Intelligent Algorithms Enable Photocatalyst Design and Performance Prediction
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsBelow are a few of my comments. Please address them accordingly.
Lines 20-21 contain the phrase "artificial neural network model" repetitively. Please remove one instance to avoid redundancy.
The first paragraph of the introduction section contains a history of artificial intelligence, which I think is irrelevant. I suggest removing it and focusing on the application of AI in the photocatalyst design.
Various recently published important papers on machine learning applications in wastewater treatment are missing from the manuscript. Please discuss the following publications in your manuscript (J. Hazard. Mater. 465 (2024) 132995; J. Mater. Chem. A., 11 (2023) 9009-0918).
The introduction section lacks a clear statement on novelty and identification of research gaps. Please review and revise accordingly.
The description of Figure 1 is insufficient. Please provide more details to enhance clarity and understanding.
Section 3.1, "Types of neural network models," appears to be redundant. The authors have listed various neural network models without providing justification or reasoning. This section does not offer any useful information and could be omitted.
The application of ML models in the field of photocatalysis requires significant revision as many essential details are currently missing. A thorough revision is needed to ensure that all pertinent information is included.
Numerous typos and grammatical errors were observed throughout the manuscript.
Reference numbers 34 and 53 are the same, please remove one.
Comments for author File: 
Comments.pdf
Numerous typos and grammatical errors were observed throughout the manuscript.
Author Response
Detailed Response to Reviewer:
Below are a few of my comments. Please address them accordingly.
Lines 20-21 contain the phrase "artificial neural network model" repetitively. Please remove one instance to avoid redundancy.
Response: Thank you very much for your valuable Suggestions and comments. This error has been corrected.
The first paragraph of the introduction section contains a history of artificial intelligence, which I think is irrelevant. I suggest removing it and focusing on the application of AI in the photocatalyst design.
Response: Thank you very much for your valuable Suggestions and comments. We have removed the following contents:
“Since the introduction of the concept of neural networks in 1800, people have continually pursued the development of neural network models and applied these models to solve problems encountered in physics, chemistry, biology, medicine, power electronics and engineering technology [1]. After a long period of development, Frank Rosenblatt [2] proposed the famous perceptron model, which set off the first research upsurge of artificial neural networks. But then Marvin Minsky and Seymour Papret [3] decided that the perceptron was an area not worth studying, and neural network research hit a low point. Despite several setbacks, the development of neural network has been stagnant for many years. After Hopfield [4] proposed Hopfield neural network model, a new round of research upsurge of neural network has ushered in a new age of exploration. ”
We cite the following literature after the first sentence to support our point:
1.“Kim, C. M.; Jaffari, Z. H.; Abbas, A.; Chowdhury, M. F.; Cho, K. H. Machine learning analysis to interpret the effect of the photocatalytic reaction rate constant (k) of semiconductor-based photocatalysts on dye removal. J. Hazard. Mater. 2024, 465, 132995.
2.Jaffari, Z. H.; Abbas, A.; Umer, M.; Kim, E. S.; Cho, K. H. Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb 2 CT x towards Pb (ii) and Cd (ii) ions. J. Mater. Chem. A 2023, 11, 9009-9018.
3.Rashtbari, S.; Dehghan, G.; Marefat, A.; Khataee, S.; Khataee, A. Proficient sonophotocatalytic degradation of organic pollutants using Co3O4/TiO2 nanocomposite immobilized on zeolite: Optimization, and artificial neural network modeling. Ultrason. Sonochem. 2024, 102, 106740.
4.Ramkumar, G.; Tamilselvi, M.; Jebaseelan, S. S.; Mohanavel, V.; Kamyab, H.; Anitha, G.; Thandaiah Prabu R.; Rajasimman, M. Enhanced machine learning for nanomaterial identification of photo thermal hydrogen production. Int. J. Hydrog. Energy 2024, 52, 696-708. ”
Various recently published important papers on machine learning applications in wastewater treatment are missing from the manuscript. Please discuss the following publications in your manuscript (J. Hazard. Mater. 465 (2024) 132995; J. Mater. Chem. A., 11 (2023) 0909-0918).
