Category: Experiment

It is well-known that reproductive mechanisms are arranged in many ways much more complex than in humans as a social species. In order to continue their lineage, birds rarely have families and live with a partner for long periods but often team up with males to obtain sperm only during the mating season. This phenomenon creates an important question: how exactly do female birds know that the male in front of them is suitable for them to continue their lineage? There are several assumptions on this question: on the one hand, it is believed that the more striking appearance of a male, the more likely he is to attract a female (“Why Are Male Birds”). On the other hand, male birds often use songs and loud noises to “defeat” mating competitors (Podos and Cohn-Haft 1068). In addition, the presence of specific odors can also be a severe predictor of the selection of a particular male (Daley). Danielle Whittaker of Michigan State University was interested in this question for unsightly junco birds and decided to test the assumptions.

Description of the Experiment

Whittaker hypothesized that there are at least two equivalent predictors that may cause female juncos to choose males to mate with. These include the presence of white spots on the birds’ tails and feathers and the concentration of 2-pentadecanone in the fatty secretion covering the bird’s feathers (Whittaker 1). The presence and content of 2-pentadecanone in the male may be related to the saturation of particular odors that excite the reproductive call of female birds (Whittaker 70). Whittaker decided to test her hypotheses through observation: the woman collected 22 male Junko birds and measured the average percentage of white feathers. This figure was a non-numerical measure ranging from 1.7 to 3.0. At the same time, Whittaker measured the percentage of 2-pentadecanone in the males’ secretions: this total ranged from 1.4% to 6.6%. Observing the birds during the mating season, Whittaker recorded the number of offspring from them to test her hypotheses. Thus, by the time the data was processed, the researcher had a table with measurements for each bird. To avoid confusing the animals, Whittaker used unique IDs for each of the males. On the row of the data table, each ID had its measure of the number of chicks per mating period, the white feather content of that bird, and the substance content of the fat-like secretion.

Research Question

Do the percentage of 2-pentadecanone in the fat-like secretion and the number of white feathers in male juncos affect their attractiveness among females during the mating period?

Hypotheses

1. The greater the number of white spots characteristic of a male junco, the more likely it was that a female would choose him.
2. The more significant the percentage of 2-pentadecanone contained in the fatty secretion of male juncos, the more likely it was that the female would choose him.

Variables

There were two independent variables and one dependent variable in this trial. The independent variables were 2-pentadecanone content and the proportion of white feathers in the male. Both of these variables seemed to influence the dependent variable, which was the number of offspring from that bird during the mating period. More specifically, changes in 2-pentadecanone content and the proportion of white feathers may have changed the number of offspring from this male. The accuracy of this relationship has yet to be elucidated by correlation and regression analyses, which is part of the next steps of the study (Calvello). This will allow us to know the degree, strength, and direction of the relationship between the variables.

Works Cited

Calvello, Mara. “Correlation vs. Regression Made Easy: Which to Use + Why”. G2, 2020, Web.

Daley, Jason. “Birds Sniff Each Other’s Bacteria to Help Choose a Mate”. Smithsonian, 2019, Web.

Podos, Jeffrey, and Mario Cohn-Haft. “Extremely Loud Mating Songs at Close Range in White Bellbirds.” Current Biology, vol. 29, no. 20, 2019, pp. 1068-1069.

Whittaker, Danielle. “Sexy Smells”. Data Nugget, 2021. Web.

“Why Are Male Birds More Colorful Than Female Birds?” Lyric, 2020, Web.

Whittaker, Danielle J., et al. “Chemical Profiles Reflect Heterozygosity and Seasonality in a Tropical Lekking Passerine Bird.” Animal Behaviour, vol. 151, 2019, pp. 67-75.

Introduction

Every chemical reaction has a certain degree of complexity: while the kinetic parameters of some can be described relatively quickly using fundamental equations of the first or second order, more complex reactions, most often of biochemical nature, cannot be interpreted as easily. The experiment will monitor the reduction of Cr(VI) by glutathione, record the results, and investigate the reaction’s double-exponential dependency. The reaction’s rate equation and rate constants will be determined using exponential stripping and computer-assisted nonlinear regression analysis. Numerous biological processes and their rates cannot be readily explained using first or 2nd order kinetics. Numerous processes are very complicated, including several stages, reversible reactions, and intermediates; descriptions of such processes often assume the form of complex equations, necessitating the use of computers in data analysis and algorithms to understand the gathered experimental data.

Discussion

The current study intended to calculate the rate constants of Cr (VI) reaction with glutathione at near-neutral pH. The first trial results revealed that k₁ = 0.00300 ????^-1, k₂ = 0.000510 ????^-1, and k_-1 = 0.00189 ????^-1. On the other hand, during the second trial the obtained results were k₁ = 0.00275 ????^-1, k₂ = 0.000684 ????^-1, and k_-1 = 0.00167 ????^-1. It implies that the concentration of Cr(VI) in Cr(VI) -> Cr(VI) – GSH (thioester) reaction reduces by 0.00300 ????^-1 (0.00275 ????^-1) per second, and increases by 0.00189 ????^-1 (0.00167 ????^-1) during the reverse reaction Cr(VI)-GSH -> Cr(VI). In a similar vein, during the reaction thioester + GSH -> GSSG + Cr(III) the concentration of Cr(VI) reduces by 0.000510 ????^-1 (0.000684 ????^-1) per second. Consequently, these results can be used to determine a chemical reaction rate based on a rate law, where rate = k[R]ⁿ[I]^m, which, however, is out of the scope of this research.

To achieve these results, the double-exponential dependence was investigated. In this regard, using this method helped to break one non-linear function into two linear ones and analyze each one separately. Firstly, the dependency between absorbance and time when t > t’ was calculated. As a result of two trials the following regression formulas were received: y = – 0.0003x – 1.4774 and y = – 0.0004x – 1.5206, where 0.0003 and 0.0004 are ????₁ and -1.4774 and -1.5206 are c₁. Applying similar logic to find the dependency between absorbance and time when t < t’ it was found that ????₂ equals 0.0047 and 0.0041 for the first and the second trials accordingly, while c₂ equals -1.2475 and -1.3305. Based on these values, amplitude ratio (A) was determined to be 1.258 in the first and 1.209 in the second trial.

Next, the optimized values for all the aforementioned values were determined. As such it was found that ????₁ = 0.0003 (0.0004), ????₂ = 0.0051 (0.0047), c₁ = -1.4589 (-1.4818), c₂ = -1.2050 (-1.2916), and A = 1.289 (1.209). Here it is important to mention that there were some differences between trials, although not significant, which indicates the greater reliability of the results. On the other hand, most of the time, the small differences in the trials are inevitable due to a number of external and internal reasons. Finally, using the optimized values of ????₁, ????₂, and A, the values of k1, k2, and k3 were calculated.

Conclusion

The experiment investigated the time dependency of glutathione reduction of Cr(VI) in an aqueous solution. The absorption of the oxidant and intermediate was used to calculate the rate constants. Nonlinear regression was used to examine the data. This reaction’s reactant was continually eaten while the reaction’s intermediate was created and devoured. It links two GSH units and oxidizes these to glutathione disulfide. The oxidation decreases the chromium ion’s oxidation state from +6 to +3. The thioester intermediate is anticipated to be formed by Cr(VI). In this case, the thioester intermediate joins with another GSH molecule. Because simple kinetic models cannot capture real-life rate processes, reversible multi-step sequential reaction mechanisms arise. This experiment’s reaction mechanism proposes that the reactant generates an intermediate and reverts to the reactant to form the product. This was occurring in the responses mentioned above. The blue line represents the outcome, the mediator’s black line, and the demarcation dot the intermediate. Since the concentration curve climbs and lowers towards the end, the intermediate is generated, and the reactant is continually consumed.

Reference

Experiment 6: Kinetics of a Reversible, First-Order, Consecutive Reaction: Reduction of Cr(VI) by Glutathione

Introduction

I finely crumbled a given bar of school white chalk with a mortar and pestle into a fine homogeneous powder without lumps. Then I dissolved the substance in 50 mL of ordinary drinking water and stirred vigorously. The calcium-gypsum mixture, which forms the base of the chalk, was not completely dissolved in water, and once the equilibrium in the glass had settled, I filtered the mixture entirely through filter paper. The clear water was collected as a filtrate in a beaker under the filter paper, while the wet chalk powder, which had acquired an intense gray hue, was in the filter. After the collected “purified” water was boiled on the stove at 95-100°C, all the liquid evaporated, and white sediment grains remained at the bottom of the beaker.

Checking with an Indicator

In the second part of the work, a mixture of 10 g of solid calcium hydroxide and 50 mL of drinking water in a beaker was initially created. I closed the beaker tightly with a rubber stopper and stirred it vigorously. The mixture was passed through filter paper, resulting in “purified water” collected in a flask under the filter. Additionally, a sterile flask with pure drinking water, devoid of impurities, was created. I added two drops of phenolphthalein indicator to each of the two flasks, after which visual changes were observed, if appropriate.

Data

The presence of minerals in the composition of the filtered water was observed on the fact of the two procedures performed. The boiling allowed all excess moisture from the beaker to evaporate, but this evaporation left a fine pale murky powder of calcium-gypsum on the bottom. In the second part of the work, phenolphthalein added to pure water showed no result: the color of the solution did not change. However, the addition of clear phenolphthalein to the water collected after filtration resulted in a faint violet hue inside the flask.

Results

The present work tested the solvent property of water and its ability to transfer minerals from a solid to a liquid state. Some of the chalk powder remained at the bottom of the plate after water evaporation indicates that not all of the chalk was left in the filter. To put it another way, some of the sediment was dissolved by the water molecules, then returned to solid form when all the water was evaporated. At the same time, the formation of a purple coloring of the filtered water was a sign of a slightly alkaline water environment. This probably indicates that some of the calcium hydroxides were converted to a liquid form, which gave a reaction to the indicator.

Interpretation

In contrast to soil, water has better solubilizing properties, and therefore its use proves to be an advantage. Thus, hydroponic horticulture seems to bring better results for the agricultural industry because water actively brings minerals that have been dissolved in it from fertilizers to the plants. Water is a kind of conductor that creates the opportunity for substances to penetrate the plants by root pressure, and this effect is probably stronger than in the presence of solid soil. However, more research is needed to verify this, including the actual growing of plants on hydroponic systems and soil to track the dynamics. In addition, it is interesting to know which fertilizers prove to be most effective in such systems.

Conclusion

The proposed hypothesis was true, fully proven by the tests performed. Water did indeed prove to be an excellent solvent, as it could dissolve even solid, seemingly insoluble substances. This was confirmed in two ways, ensuring that systematic error was minimized. This work was helpful for me: I learned about hydroponics and its advantages over classical gardening.

Objective

The experiment aimed at isolating flagella protein and carrying out SDS electrophoresis.

Motile flagella and cilia are microtubule-based structures highly organized, comprising approximately 250 proteins. The flagella and ciliary components are found in vertebrates and other eukaryotic organisms. Studies using Chlamydomonas reinhardtii have revealed that most of flagellar proteins assemble into complexes in the cell’s cytoplasm. Radial spokes inner arm dynein and dynein flagella components reassemble. This is per the research done by [FOWKES et al 1998], [QIN et al 2004] and [PIPERON 2004].

Intra-flagella transport revealed in a model of both membrane and non-membrane bound flagellar protein shuttling between the cytoplasm and flagellar compartments. Accumulations of proteins for the flagella occur in the cytoplasm. Here interaction between the proteins and intra-flagella transport takes place. Absence of intra-flagella transport kinesin results to cells with stubby flagella. This suggests that IFT is necessary for the transport and assembly of each and every axonemal component. The final assembly of proteins takes place after the proteins enters the flagellar compartment.

Materials

Chalydomonas reinharditii culture, Centrifuge, Pipettes, Ice, SDS polyacrylamide gel,

HEPES-10mM 4 (2-hydroxyethyl)-1-piperazinethanesulfonic acid pH 7.4

HMDS- 10mM HEPES, 5nM MgSO4, 1mMDTT, 4% sucrose, pH 7.4 ,HMDES-10mM HEPES, 5mM MgSO4, 1mMDTT, 2mM EGTA and ImM PMSF, 4% sucrose pH7.4

HMDEK-30mM HEPES, 4% mM MgSO4, 1mMDTT, 0.5mM EGTA and I mM PMSF, 25mM CH3COOK, Ph 7.4, 50mM Dibucaine

