bi2Te3nanorods in FC72 and in oil had been experimentally found to decrease with increasing temperature . Micael et al. measured the thermal conductivities of EG-based Al2O3 nanofluids at temperatures ranging from 298 to 411 K. A maximum in the thermal conductivity was observed at all mass fractions of NPs .
Figure 2 shows our measured temperature-depended thermal conductivity enhancements of nanofluids [8,38–42]. For EG-based nanofluids containing MgO, ZnO, SiO2, and graphene NPs, the thermal conductivity enhancements almost remain constant when the tested temperature changes (see Figure 2a), which means that the thermal conductivity of the nanofluid tracks the thermal conductivities of the base liquid in the experimented temperature range of this study. The thermal conductivity enhancements of DW-EG-based nanofluids containing MgO, ZnO, SiO2, Al2O3, Fe2O3, TiO2, and graphene NPs also appear to have the same behavior. It was further found that kerosene-based Fe3O4 nanofluids presented temperature-independent thermal conductivity enhancements. Patel et al.  reported that the thermal conductivity enhancement ratios of Cu nanofluids are enhanced considerably when the temperature increases. The experimental results of this study shown in Figure 2b demonstrated similar tendency. At 10°C, the thermal conductivity enhancement of EG based Cu nanofluid with 0.5% nanoparticle loading is less than 15.0%. When the temperature is increased to 60°C, the enhancement reaches as large as 46.0%. Brownian motion of the NPs has been proposed as the dominant factor for this phenomenon. For the EG-based CNT nanofluids, cylindrical nanotubes with large aspect ratios were used as additions. The effect of Brownian motion will be negligible. Typical conduction-based models will give (k – k0)/k0, independent of the temperature. However, results shown in Figure 2b illustrate that (k – k0)/k0increases, though not drastically, with the temperature. CNT aggregation kinetics may contribute to the observed differences . It is worthy of bearing in mind that the temperatures of the base fluid and the nanofluid should be the same when compared with the thermal conductivities between them. Comparison of the thermal conductivities between the nanofluid at one temperature and the base at another one is meaningless.
Figure 2. Thermal conductivity enhancement varying with the tested temperatures. (a) Oxide nanofluids: MgO-EG ; ZnO-EG; Graphene-EG ; (b) Nonoxide nanofluids: Cu-EG ; CNT-EG; DNP-EG .
Figure 3 shows the relation between the enhanced thermal conductivity ratios of the nanofluids and the thermal conductivities of the base fluids [7,8,40,41]. It is clearly seen that no matter what kind of nanoparticle was used, the thermal conductivity enhancement decreases with an increase in the thermal conductivity of the base fluid. For pump oil (PO)-based Al2O3 nanofluid with 5.0% nanoparticle loading, the thermal conductivity can be enhanced by more than 38% compared to that of PO. When the base fluid is substituted with water, the thermal conductivity enhancement achieved is only about 22.0% . A greater dramatic improvement in thermal conductivity of CNT nanofluid is seen for a base fluid with lower thermal conductivity. At 1.0% nanoparticle loading, the thermal conductivity enhancements are 19.6, 12.7, and 7.0% for CNT nanofluids in decene, EG, and DW, respectively. No matter what kind of base fluid is used, the thermal conductivity enhancement of CNT nanofluids is much higher than that for Al2O3 nanoparticle suspensions  at the same volume fraction. The reason would lie in the substantial difference in thermal conductivity and morphology between alumina nanoparticle and carbon nanotube.
Figure 4 presents the thermal conductivity enhancement of the nanofluids as a function of the specific surface area (SSA) of the suspended particles . It is seen that the thermal conductivity enhancement increases first, and then decreases with an increase in the SSA, with the largest thermal conductivity at a particle SSA of 25 m2 · g-1. We ascribe the thermal conductivity change behavior to twofold factors. First, as particle size decreases, the SSA of the particle increases proportionally. Heat transfer between the particle and the fluid takes place at the particle-fluid interface. Therefore, a dramatic enhancement in thermal conductivity is expected because a reduction in particle size can result in large interfacial area. Second, the mean free path in polycrystalline Al2O3 is estimated to be around 35 nm, which is comparable to the size of the particle that was used. The intrinsic thermal conductivity of nanosized Al2O3 particle may be reduced compared to that of bulk Al2O3 due to the scattering of the primary carriers of energy (phonon) at the particle boundary. It is expected that the suspension’s thermal conductivity is reduced with an increase in the SSA. Therefore, for a suspension containing NPs at a particle size much different from the mean free path, the thermal conductivity increases when the particle size decreases because the first factor is dominant. However, when the size of the dispersed NPs is close to or smaller than the mean free path, the second factor will govern the mechanism of the thermal conductivity behavior of the suspension.
