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Fiber Optic Sensor for Analyzing Azo Dyes in Liquid Samples

In a recent article published in Sensors and Actuators Reports, researchers proposed a novel fiber optic sensor using light diffusing fiber (LDF) for spectrophotometric analysis of liquid samples, focusing on detecting and measuring azo dyes in water. The goal was to monitor the presence and concentration of these dyes reliably and cost-effectively.

Fiber Optic Sensor for Analyzing Azo Dyes in Liquid Samples

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Background

Azo dyes are synthetic organic colorants extensively used in industries like food, medical, and cosmetics. Despite their widespread use, they pose significant risks to human health and the environment, including cancer, hyperactivity, and water pollution. This underscores the need for reliable, cost-effective methods to monitor azo dyes in liquid samples.

Optical absorption spectroscopy is a powerful tool for analyzing liquids, as it can provide information about the chemical composition and concentration of the sample.

However, conventional spectroscopic sensors rely on bulky and expensive equipment, such as light sources, detectors, and spectrometers, and often require sampling or in-situ measurements at a single point. This makes them less suitable for applications where spatial mapping of concentration is needed, like in chemical reactors or environmental monitoring.

About the Research

The authors developed a fiber optic sensor for distributed spectrophotometric analysis of liquid. The sensor utilizes a side-coupling technique between a flexible light-emitting diode (LED) strip and an LDF.

The LED strip consists of 144 addressable LEDs spaced 7 mm apart, which can emit white light across the entire visible wavelength range. The LDF is a commercially available glass fiber with non-periodically distributed scattering centers within its core, which uniformly diffuses light around its circumference and length.

The sensor operates by sequentially activating each LED on the strip. As each LED lights up, light passes through the liquid sample, which couples into the LDF. This light is then guided to a low-cost spectrometer that measures the absorption spectrum at each LED position.

This setup allows for distributed spectroscopic analysis along the entire length of the fiber, achieving a sampling resolution of 7 mm and a spatial resolution of 12 mm.

Notably, the sensor requires no modifications to the optical fiber, such as cladding removal or etching, making it robust and straightforward to fabricate. It is also insensitive to the refractive index and temperature of the liquid sample and can function across the entire visible spectrum and into the near-infrared range.

Research Findings

The authors tested their sensor using two azo dyes, Allura Red and Fast Green, commonly used in the food, medical, and cosmetic industries. These dyes can pose significant health and environmental risks if consumed in large quantities or released into wastewater. They prepared water solutions with varying dye concentrations, ranging from 0.03 ppm to 28.10 ppm, and measured their absorption spectra using the sensor.

The sensor detected both dyes in water solutions with concentrations ranging from 0.15 ppm to 28.10 ppm for Allura Red and from 0.03 ppm to 15.69 ppm for Fast Green, with detection limits of 0.37 ppm and 0.07 ppm, respectively. It also effectively distinguished between the two dyes when they were mixed by analyzing their unique absorption peaks at different wavelengths.

The sensor proved its ability to identify and quantify the dyes along a 1-meter fiber with high spatial resolution. It could track the diffusion of dyes over time and precisely measure the concentration along the fiber at 12 mm intervals.

Additionally, by injecting localized dye drops into a water-filled container, the sensor demonstrated its capability to detect and quantify the dyes even when they were located at the same position, maintaining a spatial resolution of 12 mm.

Applications

The proposed sensor has implications across several fields that demand real-time monitoring of liquid samples, including food analysis, bioreactor control, wastewater treatment, and environmental protection. It effectively detects and quantifies azo dyes and other colorants in liquids.

In food safety, the sensor can verify compliance with regulatory standards for synthetic colorants. In environmental applications, it can identify and measure contaminants in industrial wastewater.

Beyond dyes, the sensor’s technology can be tailored to detect organic pollutants, heavy metals, and biological agents by analyzing their distinct absorption spectra. Additionally, it can be adapted to measure pH, dissolved oxygen, or metal ions by using different LEDs or modifying the LDF with specific coatings or indicators.

Conclusion

The novel sensor demonstrated effectiveness in the spectrophotometric analysis of liquid samples. It enabled continuous mapping of absorption spectra along its entire fiber length, facilitating precise localization and quantification of azo dyes in water with high spatial resolution and a low detection limit.

The sensor is submersible, robust, and simple to manufacture, suitable for diverse applications requiring real-time and on-site monitoring of liquid samples.

The researchers suggested enhancing sensor performance by optimizing the design of the LED strip and the LDF. They also recommended exploring multiplexing capabilities to enable parallel measurements with multiple sensors.

Journal Reference

Testa, G., Persichetti, G., Bernini, R. (2024). Low-cost high-resolution distributed optical fiber system for spectrophotometric analysis of liquid samples: Application to detection of azo dyes. Sensors and Actuators Reports. DOI: 10.1016/j.snr.2024.100190

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Muhammad Osama

Written by

Muhammad Osama

Muhammad Osama is a full-time data analytics consultant and freelance technical writer based in Delhi, India. He specializes in transforming complex technical concepts into accessible content. He has a Bachelor of Technology in Mechanical Engineering with specialization in AI & Robotics from Galgotias University, India, and he has extensive experience in technical content writing, data science and analytics, and artificial intelligence.

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