Response: Thank you very much for your valuable Suggestions and comments. We cite the following literature to support our claim:
1.“Kim, C. M.; Jaffari, Z. H.; Abbas, A.; Chowdhury, M. F.; Cho, K. H. Machine learning analysis to interpret the effect of the photocatalytic reaction rate constant (k) of semiconductor-based photocatalysts on dye removal. J. Hazard. Mater. 2024, 465, 132995.
2.Jaffari, Z. H.; Abbas, A.; Umer, M.; Kim, E. S.; Cho, K. H. Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb 2 CT x towards Pb (ii) and Cd (ii) ions. J. Mater. Chem. A 2023, 11, 9009-9018. ”
The introduction section lacks a clear statement on novelty and identification of research gaps. Please review and revise accordingly.
Response: Thank you very much for your valuable Suggestions and comments. We have added the following contents to the manuscript:
“Simultaneously, we reviewed the application of all intelligent algorithm optimized neural network models in predicting the photocatalytic activity of photocatalysts, providing technical reference for the subsequent development of new intelligent algorithm optimized neural network models to predict the photocatalytic activity of photocatalysts.”
“Intelligent algorithm optimized neural networks have also been used to develop new photocatalysts, predict the photocatalytic activity of photocatalysts, and predict the absorbance curves of pollutants with different irradiation times obtained during the photocatalysis process.”
The description of Figure 1 is insufficient. Please provide more details to enhance clarity and understanding.
Response: Thank you very much for your valuable Suggestions and comments. We have added the following contents to the manuscript:
“Among these software, MATLAB is the most popular software, with a usage rate of more than 60% [45]. Statistica software not only provides users with general purposes such as statistics, plotting and data management programs, but also provides data analysis methods such as neural networks for users to use, the usage rate is almost second only to MATLAB software. The software programmed by Python language requires users to have a strong background in neural network theory, which makes the use of related software relatively small, but also accounts for a large proportion. The other software listed in Figure 1 has a usage rate of less than 5%.
- Xu, A.; Chang, H.; Xu, Y.;Li, R.; Li, X.; Zhao, Y. Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review. Waste Manage. 2021, 124, 385-402.”
Section 3.1, "Types of neural network models," appears to be redundant. The authors have listed various neural network models without providing justification or reasoning. This section does not offer any useful information and could be omitted.
Response: Thank you very much for your valuable Suggestions and comments. We have revised this part, especially the title.
“3.1. Neural network model suitable for photocatalyst development
For the prediction of photocatalytic activity of photocatalyst, the choice of neural network model is particularly important. Therefore, it is necessary to understand the current development of neural network models, especially their scope of application, advantages and disadvantages. ”
“Among these models, P, FF, RBF, SVM, and NTM are multi-input single-output models. MC, HN, BM, RBM, and KN are neural network models with no output. All the other models are multiple-input multiple-output models. According to the law that the photocatalytic activity of photocatalyst is affected by environmental parameters such as pH, catalyst content, initial concentration of pollutants, reaction time, reaction temperature, etc., a suitable model is selected for prediction. Due to the influence of computing resources and the number of samples, selecting the appropriate activation function can save the storage space or improve the computing speed. Therefore, the choice of neural network model should be considered in combination with the available computing resources and the original data. Under normal circumstances, it is necessary to normalize the original data, and then perform the anti-normalization processing after the calculation is completed. ”
“To make up for the shortcomings of a single neural network model, it has become a trend to combine multiple neural network models to predict the photocatalytic activity of photocatalysts.”
The application of ML models in the field of photocatalysis requires significant revision as many essential details are currently missing. A thorough revision is needed to ensure that all pertinent information is included.
