Influence of Graphene, Carbon Nanotubes, and Carbon Black Incorporated into Polyamide Yarn on Fabric Properties

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. For all experimental samples, it is necessary to present the technical characteristics of the precise textile weave, the amount of porosity, and it is also necessary to present images of the microstructure of materials that are required for the analysis of the results obtained. 

2. In the research methodology, it is necessary to present and prove by statistical calculations that all textile images were prepared with an accurate uniform layer of functional compositions including graphene. 

3. How many identical samples were examined for each sample under study? It's not clear.  It is necessary to provide the necessary information about the mathematical planning of experiments. 

4. For all graphs that the experimental results represent, it is necessary to provide a statistical assessment of the accuracy of the results and established trends. 

5. The authors conclude that "Graphene increases thermal efficiency at the lowest concentration, but at higher concentrations, it decreases, suggesting that higher amounts of graphene may lead to aggregation, reducing its thermal efficiency." What are the provisions 

do physicists explain a phenomenon that is not described by a steady trend in this article? 

6. It is necessary for all the results obtained to present the conclusions not in the form of probability or assumption, but in the form of an evidence-based explanation of the patterns from the point of view of physics with a mandatory mathematical assessment of the reliability of the experimental data obtained. 

7. The authors should convincingly (with references to reliable data) explain why the inclusion of graphene with ultrahigh thermal conductivity in polyamide samples with relatively low thermal conductivity leads to unstable, but still to a decrease in its thermal conductivity at all graphene concentrations in textile decoration (Fig.9)? There are modern studies showing the opposite. What is the reason for the change in the thermal properties of graphene in polyamide tissue from the point of view of physics?

Author Response

Reviewer 1: Comments and Suggestions for Authors

Comments 1: For all experimental samples, it is necessary to present the technical characteristics of the precise textile weave, the amount of porosity, and it is also necessary to present images of the microstructure of materials that are required for the analysis of the results obtained. 

Response 1: We appreciate the reviewer's insightful comments. We would like to clarify that all experimental fabric samples were produced under consistent and controlled conditions to ensure uniformity across the study. Specifically, the fabrics were manufactured using the same yarn denier with an identical number of filaments, and the same fabric structure (single jersey knitted). The same gauge was maintained using the same knitting machine, and consistent loop length was ensured throughout the production process. As a result, key physical characteristics such as fabric GSM, thickness, and most importantly, porosity have not shown significant variation across the samples which is highlighted in Page 7-8; Line: 236-249. This uniformity ensures that the comparative analysis focuses solely on the influence of the different carbon additives and their varying concentrations, without any interference from fabric structural differences. Consequently, whether the observed effects are on the micro or macro scale, they are attributed to the additives rather than any variations in fabric structure. To support this, we have conducted a thorough analysis of the samples and can provide additional data or images of the microstructure if necessary, although the uniformity of the fabric properties has been strictly maintained

Comments 2: In the research methodology, it is necessary to present and prove by statistical calculations that all textile images were prepared with an accurate uniform layer of functional compositions including graphene. 

Response 2: The reviewer’s request for "statistical calculations that all textile images were prepared" is somewhat unclear. However, we interpret this as a request for evidence, likely through statistical analysis, to demonstrate that the application of functional compositions, such as graphene, was uniformly applied across all textile samples. In other words, the reviewer seems to be seeking confirmation, using statistical methods, that the functional layer was consistently applied across all fabric samples. We would like to clarify that in this study, the carbon additives were not applied to the surface of the fabrics, where concerns about uniformity and potential issues such as peeling during washing or abrasion might arise. Instead, the core objective of our research was to permanently incorporate carbon nano-additives into the yarn itself. This was achieved through a process of melt mixing and compounding the additives with polymers, followed by extrusion into multifilament yarns using a melt spinning machine. To elaborate, the yarns were produced using a semi-industrial yarn spinning machine, with a minimum pellet load of 5 kg. The yarns used in the study were taken only after the machine had reached its normal operating mode, which suggests a level of consistency in the produced yarn due to the machine's stable functioning. Consequently, the additives are embedded within the yarn, ensuring their permanent presence and uniform dispersion, thus avoiding the issues typically associated with surface applications. Please refer to the highlighted text on Page 3, Lines 108-114, for further details.