Procedure

1. Washing culture to concentrate ‘Chamy’ culture.
2. From 20ml cell suspension, keep 1mml aliquot for protein analysis and electrophoresis. Label the sample WC (whole sample).
3. Spin the cells for 8minutes at 1300xg in a clinical centrifuge. Aspirate to remove the supernatant and re-suspend the washed cells in 5ml of cold HMDS.
4. Add 0.5ml of 50mM dibucaine to the cells and mixed by swirling. This makes cells lose their flagella.
5. Add 0.5ml of cold HMDES and mix gently. Spin cells at 2000xg for 5minutes at 4 degrees. The cold pellet contains the cell bodies. Re-suspend the pellet in 2ml of ice-cold HMDEK. Label the tube CB.
6. Transfer the supernatant containing the flagella to another tube, and centrifuge at 31,000x g for 20minutes for the flagella to sediment. Aspirate to remove supernatant and then re-suspend the pellet in 250 µliters HMDEK. Label the tube F.
7. The purpose of the protein assay is to determine the concentration of protein in the samples WC, CB and F, so as to determine appropriate volumes to load onto an SDS–PAGE gel.
8. SDS polyacrylamide gel electrophoresis separates the flagella proteins on the basis of their charge and mass. Mix 80 µliters of each of the samples with 5x sample buffer. The loading buffer contains a dye for tracking the movement of protein in the gel. Glycerol for making the sample solution dense to prevent it from dispersing into the upper buffer is present. Β-mercaptoethanol, which denatures proteins, into polypeptides is a component of the sample buffer.
9. Stain the gel using Coomassie Brilliant Blue that binds to protein. A de-staining process is essential to remove unbound stain. This is done using high methanol for 15 minutes followed by low methanol overnight. The gel is left with blue bands which correspond to polypeptides of different molecular weight.
10. Measure the distance of migration of the bands and use the values to calculate retention values for each sample.

Results

• WC- Whole cells
• CB- cell bodies (deflagellated cells)
• F -Isolated flagella

Y axis- log Molecular weight
X axis= retention factors

The molecular weight for the bands is:

Discussion

Sodium sodecylsulfate polyacrylamide gel electrophoresis is a technique that separates protein on the basis of molecular weight. The gel porosity can be controlled through regulating the cross-linking extent. As the acrylamide concentration increases, the gel porosity decreases and any protein molecule migrates more slowly after application of the same electrical current. Presence of SDS detergent in the gel disrupts the non-covalent bonds of proteins and coats the polypeptides resulting to a net negative charge. Reducing agent β-mercaptoethanol addition disrupts the disulfide bridges present in the protein. The larger protein moves slower than small molecules in the polyacrylamide gel. The approximate size of the protein estimation is by running a protein molecular weight marker along the samples.

A standard curve can generation can be done using the standard proteins of known molecular weight. A plotted graph with the distance of migration versus molecular weight can be used to estimate the molecular weight of unknown protein. The unknown protein is electrophoresed parallel to the standard molecular weight samples. The resolution of SDS-PAGE can be increased by using two polyacrylamide gel layers having different levels of cross-linking. The upper gel (stacking gel) contains a lesser degree of cross-linking, making polypeptides move fast in this layer. The running gel (lower gel) contains a higher degree of cross-linking and polypeptides move slowly in this layer. The slowing down of the speed of movement results to all polypeptides piling up and get condensed to a tight band that enters the running gel. A polyacrylamide gradient use such that the polyacrylamide percentage increases downwards also increases protein resolution. This allows protein separation into a greater range of molecular weights than the use of uniform concentration of acrylamide throughout the gel.

The total numbers of protein bands in the samples WC, CB, and F were varying. The whole fraction samples, WC shows more bands than CB and F while CB has more protein bands than F. Sample WC contained all possible proteins of the Chalydomonas reinharditii while sample F contained a purified flagella protein.

During protein isolation process, Dibucaine a local anesthetic addition to the sample flagellated the cells before collecting sample CB. This means that sample CB only contained the proteins present in the cell bodies and not flagellar protein. The supernatant containing the flagellar is further processed through centrifugation and addition of solution HMDEK.

Reference List

ALBERTS B, JOHNSON A, LEWIS J. 2002 Molecular Biology of the cell 5th Edition Garland science New York

DAVID R, WILLIAM D. 2005 Methods in cell Biology 6^th Edition Gerald science New York

JOHN R. 1999 Methods in Microbiology Oxford University press USA

LINDA J, VAN E, GIANNI P, MARTIN W. 2000 Cell Biology Rockefeller University New York

Gram Stain

Bacteria have their unique morphological features which helps them to distinguish from one another. This is better accomplished by techniques that play an important role. These are nothing but staining techniques. Various methods of staining exist.

The first to be dealt is Gram Staining. This technique was invented by a Danish bacteriologist, Christian Gram and is most popular. This method includes the utility of sequence of dyes that enable certain bacteria to become purple while others pink. The bacteria which become purple color on staining are known as Gram –positive while that become pink colored are known as Gram-negative (Tami port, 2010).

The bacterial cell wall is responsible for the stain color to develop(Tami port, 2010). The materials required are Slides, Crystal Violet (the primary stain), water, Acetone alcohol, safranain, staining rack, oil immersion, compound microscope.

Methods

The steps are as follows: Add slowly drops of crystal violet on the surface of the slide completely and for 1 minute allow it to stand. Fill the slide surface with Gram’s iodine and wait for 1 minute.

Following that the slide is rinsed with water. Next, the slide is flooded with iodine and allowed to stand for1 minute. It is then rinsed with water. The slide is flooded with Acetone Alcohol and after standing for 15 seconds, it is rinsed with water. Finally, the slide is flooded with safranin and allowed to stand for 1 minute and then rinsed with water. After this slide should be allowed to dry and observed under compound microscope using an oil immersion (1000x TM).

Result

The Gram positive cells develop purple color, indicating that they have taken up the primary stain. The Gram negative cells develop pink indicating that they have taken up counter stain.

In this test Pseudomonas ssp was identified and it is Gram negative. Therefore, the gram staining procedure helps in identifying the bacteria that may be gram positive and gram negative.

Catalase Test

This test is performed to identify the catalase enzymes by their catalytic action on Hydrogen peroxide to yield oxygen and water.Hydrogen peroxide is generally produced by the bacteria as an oxidative final product of sugars that are degraded aerobically. When hydrogen peroxide gets stored in bacterial cells, the consequence is cell demise.catalse generally, degrades hydrogen peroxide or enables oxidation of secondary substrates. Catalase does not exert oxidizing action on different peroxides.

Materials

Microscope slide, sterile loop (made of nichrome), Bacterial colonies, 3-6% Hydrogen peroxide solution, clean capped test tubes. Procedure: Take a slide and with the help of a sterile loop pick few colonies. These are smeared onto the glass slide. Next, a drop of Hydrogen peroxide solution is added.

The slide is later observed for vigorous air bubbles within 10 -15 seconds. Result: Bubbles generated reflect the presence of catalase in bacterial cells whereas no bubbles reflect a negative test. Therefore, the presence or absence of air bubbles from the bacterial cells placed on the glass slide in a smear form helps us to distinguish the bacteria that are catalase positive or catalase negative.

Oxidase Test

Microorganisms identified by this test yield cytochrome oxidase). This enzyme is involved in the process of electron transfer process by the transport of electrons to oxygen from a molecule that acts like donor. The oxidase reagent possesses a chromogenic reducing agent. This substance when undergoes oxidation changes its color.

