$1 Graphene Sensor Identifies Safe Water

If you live in a place where you can buy Arduinos and Raspberry Pis locally, you probably don’t spend much time worrying about your water supply. But in some parts of the world, it is nothing to take for granted, bad water accounts for as many as 500,000 deaths worldwide every year. Scientists have reported a graphene sensor they say costs a buck and can detect dangerous bacteria and heavy metals in drinking water.

The sensor uses a GFET — a graphene-based field effect transistor to detect lead, mercury, and E. coli bacteria. Interestingly, the FETs transfer characteristic changes based on what is is exposed to. We were, frankly, a bit surprised that this is repeatable enough to give you useful data. But apparently, it is especially when you use a neural network to interpret the results.

What’s more, there is the possibility the device could find other contaminants like pesticides. While the materials in the sensor might have cost a dollar, it sounds like you’d need a big equipment budget to reproduce these. There are silicon wafers, spin coating, oxygen plasma, and lithography. Not something you’ll whip up in the garage this weekend.

Still, it is interesting to see a FET used this way and a cheap way to monitor water quality would be welcome. Using machine learning with water sensors isn’t a new idea. Of course, the sensor is one part of the equation. Monitoring is the other.

Remote Water Quality Monitoring

While it can be straightforward to distill water to high purity, this is rarely the best method for producing water for useful purposes. Even drinking water typically needs certain minerals in it, plants may need a certain pH, and wastewater systems have a whole host of other qualities that need to be measured. Measuring water quality is a surprisingly complex endeavor as a result and often involves a wide array of sensors, much like this water quality meter from [RowlesGroupResearch].

The water quality meters that they are putting to use are typically set up in remote locations, without power, and are targeting natural bodies of water and also wastewater treatment plants. Temperature and pH are simple enough to measure and grasp, but this device also includes sensors for total dissolved solids (TDS) and turbidity which are both methods for measuring various amounts and types of particles suspended in the water. The build is based around an Arduino so that it is easy for others to replicate, and is housed in a waterproof box with a large battery, and includes data logging to an SD card in order to make it easy to deploy in remote, outdoor settings and to gather the data at a later time.

The build log for this device also goes into detail about all of the steps needed to set this up from scratch, as well as a comprehensive bill of materials. This could be useful in plenty of professional settings such as community wastewater treatment facilities but also in situations where it’s believed that industrial activity may be impacting a natural body of water. For a water quality meter more focused on drinking water, though, we’d recommend this build that is trained on its own neural network.

Supercon 2022: All Aboard The SS MAPR With Sherry Chen

How do you figure out what is in a moving body of water over a mile wide? For those in charge of assessing the water quality of the Delaware river, this is a real problem. Collecting the data required to evaluate the water quality was expensive and time-consuming, taking over six years. Even then, the data was relatively sparse, with just a few water quality stations and only one surface sample for every six miles of river.

Sherry Chen, Quinn Wu, Vanessa Howell, Eunice Lee, Mia Mansour, and Frank Fan teamed up to create a solution, and the SS MAPR was the result. At Hackaday Supercon 2022, Sherry outlined the mission, why it was necessary, and their journey toward an autonomous robot boat. What follows is a fantastic guide and story of a massive project coming together. There are plans, evaluations, and tests for each component.

Sherry and the team first started by defining what was needed. It needed to be cheap, easy to use, and able to sample from various depths in a well-confined bounding box. It needed to run for four hours, be operated by a single person, and take ten samples across a 1-mile (2 km) section of the river. Some of the commercial solutions were evaluated, but they found none of them met the requirements, even ignoring their high costs. They selected a multi-hull style boat with off-the-shelf pontoons for stability and cost reasons.
Continue reading “Supercon 2022: All Aboard The SS MAPR With Sherry Chen”

A blue enclosure with "IoT AI-assisted Deep Algae Bloom Detector w/Blues Wireless" written on the front. Two black cables run over a wooden desk to a cylinder with rocks on the bottom and filled with murky water. A bookshelf lurks in the background.

Detecting Algal Blooms With The Help Of AI

Harmful Algal Blooms (HABs) can have negative consequences for both marine life and human health, so it can be helpful to have early warning of when they’re on the way. Algal blooms deep below the surface can be especially difficult to detect, which is why [kutluhan_aktar] built an AI-assisted algal bloom detector.

After taking images of deep algal blooms with a boroscope, [kutluhan_aktar] trained a machine learning algorithm on them so a Raspberry Pi 4 could recognize future occurrences. For additional water quality information, the device also has an Arduino Nano connected to pH, TDS (total dissolved solids), and water temperature sensors which then are fed to the Pi via a serial connection. Once a potential bloom is spotted, the user can be notified via WhatsApp and appropriate measures taken.