Comments 3: How many identical samples were examined for each sample under study? It's not clear.  It is necessary to provide the necessary information about the mathematical planning of experiments. 

Response 3: Thank you for this comment. As mentioned in the manuscript and in previous responses, the carbon additives were mixed in different concentrations into the yarns before the melt spinning process, using melt mixing and compounding techniques. The yarns were produced using a semi-industrial yarn melt spinning machine, with a minimum pellet load of 5 kg. The yarns used in the study were taken only after the machine had reached its normal operating mode, which suggests a level of consistency in the produced yarn due to the machine's stable functioning. This process ensures a uniform dispersion of the additives within the yarns. For each sample, 3 meters of fabric with a width of 60 cm were produced using the yarns with different additives and concentrations. Given the size of the fabric, it is expected to exhibit consistent physical characteristics, including the concentration of carbon additives included in the yarns. To ensure this uniformity, we statistically analyzed the fabric’s areal density and thickness by measuring multiple locations across the fabric. The results of these measurements, which confirm the consistency, are highlighted in the manuscript for the reviewer's reference. Meanwhile for each functional property testing, 5 specimens from different locations of each fabric sample were taken, tested and included I the paper after statistically accepted. Additionally, the yarn linear density was also statistically analyzed to ensure that the standard errors fell within the 95% confidence interval as given and highlighted in Page 6, Line: 202-204. This data is also provided in the manuscript to demonstrate the reliability and uniformity of the samples under study.

Comments 4: For all graphs that the experimental results represent, it is necessary to provide a statistical assessment of the accuracy of the results and established trends. 

Response 4: In response to your suggestion, we have conducted a statistical analysis of the experimental results presented in the graphs in submitted manuscript. This includes assessing the accuracy of the results and validating the established trends using appropriate statistical methods. The graphs include error bars, and in addition I have provided a detailed explanation of the statistical significance in the revised manuscript. I hope this addresses your concern effectively. Please refer highlighted figure 3, 7, 8, 9 &10.

Comments 5: The authors conclude that "Graphene increases thermal efficiency at the lowest concentration, but at higher concentrations, it decreases, suggesting that higher amounts of graphene may lead to aggregation, reducing its thermal efficiency." What are the provisions 

do physicists explain a phenomenon that is not described by a steady trend in this article? 

Response 5: Thank you for your valuable feedback. The findings highlighted all additives reduce thermal conductivity of fabric with composite yarns, among them CNTs and graphene are most effective at lower concentrations with lower thermal conductivity, with graphene showing the greatest reduction at 0.5%, which are efficient concentration for potential winter clothing application with high thermal resistance when compared with higher concentration because of higher thermal conductivity. Based on feedback, the discussion and conclusion parts are modified for readers to get understand better which is highlighted in Page 14 & 16. The phenomenon where graphene incorporated polymeric composite’s thermal resistance diminishes at lower concentrations and increased at higher levels is indeed a complex one. Physicists often explain this non-linear behavior by considering factors such as the percolation threshold, phonon scattering, and the formation of conductive networks within the polymer matrix. Below the percolation threshold, graphene particles are dispersed and isolated, limiting heat transfer. As the concentration increases and the threshold is surpassed, a continuous network forms, enhancing thermal conductivity. However, beyond a certain concentration, aggregation of graphene particles occurs, leading to increased phonon scattering and localized thermal barriers, which reduces overall thermal efficiency. This non-steady trend highlights the delicate balance between dispersion and network formation, which is crucial for optimizing thermal properties in nanocomposites. Graphene's alignment within a polymer matrix is a dynamic process that begins with its dispersion and continues through various stages of material processing. The drawing process plays a pivotal role in orienting graphene sheets along the polymer chains, which can significantly enhance the composite's properties. However, achieving and maintaining this alignment requires careful control of processing conditions to avoid issues like aggregation, which could otherwise diminish the material's overall performance

Comments 6: It is necessary for all the results obtained to present the conclusions not in the form of probability or assumption, but in the form of an evidence-based explanation of the patterns from the point of view of physics with a mandatory mathematical assessment of the reliability of the experimental data obtained. 

