Electrochemical Insights
Empowering Innovators in Electrochemistry and Biosensing
Daniel Carroll | 15th edition
Welcome to the 15th edition of Electrochemical Insights! Whether you’re designing biosensors, troubleshooting experiments, or looking to stay ahead in electrochemistry, this newsletter delivers practical insights you can apply today.
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Why Some Electrochemical Sensors Shouldn’t Be Scaled Up (Yet)
In recent years, the pace of sensor innovation has accelerated significantly. New papers arrive weekly proposing novel receptor molecules, materials, and device architectures — many accompanied by the claim that the sensor is “ready for deployment” or “suitable for point-of-care use.” But in many cases, the reality does not support the rhetoric.
There is a widening gap between what works under controlled laboratory conditions and what is truly viable at scale — technically, operationally, and economically. The issue is not one of scientific creativity, but of premature translation. Some electrochemical sensors, however promising in theory or on paper, are simply not ready for scale-up.
Lab Data is not Field Data
A common pattern in early-stage sensor development is to report high sensitivity, linearity, and low limits of detection using highly controlled, specifically selected solutions. These results are often clean and reproducible — but they rarely reflect the complexity of real samples. Whether the intended application is clinical, environmental, or industrial, not only the matrix matters but the variability of the matrix in the real world.
Proteins, surfactants, particulates, variable pH, and ionic strength all impact sensor behaviour. Non-specific adsorption, electrode fouling, and signal suppression are often significantly reduced in research environments resulting in claims that the platform is ready for real world deployment. However, without extensive field/clinical trails it is nearly impossible to determine how the platform will perform when faced with the unpredictable nature of real samples. Even within a single application area, no two samples are truly alike — patient-to-patient variability in clinical diagnostics, seasonal and geographical shifts in environmental samples, or differing contamination profiles in industrial settings can all introduce significant challenges.
On top of this, real-world deployment often means handing the sensor over to non-specialist users, where inconsistent handling, storage, and interpretation introduce yet another layer of complexity.
Surface Chemistry Remains a Bottleneck
A significant number of biosensors rely on fragile or poorly characterised surface chemistries. Techniques such as thiol-based self-assembled monolayers (SAMs) or carbodiimide coupling are widely used, but their reproducibility is often low, and their stability over time is poorly understood on different surfaces in different environmental conditions.
In laboratory settings, this is manageable — surfaces can be prepared fresh, and variation controlled through repetition. In production, however, these inconsistencies are not merely inconvenient; they are commercially unacceptable.
Reproducibility Is Not Just a Protocol Problem
Another frequent barrier to scale is lack of device-level reproducibility. Sensors fabricated by hand using screen-printed or drop-cast electrodes often show good internal consistency within a batch — but reproducibility between batches, operators, or storage conditions can be poor.
This is rarely addressed until late in development, by which point significant time and resources have already been invested. Simple variations in electrode morphology, reference electrode placement, or ambient humidity during fabrication can subtly alter electrochemical responses. If robustness isn't built in from the start, it becomes difficult and expensive to impose later.
Scaling Requires More Than a Signal
The temptation to move quickly from proof-of-concept to prototype is understandable — particularly for researchers under pressure to demonstrate translational impact. But the cost of premature scaling is not just financial; it erodes confidence in the technology and, by extension, in the broader field.
A robust biosensor platform is not defined by a single dataset. It is defined by its consistency, its tolerance to variability, and its capacity to operate under the conditions it was designed for. Until those parameters are properly established and stress-tested, scale-up is not a next step — it’s a risk.
Innovation in electrochemical sensing is essential. But for innovation to translate, it must be matched by critical evaluation, engineering discipline, and a realistic understanding of system limitations. Some sensors are indeed ready for scale — many are not. Knowing the difference is what separates exploratory research from viable technology.
Quick Tips
Check for Equilibrium (Without Disrupting It)
In electrochemistry, knowing whether your system is at equilibrium is critical — especially before applying potential or interpreting kinetic data. But checking for equilibrium can be tricky, since most measurements inherently disturb it.
One solution? Use Electrochemical Impedance Spectroscopy (EIS) at Open Circuit Potential (OCP).
Some potentiostats allow you to apply a very small AC voltage (e.g. 5–10 mV) around the rest potential. This low-amplitude perturbation is subtle enough to avoid meaningfully shifting the system, yet still gives you valuable information such as:
Interfacial capacitance,
Charge transfer resistance,
Double-layer stability.
A smart way to confirm whether your system is stable — before diving into more invasive tests.
Drying your Electrode Surface (Gently)
Biosensor fabrication is often a multi-step process involving chemical and biological surface modifications. In some cases, it’s important to fully dry the electrode surface before proceeding to the next modification step or before storing the sensors for future use.
In the literature, you will frequently find reports of surfaces being dried using streams of inert gas (typically nitrogen or argon) or by placing electrodes in a vacuum chamber to remove residual solution from the working electrode surface.
However, during early-stage R&D, researchers may be uncertain about the effects these drying methods have on the electrode surface, especially when delicate bioreceptor components such as enzymes, aptamers, or antibodies are involved. Moreover, applying a pressurised gas stream can be difficult to control and may introduce inconsistencies.
An alternative, gentler approach is to remove the bulk of the solution from the surface—without physically touching the electrodes—and then place them inside a fume hood. The continuous airflow within the fume hood facilitates the gentle evaporation of residual liquids from the electrode surface, effectively drying them without risking damage or variability.
What I’m am:
Reading: ‘The Fundamentals of Electrochemistry’ - By Yuliy D. Gamburg
In particular:
Chapter 5: Double Electric Layer and Adsorption of Substances on Electrodes.
