Lab vs commercial EIS (part 2): A practical comparison
Our previous article on lab vs. commercial EIS (part 1) explored why EIS looks fundamentally different at lab versus commercial scale. Now let’s dive into the specifics: what exactly changes at each level, and what does that mean for your operations?
What changes at scale: A visual comparison
Why you can’t simply transplant lab results
Understanding these differences helps explain why you can't simply transplant lab EIS results to commercial operations, or vice versa.
A catalyst that shows a charge transfer resistance of 0.1 Ω·cm² in a three-electrode cell might contribute differently to total stack impedance when integrated with real-world current distribution non-uniformities, contact resistances and thermal gradients.
For commercial operators, the key insight is this: industrial EIS won't give you the same frequency resolution or electrode-specific detail as lab measurements (you typically can't scan below 0.1 Hz in continuous operation, and you're measuring an aggregate stack response).
But what you gain is far more valuable – the ability to track impedance evolution continuously without interrupting hydrogen production. Changes in characteristic resistance or capacitance over time become leading indicators of membrane dehydration, catalyst degradation or seal failures – problems you can address during planned maintenance rather than catastrophic failure.
The real value of commercial EIS
The transition from lab to field isn’t about losing precision; it's about gaining operational intelligence that directly impacts your stack’s lifetime economics.
Lab measurements tell you what's theoretically possible with your materials. Commercial EIS tells you what's actually happening in your stack, right now, under real operating conditions.
This continuous monitoring capability transforms maintenance from reactive to predictive.
Instead of waiting for performance to degrade noticeably (or worse, for a stack to fail), you can identify subtle trends in impedance signatures that indicate developing issues – often weeks or months before they would otherwise be detected.
Coming next: How to interpret EIS data from commercial stacks and identify specific degradation signatures before they impact performance.