Response 6: As advised and suggested, the conclusion is revised accordingly in revised manuscript (Page 16).

Comments 7: The authors should convincingly (with references to reliable data) explain why the inclusion of graphene with ultrahigh thermal conductivity in polyamide samples with relatively low thermal conductivity leads to unstable, but still to a decrease in its thermal conductivity at all graphene concentrations in textile decoration (Fig.9)? There are modern studies showing the opposite. What is the reason for the change in the thermal properties of graphene in polyamide tissue from the point of view of physics?

Response 7:  We sincerely appreciate reviewer feedback and recommendations, as advised the revised manuscript has been modified and highlighted in pages 14& 15, Line: 426-468. Graphene is well-known for their exceptional thermal conductivity. When these particles are densely distributed and incorporated into a polymer matrix in sufficient quantities, they can form a continuous network with bridges between the particles. This network allows for the maintenance of high thermal conductivity throughout the composite material. The concept of the percolation threshold is critical here, it refers to the specific percentage of conductive filler at which these particles begin to form a network, rather than remaining isolated within the insulating matrix. Below the percolation threshold, the thermal conductivity of polymeric composites containing carbon is primarily limited due to the isolated nature of the conductive filler graphene (Gn). Additionally, the large interfacial thermal resistance between the conductive filler and the polymer further reduces thermal conductivity. This issue arises because the interface between the polymer and the filler material typically exhibits poor thermal conductance. However, once the percolation threshold is exceeded, the formation of interconnected filler networks significantly enhances thermal conductivity. These networks create highly efficient, continuous percolative heat transfer paths throughout the composite. Consequently, as the filler loading increases, there is a rapid improvement in thermal conduction. On the other hand, if the concentration of these particles within the polymer matrix is insufficient, it may lead to a discontinuous network. In such scenarios, rather than facilitating heat transfer, the dispersed nanoparticles may hinder it by absorbing and accumulating heat, potentially leading to localized thermal hotspots. Another possibility for the thermal resistance of the graphene nanopolymeric composite could be attributed to graphene's high capacity to reflect infrared (IR) radiation. However, as the concentration further increases, graphene sheets tend to aggregate. This aggregation disrupts the uniform distribution of heat, creates thermal barriers, and reduces the overall thermal conductivity of the material. This behavior is well-documented in the literature. These two references and modified discussion part are included in revised manuscript (Page 18, Line: 594-598)

1.      Kargar, F.; Barani, Z.; Salgado, R.; Debnath, B.; Lewis, J.S.; Aytan, E.; Lake, R.K.; Balandin, A.A, Thermal Percolation Threshold and Thermal Properties of Composites with High Loading of Graphene and Boron Nitride Fillers. ACS Appl. Mater. Interfaces. 2018, 10 (43), 37555-37565. https://doi.org/10.1021/acsami.8b16616

2. Jang, J.; So, S. O.; Kim, J.H.; Kim, S.Y.; Kim, S.H. Enhanced thermal conductivity of graphene nanoplatelet filled polymer composite based on thermal percolation behavior. Composites Communications, 2022, 31. 101110, https://doi.org/10.1016/j.coco.2022.101110.

 

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The article lacks a critical discussion with proper references. The abstract and the conclusions must be specific.

Author Response

Reviewer 2: Comments and Suggestions for Authors

Comments 1: The article lacks a critical discussion with proper references. The abstract and the conclusions must be specific.

Response 1: Thank you for your feedback. The manuscript has been significantly enhanced by incorporating a more critical discussion that thoroughly engages with relevant literature. We have included additional references to support our analysis, discussion and provide a more comprehensive context for our findings. Additionally, we have revised both the abstract and conclusion sections to be more precise and specific, clearly reflecting the key observations and contributions of our study.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

In this manuscript, the authors aim to address the research gap by investigating the integration of three different carbon-based nanofillers—carbon nanotubes (CNTs), carbon black (CB), and graphene (Gn)—into polyamide 6 during the melt spinning process to create multifilament yarns. These yarns will subsequently be woven into fabrics to evaluate and enhance their functional properties. The study will systematically vary the concentration of nanofillers to investigate and compare their effects on the mechanical and thermal properties of the composite textile materials. However, some major revisions should still be made to make the paper more convinced before its publication.