Response: Thank you very much for your valuable Suggestions and comments. We have added the following contents to the manuscript:
In Section 5.1
“In short, the structure-function relationship between the crystal structure information of semiconductor materials and its Eg value is established, and the intrinsic relationship between the Eg value and the photocatalytic activity of semiconductor materials is established, so as to achieve the prediction of the photocatalytic activity of new photocatalysts.”
In Section 5.2
“The degradation percentage (DP%) of pollutants degraded by semiconductor materials can be approximated by the following formula (38):
(36)
Where, C0 and Ct are the concentration of pollutants at the initial time and the concentration of pollutants at the time t, respectively.”
In Section 5.3
“Where, A0 and At are the absorbance value of pollutants at the initial time and the concentration of pollutants at the time t, respectively. When the ultraviolet visible spectrophotometer is used to test the pollutants with different irradiation time, the absorbance curves of different time are obtained. It is no longer just one value as measured by the 721 powder photometer, but a curve. ”
“The measurement process of photocatalysis can be more dynamically reflected by predicting the absorbance curve. For the prediction of the above degradation percentage, it belongs to the multi-input single-output model. However, the prediction of absorbance curve belongs to the model of input and output, which is more difficult. ”
Numerous typos and grammatical errors were observed throughout the manuscript.
Response: Thank you very much for your valuable Suggestions and comments. In order to better correct the grammatical errors in the manuscript, we asked experts in the field to make corrections. In addition, we modified the manuscript through the paid version of Ginger Writer software.
Reference numbers 34 and 53 are the same, please remove one.
Response: Thank you very much for your valuable Suggestions and comments. This error has been corrected.
Author Response File: 
Author Response.docx
Reviewer 2 Report
Comments and Suggestions for AuthorsI have reviewed the manuscript titled "Intelligent Algorithms Facilitating Photocatalyst Design and Performance Prediction." In this review article, the authors explore a process for photocatalyst design utilizing AI, from data collection to algorithm model construction. The manuscript is suitable for acceptance pending minor revisions to several aspects outlined below.
1. I did not find the statement "Since the introduction of the concept of neural networks in 1800" in the references. Please verify its accuracy.
2. To enhance clarity, please revise the language throughout the manuscript. For example, avoid expressions such as "To obtain these data, it can be obtained in three ways."
3. I also suggest incorporating synonyms to aid comprehension.
4. The manuscript's structure could be refined to ensure smooth readability and better convey the content to readers.
5. Python should not be referred to as software but rather as a computer programming language commonly employed in software development.
6. It may be beneficial to include comparisons between different data processing software.
7. Furthermore, enhancing the quality of the images will improve their visibility.
Comments on the Quality of English LanguageThe quality of the English language in the manuscript is poor and requires significant improvement. Minor editing alone will not suffice; substantial revisions are necessary to enhance clarity and coherence. Many sentences lack fluency and organization of ideas.
Author Response
Detailed Response to Reviewer:
I have reviewed the manuscript titled "Intelligent Algorithms Facilitating Photocatalyst Design and Performance Prediction." In this review article, the authors explore a process for photocatalyst design utilizing AI, from data collection to algorithm model construction. The manuscript is suitable for acceptance pending minor revisions to several aspects outlined below.
- I did not find the statement "Since the introduction of the concept of neural networks in 1800" in the references. Please verify its accuracy.