When th organism generates cytochrome oxidase, in 15 seconds the oxidase reagent takes up blue or purple or blue color.

Materials

Petri dish, filter paper, N, N, N,N-tetramethylparaphenylene-diamine hydrochloride, sterile loop or wooden stick.

Procedure

Obtain a petridish and keep a small filter paper exactly at the bottom. Add few drops of N,N,N,N-tetramethylparaphenylene-diamine hydrochloride such that the paper turns wet.

Take the sterile loop and pick few colonies. Place them on the filter paper by rubbing. Observe for blue color within 1 – 2 minutes.

Result

Positive culture is detected by color difference.

API system

The API stands for Analytical Profile Index. This test identifies various microorganisms. There are several API products available commercially. Some of them are API 20E, API 20 NE, API 20 Rapid 20 E, API NH, API 20 A etc. The API includes strips which contain small miniaturized tubes.

Materials

Agar plates containing bacterial species, – sterile mineral oil, saline tube, sterile Pasteur pipettes + bulbs, 0.85% NaCl solutions (5ml), API 20E test strip (for oxidase – gram negative rods). API test strip incubation chamber. Kovac’s reagent, 10% FeCl3, Barrett’s reagents A and B, Result sheets.

Methods

Take a saline tube and prepare a suspension of the bacteria. Now, take a large colony with the help of a sterile loop and inoculate bacterium (pure culture) into the 0.85% NaCl solution. Maintain that the suspension is homogenous and devoid any floating bacterial clumps.

In order to quantify the suspension, employ McFarland barium sulfate standard No. 3. for API strip inoculation: Take the strip and handle above the table at a small angle, with the pipette introduce the bacterial suspension into well. At the sides of the cupule touch the pipette end. This enables capillary pressure into well such that fluid enters inside.This is achieved by gentle squeezing of the bulb ,which eliminates bubble formation in wells. Here, care should be taken such that each well filled till the neck.

Incubation of the strip in its chamber

In this step, take incubation chamber and pour water to fill. In the bottom, incubation chamber possesses contains small holes.

The filling of chamber is such that the indentations are just filled. Next, keep the strip in the bottom of the chamber. Take care to avoid much water as the API strip may totally get wet. Now at the bottom keep incubation chamber top and name it. Keep the strip in incubation at 37° C for 18-24 hours (www.biotech.univ.gda).

Reading the strips/interpretation

With the help of supplied result sheets, the results are read. This would create an API number profile on the results sheet. This information can be recorded in a computer or entered in a laboratory manual.

References

1. Tami Port. Bacteria Gram Stain Reaction. Test for Gram-positive and Gram-negative Bacterial Identification. Web.
2. Common Laboratory Techniques. Web.
3. Sputum culture. Web.
4. https://biology-forums.com/index.php?acti%20on=dlattach;topic=573.0;attach=253
5. Oxidase Test. Web.
6. Analytical Profile Index. Web.

Introduction

Thermoelectric coolers are gadgets are made from junctions of two semiconductors of different materials usually stacked on top of each other (Anatychuk 1979, p.151). Thermoelectric coolers work with the Peltier effect, which is created by heat instability between the two semiconductor junctions (Vayner 1983, p.30). They consume electric current energy to transfer the heat against the heat gradient. One of the sides of the thermoelectric cooler junction absorbs heat energy when the electric current is passed while the other side dissipates energy in form of heat (Woodbury, Levinson, & Lewandowski 1995, p. 184). The main advantage offered by thermoelectric coolers over other conventional cooling apparatus is that their reliability is extremely high, their nature of being small is highly advantageous, and they are light in weight (Stockholm 1991, p. 228).

Objectives

• To understand the phenomenon of thermoelectric cooling
• To use thermocouples for temperature measurement
• To investigate the roles played by heat sinks, thermal paste, and insulation in the operation of a thermoelectric cooler

Materials

• Thermoelectric cooler assembly
• Heat sink paste
• Thermometer
• Brass, aluminum, steel, copper

Methods

Investigating the heat sink

Experiment 1: Effect of current on temperature difference created by a TEC

The thermoelectric cooler assembly used consisted of a thermoelectric cooler glued to a small heat sink using heat sink paste, without which the thermoelectric cooler would heat up dangerously even with small currents.

1. The thermoelectric cooler assembly was attached to the copper heat sink using a thin coating of heat sink paste.
2. The temperatures of both the cold surface of the thermoelectric cooler and the hot surface of the heat sink were measured as a function of current ranging from 0.0-1.0A.
3. The thermocouple was covered in heat sink paste first before pressing them to the surface of the heat sink and thermoelectric cooler.
4. Time was allowed for the temperatures to reach steady values after the current was changed. This was about one minute.

Results and analysis

Table 1.0: Table showing Effect of current on temperature difference created by a TEC

The above table translated into the graph shows that the temperature difference created is directly proportional to the current that is passed through the heat sink. This means that the temperature difference increases with an increase in current. However, the limit that was used in this experiment was a current of 1.0 A in order to avoid damage to the sensitive thermoelectric cooler assembly.

Therefore, extrapolating the line graph, it can be found that the temperature difference created by the current of 1.2 A is 33.7°C.

Experiment 2: Effect of size of the heat sink

1. The thermoelectric cooler assembly was attached to the various sizes of the aluminum in turn using the heat sink paste.
2. The current was then fixed at 1.0 A and passed through the thermoelectric cooler assembly.
3. The temperature of the cold surface of the thermoelectric cooler assembly was then measured three minutes after the current had been switched on for all the sizes of the aluminum.
4. The heat sink and thermoelectric cooler assembly were at room temperature before the current was switched on.

Results and analysis

Table 2.0: Effect of size of the heat sink on the temperature difference.

The above graph shows that the temperature of the cold surface of the heat sink is much lower when the size in terms of volume, of the heat sink, is greater. However, this graph shows that the temperature of the cold surface to be 45°C when the volume is 21.7 cm3. This is an anomaly probably due to the measuring instrument and it does not follow the trend of the graph.

It can be concluded that the temperature of the cold surface of the heat sink is inversely proportional to the size (volume) of the heat sink up to a certain value when the temperature increases with an increase in the volume of the heat sink.

Experiment 3: Effect of heat sink material

Different materials were used for the heat sink in order to determine the type of material, which has an effect on the heat sink performance, and to determine the best material for the heat sink purpose.

1. Several heat sinks of the same dimensions were used for this experiment. These include aluminum, brass, copper, and steel.
2. The material which was best for use as a heat sink was established by passing a current through them
3. The current through the thermoelectric assembly was fixed at 1.0 A.
4. The temperature of the cold surface of the thermoelectric assembly was then measured after the current had been switched on for about three minutes.