If you’re looking for more environmental sensing hacks, check out the OpenCTD, this swarm of autonomous boats, or this drone buoy riding the Gulf Stream.

A portable water quality monitor

Monitoring Water Quality Using Lots Of Sensors And Machine Learning

Despite great progress over the past century, more than a billion people still don’t have access to clean drinking water today. Much of the water on Earth’s surface is polluted, but it’s not always easy to tell a dirty stream from a clean one. Professional kit for water analysis can be expensive, which is why [kutluhan_aktar] decided to design a portable, internet-connected water pollution monitor.

A bowl of water with several sensors immersed in it, and a blue box connected to them
Calibrating the system using a bowl of clean water.

There is no single parameter that determines the quality of a water sample, so the pollution monitor has no less than five different sensors. These can determine the oxidation-reduction potential (a chemical indicator), the pH (acidity), total dissolved solids (mainly salts), turbidity (suspended particles) and temperature. To combine all these numbers into a simple “yes/maybe/no” indicator, [kutluhan] trained a neural network with data gathered from a large number of places around his hometown.

This neural network runs on an Arduino MKR GSM 1400 module. While not a typical platform for AI applications, the neural network runs just fine on it thanks to the Neuton framework, a software plaform designed to run machine learning applications on microcontroller systems like the Arduino. It also has a GSM/3G modem, allowing it to report the measured water quality to a central database.

All of this is housed in a 3D-printed enclosure that makes the whole setup easy to carry and operate in any location. Collecting data across a wide area should help to locate sources of pollution, and hopefully contribute to an improvement in water quality for everyone. Here at Hackaday we love citizen science initiatives like this: previously we’ve featured projects to measure things as varied as air quality and ocean waves.

UnifiedWater Finds Potable Water And Stops Polluters

Millions of people all over the world don’t have access to clean drinking water, and it’s largely because of pollution by corporations and individuals. Solving this problem requires an affordable, scalable way to quickly judge water quality, package the data, and present it to an authority that can crack down on the polluters before the evidence dissipates. Ideally, the solution would be open source and easy to replicate. The more citizen scientists, the better.

[Andrei Florian]’s UnifiedWater flows directly from this line of thinking. Dip this small handheld device below the surface, and it quickly takes a bunch of water quality and atmospheric readings, averages them, and sends the data to a web dashboard using an Arduino MKR GSM.

UnifiedWater judges quality by testing the pH and the turbidity of the water, which gauges the amount of impurities. Commercial turbidity sensors work by measuring the amount of light scattered by the solids present in a liquid, so [Andrei] made a DIY version with an LED pointed at a photocell. UnifiedWater also reads the air temperature and humidity, and reports its location along with a timestamp.

This device can run in one of two modes, depending on the application. The enterprise mode is designed for a fleet of devices placed strategically about a body of water. In this mode, the devices sample continuously, taking readings every 15 minutes, and can send notifications that trigger on predefined thresholds. There’s also a one-and-done individual mode for hikers and campers who need to find potable water. Once UnifiedWater takes the readings, the NeoPixel ring provides instant color-coded judgment. Check out the demo after the break.

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Underwater distributed sensor network

Open Source Underwater Distributed Sensor Network

One way to design an underwater monitoring device is to take inspiration from nature and emulate an underwater creature. [Michael Barton-Sweeney] is making devices in the shape of, and functioning somewhat like, clams for his open source underwater distributed sensor network.

Underwater distributed sensor network descent and ascentThe clams contain the electronics, sensors, and means of descending and ascending within their shells. A bunch of them are dropped overboard on the surface. Their shells open, allowing the gas within to escape and they sink. As they descend they sample the water. When they reach the bottom, gas fills a bladder and they ascend back to the surface with their data where they’re collected in a net.

Thus far he’s made a few clams using acrylic for the shells which he’s blown himself. He soldered the electronics together free-form and gave them a conformal coating of epoxy. He’s also used a thermistor as a stand-in for other sensors and is already working on a saturometer, used for measuring the total dissolved gas (TDG) in the water. Knowing the TDG is useful for understanding and mitigating supersaturation of water which can lead to fish kills.

He’s also given a lot of thought into the materials used since some clams may not make it back up and would have to degrade or be benign where they rest. For example, he’s been using a lithium battery for now but would like to use copper on one shell and zinc on another to make a salt water battery, if he can make it produce enough power. He’s also considering using 3D printing since PLA is biodegradable. However, straight PLA could be subject to fouling by underwater organisms and would require cleaning, which would be time-consuming. PLA becomes soft when heated in a dishwasher and so he’s been looking into a PLA and calcium carbonate filament instead.

Check out his hackaday.io page where he talks about all these and more issues and feel free to make any suggestions.