1.       In this study, the author examined the relationship between the material's strength and the concentration of the additive at various addition levels; however, the overall change in concentration was minimal. To better illustrate the relationship between the additive concentration and the material's strength, the concentration range should be systematically adjusted.

2.       In Figure 4, the author demonstrated significant chemical interactions and structural changes in the fabric solely through infrared spectroscopy. It is recommended to supplement this analysis with XPS spectroscopy to provide a more comprehensive understanding.

3.       The author posits that higher concentrations of additives result in more pronounced effects, suggesting improved interaction and dispersion within the polyamide matrix, as well as enhanced crystallinity and thermal stability. To better support this claim, it is recommended to include particle size distribution data at different concentrations and thermogravimetric analysis (TGA) curves for various fibers.

4.       In this study, the authors suggest that the performance of the material is primarily related to the interaction and dispersibility of the additives within the polyamide matrix. To further enhance these properties, it is recommended to explore the use of surfactants to improve dispersibility or to modify CNTs with functional groups to increase their interaction with the matrix.

Comments on the Quality of English Language

need to be polished.

Author Response

Reviewer 3: Comments and Suggestions for Authors

In this manuscript, the authors aim to address the research gap by investigating the integration of three different carbon-based nanofillers—carbon nanotubes (CNTs), carbon black (CB), and graphene (Gn)—into polyamide 6 during the melt spinning process to create multifilament yarns. These yarns will subsequently be woven into fabrics to evaluate and enhance their functional properties. The study will systematically vary the concentration of nanofillers to investigate and compare their effects on the mechanical and thermal properties of the composite textile materials. However, some major revisions should still be made to make the paper more convinced before its publication.

Comments 1:     In this study, the author examined the relationship between the material's strength and the concentration of the additive at various addition levels; however, the overall change in concentration was minimal. To better illustrate the relationship between the additive concentration and the material's strength, the concentration range should be systematically adjusted.

Response 1: Thank you for your insightful comment. In the context of textile manufacturing, particularly with melt spinning processes, the concentration of additives is typically limited to a maximum of 2%. This limitation is influenced by several factors, including the particle size of the additive and the filament diameter/denier. Exceeding this concentration can result in significant challenges during yarn extrusion, such as increased breakages and compromised yarn strength, which in turn lead to difficulties in subsequent processing stages, such as fabric formation, flexibility issues, and increased surface abrasion with yarn guides. In our practical experience, even when we attempted to increase the additive concentration, especially Graphene to 5% during melt spinning, we encountered substantial difficulties in maintaining continuous yarn spinning. Additionally, while we produced yarns with a 2% additive concentration, we observed that the fabric surface became rougher, making it less suitable for wear against human skin. These factors are crucial considerations in the practical application of additives in melt spinning, which is why the concentration range explored in this study was kept minimal to avoid these adverse effects.

Comments 2:       In Figure 4, the author demonstrated significant chemical interactions and structural changes in the fabric solely through infrared spectroscopy. It is recommended to supplement this analysis with XPS spectroscopy to provide a more comprehensive understanding.

Response 2: Thank you for your suggestion and for highlighting the potential benefits of supplementing the infrared spectroscopy analysis with XPS spectroscopy. We agree that XPS could provide additional insights into the chemical composition and surface interactions, further enriching the understanding of the observed structural changes. However, due to the current scope and limitations of this study, including access to XPS instrumentation and time constraints, it was not feasible to conduct this additional analysis at this stage. Nonetheless, the infrared spectroscopy results presented in Figure 4 offer a strong indication of the chemical interactions and structural changes in the fabric, which align well with the study's objectives. For future research, we plan to incorporate XPS spectroscopy to build upon the findings of this study and provide a more comprehensive analysis. We appreciate your understanding and hope that the current data sufficiently supports the conclusions drawn in this work.

Comments 3:    The author posits that higher concentrations of additives result in more pronounced effects, suggesting improved interaction and dispersion within the polyamide matrix, as well as enhanced crystallinity and thermal stability. To better support this claim, it is recommended to include particle size distribution data at different concentrations and thermogravimetric analysis (TGA) curves for various fibers.

Response 3: Thank you for your valuable suggestion. We understand that including particle size distribution data and thermogravimetric analysis (TGA) curves would indeed provide valuable additional support for our claim regarding the effects of higher additive concentrations on interaction, dispersion, crystallinity, and thermal stability within the polyamide matrix. We conducted a study on the particle size distribution of all three additives, and the results are provided below for your reference. However, we encountered some inconsistencies, particularly with the results for CNT and graphene, most likely related to the aggregation of particles. Specifically, the challenge lies in understanding how the particle sizes, especially the D90 for graphene and carbon nanotubes (CNT), fit within the yarn denier (80D/48f), given each filament's denier of 1.8D and a calculated diameter of 15 micrometers (assuming a round cross-section and based on a polyamide density of 1.14 g/cc). tHis contradiction led us to exclude this data from the manuscript. We believe that agglomeration is the most likely cause of these unexpected results. Notably, the graphene particles appeared quite large (322 µm), suggesting significant agglomeration or clumping in water. Given graphene's hydrophobic nature, especially if these are graphene nanoplates, this observation aligns with its tendency to stick together easily and not disperse thoroughly.

Due to the scope and time constraints of this study, we were unable to include these additional analyses of TGA. While the current data, including infrared spectroscopy and differential scanning calorimetry (DSC), provide strong evidence of the improved interaction and enhanced properties with higher additive concentrations, we acknowledge that the inclusion of particle size distribution and TGA curves would further substantiate our findings. For future studies, we plan to conduct these additional analyses to provide a more detailed understanding of the mechanisms at play.

Particle Size Data: CB: D90:14um; D10:0.2um; CNT: D90:40.6um; D10:3.3um;  Graphene: D90:322.7um; D10:8.2um

 

Figure 1. Particle Size Distribution Analysis

 

Comments 4:     In this study, the authors suggest that the performance of the material is primarily related to the interaction and dispersibility of the additives within the polyamide matrix. To further enhance these properties, it is recommended to explore the use of surfactants to improve dispersibility or to modify CNTs with functional groups to increase their interaction with the matrix.

Response 4: We value the reviewer's constructive recommendation regarding the potential use of surfactants to improve dispersibility and the modification of CNTs with functional groups to enhance their interaction with the polyamide matrix. We agree that these approaches could significantly contribute to the material's performance. While these strategies were not explored in the current study, we acknowledge their importance and will consider them for future research. In our ongoing and future studies, we aim to investigate the effects of surfactants and functionalized CNTs on the dispersibility and interaction within the polyamide matrix, which will allow us to provide a more comprehensive understanding of these factors and their impact on the material's overall performance.

 

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The article can be published.

Author Response

 

Comments and Suggestions for Authors: The article can be published.

 

Response: Thank you for your positive feedback and for recommending our article for publication.

Reviewer 2 Report

Comments and Suggestions for Authors

The same issues continue except for abstract. No references were used in Materials and Methods. Only 2 References were used in Results and Discussion which is not acceptable for a proper discussion. The conclusions must be clear and specific which separate them from the results and discussion section. 

Author Response

Comments and Suggestions for Authors: The same issues continue except for abstract. No references were used in Materials and Methods. Only 2 References were used in Results and Discussion which is not acceptable for a proper discussion. The conclusions must be clear and specific which separate them from the results and discussion section. 

Response: Thank you for your constructive feedback. We appreciate your time and effort in reviewing our manuscript. We acknowledge the need to provide references in the Materials and Methods section. We have now included appropriate references to support the methodology used in the study. We understand the importance of incorporating relevant literature to enhance the discussion. We have reviewed and added additional references to ensure a comprehensive and well-supported discussion of the results. And also, we have revised the Conclusions section to make it clearer and more specific, distinctly separating it from the Results and Discussion section. We hope these revisions address your concerns and improve the quality of our manuscript. Thank you once again for your valuable suggestions.

 

Reviewer 3 Report

Comments and Suggestions for Authors

Agree to publish.

Comments on the Quality of English Language

Qualified.

Author Response

Comments and Suggestions for Authors:  Agree to publish.

 

Response: Thank you for your feedback and support. We are glad you found the article suitable for publication.