I was searching for YouTube videos about ERC, when noticed this cover with me at the Estonian Research Agency homepage: https://www.youtube.com/watch?v=tBMsxvifxhw

That feel like:

As it is the 31th of December 2025, wish everyone a Happy New Year!
I was searching for YouTube videos about ERC, when noticed this cover with me at the Estonian Research Agency homepage: https://www.youtube.com/watch?v=tBMsxvifxhw

That feel like:

As it is the 31th of December 2025, wish everyone a Happy New Year!
Vladislav Ivanistsev, Ritums Cepitis, Jan Rossmeisl, Nadezda Kongi
to be published in Chemical Society Reviews
https://chemrxiv.org/engage/chemrxiv/article-details/67ed469081d2151a02b33a98
All figures:
https://gitlab.com/doublelayer/chemsocrev_2025_scaling-relations
Some cool notebooks:
https://gitlab.com/doublelayer/electrocatalysis-on-a-laptop
AI-created podcast:
Youtube video:
The following video-presentation – for the CHEAC Summer school 2025 – retells our review on the scaling relations electrocatalysis https://chemrxiv.org/engage/chemrxiv/article-details/67ed469081d2151a02b33a98
From the beginning I decided to try AI to prepare the presentation. Eventually the only to record the video turned out to be by the traditional way. Together with co-authors Ritums and Nadezda, we used PowerPoint with its slide-by-slide recording feature. As we were in 3 different locations, we exchanged the presentation several time while recording. I used chatgpt 4o and 5 to write lecturer’s notes for every slide. In particular, I gave the chat our article’s pdf-file and then discussed every slide-text using canvas-feature to polish it iteratively. Nadezda also used chatgpt to refined her slides before reading them aloud. Overall, I have spend over two weeks planning the presentation. Then a week polishing the slides. Then several days to record and re-record slides. And finally I have got this the final video-presentation.
app.pictory.ai does a relatively good job on reading the lecturer’s notes in a ready presentation. Thought, it reads “Jan” and “OOH” in a funny way. And it adds a lot of 10–20 second pauses. Also the slide numbering is off as well as all animation. The picture is also cut from below. But overall, it takes around 2 hours to generate this voiced video and process it.
Does not work for me. Gemini wants to draw images. I just want to enter my own figures.
https://www.magicslides.app promises to do exactly that but I failed with a notice that below 5 Mb files are allowed.
SlideAI extension also does not do what I want.
Ufff … manual upload is still the fastest and most robust. Well, it is not so simple, as most of my figures are in pdf, so I wrote this script to convert everything to png. When it took me 2 mins to drag-and-drop all png figure to my presentation. Hurray!
#!/bin/bash
# Create output folder
mkdir -p png
# List of input files
files=(
"Figure 1 mechanisms.png"
"Figure 18 Timeline.png"
"FIgure 14 distances.pdf"
"Figure 11 relative.pdf"
"Figure 6 3dvolcano_withscaling.pdf"
"Figure 2 publications.pdf"
"Figure 5 3dvolcano.pdf"
"Figure 17 perspectives.pdf"
"Figure 16 O_bypassing.pdf"
"Figure 15 O_pushing.pdf"
"Figure 12 O_breaking.pdf"
"Figure 13 O_switching.png"
"Figure 10 O_tuning.pdf"
"Figure 7 projection_potential.pdf"
"Figure 9 projection_ads.pdf"
"Figure 8 timeline.pdf"
"Figure 3 ass_diss.png"
"Figure 4 scalings.png"
)
# Loop through files
for f in "${files[@]}"; do
base=$(basename "$f")
name="${base%.*}"
ext="${base##*.}"
if [[ "$ext" == "pdf" ]]; then
convert -density 300 "$f" -quality 100 "png/${name}.png"
elif [[ "$ext" == "png" ]]; then
cp "$f" "png/${name}.png"
else
echo "Unsupported file type: $f"
fi
done
Pretty cool – NotebookLM make a FAQ.
Scaling relations are correlations between the adsorption energies of reaction intermediates on a catalyst’s surface. They are crucial in multi-step electrocatalytic reactions, such as the oxygen reduction reaction (ORR), carbon dioxide reduction (CO2R), and nitrogen reduction (N2RR). The concept emerged in 2005 with the discovery of linear relations between adsorption energies of intermediates like OH, OOH, and O on metal surfaces. Understanding these relations is vital because they define fundamental chemical limitations in electrocatalytic reactions, impacting the design of more efficient catalysts for energy conversion technologies like electrolysers, fuel cells, and metal-air batteries.
In oxygen electrocatalysis, particularly the oxygen reduction reaction (ORR), the adsorption energies of key intermediates (OOH, OH, O) are correlated by scaling relations. These correlations constrain the achievable catalytic activity, often visualised on “volcano plots.” The OOH-OH and O-OH scaling relations, for instance, mean that if a catalyst binds one intermediate optimally, it might bind another too strongly or too weakly, preventing it from reaching the ideal catalytic activity (the “volcano top”). This limitation is significant, as experimental results have shown catalytic overpotentials converging to a limit set by these relations for over two decades, hindering progress in sustainable energy solutions.
Oxygen electrocatalysis primarily proceeds via two mechanisms: associative and dissociative. The associative mechanism, which dominates most known catalysts, involves intermediates like OOH, OH, and O adsorbing at a single active site. Geometrically, this requires only one atom in the active site. The dissociative mechanism, conversely, requires at least two neighbouring atoms to accommodate dissociation products (O and OH). On metal surfaces, a spatial mismatch often prevents the dissociative mechanism, as O preferentially adsorbs on hollow sites and OH on top sites. However, dual-atom site catalysts (DACs) can facilitate dissociative pathways by providing two adjacent sites, allowing for the adsorption of dissociation products. The inter-atomic distance within these active sites is a critical geometric parameter that influences the energy barrier for dissociation, balancing thermodynamics and kinetics.
The “volcano plot” is a theoretical framework used to understand electrocatalysis, typically representing overpotential or activity as an “altitude” against adsorption energy descriptors. For ORR, it correlates adsorption energies with deviations from the thermodynamic equilibrium potential. Scaling relations define the “paths” or “fixed climbing routes” on this volcano plot that are accessible to catalysts. For example, the OOH-OH scaling relation appears as a plane on the three-dimensional volcano, and catalysts following this relation are confined to a specific line on the volcano’s surface. This means that while an “ideal catalyst” (the volcano’s apex) might exist theoretically, scaling relations prevent most catalysts from reaching it, limiting the search for optimal catalysts to a two-dimensional projection.
The review outlines five general strategies for manipulating scaling relations to enhance electrocatalytic performance:
The “breaking” strategy focuses on reducing the intercept of the OOH-OH scaling relation (from approximately 3.2 eV to an ideal value of 2.46 eV) by selectively stabilising the OOH intermediate relative to OH. This typically involves introducing spectator groups or a second adsorption site near the active site. These spectators can form hydrogen bonds or other stabilising interactions with OOH, effectively shifting its adsorption energy without proportionally affecting OH. While challenging to achieve experimentally, this strategy has been demonstrated in oxygen evolution reactions (OER) and more recently in ORR using dual-atom catalysts (DACs) with specific active sites like PN3FeN3, where the phosphorus acts as a spectator to stabilise OOH through hydrogen bonding.
Single-Atom Site Catalysts (SACs) and Dual-Atom Site Catalysts (DACs) are crucial in manipulating scaling relations due to their distinct geometric and electronic properties. SACs typically allow for “on-top” adsorption, primarily favouring the associative mechanism in ORR. DACs, with their two neighbouring active sites, offer the possibility of accommodating two dissociation products simultaneously, thereby enabling the dissociative mechanism. This ability to switch mechanisms is key to the “switching” strategy, where DACs can replace the OOH intermediate with two distinct O and OH intermediates adsorbed at separate sites. Furthermore, the precise control over inter-atomic distances and curvature in DACs allows for fine-tuning of electronic structures and promoting specific interactions (like hydrogen bonding), contributing to “breaking” and “pushing” strategies.
