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- UPC - IED - ESADE
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Once we have a clear understanding of the concept and design of the machine, it’s important to ensure that it is accessible and understandable to the general public. For this purpose, we have created two simple prototypes. Although these prototypes differ in some aspects from how the actual devices will be, they will aid the audience in comprehending the process and functionality of these devices.
We have designed a machine featuring a filter and an air pump, aimed at collecting air samples for future analysis to detect microplastic concentrations using Raman Spectroscopy. The machine is equipped with a removable tape on top, allowing for easy placement and retrieval of the filter. Each filter is labeled with an identification number for easy tracking when shipped to the analysis lab.
The filter is paired with an air pump. In this prototype, the air pump is not as powerful as initially expected due to resource constraints. However, it still performs the intended function, albeit with reduced air absorption. This lower power consumption also means less air is absorbed.
A flexible material is incorporated at the bottom part of the filter. This design feature assists technicians in monitoring the absorption power at any given time.
All components are housed within a box designed for stability and durability, as the device will be table-mounted and might be subjected to movement or accidental falls. While the final device will be fully enclosed, the prototype is intentionally left open for demonstrative purposes, allowing viewers to see how the device functions.
The materials we used are: air pump, engine, switch, 9 V battery, tube, 3D pieces (to put the filter). And then we proved that our prototype is working.
On the other hand, we adapted a setup in order to simulate the Raman Spectroscopy, the best analogy we had in order to make this simulation which we had access to is a visual range spectrophotometer which is part of a final degree work which works with Arduino and a shield which connects the board to the spectrophotometer sensors.
As you can see, our setup is composed of an incandescent bulb, an Arduino with sensor shield, and a piece to support the filter. We are using colored papers to simulate filters. And our spectrum is a wavelenghth one, that’s why on the result, we find a minimum on the wavelenghth of the color we are detecting.
Given the high cost associated with Raman Spectroscopy, a prototype using color spectroscopy has been developed as an analogy. This prototype includes an air pump with a removable cover for easy filter placement. The filter is connected to an Erlenmeyer flask, with the air pump facilitating the air flow.
Before interpreting the results we have to explain what the colorspectrum is.
The color spectrum refers to the range of colors produced when light is dispersed according to its wavelength. This dispersion often occurs when light passes through a prism or a diffraction grating. The color spectrum is most commonly associated with the visible light spectrum, which ranges from approximately 380 nanometers (nm) to 750 nm.
Table from: http://en.wikipedia.org/wiki/Color
This are the Scientific Phenomena which explain colorspectrum:
Dispersion: The separation of light into its different colors occurs due to the varying speeds of different wavelengths as they pass through a medium like glass or air.
Refraction: The bending of light when it passes from one medium to another (e.g., air to glass) causes different colors to bend by different amounts, resulting in the spread of colors.
Absorption and Emission: Different materials absorb and emit light at specific wavelengths, contributing to the unique colors observed in the spectrum.
We also have to consider that c=fλ to understand the relationship between colors and wavelength (λ), being aware that c=3*10⁸ m/s is the light propagation velocity and f the frequency value of the light.
To operate this prototype, two primary software interfaces are required: MATLAB, for plotting the final results visible to the user, and Arduino, which functions as the control system for our spectrophotometer.
Our setup includes an incandescent light, which, through a steel component, directs light onto a paper held by a 3D-printed support. Additionally, there’s a sensor that detects light and provides the system with information about the color. To optimize performance and prevent the bright light from influencing the sensor’s readings, we’ve enclosed the light source and positioned the sensor to directly face the targeted color, minimizing interference. This entire setup is housed inside a box to ensure that external light does not affect our results.
The calibration process begins by setting the system to black (achieved by turning off the light) to establish the minimum value. Then, we calibrate to white by placing a white paper on the support and turning the light back on, setting the maximum value. Following this, we introduce the actual sample for analysis. The system automatically normalizes the value using these established maximum and minimum limits. If the machine remains in a static location, it won’t require recalibration each time a sample is analyzed.
Finally, in MATLAB, we can view the final plot. The minimum point on the plot corresponds to the wavelength of the paper color, giving us the analytical data we need. This system, while intricate, is designed for accurate and efficient spectral analysis of samples.
Just as this simulation operates on the visible spectrum of light, the same principle applies to the non-visible spectrum, such as the Raman spectrum. In this scenario, the spectrometer, which would be in motion, would use a laser instead of a regular light source. The way the laser light reflects off various types of microplastics would cause a decrease in the function at different wavelengths. By studying the peaks in the Raman spectrum, we can determine the concentration of each peak. Consequently, this allows us to identify the quantity and type of different materials present.
In essence, the Raman spectrometer analyzes how light is scattered by the microplastics. Different materials will scatter light in unique ways, resulting in distinct peaks at different wavelengths. These peaks are indicative of the molecular composition and structure of the material. By analyzing these peaks, we can not only identify the type of microplastics present but also quantify their concentration. This method is highly effective for detailed and specific identification of various microplastic materials in a sample.
On the other hand, an innovative implementation that has been applied to the system are filters. These filters enable visualization of regions corresponding to specific colors, such as red, blue, or green, making it easier for the user to identify them without constantly referring to a color table and its corresponding wavelength. Furthermore, these filters act as a perfect simulation of how a computer program can be used to select only certain regions of interest. These regions are pre-tabulated, allowing the system to focus solely on parts of the spectrum where it’s known that plastic, which could become microplastics, is reflected. This feature can be greatly beneficial in post-analysis and final interpretation.
The development of a prototype for analyzing microplastics poses certain challenges, particularly regarding the cost and practicality of employing Raman Spectroscopy. Given these constraints, an alternative approach using color spectroscopy has been adopted as a viable and cost-effective analogy.
Raman Spectroscopy, despite its accuracy and reliability in identifying and analyzing microplastics, is a notably expensive technique. The high cost is primarily due to the sophisticated equipment and technology required. In a prototype setting, where budget and resources may be limited, deploying a full-scale Raman Spectroscopy setup is often not feasible.
To circumvent this challenge, color spectroscopy has been chosen as an alternative for the prototype. Color spectroscopy, which analyzes the color spectrum of light reflected from a sample, offers a more accessible and less expensive method for preliminary analysis. While it does not provide the same level of detail as Raman Spectroscopy, it can still yield valuable information about the presence and characteristics of microplastics in a sample. This approach allows for a rudimentary analysis that can be used as a stepping stone for more detailed studies using Raman Spectroscopy at a later stage.
The prototype incorporates an innovative air pump and filtering system. Central to this system is a uniquely designed air pump with a removable cover. This feature is crucial as it allows for the easy placement and removal of the filter, facilitating sample collection and analysis.
The filter, a critical component in the collection of microplastics, is connected to an Erlenmeyer flask. This setup ensures that the air drawn in by the pump passes through the filter, capturing microplastics and other particulate matter present in the air sample.
“Empowering Vietnam textile factories workers by reducing their overexposure to microplastics to avoid lung disseasses.”
Making the invisible impossible to ignore
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