Evaluation of Length and Fiber Count Determinations for Natural and Synthetic Microfibers Using the OpTest Fiber Quality Analyzer

Abstract

Microfibers are small fiber particles that range from 1 µm to 5 mm in length, generated through the home laundering and daily wear of textile garments. Microfibers stemming from synthetic textiles are a global pollution problem marked by their slow biodegradation and steady environmental accumulation. Thus, the quantification and study of factors controlling their generation is of interest. The aim of the current study included exploring the use of a Fiber Quality Analyzer-360 (FQA) for examining fiber counts and lengths of microfibers derived from cotton, flax, ramie, hemp, acrylic, polyester, viscose, and polyamide, and to explore if additional preparation steps, such as sonication, would improve microfiber detection by the system. While probe sonication led to higher fiber counts for most microfiber types, average microfiber lengths were statistically similar for most samples, with only the hemp and ramie samples showing statistically shorter microfibers following sonication. FQA detection estimates for cotton, viscose, and ramie microfibers were high, at 99, 101, and 116% for viscose, flax, and cotton, respectively. In contrast, synthetic microfibers of acrylic, polyamide and polyester showed 77, 43, and 14% detection rates, respectively. The high detection rate for the cotton sample is partly due to the higher fineness value obtained from the gravimetric determination. A similar calculation using AFIS fineness showed 86% detection. These observations confirm the significance of properly suspending the samples to accurately quantify microfibers while using the FQA system. Furthermore, the reduced detection of the examined synthetic microfibers suggests the limitations of the FQA as a technique for the direct comparison of natural and synthetic microfiber counts.

1. Introduction

Microfibers, as the term is used herein, are small fiber particles that range from 1 µm to 5 mm in length that have separated from larger textiles typically through casual wear, washing, or tumble drying [1,2,3]. Microfibers is a general term that applies both to fibers generated from synthetic textiles, such as polyester, acrylic, and polyamide, and those generated from naturally sourced fibers, such as cotton, rayon, or ramie. Synthetic microfibers are more commonly described as microplastics [4,5,6]. While degradation studies of natural microfibers have confirmed their prompt degradation in the environment, many of the synthetic fibers have been shown to endure and accumulate in the environment [2,7,8]. Establishing quick and reliable methods for the quantification of microfibers, both from natural and synthetic sources, could aid efforts to better understand their environmental incidence, and to employ textile processing and garment construction techniques that could lead to the production of more durable garments less prone to generating microfibers.

Microfiber generation studies have been reported for a wide range of fabric and garment types, including knit and woven fabrics [2,9,10,11,12,13], fleece fabrics [11,14], blankets [14], non-woven materials, t-shirts [14], and outerwear [11]. Similarly, a diverse set of testing conditions have been reported. These include microfibers generated following home washing machines cycles [15], from top and frontloading machines [16], drying sequences in home drying machines [17], washings in accelerated laundering systems [2,18,19], and Martindale abrasion tester examinations [20,21,22]. Methods for quantifying the number and length parameters of the generated microfibers vary between studies, with some methods relying on gravimetric methods [12], traditional image analysis [8,13,14], IR-mediated mapping studies, and automated visual microscopes [10,23]. While comparison among studies is complicated by the variety of experimental approaches, some general observations can be drawn. Studies that incorporated both synthetic and naturally sourced fabrics found a higher incidence of microfiber generation from the natural fibers when compared to the synthetic fiber materials. Additionally, garments made from lower twist yarns, such as fleece fabrics, generally produce a higher count of microfibers [2,24,25,26].

Zambrano and collaborators adapted an accelerated washing system to examine microfiber generation in a manner that mimicked household washers [2]. In this series of studies, an Optest FQA was used for measuring microfiber counts and lengths for cotton, rayon, and polyester microfibers. It was reported that based on their results, cellulose-based fibers (cotton and rayon) were expected to biodegrade faster than polyester in aquatic aerobic environments [2]. The FQA is an automated digital image analysis system routinely used for the examination of fibers in pulp fibers. To achieve this, a microfiber suspension is taken by the system into the internal flow cell of the FQA, and each microfiber particle is photographed and measured. The system automatically generates a report with average fiber lengths (arithmetic, length-weighted, and weight-weighted), and several additional determinations useful in the pulp industry. Given its ease of use and relatively quick examination time (~8 min. per 600 mL suspension), wide adoption of the FQA system or a similar automated imaging system for the quantification of microfibers could aid comparison across studies. Still, several potential limitations of the system should be further examined. In our previous study, we showed that improper sample suspension leads to incomplete fiber counts [1]. To counter this issue, pulse sonication of the sample suspension was performed prior to FQA examination. Notably, differences in detection by the FQA systems for the various cotton samples were not attributed to the degree of secondary cell wall thickness, an indication of fiber maturity.

In the current study, we further explored the use of an FQA system for quantifying microfiber particles. For the study, eight microfiber samples from synthetic and cellulose-based fibers were selected to better examine the ability of the FQA system to detect a wide range of fibers, both natural and synthetic. Of particular interest is understanding whether synthetic microfibers, particularly from hydrophobic polyester, achieve a similar level of suspension in water. Inadequate suspension would lead to lower detection of these microfibers. Additionally, the impact of pulse sonication of the microfiber sample detection by the FQA system was again explored. Fiber counts and fiber lengths were analyzed using statistical models.

2. Materials and Methods

2.1. Microfiber Samples

The polyester microfiber samples were prepared from a length refence sliver (L1A) obtained from USTER Technologies Inc. (Knoxville, TN, USA). Acrylic, polyester, flax, hemp, ramie, and viscose microfiber samples were prepared from spinning slivers obtained from Falls Farm, LLC (Young Harris, GA, USA). Polyamide fibers were obtained from an industry group. Cotton microfibers were sourced from the micronaire calibration standard CM-129 and obtained from the Agricultural Marketing Service (AMS). All microfiber samples were generated using a Thomas Model 4 Wiley mill (Thomas Scientific, Swedesboro, NJ, USA) using the 30-mesh guard. Polyester, acrylic, flax, hemp, ramie, viscose, and polyamide fibers were converted into microfibers without prior modification. Cotton fibers were scoured prior to Wiley mill treatment, as described further below. The resulting microfibers from each sample were then placed in a stacked array of U.S. standard sieves with ASTM specifications [27] of 100 (Advantech, Mentor, OH, USA), 200 (Advantech), and 325 (W.S. Tyler Company, Mentor, OH, USA), and shaken for 15 min using a Humboldt H-4330 (Humboldt Mfg. Co., Elgin, IL, USA) motorized sieve shaker. Flax, viscose, polyester, polyamide, and acrylic microfibers were collected from the 100-sized sieve, while cotton, ramie, and hemp were collected from the 200-sized sieve.

2.2. Cotton Fiber Scouring

For the scouring procedure, 5 g of the cotton sample was selected at random from three different locations of the bulk of the calibration standard. The subsample was then blended three times using a fiber blender (Custom Scientific Instruments, Easton, PA, USA). The blended sample was transferred to a 1000 mL beaker containing 750 mL of distilled water. A dilute solution of the surfactant Triton-X 100 (1%; Fisher Scientific, Pittsburgh, PA, USA) was added to the beaker solution to improve the wettability of the cotton. The mixture was stirred for 15 min and heated to 95 °C. Next, sodium hydroxide (50% solution, Fisher Scientific) was added to the mixture to reach a 1.75% concentration. The mixture was left stirring for two hours at 95 °C. The treated cotton was then rinsed three times by transferring to beakers containing ~750 mL of warm, distilled water. After rinsing, the cotton was then treated with a 0.125% solution of acetic acid and stirred for 15 min. at room temperature. The scoured fibers were then rinsed thoroughly with distilled water, squeezed to remove excess water, and left to air dry overnight. After drying, the treated cotton fibers were again blended three times using the fiber blender to obtain a more consistent sample.

