Navigating Alkaline Hydrogen Evolution Reaction Descriptors for Electrocatalyst Design

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

 

The review provides a comprehensive overview of the alkaline hydrogen evolution catalysts. The authors have addressed the following shortcomings before the publication.

1.      While discussing different factors, author should provide a critical analysis of their limitations and challenges.

2.      The figures and diagrams effectively illustrate ideas, author should avoid improper captions.

3.      The review excellently summarizes the current state of the field, but I encourage a future aspects section discussing the most promising directions for future research, potential breakthroughs, and challenges that need to be overcome.

4.      In the conclusion, author should succinctly summarize the key takeaways from the review.

5.      An extensive English correction is needed.

6.      The background of the review needs to be further highlighted, and relevant literature needs to be cited, such as scientific issues of energy and the environment. The following recent literature can be referred to light the topic background. Such as Energy Fuels 2024, 38, 3, 2235–2247; International journal of hydrogen energy 2024, 72, 755-763.

Comments on the Quality of English Language

Minor editing of English language needed

Author Response

The review provides a comprehensive overview of the alkaline hydrogen evolution catalysts. The authors have addressed the following shortcomings before the publication.

Comment 1.      While discussing different factors, author should provide a critical analysis of their limitations and challenges.

Response: Many thanks for this valuable suggestion. We now added the discussion about the limitations and challenges of different descriptors in the discussion section as shown below.

It is crucial to remember that each descriptor has its own limitations and challenges when studying alkaline HER in alkaline media. Descriptors such as water adsorption energy and water dissociation energy barriers are indicative of material reactivity and are closely associated with the Volmer step, which involves the adsorption of water molecules and their subsequent dissociation into adsorbed hydrogen (H*) and hydroxide ions (OH-). These steps are fundamental to the overall HER process, yet they do not provide a complete picture of the catalytic site's capabilities, especially concerning the Heyrovsky and Tafel steps. The Heyrovsky step involves the electrochemical desorption of H* to form hydrogen gas.

The Δ?_H* has been the most prevalent descriptor for assessing HER performance at the Heyrovsky and Tafel steps. It offers a measure of the free energy change when H* is adsorbed on the catalyst surface, which is a critical factor in determining the rate of the HER. However, Δ?_H* alone is insufficient for evaluating the Volmer step or the desorption efficiency of adsorbed OH, which are also essential for a complete understanding of the HER mechanism.

The Gibbs free energy of hydroxide adsorption (Δ?_OH*) can be used to evaluate the desorption efficiency of adsorbed OH, providing insights into the potential for water dis-sociation, as suggested by the Bronsted-Evans-Polanyi relationship. This relationship posits a linear correlation between the activation energy of a reaction and the reaction energy, allowing for the prediction of reaction barriers based on thermodynamic parameters. However, it does not offer information on the catalyst's performance during the Heyrovsky or Tafel steps. Therefore, relying solely on Δ?_OH* would give an incomplete assessment of a catalyst's overall activity and efficiency.

Given these considerations, it is evident that no single descriptor can fully encapsulate the complexities of the HER process. Researchers must carefully select and combine multiple descriptors to gain a comprehensive understanding of the catalytic activity and to design more efficient catalysts. This approach allows for the evaluation of catalysts across all steps of the HER, ensuring a more accurate prediction of their performance in real-world applications.

 

Comment 2.      The figures and diagrams effectively illustrate ideas, author should avoid improper captions.

Response:  Many thanks for the suggestion. We have rephrased the caption of Figures 2-4.

Comment 3.      The review excellently summarizes the current state of the field, but I encourage a future aspects section discussing the most promising directions for future research, potential breakthroughs, and challenges that need to be overcome.

