If you're in the business of measuring transport properties in porous electrodes (and/or modeling them), you may find our latest paper in @ECSorg interesting: A 🧵below on some highlights.

Please share with any prospective graduate students thinking about applying to @ChemeCU. Goal is to make the process more accessible!.

RT @McNeillGroup: Congratulations, Dr. Han Huynh!

RT @CEEC_CU: Congrats Dr. Steven Denny, a member of the Chen group, for successfully defending his doctoral thesis "Catalytic Properties of….

Brb calling NYT customer support

RT @wesleykchang: Here's a newly published 2D operando imaging technique I've been working on the past year, using ultrasound: https://t.co….

The uncertainty analysis on the EIS measurements was done using the very cool open source from @matt_murbach and team @UWChemE. you can find my code for this uncertainty analysis here:

ps - I tried to frame parameter estimation problems through a Bayesian perspective. Hopefully, this makes thinking about problems in electrochemistry/modeling through the lens of Bayes rule more accessible. S/o to @kjmbishop's Data Analysis class I took in my first year @ChemeCU.

As always, this was a collaborative effort and I need to shout out my labmates Zeyu, Kedi, and @johncbernard1, as well as our collaborators in the Takeuchi-Marschilok group @StonyBrookChem.

Finally, I did an analogous uncertainty analysis on published EIS measurements and found that comparable, if not better, precision can be obtained - probably why it's so common!.

I was especially interested in understanding how uncertainty in other model parameters (e.g. particle-scale Li diffusivity) influenced the tortuosity estimate. How do your results change if you acknowledge you don't know a parameter well vs if you assume it has an exact value?

We estimated tortuosity for NMC and compared our estimates to published results. We also report estimates for LVO electrodes for the first time. Side note (that already seems relatively well established in the literature) - be careful with the Bruggeman relation

This led me down the path of leveraging porous electrode theory to design electrodes & experimental conditions under which the models would be most sensitive to ion transport, leading to more confident estimates of tortuosity

We started by asking the question: if you just fit the tortuosity parameter in the physics-based model to readily available experimental data (rate capability tests), how good of an estimate could you get?.

Measurements of transport properties like tortuosity are used to compare different electrodes and active materials. They are inputs into physics-based models that can simulate cells with these electrodes.

was just thinking that having some visual to look at where bets are being placed would be super helpful. this is it!.

RT @LeaRWinter: I am thrilled to share the news that I will be joining the Yale Department of Chemical and Environmental Engineering @YaleE….

Article in @ECSorg, open access:.

We also used our model to design modified cycling protocols that discriminated between proposed causes for degradation that were convoluted in the original cycling data. This project is a great example of the power of modeling-experiment synergy!

This was a really cool collaboration with The Marschilok-Takeuchi group @StonyBrookChem where my modeling analysis was able to identify particle cracking in these two-phase LVO electrodes upon cycling, which was later verified experimentally.