Results and analysis

Table 3.0: Effect of heat sink material on temperature difference.

From the table above, it was evident that the cooling effect was more pronounced when the material of the heat sink was made of brass, steel, aluminum, and copper in that order. This implies that brass would make a very good heat sink material.

System design using thermoelectric coolers

1. The experiment was done by initially using a small mass of water in the small copper container (C_cu =386 J kg-1 K-1).
2. The temperature change of the water was measured after 4 minutes. This was also done for the thermoelectric cooler assembly.
3. This procedure was repeated for different conditions including a bigger heat sink, the heat sink in ice, a smaller heat sink among others.
4. The conditions that gave the best results for cooling the water for 30 minutes were selected and the temperature of the water T_w and the temperature of the thermoelectric cooler assembly T_tec was recorded after every 5 minutes.

Results and analysis

Table 4.0: Effect of different types of heat sink materials on the cooling effect of water.

M_w= Mass of water

M_c= Mass of colorimeter

dQ/dt= (M_wC_w+ M_cC_c) dT/dt

dt= Tf- Ti (total time in seconds)

Table 5.0: Temperature difference created by one heat sink over time.

Volume of Aluminum= 40X 40 X4

Density of Aluminum= 2710 kgm^-3

=6400 X 10^-9 m³

Mass= Density X Volume

= 2710X 6400X 10^-9

Rate of energy removal= 5J/s

dQ/dt= m* C* ΔT/Δt

Δt= 60s, ΔT=Tf- Ti, C= 900J/ Kg/ °C

-5= dQ= (2710 *6900* 10^-9 *900 ΔT)/ 60

-5= 15.6 ΔT

-5= 15.6 (Tf-Ti)

-5= 15.6 (Tf-25)

15.6Tf= 390-5= 385

Tf=24.7°C

Three factors that might cause the temperature of the aluminum to fail to reach the final temperature you calculated in part 2 above include:

1. Accuracy of measuring instruments
2. Loss of energy due to dissipation to the air
3. Prevailing room temperatures

Discussion and conclusion

The amount of current that passes through the thermoelectric cooler will determine the amount of heat energy difference created. The higher the current that flows through the TEC, the higher the temperature difference created. The size of the heat sink will also affect the temperature difference of the TEC. The bigger the size of the heat sink, the smaller the temperature difference created. Also affecting the efficiency and effectiveness of the TEC is the type of material used as the heat sink. The cooling effect decreases when the material used is brass, steel, aluminum, and copper in that order.

List of References

Anatychuk, L 1979, Thermo elements, and thermoelectrically devices, Prentice Hall, New York.

Stokholm, L 1991, Reliability of thermoelectric cooling systems, Oxford publishers, London.

Vayner, A 1993, Thermoelectric coolers, Prentice Hall, New York.

Woodbury, H, Levinson, L & Lewandowski, R 1995, Z-meters CRC handbook of thermoelectric, CRC Press, Inc., New York.

The impacts of dropout prevention programs are usually assessed using experimental methods since the outcomes are based on similar characteristics from randomly assigned treatment and control groups. Non-experimental methods are however necessary for some situations and therefore it is worth determining whether propensity-score methods can replicate the impacts of experimental designs. This study compares outcomes of experimental designs with those of propensity-score matching using secondary data analysis from the School Dropout Demonstration Assistance Program (SDDAP) as well as the National Education Longitudinal Study (NLES). Propensity-score methods are identified as ineffective in replicating experimental impacts of school dropout programs.

Roberto Agodini and Mark Dynarski’s article “are experiments the only option? A look at dropout prevention programs” is published in The Review of Economics and Statistics 2004 volume 86, number 1 from page 180 to 194. Agodini and Dynarski determine the possibility of getting unbiased program impact estimates when propensity-score methods are used to determine dropout. This is done by comparing estimates derived using experimental methods with those derived from propensity-score methods. It is first recognized that experimental designs are valid enough in portraying the impacts of a program since the control and the treatment groups have similar observed and unobserved characteristics. However, conducting experimental studies in some settings such as when programs are not achieving the minimum capacity or in cases where treatment affects the entire population that would be qualified for program services. The propensity-score method is thus viewed as an alternative design that can evaluate programs and display similar impacts to those that would be attained if experimental methods are used. In this study, Agodini and Dynarski (p.180) seek to know the ability of propensity-score methods to duplicate experimental impacts of student absenteeism, dropout, self-esteem and educational aspirations. The authors also seek to know the extent to which propensity-score methods can replicate experimental impacts using less extensive data that is readily obtained for public use. Finally, the authors seek to determine the precision of propensity-score-based impacts estimates.

Agodini and Dynarski (p 182) primarily conduct data analysis to determine the possibility of propensity-score methods replicating experimental methods in getting impacts of services programs. Data is obtained from the School Dropout Demonstration Assistance Program (SDDAP) as well as the National Education Longitudinal Study (NLES). SDDAP addresses the school dropout problem in the U.S. with a target on middle- and high school students. An experimental design was used to evaluate SDDAP targeted programs whereas a comparison design was used to evaluate SDDAP restructuring programs. For the experimental design, randomly assigned (to either a treatment or a control group) students were assessed. In the comparison design, the treatment groups were assigned to 16 SDDAP targeted programs in a random manner. Baseline evaluations were done and follow-up was conducted (two follow-ups for one cohort and one follow-up for the other cohort) and data was collected using detailed questionnaires as well as data from school records.

Two comparison groups were selected using the propensity-score methods to match the sixteen treatment groups. One group was obtained from SDDAP restructuring programs whereas the second group was obtained from NELS data. The propensity-score matching helped in selecting a comparison group that had average similar characteristics to those of the treatment group and not an exact match. Agodini and Dynarski (p. 185) utilized the logit model estimates to determine comparison groups, followed by assignment of a propensity score to members of treatment groups as well as the equivalent comparison member and finally selecting the nearest neighbor from the comparison group and assigning to the treatment group subject. The t-test helped determine how similar propensity scores were for the treatment and comparison subjects. Further, an f-test helped in determining how similar the characteristics of the two groups were collective. A p-value greater than.05 for both tests indicated that the two groups matched well in their characteristics. Several characteristics were used to determine the eligibility of subjects and these included demographic characteristics, parental education, time use, school attendance and participation in school activities, the student’s background as well as academic performance of students among others. To determine standard errors in the experimental impacts, the authors used standard analytic formula whereas those of propensity-score were determined using bootstrap methods.

