The Microfibre Consortium Examines Why Fibers Fragment
Natural fabrics fragment more fibers than their synthetic foils, a report by The Microfibre Consortium (TMC) found.
The multi-stakeholder initiative has shared the first in-depth statistical analysis of its data portal to 'identify the material factors most likely to influence fiber fragmentation.'
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The technical research report, titled 'Root Cause Analysis: Unravelling the Root Causes Behind Fiber Fragmentation in Textiles,' builds on an October update about the multi-stakeholder initiative's efforts to analyze consortium data and better understand the root causes of fiber fragmentation.
To that end, the large-scale Root Cause Analysis (RCA) studied 1,000-plus TMC Test Method-trialed fabrics housed in The Microfibre Data Portal to glean insights on the pollution pathways contributing to initial laundering loss. The resulting, reportedly first-of-its-kind findings identified the material characteristics most likely to influence fiber fragmentation according to TMC's dataset.
'The publication of this report marks a significant milestone in the fashion and textile industry's efforts to address the challenge of fiber fragmentation,' said Kelly Sheridan, TMC's CEO. 'The research has been extensive and robust, which is only possible thanks to the collective data contributions of our signatories.'
The key fabric characteristics identified as significant contributors to fiber fragmentation include composition, fabric structure, yarn type, dyeing and finishing.
Composition-wise, fabrics made from natural fibers (like cotton and MMCFs) release more fiber fragments than their synthetic counterparts (like polyester and nylon). TMC attributed this finding to the 'higher hairiness and lower durability' of most natural materials compared to synthetics, which are often made from longer, more cohesive filament yarns.
Structurally speaking: Woven fabrics generally exhibited lower fiber fragmentation than knitted fabrics, likely due to increased strength and tighter structure. Within knitted structures, warp-knitted structures were linked to decreased fiber fragmentation compared to weft-knitted structures. Higher fabric density (thread count or stitch density) was also associated with lower average fiber fragmentation.
Fabrics made from filament yarns showed reduced fiber fragmentation compared to those made from staple yarns. Among staple yarn fabrics, increased staple length and higher yarn twist aligned with decreased average fiber fragmentation. Undyed fabrics were associated with higher fiber fragmentation rates than piece-dyed fabrics. Yarn-dyed fabrics were associated with less fiber fragmentation than piece-dyed fabrics as well.
Lastly, the finishing stage was identified as a 'strong influencer' of fiber fragmentation.
Chemically speaking, hydrophilic finishes had higher predicted fiber fragmentation, while hydrophobic finishes and some softening agents had lower fiber fragmentation. Laminated or bonded fabrics, too, showed lower fragmentation. Mechanically speaking, finishing techniques like brushing, compacting and tumbling were linked to increased fiber fragmentation. Alternatively, finishes processes designed to create smoother-feeling fabrics appeared linked to reduced fragmentation.
Despite existing research, the report noted, gaps remain. Without standardized testing or detailed fabric specs, the TMC shared challenges in comparing studies and pinpointing causes of fiber shedding, making real-world solutions 'difficult' to employ.
'We're proud to lead this global effort, but the path forward depends on continued collaboration and the sustained support of companies that sign the Microfibre 2030 Commitment,' said Sheridan. 'With this data, we're closer than ever to meaningful solutions.'
The multi-stakeholder initiative employed a 'rigorous methodological approach.' With research based on a large-scale global study involving the testing of over 1,000 diverse fabrics, the testing utilized data housed in The Microfibre Data Portal, which houses standardized test results and respective fabric specifications.
TMC's methodology involved using its 'internationally-aligned' TMC Test Method. This method simulates domestic laundering conditions to measure fiber loss by weight from fabrics. The quantification of fiber loss is performed through gravimetric analysis. For each fabric, multiple specimens are tested before the data is averaged.
TMC took a machine learning approach to analyze this dataset—primarily Random Forest (RF) modeling. This commonly used algorithm combines the output of multiple decision 'trees' to reach a single result, per IBM's definition. In turn, TMC could assess the effect of various fabric characteristics on fiber fragmentation. The RF model also helped identify patterns and relationships—and the relative importance of such variables—in predicting fiber shedding.
Outside of machine learning, TMC tapped box and whisker plots to visually compare the average fiber fragment loss against data distribution across different groups. The data used, by the way, came from signatories of the Microfibre 2030 Commitment who submitted testing data and fabric technical specifications to the aforementioned portal—likely influencing why only 56 percent of the portal was analyzed.
'For the machine learning methods applied in this study, 634 of 1129 fabrics were analyzed, as only fabrics with complete data in the variables selected for analysis can be used for the statistical methods employed,' the report said.
TMC said the study marks a 'pivotal step' in addressing fiber fragmentation at its root, and should, ultimately, equip the industry with 'data-driven insights' to design lower-shedding fabrics. With the RCA report now completed, TMC is moving into the next phase: filling those gaps.
'To build on this progress, continued research and collective learning are essential,' the report concluded. 'By fostering an open exchange of data and insights, we can drive meaningful advancements in textile design, development and manufacturing, ensuring that future mitigation solutions are both scientifically robust and widely applicable across the industry.'
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Undyed fabrics were associated with higher fiber fragmentation rates than piece-dyed fabrics. Yarn-dyed fabrics were associated with less fiber fragmentation than piece-dyed fabrics as well. Lastly, the finishing stage was identified as a 'strong influencer' of fiber fragmentation. Chemically speaking, hydrophilic finishes had higher predicted fiber fragmentation, while hydrophobic finishes and some softening agents had lower fiber fragmentation. Laminated or bonded fabrics, too, showed lower fragmentation. Mechanically speaking, finishing techniques like brushing, compacting and tumbling were linked to increased fiber fragmentation. Alternatively, finishes processes designed to create smoother-feeling fabrics appeared linked to reduced fragmentation. Despite existing research, the report noted, gaps remain. 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The quantification of fiber loss is performed through gravimetric analysis. For each fabric, multiple specimens are tested before the data is averaged. TMC took a machine learning approach to analyze this dataset—primarily Random Forest (RF) modeling. This commonly used algorithm combines the output of multiple decision 'trees' to reach a single result, per IBM's definition. In turn, TMC could assess the effect of various fabric characteristics on fiber fragmentation. The RF model also helped identify patterns and relationships—and the relative importance of such variables—in predicting fiber shedding. Outside of machine learning, TMC tapped box and whisker plots to visually compare the average fiber fragment loss against data distribution across different groups. 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