Response: Many thanks for this valuable comments. We now summarise the promising directions for future research at the end of the manuscript as shown below:

In sum, the exploration of electrocatalytic mechanisms in alkaline HER is a complex field that necessitates a comprehensive approach. Advancements beyond the DFT-calculated descriptors are critical for a deeper understanding. Operando simulations offer a dynamic perspective by considering actual working conditions, providing insights into the real-time structural and chemical changes during the reaction process. Meanwhile, ML-based force fields for classical molecular dynamics and Monte Carlo simulations represent a significant leap in mesoscale modeling, enabling simulations that capture the nuanced interactions within molecular systems. These ML models can bridge the gap between classical and quantum mechanical accuracy, offering a more detailed view of the catalytic processes at play. Lastly, ML-driven high-throughput screening is revolutionizing the way electrocatalysts are discovered and optimized. By analyzing vast datasets, ML algorithms can predict performance, stability, and efficiency, thereby accelerating the development of new materials for HER. Together, these methodologies form a multi-faceted approach that could significantly advance the field of electrocatalysis.

Comment 4.      In the conclusion, author should succinctly summarize the key takeaways from the review.

Response:  As suggested by the reviewer, we now add one paragraph at the end of the ‘Discussion’ section and the ‘Conclusions and outlook’ section.

Comment 5.      An extensive English correction is needed.

Response:   The review has been extensively corrected by the native English speaker.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The presented review “Navigating Alkaline Hydrogen Evolution Reaction Descriptors for Electrocatalyst Design” is devoted to the important topic of finding good catalyst activity descriptors for the hydrogen evolution reaction in an alkaline media. The review is of great practical importance since electrolysis in an alkaline medium is extremely important for the production of green hydrogen and increasing the efficiency of catalysts will lead to increased electrolysis efficiency and a reduction in the cost of hydrogen.

This review consistently examines the mechanism of the hydrogen evolution reaction in an alkaline medium, theoretical calculations associated with this process and experimental data from various authors on the relationship between the energy of adsorption or desorption of various intermediate products and the activity of catalysts. Analysis and comparison of a large amount of experimental data quite logically confirmed the absence of one universal descriptor of catalyst activity. It is worth noting that the complexity and multi-stage nature of the process clearly indicate the need to use a complex of several descriptors, as, for example, the authors of work 17 did, who used parameters such as ΔGH* and ΔGOH*. Overall, the review raises the important issue of finding reliable descriptors for predicting catalytic activity, which will help accelerate the development of new materials to create highly efficient and low-cost catalysts. Unfortunately, an unambiguous and simple descriptor was not found in the review, which is associated both with the multi-stage process and with different approaches to determining the activity of catalysts by different authors, which complicates the task.

As a comment, I would like to note the need to compile a list of symbols and abbreviations.

As a serious note, I would like to note that the authors provide many tables with data from the literature, but they do not contain quantitative information about the activity of materials. Given such data, the authors would be able to construct various correlations between activity and the proposed descriptor, and not just use previously published relationships.

Nevertheless, in general, the review systematizes a large amount of relevant data on current topics and can be useful to researchers in the field of creating new materials for electrolyzes.

Author Response

Comment 1: The presented review “Navigating Alkaline Hydrogen Evolution Reaction Descriptors for Electrocatalyst Design” is devoted to the important topic of finding good catalyst activity descriptors for the hydrogen evolution reaction in an alkaline media. The review is of great practical importance since electrolysis in an alkaline medium is extremely important for the production of green hydrogen and increasing the efficiency of catalysts will lead to increased electrolysis efficiency and a reduction in the cost of hydrogen.

This review consistently examines the mechanism of the hydrogen evolution reaction in an alkaline medium, theoretical calculations associated with this process and experimental data from various authors on the relationship between the energy of adsorption or desorption of various intermediate products and the activity of catalysts. Analysis and comparison of a large amount of experimental data quite logically confirmed the absence of one universal descriptor of catalyst activity. It is worth noting that the complexity and multi-stage nature of the process clearly indicate the need to use a complex of several descriptors, as, for example, the authors of work 17 did, who used parameters such as ΔGH* and ΔGOH*. Overall, the review raises the important issue of finding reliable descriptors for predicting catalytic activity, which will help accelerate the development of new materials to create highly efficient and low-cost catalysts. Unfortunately, an unambiguous and simple descriptor was not found in the review, which is associated both with the multi-stage process and with different approaches to determining the activity of catalysts by different authors, which complicates the task.

Response: We are very grateful to the reviewer for her/his positive feedback.

Comment 2: As a comment, I would like to note the need to compile a list of symbols and abbreviations.

Response: Many thanks for this suggestion. However, the authors wish to comply with the publication format of the journal. The list of symbols and abbreviations has not been included in previous reviews published in this journal; therefore, we did not compile this list here.

Comment 3: As a serious note, I would like to note that the authors provide many tables with data from the literature, but they do not contain quantitative information about the activity of materials. Given such data, the authors would be able to construct various correlations between activity and the proposed descriptor, and not just use previously published relationships.

Response: Many thanks for this critical point. We opted not to conduct a quantitative comparison as our review primarily centers on the theoretical descriptors rather than the materials' performance. Furthermore, the data showcased in the review stem from computations using various DFT methodologies, making it challenging to perform a quantitative comparison. As a result, we have chosen to include the data in tables alongside the corresponding DFT methodologies.

Comment 4: Nevertheless, in general, the review systematizes a large amount of relevant data on current topics and can be useful to researchers in the field of creating new materials for electrolyzes.

Response: Many thanks for this positive comment.

Reviewer 3 Report

Comments and Suggestions for Authors

In many cases, the results of theoretical research are very different from the results of laboratory and experimental research. It would have been better for the authors of this article to focus more on laboratory studies. However, some suggestions for completing the article are mentioned:
1- Most of the authors focus on DFT calculation. Other methods should also be mentioned and reviewed in the article.
2- The review article will be standard when the authors devote much of the article to their research. What percentage of this article is the result of your team's studies?
3- The article should include comparing the results and checking the error rate with laboratory studies, and the role of parameters such as morphology and defects should also be investigated.
4- The discussion and criticism of this article is very weak. The analysis and suggestions in this article have been examined very superficially.

Comments on the Quality of English Language

Minor editing of English language required

Author Response

In many cases, the results of theoretical research are very different from the results of laboratory and experimental research. It would have been better for the authors of this article to focus more on laboratory studies. However, some suggestions for completing the article are mentioned:
Comment 1: Most of the authors focus on DFT calculation. Other methods should also be mentioned and reviewed in the article.

Response: We have now added the methods beyond the DFT-calculated descriptor at the end of the ‘Conclusions and outlook’ section as shown below:

In sum, the exploration of electrocatalytic mechanisms in alkaline HER is a complex field that necessitates a comprehensive approach. Advancements beyond the DFT-calculated descriptors are critical for a deeper understanding. Operando simulations offer a dynamic perspective by considering actual working conditions, providing insights into the real-time structural and chemical changes during the reaction process. Meanwhile, ML-based force fields for classical molecular dynamics and Monte Carlo simulations represent a significant leap in mesoscale modeling, enabling simulations that capture the nuanced interactions within molecular systems. These ML models can bridge the gap between classical and quantum mechanical accuracy, offering a more detailed view of the catalytic processes at play. Lastly, ML-driven high-throughput screening is revolutionizing the way electrocatalysts are discovered and optimized. By analyzing vast datasets, ML algorithms can predict performance, stability, and efficiency, thereby accelerating the development of new materials for HER. Together, these methodologies form a multi-faceted approach that could significantly advance the field of electrocatalysis.

Comment 2: The review article will be standard when the authors devote much of the article to their research. What percentage of this article is the result of your team's studies?

Response: Many thanks for this critical point. We have included more than 10% references from our studies. Although we have published numerous papers in this area, we aim to enhance the comprehensiveness of this review by incorporating outstanding papers from other sources.

Comment 3: The article should include comparing the results and checking the error rate with laboratory studies, and the role of parameters such as morphology and defects should also be investigated.

Response: This is a very good point. The DFT-calculated descriptors are mostly derived from the oversimplified computational hydrogen electrode (CHE) methods developed by Noskov and his team. Therefore, it is not appropriate to use these descriptors to measure the error rate in laboratory studies. As a solution, employing operando multiscale simulations combined with machine-learning techniques shows promise. We need to emphasize this in the 'Conclusions and Outlook' section.
Comment 4: The discussion and criticism of this article is very weak. The analysis and suggestions in this article have been examined very superficially.

Response: Many thanks for this valuable comment. We now added the discussion about the limitations and challenges of the different descriptors in the ‘Discussion’ section as shown below:

It is crucial to remember that each descriptor has its own limitations and challenges when studying alkaline HER in alkaline media. Descriptors such as water adsorption energy and water dissociation energy barriers are indicative of material reactivity and are closely associated with the Volmer step, which involves the adsorption of water molecules and their subsequent dissociation into adsorbed hydrogen (H*) and hydroxide ions (OH-). These steps are fundamental to the overall HER process, yet they do not provide a complete picture of the catalytic site's capabilities, especially concerning the Heyrovsky and Tafel steps. The Heyrovsky step involves the electrochemical desorption of H* to form hydrogen gas.

The Δ?_H* has been the most prevalent descriptor for assessing HER performance at the Heyrovsky and Tafel steps. It offers a measure of the free energy change when H* is adsorbed on the catalyst surface, which is a critical factor in determining the rate of the HER. However, Δ?_H* alone is insufficient for evaluating the Volmer step or the desorption efficiency of adsorbed OH, which are also essential for a complete understanding of the HER mechanism.

The Gibbs free energy of hydroxide adsorption (Δ?_OH*) can be used to evaluate the desorption efficiency of adsorbed OH, providing insights into the potential for water dissociation, as suggested by the Bronsted-Evans-Polanyi relationship. This relationship posits a linear correlation between the activation energy of a reaction and the reaction energy, allowing for the prediction of reaction barriers based on thermodynamic parameters. However, it does not offer information on the catalyst's performance during the Heyrovsky or Tafel steps. Therefore, relying solely on Δ?_OH* would give an incomplete assessment of a catalyst's overall activity and efficiency.

Given these considerations, it is evident that no single descriptor can fully encapsulate the complexities of the HER process. Researchers must carefully select and combine multiple descriptors to gain a comprehensive understanding of the catalytic activity and to design more efficient catalysts. This approach allows for the evaluation of catalysts across all steps of the HER, ensuring a more accurate prediction of their performance in real-world applications.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors did not respond to comment3.

Comment 3: As a serious note, I would like to note that the authors provide many tables with data from the literature, but they do not contain quantitative information about the activity of materials. Given such data, the authors would be able to construct various correlations between activity and the proposed descriptor, and not just use previously published relationships.

The fact is that Figure 5 shows the relationships between the activity of the catalysts and the values ​​of the calculated energy descriptors. Accordingly, adding catalyst activity values will determine the effectiveness of the descriptors in predicting catalyst activity.

Author Response

Comment 3: As a serious note, I would like to note that the authors provide many tables with data from the literature, but they do not contain quantitative information about the activity of materials. Given such data, the authors would be able to construct various correlations between activity and the proposed descriptor, and not just use previously published relationships.

The fact is that Figure 5 shows the relationships between the activity of the catalysts and the values of the calculated energy descriptors. Accordingly, adding catalyst activity values will determine the effectiveness of the descriptors in predicting catalyst activity.:

Reply: Thank you very much for your suggestion. As recommended, we have now added the "overpotential" to indicate the catalyst activity values. This addition has been highlighted in yellow in Tables 1-4 of the revised manuscript.

Since these data are summarized from various sources, it is challenging to discuss them comprehensively. Therefore, we do not include a detailed discussion regarding "overpotential".

Reviewer 3 Report

Comments and Suggestions for Authors

Accept in present form

Comments on the Quality of English Language

Minor editing of English language required.

Author Response

Minor editing of English language required.

Reply: As suggested, we have checked the English language throughout the whole manuscript. Revision has been highlighted in yellow colour on the manuscript.