Team:IISER Mohali/Hardware

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Hardware

Hardware: Smartphone Based Spectrometer


Achievements:

  • Low cost
  • Portable
  • No need for companion laptop or desktop

Motivation:

  • Since we were inspired by existing optical solutions to build OSCCit, we explored methods that we could accomplish keeping our design aspects in mind. A very popular method to detect protease activity is via fluorescence. But to detect it, we needed a very sophisticated light-capturing technique that could detect fluorescent intensities from low concentration samples (ng/ml - something in the range of biomarkers in saliva) and calculate the concentration of the biomarker. Additionally, like any spectrometer, it should be able to detect the wavelength of the emitted light. Most importantly, it was to be part of a low-cost device. Thus, we sought to build a spectrometer that could solve both these problems.


Introduction:

  • The spectrometer is a very important tool in a laboratory. It has wide applications ranging from astronomy to biochemistry. But most of the spectrometers are costly, bulky, need a companion computer for their operation.

    So we attempted to make a custom spectrometer printed with a 3D printer. One can customize it according to their needs. We utilized the smartphone camera for the collection of data and built a custom app for data processing. It is capable of doing quantitative analysis. It can take the spectrum of a given light source, emission/fluorescence at an external laser source.


Layout:

  • The spectrometer is based on the lens-grating-lens LGL configuration. Light from source/sample enters through entrance slit. We can control the slit width depending on the resolution, input intensity we want. The light is then colimated by colimating lens, which is then passed through a transmission grating. Dispersed light from the grating is then focused on the CMOS sensor of the smartphone camera by smartphone lens.
    Data/images from the smartphone can be processed on the smartphone itself by OSCCit App.



Simulation:

The setup was simulated in COMSOL Multiphysics to visualize the ray tracing.

The entire protocol can be found in our lab notebook, here.


Calibration

The spectrometer is calibrated using a CFL (Compact Fluorescent Lamp) bulb. The calibration is done by identifying 3 peaks in the spectrum of light from CFL bulb. We used a Thor labs spectrometer and identified 3 peaks.

λ1= 435.12nm
λ2= 545.48nm
λ3= 611.02nm

With 3 peaks as standard we can calibrate OSCCit spectrometer upto the quadratic order.

λ = ax2 + bx + c

where x is the pixel number.

Image: Spectrum of CFL bulb took with CCS100 Thorlabs spectrometer

λ1= ax12 + bx1 +c
λ2= ax22 + bx2 +c
λ3= ax32 + bx3 +c

Image: Spectrum of CFL bulb took with OSCCit spectrometer

The OSCCit app identifies x;1,x;2,x;3, solves for a,b,c, and determines wavelength function.


Resolution:

If we see the spectrum of light from CFL, there are two close peaks at λ1 = 576.359nm and λ2 = 578.378nm.

So, &#x394 &#955 = 2nm

With OSCCit spectrometer, we are able to resolve these two peaks very clearly. Hence, the resolution of the OSCCit spectrometer is at least 2nm.


Mode of Operation:

We can operate OSCCit spectrometer in two modes:

  1. Mode 1: Spectrum of any given light source.
  2. Mode 2: Emission Spectrum of a given sample at constant excitation.

MODE 1:

In this mode, we can take spectrum of any given source. We have to place the light source in from of entrance slit. We record the spectrum and process it in OSCCit assistant app. Detailed instructions can be found on operation manual.

MODE 2:

In this mode we record the emission spectrum of the sample at constant excitation using an external laser. We can record the spectrum for different known concentrations and unknown concentrations. The OSCCit Assistant App can predict the unknown concentration. The detailed instructions can be found in the operation manual and the app can be downloaded from our Github page.


REFERENCES:

https://www.upb.edu/en/contenido/spectrometry-software-for-android

Fluorescent Imaging of Single Nanoparticles and Viruses on a Smart Phone


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