2.3. Microfiber Examination with FQA

Microfiber samples were examined with the FQA (OpTest Equipment Inc., Hawkesbury, ON, Canada). For each examination, 0.0020 g of the microfiber sample was weighted in a 20 mL scintillation vial using an analytical balance (Mettler Toledo XSR205DU, Mettler-Toledo, LLC, Columbus, OH, USA). Prior to weighing, microfiber samples were allowed to equilibrate for 24 h in a conditioned room set to 21 ± 1 °C and 65% relative humidity ±2%. Then, 15 mL of deionized water were added to each vial, and the sample was pulsed for 5 min using a 50 W probe sonicator (Fisherbrand model 50, Fisher Scientific, Pittsburgh, PA, USA). The mixed suspension was transferred to a 600 mL Nalgene plastic beaker (Nalge Nunc International, Rochester, NY, USA) and an additional 585 mL of water were added to the beaker. The suspension was mechanically stirred for 3 additional minutes, which was immediately followed by examination with the FQA. At the beginning of each FQA run, the instrument was flushed 3 times and degassed once. After each examination, the FQA was further flushed and degassed one additional time. A total of 10 samples were examined for each sample type. Non-sonicated examinations followed the same general procedure without the sonication step. Arithmetic mean lengths (L_n) and length weighted mean lengths (L_W) were calculated by the FQA system using the equations described by the manufacturer [1].

2.4. Fiber Property Measurements

Length based on weight, L (w), upper quarter length based on weight, UQL (w), short fiber content based on weight, SFC (w), immature fiber content (IFC), maturity ratio, and fiber fineness were measured for cotton, polyester, and polyamide slivers using the Advanced Fiber Information System (AFIS, USTER Technologies Inc., Knoxville, TN, USA). AFIS examinations were performed under standard cotton testing conditions (see above). For each examination, a sliver of the material was placed in the AFIS sample holder. Fiber samples were examined in three replicates of 3000 fiber examinations and the reported values represent an average of the three examinations. Linear density measurements (fiber fineness) are given in millitex (mTex) and were performed for all fiber types following the ASTM standard method D1577-07 [28].

2.5. Microfiber Characterization

Field emission scanning electron microscopy (FE SEM) studies of cotton, flax, hemp, and rayon, and viscose microfibers were conducted using a Quanta 3D FEG FIB/SEM (FEI Company, Hillsboro, OR, USA). The field emission gun was operated at an accelerating voltage of 5 kV and gun current of 6.3 pA. Each sample was mounted on the stub using a double-sided tape, and a platinum coating was sputtered onto the sample. Optical images of the source fibers of cotton, acrylic, polyester, flax, hemp, ramie, viscose, and polyamide were acquired using an Hirox digital microscope model KH 8700 (Hirox Co., Ltd., Hackensack, NJ, USA) equipped with a revolver zoom lens attachment (MXG-2500REZ, Hirox Co., Ltd.) set in the mid-range lens at a resolution of 1000×.

2.6. Statistical Analysis

FQA test results were extracted from the reports generated by the FQA instrument for each examination using Excel (Excel for Microsoft 365, Version 2304, Microsoft Corporation, Redmond, WA, USA). Collected data were then transferred into JMP 17.0 (SAS Institute Inc., Cary, NC, USA). One way ANOVA analyses (alpha level of 0.05) were performed for all non-sonicated samples (eight groups, n = 80) and for all sonicated samples (eight groups, n = 80). All-pair comparisons and Tukey’s honestly significant difference test (HSD) with 0.05 alpha were also performed. Additionally, each microfiber type was compared individually with and without sonication (two groups, n = 20). For the connecting letters reports, groups not connected by the same letter are statistically different.

3. Results and Discussion

Fiber samples from cotton, viscose, hemp, ramie, flax, polyester, acrylic, and polyamide were examined with the FQA automated microscope system. Given the variety of intrinsic fiber lengths and fiber coarseness of these fibers, routine fiber characterization methods that can examine all the fiber types proved difficult to accomplish. For example, the hemp, flax, and ramie fibers were too coarse for AFIS tests, while the acrylic fibers were too long for proper examination with the AFIS system. Given these limitations, only fibers from cotton, polyester, and polyamide were examined with the AFIS,

Table 1. Fiber characteristics of cotton, flax, hemp, ramie, viscose, acrylic, polyester, and polyamide fibers. AFIS examinations were made on slivers of cotton, polyester, and polyamide.

Sample	Cotton	Flax	Hemp	Ramie	Viscose	Acrylic	Polyester	Polyamide
Fineness * [mTex]	202	2957	3288	676	322	386	169	178
Fineness ‡ [mTex]	150	-	-	-	-	-	188	222
Maturity ratio	0.81	-	-	-	-	-	1.11	1.12
L (w) [mm]	23.6	-	-	-	-	-	1.43	1.40
UQL (w) [mm]	30.0	-	-	-	-	-	28.2	28.4
SFC (w) [%]	12.4	-	-	-	-	-	0.25	1.52
IFC [%]	8.67	-	-	-	-	-	2.70	2.00

* = gravimetric measurement of linear density ^‡ = AFIS measurement of fineness.

. As a result, a gravimetric measurement of linear density was performed for all fiber types, as in Table 1. The linear density of each fiber type provides an indication of how much mass is expected for a certain length of fiber [29] and is also described as fiber fineness. For the measurement, 300 fibers sourced from the slivers were cut to a known length (14.9 mm) and weighted. Polyester showed the smallest gravimetric linear density, followed closely by polyamide and the cotton fibers, at 169, 178, and 202 mTex, respectively. Viscose, acrylic, and ramie followed in the linear density ranking with 322, 386, and 676 mTex values, respectively. Notably, the flax and hemp linear density measurements showed drastically higher values of 2957 and 3288 mTex, respectively. This finding is not surprising, since bast fibers, like those from hemp and ramie, structurally organize as multiple fibrils that are fused together by lignin and other components. Optical images of the fibers support some of the general observations made with the linear density measurements, as in Figure 1. For example, polyester, polyamide, and cotton fibers appear to have the narrowest widths of all the examined samples, while hemp, flax, and ramie fibers have drastically wider fibers. Still, it is important to note that linear density accounts for more than just fiber width. The arrangement of the fiber structure and inner thickness of the fiber play a role as well. In understanding this principle, the cotton and polyester comparison proves particularly useful. While both fibers provided similar linear density measurements, the polyester fiber appears much narrower than the cotton, as in Figure 1. Notably, the fineness measurements from AFIS provided a smaller number for the cotton sample, but higher figures for polyester and polyamide.

The FQA fiber count measurements for mechanically stirred suspensions of microfibers are listed in

Table 2. Fiber count, mean length (L_n), and weighted mean length (L_W) for mechanically stirred microfiber samples of cotton, flax, hemp, ramie, viscose, acrylic, polyester, and polyamide. Average determinations reflect the examination of 10 replicates each weighing 2 mg. Connecting letters, reporting alpha 0.05, are indicated next to each average. Groups not connected by the same letter are statistically different.

Sample	Fiber Count		Ln (mm)		LW (mm)
Cotton	7765 ± 2138	A	0.60 ± 0.03	A	0.73 ± 0.04	AB
Flax	7809 ± 738	A	0.69 ± 0.03	B	0.85 ± 0.03	A
Hemp	2312 ± 1055	B	0.51 ± 0.02	CD	0.65 ± 0.03	BC
Ramie	5505 ± 519	C	0.53 ± 0.01	C	0.69 ± 0.02	B
Viscose	9338 ± 1285	D	0.49 ± 0.02	D	0.62 ± 0.03	BC
Acrylic	4950 ± 370	C	0.53 ± 0.02	C	0.70 ± 0.03	B
Polyester	2088 ± 1053	B	0.38± 0.02	E	0.50 ± 0.01	C
Polyamide	6239 ± 991	E	0.49 ± 0.02	F	0.63 ± 0.2	AB

. The cellulose-containing microfibers of viscose, cotton, flax, and ramie showed the highest fiber counts, ranging from 5505 ± 519 for ramie to 9338 ± 1285 for viscose. Still, it is notable that the viscose fiber counts are almost double the fiber counts of ramie. While the fiber counts for synthetic microfibers of acrylic and polyamide were comparable to those of ramie (4950 ± 370 for acrylic; 6239 ± 991 for polyamide), polyester had noticeably lower fiber counts of 2088 ± 1053. Indeed, several of the microfiber counts were not found to be statistically different and are represented with matching letters in the fiber count column of Table 2. Mean fiber lengths for the corresponding FQA runs are also summarized in Table 2. A range of fiber lengths were observed, with the natural fibers, cotton, hemp, flax, and viscose, showing the longest mean length mean values for L_n and L_W. Of these, flax showed the longest L_n values at 0.69 ± 0.03 mm, while viscose showed the shortest fibers at 0.49 ± 0.02 mm. For the synthetic microfibers, acrylic microfibers showed the largest L_n value, at 0.53 ± 0.02 mm, although this value was not statistically different to those of ramie or hemp. The polyamide microfibers were slightly shorter at 0.49 ± 0.02, but the polyester microfibers showed the shortest L_n of all the samples examined, namely 0.38 ± 0.02. It should be noted that the lengths observed in this study are not entirely intrinsic to the samples or singularly related to their cellulose-based composition. During the sieving step that followed the Wiley mill treatment, microfibers were recovered from the 100 and 200 sized sieves, and both subsamples were tested for L_n using the FQA. The subsample with the L_n value closest to the rayon calibration standard supplied by the manufacturer were selected for further study. The rayon standard has a reported L_n of 0.53 ± 0.02 mm. Only the L_n for the polyester microfibers was significantly shorter. In a previous study, the L_n values of six cotton micronaire standards showed a strong correlation with the fineness of the source fiber [1]. Thus, it is not surprising that the shortest L_n value is observed for the fiber with the smallest fineness value. As expected, L_W values were longer than L_n, as squaring of the length measurements reduces the importance of fine to the overall length means. For the natural fibers, flax also showed the longest L_W values, at 0.85 ± 0.03 mm, while viscose showed the shortest, at 0.62 ± 0.03 mm.

Probe sonication of microfiber samples increased the fiber count for the majority of examined microfibers, as in

Table 3. Fiber count, mean length (L_n), and weighted mean length (L_W) for microfiber samples of cotton, flax, hemp, ramie, viscose, acrylic, polyester, and polyamide examined following pulse sonication. Fiber count increases were calculated using the mechanically stirred fiber counts in Table 2 as the initial number. Pct detection was calculated using Equation (3). Average determinations reflect the examination of 10 replicates each weighing 2 mg. Connecting letters, reporting alpha 0.05, are indicated next to each average. Groups not connected by the same letter are statistically different.

Sample	Fiber Count		Ln (mm)		LW (mm)		Fiber Count	Detection
							Increase (%)	(%)
Cotton	15,599 ± 676	A	0.61 ± 0.02	A	0.75 ± 0.04	A	101	116
Flax	8839 ± 1127	B	0.64 ± 0.04	A	0.83 ± 0.03	B	13	101
Hemp	8052 ± 438	B	0.51 ± 0.02	BC	0.66 ± 0.02	CD	248	-
Ramie	5507 ± 529	C	0.45 ± 0.02	D	0.65 ± 0.02	CD	0.04	-
Viscose	10,377 ± 1121	D	0.49 ± 0.04	C	0.63 ± 0.05	D	11	99
Acrylic	6285 ± 326	C	0.53 ± 0.02	B	0.68 ± 0.02	C	26	77
Polyester	3609 ± 576	E	0.39± 0.02	E	0.52 ± 0.03	E	73	14
Polyamide	8474 ± 1269	B	0.47 ± 0.02	CD	0.63 ± 0.03	D	26	43

. In a previous study, sonication of cotton micronaire standards increased the fiber counts detected by the FQA; however, sonication of the rayon standard used to calibrate the instrument did not impact fiber counts [1]. Following the trend observed in the previous study, sonication increased the fiber counts detected for the cotton microfibers by 101% to 15,599 ± 676. Notably, the biggest increase in fiber count following sonication was observed in the hemp microfibers, with a 248% increase to 8052 ± 438 fibers. The remaining cellulosic microfibers only saw modest or insignificant increases in fiber counts; flax and viscose saw a 13 and 11% increase, respectively, while the ramie fiber count changed by two microfiber counts. Of the synthetic microfibers, polyester showed the greatest fiber count increase following sonication, with a 73% increase to 3609 ± 576, while polyamide showed an 26% increase in fiber counts. Probe sonication did not significantly alter the length averages for most of the microfibers. Indeed, relative distribution plots for each of the eight microfiber samples before and following sonication show overlapping distributions for most microfiber types, as in Figure 2. Three significant deviations are observed. First, the flax and ramie samples show marked increases in very short microfibers counts following sonication, and second, the polyester sample showed a slight increase in relative counts for fibers about 0.4 mm or longer. Statistical analysis of the length determinations showed that only sonicated samples of flax and hemp had statistically shorter fibers, both L_n and L_W values, than their non-sonicated counterparts. Additional analysis is needed to determine if this reduction in length and increase in fiber counts for flax and ramie results from the natural variability of the samples, or from damage from the sonication step. Still, the decrease in mean lengths is relatively small, with a ~7% decrease in length for the L_n value of flax. The increase in mid-range microfibers for polyester likely points to the better suspension and detection of these microfiber following sonication. Taken together, these results strongly support the addition of a sonication step to the sample preparation of microfibers prior to examination with the FQA system.

Linear density determinations point to the high detection by the FQA system of the sonicated suspensions of several of the natural microfibers and the acrylic synthetic microfibers; however, polyamide and polyester microfibers appear to be undercounted by the FQA system. The principle behind this estimation method, based on Equations (1)–(3) [1], is that knowing the linear density of each of the fibers, and knowing the mass of the sample examined, the total length measured by the system should track the fiber count and mean length detected by the FQA system. One limitation of this practice is that the estimate assumes that the width of the fiber is not considerably impacted by processing in the Wiley mill. ESEM images of the cellulose-based fibers displayed in Figure 3 show that the widths of the flax and hemp microfibers are much narrower than what was observed in the optical microscope image of the source fibers shown in Figure 1. A more direct comparison is shown in Figure S1, with estimated fiber widths from the optical images shown as 66 and 114 μm for flax and hemp, respectively, and microfiber width estimated from the ESEM images shown as 32 and 28 μm for the flax and hemp microfibers, respectively. As such, flax and hemp were left out of the detection calculations. Both the ramie and viscose samples are identified at close to 100% by the system, while the cotton samples show the highest detection at 116%. The higher-than-expected detection of the cotton microfiber could be due to the variability in fineness of the sample. The gravimetric method also assumes even fineness in the sample based on 300 fibers. The natural variability in cotton and changes in width, particularly near fractures caused by the Wiley mill, will have an impact on the sample fineness. The extra weight of the fiber cuticle, mostly composed of waxes and pectin, that is removed during scouring could also influence the gravimetric measurement for the cotton sample. We note that when the calculations rely on the AFIS fineness determinations, the percentage detection for the cotton samples is closer to 86%. The synthetic microfibers of acrylic, polyester, and polyamide showed reduced detection by the FQA system, calculated at 77, 14, and 43%, respectively. Even when using fineness values from AFIS, which provided higher values for polyester and polyamide than the gravimetric measurements, the detection levels are relatively low at 16 and 54%, respectively.

$$M_D \left(\%\right)={\displaystyle \frac{W_T \left(\mathrm{mg}\right) }{Sample weight \left(\mathrm{mg}\right)}}\times 100$$

(1)

where

$$W_T \left(\mathrm{mg}\right)=L_T \left(\mathrm{m}\right)\times Fineness \left({\displaystyle \frac{\mathrm{mg}}{\mathrm{m}}}\right)$$

(2)

and

$$L_T \left(\mathrm{m}\right)=L_n \left(\mathrm{m}\right)\times Fiber Count$$

(3)

where $M_D$ equals percent microfiber weight detected, $W_T$ equals total microfiber weight, and $L_T$ equals total microfiber length.

In the previous study of six micronaire cotton standards it was observed that microfiber count was inversely correlated to the fineness of the cotton samples. Similarly, in that study, the cotton samples showed a linear relationship between fiber count and L_n. Considerations of these relationships is complicated for the current study, given the difference in experimental conditions. Still, the bivariate plots in Figure 4 are informative in their presentation of the polyester and polyamide fiber counts and how these are related to their low detection by the FQA. Both plots include the six micronaire standards previously reported [1], and the size of each replicate is proportional to its L_n (left plot) or fineness value L_W (right plot). For the fiber count versus fineness plot, it can be observed that while the cotton used in this study has the same fineness value as one of the micronaire standards in the previous study, the fiber counts for the current study are much lower. This can be explained by the significantly larger L_n value of the cotton used in this study, since the sieving process employed in this study removed more of the shorter microfibers. The polyamide and polyester samples both showed higher fineness AFIS values than the micronaire standards, and, as such, their fiber counts could be expected to be lower than those of the standards; however, their L_n were amongst the shortest seen in both studies. Generally, shorter microfibers L_n values would result in a higher fiber count. This point is further explored in the bivariate plot for L_n versus fiber count. Polyester and polyamide replicates are seen, simultaneously, in the lower half of observed L_n values and lower third region of the fiber counts of the plot, while all other samples in the lower L_n region are found in the upper half for fiber counts. While fiber fineness plays a role in the fiber count observed, it would not account for the significantly lower detection levels of polyamide and polyester. While further work is needed to fully explain the lower detection of the synthetic microfibers, improper suspension of the polyester and polyamide samples is in, in part, responsible. Despite sonication, these fibers do not appear to be as well suspended as the cellulose-based microfibers examined in this study. In the case of polyester, its hydrophobicity might play a role in this limitation. Additional preparation steps might be needed to properly suspend the synthetic microfiber samples.

4. Conclusions

The FQA imaging system was used in this study to detect fiber counts and fiber lengths of natural and synthetic fibers. It was demonstrated that the FQA imaging system proved more suitable for the examination of cellulose-based microfibers, including cotton, viscose, flax, and ramie, as shown by their higher fiber counts, compared to synthetic fibers, like polyester, which was notably lower. Analyzing synthetic microfibers, such as acrylic, polyamide, and polyester, posed a challenge for the accurate fiber count and length measurements by the FQA. It is proposed that these observations stem from the reduced suspension characteristics observed for these samples even after sonication. Moreover, the low detection by the FQA automated microscope observed for polyester, polyamide, and acrylic microfibers suggests that these samples should have additional sample preparation performed before analysis to improve the observed results, and that direct comparison of counts observed from these fibers to the cellulose-based fibers that showed high detection should be limited.

Higher flax and hemp gravimetric linear densities were observed compared to the other six microfiber types. It was concluded that these observations were garnered due to the bast fiber type and microscopic findings of these microfibers. The fused fiber nature of these microfibers, which becomes disconnected during Wiley milling, can also account for these results. Given the significant change in width for the flax and hemp microfiber, detection calculations were not performed. Further work is needed to determine their degree of detection by the FQA system.

Pulsed-probe sonication treatment of microfibers was demonstrated to enhance their suspension in water and increase the fiber count of most microfibers. However, sonication had a minimal effect on microfiber length measurements compared to non-sonicated samples, suggesting that the microfiber lengths were not significantly altered for the majority of microfiber types by the probe sonication. Only flax and ramie showed significantly shorter microfibers following sonication. More work is needed to understand if the changes in length and short microfiber counts for these those types of microfibers are due to damage from sonication. Taken together and with the exemption of samples known to contain ramie or flax microfibers, the data from this study strongly suggest adding a sonication step to the sample preparation before microfiber analysis by the FQA to positively influence the detection of cotton and synthetic microfibers generated from fabrics.