eral other programs. This simplifies subsequent data analysis and documentation.
I have also written a simple Pygame script to show the color defined by the measured RGB values on your computer screen, so you may compare the actual and the measured colors. For various reasons they might differ a bit.
Make sure that the dye concentrations are not too high. Be aware that e.g. a bright blue solution might already be pitch black under the light of a yellow or red LED.
Step 5: Example #1: Kinetics (colorimeter and Photometer)
So far I have performed only a limited set of measurements.
An example for a kinetic measurement is the slow evaporation of carbondioxide (CO2) from sparkling water placed in an open cuvette. For this purpose, a bit of bromothymol blue was added to normal water and a small volume of CO2-enriched, "sparkling" water was added until the color changed from blue to yellow. Now the kinetics measurement was started and measurements were taken every 20 minutes for several hours. The data was automatically written to a CSV file and later analysed using LibreOffice Calc.
So what is happening? By addition of carbon dioxide to water it gets acid. As the concentration of CO2 in air is much lower than in the water, it evaporates slowly from the water, which results in a rising pH. In an acid solution bromothymol blue is faintly yellow, while under basic conditions it is bright blue. Around pH 7.0, the pKa of the dye, the color of the solution is green, due to a mixture of yellow and blue forms of the dye.
Using the RGB sensor, it is possible to detect the decay of the yellow form in the blue channel and the increase of the blue form in the red channel, while the effect on the green channel is only moderate. As you can see in the chart, over time the absorption in the blue channel decreases (i.e. less yellow dye) and the absorption in the red channel increases (i.e. more blue dye). As expected, the color changes over time indicate an approximate first order decay kinetic, values asymptotic nearing maximum or minimum values
The same process has also been measured with the photometer. The sensor gives you two values: a full spectrum value (Full) and a value of a second incorporated sensor which measures only the infrared (IR) part of the spectrum. The values of the visible part of the spectrum (VIS) can be estimated by subtraction of IR from Full values. The dye used here was phenol red, which is yellow in acids, red under neutral and violet under basic conditions. Its pKa is also around 7.0. The light source used here was a green LED, so the increase in "redness" resulted in a diminished amount of green light reaching the sensor.
Step 6: Example #2: Dilution Curve (photometer)
The second example is a dilution curve of a red dye.
As shown in the image, a serial 1:2 dilution was set up, with an additional blank value. The dilutions were measured from the lowest to the highest concentration, using the photometric sensor (TSL2591) and a green LED (Nichia NSPG500DS) as the light source. The measured values were written to a CSV file and analysed using LibreOffice Calc.
The resulting lin-log graph (linear: absorption vs. logarithmic: concentration) gave the typical S-shaped curve seen in such measurements. At low concentrations there is a direct relationship between concentration of the dye and absorption of light, while at higher concentrations it is asymptotically approaching a maximum level (total absorption). Using the curve, the usable detection range can be estimated to cover about two logs (100x) of concentration, which is reasonable for a photometric device.
Step 7: Some Final Remarks
- To make the devices described here available to the public, we are planing to place a optimized, checked and proved layout file for the acrylic parts at Pokono, so you may download it there for a small licence fee. I would like to insert a few changes to the current layout, which may require a few weeks to be evaluated.
For all that want to start right now: A slightly modified version of the last evaluated one is available at Ponoko.
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