Bisphenol Detection in Sunlit Bottled Water

Bisphenol Detection in Sunlit Bottled Water

ISEF Category: Biochemistry

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

The Hook

Plastic bottles can pick up more than just water. Heat and sunlight can push small chemicals from the bottle into the drink, and some of them act like hormone mimics. You can turn that hidden change into a visible signal with a paper sensor and a phone camera.

What Is It?

This project looks at how much bisphenol-like material may move from plastic into bottled water after sunlight exposure. Bisphenol analogs are chemicals used in some plastics, and they can behave like tiny signal switchers in biological systems. In this project, your sensor does not “see” the chemical directly. Instead, it reacts when the chemical changes fluorescence, which is light emission from a material after UV exposure.

Think of the sensor like a mood ring for water. When the target chemical is present, the paper spot gets darker or less bright under UV light. Your smartphone image becomes the measurement tool, so you can compare samples by signal intensity instead of guessing by eye.

Why This Is a Good Topic

This is a strong science fair topic because you can change one clear factor, sunlight exposure, and measure a real signal. It connects to everyday safety, packaging chemistry, and water quality. You can learn sample design, calibration, image analysis, and control planning without needing a full research lab.

Research Questions

  • How does sunlight exposure time change the fluorescence signal of bottled water samples?
  • What is the effect of bottle material on bisphenol-related fluorescence quenching?
  • Does storage in direct sunlight produce a larger signal change than storage in shade?
  • To what extent does water brand or bottle type affect the sensor response?
  • Which wavelength band in the smartphone image gives the clearest separation between exposed and control samples?
  • How does repeated sunlight exposure across days change the measured quenching signal?

Basic Materials

  • Commercial bottled water from at least two brands.
  • Clear and tinted plastic bottles of known type.
  • Fluorescence-quenching paper sensor strips or prepared sensor paper.
  • Smartphone with a 365 nm flashlight or UV light source.
  • Dark box or blackout folder for consistent imaging.
  • White background card for photo setup.
  • Digital scale for sample handling.
  • Ruler or printed alignment grid.
  • Nitrile gloves and safety glasses.
  • Image analysis app or computer for reading brightness values.

Advanced Materials

  • Spectrofluorometer for confirmation measurements.
  • UV-Vis spectrophotometer for absorbance checks.
  • Controlled light box for exposure tests.
  • HPLC or LC-MS for validation of suspected leachates.
  • Laboratory balance with finer precision.
  • Micropipettes and tips.
  • Analytical-grade standards for bisphenol analog comparison.
  • Filtered glassware for blank preparation.
  • Data logger for light intensity and temperature.
  • pH meter for sample characterization.

Software & Tools

  • ImageJ: Measures fluorescence intensity from phone images and helps compare samples consistently.
  • Python: Cleans data, graphs calibration curves, and runs basic statistics.
  • Google Sheets: Organizes sample groups, controls, and replicate readings.
  • PubChem: Helps you check chemical structures and names of bisphenol analogs.
  • NIH PubChem Sketcher: Lets you compare related compounds and plan which analogs to test.

Experiment Steps

  1. Define the one exposure variable you will change first, such as bottle type or sunlight duration.
  2. Choose a sensor response metric, then decide how you will turn a photo into a number.
  3. Build a calibration plan with known standards so your readings mean more than simple brightness.
  4. Set up controls that separate sunlight effects from storage effects and camera drift.
  5. Plan replicate groups and a simple statistical test before you collect any samples.
  6. Map out one validation path, such as confirming your strongest signal with a second method or comparison sample.

Common Pitfalls

  • Using different phone angles or distances, which changes the apparent brightness of the sensor spot.
  • Letting room light leak into UV photos, which masks small fluorescence changes.
  • Comparing bottles without matching their starting brand, age, or plastic type.
  • Treating cloudy or tinted samples like clear ones, which shifts the signal and confuses the analysis.
  • Skipping blank controls, which makes you mistake the paper background for a chemical response.

What Makes This Competitive

A class-level version stops at “sunlight changes the signal.” A stronger version links the sensor output to bottle type, exposure history, and a calibration curve that can estimate concentration. You can push it further by comparing multiple bisphenol analogs, testing matrix effects, or using a second analytical method to confirm the signal. Strong controls and clean statistics matter more than flashy equipment.

Project Variations

  • Test different bottle polymers, such as PET versus polycarbonate, to compare which one shows the strongest signal.
  • Compare sunlight with artificial UV exposure to see whether the source of light changes the quenching pattern.
  • Add temperature as a second variable to test whether heat speeds up leaching beyond light alone.

Learn More

  • PubChem: Search chemical profiles for bisphenol A, bisphenol S, and related analogs to compare structures and properties.
  • PubMed: Search review articles on bisphenol leaching, plastic migration, and fluorescence-based sensing.
  • National Library of Medicine Bookshelf: Look for open textbook chapters on analytical chemistry and fluorescence methods.
  • NOAA Marine Debris Program: Find background on plastics, degradation, and environmental pathways for plastic-associated chemicals.
  • MIT OpenCourseWare: Search free course notes on analytical chemistry, spectroscopy, and calibration curves.
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