To address the lack of a device that incorporates all of the reagents required for the detection of biomarkers, we developed APTASTELES, a modular microfluidic device.

Microfluidics is the study of the characteristics of fluids at microscopic scales where surface forces outweigh volumetric forces [1].

Several sketches were made while designing the device, and after consulting experts in the field of microfluidics, we finally narrowed it down to our design. Taking in suggestions from Dr Prosenjit Sen we decided to design a chip for one of the biomarkers and parallelise it to the others.

Our detection system is divided into two modules:

  1. Protein and hormone detection
  2. miRNA detection.

    (For more information, visit blueprint)

We initially made designs incorporating both these modules, but due to complications in the integration, we decided to begin with a prototype for protein and hormone detection. 

 Our Hardware consists of 3 parts:

  1. Microfluidic chip
  2. The optical detection unit
  3. Processing unit


After considering multiple materials to fabricate our microfluidic device, we have chosen Polydimethylsiloxane (PDMS)

Reasons for Choosing PDMS

Accounting for the different processes involved in the kit, our microfluidic chip is divided into three chambers.

  1. Input chamber
  2. Reaction Chambers
  3. Storage Chambers

Input chamber:

Processes in this chamber: Sample collection and filtration. 

The sample collected from the patient is added to the input chamber using a syringe. According to our literature reviews, the volume of the sample to be taken depends on the sample reservoir and the amplification technique and quantity [2]. 


Volume of the final reaction chamber can hold = 10 𝜇l of the sample

Number of reaction chambers = 10

So the volume of plasma sample required = 100 𝜇l 

However, it was found that we need to draw 2.5 times more blood than the plasma we require to account for the losses [3].

Therefore the volume of blood sample required to be taken from the person = 250 𝜇l 

To filter out the plasma from the blood, we use a plasma filter - Vivid™ [4]. The input chamber is coated with anticoagulant (EDTA) to prevent coagulation of blood.

Reaction Chamber 1 :

Process that occur: Release of trigger

The plasma from the input chamber is directed to the reaction chamber 1.


  1. Immobilised aptamer-trigger complexes 
  2. Tris-Magnesium sulphate (TMS) buffer required for maintaining the aptamer-trigger complex. 

The biomarkers in the plasma bind to the aptamers-trigger complexes and result in the trigger release. The aptamers are immobilised to ensure that only the triggers go out to the reaction chamber 2. The contents are enclosed to give it the required incubation time of around 20-30 mins. 

Reaction Chamber 2:

Processes that occur: Amplification and detection

The triggers released from reaction chamber 1 go to reaction chamber 2.


  1. Amplification module consisting of target sequences
  2. Φ29 polymerase
  3. Φ29 buffer
  4. dNTPs.

Reactions that occur: 

  1. Strand Displacement
  2. Amplification
  3. Dye-binding 

Storage Chambers:

The following reagents are stored in storage chambers:

  1. T7 polymerase, buffer, and NTPs
  2. DFHBI dye
  3. Sensor buffer
  4. Protease

Evolution of our Chip Design

Design 1

The initial design consists of 10 microchannels, exclusive for the detection of each of the biomarkers.


  1. Wastage of Sample
  2. Lack of Storage Chambers

Tested Design:

The primary goal of this design was to implement timed, sequential delivery of reagents and to test for fluorescence.

3D Models

3D printed chip

Fabricated Chip

A silicon wafer was coated with 2 layers of photoresist to achieve a height of 180 μm. It was 3D printed with our design and developed using a developer. This was the mould over which we successfully fabricated PDMS chips of the dimensions mentioned above. Two PDMS chips were bonded together by plasma treatment to increase the volume of the chip. After the bonding process, the chip was made hydrophobic by treatment with Teflon in the first attempt and Silane in the second attempt. The hydrophobicity ensures storage and prevents the leakage of the contents. 

The contents in the storage chamber are transferred to the reaction chamber by a gentle press. And this helped us in the sequential and timed delivery of the reagents

Experimental Protocol

We wanted to test the working of our design and observe the fluorescence emission from the chip.

In a tube, the following reaction mixture was added:

• 5X sensor Buffer- 20 𝜇L

• Broccoli aptamer- 1 𝜇L

• DEPC water- 70 𝜇L

• The mixture was briefly vortexed 

• Added a few drops of the mixture into the reaction chamber

• DFHBI was added to the storage chamber

Before Illumination

After Illumination

By a gentle press to the storage chamber, DFHBI was transferred to the reaction chamber. The mixture was incubated for 30 mins. Later, we placed the sample under a fluorescence microscope, excited it, and observed the fluorescence.

The movement of the mixture in and out of the chambers by the application of pressure is shown in the video.

Implementation Design 


Here we have storage chambers containing the lyophilised content. As the water flows through the system, the reagents are reconstituted and stored in the chamber. Application of pressure releases the contents to the reaction chambers. 

Multiplexed Design


The key steps are as follows:

  1. Illumination
  2. Fluorescence emission
  3. Photodetection

Light Emitting Diode (LED):

The excitation maxima of the Aptamer-Dye complex = 447 nm

Emission maxima of the Aptamer Dye complex = 501 nm [7]

The light-up aptamer-dye complex requires an illumination source to fluoresce. The battery-powered LED of a specific wavelength (450 nm) is the illumination source in our system. The excitation of the aptamer-dye complex results in fluorescence output. We use the LED MTE4600P-C for our system.

Band Pass Filter:

To increase the accuracy of our output, we filter out the background signals by incorporating a bandpass filter that allows only light of wavelength 510 ± 10 nm to pass through it. 


A photodiode detects the fluorescence output from the complex, which gives the current output.

After a discussion with Dr Kanagasekharan, we selected photodiode S5821-03  (Hamamatsu) for our system. The spectral response of the photodiode is from 320 - 1100 nm. High response speed and Low dark current are the special features of this photodiode.

Set Up for the optical detection:

Schematic Representation of Photodetection

Our excitation source is an LED which illuminates the sample, resulting in the fluorescence output. The fluorescence output emitted around 500- 510 nm passes through a bandpass filter of centre wavelength 510 nm and FWHM ±10nm. This blocks out the background noises including the light from the illumination source, thereby filtering our required spectrum. The filtered light now goes to the photodiode S5821-03, which gives the current output proportional to the intensity of the Fluorescent light. The current output goes as the input to the Arduino board, which gives the final output.


Both the current output from the photodiode and biomarker concentration in the sample is proportional to the fluorescence intensity. Making use of these two proportionalities, we establish a correlation between the current output and biomarker concentration.

The fluorescence intensity measured by the photodiode will be processed by an Arduino UNO

Signal Processing:

The electrical signal is received by an Arduino UNO board.The input is analysed based on the threshold value from the Calibration curve of the Voltage vs Conc of Aptamer [8]. The Arduino is coded to receive the electrical input and displays the level of risk of PCOS that the user is prone to, in the LCD screen.

Calibration curve:

As an initial step, we did an experiment to plot the Calibration curve of the Current vs Conc of Aptamer. Due to the unavailability of the bandpass filter, to test the setup's working, we used a Pinhole beneath the LED to ensure that the light beam was concentrated into a region of interest. But due to heavy interference from the LED were not successful in accurately measuring the electric output.


Circuit Diagram for photodiode:

Circuit Diagram

Arduino Configuration

The first experiment was to see the variation of the voltage signal with the variation in the light intensity. Initially, we simulated the experiment on Tinkercad. To verify this  the arduino board was connected to a laptop, and the output was displayed on the serial monitor. We observed that as the light source is moved towards the photodiode the output value increases.

Arduino Circuit

Experiment 2: Photodetection with Arduino

We performed an experiment where we vary the light intensity and get an LCD to display “HIGH” or ‘LOW” compared to a fixed value.

The output from the photodiode goes to the Analog pin of the Arduino. The Arduino is fed with the program which takes in the Analog input, does conversions, and compares it with a fixed value.

If the value is lesser than the fixed value, it displays: LOW, along with the value. Otherwise, it displays “HIGH” along with the value.

Click here to access the code as a PDF

For our system:

The input given to the Arduino board from the photodiode is compared to the threshold value. If the voltage exceeds the threshold value, the user gets a YES for that particular biomarker in the LCD screen and a NO otherwise.

Breadboard Setup


  1. Novotný, J., & Foret, F. (2017). Fluid manipulation on the micro‐scale: Basics of fluid behavior in microfluidics. Journal of separation science, 40(1), 383-394.

  2. Design for Microfluidic Device Manufacture Guidelines Editors: Henne van Heeren, Peter Hewkin. Version 5,April 2014

  3. https://www.labcorp.com/resource/blood-specimens-chemistry-and-hematology

  4. Olanrewaju, A., Beaugrand, M., Yafia, M., & Juncker, D. (2018). Capillary microfluidics in microchannels: from microfluidic networks to capillaric circuits. Lab on a Chip, 18(16), 2323-2347.

  5. Hitzbleck, M., Avrain, L., Smekens, V., Lovchik, R. D., Mertens, P., & Delamarche, E. (2012). Capillary soft valves for microfluidics. Lab on a Chip, 12(11), 1972-1978.

  6. Ghosh, S., & Ahn, C. H. (2019). Lyophilization of chemiluminescent substrate reagents for high-sensitive microchannel-based lateral flow assay (MLFA) in point-of-care (POC) diagnostic system. Analyst, 144(6), 2109-2119.

  7. Filonov, G. S., & Jaffrey, S. R. (2016). RNA imaging with dimeric broccoli in live bacterial and mammalian cells. Current protocols in chemical biology, 8(1), 1-28.

  8. Namasivayam, V., Lin, R., Johnson, B., Brahmasandra, S., Razzacki, Z., Burke, D. T., & Burns, M. A. (2003). Advances in on-chip photodetection for applications in miniaturized genetic analysis systems. Journal of Micromechanics and Microengineering, 14(1), 81

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