Phone Fluorescence Chlorophyll Testing

ISEF Category: Embedded Systems

Subcategory: Optics  ·  Difficulty: Intermediate  ·  Setup: School Lab  ·  Time: 1 to 2 Months

The Hook

Pond water can look clear and still hide a bloom in progress. Your phone can help catch that signal before your eyes do. With a UV LED, a filter, and a simple insert, you can turn a normal camera into a tiny detector. That gives you a real engineering project with a real environmental use.

What Is It?

This project uses fluorescence, which means a material absorbs one color of light and gives off another. Chlorophyll does this well. When you shine the right light on water samples, chlorophyll glows, and your phone camera can measure that glow. Think of it like turning invisible ink into something your camera can read.

The insert does the hardware part. A microcontroller, or MCU, controls the UV LED and helps keep the lighting consistent. The filter cassette blocks extra light so the camera sees mostly the glow you care about. That lets you compare samples more fairly. You can then use serial dilutions, which are step-by-step weaker versions of a standard sample, to build a calibration curve and convert brightness into concentration.

Why This Is a Good Topic

This is a strong science fair topic because you can test a clear variable, measure a real signal, and improve the design with iteration. It connects to water quality, harmful algal blooms, and environmental monitoring, so the work has real-world value. You can learn optics, sensors, calibration, and error analysis without needing a university lab. You also get room to compare different filters, phone models, or sample types.

Research Questions

• How does chlorophyll concentration affect fluorescence intensity in pond water samples?
• What is the effect of different UV LED wavelengths on measured signal strength?
• To what extent does a filter cassette improve the repeatability of phone-based fluorescence measurements?
• Which serial dilution range gives the most linear calibration curve for chlorophyll standards?
• How does sample turbidity change the accuracy of fluorescence readings?
• Does automatic MCU timing improve measurement consistency compared with manual capture?
• To what extent do different phone camera settings change the final chlorophyll estimate?

Basic Materials

• Smartphone with a manual camera app
• UV LED source
• MCU board such as Arduino Nano or ESP32
• Lightproof phone insert or 3D-printed cassette
• Optical filter sheet or color filter gel
• Clear cuvettes or sample vials
• Distilled water
• Chlorophyll standard or dyed calibration solution from a school lab
• Micropipettes or graduated droppers
• Digital kitchen scale or balance, if making standards from plant extract
• White poster board or matte black cardboard for light control
• Notebook for logging samples and settings.

Advanced Materials

• Smartphone with RAW capture support
• MCU board with stable LED control
• UV LED at a chosen wavelength
• Long-pass or band-pass optical filters
• 3D-printed housing with aligned optical path
• Spectrometer for reference validation
• Fluorometer access for comparison data
• Laboratory glassware for serial dilutions
• Chlorophyll a standard or extracted pigment standards
• Data collection target or calibration card
• Light meter or photodiode sensor for LED stability checks
• Image calibration target for camera response correction.

Software & Tools

• ImageJ: Measures pixel intensity in each sample image and helps compare calibration curves.
• Python: Fits calibration models, plots results, and checks repeatability across trials.
• Google Sheets: Organizes sample labels, dilution series, and basic summary statistics.
• Arduino IDE: Programs the MCU to control the LED and capture timing.
• PubChem: Helps you look up chlorophyll-related compounds and their optical properties.

Experiment Steps

1. Define the exact optical signal you want to measure, then decide whether your output will be average brightness, color channel ratio, or another image feature.
2. Design the phone insert so the LED, filter, and camera stay aligned and keep outside light out.
3. Plan a calibration set with known standards, then build a dilution series that spans low to high signal without saturating the camera.
4. Choose controls that separate true fluorescence from background color, glare, and changes in sample clarity.
5. Set a camera workflow that keeps exposure, focus, and white balance consistent across all trials.
6. Map out the analysis pipeline before collecting data, including how you will convert image measurements into concentration and test error.

Common Pitfalls

• Letting ambient room light leak into the cassette, which raises background signal and ruins comparisons.
• Using automatic camera exposure, which changes brightness from shot to shot and breaks calibration.
• Picking a filter that blocks too little stray light, which makes the chlorophyll signal hard to separate from the background.
• Skipping a true standard curve, which leaves you with image brightness but no way to estimate concentration.
• Testing only clean standards and not real pond water, which hides matrix effects from turbidity and debris.

What Makes This Competitive

A competitive version of this project goes beyond a simple phone detector. You would compare your system against a reference method, test multiple sample matrices, and quantify error across the full calibration range. Strong entries also examine repeatability, camera-to-camera variation, and how well the design works in real pond water, not just clear standards. A careful analysis plan matters as much as the hardware.

Project Variations

• Use lake water, aquarium water, or runoff samples to compare how turbidity changes fluorescence readings.
• Swap the UV LED wavelength or optical filter type and measure which setup gives the cleanest calibration curve.
• Compare raw RGB intensity, grayscale brightness, and color-channel ratios to see which analysis method predicts chlorophyll best.

Learn More

• NOAA Harmful Algal Blooms resources: Search NOAA for background on bloom monitoring, water quality, and why chlorophyll matters.
• USGS Water Data and Water Research topics: Search USGS for freshwater monitoring methods and sample context.
• NIH PubMed: Search for review articles on chlorophyll fluorescence, smartphone sensing, and colorimetric imaging.
• MIT OpenCourseWare, Circuits and Electronics: Use the free course materials to review sensor control, signal handling, and basic embedded design.
• ImageJ documentation: Read the free guides for measuring pixel intensity and building image-analysis workflows.
• PubChem: Search chlorophyll a and related pigments to find spectra, structures, and basic chemical data.