At the end of Part 6 I had a custom PCB, but it was really just a carrier board for modules: an ESP32 dev board, a sensor breakout and an OLED module, all soldered on by hand. It worked, but I wanted to get rid of the soldering completely. So this time the plan was simple to say and harder to do: design a board that JLCPCB assembles for me, from bare parts, so it arrives ready to flash.

It’s finally here, and it’s the first version of this project that I’d call almost production.

After successfully miniaturizing our CO2 monitor with a 3D printed enclosure in Part 5, it was time to tackle the final stage outlined in our original roadmap: Stage Three - Custom PCB & Cost Optimization for Potential “Mass Production”. What I discovered during this journey was that custom PCB design and manufacturing is far more accessible and affordable than I ever imagined!

After successfully integrating the SCD41 sensor in Part 4, it was time to tackle the next major challenge: miniaturization. The goal was to transform our bulky breadboard prototype into a sleek, compact device that could fit into a custom 3D printed enclosure. This journey introduced me to the world of CAD modeling, 3D printing technique, and opened up an entirely new hobby!

The components I ordered for the next iteration of our DIY Smart CO2 Monitor have finally arrived! In this part, we’ll be focusing on cost optimization by switching to more affordable hardware while significantly improving accuracy with the Sensirion SCD41 sensor. This upgrade brings us closer to a production-ready design while maintaining the core functionality we built in Part 3.

In Part 2 of our DIY Smart CO2 Monitor project, we successfully built a proof-of-concept (PoC) using an ESP32-S3, an SH1106 OLED display, a DHT11 sensor for temperature and humidity, and an SGP30 sensor for CO2 and TVOC levels. We also got our first taste of coding with GitHub Copilot. Now, in Part 3, we’ll take a significant leap forward by connecting our ESP32 to WiFi, exposing the collected air quality data in Prometheus format, and setting up Prometheus and Grafana with Docker Compose for long-term data storage and visualization.