An Intelligent Management Model for College-Level Reagent Repositories in Universities

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript addresses an important and timely topic with strong scientific merit. The research design is appropriate, and the results are generally well presented and support the conclusions. However, several minor issues should be addressed to improve clarity and coherence:

- The Introduction would benefit from a more explicit statement of the existing knowledge gaps and from tightening the narrative flow between sections.

- The Methods section lacks sufficient detail regarding dataset selection criteria and pre-processing steps. Adding this information would improve reproducibility.

- Some terms are used inconsistently across the manuscript (e.g., “workflow”, “pipeline”). Standardizing terminology will improve readability.

- Figure legends should include all abbreviations and acronyms to ensure comprehension without needing to refer back to the main text.

Overall, the study is scientifically sound and requires only minor revisions to reach publication quality.

Comments on the Quality of English Language

Please revise the English for clarity and grammatical precision.

Author Response

1) The Introduction would benefit from a more explicit statement of the existing knowledge gaps and from tightening the narrative flow between sections.

Response: We thank the reviewer for this constructive suggestion and fully agree that clearly articulating knowledge gaps is important. In the current version, the Introduction already 1) reviewed existing LIMS and chemical management solutions, 2) highlighted their limitations in providing full life-cycle, IoT-enabled management for university teaching and research laboratories, and 3) stated our contributions at the end of the section. For this reason, given the strict overall word limit of the journal, we have chosen to retain the present structure and length of the Introduction rather than further expanding or restructuring it.

2) The Methods section lacks sufficient detail regarding dataset selection criteria and pre-processing steps. Adding this information would improve reproducibility.

Response: Regarding the comment on dataset selection and pre-processing, we would like to clarify the core nature of our study. This paper primarily presents the design and implementation of a management framework and system architecture, rather than a data-driven or algorithmic model that relies on a specific dataset for training or validation. The “methods” involved are centered on system engineering, workflow design, and technology integration (e.g., LIMS, IoT, RFID), not on the curation or processing of a dataset for quantitative analysis. Therefore, while we agree that such details are crucial for machine learning or statistical studies, they are not applicable to the design-science and case-study approach taken in this manuscript. The reproducibility of our work lies in the detailed description of the system architecture, business logic, and implementation pathways, which we have strived to provide in Sections 2 and 3.

3) Some terms are used inconsistently across the manuscript (e.g., “workflow”, “pipeline”). Standardizing terminology will improve readability.

Response: We have carefully rechecked the manuscript and standardized the terminology to improve clarity and consistency.

4) Figure legends should include all abbreviations and acronyms to ensure comprehension without needing to refer back to the main text.

Response: We have revised the entire manuscript according to reviewer’s suggestion. Please see the revised manuscript.

5) Please revise the English for clarity and grammatical precision.

Response: The language has been edited for clarity and grammatical accuracy by a native English speaker.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

In this work an intelligent management model for college-level reagent repositories in universities is described. Manual management models cannot ensure traceability, real-time monitoring or safety compliance leading to laboratory accidents. Therefore, authors of this study propose an intelligent management model for chemical reagent repositories which is built on a LIMS based architecture and augmented by Internet of Things sensing, RFID identification, intelligent hardware. The work is of interesting because the proposed model provides a reliable framework for data-driven management of hazardous chemicals in academic settings. I think that this article may be published after minor revision.

Notes:

1. The meaning of LIMS, IoT, RFID abbreviations should be added in the text at the first mention.
2. Can an intelligent management model developed in this work be used for other chemistry laboratories besides universities?
3. Are there any limitations to the developed intelligent management system for college-level reagent repositories? If so, what are they?

Author Response

1) The meaning of LIMS, IoT, RFID abbreviations should be added in the text at the first mention

Response: In the revised manuscript, we have spelt out the full terms at their first appearance in the text (i.e., Laboratory Information Management System (LIMS), Internet of Things (IoT), and Radio Frequency Identification (RFID)), and use the abbreviations consistently thereafter.

2) Can an intelligent management model developed in this work be used for other chemistry laboratories besides universities?

Response: The intelligent management model was designed and validated for university chemistry laboratories, but its core architecture is generic, including role-based access control, full life-cycle reagent tracking, IoT/RFID integration, and middleware-based linkage to procurement and identity systems. With appropriate adaptation of user roles, safety rules, and system interfaces, it can be extended to other chemistry laboratories, such as research institutes, hospitals, and industrial labs in the future.

3) Are there any limitations to the developed intelligent management system for college-level reagent repositories? If so, what are they?

Response: Yes, the current intelligent management system has several limitations. Firstly, it has been designed and piloted for a single university college-level repository, so its performance and usability in larger, multi-campus deployments remain to be validated. Secondly, the architecture assumes the presence of a university-wide procurement platform, campus identity management, and IoT/RFID hardware, so adaptation and additional investment would be required in institutions without such infrastructure.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear author,

Thank you for your contribution.

The paper outlines the process of designing and deploying an intelligent chemical reagent management system to fit the university laboratory setting, incorporating state-of-the-art technology, RFID, IoT sensors, and Laboratory Information Management Systems (LIMS) to automate storage, distribution, and lifecycle tracking of the reagents.

he system focuses on compliance with rules and regulations, such as closed-loop distribution and two-person, two-lock protocol on the use of hazardous chemicals, and tackles technical issues such as system integration, data interoperability and user behaviour with solutions such as middleware API and guided workflows.

The purpose of this model is to accelerate the reagent management system to be more modern and safe, as well as to be more efficient and traceable, which is a full-organization scheme of the digital transformation of the academic laboratory environment.

Some suggestions are:

• Include a more detailed description of the methods and experimental procedures so that they can be replicated and the soundness of the science can be more easily judged.  
• Develop the topic of novelty of the proposed intelligent management model more by making a clear distinction between the new system and the current systems and emphasizing on specific contributions.  
• Add stricter validation or case studies that proved the model to be effective and robust in real world laboratory conditions.  
• Discuss user behavior adaptation issues in more detail, perhaps suggesting the ways to enhance compliance and data quality assurance.  
• Enhance the quality of figures and tables they should be clear, well labeled and directly reflect the contents of the text.  
• Make the conclusion stronger by clearly connecting the results to the assertions and commenting on the possible constraints and future research.

I hope these comments and suggestions may help improve the quality of the research.

Kindest regards

Comments on the Quality of English Language

The manuscript requires significant improvement in the clarity and fluency of the English language to ensure that the research objectives and findings are communicated precisely and effectively.

At the moment, the text has awkward phrasing, grammar mistakes, and uneven usage of terminology that impede the perception of the reader and decrease the overall professionalism of the text. Clear and fluent language is not only necessary in the transmission of complex technical aspects but it also serves in engaging a wider audience in the academic context.

Enhancement of sentence structure, removing redundancy, and applying the right scientific vocabulary will make the manuscript more readable. Also, it is suggested to pay thorough attention to proofreading and potentially engage professionals to edit the language as a way of addressing these problems in their entirety.

The improved quality of the language will also contribute to a more accurate presentation of the originality of the intelligent management model and its practical implication, that is at the center of the contribution that the paper makes. The failure to make these improvements will run the risk of the manuscript being misinterpreted about the main points, and making it less effective in the field of laboratory chemical management.

Thus, language clarity is an important measure to be taken, and only then the paper can be taken to consideration.

Author Response

1) Include a more detailed description of the methods and experimental procedures so that they can be replicated and the soundness of the science can be more easily judged. 

Response: We thank the reviewer for this suggestion. This manuscript is positioned primarily as a system design and implementation study for an intelligent reagent management platform, rather than as a report of laboratory experiments or analytical protocols. Thus, there are no methods and experimental procedures in the conventional sense to be detailed.

2) Develop the topic of novelty of the proposed intelligent management model more by making a clear distinction between the new system and the current systems and emphasizing on specific contributions.

Response: We thank the reviewer for this valuable suggestion. The novelty of our intelligent management model is its integrated closed loop and data driven architecture, which is far from the current traditional, manual and fragmented systems. From passive recording to proactive control: Traditional methods are reactive and document incidents only after they occur. From subjective approval to rule-based workflows: We introduced multi-level electronic approval workflows tied to reagent risk (general, hazardous, controlled), replacing inconsistent and often subjective manual approvals with standard rule-based procedures.

 

3) Add stricter validation or case studies that proved the model to be effective and robust in real world laboratory conditions.

Response: We agree with the reviewer on the importance of real-world validation. In the present work, the intelligent management model has been deployed in a pilot college-level reagent repository and exercised with routine teaching and research use. During this pilot, system logs and user feedback indicated improved traceability, fewer inventory discrepancies, and more timely return or status confirmation of reagents compared with the previous manual workflow. A more extensive, multi-site longitudinal evaluation with quantitative performance indicators (e.g., error rates, time savings, and safety incidents) is planned as future work but is beyond the scope of this initial design and implementation study.

 

4) Discuss user behavior adaptation issues in more detail, perhaps suggesting the ways to enhance compliance and data quality assurance.

Response: We appreciate the reviewer's insightful comment regarding user behavior adaptation. This is indeed a critical factor for the successful implementation of any new management system.

In response, we have expanded the scope of discussion in section 4.3.

Guided UI with Integrated Validation: A guided workflow and field verification are implemented. If any operation is skipped, submission will be blocked. For example, menu selections for "Reagent Status" and "Bottle Cleanliness" are compulsory during reagent restocking.

Hardware-Interlocked Process Flow: The approval system fully integrates with the access control system (approval and collection).For instance, access to the reagent repository is electronically locked; the door is unlocked only after a user receives elec-tronic approval from their Principal Investigator (PI). Similarly, upon reagent return, the system requires a re-measurement of weight, ensuring that inventory updates are based on objective data rather than manual entry.

Continuous Data Quality Monitoring: The system generates weekly data quality reports, highlighting abnormal operations, and the administrator provides targeted guidance.

 

5) Enhance the quality of figures and tables they should be clear, well labeled and directly reflect the contents of the text.

Response: We thank the reviewer for this suggestion. In response, we have improved the quality and labelling of all figures and tables, so that they more clearly and directly reflect the contents of the text. Please see the revised manuscript.

6) Make the conclusion stronger by clearly connecting the results to the assertions and commenting on the possible constraints and future research.

Response: We thank the reviewer for this suggestion. In the present manuscript, the Conclusion section already (1) summarized the key results regarding the architecture, functions, and pilot application of the intelligent management model, (2) restated the main contributions in relation to existing reagent management practices, and (3) briefly noted practical constraints and its future application in universities.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The paper presents both a conceptual framework and a practical roadmap for the implementation of intelligent reagent management within academic institutions. The manuscript is systematically structured and exhibits evident scholarly rigor. Nevertheless, it would gain from slight stylistic enhancements to enhance its flow and readability. Certain sections, especially those concerning system architecture and technical challenges, could be made more succinct. Therefore, I recommend a minor revision.

1.    Could the author briefly clarify how data privacy and user authentication are handled within the LIMS to ensure compliance with institutional cybersecurity policies?

2.    In Section 4.1, the paper introduces a lightweight API middleware. Could the author specify whether this middleware has been tested for performance bottlenecks or latency under real data loads?

3.    RFID and QR codes are mentioned as complementary methods for reagent identification. Would the author consider providing a short explanation or diagram of how these two methods are integrated during actual use?

4.    The paper describes a “Mandatory Status Return” mechanism for reagent tracking. Could the author add a concise explanation of how this mechanism improves accountability compared with conventional tracking systems?

5.    Could the author include a short note on future scalability—how easily this framework could be extended to university-wide or multi-campus reagent repositories?

Author Response

1). Could the author briefly clarify how data privacy and user authentication are handled within the LIMS to ensure compliance with institutional cybersecurity policies?

Response: We thank the reviewer for raising this critical point. Although data privacy and cybersecurity are not the central focus of this manuscript, the proposed LIMS is designed to comply with our university’s institutional policies. User authentication is integrated with the existing campus identity management (single sign-on), so only authenticated institutional accounts can access the system. A role-based access control model (administrator, department administrator, PI, lab researcher) follows the principle of least privilege, and all key operations are logged. In addition, middleware communication relies on authenticated, encrypted API calls to protect data in transit.

 

2). In Section 4.1, the paper introduces a lightweight API middleware. Could the author specify whether this middleware has been tested for performance bottlenecks or latency under real data loads?

Response: This is a valuable question. At this stage, the lightweight API middleware has been implemented and exercised in a pilot deployment for a single college-level repository, where it handles real procurement and inventory synchronisation traffic. During this pilot, we monitored throughput and end-to-end response times and did not observe obvious bottlenecks or user-perceived latency under typical loads in our setting. However, we have not yet carried out large-scale synthetic stress tests beyond the expected operating range; this will be part of the hardening process before any university-wide roll-out. For this reason, the present manuscript focuses on the architectural design of the middleware rather than detailed benchmark results.

3). RFID and QR codes are mentioned as complementary methods for reagent identification. Would the author consider providing a short explanation or diagram of how these two methods are integrated during actual use?

Response: In practice, RFID and QR codes are used in a complementary way in our LIMS. Each reagent bottle is printed with a QR label for fast manual scanning by users during checkout/return, while selected high-value or hazardous reagents are additionally equipped with RFID tags that are automatically detected by the smart cabinet reader. When a user opens the cabinet and scans the QR code, the middleware associates the user ID, QR-based reagent ID, and the real-time RFID inventory snapshot to confirm the transaction and update stock records.

 

4). The paper describes a “Mandatory Status Return” mechanism for reagent tracking. Could the author add a concise explanation of how this mechanism improves accountability compared with conventional tracking systems?

Response: We appreciate this suggestion. The reviewer has raised an excellent point regarding the accountability benefits of the “Mandatory Status Return” mechanism. In the current manuscript, we have aimed to concisely integrate this concept within Sections 3.3 and the Conclusion, focusing on how it enforces a digital and verifiable closure of the reagent lifecycle to bridge the accountability gap left by conventional systems. We believe the existing descriptions, particularly the contrast with the “write-off after checkout” practice and the emphasis on verifiable physical return and data recording, already address this core improvement in accountability. Therefore, we have decided to retain the current wording to maintain the conciseness of the paper.

 

5). Could the author include a short note on future scalability—how easily this framework could be extended to university-wide or multi-campus reagent repositories?

Response: We thank the reviewer for this question. The framework was intentionally designed with scalability in mind: the LIMS core, lightweight API middleware, and IoT/RFID layer are modular, and user roles, approval rules, and cabinet nodes can be configured without changing the overall architecture. This allows additional college-level repositories to be onboarded and, in principle, extended to university-wide or even multi-campus deployments by replicating and parameterizing the same model. However, the present work reports only a single college-level pilot, and a full scalability study is planned as future work and lies beyond the scope of this initial design and implementation paper.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

Dear authors,

Thank you for submitting a revised version of your manuscript.

Following a thorough examination, I observe that you have addressed some of the earlier issues especially by expounding on the technical implementation issues and proposing viable solutions to the issues such as the lightweight API middleware and integration to the IoT and RFID technologies. The methodological clarity is enhanced by the fact that a detailed workflow and lifecycle management description are included.

Nonetheless, certain aspects are yet to be improved:

• Methods and Experimental Validation: Although the system architecture and workflow could be described in a more detailed manner, the manuscript does not include rigorous validation or case studies that would prove the effectiveness and strength of the model in the laboratory environment. Credibility would be much higher with the inclusion of some quantitative results of performance or feedback left by the users.
• Novelty and Contribution: The difference between your proposed model and the existing systems is somehow tacit. A more direct, overt comparison of distinguishing characteristics or advantageous increased features in comparison to current LIMS or chemical management strategies would be more focused on novelty.
• User Behavior and Compliance: The user adaptation and compliance are not extensively discussed. Discussing methods to enhance user interaction, data quality control, and behavioral change management would cover an important issue of the system success.
• Figures and Tables: Some figures and tables might be enhanced in terms of their clarity and labels to show the direct reflection of the text content and make the reading easier.

Overall, these revisions have made the paper stronger, but it still has several aspects that need to be improved so that the scientific rigor, clarity, and impact of your work could be even greater.

Kind regards.

Comments on the Quality of English Language

Although improved, the manuscript would benefit from further professional proofreading to enhance fluency, reduce awkward phrasing, and ensure consistent scientific terminology throughout.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 3

Reviewer 3 Report

Comments and Suggestions for Authors

Dear authors,

It is a pleasure to receive the revised copy of your manuscript. At a glance, I observe the following with the former remarks:

• Methods and Experimental Validation: The workflow/lifecycle description is now more detailed, and technical implementation issues are discussed with more solutions, including the lightweight API middleware and integrating the IoT/RFID [1,3,5]. Nevertheless, no serious experimental validation and quantitative performance data to show the effectiveness of the model in the real laboratory conditions are provided in the paper. The credibility could be enhanced with the case studies or user feedback.
• Novelty and Contribution: The comparison to the existing systems is better, especially with Table 1 and the discussion of three customized mechanisms that make your model different at the college level. However, a more straightforward, direct comparison that points to exclusive benefits or better performance over existing LIMS or chemical management systems would better make the novelty more clear.
• User Behavior and Compliance: Although the digital closed-loop and access control are outlined in the system, little is said about the user adaptation strategies, compliance, or behavioral change management to guarantee the data quality and system success. These aspects are significant to practical implementation, and expanding on them would cover an important aspect.
• Figures and Tables: The figures and tables are more understandable than ever, but some could be improved in terms of labeling and direct connection with the text material to make it easier to read and understand.
• Quality of English Language: The language of the manuscript has been improved; although, some additional proofreading by a professional is necessary to make it fluent and decrease clumsy expressions, as well as to use the same scientific terms throughout.

Additional suggestions:

• Regarding the limitations and future work, it is possible to consider adding a section to recognize the existing gaps and future enhancements.
• Add further information about security and data privacy, particularly RFID and middleware data exchange.
• Talk of scalability and applicability of the model to other university environments or disciplines.

In sum, the additions have made the manuscript more solid, yet these points that can be identified will make it more scientifically rigorous and effective.

Kind regards.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf