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Group 9: Results and Conclusions

The bar chart below displays the adjusted opacity values of our samples. The longer the bar, the more transparent the liquid, and hence, the lower the opacity.

We expected our opacity readings to be strongly correlated with our spectroscopic data, but this was not always the case. For example, Axe Shower Gel had a high opacity (low lux reading) and had full absorbance (to the limit of the spectrometer) across the visible spectrum.

Coffee, on the other hand, was the most opaque sample we measured, but only displayed full absorbance from 400-580 nm. We did not expect this result, and are not sure why this is the case. Maybe because the light which is absorbed is all higher energy wavelengths?

The most transparent liquids all had very low absorbances across the spectrum, which is what we expected. Locations of minor spikes in absorbance varied, but this did not affect opacity because the lux readings covered the entire visible spectrum.

Many of the liquids were colored, and these colors were based on their absorbance patterns. Coffee, for example, absorbs everything up to 580 nm, and then trails off, which means it reflects light in the red to orange area of the spectrum, giving it its brown color.

We also diluted coffee with unknown pineapple juice (what pineapple juice does the Deece use?) and found that the mixture had some yellow and green absorbance readings as well. The problem is that since we don’t have the spectrum of pineapple juice, we can’t determine whether the yellows and greens are due to the pineapple juice’s absorbance wavelengths, or simply through the dilution of coffee.

Orange juice absorbs everything in the visible spectrum except the wavelengths from 640 to 700 nm. From 640 to 700 nm the absorbance varies, but it is never completely saturated. Thus its orange color.

The spectroscopic readings for Axe Shower Gel were fully saturated, but when we mixed it with the almost completely transparent listerine, we wound up with an interesting spectrum. Since the two substances did not react chemically, the spectrum produced is most likely a reflection of the spectrum of Axe Shower Gel.

This project gave us a better grasp of the principles of atomic spectroscopy. At the risk of over-simplifying, a spectroscope shines light through the sample, and then measures how much is absorbed at each wavelength. Since each element only absorbs certain wavelengths, the spectrographic measurement of a substance is akin to a fingerprint, although it requires a trained spectroscopist to analyze it accurately. We utilized the spectroscope to examine the absorbance properties of our samples, and compared that to the overall opacity of the samples.

If we had this project to do over, we would make a few corrections to our approach. We would record the concentrations of each liquid in our mixtures, and vary the concentrations to examine the difference in absorbance. We would handle the cuvettes with more care, because fingerprints on the cuvettes could subtly alter the spectroscopic readings.

If we had another six weeks, we would dilute all of our fully absorbent liquids with water (since water would not cause much interference, as a relatively clear, non reactive, liquid) so we could measure their absorbances. We really liked the idea of analyzing the composition of sunset lake. We would like to suggest hiring a trained spectroscopist to work with us to help us interpret the spectroscopic readings. 😉

Group 2 Data: Lasers and Sound

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For our project, we build a device to convert sound waves into patterns of laser light:

diagram

We recorded each member of the team saying a standard sentence into the device, and then took a video of the resulting pattern. We also took data regarding the volume (dB) and frequency (Hz) of each speaker.

Displayed here are three different voice recordings of one sentence, “the quick brown fox jumps over the lazy dog.” This sentence was chosen because it contains all the letters of the alphabet and would hopefully provide variation in the laser patterns produced. The patterns of one boy’s voice and two girls’ voices were measured. As seen in the video clips, deeper/lower voices/frequencies created a larger, circular laser pattern; higher pitched voices created a smaller ring-shaped pattern and also figure 8 shapes, as well.

Elizabeth’s Sentence:

Tim’s Sentence:

Becca’s Sentence:

The length of the sentence was about four seconds. The data recorded includes the video of the laser patterns, the audio recording, and the decibel recording, as well.

While further playing around with the device, we found that “P” noises (like the word “pop”) elicit a large circle, whistling into the cup and also playing music through an iPhone (classical and also bass/drum heavy) do not vibrate the balloon enough for much of a visible laser response.

Decibel levels of each speaker’s sentence:

db_graph

Frequency plot of Elizabeth’s voice:

Frequency plot of Tim’s voice:

Frequency plot of Becca’s voice:

Looking at Tim’s frequency plot, there is a steep drop-off after 500Hz. This lines up with our observation that lower frequencies had more of an impact on the laser pattern, as his was much larger than the other two.

Decibel level did not have a discernible effect on the results. Much more important was the low frequencies.

We also recorded each member singing a set of three notes:

Elizabeth Singing:

Tim Singing:

Becca Singing:

Problems Encountered:

-The correct assembly of the device. For instance, less stretched balloon drum on top of the cup responded better to sounds. The application of the piece of mirror to the balloon drum was best executed using glue instead of tape, which buffered and absorbed some of the vibrations of the balloon, desensitizing the mirror and laser’s responses. The size of the mirror was also important–the heavier and larger the mirror piece, the less it would respond to vibrations. The material for the cup was also important and plastic works best (over paper). Mounting the laser onto the cup using a paperclip was best done by unfolding the clip to have two legs to tape to the cup, rather than one.

-The distance of the device and input source from the observation point, a black board. A distance of about 12.5 feet worked well for us; a longer distance increases the amount of the pattern seen.

-Playing music from a phone didn’t have enough low frequencies to move the laser a discernible amount.

Group 3 Data

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Graph of Data

Table of data

Data Point	Location, date, and time	Description	Average dB	dB Range	d(dB)/dt in dB/s	Settings	Inputter	Graph
1	Jewett. 9:23pm on 2/13/14	Noise level during night time in my dorm (wellness and quiet).	38.98	38.00 – 48.86	-0.002022	S/Reset/A/Lo	Christopher	Graph
2	Library. 3:15pm on 2/12/14	Studying in library. No conversations.	39.82	37.70 – 53.74	-0.006618	S/Reset/A/Lo	Christopher	Graph
3	Jewett. 12:40pm on 2/14/14	Noise level during day time in my dorm (wellness and quiet).	42.65	41.91 – 48.52	-0.002447	S/Reset/A/Lo	Christopher	Graph
4	Raymond. 9:45pm on 2/18/14	Listening to “Arabella” by Arctic Monkeys on computer. Microphone 72.5cm from computer (comfortable ear distance when computer on lap) at 50% volume.	55.12	10.82-70.22	0.0118	S/Reset/A/Lo	Phe	Graph
5	ACDC. 3:30pm on 2/12/14	Closing time for ACDC (big side).	55.75	49.75-67.47	-0.003545	S/Reset/A/Lo	Hannah	Graph
6	OLB 205. 12:00pm on 2/17/14	Physics 152 lecture.	56.45	43.40 – 67.35	0.03432	S/Reset/A/Lo	Christopher	Graph
7	Jewett. 7:50pm on 2/13/14	Conversation with a friend in my dorm.	57.3	42.40 – 80.32	-0.002987	S/Reset/A/Lo	Christopher	Graph
8	Raymond. 9:41pm on 2/18/14	Listening to message on iPhone at 100% volume. Microphone against speaker. Message begins at t=5s.	57.51	0.08392-80.95	0.1028	S/Reset/A/Lo	Phe	Graph
9	Jewett. 9:13pm on 2/13/14	Listening to Kings&Queens – 30STM, volume at 100%, using headset with volume at 100%, however headset is on my head and microphone is 0cm from left ear.	60.86	50.78 – 67.38	0.05022	S/Reset/A/Lo	Christopher	Graph
10	ACDC. 12:00pm on 2/13/14	Noise level at ACDC (big side) during lunch.	61.75	51.99-77.08	-0.009397	S/Reset/A/Lo	Phe	Graph
11	Jewett. 8:58pm on 2/13/14	Listening to Kings&Queens – 30STM, volume at 60% and microphone 10cm from speakers.	61.86	41.28 – 71.53	0.08031	S/Reset/A/Lo	Christopher	Graph
12	Retreat. 6:00pm on 2/18/14	Noise level at Retreat during dinner.	64.98	58.11-74.58	0.01566	S/Reset/A/Lo	Phe	Graph
13	Vassar’s Infant Toddler Center. 2:30pm on 2/18/14	The infant room at the ITC, with babies crying sporadically	67.73	54.50-80.95	-0.02128	S/Reset/A/Lo	Hannah	Graph
14	Jewett. 9:07pm on 2/13/14	Listening to Kings&Queens – 30STM, volume at 100%, using headset with volume at 100%, microphone in place where head should be (2-4cm apart from each ear piece).	69.9	42.28 – 81.31	0.1129	S/Reset/A/Lo	Christopher	Graph
15	Retreat. 3:00pm on 2/17/14	Lunchtime at the retreat, crowded	70.8	64.10-83.32	-0.0111	S/Reset/A/Lo	Hannah	Graph
16	Retreat. 12:08pm on 2/18/14	Noise level at Retreat during lunch.	71.83	63.85-80.08	-0.03982	S/Reset/A/Lo	Phe	Graph
17	ACDC. 6:20pm on 2/12/14	Dinner time on the west side (big side) of ACDC.	79.9	69.97-91.31	0.02131	S/Reset/A/Lo	Hannah	Graph
18	Raymond. 10:40 on 2/17/14	Listening to “What Makes You Beautiful” on iPhone from apple earbuds at 100% volume. Earbuds up against microphone.	83.39	72.49-91.48	-0.01072	S/Reset/A/Hi	Phe and Hannah	Graph
19	Raymond. 10:34pm on 2/17/14	Listening to “What Makes You Beautiful” on iPhone from jvc earbuds at 100% volume. Earbuds up against microphone.	90.66	79.48-97.23	0.01003	S/Reset/A/Hi	Phe and Hannah	Graph
20	Jewett. 8:35pm on 2/17/14	One of my friends practicing with her oboe from t=4 to t=113. 109 seconds of data.	91.29	77.99 – 101.40	-0.05215	S/Reset/A/Lo	Christopher	Graph
21	Raymond. 10:45 on 2/17/14	Listening to “What Makes You Beautiful” on iPhone at 100% volume. Apple earbuds with holes on back covered. Earbuds up against microphone.	91.46	77.48-101.9	0.07732	S/Reset/A/Hi	Phe and Hannah	Graph
22	Villard room. 12am on 2/15/14	Villard room party 100 nights, dubstep-like music was playing. 5-6 feet away from speakers.	93.8	86.69-101.5	-0.06069	S/Reset/A/Hi	Hannah	Graph

Sample experimental Setup Photo

Experimental Setup Explanation

Level of sound (in decibels) was collected using a Vernier Sound Level Meter (Vernier SLM-BTA) in conjunction with a Vernier LabQuest 2. The data was collected under 22 different conditions, as specified in the table above. All data, with the exception of data point 20 (see table), was recorded for 180 seconds. The Time Weighting switch on the Sound Level Meter (SLM) was set to “S” (slow), as is standard for most measurements. The Maximum Level Hold switch on the SLM was set to “Reset,” so that the screen would continuously display the sampled reading as opposed to the maximum reading. The Frequency Weighting switch on the SLM was set to “A,” which measures sound levels that most match those in the human hearing range. The Power/Measurement Range Switch on the SLM was most often set to “Lo,” which measures amplitudes in the range of 35-90 dB. When appropriate (when dB exceeded 90), the switch was set to “Hi,” which measures amplitudes in the range of 75-130 dB.

Explanation of Data:

The data is displayed in the table above. After collected, all data was transferred to the computers and analyzed in Logger Pro 3.8.6.1. The mean amplitude (dB), range, and slope of each graph was extracted using Logger Pro. This information, along with a photo of each graph is in the table. The mean amplitudes will be used to determine which conditions are damaging to human hearing after prolonged exposure. According to the National Institute on Deafness and Other Communication Disorders and to The American Speech-Language-Hearing Association, repeated or prolonged exposure to sounds at or above 85 dB can be damaging. Based on this statistic, the data will be analyzed to determine which sounds (based on mean dB) are dangerous.

Technology involved:

Two Sound level meters: Vernier SLM-BTA (Type 2) were used in conjunction with two Vernier LabQuest 2 to record the sound level of each condition. The Vernier LabQuest 2 recorded the data and provided graphical representation of the data for each of the conditions. For further analysis, the Vernier LabQuest 2 were connected to computers (a MacBook Pro and a Samsung Series 9 Laptop), which had Logger Pro 3.8.6.1 installed. We utilized the stats tool and the linear fit tool to analyze the data in Logger Pro. Further more, we created a graph of the average dB level exposure per activity (Average dB for each data point) and created a chart of all of the data using Google Docs.

Conditions under which data was taken:

Twenty-two conditions with various amplitude levels, from studying in the library to a Villard room party, were studied. Please see table above for specifics.

Citations:

“Noise.” ASHA. n.p., n.d. Web. 17 Feb. 2014.

“Noise-Induced Hearing Loss.” NIDCD. NIH, Oct. 2013. Web. 17 Feb. 2014.

Group 6 Data

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We performed tests to compare a smartphone, a laptop, and a tablet with one another. The smartphone used was an iPhone 4 running iPhone 5 software, the laptop used was a Macbook pro, and the tablet used was a nook running android software. We compared each device with regards to battery life, overheating issues, energy consumption, and cost efficiency.

Battery Life: This test was to see how much charge each device’s battery lost over the course of an hour. We tested the battery life of each device using a stopwatch and the device’s charge reading. Each device was tested while they were running Netflix (Netflix test group), and while they were on but not performing any additional tasks (Control test group). A reading of the battery’s remaining charge was taken every five minutes. All data readings were taken in percentage. After all data was collected we calculated the amount of charge the devices lost every five minutes respectively. We then took the average of the charge lost every five minutes to show the average charge lost every five minutes.

Test set up pictured below (Netflix):

Loss of charge (Netflix):

Time (minutes)	Macbook (%)	Tablet (%)	Smartphone(%)
0	100	100	100
5	98	97	99
10	95	96	98
15	92	95	95
20	89	93	93
25	87	91	91
30	83	89	88
35	80	87	86
40	77	86	84
45	74	83	82
50	71	81	80
55	68	78	78
60	64	75	76

Charge lost every five minutes (Netflix):

Time (minutes)	Macbook (%)	Tablet (%)	Smartphone (%)
0	0	0	0
5	2	3	1
10	3	1	1
15	3	1	3
20	3	2	2
25	3	2	2
30	4	2	3
35	3	2	2
40	3	1	2
45	3	3	2
50	3	2	2
55	3	3	2
60	4	2	2

Average:

2.846153846

1.846153846

Loss of charge (Control):

Time (minutes)	Macbook (%)	Tablet (%)	Smartphone (%)
0	100	100	100
5	97	98	100
10	96	97	100
15	94	95	100
20	92	94	100
25	91	93	99
30	89	91	98
35	88	90	97
40	86	88	96
45	84	86	94
50	82	85	93
55	81	83	91
60	80	81	90

Charge lost every five minutes (Control):

Time (minutes)	Macbook (%)	Tablet (%)	Smartphone (%)
0	0	0	0
5	3	2	0
10	1	1	0
15	2	2	0
20	2	1	0
25	1	1	1
30	2	2	1
35	1	1	1
40	2	2	1
45	2	2	2
50	2	1	1
55	1	2	2
60	1	2	1

Average

1.538461538

1.461538462

0.769230769

We then took this data and graphed it to compare the devices and the test groups:

Comparison of the charge lost over an hour in all three devices (Netflix):

Comparison of the charge lost over an hour in all three devices (Control):

Comparison of the loss in charge every five minutes for all devices (Netflix):

Comparison of the loss in charge every five minutes for all devices (Control):

Comparison of the charge lost every five minutes for the Netflix group and the Control group (Smartphone):

Comparison of the charge lost every five minutes for the Netflix group and the Control group (Tablet):

Comparison of the charge lost every five minutes for the Netflix group and the Control group (Macbook):

Average Charge Lost every 5 minutes for all devices (Netflix):

Average charge lost every five minutes (Control):

Temperature Change: We recorded the temperature change in all three devices using an Infrared temperature probe. We used this probe in two tests: a control test where the devices were on, but not performing any additional tasks, and a Netflix test where the devices were running Netflix. A temperature reading was taken every five minutes, and each trial lasted 40 minutes. The probe was placed at the same spot on the device for every reading, the placement of the device was determined prior to testing using the infrared temperature probe to find the hottest spot on the device. After each trial we calculated the overall change in temperature by subtracting the final temperature reading from the initial reading. All temperature data was taken in degrees celsius.

Infrared temperature probe pictured below:

Change in Temperature (Netflix):

Time (min)	Macbook Temp (°C)	Tablet Temp (°C)	Smartphone Temp (°C)
0	36.5	27.5	27.7
5	39.6	31.5	30.6
10	42.5	36.6	32.7
15	45.6	38.6	34.7
20	48.7	41.5	35.2
25	50.6	41.6	34.6
30	51.7	42.6	34.8
35	52.6	43.6	34.8
40	54.6	44.6	34.9
Overall Change	18.1	17.1	7.2

Change in Temperature (Control):

Time (min)	Macbook Temp (°C)	Tablet Temp (°C)	Smartphone Temp (°C)
0	38.5	25.5	26.6
5	37.7	28.5	29.5
10	38.6	30.5	31.1
15	39.7	31.5	32.6
20	39.8	31.5	32.6
25	39.7	32.7	32.7
30	38.6	33.7	32.8
35	39.7	33.7	32.8
40	39.7	35.7	33.5
Overall Change:	1.2	10.2	6.9

We then took this data and graphed it to compare the differences in all of the test groups:

Comparison of all three devices in the Control group:

Comparison of all three devices in the Netflix group:

Comparison of the temperature change between the Netflix group and the Control group (Smartphone):

Comparison of the temperature change between the Netflix group and the Control group (Macbook):

Comparison of the temperature change between the Netflix group and the Control group (Tablet):

Comparison of the difference in temperature between all three technologies in both the Control and the Netflix test groups:

Comparison of all the devices with regards to the overall temperature change (Netflix and Control):

Energy Consumption: We recorded how much power (in watts) each of these devices uses in an hour while idling (control) and while watching a film on Netflix. This was done using a Watts Up Pro and Logger Pro software. First, each device was fully charged, then the control test was done. In the control test, each device’s power consumption was measured while their displays were left on. Afterwards, still with a full charge, the Netflix test was done. Both tests measured the wattage of each device for 30 minutes. The Watts Up Pro took real time measurements of energy consumption and Logger Pro graphed these measurements. The overall wattage used by each device was obtained by taking the integral of each graph in Logger Pro.

Watts Up Pro setup shown above.

The iPhone, while idling, used a total of 2,069 watts in 1,775 seconds, which is about 4,196 watts/hour. The iPhone, while watching a video on Netflix, used a total of 3,018 watts in 1,797 seconds, which is about 6,046 watts/hour.

