ZnO-Based Photocatalysts: Synergistic Effects of Material Modifications and Machine Learning Optimization

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript reviews recent advances in ZnO-based photocatalysts, highlighting the growing role of computational modeling (atomistic calculations and machine learning) in accelerating the design of ZnO composites. However, there are a few places in this manuscript where more details or explanations are needed to clarify the points or support the arguments. The manuscript is suitable for publication in this journal after minor revisions. In particular, the authors should answer the following remarks:

1. There have been some impactful review articles with a similar topic to this review. The uniqueness and significance of this review should be further strengthened in the introduction section.
2. The article primarily functions as a compilation of existing studies, lacking a substantive critical analysis and compelling highlights to capture the reader's attention.
3. 4 should be revised to emphasize the role of carbon-based materials in ZnO composites.
4. As a review paper, authors should add more comments and summaries about this field, rather than only listing the related literature. More comments like the current research situation, the challenge in this filed, as well as analyzing and comparing the published works should be done.
5. The article currently has almost 260 references, with only few since 2024. Consider adding references from 2024 onwards and compare the results.
6. The manuscript should include extensive studies on the catalytic mechanism to enhance the depth of understanding and provide a more robust analysis in Section 6.
7. Conclusions should be revised accordingly. More details should be given in future perspectives to provide clearer and valuable hints to researchers in the field of data-driven photocatalysis. A figure is suggested to highlight the perspectives. For example: Coordination Chemistry Reviews, 2024, 509: 215765.

Author Response

The reply is as shown in the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Review of the manuscript "Zno-based photocatalysts: synergistic effects of material modifications and machine learning optimization"

The above-mentioned manuscript is a pretty long review about a vast topic. ZnO is a widely explored materials due to multiple functionalities, including but not limited to photocatalytic properties. From that, it follows that the task the authors have set themselves is very challenging: the intended subject covers a broad scope of topics, which makes it difficult to produce a paper providing the most important information without appearing too without appearing too generic and unfocused, and without overlapping with the multitude of reviews that are continuously published on these topics.

When reviewing a long review manuscript I always find my uncofortable, especially since generating large amounts of coherent text has become remarkably easy. We are witnessing a proliferation of review articles characterized by a high degree of generality. For such a reason, I am becoming quite selective in evaluating review papers: I find them engaging and valuable when they have a clear, precise focus (whether on a specific technological challenge, a particular methodology, or another well-defined topic) and when it presents information that is not readily accessible elsewhere.

In this work, the sections concerning the use of ZnO as a stand-alone photocatalyst or as a component in composite photocatalytic materials or systems are, in my opinion, not particularly compelling. The information provided in these sections is already well-known, easily accessible, and extensively documented in countless research papers and books. 
This applies, in particular, to the first 4 section of the manuscript which cover, among others:
- The structure of ZnO, its physical parameters, crystalline properties, bandgap, and photoluminescence characteristics;
- General concepts of semiconductor nanocatalysts, their operating mechanisms, advanced oxidation processes, and the role of radicals;
- Integration of ZnO with carbon-based materials, other oxides, and its use as a co-catalyst with metals and non-metals.
- General consideration about atomistic calculation in materials science, 

Any of that is extensively covered in existing literature, and the information is delivered by the authors in a non-structured manner, which is expectable given the generalistic subject of their manuscript. I find difficult to believe that another review on such topics which have now been explored in various ways can draw the attention of potential readers.
As authors, we shall take note that relevant information can literally be found from everywhere and literally in a span of minutes. I recognize the practical value of consolidating such information within a single article, but I find it difficult to identify a distinctive contribution in this particular review, given how  extensively these topics have already been covered in existing literature.

A different argument can maybe be mande for the 5th section which deals with the application of machine learning (ML). In brief, the use of ML techniques for predicting the properties of novel materials and for designing materials or composites to fulfill specific functions remains a relatively underexplored topic. Dissemination of knowledge in this area is highly relevant, and well-prepared review articles would be especially beneficial at this stage of research—for example, as study material for PhD students or researchers newly approaching the field.

However, I do not find this section really sufficient to justify in itself the publication of the review. Just to mention some consideration:
- As also sated by the authors, the ML techniques allow to optimize material properties. I can see how that can affect chemical design of novel molecules or composites, but I cannot see how can this be important when the one specific material (namely ZnO here) is considered. The author do not clarify this point in the section  5.1, where they cite some references (235, 236) but do not really discuss what has been done in these works. The section 5.2 also seems to me not so relevant, reporting a sort of summary of what a SHAP techique analysis is which seems unnecessary to me, and figures 9 and 10 feel unnecessary as well. 

Overall, it seems that the the work lacks a specific focus and a coherent leitmotif, does not sufficiently discuss the findings reported in the cited artiocles and provides rather generic and unnecessary information, without helping to really advance concretely the understanding of the topic of the use of ML in photocatalyst science, which would probably be a topic that would deserve a review entirely dedicated to it.

For these reason, I am sorry to have to say that I do not recommend the pubblication of the manuscript.

Author Response

The reply is as shown in the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Authors are requested to confirm and properly cite any figures reproduced or adapted from previously published literature, ensuring that appropriate copyright permissions are obtained where necessary.

The Introduction section should place greater emphasis on binary and ternary photocatalysts to provide a broader context. In this regard, the following references are recommended for inclusion and discussion:
ACS Omega, 3(7), 7587–7602;
https://pubs.acs.org/doi/10.1021/acs.iecr.0c03192

A new section should be added to highlight the fundamental principles and mechanisms underlying different photocatalytic processes, which will enhance the conceptual clarity of the work.

The manuscript currently contains multiple typographical and formatting errors. A thorough revision is required to improve overall language quality and presentation.

The quality and resolution of the figures should be enhanced to meet the journal’s publication standards.

A comparative table summarizing the photocatalytic performance of the developed materials against existing catalysts is currently missing and should be included to support the discussion.

The future prospects section should be refined, with particular attention to the role of artificial intelligence (AI) tools in catalyst design.

Additionally, future challenges and limitations associated with integrating AI tools for the rational design of efficient photocatalysts should be discussed to provide a balanced perspective.

Lastly, please ensure that all references are formatted according to the journal’s guidelines.

Comments on the Quality of English Language

Need to improve

Author Response

The reply is as shown in the attachment.

Author Response File: Author Response.pdf