The ultimate goal of manipulating scaling relations is to achieve ideal catalyst performance, ideally with zero overpotential, by overcoming the fundamental limitations imposed by these correlations. The “bypassing” strategy represents the most ambitious approach towards this goal. It seeks to completely decouple the adsorption energies of reaction intermediates by allowing the catalyst to switch between two or more distinct states (e.g., geometric, electronic, or photonic) during the reaction cycle. Each state would be optimally configured to bind specific intermediates at the ideal energy values required for efficient catalysis. While seemingly challenging in practice, this concept, inspired by natural enzymes like cytochrome c oxidase, offers a theoretical pathway to eliminate all scaling constraints and achieve the theoretical apex of the volcano plot, pushing the boundaries of what is currently achievable in electrocatalysis.
In this work [0], we have applied the DFT-based delta Kohn–Sham (ΔKS) method to ion pairs in a vacuum to obtain X-ray photoelectron spectra of corresponding ionic liquids (IL). On the example of forty ion pairs, we demonstrate how the core level binding energy (BE) values can be calculated and used to plot theoretical spectra at a low computational cost. Furthermore, we compare the ΔKS results, 1s Kohn–Sham orbital energies, and atomic charges against the experimental X-ray photoelectron data. Recently, in connection to the electrochemical application in the supercapacitors, we have measured spectra for EMImBF4 and EMImB(CN)4 ionic liquids at the carbon–IL interface [1–3]. Other experimental spectra were obtained from the literature [4,5]. Both the ΔKS BE values and the 1s Kohn–Sham orbital energies show a correlation, yet with a different order of the BEs assigned to specific atoms. We find that neither DDEC6 nor Bader charges correlate with the experimental data. Thus, the DFT calculations of 1s Kohn–Sham orbital energies provide the fastest way of predicting the XPS spectra. However, more detailed experimental studies are required to resolve the right order of the BE values and its relation to the atomistic structure of the ILs. The ΔKS calculations provide the most precise estimations of the BEs. Herewith, they also demand more resources and cause computational difficulties discussed in the presentation. Besides the prediction power, a robust computational method can help in intepretating experimental data when the appropriate reference values are either not available nor directly applicable. Thus, the ΔKS method can find its application in various fields of physics and chemistry where the XPS is used for resolving electronic and geometric structures of pure ILs, their mixtures, and at interfaces.
In this work, we have applied the DFT-based delta Kohn–Sham (ΔKS) method to ion pairs in a vacuum to obtain X-ray photoelectron spectra of corresponding ionic liquids (IL). On the example of forty ion pairs, we demonstrate how the core level binding energy (BE) values can be calculated and used to plot theoretical spectra at a low computational cost. Furthermore, we compare the ΔKS results, 1s Kohn–Sham orbital energies, and atomic charges against the experimental X-ray photoelectron data. Recently, in connection to the electrochemical application in the supercapacitors, we have measured spectra for EMImBF4 and EMImB(CN)4 ionic liquids at the carbon–IL interface [1–3]. Other experimental spectra were obtained from the literature [4,5]. Both the ΔKS BE values and the 1s Kohn–Sham orbital energies show a correlation, yet with a different order of the BEs assigned to specific atoms. We find that neither DDEC6 nor Bader charges correlate with the experimental data. Thus, the DFT calculations of 1s Kohn–Sham orbital energies provide the fastest way of predicting the XPS spectra. However, more detailed experimental studies are required to resolve the right order of the BE values and its relation to the atomistic structure of the ILs. The ΔKS calculations provide the most precise estimations of the BEs. Herewith, they also demand more resources and cause computational difficulties discussed in the presentation. Besides the prediction power, a robust computational method can help in intepretating experimental data when the appropriate reference values are either not available nor directly applicable. Thus, the ΔKS method can find its application in various fields of physics and chemistry where the XPS is used for resolving electronic and geometric structures of pure ILs, their mixtures, and at interfaces.
[0] M. Lembinen, E. Nõmmiste, H. Ers, B. Docampo‐Álvarez, J. Kruusma, E. Lust, V.B. Ivaništšev, Calculation of core‐level electron spectra of ionic liquids, Int. J. Quantum Chem. 120 (2020). https://doi.org/10.1002/qua.26247.
[1] J. Kruusma, A. Tõnisoo, R. Pärna, E. Nõmmiste, I. Tallo, T. Romann, E. Lust, Electrochimica Acta 206 (2016) 419–426.
[2] J. Kruusma, A. Tõnisoo, R. Pärna, E. Nõmmiste, I. Kuusik, M. Vahtrus, I. Tallo, T. Romann, E. Lust, J. Electrochem. Soc. 164 (2017) A3393–A3402.
[3] A. Tõnisoo, J. Kruusma, R. Pärna, A. Kikas, M. Hirsimäki, E. Nõmmiste, E. Lust, J. Electrochem. Soc. 160 (2013) A1084–A1093.
[4] A. Foelske-Schmitz, D. Weingarth, R. Kötz, Surf. Sci. 605 (2011) 1979–1985.
[5] I.J. Villar-Garcia, E.F. Smith, A.W. Taylor, F. Qiu, K.R.J. Lovelock, R.G. Jones, P. Licence, Phys. Chem. Chem. Phys. 13 (2011) 2797–2808.
Simple demonstration of a molecular dynamics simulation of 408 BMPyrDCA ionic pairs between two graphene walls.
Inputs (packmol.inp, STEEP.mdp, RUN.mdp, topol.top) and force field parameters: github.com/vilab-tartu/LOKT.02.048/tree/master/MD_Gr-BMPyrDCA_pbc. The force fields are taken from github: github.com/vladislavivanistsev/RTIL-FF. References are given within the files.
Continue reading “MD simulation of BMPyrDCA between graphene walls”
Simple demonstration of a molecular dynamics simulation of 25 BMPyrDCA ionic pairs in a box.
Inputs (packmol.inp, STEEP.mdp, RUN.mdp, topol.top) and force field parameters: github.com/vilab-tartu/LOKT.02.048/tree/master/MD_BMPyrDCA_box. The force fields are taken from github: github.com/vladislavivanistsev/RTIL-FF. References are given within the files.
Continue reading “MD simulation of bulk BMPyrDCA ionic liquid”
Vladislav, Meeri and Karl at the FMTDK2018 near the Karl’s poster.

Summer is a great time for research visits, schools as well as vacations. Here is my check list for a safe trip from 2016.
Here is what I would like to add to the list in 2023 (thanks to chatGPT for ideas):
The 6th Baltic Electrochemistry Conference held in Helsinki, Finland during a period of 14-17 June and collects researchers dedicated to the science and technology of electrochemistry around the Baltic. This conference provides a forum for individuals from research organizations and companies to learn about the latest developments in this rapidly evolving field, to discuss with renowned experts and to build their networks in an informal and friendly atmosphere. The conference covers all forms of electrochemistry, including, but not limited to experimental and theoretical aspects of charge transfer at electrochemical interfaces, electrochemical materials science, and electrocatalysis. In addition, emergent technologies like electrodeposition of nanomaterials and functionalized electrodes, and electrochemical nanostructuring feature along with related poster presentation sessions.
I am taking part in these conference with the poster presentation “DFT-based modeling of associates of ionic liquid ions” where discuss the ability of prediction properties of novel type electrolytes by applying the results of the DFT calculations of simpler ionic associates. For this reason, the effect of the self-interaction and dispersion corrections on the results of DFT calculations for 48 ionic associates has been investigated [1]. The magnitude of the corrections strongly depends on the anion choice and especially in the case of halide anions. It is very important to pay particular attention to that fact because ionic liquid mixture with the addition of halides has attracted attention as a possible electrolyte for supercapacitors [2].