The tablet, while idling, used a total of 9,133 watts in 1,800 seconds, which is 18,266 watts/hour. The tablet, while watching a video on Netflix, used a total of 14,910 watts in 1,800 seconds, which is 29,820 watts/hour.

The MacBook Pro, while idling, used a total of 17,640 watts in 1,800 seconds, which is 35,280 watts/hour. The MacBook Pro, while watching a video on Netflix, used a total of 37,180 watts in 1,800 seconds, which is about 74,360 watts/hour.

Device	Control Test (W)	Netflix Test (W)
iPhone	4,196	6,046
Tablet	18,266	29,820
MacBook Pro	35,280	74,360

Cost Effectiveness: By converting each of these power consumption values to kilowatt hours, we determined the cost to run each of these devices for an hour while both idling and watching Netflix. The average cost of a kilowatt hour in the United States is 12 cents.

iPhone (Control): 2069 W / 1775 s = 2.069 kW / 0.493055555 h = 4.1963 kW/h

4.1963 (12) = 50.3556 cents ($0.50 per hour)

iPhone (Netflix): 3018 W / 1797 s = 3.018 kW / 0.499166666 h = 6.0461 kW/h

6.0461 (12) = 72.5532 cents ($0.73 per hour)

Tablet (Control): 9133 W / 1800 s = 9.133 kW / 0.5 h = 18.266 kW/h

18.266 (12) = 218.712 cents ($2.19 per hour)

Tablet (Netflix): 14910 W / 1800 s = 14.910 kW / 0.5 h = 29.82 kW/h

29.82 (12) = 357.84 cents ($3.58 per hour)

MacBook Pro (Control): 17640 W / 1800 s = 17.640 kW / 0.5 h = 35.28 kW/h

35.28 (12) = 423.36 cents ($4.23 per hour)

MacBook Pro (Netflix): 37180 W / 1800 s = 37.180 kW / 0.5 h = 74.36 kW/h

74.36 (12) = 892.32 ($8.92 per hour)

Device	Control Test ($/hour)	Netflix Test ($/hour)
iPhone	0.50	0.73
Tablet	2.19	3.58
MacBook Pro	4.23	8.9

Group 2 Project Plan

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Roles:

Rebecca Gluck, Tim Brown and Elizabeth Berridge will all take on equal roles in Data Collecting, Recording and Analyzing.

Data Collection of Human Voices: Rebecca Gluck

Data Collection of musical Instruments: Tim Brown, Elizabeth Berridge

Data Collection of Concerts: Rebecca Gluck, Tim Brown, Elizabeth Berridge

Data Recorders: Rebecca Gluck, Tim Brown, Elizabeth Berridge

Data Analysis/ Comparing differences in Human Voices/Instruments /Discerning Patterns in images from Concert: Rebecca Gluck, Tim Brown, Elizabeth Berridge

List of equipment and supplies:

1. Device to view sound waves(plastic cup, balloon, duct tape, mirror shard, laser pointer, paper clip), 2. phone camera, 3. computer 4. Vernier LabQuest Pro Sound Level Meter

What is the science/technology involved?

We will use the device created with a plastic cup, balloon, duct tape, mirror shard, laser pointer, paper clip etc. to have a visual means of reviewing sound waves. The sound will enter the end of the plastic cup and vibrate the balloon stretched across the other end. Attached to the balloon will be a small shard of mirror which will vibrate with the balloon. A laser pointer will be reflecting off the mirror onto a flat surface (most likely a wall). The reflected light on the wall will move in correspondence with the vibrations caused by the sound waves entering the cup. Different sounds have different waves and will create different patterns. By analyzing the visuals we can discern differences in voices and instruments as well as recognize patterns between sounds and their sources.

We will also record the sounds that enter the cup using the Vernier LabQuest Pro Sound Level Meter, which will measure the frequency and decibel level to compare with the visuals and recognize patterns between the two types of data(visual and numerical).

Activity plan (how will you take your data, what equipment will you use). Include dates and meeting times.

We will take our data using the aforementioned devices. We will be meeting at 1 pm on sunday afternoons and begin by creating the two or three cup/balloon devices. Individually throughout the week and on sundays together we will collect data by having different people speak into the cup while one group member records the visuals and another records the numerical data with the Vernier LabQuest Pro Sound Level Meter. We will repeat this process with different subjects as well as with different instruments. Recording data from concerts will depend on when the next concert is scheduled, we will attend said concert and let the sound penetrate the device while recording the data.

What outcome(s)/data do you expect? Why?

We expect to have different data for every sound/voice/instrument because each sound uses a different frequency and therefor has a different wave length which will correspond to a different kind of pattern from the reflected laser pointer. We expect there will be a greater similarities between same sex voices versus opposite sex voices, most likely because of similar frequencies among same sex voices. We expect there will be erratic numerical and visual data from a concert, as there will be a multitude of sounds and sound waves. We also expect to see data correspond between the visuals, and the numerical data taken with the Vernier LabQuest Pro Sound Level Meter. The patterns that are projected will no doubt change based on the sound wave length and hopefully with the numerical data we will be able to discern which frequencies/sound waves correspond with which patterns.

Group 8 Project Plan

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Group Roles: In order to effectively collect and analyze data while ensuring that each group member is a part of each step of the process.

Data Collecting: Data Recorder – Hannah; Data Collector 1 – Hunter; Data Collector 2 – Emma

Analyzing/Synthesis: Comparing Differences in Radiation – Hannah; Comparing Radiation to Power – Emma; Research on Safety of Radiation Levels – Hunter

Equipment Used: RF Meter (to test EM field strength around the microwaves at various locations), WattsApp (to measure the microwaves’ power), ~9 microwaves (of various models, ages, and conditions), TI-30X Calculator, Pencils, Notebooks

Science/Technology Involved: When the microwave is turned on, the magnetron, an electron tube in the upper part of the oven, generates microwaves to excite molecules and heat the food. Despite protective measures to ensure as little radiation seeps through the microwave as possible, such as the metal behind the door and the metal walls meant to reflect the radiation, absorption and leakage occur nonetheless while the microwave is on. These waves penetrate past the microwave, exciting molecules, to generate an electromagnetic field that emits some amount of radiation. The government has deemed this radiation safe to the human body based on the Specific Absorption Rate (SAR), the rate at which our bodies absorb energy, but others disagree that this exposure is dangerous nonetheless.

The Watts Up Pro meter will also provide us with the technology to measure the power (watts) that each microwave uses to function. With this data we can track correlations between power, and the strength of the generated EM fields.

“Microwave Ovens,” Federal Office of Public Health, 2009. http://www.bag.admin.ch/themen/strahlung/00053/00673/03752/index.html?lang=en

Activity Plan: We will measure the strength of the EM field while a microwave is on and compare how different microwaves emit more or less radiation. Furthermore, we will test different sides and distances from a microwave to determine if the radiation is 1) stronger at a certain side of the microwave (in the front, or closer to the magnetron, for example) and 2) if the field drops off after a certain distance.

On Friday, February 7th, at 1:00 pm we will walk around to different dorms to determine the status of each microwave. We predict many of them will be relatively the same model, but if some seem much older or have a lot of wear and tear (for example, the front screen has a hole in them) we will collect data on those individuals to see if there is a correlation between age/wear and tear and EM radiation. We will also compare power output and radiation. We will record the power output labeled on each microwave to do so.

We will collect our data on Saturday, February 8th at noon. We would like to test different microwaves both provided by the college and those provided by MicroFridge. We hope to test multiples of each brand to ensure our results are consistent. We will use an RF meter to measure the strength of the EM field and use the setting “Max Average” to get an average measurement over the course of a few seconds of radiation emission. We will collect data in the following table:

Sample #	Location	Brand	Wear and Tear?
M1
M2
M3
M4
M5
M6
M7
M8
M9

Sample #	Power (Watts)	EM Radiation from Front (1 cm)	EM Radiation from Front (10 cm)	EM Radiation from Front (20 cm)	EM Radiation from Right (1 cm)	EM Radiation from Right (10 cm)	EM Radiation from Right (20 cm)
M1
M2
M3
M4
M5
M6
M7
M8
M9

After we have collected the data, we will compile research on various proposed safety levels of microwave radiation, and compare our findings.

Expected Outcomes: Our group expects to confirm the safety of standard consumer model microwaves in regards to the level of microwave radiation emitted. This is due to the rapid falloff in radiation over distance as well as the strict safety standards established by the FDA. The more interesting analysis will be any correlation between the level of radiation, power usage of the unit, and cost of the unit. We expect to find high power microwaves emit higher levels of radiation (though still at safe levels). While cheaper units may theoretically result in less safety precautions, FDA standards should prevent this at any noticeable level.

Emma Foley; Hunter Furnish; Hannah Tobias

Group 6 Project Plan: A comparison of Smart Technologies

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Project Goal: The goal of this project is to determine which of the three smart technologies we are testing is the best with regards to overheating, cost effectiveness, energy consumption, and battery life.
Roles: We are splitting the test and the resulting analysis equally. Ryan will conduct the tests/analysis regarding cost effectiveness and energy consumption. Nora will conduct the tests/analysis regarding battery life and overheating.
Equipment/Supplies: Our experiments will require the following materials: 1. A Watts Up? Pro 2. A temperature probe 3. An infrared temperature probe (if available) 4. Duct tape 5. A stopwatch 6. A Nook HD+ tablet 7. An HTC smartphone 8. A Macbook Pro laptop
Science/Technology Involved: We are using a Watts Up? Pro to test both the energy consumption levels of each device and the cost efficiency of each device. A Watts Up? Pro measures the amount of electricity being consumed by a device in real time, it can measure this in both Watts and KWH. We are also planning on using a Vernier temperature probe and an infrared temperature sensor (if available). The Vernier temperature probe will be duct taped to the back of each device to read temperature increases. The infrared sensor works by reading the black-body radiation (energy) emitted by the device and converting it into a temperature. The infrared temperature probe would be more affective because it does not require contact with the device to measure an increase in temperature. The three devices we are testing are an HTC evo 3D smartphone, a Nook HD+ tablet running Android 4.1 software, and a Macbook Pro Laptop running Mac 0SX version 10.7.
Activity Plan: Overheating Test: To test the severity to which each device overheats we will open up Netflix on each device (each device will be tested individually for more accurate data). We will make sure that there are no background programs running on the devices while Netflix is playing. We will choose a show that is either an hour or 30 minutes long depending on the time it takes for the first device’s temp to increase by approximately 10 degrees. At the start of the show we will begin taking the temp, this reading will be at t=0, after every 5 minutes we will record the temperature reading. We will run 2 trials for each device using both a Vernier temp probe and an infrared temperature probe. After all data is collected we will graph temperature versus time and compare the results accordingly. Battery life test: First, after fully charging it, we will turn on the device. As soon as the device powers on fully we will start a stopwatch. We will record how long it takes for the battery to use up 20%, 40%, 60%, 80%, and finally 100% of its charge. Repeat the test now using Netflix while conducting the test recording how long it takes for the battery to run out of charge. Once all data is recorded we will graph loss of charge versus time and compare the results. Energy Consumption: We will be using the Watts Up Pro to measure how much energy, in watts, each device uses in an hour. We will have each device play a video on Netflix to use up energy. After recording the wattage each device uses, we will import the data from the Watts Up Pro into Logger Pro to get a graph of energy used over time. Cost Effectiveness: Once we determine the energy consumption for each device, will we find the kilowatts used per hour of all three devices. Knowing the price per kilowatt hour, we can convert these numbers into a cost. Taking into account things like the down payment costs of each device and how much a phone plan costs, we can determine which device gives you the most for its cost.
Meeting Times: We will meet three times a week. Thursdays at 5:00 pm, and then on Saturdays and Sundays at noon.
Expected Data/Outcomes: We expect that with regards to energy consumption the Mac laptop will use the most energy because it has the largest processing system out of the three devices tested. The tablet will follow the laptop in energy consumption, and the phone will use the least amount of energy. With regards to cost effectiveness we expect that the tablet will be the most cost effective because it costs less than a laptop and, though it probably uses more energy, it does not have the cell phone/data plan that a smartphone has. We expect that the laptop will have the best battery life, because it has the biggest battery and therefore has more energy to consume than both the tablet and the smart phone. The tablet will probably have the second best battery life, and the phone will probably have the worst. Once again, this hypothesis is based off the difference in battery size and the fact that battery life is most likely directly proportional to energy consumption. Lastly, with regard to temperature data, we expect that the phone’s temperature will increase the most when using Netflix, then the tablet, and then the laptop. This is because the phone has the smallest processor so using Netflix will be the most taxing to this device.

Group 3 Project Plan: The effects of a variety of sounds on the human ear

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Roles:

All group members are responsible for collecting data, as well as posting information on the blog regarding their data. They will also input data onto a spreadsheet for the entire group to see.

List of equipment and supplies:

We will be using a sound meter to measure the amplitude of the sound waves we are exposed to.

What is the science/technology involved?

Sound meters use microphones to detect sound waves and measure the properties (amplitude, frequency, etc) of these waves.

Activity plan (how will you take your data, what equipment will you use). Include dates and meeting times.

Each group member will have their own sound meter. The members will then record the sounds they encounter throughout the week of February 16^th. Such sounds will include music through speakers/computer, music through headphones, conversations through cell phones, orchestra concerts, class lectures, crowded dining halls (both ACDC and The Retreat), Villard room party, noise heard through dorm walls/doors, and Vassar’s Infant Toddler Center. Whenever possible, each type of sound will be recorded by at least two different group members for comparison. Throughout the week, group members will add their data to the group’s spreadsheet. At the end of the week (February 22nd), the group will decide (via email) whether more data needs to be taken. The group will meet to discuss and analyze data on February 23rd.

What outcome(s)/data do you expect? Why?

We expect to find consistent, but not identical, data between group members for each activity, since the activities should produce around the same amplitude of sound. Any individual differences may be due to the use of different equipment or to minor, uncontrollable variables. We also expect to find that some activities have a sound amplitude that can be dangerous over extended periods of time, whereas others are harmless.