It is established that random assignment of subjects helps in reducing experimental bias (Nancy & Grove, p. 245). This is attained in this study among the treatment groups but it is lacking in the comparison groups. It is also important to note that experimental research design ensures that internal validity is controlled in that the independent variables are kept uniform for all subjects. As such, the experimental design in this study maintained internal validity which is not guaranteed in the comparison group even though the selection criteria maximizes on a similarity of characteristics. It is, therefore, no wonder that the findings of this study establish propensity-score methods as highly unlikely to replicate the experimental impacts on school dropout programs. By using secondary data, the study may not be reliable for making comparisons even for the experimental design since the possibility of manipulating the independent variables is highly limited (Calmorin & Calmorin, p. 74). The use of primary data is therefore advisable to enable the manipulation of variables and effective comparison.

It is commendable that the sample size for the entire comparison group study is large enough (about 3,000 subjects) and therefore may have substituted for the internal validity which is achievable in experimental studies even when small samples are used. In the selection process for the comparison group design, important factors such as the region where students were studying were not a major consideration yet the environment (rural or urban) may have determined the outcome variables. It is also important to mention that the propensity-score method being a non-experimental method; failed to determine the effects of observed and non-observed factors on the outcome. As such, there is no firm basis for comparing the experimental and propensity-score methods outcomes. This study also ignores confounding variables such as an individual’s motivation which may affect the outcomes of the study.

Based on the findings that propensity-score methods can barely replicate experimental effects in dropout prevention initiatives, it is advisable for policymakers to advance experimental methods. This would avoid working on assumptions related to outcomes. Whereas propensity-score methods are not appealing for replicating experimental methods, they should be considered in settings that allow the researcher to direct participation, since this would deter unobserved factors from the study.

References

Agodini, Roberto and Dynarski, Mark. Are experiments the only option? A look at dropout prevention programs. The Review of Economics and Statistics, 2004, 86,1: 180-194.

Burns, Nancy and Grove, Susan K. The practice of nursing research: conduct, critique, and utilization, (5th edition). St. Louis, Missouri: Elsevier Saunders, 2005.

Calmorin, Laurentina Paler and Calmorin, Melchor A. Research methods and thesis writing’ 2007 Ed. (2nd edition). Samaplaoc, Manila: Rex Bookstore, Inc., 2007.

X-ray fluorescence (XRF) is an investigative method that ascertains the chemical composition of a substance by identifying the individual elements present. However, the sample remains intact during the entire analytical process (Tykot 2016). A special type of equipment referred to as an XRF analyzer verifies a sample’s chemical makeup by measuring the level of fluorescence given off when the material is excited by X-ray radiations (Goldstein et al. 2017). XRF is a flexible method that can be used successfully on solid or liquid samples. Furthermore, simple sample preparation steps are needed before analysis, which has made XRF the preferred analytical tool in qualitative and quantitative investigations involving geological materials (Sharma et al. 2015; Lutterotti et al. 2016).

An X-ray tube in the analyzer produces radiations that are absorbed by atoms in the sample. Consequently, the atoms ionize and release electrons from the lower K and L energy levels in a process that destabilizes the atom. The ousted electrons are substituted by electrons from a higher energy level in a process that releases energy in the form of fluorescent X-rays because these electrons have more energy than the previously expelled ones (Anand et al. 2018). The emitted rays are characteristic of each element and can be used to identify them.

Experimental Procedure

About 20 unknown salt samples with unique identification numbers were provided to the class. One unknown sample (number 3) was chosen and used for the subsequent investigations. Two other students also handled the same salt sample. An XRF film was put over a cup, which was covered by a different cup thereby pushing down the film. A clean weighing balance was used to weigh about 2.57 grams of the unknown salt.

It was important to ensure that the weighting balance was clean to avoid introducing any extra material that could interfere with the accuracy of the measurements. The correct weight was expected to range from 2 to 3 grams. The salt sample was then put in a plunger to be compressed followed by measuring the height of the compacted sample, which was 7 cm. Appropriate labeling of the sample was done on the cover of the cup by including pertinent details such as the sample number, student’s initials, weight, laboratory session, and height after compression. The prepared sample was then placed in the RIGAKU XRF machine for evaluation alongside the unknown samples prepared by other students. The results of the analyses were later provided to facilitate the compilation of the report.

Result

Table 1 below shows the concentrations of different trace elements in the salt samples. The findings of the individual sample that was assigned are highlighted in green. The results showed that sodium and chloride were the main cation and anion at concentrations of 522,000 and 379,000 parts per million (ppm), respectively. Sample 3 had the highest concentration of sodium of all the salts. Mineral elements that were present in sample 3 included bromine at 68.8 ppm, calcium at 33.6 ppm, copper at 7.23 ppm, iron at 19.7 ppm, and phosphorous at 649 ppm.

Other chemical elements that were found in the salt specimen included rubidium, silicon, stannum (tin), and tellurium at concentrations of 5.37, 730, 57.8, and 8.91, respectively. Trace elements that were seen in other samples but were absent in sample 3 included aluminum, chromium, potassium, magnesium, rhenium, rhodium, sulfur, strontium, tantalum, titanium, vanadium, and zinc. In contrast, some elements were not completely detected in any of the salt samples.

They included silver, arsenic, barium, bismuth, cobalt, cesium, gallium, germanium, mercury, iodine, indium, manganese, molybdenum, niobium, nickel, lead, palladium, ruthenium, stibium (antimony), selenium, thallium, uranium, and tungsten. Disparities existed in some of the elements identified in the other two duplicate analyses. For example, aluminum and potassium were detected in session 1S03, whereas chromium, strontium, and titanium were observed in session 3S08.

Discussion and Conclusion

The XRF findings and physical appearance of the unknown specimen indicate that it is likely to be table salt. The industrial processing of table salt involves high levels of refinement to get rid of impurities and grinding into a fine powder (Warren 2016). The pounding increases the tendency of the salt particles to clump together, which was observed in sample 3. Conversely, the refining process removes some of the impurities that contribute to discoloration in salt, thereby resulting in a pure white product (Hopkins 2015). The specimen also contained large quantities of sodium and chlorine, which is usual for table salt (Fang et al. 2019).

The sample could not be sea salt because it lacked sulfur, which makes up sulfates that are found in sea salt. Trace minerals that can be found in sea salt include potassium, bromine, phosphorus, boron, iron, zinc, copper, manganese, and silicon (Yang et al. 2015). Nonetheless, some of these components such as potassium, zinc, and manganese that are commonly found in sea salt were absent in the analyte.

The possibility of the unknown sample being rock salt was ruled out because barium and strontium, the two major elements that are found in rock salt, were absent (Chen et al. 2019). The outcomes of this experiment prove that XRF is a dependable technique in the chemical analysis of the elemental organization of substances.

Reference List

Anand, LFM, Gudennavar, SB, Bubbly, SG & Kerur, BR 2018, K-shell X-ray fluorescence parameters of a few low Z elements’, Journal of Experimental and Theoretical Physics, vol. 126, no. 1, pp. 1-7.

Chen, A, Yang, S, Xu, S, Ogg, J, Chen, H, Zhong, Y, Zhang, C & Li, F 2019, ‘Sedimentary model of marine evaporites and implications for potash deposits exploration in China’, Carbonates and Evaporites, vol. 34, no. 1, pp. 83-99.

Fang, D, Huang, W, Antkiewicz, DS, Wang, Y, Khuzestani, RB, Zhang, Y, Shang, J, Shafer, MM, He, L, Schauer, JJ & Zhang, Y 2019, ‘Chemical composition and health risk indices associated with size-resolved particulate matter in Pearl River Delta (PRD) region, China’, Environmental Science and Pollution Research, pp. 1-11.

Goldstein, JI, Newbury, DE, Michael, JR, Ritchie, NW, Scott, JHJ & Joy, DC 2017, Scanning electron microscopy and X-ray microanalysis, Springer, New York, NY.

Hopkins, J 2015, Everyday nutritional mistakes making you fat: things you need to know about food, that keeps sabotaging your waistline, BookBaby, Pennsauken, NJ.

Lutterotti, L, Dell’Amore, F, Angelucci, DE, Carrer, F & Gialanella, S 2016, ‘Combined X-ray diffraction and fluorescence analysis in the cultural heritage field’, Microchemical Journal, vol. 126, pp. 423-430.

Sharma, A, Weindorf, DC, Wang, D & Chakraborty, S 2015, ‘Characterizing soils via portable X-ray fluorescence spectrometer: 4. cation exchange capacity (CEC)’, Geoderma, vol. 239-240, pp. 130-134.

Tykot, RH 2016, ‘Using nondestructive portable X-ray fluorescence spectrometers on stone, ceramics, metals, and other materials in museums: advantages and limitations’, Applied Spectroscopy, vol. 70, no.1, pp. 42-56.

Warren, JK 2016, Evaporites: a geological compendium, Springer, New York, NY.

Yang, D, Shi, H, Li, L, Li, J, Jabeen, K & Kolandhasamy, P 2015, ‘Microplastic pollution in table salts from China’, Environmental Science & Technology, vol. 49, no. 22, pp. 13622-13627.

Overview of XRF

XRF (X-ray fluorescence) is an analytical technique that verifies the chemical elements found in a material without destroying it during analysis (Pinto 2018). Specialised equipment known as XRF analysers establish a sample’s chemistry by quantifying the extent of fluorescence emitted following the excitation of the material by X-ray radiations. During XRF, a sample (can be in solid or liquid form) is exposed to high-energy X-rays originating from a regulated X-ray tube.

When radiations possessing energy that exceeds the binding energy of atomic K or L shells hit the atoms in the material, an electron from the innermost orbital shells is displaced, which destabilises the atom (Leyden 2018).

As the atom returns to stability, the void left by the excited electron is filled with another electron from an orbital with higher energy. Fluorescent X-rays are emitted during this transition. Therefore, the irradiation of a chemical element with X-rays generates a set of distinct fluorescent X-rays that are unique to a given element. This array of fluorescence is referred to as a fingerprint and can be used to identify various chemical elements (Klockenkämper & Von Bohlen 2014). As a result, XRF spectroscopy can be used to conduct quantitative and qualitative investigations of material composition (Rouillon & Taylor 2016).

Experimental Procedure

About 20 unknown, numbered salt samples were provided to the class. One unknown sample (number 16) was chosen and used for individual analyses. An XRF film was placed over one part of a cup. A second part of the cup was then put on top of the film and used to push it down. A weighing balance was cleaned to remove any impurities that could have altered the accuracy of the measurements after which approximately 2.48 grams of the unknown sample were weighed.

The required weight was supposed to be between 2 and 3 grams. The weighed salt sample was then put in a sample press machine to be flattened after which the height of the flattened sample was measured and found to be 4 cm. The sample was then labelled by indicating the sample number, weight, name of the student and height after flattening. These values were indicated on the cover of the cup. The prepared sample was then put in the RIGAKU XRF machine for analysis together with additional unknown samples prepared by other students. The results of the analyses were later provided by the instructor to facilitate the writing of the report.

Results

Table 1 below shows the concentrations of various trace elements in different salt samples. The individual sample is highlighted in red. It was noted that sodium (Na) was the main cation with a concentration of 521,000 parts per million (ppm), whereas chloride (Cl) was the predominant cation with a concentration of 363,000 ppm. Sample 16 had the second highest concentration of sodium.

Trace elements that were present in sample 16 included aluminium at 6420 ppm, bromine at 57.4 ppm, calcium at 119 ppm, chromium at 10 ppm, copper at 4.91 ppm and potassium at 112 ppm. Other chemical elements that were found in the salt specimen included phosphorous, rubidium, silicon and stannum at concentrations of 640, 4.29, 616 and 50.6, respectively. Trace elements that were detected in other samples but were absent in sample 16 included iron, magnesium, rhodium, sulphur, strontium, tantalum, titanium, tellurium, vanadium and zinc.

In contrast, some elements were not completely detected in any of the salt samples. They included silver, arsenic, barium, bismuth, cobalt, caesium, gallium, germanium, mercury, iodine, indium, manganese, molybdenum, niobium, nickel, lead, palladium, ruthenium, antimony (stibium), selenium, thallium, uranium and tungsten. Sodium and chloride were the most abundant elements in all salt samples.

Discussion and Conclusion

Based on the XRF findings of the salt specimen, the unknown salt sample was likely to be table salt. The unidentified sample was pure white with very fine crystals, an observation that was in line with the appearance of table salt (Anchell 2016). The specimen also contained large quantities of sodium and chlorine, which is usual for table salt (De la Guardia & Garrigues 2015). The sample could not be sea salt because it lacked sulphur, which makes up sulphates that are found in sea salt. Trace minerals that can be found in sea salt include bromine, phosphorus, boron, iron, zinc, copper, manganese and silicon (Ohji et al. 2017). However, the unknown analyte did not contain constituents such as zinc and manganese, which are among the most common trace elements in sea salt (Kadko, Landing & Shelley 2015).

The sample could not be rock salt because it did not contain barium and strontium. Studies show that rock salt contains very large quantities of these two elements (Kelly, Findlay &Harris, 2018). Therefore, their absence in the specimen ruled out the possibility of rock salt. The findings of this study show that XRF is a very reliable analytical technique in the determination of the elemental composition and concentration of chemical substances.

Reference List

Anchell, S 2016, The darkroom cookbook, 4th edn, Routledge, New York, NY.

De la Guardia, M & Garrigues, S 2015, ‘Handbook of mineral elements in food, John Wiley & Sons, Hoboken, NJ.

Kadko, D, Landing, WM & Shelley, RU 2015, ‘A novel tracer technique to quantify the atmospheric flux of trace elements to remote ocean regions’, Journal of Geophysical Research: Oceans, vol. 120, no. 2, pp. 848-858.

Kelly, VR, Findlay, SE & Harris, C 2018, ‘Chemical composition of rock salt brine compared with brine from oil and gas wells’, Journal of Environmental Engineering, vol. 144, no. 9, p. 06018006.

Klockenkämper, R & Von Bohlen, A 2014, Total-reflection X-ray fluorescence analysis and related methods, John Wiley & Sons, Hoboken, NJ.

Rouillon, M & Taylor, MP 2016, ‘Can field portable X-ray fluorescence (pXRF) produce high quality data for application in environmental contamination research?’, Environmental Pollution, vol. 214, pp. 255-264.

Ohji, T, Colorado, H, Matyáš, J & Kanakala, R 2017, Advances in materials science for environmental and energy technologies vi, volume 26, John Wiley & Sons, Hoboken, NJ.

Pinto, AH 2018. ‘Portable X-ray fluorescence spectrometry: principles and applications for analysis of mineralogical and environmental materials’, Aspects in Mining and Mineral Science, vol. 1, pp. 1-6.

Introduction

Most power systems in the electrical field experience overvoltage and therefore require protection from voltage spikes, especially those that involve inductors and capacitors. These transients are usually caused by circuit breakers that have tripped, power outages, inadequate insulation strength in power systems, short circuits, transition of power in large equipment working on the same line, lightning strikes, as well as in unprotected transformers, among others (Jacks, 1975, p. 231).To combat such damages, the following methods are usually employed, designing equipment capable of tolerating such abnormal overvoltage or by use of lightning arresters or power surge.

Transients occur in power systems when over-voltages exceed insulation strength. Severe consequences may be experienced under such conditions. This happens mainly because the inductors capacitors usually respond very slowly to such changes in voltages. Inductors and capacitors tend to absorb and release energy as opposed to resistors, which are instantaneous in their response to the application of voltages (Greenwood, 1991, p. 356).

Objectives

• To explore the observable facts in transient voltage recovery
• To explore the observable facts in magnetizing inrush current

Background theory

Overvoltage tends to cause drastic happenings such as total breakdown to power systems. This happens whenever the voltage exceeds that of the insulating material in power systems. There are two ways that are usually employed in protecting powers systems from overvoltage. One of them is by designing a machine that is well adapted to such conditions of overvoltage, while the other method involves the use of protectors such as lightning arresters or power surges (Jacks, 1975, p. 231).

The term overvoltage is a general term used to refer to the different voltages. These include the steady over-voltages and transient overvoltage. The former involves a slight increase in frequency level of voltage while the latter involves changes in amplitudes. The one which causes the most concern to the power system is the power surge voltages which are transient voltages occurring over a short duration. They have very severe implications on power systems as they exceed electrical insulation. Therefore it is a pulse or surge voltage that causes transients in power systems. The decay half-life is as shown in figure 1 below.

Pulse voltages are usually classified as internal or external. As their name suggests, their generation comes from internal and external respectively. It is quite important to note that lightning acts as the primary cause of external impulse (Jacks, 1975, p. 231).

Experimental results and analysis

Experiment Part 1

The experiment was set up as shown in the figure below (insert diagram)

Results

L=1/(4*π²*c*f²)

For the circuit breaker:

For the 2A fuse: V_p = sqrt(V_m² + i₀²*L/C)

For the 3A fuse:

For the 5A fuse:

For the 7A fuse:

For the 10A fuse:

Experiment Part 2

The experiment was set up as shown in the figure below (insert diagram)

Results

(Insert the results)

Choosing to use the waveform with I_max=1.07(A?)

Analysis

Peaks 1 to 4 happens over a 62ms time span. After peak 4, it was in the steady-state.

Using CTFTOOL in Matlab: >> CFTOOL ([0, 62/3, 62*2/3, 62], [149.8-8.162, 25.48-8.162, 10.724-8.162, 0])

In addition, specifying an exponential decay, leads to the following curve fit:

With the following details:

General model Exp1:

• f(x) = a*exp(b*x)
• Coefficients (with 95% confidence bounds):
• a = 141.6 (140.1, 143.2)
• b = -0.1014 (-0.1056, -0.09725)

Goodness of fit:

• SSE: 0.2557
• R-square: 1
• Adjusted R-square: 1
• RMSE: 0.3576

Discussion

The transient recovery voltage is reduced by about half the value, and in this case, the breaker switches the setup circuit, the breaker integrates a switching resistor with values, which reduces the transient recovery voltage. Moreover, the conditions under which the fuse breaks is the same as the conditions where in there is no resistor. For each increase in fuse values, I²t increases, as can be seen from the results. Therefore, the higher the values, the higher the ability of fuse to limit thermal effects observed in high fault currents (Flurscheim, 1982, p. 455).

The measurement technique applied is appropriate as it allows for a faster and accurate way of attaining voltage and current. When both capacitor and inductor are used, energy is dissipated in form of heat on the resistor as shown in figure 6 below. The peak values (Vp) decrease as the fuse values increase as shown in figure 5 above.

Conclusion

Fuses with higher values have low peak voltages (AllAboutCircuits, 2011, p. 1).

The higher the fuse values, the higher the ability of fuse and breakers to limit thermal effect from high fault currents linear (Lythall, 1972, p. 112).

Overvoltage is a danger to power systems, especially when they are not adapted or protected like in transformer systems.

Reference List

AllAboutCircuits. 2011. Electrical Transients. AllAboutCircuits.com. Web.

Flurscheim C.H. (ed.), 1982. Power Circuit Breaker Theory and Design. Peregrinus, 1982.

Greenwood A., (1991). Electrical Transients in Power Systems, 2nd ed., Wiley, 1991.

Jacks, E. 1975. High Rupturing Capacity Fuses: Design and Application for Safety in Electrical Systems. Wiley. 1975.

Lythall R.T. (ed.), 1972. The J&P Switchgear Book: an Outline of Modern Switchgear Practice for the Non-specialist Use, 7th ed., Newnes-Butterworths, 1972.

Footnotes

1. This should be higher than the 3A fuse V_p, need to recalculate from CRO printout
2. dt=44 μs, check dt