Chapter 6: Electrochemical kinetics
Listening to: ‘Mindscape’ - Sean Carroll
While Sean Carroll’s Mindscape podcast isn’t directly related to electrochemistry or biosensing, I find it incredibly valuable to stay connected to developments in other fields. Hosted by physicist and philosopher Sean Carroll, the podcast features in-depth conversations with some of the most thought-provoking minds across disciplines—from neuroscientists and engineers to authors, filmmakers, and beyond. Together, they explore some of the biggest questions in science, philosophy, culture, and the nature of reality itself.
Using: Python for Automating Electrochemical Work Flows
With the rapid advancement of AI and large language models (LLMs), it's now more practical than ever to automate key aspects of electrochemical workflows. Lately, I've been using tools like ChatGPT to introduce some level of automation at nearly every stage of my R&D process—whether it's running electrochemical scripts, processing data, organising and storing results, or generating reports and summaries. Even small automations can significantly boost efficiency and free up time for deeper problem-solving.
The good thing is, once you do this once, the process becomes highly adaptable. You can tweak parameters, reuse scripts across projects, and scale your workflow as your data or experimental complexity grows. Whether you're working on biosensors, materials characterisation, or electrode optimisation, these automation tools can be customised with minimal effort—saving you time and reducing human error in the long run.
Science is Becoming Uncertain!
Across the UK and the US, science funding is facing serious challenges. In the UK, despite headlines of increased investment, the reality is different: R&D spending is flat in real terms. Once inflation is factored in, there’s little left for meaningful growth—placing added pressure on researchers already stretched thin.
In the US, the situation is even more alarming. Proposed federal budget cuts would slash funding to core agencies like NASA, the NIH, and the NSF by up to 50–70%. These cuts threaten to derail active research, halt future missions, and force thousands of scientists out of work. Some of the world’s leading institutions are already reporting massive grant losses and staff layoffs.
While the outlook in the UK and US grows uncertain, Europe is taking a different path—investing in new programmes to attract global research talent and strengthen its scientific leadership.
This isn’t just a political issue. Undermining science in this way has serious consequences for humanity. It slows the development of life-saving technologies, weakens our ability to respond to global crises, and risks fuelling a long-term brain drain. Without public support, the benefits of science could become concentrated in private hands—leaving entire populations behind. And in a world where misinformation spreads faster than truth, defunding science sends a dangerous message: that facts and evidence no longer matter.
Now more than ever, we need to protect and prioritise the systems that help us understand, innovate, and prepare for the future.
From The Archive
Revisiting My First Lab Notebook
I recently came across my first proper lab notebook, dating back to my undergraduate final-year research project. At first glance, it brought back a wave of nostalgia—but as I flipped through the pages, a more sobering realisation set in: if I had to reproduce any of that work today based solely on what I wrote down, it would be almost impossible.
Then I asked myself a tougher question: would my current lab notebooks be any better if I revisited them in 10 years time? Honestly... probably not!
Despite all the experience I’ve gained, good documentation still takes discipline, clarity, and time—three things that often get sacrificed in the fast-paced rhythm of research. Abbreviated notes, missing reagent details, unlabelled spectra, vague references to “standard protocols”… it’s a pattern we all fall into.
Yet accurate, reproducible record-keeping is the bedrock of good science. Without it, we're left with scattered clues rather than a clear roadmap.
This experience reminded me of the value in taking a step back to audit our own habits:
Would your notes make sense to someone else?
Could you replicate your own experiment 1 year from now?
Do you leave enough context for the why, not just the what?
We often think we understand what our notebooks are telling us—but that’s usually only because we’re still immersed in the work written on the most recent pages. The details feel obvious now because they’re fresh in our minds. But give it a few months, or hand it to someone else, and suddenly the shorthand, half-sentences, and missing context become real barriers to understanding.
That illusion of clarity is dangerous. It convinces us we're documenting well—when in reality, we’re just riding on memory.
Community Q&A – Ask Me Anything!
Have a question about biosensors, electrochemical techniques, or experimental design? Every week, I’ll select and answer reader-submitted questions, offering insights, practical tips, and expert guidance. Whether you're troubleshooting an experiment, exploring new methods, or just curious about the field, feel free to ask!
Submit your question with as much detail as possible to help me provide the most useful response!
For paid subscribers: You can send me a direct message and even set up a one-on-one meeting to discuss your research challenges in depth.
The Electrode Exchange
Are you working in electrochemistry, biosensing, or a related field? This is your chance to share it with a community of like-minded researchers and professionals!
How to Participate:
Submit a summary: Share a brief overview of your research that includes:
Where you are working/studying
Your position: Masters student, senior scientist etc..
The focus of your work
Key findings or progress so far
Challenges or problems you are currently facing
Why it matters
What’s in it for you?
Visibility: Get your work featured in the newsletter and in front of a growing, engaged audience of professionals and researchers.
Engagement: Showcase your expertise and connect with others in the community.
On the Shoulders of Giants
This week we stand on the shoulders of Ernst Julius Cohen — a pioneering Dutch chemist whose work bridged electrochemistry, crystallography, and thermodynamics at a time when physical chemistry was just taking shape. As a professor at Utrecht and editor of one of Europe's leading chemical journals, Cohen helped build the foundation of modern materials science. But his life was tragically cut short in 1944, when he was deported and murdered at Auschwitz. His legacy reminds us that the pursuit of knowledge does not exist in isolation—and that defending science also means defending the people who carry it forward.
If you find this work valuable, consider supporting it by becoming a paid subscriber. For just £3.50/month, you'll unlock full access to everything Electrochemical Insights has to offer.
Daniel Carroll
Electrochemical Insights | 15th Edition