Figure 5 depicts the thermal conductivity enhancements of nanofluids containing CNTs with different sizes . The base fluid is DW, and the volume fraction of the CNTs is 0.0054. It is observed from Figure 5 that the thermal conductivity enhancements show differences among these three kinds of nanofluids containing SWNTs, DWNTs, and MWNTs as the volume fraction of CNTs is the same. Two influencing factors may be addressed. The first one is the intrinsic heat transfer performance of the CNTs. It is reported that the thermal conductivity of CNTs decreases with an increase in the number of the nanotube layer. The tendency of the thermal conductivity enhancement of the obtained CNT nanofluids accords with that of the heat transfer performance of the three kinds of CNTs. The second one is the alignment of the liquid molecules on the surface of CNTs. There are greater number of water molecules close to the surfaces of CNTs with smaller diameter due to the larger SSA if the volume fractions of CNTs are the same. These water molecules can form an interfacial layer structure on the CNT surfaces, increasing the thermal conductivity of the nanofluid .
In the preparation of nanofluids, solid additives are always subjected to various pretreatment procedures. The initial incentive is to tailor the surfaces of the NPs to enhance their dispersibility, thereby to enhance the stability of the nanofluids. The morphologies would be significantly changed when CNTs were subjected to chemical or mechanical treatments. Theoretical research into the thermal conductivity of composites containing cylindrical inclusions has demonstrated that the morphologies, including the aspect ratio, have influence on the effective thermal conductivity of the composites. Therefore, it can be expected that the thermal conductivity of CNT contained nanofluids would be affected by the pretreatment process.
Figure 6 shows the dependence of the thermal conductivity enhancement on the ball milling time of CNTs suspended in the nanofluids . From theoretical prediction, the thermal conductivity of a composite increases with the aspect ratio of the included solid particles [49–51]. Intuition suggests that increasing the milling time should therefore decrease (k – k0)/k0 because of the reduced aspect ratio. Figure 6, however, shows clear peak and valley values in the thermal conductivity enhancement with respect to the milling time for all the studied CNT loadings. For nanofluid at a volume fraction of 0.01, the thermal conductivity enhancements present a peak value of 27.5% and a valley value of 10.4% when the milling times are 10 and 28 h, respectively. The maximal enhancement is intriguingly more than two and half times as the minimal one. Interestingly, when further increased the milling time from 28 to 38 h, (k – k0)/k0 increases from the valley value of 10.4 to 12.8%. Though the increment is not pronounced, it illustrates a difference in tendency from that in the milling time range from 10 to 28 h. Temperature-dependent thermal conductivity enhancement data further indicate that, at all the measured temperatures, nanofluid with CNTs milled for 10 h has the largest increment in thermal conductivity. Glory et al.  reported that the enhancement of the thermal conductivity noticeably increases when the nanotube aspect ratio increases. However, the thermal conductivity enhancement behavior of our CNT nanofluid is very different and cannot be explained only by the effect of the aspect ratio.
The above results suggest other dominant factors that have the influence over the thermal conductivity of the CNT nanofluids. The authors proposed that the nonstraightness and the aggregation would play significantly roles. As is known, the walls of CNTs have similar structure of graphene sheet, and the thermal conductivity of CNTs shows greatly anisotropic behavior. Heat transports substantially quicker through axial direction than through radial direction . For a nonstraight CNT, the high thermal anisotropy of CNTs induces a unique property that individual CNTs are nearly perfect one-dimensional thermal passages with negligibly small heat flux losses during long distance heat conductions . For a nonstraight CNT with length L under a two-end temperature difference, the heat flux q goes through a curled passage. This CNT can be regarded as an equivalent straight thermal passage with a distance of Le. The same heat flux q is conducted between the two ends of this straight passage. Obviously, the equivalent length Le depends on the curvature of the actual nanotube in the nanofluid. A concept, straightness ratio η (η = Le/L), can be adopted to describe the straightness of a curled CNT. The lowest straightness ratio arises when a suspended nanotube forms ring closure .
When subjected to ball milling, CNTs were broken and cut short with appropriate average length. The straightness ratio was significantly increased and heat transports more effectively through the CNTs and across the interfaces between the CNT tips and the base fluid, resulting in the highest thermal conductivity enhancement in a nanofluid containing CNTs milled for 10 h. For nanofluids containing relatively straight nanotubes, the influence of the aspect ratio will surpass that of straightness ratio. Therefore, by further treatment on nanotubes with relatively high straightness ratio, the excessive deterioration of the aspect ratio would decrease the thermal conductivity of nanofluids, causing (k – k0)/k0 decrease from 10 to 28 h. Recent theoretical analysis has revealed that the aggregation of nanoparticle plays a significant role in deciding (k – k0)/k0 . Percolation effects in the aggregates, as highly conducting nanotubes touch each other in the aggregate, help in increasing the thermal conductivity. Our experiments demonstrate that aggregates are the dominant appearance of CNTs when the ball-milling time is increased to 38 h. The aggregation accounts for the increment of thermal conductivity enhancement when the ball-milling time is increased from 28 to 38 h. This result implies that the positive influence of the aggregation surpasses the negative influence of the aspect ratio deterioration.
