DigitalGlassware™ : Different Locations, Reproducible Results

Ensuring chemical reproducibility can be tricky at the best of times, especially when following a procedure written by another scientist based in a different laboratory. User ability, equipment and even ambient conditions can all affect the outcome of a reaction. DigitalGlassware™ from deepmatter™ reduces this variability by capturing the impact each of the user’s actions have on their chemistry, contextualising the experimental procedure with real-time sensor data. As DigitalGlassware™ is cloud-based, users can share their reactions and run data easily and quickly with the global community.

In the previous post, we saw how Hannah achieved reproducible results in her Glasgow lab by using DigitalGlassware™. You can view her reaction data, and explore the RunManager module of DigitalGlassware™, by following this link. Today, Tobias, a chemist working in industrial R&D in Basel, who has never previously discussed this method with Hannah, is trying the same alcohol oxidation reaction for the first time.

To see how Tobias’s results compare with Hannah’s follow this link.or continue reading below to find out how easy it is for Tobias to not only find Hannah’s reaction profile but also compare his results with hers.

Tobias is interested in performing the same alcohol oxidation reaction as Hannah has performed and uses RecipeBuilder to search for a recipe. Using the search function, Tobias easily locates the exact method Hannah used and can see at a glance the reagents, equipment and conditions as well the relevant safety information.

Tobias using RecipeBuilder to find and examine Hannah’s recipe for the synthesis of 2,2,2-trichloro-1-phenylethan-1-one.

Excited, Tobias heads to the lab and accesses the recipe from the deepmatter™ RecipeRunner tablet app, which presents the recipe in a simple to follow and manageable format. From RecipeRunner he also has access to real-time sensor data captured from his connected devices, providing unique insights as they occur.

Tobias carrying out a run of a recipe using the deepmatter™ RecipeRunner tablet app highlighting the ‘Run’, ‘Graph’ and ‘Recipe’ tabs.

Tobias sets up the reaction including the unique DigitalGlassware™ sensor platform (DigitalController, DeviceX and the EnvironmentalSensor) capturing context against his actions as they happen. DeviceX sits inside the flask, recording data both inside the reaction solution and in the flask headspace, while the EnvironmentalSensor records ambient conditions in the fume cupboard including temperature, pressure, humidity and light levels. The data from the EnvironmentalSensor allows Tobias to compare the conditions in his laboratory in Basel, to those recorded by Hannah when she carried out the reaction in Scotland, allowing him to compare not only the conditions in the flask but also external conditions that may have an effect.

The RecipeRunner app prompts Tobias throughout the reaction process. As he completes each step, he taps ‘Completed’ to move on to the next stage, which creates a timestamp. In this way, the reaction data from the sensors can be correlated precisely to each action Tobias takes.

EnvironmentalSensor, the multi sensor device that measures the ambient conditions (temperature, pressure, humidity, light level) of your laboratory space.

At the end of the reaction, Tobias records a 67% yield. Using the RunManager software, he can compare his results to Hannah’s reaction last week, which resulted in a 69% yield.  Although he notes the ambient temperature and humidity were different during his reaction compared to Hannah’s, the temperature profiles of the reactions were similar, with a sharp decrease in temperature following the addition of dichloromethane, indicating that both reactions proceeded properly. (Hannah’s reaction is shown in orange, Tobias’s in blue).

Because DigitalGlassware™ is cloud-based, Hannah can also follow Tobias’s reaction from Glasgow – or anywhere in the world. In this case, all went according to plan, but what if something goes wrong? The next post will show how this real-time data can give an early warning and save both time and money.

You can compare Tobias’s and Hannah’s reaction data and explore the RunManager module here, and register here for further information. Remember to check back for regular blog updates that will show how DigitalGlassware™ can work in your lab.

Note all captured reaction data is real as presented. User identities have been anonymised.