Response: Thank you very much for your valuable Suggestions and comments. Based on the comments of the two reviewers, we have removed this part:
“Since the introduction of the concept of neural networks in 1800, people have continually pursued the development of neural network models and applied these models to solve problems encountered in physics, chemistry, biology, medicine, power electronics and engineering technology [1]. After a long period of development, Frank Rosenblatt [2] proposed the famous perceptron model, which set off the first research upsurge of artificial neural networks. But then Marvin Minsky and Seymour Papret [3] decided that the perceptron was an area not worth studying, and neural network research hit a low point. Despite several setbacks, the development of neural network has been stagnant for many years. After Hopfield [4] proposed Hopfield neural network model, a new round of research upsurge of neural network has ushered in a new age of exploration. ”
We cite the following literature after the first sentence to support our point:
1.“Kim, C. M.; Jaffari, Z. H.; Abbas, A.; Chowdhury, M. F.; Cho, K. H. Machine learning analysis to interpret the effect of the photocatalytic reaction rate constant (k) of semiconductor-based photocatalysts on dye removal. J. Hazard. Mater. 2024, 465, 132995.
2.Jaffari, Z. H.; Abbas, A.; Umer, M.; Kim, E. S.; Cho, K. H. Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb 2 CT x towards Pb (ii) and Cd (ii) ions. J. Mater. Chem. A 2023, 11, 9009-9018.
3.Rashtbari, S.; Dehghan, G.; Marefat, A.; Khataee, S.; Khataee, A. Proficient sonophotocatalytic degradation of organic pollutants using Co3O4/TiO2 nanocomposite immobilized on zeolite: Optimization, and artificial neural network modeling. Ultrason. Sonochem. 2024, 102, 106740.
4.Ramkumar, G.; Tamilselvi, M.; Jebaseelan, S. S.; Mohanavel, V.; Kamyab, H.; Anitha, G.; Thandaiah Prabu R.; Rajasimman, M. Enhanced machine learning for nanomaterial identification of photo thermal hydrogen production. Int. J. Hydrog. Energy 2024, 52, 696-708. ”
- To enhance clarity, please revise the language throughout the manuscript. For example, avoid expressions such as "To obtain these data, it can be obtained in three ways."
Response: Thank you very much for your valuable Suggestions and comments. This error has been corrected. In order to better correct the grammatical errors in the manuscript, we asked experts in the field to make corrections. In addition, we modified the manuscript through the paid version of Ginger Writer software.
- I also suggest incorporating synonyms to aid comprehension.
Response: Thank you very much for your valuable Suggestions and comments. We have carefully revised our manuscript and reevaluated some technical terms.
- The manuscript's structure could be refined to ensure smooth readability and better convey the content to readers.
Response: Thank you very much for your valuable Suggestions and comments.
- Python should not be referred to as software but rather as a computer programming language commonly employed in software development.
Response: Thank you very much for your valuable Suggestions and comments. We have revised this misstatement.
- It may be beneficial to include comparisons between different data processing software.
Response: Thank you very much for your valuable Suggestions and comments. We have added the following contents to the manuscript:
“Among these software, MATLAB is the most popular software, with a usage rate of more than 60% [45]. Statistica software not only provides users with general purposes such as statistics, plotting and data management programs, but also provides data analysis methods such as neural networks for users to use, the usage rate is almost second only to MATLAB software. The software programmed by Python language requires users to have a strong background in neural network theory, which makes the use of related software relatively small, but also accounts for a large proportion. The other software listed in Figure 1 has a usage rate of less than 5%.”
Meanwhile, we also added the following references to support our view:
“45. Xu, A., Chang, H., Xu, Y., Li, R., Li, X., & Zhao, Y. (2021). Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review. Waste Management, 124, 385-402.”
- Furthermore, enhancing the quality of the images will improve their visibility.
Response: Thank you very much for your valuable Suggestions and comments. We changed the pictures as best we could.
Comments on the Quality of English Language
The quality of the English language in the manuscript is poor and requires significant improvement. Minor editing alone will not suffice; substantial revisions are necessary to enhance clarity and coherence. Many sentences lack fluency and organization of ideas.
Response: Thank you very much for your valuable Suggestions and comments. In order to better correct the grammatical errors in the manuscript, we asked experts in the field to make corrections. In addition, we modified the manuscript through the paid version of Ginger Writer software.
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsAccept
