Hi Reader, have you ever changed a running system?
It’s not a good idea. I've never done it in research, but I’ve unfortunately done it outside of it.
However, changing the system at the right time is one of the best ideas - and this subtle difference is important.
Let’s find out how to drive change safely and why it’s more a question of tactics than science:
Today's Lesson: How To Drive Change Safely
Tactics and insights on how to make processes greener
Number Of The Day
Through 3 simple actions, namely reduction, reuse, and miniaturization, it was possible to save more than 65% of exchangeable plastic waste, even under sterile conditions. While done in conditions few consider feasible for change, all time-sensitive steps intact too. The key is understanding that we change our practices, such as how we handle single-use items, not the underlying methodology that influences data generation.
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Safely Towards Sustainability
First, we must understand that in our planning we want to leave the underlying scientific process of our experiments untouched.
In other words, the goal is to drive change where it doesn’t affect our science.
We optimize the way in which we handle the objects that help us assess our samples. We don’t want to alter anything that links to the fundamental experimental principles. Meaning we might change the gradient of our HPLC but the elution principle and resolution stay the same. The two thin lines connecting Handling and Measuring should indicate that sometimes we have to handle our items in a very specific way, as they would otherwise influence the system we measure. A slightly extend version of this article can be found here - with nuances on scientific benefits.
This is why sustainability-related change doesn’t mean significant investments of additional time, effort, or risk.
The biggest reason we are afraid of change is that we prefer to keep things steady because we are already busy with everything else.
Understanding Perceived Risk
We rarely learn how to optimize, how to drive change, and sometimes we don’t even know why our protocols work.
Refer back to our lesson on experimental design, where we discussed sample sizes, statistical planning, and examined an example of how to implement step-wise designs, given the time savings for scientists and the resource savings for the lab.
This makes change feel overwhelming. It makes our mind rationalize irrational concerns.
Generally, it is often possible to find a technical solution - the difficult part is clearly expressing the kind of anxiety that holds us back.
While rationalization was first characterized by psychoanalysis, I found this very well-designed graphic in a publication by Cushman titled fittingly: “Rationalization is Rational.” Exactly this makes it so difficult for us scientists to realize that we are searching for reasons to support our habits instead of analyzing the data and odds in the present situation before deciding.
To allow us to think accurately, we need to identify and label the anxiety that we are often not aware of.
> Otherwise, we see dangers everywhere or nowhere. But we want to see the actual risks and address them.
Let us therefore look at some examples.
A common perceived risk is overlooking a danger and thereby causing contamination. However, if we plan our optimization ahead and review it on two different days, it is very unlikely that we would make such a blunder.
Secondly, one might be afraid of being distracted and thereby making mistakes. For example, when reusing the same pipette tip and causing contamination. While this can always happen (why else would cell cultures get contaminated), one might even argue that because we work more sustainably, we pay more attention to best practices and focus more on our experiments.
Mistakes happen to all of us … but the point about anxiety, for example, when it comes to contamination, is simply related to human error. Either you can reuse your tip or you cannot. Keeping the tip and pipetting into the wrong well is a human mistake. And don’t rationalize, you won’t truly “get used to” keeping your tip or serological pipette, as the cases where reuse is possible are limited.
Also, some fear that after a change the experiment no longer works as it did previously. But if this were the case, we can simply return to the original method. Of note, investigating surprising failures has been the starting point for breakthrough findings.
The True Danger
This means most dangers introduced to scientific processes are linked to human failure, not the riskiness of optimization.
In other words, we (rightfully) perceive excessive and therefore blinding eagerness to save resources as the danger.
However, this eagerness fades once we understand it’s not about reaching a zero footprint.
Big changes like exchanging plastic for glass do not make the biggest impact. Reduction does. These graphs come from Farley & Benoit. In short, they tried to extrapolate the footprint of single-use plastic vs. reused plastic vs. glass and found that, in some scenarios, reusing plastic can be as sustainable, if not more sustainable, if we look at CO₂e.
My goal is to make you see sustainability-driven changes as a synonym for optimization.
We essentially optimize; we don’t change. (More about optimization of scientific processes and the underlying theory can be found in the extended version of this lesson right here.)
And that also means it’s not primarily about the environment - it is about optimizing workflows.
The point is that saving time, chemicals, or plastics naturally translates into sustainability.
How To Do It
To implement change safely, following five core principles might help:
Differentiation There is no single strategy that works everywhere. Reusing a pipette tip might be appropriate for certain controls where only the concentration differs but the analyte is identical, whereas it would not be appropriate for specific sample types. We should avoid oversimplification.
Stepwise implementation Many protocols offer multiple points for optimization. Although it may be tempting, the best approach is stepwise change. This reduces cognitive burden and ensures that if unexpected difficulties arise, we can trace them back and handle them on the spot.
Mindset When implementing change for the first time, we need the right mindset. This means planning and analyzing the change beforehand so that we can remain fully present when conducting the experiment. It also means working in a good flow—not being distracted by worries about colleagues’ reactions or anxiety about the change itself. Confidence, focus, and concentration are essential.
Experience We must be sufficiently familiar with the protocol before optimizing it. Protocols that are handed down should first be learned and implemented as they are. Then, optimizing includes talking to lab colleagues about potential difficulties and thoroughly reviewing literature to see whether similar changes have been reported.
Protocols can be long and nuanced. Make sure you understand why you do each step and that you remember it sufficiently well to be able to focus on the changes. We often underestimate how quickly we forget - so making a plan and believing you don’t need to take notes or that you can implement changes on the fly is not a good idea. As shown in the diagram about spaced repetition, we generally want to have the original protocol properly established before making changes.
Controls This involves performing trial runs to verify whether optimizations are still valid. Then, it’s about documenting changes through controls - in cell culture, for example, this means checking whether cells grow with the same morphology, speed, and metabolism when a new dish type or dish-reuse strategy is employed.
Applying The Knowledge
The key is not to change a running system in the middle of an experiment.
Instead, change should be implemented after an experimental series is completed or when a new project is started.
As we discussed, seven funding bodies met in Heidelberg to support these statements, companies support you with innovations, data, or tools, and initiatives like those by My Green Lab show that pharma companies regularly achieve a positive ROI. Most often, optimization is missing due to psychological barriers. One such barrier is insufficient trust in oneself. A very different one is the reluctance to accept that improvements already exist, as this would mean admitting that one could have been more efficient for a long time or that someone else might find a solution one did not. When leaders raise doubts, resistance might stems from distrust in a person, not distrust in the change itself (although expressed as such). Nevertheless, if doubts remain after meticulous planning, change should be aborted. This may indicate that the optimization has the wrong target, impacting aspects of the underlying process.
As scientists, we constantly work on new projects and approaches, meaning we are inherently used to change. However, the circumstances matter and this is what we need to express.
If you want to convince yourself, colleagues, or supervisors:
1. Adhere to best practices: research the literature, plan the change carefully, and run a trial.
There are several publications nowadays, whether in microbiology, biochemistry, analyticalchemistry, or synthesis. Read them to understand how change can be realized. Moreover, they are great for convincing others that change is safely possible.
2. Assure superiors that you will invest the extra time required to avoid losses in productivity, even though long-term benefits are almost always observed.
3. Prove that you are capable of managing the change by clearly articulating what we know and plan to do, even if it feels obvious or trivial because others don't know what you do.
Once again, the biggest challenge is not the science. it's the psychological barriers we are rarely aware of.
How We Feel Today
References
Penndorf, P., et al., 2023. A new approach to making scientific research more efficient – rethinking sustainability. FEBS Letters, 597(19), pp.2371–2374. doi:10.1002/1873-3468.14736.
Farley, M., et al., 2023. Re-use of laboratory utensils reduces CO₂ equivalent footprint and running costs. PLOS ONE, 18(4), e0283697. doi:10.1371/journal.pone.0283697.
Cushman, F., et al., 2020. Rationalization is rational. Behavioral and Brain Sciences, 43, e28. doi:10.1017/S0140525X19001730.
Alves, J., et al., 2020. A case report: insights into reducing plastic waste in a microbiology laboratory. Access Microbiology, 3(3), 000173. doi:10.1099/acmi.0.000173.
Kilcoyne, J., et al., 2022. Reducing environmental impacts of marine biotoxin monitoring: A laboratory report. PLOS Sustainability and Transformation, 1(3), e0000001 doi:10.1371/journal.pstr.0000001.
Mazzali, D., et al., 2025. Sustainable and surfactant-free synthesis of negatively charged acrylamide nanogels for biomedical applications. Macromolecules, 58(3), pp.1206–1213. doi:10.1021/acs.macromol.4c02128.
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