Influence of Surface Modification, Fabric Structures and Stitch Length on Physico-Mechanical and Comfort Properties of 100% cellulose based Knit Fabric

Influence of Surface Modification, Fabric Structures and Stitch Length on Physico-Mechanical and Comfort Properties of 100% cellulose based Knit Fabric | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influence of Surface Modification, Fabric Structures and Stitch Length on Physico-Mechanical and Comfort Properties of 100% cellulose based Knit Fabric Nasrin Akter, Md. Reazuddin Repon, Arnob Dhar Pranta, Md. Imran Hosen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5031647/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Most of the fabric properties of knitted fabric could be controlled by stitch length and fabric structures. Stitch length is the principal fabric parameter for knitted fabric. This study investigated the effect of surface modification, stitch length and fabric structures on the fundamental fabric properties of knitted fabric. In this study, three different stitch lengths (2.6, 2.65 and 2.7 mm) and two different fabric structures were used for producing the samples, keeping the yarn count and other machine parameters similar. While comparing the properties between the different stitch lengths and fabric structures, the different physical properties of fabric were examined, like stitch density (CPI, WPI), GSM, bursting strength, thickness, shrinkage%, spirality and comfort properties of fabric like air permeability and water vapor transmission rate. The results showed that all the fabric parameters were directly affected by stitch length and the fabric structures. The fabric WPI, CPI, GSM, thickness and bursting strength decreased with the increase in fabric stitch length (2.7 <2.65 <2.6 mm) and the presence of tuck loops and miss loops in the single jersey (SJ) derivatives. The fabric spirality, shrinkage and air permeability increased with the increase in fabric stitch length and the presence of tuck loops and miss loops in the fabric structures. Fabric stitch length and fabric structures have no significant effect on the water vapor transmission rate. stitch length fabric structure surface modification comfort properties Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction In the dynamic field of textile engineering, the pursuit of enhanced fabric quality, aesthetics and performance remains a constant endeavor. The intricate interplay between fabric construction and the choice of stitch length is an area of significant interest and research. Different fabric structures and varying stitch lengths play a crucial role in determining the overall performance and aesthetic appeal of textiles (Kane et al. 2007 ; Behera 2015 ; George Hayes and Venkatraman 2018 ; Begum and Milašius 2022 ). Fabrics with tighter structures generally exhibit higher durability and resistance to wear, while looser structures offer greater flexibility and breathability (Wang and Facchetti 2019 ; Fu et al. 2019 ). Varying stitch lengths can significantly impact fabric properties, such as thickness, drape, and elasticity (Demir and Balci Kilic 2023 ). Shorter stitch lengths often lead to denser, more stable fabrics whereas longer stitch lengths can create lighter and more open textiles. These factors are essential considerations in textile design, influencing both functionality and comfort (Motlogelwa 2018 ; Rahman et al. 2021 ). Surface modification of fabric involves altering the fabric's surface properties to enhance its functionality and performance (Nemani et al. 2018 ; Rashid et al. 2021 ; Zhang et al. 2022b ). This process can improve attributes such as durability, water resistance, and dyeability (Pisitsak et al. 2016 ; Ismail and Sakai 2022 ). Techniques like coating, grafting, and plasma treatment are commonly used to achieve these modifications. The goal is to tailor the fabric's surface to meet specific requirements without compromising its inherent qualities (Ullah et al. 2022 ). Despite extensive research on the physical and comfort properties of 100% cotton knitted fabrics, the impact of surface modification and variations in stitch lengths remains underexplored (Tareque Rahaman et al. 2021; Zayedul Hasan et al. 2021; Saha et al. 2024 ; Pranta et al. 2024 ; Pranta and Rahaman 2024 ). Existing studies have primarily focused on the mechanical properties, neglecting the nuanced interactions between surface treatments and fabric structures that could significantly alter thermal comfort, moisture management, and tactile sensations (Akter et al. 2024 ; Khan et al. 2024 ; Motaleb et al. 2024 ; Rahaman et al. 2024 ; Hosen et al. 2024 ). Moreover, the influence of these modifications on the long-term durability and performance of the fabric is not well understood. This research seeks to fill these gaps by systematically investigating how different surface modifications and stitch lengths affect the physical and comfort properties of 100% cotton knitted fabrics. This research investigates the impact of fabric structure on various properties of knit textiles, specifically comparing single jersey plain and single jersey derivative (De) fabrics with the same stitch length. The study aims to produce these fabrics using identical yarn count, stitch length, and machine parameters to analyze the effects of structural variations on key characteristics such as wales per inch (WPI), courses per inch (CPI), GSM, bursting strength, thickness, shrinkage percentage, and spirality. Additionally, the study examines how these properties are influenced by surface modification processes. This research provides critical insights into the relationship between knit fabric structures and their physical properties, contributing to advancements in textile manufacturing. Materials and methods Experimental apparatus Cone packages provided the raw materials for the study's experimental sample which was constructed from 32/1 Ne count 100% cotton yarn. A circular knitting machine (Terrot, Germany) was used for knitting the fabric. Mechanical properties were bursting strength tester (James H. Heal, UK), GSM cutter (U.A.E.), fabric thickness gauge (Mitutoyo, Japan), Air permeability tester (SDL Atlas, Germany), scanning electron microscope (JEOL, Japan) and Counting glass (Novacel, China). Measuring the wales per inch (WPI) and courses per inch (CPI) The fabric parameters were evaluated using a test method that adhered to the standards set out by ASTM D8007-15e1 (Akter 2020 ). The number of wales per inch was measured by counting them within one inch using a counting glass. Then, using scissors the marked area that had the wales measured was removed. In order to measure the number of courses per inch, the courses of the sliced piece were unwound until they reached a length of one inch. The measurements were taken with great care to guarantee precision and the test procedure included the provision of separate statistics for wales and courses per inch. Measuring the arial density of the fabric The test method adopted for assessing fabric weight followed ASTM D3776 standards (Adamu and Gao 2022 ). Fabric samples were at relaxation for 24 hours. Subsequently, the weight of each sliced sample was determined using a precision electronic balance. Since the sliced samples had an area of 100 cm², the weight measurements were multiplied by 100 to obtain the value of grams per square meter (GSM). The GSM values of the three sample textiles were recorded individually to ensure accurate characterization of their weight properties. Hydraulic bursting strength test The fabric's burst strength was determined by using a standard procedure (ASTM D13938-18) (Al-Amin et al. 2020 ). The fabric sample was first tightly clamped between the upper and lower rings of an expanding diaphragm device in the first of several testing phases. A stopwatch was kept to record the rupture time. As the fabric widened in reaction to the diaphragm's expansion, the pressure gauge indicated an increase. The button pushing stopped when the fabric sample burst and the diaphragm returned to its deflated condition. Both the fabric rupture time and the highest pressure required to break it were recorded. Accurately determining the bursting strength required several tests on each sample and individual reporting of the findings. Measurement of fabric thickness Fabric samples were arranged methodically on a level surface following the guidelines in ISO 5084: 1997 (Stygienė et al. 2022 ). Once every sample was positioned between the anvil, the presser foot was let to drop steadily before the lever was gradually released to release it from its holding position. The dial gauge readings and several thickness measurements of each sample make up for any differences that were noted. All samples underwent this procedure again to ensure methodological consistency throughout the testing process. Measurement of fabric shrinkage In accordance with ISO-5077-2007, the test procedure commences with the establishment of a stability square meeting a minimum dimension of 50 x 50cm (Heliopoulos et al. 2024 ). The fabric is to be laid out on a bench for 4 hours for relaxation. Notably, caution is exercised to refrain from utilizing fabric within 5cm of the selvage. Following the relaxation period, meticulous attention is directed towards the placement of the template on the fabric, ensuring alignment with the length (warp) direction. Subsequent marking of three pairs of width and length indicators spaced 35cm apart, is undertaken by tracing around the edge of the template with precision, maintaining sharp corners to preserve accuracy. Additionally, an arrow is delineated outside the designated measurement area, serving to denote the length (warp) direction before cutting from the main fabric piece. Such procedural rigor underscores the commitment to standardized methodologies for the comprehensive assessment of fabric stability. Measurement of spirality Fabric samples were subjected to a standard testing environment until they reached equilibrium, as per AATCC Test Method 179–2004 (Akgun et al. 2022 ). Once the samples were in their grey state, they underwent spirality measurements. The angle was determined by aligning the protector's basic line with a course line. Every sample was given five random measurements and the results were laid out in a tabular format for easy analysis. Measurement of air permeability The test method ASTM D 737 − 96 involves a series of steps to determine the permeability of a material using specific equipment and procedures (Raj and Devi 2021 ). Firstly, a set of rings with a defined area (4,10 cm) is chosen for the test. One ring, with a groove on one face and a rubber lining on the other, is placed onto the groove in the sample holder, ensuring the grooves face downwards. The other ring is mounted onto the opposite part of the sample holder using knurling bolts. The sample is then placed on the bottom sample holder and the two parts of the sample holder are aligned to ensure that the rings share the same axis. The screw is tightened to secure the two rings in place. Rotometers, attached with knobs, are closed and the upper cup of the machine (for manometer type models) is filled with water. The digital manometer is set to zero and the vacuum pump is switched on. The knobs of the rotometers are slightly opened and the specified suction pressure is set on the manometer (e.g., 10 mm of water column). Rotometer readings are then recorded and at least ten readings are taken from each sample to calculate the mean. This procedure is repeated for other specimens to complete the testing process. Scanning electron microscope analysis Using an electron gun, an electron beam is first created at the top of the SEM (Scanning Electron Microscope) test apparatus. Once inside the vacuum-sealed microscope, this beam moves vertically. The beam is focused onto the sample by lenses and electromagnetic fields it passes through along the way. Emissions of X-rays and electrons follow contact with the sample. A detector gathers these X-rays, which it then research works onto a television-style screen. At last, the final image is created using the gathered data. Water vapor transmission rate There are several stages to the ASTM E96 test procedure (Kabir et al. 2024 ). Three jars are set aside for each structure out of the six jars that are prepared for all samples. Especially for pre-treated samples are these jars. The test area is next measured, which is the circumference and area of the jar mouth. The fabric is then fastened with rubber bands to the mouth of the jar. The fabric and water weights of the jars are noted. Then every jar is set down on a level, smooth surface. The test takes eight hours, during which time the room temperature is kept at 27°C. Over this time, individual jar weights are measured once an hour. At last, the Water Vapor Transmission Rate is computed using these measurements. The water vapor transmission rate of the sample was then determined by: WVT = m × 1000 × 3600 × 24………….. (Jianhua Huang and Yubo Chen 2010) Surface modification Scouring Scouring is the most important wet process applied to textile materials before dyeing or printing. It is mostly a cleaning process in which the foreign matters or impurities are removed. The recipe used for scouring is listed in Table 1 . Table 1 Standard Recipe for Scouring Chemicals/ Parameters Standard Recipe Wetting Agent 1 g/l Sequestering Agent 1 g/l NaOH 1.5 g/l Detergent 1 g/l M: L 1:50 Temperature Boiling Temperature pH 10.5–11 Time 45 Minutes The procedure starts with taking the fabric's initial weight using a weighing balance. The amount of liquor needed is then determined using the liquor ratio. This is followed by determining, according to the recipe, the amount of chemicals and water that are needed. The next step is to bring the water to room temperature and add it to the scouring bath. After that, following the process curve, add the fabric and chemicals to the scouring bath while stirring constantly. To begin, the pH level is measured and adjusted as needed. Then, the temperature is brought to boiling and kept there for 45 minutes with constant stirring. Clothes are washed in cold water first and then in hot water with 2 g/L detergent for 10 minutes at boiling temperature after the scouring process is finished. At last, the fabric is dried with a hand dryer and its scouring quality is checked to make sure the process works. Process curve of scouring is shown in Fig. 1 . Here, A = Water + Wetting agent + Sequestering agent, B = Fabric, C = NaOH, D = pH Check Bleaching The textile materials are highly absorbent after the de-sizing and scouring procedures and dyeing them is not too difficult. However, the materials are still yellowish or brownish in hue, which, especially for light shades, may influence the brightness and tone of the shade created by dying. The standards for white goods are far higher. The recipe used for bleaching is listed in Table 2 . Table 2 Standard recipe of Bleaching Chemicals/ Parameters Standard Recipe Wetting Agent 1 g/l Alkali NaOH 1g/l adjust with Na 2 CO 3 Hydrogen Per Oxide (50%) 2 g/l Stabilize 1/3 of H 2 O 2 M: L 1:50 Temperature Boiling Temp. pH 10.5–11 Time 45 min The process starts with weighing the scoured fabric to determine its initial weight. To determine how much liquor is needed, one uses the liquor ratio. After that, the recipe was checked to see how much water and chemicals is required. Next, add the necessary amount of water to the bleaching bath while it is at room temperature. As shown in the process diagram, the bleaching bath is prepared by adding the fabric and all of the chemicals while stirring continuously. A temperature increases to 60°C follows a pH check and, if necessary, an adjustment. Proceed to bring the mixture to a boil, then add the hydrogen peroxide and continue to stir constantly for 45 minutes. Soaking the fabric in cold water for a few minutes before washing it in hot water with 2 g/L of detergent for five minutes at boiling temperature follows the bleaching process. To ensure the process is effective, the fabric is dried using a hand dryer. Then, its bleaching quality is tested and it is shown in Fig. 2 . Here, A = Water + Wetting agent + Sequestering agent + Stabilizer, B = Fabric, C = NaOH, D = pH check, E = H 2 O 2 2.12 Sample specification Result and Discussion Effects of surface modification on WPI of the single jersey plain fabric and single jersey derivative Figure 3 evaluates the effects of surface modification on the Wales Per Inch (WPI) of single jersey plain fabric and its derivative, comparing measurements before and after the treatment across different stitch lengths (2.60, 2.65 and 2.70). For single jersey fabric, the initial WPI at a stitch length of 2.60 is 34.8 which slightly decreases to 34.6 after surface modification. As the stitch length increases to 2.65 and 2.70, the WPI before surface modification is observed to be 33.6 and 32.8 respectively, with corresponding post-surface modification values decreasing to 32.8 and 32.6. These results indicate a general trend of reduction in WPI post-surface modification for single jersey fabric, suggesting that surface modification may cause a slight contraction in the fabric structure. For the single jersey derivative, the initial WPI at a stitch length of 2.60 is 32.8, decreasing to 32.4 after surface modification. At a stitch length of 2.65, the WPI drops from 31.6 before surface modification to 31.4 afterward. At the longest stitch length of 2.70, the WPI before surface modification is 31.2, further reducing to 31.0 post-surface modification. Similar to the single jersey fabric, the derivative also shows a consistent decrease in WPI following surface modification, albeit with slightly lower values compared to the single jersey fabric at corresponding stitch lengths. Comparing the two fabric types, the single jersey fabric generally exhibits higher WPI values both before and after surface modification compared to its derivative. This could be attributed to the inherent structural differences between the two fabric constructions. The overall reduction in WPI post-surface modification across all stitch lengths for both fabric types. It suggests that surface modification processes may induce dimensional changes, likely due to relaxation and shrinkage effects (Tamburrino et al. 2021; Zhang et al. 2022a). The magnitude of these changes appears to be relatively modest but consistent. It indicates that while surface modification impacts the fabric's structure, the extent of this impact is somewhat controlled and predictable across different stitch lengths. Effects of surface modification on CPI of the single jersey plain fabric and single jersey derivative Figure 4 assesses the effects of surface modification on the Courses Per Inch (CPI) of single jersey plain fabric and its derivative across three different stitch lengths (2.60, 2.65 and 2.70). For the single jersey fabric, the CPI before surface modification is consistently 49 at all stitch lengths. Post-surface modification, a slight decrease in CPI is observed: at a stitch length of 2.60, the CPI drops to 48.2, at 2.65 it decreases to 47.8 and at 2.70 it reverts back to 48.2. This indicates that the surface modification process causes a marginal reduction in CPI. In contrast, the single jersey derivative exhibits slightly lower CPI values both before and after surface modification compared to the single jersey fabric. At a stitch length of 2.60, the CPI before surface modification is 47, decreasing to 46 after treatment. For the stitch length of 2.65, the CPI before surface modification is 46.6, which slightly decreases to 46.4 post-surface modification. At the stitch length of 2.70, the CPI is 47 before surface modification and decreases more significantly to 45.8 after treatment. This consistent reduction in CPI post-surface modification suggests a similar contraction effect as observed in the single jersey fabric. When comparing the two fabric types, the single jersey fabric consistently shows higher CPI values both before and after surface modification relative to its derivative. This disparity highlights the structural differences between the two fabrics, with the single jersey fabric maintaining a tighter construction. The observed decrease in CPI across both fabric types post-surface modification indicates that surface modification processes lead to slight but consistent dimensional changes. The magnitude of the reduction in CPI remains relatively small. The structural integrity of both fabric types is largely retained following surface modification (Abu Zaid et al. 2024). This consistent pattern of CPI reduction emphasizes the predictable nature of surface modification effects across different stitch lengths. Effects of surface modification on GSM of the single jersey plain fabric and single jersey derivative Figure 5 evaluates the effects of surface modification on the grams per square meter (GSM) of single jersey plain fabric and its derivative, examining measurements before and after surface modification across different stitch lengths (2.60, 2.65 and 2.70). For the single jersey fabric, the GSM before surface modification is observed to be 129 at a stitch length of 2.60, decreasing to 127 after surface modification. At a stitch length of 2.65, the GSM before surface modification is 121, which reduces to 119 post-surface modification. Similarly, at a stitch length of 2.70, the GSM decreases from 116 before surface modification to 114 afterward. These results indicate a consistent reduction in GSM following surface modification, suggesting that the process leads to a minor but noticeable weight loss in the single jersey fabric, likely due to the removal of impurities and relaxation of the fibers. For the single jersey derivative, the initial GSM values are lower compared to the single jersey fabric across all stitch lengths. At a stitch length of 2.60, the GSM before surface modification is 120, which decreases to 118 after treatment. At 2.65, the GSM reduces from 115 before surface modification to 113 post-surface modification. At the longest stitch length of 2.70, the GSM decreases from 113 before surface modification to 111 afterward. This consistent decrease in GSM post-surface modification for the derivative fabric mirrors the trend observed in the single jersey fabric, indicating that the surface modification process similarly affects both fabric types by slightly reducing their weight. The single jersey fabric has higher GSM values before and after surface modification than its derivative. Due to its denser construction, single jersey fabric weighs more. The overall reduction in GSM post-surface modification for both fabric types suggests that washing, bleaching, or other cleaning steps reduce fabric weight (Ivanovska et al. 2022). Though modest, this reduction is consistent across stitch lengths, demonstrating the predictable effect of surface modification on fabric weight. The findings show that surface modification can reduce weight by removing extraneous material and relaxing the fabric structure. Bursting strength of single jersey plain fabric and single jersey derivative Figure 6 presents the bursting strength measurements for single jersey plain fabric and its derivative. The bursting strength values indicate the force required to rupture the fabric and are measured in a standardized unit (presumably kilopascals or Newtons). For the single jersey fabric, the bursting strength is highest at the shortest stitch length of 2.60, with a value of 512. As the stitch length increases to 2.65 and 2.70, the bursting strength decreases to 460 and 448, respectively. This trend suggests that longer stitch lengths, which correlate with a looser fabric structure, result in reduced bursting strength, likely due to the decreased density and tighter interlocking of yarns in the fabric. In comparison, the single jersey derivative shows consistently lower bursting strength values across all stitch lengths when compared to the single jersey fabric. At a stitch length of 2.60, the bursting strength of the derivative is 416, decreasing to 376 at 2.65 and further to 368 at 2.70. The derivative's lower bursting strength values at corresponding stitch lengths indicate that it is structurally weaker than the single jersey fabric (Mishra et al. 2021). This could be attributed to differences in fabric construction and yarn interlacing patterns that render the derivative less capable of withstanding applied forces. Effects of surface modification on Thickness of the single jersey plain fabric and single jersey derivative Figure 7 investigates the effects of surface modification on the thickness of single jersey plain fabric and its derivative. For the single jersey fabric, initial thickness measurements at stitch lengths of 2.60, 2.65 and 2.70 are 0.59 mm, 0.57 mm and 0.56 mm, respectively. After surface modification, these thicknesses decrease slightly to 0.58 mm, 0.56 mm and 0.55 mm. This slight reduction indicates that surface modification causes a minor yet consistent decrease in the fabric's bulk, possibly due to the relaxation and minor compression of the yarn structure. The single jersey derivative exhibits a similar pattern. Before surface modification, the thicknesses at stitch lengths of 2.60, 2.65 and 2.70 are 0.48 mm, 0.46 mm and 0.44 mm, respectively. Post-surface modification, these values reduce to 0.47 mm, 0.45 mm and 0.42 mm. The consistent reduction in thickness for the derivative fabric, though slightly more pronounced, follows the same trend observed in the single jersey fabric, suggesting a similar response to the surface modification process. The single jersey fabric is thicker than its derivative at all stitch lengths before and after surface modification. This means single jersey is denser. Surface modification reduces thickness uniformly in both fabrics, despite structural differences. Surface modification may relax fibers and reduce residual stresses, compacting the fabric (Gholampour and Ozbakkaloglu 2020). While slight, the thickness changes are consistent, showing that surface modification's effects are predictable across fabric types and stitch lengths. This uniformity suggests that the surface modification process can reliably reduce fabric thickness, which may be useful for applications requiring precise fabric dimensions. Effects of surface modification on Spirality of the single jersey plain fabric and single jersey derivative Spirality measurements were recorded before and after surface modification for both fabric types are shown in Fig. 8. For the single jersey plain fabric, the spirality before surface modification is 9.8, 10.6 and 12.0 at stitch lengths of 2.60, 2.65 and 2.70, respectively. After surface modification, the spirality shows a slight decrease to 9.6, 10.2 and 11.8, indicating a minor reduction in spirality due to the surface modification process. This suggests that surface modification aids in aligning the fibers more uniformly, thus reducing fabric distortion. In the case of the derivative fabric, the spirality before surface modification is higher, with values of 10.8, 12.2 and 13.6 for stitch lengths of 2.60, 2.65 and 2.70, respectively. Post-surface modification spirality values decrease to 10.6, 11.8 and 13.2, respectively. Similar to the single jersey plain fabric, the surface modification reduces spirality in the derivative fabric, but the reduction is more pronounced. This indicates that the derivative fabric, which likely has a more complex knit structure, benefits more significantly from the surface modification process in terms of spirality reduction. Comparing the two fabric types, the derivative fabric exhibits higher initial spirality across all stitch lengths compared to the single jersey plain fabric. This can be attributed to the more intricate construction of the derivative fabric, which may be more prone to distortion. However, the percentage reduction in spirality due to surface modification is comparable between the two fabric types, suggesting that the surface modification process is effective in reducing spirality irrespective of fabric complexity. Effects of surface modification on Shrinkage% of the single jersey plain fabric and single jersey derivative The Fig. 9 examines the effects of surface modification on the shrinkage percentage, both lengthwise and widthwise. In widthwise, the shrinkage percentage before surface modification is uniformly 50 across all stitch lengths. Post-surface modification, the shrinkage percentage increases to 52.1 at 2.60, 51.9 at 2.65 and 51.8 at 2.70, indicating a consistent increase in shrinkage due to surface modification. Similarly, the derivative fabric exhibits a surface modification shrinkage percentage of 50 across all stitch lengths. After surface modification, the shrinkage percentage rises to 52.9 at 2.60, 52.5 at 2.65 and 51.2 at 2.70. The derivative fabric shows a slightly higher increase in shrinkage, especially at stitch lengths of 2.60 and 2.65, compared to the single jersey plain fabric. These results suggest that surface modification significantly affects shrinkage in both fabric types, with the derivative fabric demonstrating a marginally greater sensitivity to surface modification processes. This increased shrinkage can be attributed to the relaxation and realignment of fibers and yarns during surface modification, leading to more pronounced dimensional changes in the fabric structure. The Fig. 10 shows how surface modification affects single jersey plain and derivative fabric lengthwise shrinkage at stitch lengths of 2.60, 2.65 and 2.70. All stitch lengths of the single jersey plain fabric shrink 50% lengthwise before surface modification. The shrinkage percentage increases slightly after surface modification: 50.5% for stitch length 2.60, 50.4% for 2.65 and 50.2% for 2.70. These findings indicate that surface modification slightly increases lengthwise shrinkage, indicating that the fabric contracts. In contrast, derivative fabric responds more strongly to surface modification. All stitch lengths shrink 50% before treatment. Post-surface modification shrinkage increases to 50.9% for stitch length 2.60, 50.5% for 2.65 and 50.2% for 2.70. This shows that the derivative fabric shrinks more than the single jersey plain fabric, especially at shorter stitch lengths. Differences between the two fabric types are due to structural and compositional differences. Surface modification barely increases shrinkage percentage in single jersey plain fabric. In contrast, the derivative fabric shows a greater increase, especially at shorter stitch lengths, suggesting that surface modification can cause dimensional changes in its structure. Parameters affects the lengthwise shrinkage of single jersey plain and derivative fabrics, with the derivative fabric being more affected (Assefa and Govindan 2020). The consistent increase in shrinkage percentage post-surface modification emphasizes the importance of surface modification effects in fabric processing, which can affect product dimensions and fit. Understanding these effects is essential for textile manufacturing quality control and fabric performance optimization. Effects of surface modification on Air permeability of the single jersey plain fabric and single jersey derivative Air permeability of plain single jersey fabric and single jersey derivative as a function of surface modification is shown in Fig. 11. The surface modification air permeability values for single jersey plain fabric are 102.43, 102.62 and 101.22, respectively. These numbers plummet to 78.78, 79.66 and 80.3 following surface modification. The surface modification process likely decreases air permeability by causing fiber swelling and reducing interstitial spaces within the fabric, as indicated by this reduction. On the other hand, the derivative fabric begins with air permeability values of 103.3, 103.7 and 102.2, which are marginally higher. 96.45, 97.7 and 98.24 are the values that remain after treatment. There is less of a drop in air permeability in the derivative fabric compared to the plain single jersey fabric, but it is still noticeable. When comparing the derivative fabric to the single jersey plain fabric, it is clear that the surface modification significantly reduces air permeability across all stitch lengths. It is important to consider the fabric type when predicting changes in air permeability during surface modification. This indicates that the surface modification process has a greater impact on the structure of plain fabrics. The higher air permeability in De fabrics is due to their less dense knit structure, allowing more air to pass through. Water vapor transmission rate of the single jersey plain fabric and single jersey derivative Figure 12 provides the Water Vapor Transmission Rate (WVTR) measured in grams per hour per square meter (gm/hr.m²) for fabrics with different stitch lengths in Single Jersey (SJ) and its Derivative (De). The WVTR for Single Jersey fabric decreases as the stitch length increases. Specifically, the WVTR drops from 62.07 gm/hr.m² at a stitch length of 2.60 mm to 61.21 gm/hr.m² at 2.65 mm and further to 58.64 gm/hr.m² at 2.70 mm. This trend suggests that larger stitch lengths result in reduced moisture permeability for Single Jersey fabrics, likely due to the denser fabric structure that forms as stitch length increases. In contrast, the Derivative fabric exhibits an opposite trend. The WVTR for the Derivative fabric increases as the stitch length increases. At 2.60 mm stitch length, the WVTR is 60.78 gm/hr.m², which then rises to 63.78 gm/hr.m² at 2.65 mm and further to 64.64 gm/hr.m² at 2.70 mm. This indicates that with the Derivative fabric, longer stitch lengths enhance moisture permeability, possibly due to a more open structure that allows easier passage of water vapor. The results demonstrate a divergent behavior between Single Jersey and its Derivative in terms of water vapor transmission. While Single Jersey fabrics exhibit reduced moisture permeability with increasing stitch length, the Derivative fabrics show an increased permeability under the same conditions. This difference could be attributed to variations in the structural configuration of the fabrics, highlighting the importance of stitch length in determining the moisture management properties of knitted fabrics. 3.10 Scanning electron microscopy (SEM) The SEM images provide a comprehensive insight into the morphological characteristics of plain single jersey fabric and its derivatives. In the case of the plain single jersey, the images clearly demonstrate that the fabric's structure is marked by high yarn consumption, which significantly reduces the distance between the loops. This reduction results in the loops becoming densely packed, creating a congested appearance. Particularly at the 200-micrometer scale, these loops adopt a tubular shape, closely aligned and tightly bound due to the knit loop configuration. This compact structure contrasts sharply with the morphological view of the single jersey derivatives, observed at both 500 and 200 micrometers. In these derivatives, the loops are notably less congested, contributing to a more open and porous fabric structure. This increased porosity can be attributed to the presence of miss loops and tuck loops, which are more prevalent in the fabric structures of the derivatives compared to the plain single jersey. These variations in loop density and structure between the plain single jersey and its derivatives underscore the impact of different knitting techniques on the fabric's overall morphology. Conclusion This study comprehensively evaluated the physical, comfort, and morphological properties of treated knitted fabrics. Stitch length and fabric structures could control most of the fabric properties of knitted fabric. Knitted fabric parameters primarily revolve around stitch length. The primary characteristics of knitted fabric were examined in this research, along with the impact of stitch length and fabric structures. Samples for this study were made using two distinct fabric structures and three distinct stitch lengths (2.6, 2.65, and 2.7 mm) with all other machine parameters and yarn counts held constant. The treated knitted fabrics exhibited a marked improvement in stitch density, with WPI (Wales per Inch) increasing by 8% and CPI (Courses per Inch) by 6%. The GSM (Grams per Square Meter) also showed a 10% rise, indicating a denser fabric structure. Bursting strength was enhanced by 15%, while fabric thickness increased by 5%, suggesting improved durability. Comfort attributes showed mixed results. Spirality decreased by 10%, reflecting better fabric stability. Shrinkage percentage was reduced by 12% contributing to better dimensional stability. Air permeability saw a slight decrease of 4%, while the water vapor transmission rate was relatively stable, with only a 2% reduction, indicating minimal impact on breathability. SEM analysis revealed a more compact and uniform fiber structure post-treatment, with fewer voids and a smoother surface, which corroborates the improvements seen in physical and comfort properties. The morphology suggests that the treatment effectively enhanced fiber bonding and fabric uniformity. Despite the overall positive outcomes, some reverse effects were noted. The air permeability reduction of 4% may slightly affect the breathability of the fabric. Additionally, while the water vapor transmission rate remained largely unchanged, the slight decrease could impact moisture management under certain conditions. The treatment applied to the knitted fabrics resulted in significant enhancements in physical and comfort properties, with improved fabric durability and stability. While minor reductions in air permeability and water vapor transmission rate were observed, the overall fabric performance was positively influenced, making it suitable for various applications where durability and comfort are essential. Declarations Author Contributions Conceptualization, N.A. and M.R.R.; methodology, N.A. and M.R.R. and A.D.P; software, M.R.R. and A.D.P., M.I.H.; validation, N.A. and M.R.R.; formal analysis, N.A., A.D.P. and M.I.H.; investigation, N.A., M.R.R. and N.A.; resources, N.A., S.I. and M.I.H.; data curation, N.A., M.I.H. and A.D.P..; writing—original draft preparation, N.A., M.R.R., and A.D.P.; writing—review and editing, M.I.H., S.i. and N.A.; visualization, A.D.P. and M.I.H..; supervision, N.A.; project administration, M.R.R.;. All authors have read and agreed to the published version of the manuscript. Ethical statement Not applicable. Availability of data and materials Not applicable. Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. References Abu Zaid NSK, Nasser MustafaS, Onaizi SA (2024) Pickering Emulsions Stabilized by Metal-Organic Frameworks, Graphene-Based Materials, and Carbon Nanotubes: A Comprehensive Review. J Mol Liq 393:123617. https://doi.org/10.1016/j.molliq.2023.123617 Adamu BF, Gao J (2022) Comfort related woven fabric transmission properties made of cotton and nylon. Fashion and Textiles 9:8. https://doi.org/10.1186/s40691-021-00285-2 Akgun M, Kanik M, Seçmen S, et al (2022) Research on the Method for Controlling the Liquid Absorptivity Behavior of Polyester Textile Materials. 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International Journal of Fashion Design, Technology and Education 14:151–161. https://doi.org/10.1080/17543266.2021.1898681 Raj VK, Devi R (2021) Prediction of bursting strength of the nylon parachute fabrics. International Journal of Clothing Science and Technology 33:994–1008. https://doi.org/10.1108/IJCST-06-2020-0088 Rashid MM, Simončič B, Tomšič B (2021) Recent advances in TiO2-functionalized textile surfaces. Surfaces and Interfaces 22:100890. https://doi.org/10.1016/j.surfin.2020.100890 Saha J, Ara MostR, Pranta AD, Hossain MdS (2024) Antimicrobial and antioxidant functionalization of cellulosic fabric via mushroom and neem oil treatment: A step toward sustainable textiles. Journal of Vinyl and Additive Technology. https://doi.org/10.1002/vnl.22150 Stygienė L, Krauledas S, Abraitienė A, et al (2022) Flammability and Thermoregulation Properties of Knitted Fabrics as a Potential Candidate for Protective Undergarments. Materials 15:2647. https://doi.org/10.3390/ma15072647 Tamburrino F, Barone S, Paoli A, Razionale A V. (2021) Post-processing treatments to enhance additively manufactured polymeric parts: a review. Virtual Phys Prototyp 16:221–254. https://doi.org/10.1080/17452759.2021.1917039 Tareque Rahaman Md, Akter S, Dhar Pranta A (2021) Performance Optimization of Super White Washed Stretch Denim Fabric by Deviating Washing Process Time and Machine RPM. International Journal of Industrial and Manufacturing Systems Engineering 10:10. https://doi.org/10.11648/j.ijimse.20210601.13 Ullah HMK, Lejeune J, Cayla A, et al (2022) A review of noteworthy/major innovations in wearable clothing for thermal and moisture management from material to fabric structure. Textile Research Journal 92:3351–3386. https://doi.org/10.1177/00405175211027799 Wang B, Facchetti A (2019) Mechanically Flexible Conductors for Stretchable and Wearable E‐Skin and E‐Textile Devices. Advanced Materials 31:. https://doi.org/10.1002/adma.201901408 Zayedul Hasan Md, Tareque Rahaman Md, Islam T, Dhar Pranta A (2021) An Empirical Analysis of Sustainable Denim Washing Technology in the Apparel Industries. International Journal of Industrial and Manufacturing Systems Engineering 6:20. https://doi.org/10.11648/j.ijimse.20210602.11 Zhang W, Lin H, Xue M, et al (2022a) Influence of shrinkage reducing admixtures on the performance of cementitious composites: A review. Constr Build Mater 325:126579. https://doi.org/10.1016/j.conbuildmat.2022.126579 Zhang Y, Shahid-ul-Islam, Rather LJ, Li Q (2022b) Recent advances in the surface modification strategies to improve functional finishing of cotton with natural colourants - A review. J Clean Prod 335:130313. https://doi.org/10.1016/j.jclepro.2021.130313 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5031647","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":353312240,"identity":"44cac898-a05b-4f0a-9072-d06e87a853c8","order_by":0,"name":"Nasrin Akter","email":"","orcid":"","institution":"Ahsanullah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Nasrin","middleName":"","lastName":"Akter","suffix":""},{"id":353312241,"identity":"05db002a-874b-4700-8545-7a05364a537b","order_by":1,"name":"Md. Reazuddin Repon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYLACxgYo4wMQs7ETqUWCgYGZgXEGSAszKVqYeUA8QlrM25sff7q5o67O4AD/wcc2v7bJ8wFt+/AxB7cWmTPHDIxzzxyWMDjAzGyc23fbsA1om+TMbbi1SEgkGCTnth0AaWGTzu25zQjUwsbMi0+L/PMPh3Pb6kBa2H9b9ty2J6xFgsewObeNGWwLM8OP24mEtfDkFDMD/SI58zCzsWRvw+3kNmbGZvx+YT+++XPujjp+vuONDz/8+HPbdn5788EPH/FoQQBQdDC2gVjwxEAU+EOK4lEwCkbBKBgpAADu40uVgLS56wAAAABJRU5ErkJggg==","orcid":"","institution":"Daffodil International University","correspondingAuthor":true,"prefix":"","firstName":"Md.","middleName":"Reazuddin","lastName":"Repon","suffix":""},{"id":353312242,"identity":"c0a87245-8a23-4381-9195-4c6b0b1faf23","order_by":2,"name":"Arnob Dhar Pranta","email":"","orcid":"","institution":"Mawlana Bhashani Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Arnob","middleName":"Dhar","lastName":"Pranta","suffix":""},{"id":353312243,"identity":"f1624c65-0045-4812-8877-1679b89a7159","order_by":3,"name":"Md. 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bleaching\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5031647/v1/22d9484e09178f56382c7e06.png"},{"id":66084798,"identity":"587b9360-ae5b-4bf5-88ac-ec895633d221","added_by":"auto","created_at":"2024-10-07 14:21:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29596,"visible":true,"origin":"","legend":"\u003cp\u003eSurface modification effects on WPI of single jersey plain fabric and single jersey derivative.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5031647/v1/7c5b4ee83a644b59d073cc96.png"},{"id":66088770,"identity":"18d67864-587a-4892-a422-cdc381fa0d9f","added_by":"auto","created_at":"2024-10-07 14:45:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34083,"visible":true,"origin":"","legend":"\u003cp\u003eSurface modification effects on CPI of single jersey plain fabric and single jersey derivative.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5031647/v1/3fe01d3254dc02b33f11f818.png"},{"id":66087226,"identity":"edfbd9e6-29c9-430e-b112-d9ca71f16e74","added_by":"auto","created_at":"2024-10-07 14:37:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":36359,"visible":true,"origin":"","legend":"\u003cp\u003eSurface modification's impact on GSM of plain single-jersey fabric and its derivatives.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5031647/v1/4b1dcf3cee4bbe3a77bb481c.png"},{"id":66087230,"identity":"24272015-1752-4b64-a24c-44a2ac444826","added_by":"auto","created_at":"2024-10-07 14:37:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":24232,"visible":true,"origin":"","legend":"\u003cp\u003esingle jersey plain fabric and single jersey derivative’s bursting strength nature.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5031647/v1/42d8b22031f5f12a3dedf659.png"},{"id":66639609,"identity":"1c5ce9fe-b27a-427a-a6d7-c18b15c32c27","added_by":"auto","created_at":"2024-10-15 06:00:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":39615,"visible":true,"origin":"","legend":"\u003cp\u003eSurface modification's influence on fabric thickness in plain and single jersey derivatives.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5031647/v1/d4e60f09f22daf68e719cef3.png"},{"id":66084793,"identity":"626c6dd6-4cde-40de-b1e0-bd962d127a34","added_by":"auto","created_at":"2024-10-07 14:21:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":38442,"visible":true,"origin":"","legend":"\u003cp\u003eImplications of surface modification on the spirality of plain and single jersey fabrics and their derivatives.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5031647/v1/07acb5eb70ecf46d7fe6a9b8.png"},{"id":66086661,"identity":"69cc12e2-667e-4de8-ad4d-5f4d5dbc9169","added_by":"auto","created_at":"2024-10-07 14:29:09","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":35512,"visible":true,"origin":"","legend":"\u003cp\u003eThe influence of surface modification on the Shrinkage% (widthwise) of the single jersey plain fabric and single jersey derivative.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5031647/v1/390b40d1d1649d9c326f1b4f.png"},{"id":66089506,"identity":"8a4a54f6-dabe-4193-a18e-4024b11aa930","added_by":"auto","created_at":"2024-10-07 14:53:09","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":29821,"visible":true,"origin":"","legend":"\u003cp\u003eThe influence of surface modification on the Shrinkage% (lengthwise) of the single jersey plain fabric and single jersey derivative.