NUS engineers invent smartphone sensing device that quickly detects algae

A team of engineers from the National University of Singapore (NUS) has developed a highly sensitive system that uses a smartphone to detect the presence of toxin-producing algae in water within 15 minutes. The system can generate test results on-site, and report findings in real-time using the smartphone's wireless communications capabilities.

According to the researchers, this technological breakthrough could play a big role in preventing the spread of harmful microorganisms in aquatic environments, which could threaten global public health and cause environmental problems.

The NUS team, led by Assistant Professor Sungwoo Bae from the Department of Civil and Environmental Engineering at the NUS Faculty of Engineering, published the results in the scientific journal Harmful Algae.

Conventional algae detection and analysis methods are time consuming and require specialized, costly equipment, as well as skilled operators to conduct water sampling and testing. One approach is to test for the presence of chlorophyll using complex instruments that cost more than S2,000.

"Currently, it can take a day or more to collect water samples from a site, bring them back to the laboratory for testing, and analyze the results. This long lead time is impractical for monitoring of algae blooms, as the management of contamination sources and affected waters could be slowed down," explained Bae.

To address the current challenges in water quality monitoring, Bae and his team took a year to develop a device that monitors microbial water quality rapidly and with high reliability.

The NUS invention comprises three sections—a microfluidic chip, a smartphone, and a customizable 3D-printed platform that houses optical and electrical components such as a portable power source and an LED light.

The chip is first coated with titanium oxide phthalocyanine, a type of photoconductive polymer-based material. The photoconductive layer guides water droplets to move along the chip during the analysis process.

The coated chip is then placed on top of the screen of a smartphone, which projects a pattern of light and dark regions onto the chip. When droplets of the water sample are deposited on the surface of the chip, a voltage drop difference, created by the light and dark areas illuminated on the photoconductive layer, modifies the surface tension of the water droplets. This causes the water droplets to move towards the dark illuminated areas. This movement induces the water droplets to mix with a chemical that stains algae cells present in the water sample. The mixture is guided by the light patterns towards the camera of the smartphone.

Next, an LED light source and a green filter embedded in the 3D-printed platform, near the camera of the smartphone, create the conditions suitable for the camera to capture fluorescent images of the stained algae cells. The images can be sent to an app on the smartphone to count the number of algae cells present in the sample. The images can also be sent wirelessly to another location via the smartphone to quantify the number of algae cells. The entire analysis process can be completed within 15 minutes.

The portable device costs less than $220, excluding the smartphone, and weighs less than 600 grams. The test kit is also highly sensitive, requiring only a small water sample to generate reliable results.

The NUS research team tested their system using water samples collected from the sea and reservoirs. The water samples were filtrated and spiked with specific amounts of four different types of toxin-producing algae—two types of freshwater algae, C. reinhardtii and M. aeruginosa, and two types of marine water algae, Amphiprora sp and C. closterium. Experiments using the new device and a hemocytometer, a standard cell-counting technique commonly used for water quality monitoring, were conducted to test for the presence of algae.

The smartphone system was able to detect the four types of algae with an accuracy of 90%, comparable with the results generated by the hemocytometer.