Radiation on Vassar’s Campus: Group One’s Project Plan

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Roles:

We are planning on taking on the responsibility of measuring the radiation in buildings around campus as a group. Considering there is one primary task to be accomplished, we feel that it would be best if all three of us were involved in the data collection process.

We are also hoping to conduct interviews of physics and astronomy professors who have been at Vassar for a significant amount of time. Depending on our personal schedules, we will try to complete these interviews as a group as well in order to maximize our understanding and to create the best possible environment for consensus.

Equipment/Supplies:

SensorDrone with an attachment for detecting radiation if need be
Any other available radiation detectors

Science/Technology involved in experiment:

Depending on the available detectors, there are a few types of radiation detectors that we may be using. The first is known as a scintillation detector, which uses sodium-iodide (or another similar material), which glows when radiation hits it. This light is reflected and multiplied to increase the “signal”, which in turn hits a photocathode. As photons hit the photocathode, electrons are released towards a pair of plates that in turn multiply the electron signal even more until the signal is millions of times stronger than the initial radiation entering the device. To get a radiation reading, this electron signal is detected at the anode of the instrument and displayed in some way on the device.

The second possible type of detector available is known as a gas filled detector, which ultimately does function similarly to the scintillation detector using a signal amplification process. Radiation passes through a gas in the device and the molecules in that gas are ionized, leaving positive and negative ions in the chamber. These ions move toward separate charged sides (the anode and cathode), creating a current which is then measured.

Either of these types of devices would be appropriate for our experiment, but it is essential that we have a sensitive enough device that we can find very small levels of radiation (if there are any).

We hope to be able to use devices that will be able to measure all three types of radiation: $\alpha$ , $\beta$ , and $\gamma$ , but depending on the sensors available, that may not be possible.

Activity plan:

Refer to Professor Magnes for names of Physics/Astronomy professors to interview regarding known instances of radiation contamination on Vassar’s campus(Mon. 2/10)
Conduct interviews during the course of the following week
Conduct radiation research (Sun. 2/16) (Note: This date may be contingent upon access to buildings, Sander’s Physics in particular.)
The maximum and average radiation levels at each site will be recorded during a “pass through” (i.e., a steady paced walk through the building)
The academic buildings to be tested are as follows: Olmstead, Sanders English, Sanders Physics (if available), Mudd Chemistry, The Old Laundry Building, Chicago Hall, The Old Observatory, Blodgett Hall, Kenyon, Swift Hall, Rocky, Vogelstein (if access is available), Skinner Hall, and the Library.

Expected outcomes/data:

We expect to find mostly normal levels of radiation in Vassar’s buildings. Vassar is a fairly significant institution, so it would be inconceivable that its buildings would have unhealthy levels of radiation. The only building that we expect to possibly find higher levels of radiation than normal would be Sander’s Physics as there has been a recent discovery of radiation contamination. There is the possibility that some of the other science buildings (i.e. Mudd and Olmstead) may have higher than “normal” radiation levels due to the use of NMR or other radioactive equipment.

Group 9 Project Plan: The Role of Contaminants in the Opacity of Liquids

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Our project involves the analysis of different liquids spectroscopically. We will also measure the opacity of the same liquids. We want to analyze which contaminants have a stronger impact on opacity. Can we tell the difference between liquids contaminated with different substances based on their opacity? The data obtained using a spectroscope will allow us to determine the compositions of different liquids. We would like to examine any correlations between the spectroscopic data and the opacity measurements we take for the same liquids.

The equipment we hope to have access to is a spectrometer and any kind of light sensor such as the sensor drone. Spectroscopy works by analyzing the wavelengths of light that are either emitted or absorbed by a substance. Because each element possesses a unique pattern of wavelengths, we can determine the composition of different substances.

We will be collecting any and all kinds of liquids that we have access to (coffee, juices, alcohol, sodas, etc.). We will certainly use different kinds of water (tap, mineral, distilled, sparkling, etc.) and also water that we contaminate with various substances (salt, fertilizer, spices, chemicals, etc.). In terms of supplies, we will need multiple containers to hold the liquids. The experiment will be done using only one transparent jar which will house the liquids so as to keep the effects of the jar constant. The sides of the jar will be covered in a way that no light escapes and we will create a setup allowing us to place the light sensor under the jar. A constant source of light such as one flashlight will shine at the top of the setup. The light sensor placed under the jar will be housed in a small cardboard box having a hole in it. This will ensure that no other light interferes with the sensor’s measurements.

As far as roles are concerned, all 3 of us will collect as many different containers, liquids and contaminants as we have access to and we will all be present for the data collection. This is the most efficient way to go about our project because all our data can be collected in one place. We are meeting every Friday at 11 am to discuss the project and carry out any necessary research, data collection or other activities. We will set additional meeting times when necessary. We will also be doing every write-up (such as this one) together.

All in all, we expect some correlation between contaminants and opacity. However, we are unsure of what to expect without analyzing the data.