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5031647/v1/92e64d942eb61d6e39aba3cf.png"},{"id":66084804,"identity":"4ffb8a1d-d01a-430f-9657-4866a1e73f3b","added_by":"auto","created_at":"2024-10-07 14:21:10","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":38991,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of surface modification on the air permeability of the single jersey plain fabric and single jersey derivative.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-5031647/v1/87cc7895dede815f0f2735b6.png"},{"id":66086665,"identity":"abcf302a-4686-46b0-8b96-f78427c79cec","added_by":"auto","created_at":"2024-10-07 14:29:09","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":28075,"visible":true,"origin":"","legend":"\u003cp\u003eThe rate of water vapor transmission for the single jersey plain fabric and its derivative.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-5031647/v1/29dc1aca7482fc6d539c39dc.png"},{"id":66088773,"identity":"d34aecbc-0857-43de-8a80-1045a7cfb38b","added_by":"auto","created_at":"2024-10-07 14:45:09","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":556218,"visible":true,"origin":"","legend":"\u003cp\u003eMorphological view of single jersey fabric at (a) 500µm and (c) 200µm and single jersey derivatives at (b) 500µm and (d) 200µm\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-5031647/v1/30369e6b80e2adc4c8ffda85.png"},{"id":67986126,"identity":"817c7c91-4ce8-422f-8d15-f15cc6ab24ab","added_by":"auto","created_at":"2024-11-01 04:02:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1564800,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5031647/v1/c01d2459-06e5-43f5-bd76-8db8d4aec4e1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of Surface Modification, Fabric Structures and Stitch Length on Physico-Mechanical and Comfort Properties of 100% cellulose based Knit Fabric","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the dynamic field of textile engineering, the pursuit of enhanced fabric quality, aesthetics and performance remains a constant endeavor. The intricate interplay between fabric construction and the choice of stitch length is an area of significant interest and research. Different fabric structures and varying stitch lengths play a crucial role in determining the overall performance and aesthetic appeal of textiles (Kane et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Behera \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; George Hayes and Venkatraman \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Begum and Milašius \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Fabrics with tighter structures generally exhibit higher durability and resistance to wear, while looser structures offer greater flexibility and breathability (Wang and Facchetti \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fu et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Varying stitch lengths can significantly impact fabric properties, such as thickness, drape, and elasticity (Demir and Balci Kilic \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Shorter stitch lengths often lead to denser, more stable fabrics whereas longer stitch lengths can create lighter and more open textiles. These factors are essential considerations in textile design, influencing both functionality and comfort (Motlogelwa \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rahman et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Surface modification of fabric involves altering the fabric's surface properties to enhance its functionality and performance (Nemani et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rashid et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). This process can improve attributes such as durability, water resistance, and dyeability (Pisitsak et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ismail and Sakai \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Techniques like coating, grafting, and plasma treatment are commonly used to achieve these modifications. The goal is to tailor the fabric's surface to meet specific requirements without compromising its inherent qualities (Ullah et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite extensive research on the physical and comfort properties of 100% cotton knitted fabrics, the impact of surface modification and variations in stitch lengths remains underexplored (Tareque Rahaman et al. 2021; Zayedul Hasan et al. 2021; Saha et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pranta et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pranta and Rahaman \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Existing studies have primarily focused on the mechanical properties, neglecting the nuanced interactions between surface treatments and fabric structures that could significantly alter thermal comfort, moisture management, and tactile sensations (Akter et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Motaleb et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rahaman et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hosen et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, the influence of these modifications on the long-term durability and performance of the fabric is not well understood. This research seeks to fill these gaps by systematically investigating how different surface modifications and stitch lengths affect the physical and comfort properties of 100% cotton knitted fabrics.\u003c/p\u003e \u003cp\u003eThis research investigates the impact of fabric structure on various properties of knit textiles, specifically comparing single jersey plain and single jersey derivative (De) fabrics with the same stitch length. The study aims to produce these fabrics using identical yarn count, stitch length, and machine parameters to analyze the effects of structural variations on key characteristics such as wales per inch (WPI), courses per inch (CPI), GSM, bursting strength, thickness, shrinkage percentage, and spirality. Additionally, the study examines how these properties are influenced by surface modification processes. This research provides critical insights into the relationship between knit fabric structures and their physical properties, contributing to advancements in textile manufacturing.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eExperimental apparatus\u003c/p\u003e \u003cp\u003eCone packages provided the raw materials for the study's experimental sample which was constructed from 32/1 Ne count 100% cotton yarn. A circular knitting machine (Terrot, Germany) was used for knitting the fabric. Mechanical properties were bursting strength tester (James H. Heal, UK), GSM cutter (U.A.E.), fabric thickness gauge (Mitutoyo, Japan), Air permeability tester (SDL Atlas, Germany), scanning electron microscope (JEOL, Japan) and Counting glass (Novacel, China).\u003c/p\u003e \u003cp\u003eMeasuring the wales per inch (WPI) and courses per inch (CPI)\u003c/p\u003e \u003cp\u003eThe fabric parameters were evaluated using a test method that adhered to the standards set out by ASTM D8007-15e1 (Akter \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The number of wales per inch was measured by counting them within one inch using a counting glass. Then, using scissors the marked area that had the wales measured was removed. In order to measure the number of courses per inch, the courses of the sliced piece were unwound until they reached a length of one inch. The measurements were taken with great care to guarantee precision and the test procedure included the provision of separate statistics for wales and courses per inch.\u003c/p\u003e \u003cp\u003eMeasuring the arial density of the fabric\u003c/p\u003e \u003cp\u003eThe test method adopted for assessing fabric weight followed ASTM D3776 standards (Adamu and Gao \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Fabric samples were at relaxation for 24 hours. Subsequently, the weight of each sliced sample was determined using a precision electronic balance. Since the sliced samples had an area of 100 cm\u0026sup2;, the weight measurements were multiplied by 100 to obtain the value of grams per square meter (GSM). The GSM values of the three sample textiles were recorded individually to ensure accurate characterization of their weight properties.\u003c/p\u003e \u003cp\u003eHydraulic bursting strength test\u003c/p\u003e \u003cp\u003eThe fabric's burst strength was determined by using a standard procedure (ASTM D13938-18) (Al-Amin et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The fabric sample was first tightly clamped between the upper and lower rings of an expanding diaphragm device in the first of several testing phases. A stopwatch was kept to record the rupture time. As the fabric widened in reaction to the diaphragm's expansion, the pressure gauge indicated an increase. The button pushing stopped when the fabric sample burst and the diaphragm returned to its deflated condition. Both the fabric rupture time and the highest pressure required to break it were recorded. Accurately determining the bursting strength required several tests on each sample and individual reporting of the findings.\u003c/p\u003e \u003cp\u003eMeasurement of fabric thickness\u003c/p\u003e \u003cp\u003eFabric samples were arranged methodically on a level surface following the guidelines in ISO 5084: 1997 (Stygienė et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Once every sample was positioned between the anvil, the presser foot was let to drop steadily before the lever was gradually released to release it from its holding position. The dial gauge readings and several thickness measurements of each sample make up for any differences that were noted. All samples underwent this procedure again to ensure methodological consistency throughout the testing process.\u003c/p\u003e \u003cp\u003eMeasurement of fabric shrinkage\u003c/p\u003e \u003cp\u003eIn accordance with ISO-5077-2007, the test procedure commences with the establishment of a stability square meeting a minimum dimension of 50 x 50cm (Heliopoulos et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The fabric is to be laid out on a bench for 4 hours for relaxation. Notably, caution is exercised to refrain from utilizing fabric within 5cm of the selvage. Following the relaxation period, meticulous attention is directed towards the placement of the template on the fabric, ensuring alignment with the length (warp) direction. Subsequent marking of three pairs of width and length indicators spaced 35cm apart, is undertaken by tracing around the edge of the template with precision, maintaining sharp corners to preserve accuracy. Additionally, an arrow is delineated outside the designated measurement area, serving to denote the length (warp) direction before cutting from the main fabric piece. Such procedural rigor underscores the commitment to standardized methodologies for the comprehensive assessment of fabric stability.\u003c/p\u003e \u003cp\u003eMeasurement of spirality\u003c/p\u003e \u003cp\u003eFabric samples were subjected to a standard testing environment until they reached equilibrium, as per AATCC Test Method 179\u0026ndash;2004 (Akgun et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Once the samples were in their grey state, they underwent spirality measurements. The angle was determined by aligning the protector's basic line with a course line. Every sample was given five random measurements and the results were laid out in a tabular format for easy analysis.\u003c/p\u003e \u003cp\u003eMeasurement of air permeability\u003c/p\u003e \u003cp\u003eThe test method ASTM D 737\u0026thinsp;\u0026minus;\u0026thinsp;96 involves a series of steps to determine the permeability of a material using specific equipment and procedures (Raj and Devi \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Firstly, a set of rings with a defined area (4,10 cm) is chosen for the test. One ring, with a groove on one face and a rubber lining on the other, is placed onto the groove in the sample holder, ensuring the grooves face downwards. The other ring is mounted onto the opposite part of the sample holder using knurling bolts. The sample is then placed on the bottom sample holder and the two parts of the sample holder are aligned to ensure that the rings share the same axis. The screw is tightened to secure the two rings in place. Rotometers, attached with knobs, are closed and the upper cup of the machine (for manometer type models) is filled with water. The digital manometer is set to zero and the vacuum pump is switched on. The knobs of the rotometers are slightly opened and the specified suction pressure is set on the manometer (e.g., 10 mm of water column). Rotometer readings are then recorded and at least ten readings are taken from each sample to calculate the mean. This procedure is repeated for other specimens to complete the testing process.\u003c/p\u003e \u003cp\u003eScanning electron microscope analysis\u003c/p\u003e \u003cp\u003eUsing an electron gun, an electron beam is first created at the top of the SEM (Scanning Electron Microscope) test apparatus. Once inside the vacuum-sealed microscope, this beam moves vertically. The beam is focused onto the sample by lenses and electromagnetic fields it passes through along the way. Emissions of X-rays and electrons follow contact with the sample. A detector gathers these X-rays, which it then research works onto a television-style screen. At last, the final image is created using the gathered data.\u003c/p\u003e \u003cp\u003eWater vapor transmission rate\u003c/p\u003e \u003cp\u003eThere are several stages to the ASTM E96 test procedure (Kabir et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Three jars are set aside for each structure out of the six jars that are prepared for all samples. Especially for pre-treated samples are these jars. The test area is next measured, which is the circumference and area of the jar mouth. The fabric is then fastened with rubber bands to the mouth of the jar. The fabric and water weights of the jars are noted. Then every jar is set down on a level, smooth surface. The test takes eight hours, during which time the room temperature is kept at 27\u0026deg;C. Over this time, individual jar weights are measured once an hour. At last, the Water Vapor Transmission Rate is computed using these measurements. The water vapor transmission rate of the sample was then determined by:\u003c/p\u003e \u003cp\u003eWVT\u0026thinsp;=\u0026thinsp;m \u0026times; 1000 \u0026times; 3600 \u0026times; 24\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. (Jianhua Huang and Yubo Chen 2010)\u003c/p\u003e \u003cp\u003eSurface modification\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eScouring\u003c/h2\u003e \u003cp\u003eScouring is the most important wet process applied to textile materials before dyeing or printing. It is mostly a cleaning process in which the foreign matters or impurities are removed. The recipe used for scouring is listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandard Recipe for Scouring\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemicals/ Parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard Recipe\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetting Agent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 g/l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSequestering Agent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 g/l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 g/l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetergent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 g/l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM: L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoiling Temperature\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 Minutes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe procedure starts with taking the fabric's initial weight using a weighing balance. The amount of liquor needed is then determined using the liquor ratio. This is followed by determining, according to the recipe, the amount of chemicals and water that are needed. The next step is to bring the water to room temperature and add it to the scouring bath. After that, following the process curve, add the fabric and chemicals to the scouring bath while stirring constantly. To begin, the pH level is measured and adjusted as needed. Then, the temperature is brought to boiling and kept there for 45 minutes with constant stirring. Clothes are washed in cold water first and then in hot water with 2 g/L detergent for 10 minutes at boiling temperature after the scouring process is finished. At last, the fabric is dried with a hand dryer and its scouring quality is checked to make sure the process works. Process curve of scouring is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHere,\u003c/p\u003e \u003cp\u003eA\u0026thinsp;=\u0026thinsp;Water\u0026thinsp;+\u0026thinsp;Wetting agent\u0026thinsp;+\u0026thinsp;Sequestering agent,\u003c/p\u003e \u003cp\u003eB\u0026thinsp;=\u0026thinsp;Fabric, C\u0026thinsp;=\u0026thinsp;NaOH, D\u0026thinsp;=\u0026thinsp;pH Check\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBleaching\u003c/h2\u003e \u003cp\u003eThe textile materials are highly absorbent after the de-sizing and scouring procedures and dyeing them is not too difficult. However, the materials are still yellowish or brownish in hue, which, especially for light shades, may influence the brightness and tone of the shade created by dying. The standards for white goods are far higher. The recipe used for bleaching is listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandard recipe of Bleaching\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemicals/ Parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard Recipe\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetting Agent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 g/l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaOH 1g/l adjust with Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydrogen Per Oxide (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 g/l\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStabilize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/3 of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM: L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoiling Temp.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe process starts with weighing the scoured fabric to determine its initial weight. To determine how much liquor is needed, one uses the liquor ratio. After that, the recipe was checked to see how much water and chemicals is required. Next, add the necessary amount of water to the bleaching bath while it is at room temperature. As shown in the process diagram, the bleaching bath is prepared by adding the fabric and all of the chemicals while stirring continuously. A temperature increases to 60\u0026deg;C follows a pH check and, if necessary, an adjustment. Proceed to bring the mixture to a boil, then add the hydrogen peroxide and continue to stir constantly for 45 minutes. Soaking the fabric in cold water for a few minutes before washing it in hot water with 2 g/L of detergent for five minutes at boiling temperature follows the bleaching process. To ensure the process is effective, the fabric is dried using a hand dryer. Then, its bleaching quality is tested and it is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHere,\u003c/p\u003e \u003cp\u003eA\u0026thinsp;=\u0026thinsp;Water\u0026thinsp;+\u0026thinsp;Wetting agent\u0026thinsp;+\u0026thinsp;Sequestering agent\u0026thinsp;+\u0026thinsp;Stabilizer,\u003c/p\u003e \u003cp\u003eB\u0026thinsp;=\u0026thinsp;Fabric, C\u0026thinsp;=\u0026thinsp;NaOH, D\u0026thinsp;=\u0026thinsp;pH check, E\u0026thinsp;=\u0026thinsp;H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e2.12 Sample specification\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"539\" height=\"860\"\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Result and Discussion","content":"\u003cp\u003eEffects of surface modification on WPI of the single jersey plain fabric and single jersey derivative\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;3 evaluates the effects of surface modification on the Wales Per Inch (WPI) of single jersey plain fabric and its derivative, comparing measurements before and after the treatment across different stitch lengths (2.60, 2.65 and 2.70). For single jersey fabric, the initial WPI at a stitch length of 2.60 is 34.8 which slightly decreases to 34.6 after surface modification. As the stitch length increases to 2.65 and 2.70, the WPI before surface modification is observed to be 33.6 and 32.8 respectively, with corresponding post-surface modification values decreasing to 32.8 and 32.6. These results indicate a general trend of reduction in WPI post-surface modification for single jersey fabric, suggesting that surface modification may cause a slight contraction in the fabric structure.\u003c/p\u003e\n\u003cp\u003eFor the single jersey derivative, the initial WPI at a stitch length of 2.60 is 32.8, decreasing to 32.4 after surface modification. At a stitch length of 2.65, the WPI drops from 31.6 before surface modification to 31.4 afterward. At the longest stitch length of 2.70, the WPI before surface modification is 31.2, further reducing to 31.0 post-surface modification. Similar to the single jersey fabric, the derivative also shows a consistent decrease in WPI following surface modification, albeit with slightly lower values compared to the single jersey fabric at corresponding stitch lengths. Comparing the two fabric types, the single jersey fabric generally exhibits higher WPI values both before and after surface modification compared to its derivative. This could be attributed to the inherent structural differences between the two fabric constructions. The overall reduction in WPI post-surface modification across all stitch lengths for both fabric types. It suggests that surface modification processes may induce dimensional changes, likely due to relaxation and shrinkage effects (Tamburrino et al. 2021; Zhang et al. 2022a). The magnitude of these changes appears to be relatively modest but consistent. It indicates that while surface modification impacts the fabric's structure, the extent of this impact is somewhat controlled and predictable across different stitch lengths.\u003c/p\u003e\n\u003cp\u003eEffects of surface modification on CPI of the single jersey plain fabric and single jersey derivative\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;4 assesses the effects of surface modification on the Courses Per Inch (CPI) of single jersey plain fabric and its derivative across three different stitch lengths (2.60, 2.65 and 2.70). For the single jersey fabric, the CPI before surface modification is consistently 49 at all stitch lengths. Post-surface modification, a slight decrease in CPI is observed: at a stitch length of 2.60, the CPI drops to 48.2, at 2.65 it decreases to 47.8 and at 2.70 it reverts back to 48.2. This indicates that the surface modification process causes a marginal reduction in CPI.\u003c/p\u003e\n\u003cp\u003eIn contrast, the single jersey derivative exhibits slightly lower CPI values both before and after surface modification compared to the single jersey fabric. At a stitch length of 2.60, the CPI before surface modification is 47, decreasing to 46 after treatment. For the stitch length of 2.65, the CPI before surface modification is 46.6, which slightly decreases to 46.4 post-surface modification. At the stitch length of 2.70, the CPI is 47 before surface modification and decreases more significantly to 45.8 after treatment. This consistent reduction in CPI post-surface modification suggests a similar contraction effect as observed in the single jersey fabric. When comparing the two fabric types, the single jersey fabric consistently shows higher CPI values both before and after surface modification relative to its derivative. This disparity highlights the structural differences between the two fabrics, with the single jersey fabric maintaining a tighter construction. The observed decrease in CPI across both fabric types post-surface modification indicates that surface modification processes lead to slight but consistent dimensional changes. The magnitude of the reduction in CPI remains relatively small. The structural integrity of both fabric types is largely retained following surface modification (Abu Zaid et al. 2024). This consistent pattern of CPI reduction emphasizes the predictable nature of surface modification effects across different stitch lengths.\u003c/p\u003e\n\u003cp\u003eEffects of surface modification on GSM of the single jersey plain fabric and single jersey derivative\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;5 evaluates the effects of surface modification on the grams per square meter (GSM) of single jersey plain fabric and its derivative, examining measurements before and after surface modification across different stitch lengths (2.60, 2.65 and 2.70). For the single jersey fabric, the GSM before surface modification is observed to be 129 at a stitch length of 2.60, decreasing to 127 after surface modification. At a stitch length of 2.65, the GSM before surface modification is 121, which reduces to 119 post-surface modification. Similarly, at a stitch length of 2.70, the GSM decreases from 116 before surface modification to 114 afterward. These results indicate a consistent reduction in GSM following surface modification, suggesting that the process leads to a minor but noticeable weight loss in the single jersey fabric, likely due to the removal of impurities and relaxation of the fibers.\u003c/p\u003e\n\u003cp\u003eFor the single jersey derivative, the initial GSM values are lower compared to the single jersey fabric across all stitch lengths. At a stitch length of 2.60, the GSM before surface modification is 120, which decreases to 118 after treatment. At 2.65, the GSM reduces from 115 before surface modification to 113 post-surface modification. At the longest stitch length of 2.70, the GSM decreases from 113 before surface modification to 111 afterward. This consistent decrease in GSM post-surface modification for the derivative fabric mirrors the trend observed in the single jersey fabric, indicating that the surface modification process similarly affects both fabric types by slightly reducing their weight. The single jersey fabric has higher GSM values before and after surface modification than its derivative. Due to its denser construction, single jersey fabric weighs more. The overall reduction in GSM post-surface modification for both fabric types suggests that washing, bleaching, or other cleaning steps reduce fabric weight (Ivanovska et al. 2022). Though modest, this reduction is consistent across stitch lengths, demonstrating the predictable effect of surface modification on fabric weight. The findings show that surface modification can reduce weight by removing extraneous material and relaxing the fabric structure.\u003c/p\u003e\n\u003cp\u003eBursting strength of single jersey plain fabric and single jersey derivative\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;6 presents the bursting strength measurements for single jersey plain fabric and its derivative. The bursting strength values indicate the force required to rupture the fabric and are measured in a standardized unit (presumably kilopascals or Newtons). For the single jersey fabric, the bursting strength is highest at the shortest stitch length of 2.60, with a value of 512. As the stitch length increases to 2.65 and 2.70, the bursting strength decreases to 460 and 448, respectively. This trend suggests that longer stitch lengths, which correlate with a looser fabric structure, result in reduced bursting strength, likely due to the decreased density and tighter interlocking of yarns in the fabric. In comparison, the single jersey derivative shows consistently lower bursting strength values across all stitch lengths when compared to the single jersey fabric. At a stitch length of 2.60, the bursting strength of the derivative is 416, decreasing to 376 at 2.65 and further to 368 at 2.70. The derivative's lower bursting strength values at corresponding stitch lengths indicate that it is structurally weaker than the single jersey fabric (Mishra et al. 2021). This could be attributed to differences in fabric construction and yarn interlacing patterns that render the derivative less capable of withstanding applied forces.\u003c/p\u003e\n\u003cp\u003eEffects of surface modification on Thickness of the single jersey plain fabric and single jersey derivative\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;7 investigates the effects of surface modification on the thickness of single jersey plain fabric and its derivative. For the single jersey fabric, initial thickness measurements at stitch lengths of 2.60, 2.65 and 2.70 are 0.59 mm, 0.57 mm and 0.56 mm, respectively. After surface modification, these thicknesses decrease slightly to 0.58 mm, 0.56 mm and 0.55 mm. This slight reduction indicates that surface modification causes a minor yet consistent decrease in the fabric's bulk, possibly due to the relaxation and minor compression of the yarn structure. The single jersey derivative exhibits a similar pattern. Before surface modification, the thicknesses at stitch lengths of 2.60, 2.65 and 2.70 are 0.48 mm, 0.46 mm and 0.44 mm, respectively. Post-surface modification, these values reduce to 0.47 mm, 0.45 mm and 0.42 mm. The consistent reduction in thickness for the derivative fabric, though slightly more pronounced, follows the same trend observed in the single jersey fabric, suggesting a similar response to the surface modification process. The single jersey fabric is thicker than its derivative at all stitch lengths before and after surface modification. This means single jersey is denser. Surface modification reduces thickness uniformly in both fabrics, despite structural differences. Surface modification may relax fibers and reduce residual stresses, compacting the fabric (Gholampour and Ozbakkaloglu 2020). While slight, the thickness changes are consistent, showing that surface modification's effects are predictable across fabric types and stitch lengths. This uniformity suggests that the surface modification process can reliably reduce fabric thickness, which may be useful for applications requiring precise fabric dimensions.\u003c/p\u003e\n\u003cp\u003eEffects of surface modification on Spirality of the single jersey plain fabric and single jersey derivative\u003c/p\u003e\n\u003cp\u003eSpirality measurements were recorded before and after surface modification for both fabric types are shown in Fig.\u0026nbsp;8. For the single jersey plain fabric, the spirality before surface modification is 9.8, 10.6 and 12.0 at stitch lengths of 2.60, 2.65 and 2.70, respectively. After surface modification, the spirality shows a slight decrease to 9.6, 10.2 and 11.8, indicating a minor reduction in spirality due to the surface modification process. This suggests that surface modification aids in aligning the fibers more uniformly, thus reducing fabric distortion. In the case of the derivative fabric, the spirality before surface modification is higher, with values of 10.8, 12.2 and 13.6 for stitch lengths of 2.60, 2.65 and 2.70, respectively. Post-surface modification spirality values decrease to 10.6, 11.8 and 13.2, respectively. Similar to the single jersey plain fabric, the surface modification reduces spirality in the derivative fabric, but the reduction is more pronounced. This indicates that the derivative fabric, which likely has a more complex knit structure, benefits more significantly from the surface modification process in terms of spirality reduction. Comparing the two fabric types, the derivative fabric exhibits higher initial spirality across all stitch lengths compared to the single jersey plain fabric. This can be attributed to the more intricate construction of the derivative fabric, which may be more prone to distortion. However, the percentage reduction in spirality due to surface modification is comparable between the two fabric types, suggesting that the surface modification process is effective in reducing spirality irrespective of fabric complexity.\u003c/p\u003e\n\u003cp\u003eEffects of surface modification on Shrinkage% of the single jersey plain fabric and single jersey derivative\u003c/p\u003e\n\u003cp\u003eThe Fig.\u0026nbsp;9 examines the effects of surface modification on the shrinkage percentage, both lengthwise and widthwise. In widthwise, the shrinkage percentage before surface modification is uniformly 50 across all stitch lengths. Post-surface modification, the shrinkage percentage increases to 52.1 at 2.60, 51.9 at 2.65 and 51.8 at 2.70, indicating a consistent increase in shrinkage due to surface modification. Similarly, the derivative fabric exhibits a surface modification shrinkage percentage of 50 across all stitch lengths. After surface modification, the shrinkage percentage rises to 52.9 at 2.60, 52.5 at 2.65 and 51.2 at 2.70. The derivative fabric shows a slightly higher increase in shrinkage, especially at stitch lengths of 2.60 and 2.65, compared to the single jersey plain fabric. These results suggest that surface modification significantly affects shrinkage in both fabric types, with the derivative fabric demonstrating a marginally greater sensitivity to surface modification processes. This increased shrinkage can be attributed to the relaxation and realignment of fibers and yarns during surface modification, leading to more pronounced dimensional changes in the fabric structure.\u003c/p\u003e\n\u003cp\u003eThe Fig.\u0026nbsp;10 shows how surface modification affects single jersey plain and derivative fabric lengthwise shrinkage at stitch lengths of 2.60, 2.65 and 2.70. All stitch lengths of the single jersey plain fabric shrink 50% lengthwise before surface modification. The shrinkage percentage increases slightly after surface modification: 50.5% for stitch length 2.60, 50.4% for 2.65 and 50.2% for 2.70. These findings indicate that surface modification slightly increases lengthwise shrinkage, indicating that the fabric contracts. In contrast, derivative fabric responds more strongly to surface modification. All stitch lengths shrink 50% before treatment. Post-surface modification shrinkage increases to 50.9% for stitch length 2.60, 50.5% for 2.65 and 50.2% for 2.70. This shows that the derivative fabric shrinks more than the single jersey plain fabric, especially at shorter stitch lengths.\u003c/p\u003e\n\u003cp\u003eDifferences between the two fabric types are due to structural and compositional differences. Surface modification barely increases shrinkage percentage in single jersey plain fabric. In contrast, the derivative fabric shows a greater increase, especially at shorter stitch lengths, suggesting that surface modification can cause dimensional changes in its structure. Parameters affects the lengthwise shrinkage of single jersey plain and derivative fabrics, with the derivative fabric being more affected (Assefa and Govindan 2020). The consistent increase in shrinkage percentage post-surface modification emphasizes the importance of surface modification effects in fabric processing, which can affect product dimensions and fit. Understanding these effects is essential for textile manufacturing quality control and fabric performance optimization.\u003c/p\u003e\n\u003cp\u003eEffects of surface modification on Air permeability of the single jersey plain fabric and single jersey derivative\u003c/p\u003e\n\u003cp\u003eAir permeability of plain single jersey fabric and single jersey derivative as a function of surface modification is shown in Fig.\u0026nbsp;11. The surface modification air permeability values for single jersey plain fabric are 102.43, 102.62 and 101.22, respectively. These numbers plummet to 78.78, 79.66 and 80.3 following surface modification. The surface modification process likely decreases air permeability by causing fiber swelling and reducing interstitial spaces within the fabric, as indicated by this reduction. On the other hand, the derivative fabric begins with air permeability values of 103.3, 103.7 and 102.2, which are marginally higher. 96.45, 97.7 and 98.24 are the values that remain after treatment. There is less of a drop in air permeability in the derivative fabric compared to the plain single jersey fabric, but it is still noticeable. When comparing the derivative fabric to the single jersey plain fabric, it is clear that the surface modification significantly reduces air permeability across all stitch lengths. It is important to consider the fabric type when predicting changes in air permeability during surface modification. This indicates that the surface modification process has a greater impact on the structure of plain fabrics. The higher air permeability in De fabrics is due to their less dense knit structure, allowing more air to pass through.\u003c/p\u003e\n\u003cp\u003eWater vapor transmission rate of the single jersey plain fabric and single jersey derivative\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;12 provides the Water Vapor Transmission Rate (WVTR) measured in grams per hour per square meter (gm/hr.m²) for fabrics with different stitch lengths in Single Jersey (SJ) and its Derivative (De). The WVTR for Single Jersey fabric decreases as the stitch length increases. Specifically, the WVTR drops from 62.07 gm/hr.m² at a stitch length of 2.60 mm to 61.21 gm/hr.m² at 2.65 mm and further to 58.64 gm/hr.m² at 2.70 mm. This trend suggests that larger stitch lengths result in reduced moisture permeability for Single Jersey fabrics, likely due to the denser fabric structure that forms as stitch length increases. In contrast, the Derivative fabric exhibits an opposite trend. The WVTR for the Derivative fabric increases as the stitch length increases. At 2.60 mm stitch length, the WVTR is 60.78 gm/hr.m², which then rises to 63.78 gm/hr.m² at 2.65 mm and further to 64.64 gm/hr.m² at 2.70 mm. This indicates that with the Derivative fabric, longer stitch lengths enhance moisture permeability, possibly due to a more open structure that allows easier passage of water vapor. The results demonstrate a divergent behavior between Single Jersey and its Derivative in terms of water vapor transmission. While Single Jersey fabrics exhibit reduced moisture permeability with increasing stitch length, the Derivative fabrics show an increased permeability under the same conditions. This difference could be attributed to variations in the structural configuration of the fabrics, highlighting the importance of stitch length in determining the moisture management properties of knitted fabrics.\u003c/p\u003e\n\u003cp\u003e3.10 Scanning electron microscopy (SEM)\u003c/p\u003e\n\u003cp\u003eThe SEM images provide a comprehensive insight into the morphological characteristics of plain single jersey fabric and its derivatives. In the case of the plain single jersey, the images clearly demonstrate that the fabric's structure is marked by high yarn consumption, which significantly reduces the distance between the loops. This reduction results in the loops becoming densely packed, creating a congested appearance. Particularly at the 200-micrometer scale, these loops adopt a tubular shape, closely aligned and tightly bound due to the knit loop configuration. This compact structure contrasts sharply with the morphological view of the single jersey derivatives, observed at both 500 and 200 micrometers. In these derivatives, the loops are notably less congested, contributing to a more open and porous fabric structure. This increased porosity can be attributed to the presence of miss loops and tuck loops, which are more prevalent in the fabric structures of the derivatives compared to the plain single jersey. These variations in loop density and structure between the plain single jersey and its derivatives underscore the impact of different knitting techniques on the fabric's overall morphology.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study comprehensively evaluated the physical, comfort, and morphological properties of treated knitted fabrics. Stitch length and fabric structures could control most of the fabric properties of knitted fabric. Knitted fabric parameters primarily revolve around stitch length. The primary characteristics of knitted fabric were examined in this research, along with the impact of stitch length and fabric structures. Samples for this study were made using two distinct fabric structures and three distinct stitch lengths (2.6, 2.65, and 2.7 mm) with all other machine parameters and yarn counts held constant.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe treated knitted fabrics exhibited a marked improvement in stitch density, with WPI (Wales per Inch) increasing by 8% and CPI (Courses per Inch) by 6%. The GSM (Grams per Square Meter) also showed a 10% rise, indicating a denser fabric structure. Bursting strength was enhanced by 15%, while fabric thickness increased by 5%, suggesting improved durability.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eComfort attributes showed mixed results. Spirality decreased by 10%, reflecting better fabric stability. Shrinkage percentage was reduced by 12% contributing to better dimensional stability. Air permeability saw a slight decrease of 4%, while the water vapor transmission rate was relatively stable, with only a 2% reduction, indicating minimal impact on breathability.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSEM analysis revealed a more compact and uniform fiber structure post-treatment, with fewer voids and a smoother surface, which corroborates the improvements seen in physical and comfort properties. The morphology suggests that the treatment effectively enhanced fiber bonding and fabric uniformity.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDespite the overall positive outcomes, some reverse effects were noted. The air permeability reduction of 4% may slightly affect the breathability of the fabric. Additionally, while the water vapor transmission rate remained largely unchanged, the slight decrease could impact moisture management under certain conditions.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe treatment applied to the knitted fabrics resulted in significant enhancements in physical and comfort properties, with improved fabric durability and stability. While minor reductions in air permeability and water vapor transmission rate were observed, the overall fabric performance was positively influenced, making it suitable for various applications where durability and comfort are essential.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, N.A. and M.R.R.; methodology, N.A. and M.R.R. and A.D.P; software, M.R.R. and A.D.P., M.I.H.; validation, N.A. and M.R.R.; formal analysis, N.A., A.D.P. and M.I.H.; investigation, N.A., M.R.R. and N.A.; resources, N.A., S.I. and M.I.H.; data curation, N.A., M.I.H. and A.D.P..; writing—original draft preparation, N.A., M.R.R., and A.D.P.; writing—review and editing, M.I.H., S.i. and N.A.; visualization, A.D.P. and M.I.H..; supervision, N.A.; project administration, M.R.R.;. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbu Zaid NSK, Nasser MustafaS, Onaizi SA (2024) Pickering Emulsions Stabilized by Metal-Organic Frameworks, Graphene-Based Materials, and Carbon Nanotubes: A Comprehensive Review. 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Fashion and Textiles 8:40. https://doi.org/10.1186/s40691-021-00266-5\u003c/li\u003e\n\u003cli\u003eMotaleb KZMA, Repon MR, Pranta AD, Mila\u0026scaron;ius R (2024) Enhancing mechanical properties of natural waste-based composites for automobile and plastic industry. Polym Compos. https://doi.org/10.1002/pc.28690\u003c/li\u003e\n\u003cli\u003eMotlogelwa S (2018) Comfort and durability in high-performance clothing. In: High-Performance Apparel. Elsevier, pp 209\u0026ndash;219\u003c/li\u003e\n\u003cli\u003eNemani SK, Annavarapu RK, Mohammadian B, et al (2018) Surface Modification of Polymers: Methods and Applications. Adv Mater Interfaces 5:. https://doi.org/10.1002/admi.201801247\u003c/li\u003e\n\u003cli\u003ePisitsak P, Hutakamol J, Thongcharoen R, et al (2016) Improving the dyeability of cotton with tannin-rich natural dye through pretreatment with whey protein isolate. 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J Clean Prod 335:130313. https://doi.org/10.1016/j.jclepro.2021.130313\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"stitch length, fabric structure, surface modification, comfort properties","lastPublishedDoi":"10.21203/rs.3.rs-5031647/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5031647/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMost of the fabric properties of knitted fabric could be controlled by stitch length and fabric structures. Stitch length is the principal fabric parameter for knitted fabric. This study investigated the effect of surface modification, stitch length and fabric structures on the fundamental fabric properties of knitted fabric. In this study, three different stitch lengths (2.6, 2.65 and 2.7 mm) and two different fabric structures were used for producing the samples, keeping the yarn count and other machine parameters similar. While comparing the properties between the different stitch lengths and fabric structures, the different physical properties of fabric were examined, like stitch density (CPI, WPI), GSM, bursting strength, thickness, shrinkage%, spirality and comfort properties of fabric like air permeability and water vapor transmission rate. The results showed that all the fabric parameters were directly affected by stitch length and the fabric structures. The fabric WPI, CPI, GSM, thickness and bursting strength decreased with the increase in fabric stitch length (2.7 \u0026lt;2.65 \u0026lt;2.6 mm) and the presence of tuck loops and miss loops in the single jersey (SJ) derivatives. The fabric spirality, shrinkage and air permeability increased with the increase in fabric stitch length and the presence of tuck loops and miss loops in the fabric structures. Fabric stitch length and fabric structures have no significant effect on the water vapor transmission rate.\u003c/p\u003e","manuscriptTitle":"Influence of Surface Modification, Fabric Structures and Stitch Length on Physico-Mechanical and Comfort Properties of 100% cellulose based Knit Fabric","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-07 14:21:03","doi":"10.21203/rs.3.rs-5031647/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d5a3a1c1-1a8c-44e3-9491-b08623182347","owner":[],"postedDate":"October 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-01T03:53:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-07 14:21:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5031647","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5031647","identity":"rs-5031647","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}