For some nanofluids, the pH values of the suspensions have direct effects on the thermal conductivity enhancement. Figure 7 presents the thermal conductivity enhancement ratios at different pH values [7,40]. The results show that the enhanced thermal conductivity increases with an increase in the difference between the pH value of aqueous suspension and the isoelectric point of Al2O3 particle . When the NPs are dispersed into a base fluid, the overall behavior of the particle-fluid interaction depends on the properties of the particle surface. For Al2O3 particles, the isoelectric point (pHiep) is determined to be 9.2, i.e., the repulsive forces among Al2O3 particles is zero, and Al2O3 particles will coagulate together under this pH value. Therefore, when pH value is equal or close to 9.2, Al2O3 particle suspension is unstable according to DLVO theory . The hydration forces among particles increase with the increasing difference of the pH value of a suspension from the pHiep, which results in the enhanced mobility of NPs in the suspension. The microscopic motions of the particles cause micro-convection that enhances the heat transport process. Wensel’s study showed that the thermal conductivity of nanofluids containing oxide NPs and CNTs with very low percentage loading decreased when the pH value is shifted from 7 to 11.45 under the influence of a strong outside magnetic field .
For DNP-EG nanofluids, it is observed from Figure 7 that the thermal conductivity enhancement increases with pH values in the range of 7.0-8.0. When pH value is above 8.0, there is no obvious relationship between pH value and the thermal conductivity enhancement. In our opinion, the influence of pH value on thermal conductivity is that pH value has a direct effect on the stability of nanofluids. When pH value is below 8.5, the suspension is not very stable, and DNPs are easy to form aggregations. The alkalinity of the solution is helpful to the dispersion and the stability of the nanofluids. In order to verify the above statement, the influence of settlement time on the thermal conductivity enhancement was further investigated. It is found that the thermal conductivity enhancement decreases with elapsed time for DNP-EG nanofluid when pH is 7.0. However, for the stable DNP-EG nanofluids with pH of 8.5, there is no obvious thermal conductivity decrease for 6 months .
Surfactant addition is an effective way to enhance the stability of nanofluids. Kim’s study revealed that the thermal conductivity decreased rapidly for the instable nanofluids without surfactants after preparation. However, no obvious changes in the thermal conductivity of the nanofluids with sodium dodecyl sulfate (SDS) as surfactant were observed even after 5-h settlement . Assael et al. investigated the thermal conductivities of the aqueous suspension of CNTs. When Sodium dodecyl sulfate (SDS) was employed as the dispersant, the maximum thermal conductivity enhancement obtained was 38.0% for a nanofluid with 0.6 vol% CNT loadings . When the surfactant is substituted with hexadecyltrimethyl ammonium bromide (CTAB), the maximum thermal conductivity enhancement obtained was 34.0% for same fraction of CNT loading . Liu et al. reported that the thermal conductivity of carbon nanotube-synthetic engine oil suspensions is higher compared with that of same suspensions without the addition of surfactant. The presence of surfactant as stabilizer has positive effect on the carbon nanotube-synthetic engine oil suspensions.
We used cationic gemini surfactants (12-3(4,6)-12,2Br-1) to stabilize water-based MWNT nanofluids. These surfactants were prepared following the process described in . Figure 8presents the thermal conductivity enhancement ratios of the CNT-contained nanofluids with different surfactant concentrations. The volume fraction of the dispersed CNTs is 0.1%. The critical micelle concentration of 12-3-12, 2Br-1 is reported as 9.6 ± 0.3 × 10-4 mol/l . Ten times critical micelle concentration of 12-3-12, 2Br-1 is 0.6 wt%. Solutions of 12-3-12, 2Br-1 with different concentrations (0.6, 1.8, and 3.6 wt% at room temperature) were selected to prepare CNT nanofluids. It is observed that at all the measured temperatures the thermal conductivity enhancement decreases with the surfactant addition. The surfactant added in the nanofluids acts as stabilizer which improves the stability of the CNT nanofluids. However, excess surfactant addition might hinder the improvement of the thermal conductivity enhancement of the nanofluids.
Figure 8. Thermal conductivity enhancement ratios with different surfactant concentrations.
The effect of the structures of cationic gemini surfactant molecules on the thermal conductivity enhancement is shown in Figure 9. The fractions of the dispersed CNTs and the cationic gemini surfactants is 0.1 vol% and 0.6 wt%, respectively. The spacer chain length of the cationic gemini surfactant increase from 3 methylenes to 6 methylenes. It is seen that the thermal conductivity enhancement ratio increases with the decrease of spacer chain length of cationic gemini surfactant. Zeta potential analysis indicates that the CNT nanofluids stabilized by gemini surfactant with short spacer chain length have better stabilities. Increase of spacer chain length of surfactant might give rise to sediments of CNTs in the nanofluids, resulting in the decrease of thermal conductivity enhancement of the nanofluids.
Figure 9. Effect of surfactant structures on the thermal conductivity enhancement ratio.
Nanofluids have great potential for heat transfer enhancement and are highly suited to application in practical heat transfer processes. This provides promising ways for engineers to develop highly compact and effective heat transfer equipments. More and more researchers have paid their attention to this exciting field. When addressing the thermal conductivity of nanofluids, it is foremost important to guarantee the accuracy in the measurement of the thermal conductivity of nanofluids. Two aspects should be considered. The first one is to prepare homogeneous and long-term stable nanofluids. The second one is to keep the initial equilibrium before measuring the thermal conductivity. In general, the thermal conductivity enhancement increases monotonously with the particle loading. The effect of temperature on the thermal conductivity enhancement ratio is somewhat different for different nanofluids. It is very important to note that the temperatures of the base fluid and the nanofluid should be the same while comparing the thermal conductivities between them. With an increase in the thermal conductivity of the base fluid, the thermal conductivity enhancement ratio decreases. Considering the effect of the size of the inclusion, there exists an optimal value for alumina nanofluids, while for the CNT nanofluid, the thermal conductivity increases with a decrease of the average diameter of the included CNTs. The thermal characteristics of nanofluids might be manipulated by means of controlling the morphology of the inclusions, which also provide a promising way to conduct investigation on the mechanism of heat transfer in nanofluids. The additives like acid, base, or surfactant play considerable roles on the thermal conductivity enhancement of nanofluids.
CNTs: carbon nanotubes; DNPs: diamond NPs; DW: deionized water; DWNTs: double-walled CNTs; EG: ethylene glycol; KOH: potassium hydroxide; LP: liquid paraffin; MWNTs: multi-walled CNTs; NPs: nanoparticles; PVP: poly(vinylpyrrolidone); SDS: sodium dodecyl sulfate; SHW: short hot wire; SSA: specific surface area; SWNTs: single-walled CNTs; THW: transient hot wire; TCNTs: treated CNTs.
The authors declare that they have no competing interests.
HQ supervised and participated all the studies. He wrote this paper. WY carried out the studies on the nanofluids containing copper nanoparticles, graphene, diamond nanoparticles, and several kinds of oxide nanoparticles. YL carried out the studies on the nanofluids containing other oxide nanoparticles. LF carried out the studies on the nanofluids containing carbon nanotubes.
This study was supported by the National Science Foundation of China (50876058), Program for New Century Excellent Talents in University (NCET-10-883), and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning.
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Huaqing Xie*, Wei Yu, Yang Li and Lifei Chen
- *Corresponding author: Huaqing Xie email@example.com
School of Urban Development and Environmental Engineering, Shanghai Second Polytechnic University, Shanghai 201209, China
For all author emails, please log on.
Nanoscale Research Letters 2011, 6:124 doi:10.1186/1556-276X-6-124
The electronic version of this article is the complete one and can be found online at:http://www.nanoscalereslett.com/content/6/1/124
|Received:||3 September 2010|
|Accepted:||9 February 2011|
|Published:||9 February 2011|
© 2011 Xie et al; licensee Springer.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Tweet WHY TEST FOUNTAIN SOLUTIONS? Accurate fountain (dampening) solution concentration control is essential for consistent, high-quality results in lithography. Low concentration can cause drying on the non-image area of the plate resulting in tinting, scumming, blanket piling, etc. High concentrations, on the other hand, bring about over-emulsification of the ink. This results in weakening of […]
WHY TEST FOUNTAIN SOLUTIONS?
Accurate fountain (dampening) solution concentration control is essential for consistent, high-quality results in lithography. Low concentration can cause drying on the non-image area of the plate resulting in tinting, scumming, blanket piling, etc. High concentrations, on the other hand, bring about over-emulsification of the ink. This results in weakening of color strength and changes in ink rheology (body and flow properties). Correct concentration will allow the non-image areas of the plate to be appropriately wetted.
WAYS TO TEST
Traditionally, pH was the test relied on to determine fountain solution concentration. Today, however, conductivity testing is recognized as a much more accurate method. Many modern dampening solutions are pH stabilized (or buffered), so only small changes in pH are seen even when dramatic changes occur in solution strength. Conductivity measurement is a fast and easy test which is more indicative of fountain solution concentration than pH. This is true for all neutral, alkaline, and many acid type solutions.
pH is still important, however, with unbuffered acid fountain solutions. Checking both conductivity and pH can provide valuable information. Acid fountain solution is a mixture of gum arabic, wetting agents, salts, acids, buffers, etc. Conductivity will tell you if the proper amount of most ingredients are present, but pH is necessary to check acid concentrations. pH will also determine how effective one ingredient, gum arabic, will be.
What is conductivity? Conductivity is the measurement of a solution’s ability to conduct an electrical current. It is usually expressed in microsiemens (micromhos). Absolutely pure water is actually a poor electrical conductor. It is the substances dissolved in water which determine how conductive the solution will be. Therefore, conductivity is an excellent indicator of solution strength.
To properly measure the conductivity of fountain solutions:
1. Test and write down the conductivity of the water used to prepare the solution.
- Mix the fountain solution concentrate with the water, using the manufacturer’s recommendations or as experience dictates.
- Measure the conductivity of the mixed solution.
- Subtract the water conductivity value obtained in step 1. This is necessary because tap water quality can change from day to day.
The resulting number is an accurate indicator of fountain solution strength. Caution: because alcohol will lower a solution’s conductivity, always test solution conductivity before and after the addition of alcohol.
Determining the best concentration of fountain solution is mostly “trial and error.” It can be very useful to make a graph, recording readings for every one-half or one ounce of concentrate added to a gallon of water. Record readings on a graph with the vertical axis representing conductivity values and the horizontal axis representing ounces/gallon. Such a graph will help “fine tune” your system during future press runs.
For “on the spot” fountain solution tests, Myron L handheld instruments are fast, accurate, and reliable. Measurements are made in seconds simply by pouring a small sample of solution into the instrument cell cup and pressing a button. Automatic temperature compensated accuracy and famous Myron L reliability have made our instruments popular in pressrooms worldwide.
Even though pH usually is not the best method to check the concentration of fountain solution, it is still very important and must be checked regularly. The pH of acid dampening solution affects sensitivity, plate-life, ink-drying, etc. Also, pH can change during a run if the paper has a high acid or alkaline content. pH, therefore, must be maintained at the proper level for good printing.
A convenient and accurate way to test pH (as well as temperature) is Myron L’s waterproof Ultrameter II™ Model 6PFCE or TechPro II™ TH1. The 6PFCE has a 100 reading memory and the TH1 has a 20 reading memory to store test results on site. The 6P also measures conductivity. All electrodes are contained in the cell cup for protection. Model M6/PH also measures pH and conductivity.
For continuous monitoring and/or control of fountain solution concentration, Myron L offers a complete series of in-line conductivity instruments. These economical, accurate, and reliable models use a remotely installed sensor and a panel/wall mount meter enclosure. Most contain an adjustable set point and heavy duty relay circuit which can be used to activate alarms, valves, feedpumps, etc. All models contain a 0-10 VDC output for a chart recorder or PLC (SCADA) input, if required, (4-20 mA output is also available).
The 750 Series II with dual set point option has become quite popular in pressrooms. The two set points allow a “safe zone” for controlling fountain solution concentration.
Ultrameter II 6PFCE, 512M5 and M6/PH are available with the useful LITHO-KIT™. This accessory includes a foam-lined, rugged all-plastic carry case with calibrating solutions and buffers. In addition, a syringe to simplify drawing samples and a thermometer for testing fountain solution temperature are also included.
|Improperly mixed fountain solution||Carefully follow manufacturer’s directions, checking both water and mixed solution with a conductivity instrument||Ultrameter 4PII, 6PIIFCE and 9PTK; ULTRAPEN PT1; and TechPro II TPH1 or TP1 all test 0-9999 ppm TDS or microsiemens conductivity, and temperature. 512M5 por- table DS Meter™ with a 0-5000 microsiemens conductivity range.|
|Halftones sharpen and highlight dots lost during run||Check pH of fountain solution to determine if it’s too acidic||Ultrameter 4PII, 6PIIFCE and 9PTK; ULTRAPEN PT2; and TechPro II TH1.M6/PH portable pDS Meter. Ranges: 0-5000 microsiemens and 2-12 pH.|
|Reverse osmosis water treatment system monitor indicates membrane failure||Test RO water quality and verify in-line instrument accuracy||Ultrameter 4PII, 6PIIFCE and 9PTK; ULTRAPEN PT1; and TechPro II TPH1 or TP1 all test 0-9999 ppm TDS or microsiemens conductivity, and temperature.|
|Scum streaks across plate after 10,000 – 20,000impressions||Check acid/gum levels infountain solution||Ultrameter 6PIIFCE and 9PTK; ULTRAPEN PT2; and TechPro II TH1.M6/PH portable pDS Meter. Ranges: 0-5000 microsiemens and 2-12 pH.|
|Personnel unable to test fountain solution concentration||Continuously control fountain solution with conductivity Monitor/controller||758II-123 (0-5000 µS) in-lineMonitor/controller.|
You can save 10% on any recommended meter at MyronLMeters.com.
Tweet Anyone responsible for operating and maintaining a swimming pool or spa has to test, monitor, and control complex, interdependent chemical factors that affect the quality of water. Additionally, aquatic facilities operators must be familiar with all laws, regulations, and guidelines governing what these parameters should be. […]
Anyone responsible for operating and maintaining a swimming pool or spa has to test, monitor, and control complex, interdependent chemical factors that affect the quality of water. Additionally, aquatic facilities operators must be familiar with all laws, regulations, and guidelines governing what these parameters should be.
Why? Because the worst breeding ground for any kind of microorganism is a warm (enough) stagnant pool of water. People plus stagnant water equals morbid illness. That’s why pools have to be circulated, filtered, and sanitized – with any number of chemicals or methods, but most frequently with chlorine compounds. However, adding chemicals that kill the bad microorganisms can also make the water uncomfortable, and in some cases unsafe, for swimmers. Additionally, if all the chemical factors of the water are not controlled, the very structures and equipment that hold the water and keep it clean are ruined.
So the pool professional must perform a delicate balancing act with all the factors that affect both the health and comfort of bathers and the equipment and structures that support this. Both water balance – or mineral saturation control – and sanitizer levels must constantly be maintained. This is achieved by measuring pertinent water quality factors and adding chemicals or water to keep the factors within acceptable parameters.
Water is constantly changing. Anything and everything directly and indirectly affects the relationship of its chemical parameters to each other: sunlight, wind, rain, oil, dirt, cosmetics, other bodily wastes, and any chemicals you add to it. Balanced water not only keeps swimmers comfortable, but also protects the pool shell, plumbing, and all other related equipment from damage by etching or build-up and stains.
The pool professional is already well acquainted with pH, Total Alkalinity (TA), and Calcium Hardness (CH); along with Total Dissolved Solids (TDS) and Temperature, these are the factors that influence water balance. Water that is in balance is neither aggressive nor oversaturated. Aggressive water lacks sufficient calcium to saturate the water, so it is hungry for more. It will eat anything it comes into contact with to fill its need, including the walls of your pool or spa or the equipment it touches. Over-saturated water cannot hold any more minerals, so dissolved minerals come out of solution and form scale on pool and equipment surfaces.
The pH of pool water is critical to the effectiveness of the sanitizer as well as the water balance. pH is determined by the concentration of Hydrogen ions in a specific volume of water. It is measured on a scale of 0-14, 0-7 being acidic and 7-14 being basic.
You must maintain the pH of the water at a level that assures the sanitizer works effectively and at the same time protects the pool shell and equipment from corrosion or scaling and the bathers from discomfort or irritation. If the pH is too high, the water is out of balance, and the sanitizer’s ability to work decreases. More and more sanitizer is then needed to maintain the proper level to kill off germs. Additionally, pH profoundly affects what and how much chemical must be added to control the balance. A pH of between 7.2 – 7.6 is desirable in most cases.*
As one of the most important pool water balance and sanitation factors, pH should be checked hourly in most commercial pools.* Even if you have an automatic chemical monitor/controller on your system, you need to double- check its readings with an independent pH test. With salt- water pools, pH level goes up fast, so you need to check it more often. Tests are available that require reagents and subjective evaluation of color depth and hue to judge their pH. But different users interpret these tests differently, and results can vary wildly. The PooLPRo and ULTRAPEN PT2 give instant lab-accurate, precise, easy-to-use, objective pH measurements, invaluable in correctly determining what and how much chemical to add to maintain water balance and effective sanitizer residuals.
Total Alkalinity (TA) is the sum of all the alkaline minerals in the water, primarily in bicarbonate form in swimming pools, but also as sodium, calcium, magnesium, and potassium carbonates and hydroxides, and affects pH directly through buffering. The greater the Total Alkalinity, the more stable the pH. In general, TA should be maintained at 80 – 120 parts per million (ppm) for concrete pools to keep the pH stable.* Maintaining a low TA not only causes pH bounce, but also corrosion and staining of pool walls and eye irritation. Maintaining a high TA causes overstabilization of the water, creating high acid demands, formation of bicarbonate scale, and may result in the formation of white carbonate particles (suspended solids), which clouds the water. Reducing TA requires huge amounts of effort. So the best solution to TA problems is prevention through close monitoring and controlling. The PoolPro PS9 Titration Kit features an in-cell conductometric titration for determining alkalinity.
Calcium Hardness (CH) is the other water balance parameter pool professionals are most familiar with. CH represents the calcium content of the water and is measured in parts per million. Low CH combined with a low pH and low TA significantly increases corrosivity of water. Under these conditions, the solubility of calcium carbonate also increases. Because calcium carbonate is a major component of both plaster and marcite, these types of pool finishes will deteriorate quickly. Low CH also leads to corrosion of metal components in the pool plant, particularly in heat exchangers. Calcium carbonate usually provides a protective film on the surface of copper heat exchangers and heat sinks, but does not adversely affect the heating process. Without this protective layer, heat exchangers and associated parts can be destroyed prematurely. At the other extreme, high CH can lead to the precipitation of calcium carbonate from solution, resulting in cloudy water, the staining of structures and scaling of equipment. The recommended range for most pools is 200 – 400 ppm.* Calcium hardness should be tested at least monthly. The PoolPro PS9 Titration Kit features an in-cell conductometric titration for determining hardness.
Total Dissolved Solids (TDS) is the sum of all solids dissolved in water. If all the water in a swimming pool was allowed to evaporate, TDS would be what was left on the bottom of the pool – like the white deposits left in a boiling pot after all the water has evaporated. Some of this dissolved material includes hardness, alkalinity, cyanuric acid, chlorides, bromides, and algaecides. TDS also includes bather wastes, such as perspiration, urine, and others. TDS is often confused with Total Suspended Solids (TSS). But TDS has no bearing on the turbidity, or cloudiness, of the water, as all the solids are truly in solution. It is TSS, or undissolved, suspended solids, present in or that precipitate out of the water that make the water cloudy.
High TDS levels do affect chlorine efficiency, algae growth, and aggressive water, but only minimally. TDS levels have the greatest bearing on bather comfort and water taste – a critical concern for commercial pool operators. At levels of over 5,000 ppm, people can taste it. At over 10,000 ppm bather towels are scratchy and mineral salts accumulate around the pool and equipment. Still some seawater pools comfortably operate with TDS levels of 32,000 ppm or more.
As methods of sanitization have changed, high TDS levels have become more and more of a problem. The best course of action is to monitor and control TDS by measuring levels and periodically draining and replacing some of your mature water with new, lower TDS tap water. This is a better option than waiting until you must drain and refill your pool, which is not allowed in some areas where water conservation is required by law. However, you can also decrease TDS with desalinization equipment as long as you compensate with Calcium Hardness. (Do not adjust water balance by moving pH beyond 7.8.)*
Regardless, you do need to measure and compensate for TDS to get the most precise saturation index and adjust your pH and Calcium Hardness levels accordingly. It is generally recommended that you adjust for TDS levels by subtracting one tenth of a saturation index unit (.1) for every 1,000 ppm TDS over 1,000 to keep your water properly balanced. When TDS levels exceed 5,000 ppm, it is recommended that you subtract half of a tenth, or one twentieth of unit (.05) per 1,000 ppm.* And as the TDS approaches that of seawater, the effect is negligible.
Hot tubs and spas have a more significant problem with TDS levels than pools. Because the bather load is relatively higher, more chemicals are added for superchlorination and sudsing along with a higher concentration of bather wastes. The increased electrical conductance that high TDS water promotes can also result in electrolysis or galvanic corrosion. Every hot water pool operator should consider a TDS analyzer as a standard piece of equipment.
A TDS analyzer is required to balance the water of any pool or spa in the most precise way. PoolPro, PoolMeter and ULTRAPEN PT1 instantly display accurate TDS levels giving you the information you need to take corrective action before TDS gets out of hand.
Temperature is the last and least significant factor in maintaining water balance. As temperature increases, the water balance tends to become more basic and scale- producing. Calcium carbonate becomes less soluble, causing it to precipitate out of solution. As temperature drops, water becomes more corrosive.
In addition to helping determine water balance, temperature also affects bather comfort, evaporation, chlorination, and algae growth (warmer temperatures encourage growth). Myron L’s PooLPRo also precisely measures temperature to one tenth of a degree at the same time any other parameter is measured.
In the pool and spa industry water balance is calculated using the Langelier Saturation Index (LSI) formula:
SI = (pH + TF + CF + AF ) – 12.1
PH = pH value
TF = 0.0117 x Temperature value – 0.4116 CF = 0.4341 x ln(Hardness value) – 0.3926 AF = 0.4341 x ln(Alkalinity value) – 0.0074
The following is a general industry guideline for interpreting LSI values:
• An index between -0.5 and +0.5 is acceptable pool water.
- An index of more than +0.5 is scale-forming.
- An index below -0.5 is corrosive.
pH, Total Alkalinity, and Calcium Hardness are the largest contributors to water balance. Pool water will often be balanced if these factors are kept within the recommended ranges.
The PoolPro PS9 Titration Kit features an LSI function that steps you through alkalinity & hardness titrations and pH & temperature measurements to quickly and accurately determine LSI. An LSI calculator allows you to manipulate pH, alkalinity, hardness and temperature values in the equation to determine water balance adjustments on the spot.
The most immediate concern of anyone monitoring and maintaining a pool is the effectiveness of the sanitizer – the germ-killer. There are many types of sanitizers, the most common being chlorine in swimming pools and bromine in hot tubs and spas. The effectiveness of the sanitizer is directly related to the pH and, to a lesser degree, the other factors influencing water balance.
To have true chemical control, you need to monitor both the sanitizer residual and the pH and use that information to chemically treat the water. To check chlorine residual, free chlorine measurements are made. For automatic chlorine dosing systems, ORP must also be monitored to ensure proper functioning.
Free Chlorine is the amount of chlorine available as hypochlorous acid (HOCl-) and hypochlorite ion (OCl-), the concentrations of which are directly dependent on pH and temperature. pH is maintained at the level of greatest concentration of HOCl- because hypochlorous acid is a much more powerful sanitizer than hypochlorite ion. Free chlorine testing is usually required before and after opening of commercial pools. Samples should be taken at various locations to ensure even distribution. Residual levels are generally kept between 1-2 mg/L or ppm.* PooLPRo V.4.03 and later features the ability to measure ppm free chlorine in pools and spas sanitized by chlorine only. With this feature PoolPro can measure a dynamic range of chlorine concentrations wider than that of a colorimetric test kit with a greater degree of accuracy.
ORP stands for Oxidation Reduction Potential (or REDOX ) of the water and is measured in millivolts (mV). The higher the ORP, the greater the killing power of all sanitizers, not just free chlorine, in the water. ORP is the only practical method available to monitor sanitizer effectiveness. Thus, every true system of automatic chemical control depends on ORP to work.
The required ORP for disinfection will vary slightly between disinfecting systems and is also dependent on the basic water supply potential, which must be assessed and taken into account when the control system is initialized. 650 mV to 700 – 750 mV is generally considered ideal.*
Electronic controllers can be inaccurate and inconsistent when confronted with certain unique water qualities, so it is critical to perform manual testing with separate instrumentation. For automatic control dosing, it is generally recommended that you manually test pH and ORP prior to opening and then once during the day to confirm automatic readings.*
Samples for confirming automatic control dosing should be taken from a sample tap strategically located on the return line as close as possible to the probes in accordance with the manufacturer’s instructions. If manual and automatic readings consistently move further apart or closer together, you should investigate the reason for the difference.*
ORP readings can only be obtained with an electronic instrument. PoolPro provides the fastest, most precise, easy-to-use method of obtaining ORP readings to check the effectiveness of the sanitizer in any pool or spa. This is the best way to determine how safe your water is at any given moment.
A relatively new development, saltwater pools use regular salt, sodium chloride, to form chlorine with an electrical current much in the same way liquid bleach is made. As chlorine – the sanitizer – is made from the salt in the water, it is critical to maintain the salt concentration at the appropriate levels to produce an adequate level of sanitizer. It is even more important to test water parameters frequently in these types of pools and spas, as saltwater does not have the ability to respond adequately to shock loadings (superchlorination treatments).
Most saltwater chlorinators require a 2,500 – 3,000 ppm salt concentration in the water (though some may require as high as 5,000-7,000 ppm).* This can barely be tasted, but provides enough salt for the system to produce the chlorine needed to sanitize the water.
(It is important to have a good stabilizer level – 30 – 50 ppm* – in the pool, or the sunlight will burn up the chlorine. Without it, the saltwater system may not be able to keep up with the demand regardless of salt concentration.)
Taste and salt shortages are of little concern to seawater systems that maintain an average of 32,000 ppm. In these high-salt environments, you need to beware of corrosion to system components that can distort salt level and other parameter readings.
Additionally, incorrect salt concentration readings can occur in any saltwater system. The monitoring/controlling components can and do fail or become scaled— sometimes giving a false low salt reading. Thus, you must test manually for salt concentration with separate instrumentation before adding salt.
You must also test salt concentration manually with separate instrumentation to re-calibrate your system. This is critical to system functioning and production of required chlorine. Both the PoolPro and PT1 conveniently test for salt concentration at the press of a button as a check against automatic controller systems that may have disabled equipment or need to be re-calibrated.
Though no one instrument or method can be used to determine ALL of the factors that affect the comfort and sanitation of pool and spa water, PoolPro is a comprehensive water testing instrument that is reliable durable, easy-to-use and easy-to-maintain and calibrate. As a pool professional, a PoolPro will not only simplify your life, it will save you time and money.
RECORD KEEPING – WHAT TO DO WITH ALL THOSE MEASUREMENTS …
Data handling should be done objectively, and data recorded in a common format in the most accurate way. Also, data should be stored in more than one permanent location and made available for future analysis. Most municipalities require commercial aquatic facilities to keep permanent records on site and available for inspection at any time.
PoolPro makes it easy to comply with record keeping requirements. The PoolPro is an objective means to test free chlorine, ORP, pH, TDS, temperature and the mineral/salt content of any pool or spa. You just rinse and fill the cell cup by submerging the waterproof unit and press the button of the parameter you wish to measure. You immediately get a standard, numerical digital readout – no interpretation required – eliminating all subjectivity. And model PS9TK features the added ability to perform in-cell conductometric titrations for Alkalinity, Hardness and LSI on the spot. Up to 100 date-time-stamped readings can be stored in memory and then later transferred directly to a computer wirelessly using the bluDock™ accessory package. Simply pair the bluDock with your computer, then open the U2CI software application to download data. The user never touches the data, reducing the potential for human error in transcription. The data can then be imported into any program necessary for record-keeping and analysis. The bluDock is a quick and easy way to keep records that comply with governing standards.*
*Consult your governing bodies for specific testing, chemical concentrations, and all other guidelines and requirements. The ranges and methods suggested here are meant as general examples.
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