No abstract

A possible candidate as an environmentally friendly nonwoven fabric is one that can be formed from the thermal calendering of a cotton/cellulose acetate blend. Our results focus on biodegradable properties of the fibers as well as tensile properties of the fabric. Cotton, which is a comfortable, absorbent, biodegradable fiber, is the base fiber in the nonwovens. Cellulose acetate, which is a thermoplastic, hydrophilic, mod-

The theory of the fibrogram has been derived based on the probability of randomly catching and holding a fiber. The amount axis has been shown to be proportional to the relative mass when related to the original specimen. Tangential equations are used to show that theoretically, for a length l or longer, the proportional mass, the mean length by number, the relative number, and the mean length by mass of fibers in the sample can be obtained from the fibrogram, as well as the number and mass arrays. Finally, the theoretical percentage of fiber by number or by mass extending a length l or longer from a clamped random sliver of the sample is obtainable from the fibrogram.The theory of the fibrogram was first published by . Hertel [2]. He considered the abscissa as the length axis and stated that the ordinate for I = 0 is NL where N is the total number of fibers in the sample and L is their mean length by number. This multiple is equivalent to the total length of all the fibers. Since Hertel presented this theory, this axis has been considered as the weight axis [6,7], the number or relative number axis [1, 2, 3, 4], or the probability axis [6,8]. In this work, we show that the ordinate axis can be considered as a proportional mass or mass probability axis, and we examine the fibrogram for additional information pertaining to the length of the sample. ' . Theory . Statistical texts generally write P[x) for the probability that the stochastic variable is less than or equal to the real number x. P[x] is called the cumulative distribution function of the variable. The derivative is called the distribution function of x.The general function that leads to the fibrogram must be defined differently. Consider the probability that the stochastic variable is greater than or equal to the real number l; q[l is then the complement of the cumulative distribution function. Since the fibrogram relates to fiber lengths, constraints must be applied to q[/]. Fibers shorter than 0 and longer than the longest fiber within the sample do not exist. The constraints applied to the probability q[I are that where 1m is the length of the longest fiber(s) in the sample. q[l is then the probability of the existence of a fiber in the sample having a length I or longer. The derivative is the distribution function of l.Since the total number of fibers in the sample is N, where N(l ) is the number of fibers in the sample having a length equal to I or longer. This gives Differentiating Q(1) gives the frequency distribution or

Samples of two cotton cultivars were spun with high- and normal-tenacity polyester staple fibers in cotton/polyester blend ratios of 100:0, 67:33, 50:50, 33:67, and 0:100. Stress-strain measurements provided tenacities, elongations, and breaking energies. Based on the stress-strain response of the pure cotton and pure polyester yarns, breaking energies were calculated for the yarn blends, with the assumption that the two different components did not interact. Differences between calculated and experimental values, which were maximum when the polyester content was about 60%, were attributed primarily to the interaction between the constituent cotton and polyester fibers. Support for this attribution was provided through measurements of energy lost when an oscillatory shearing motion was applied to 50-gram blended specimens of carded lap.

Determining cotton fiber tenacity with high volume instrumentation (HVI) requires an accurate measure of specimen linear density. Indirect measurements of linear density such as light attenuation, by which the Zellweger Uster Spinlab HVI system accom plishes this, require additional knowledge about fiber numbers or fiber fineness, usually by introducing a Micronaire correction factor. The effectiveness of this correction is less applicable than fiber fineness determinations obtained by direct weighing methods. Furthermore, fiber fineness distribution is not accounted for, and this factor may be important to enhanced HVI technology. For purposes of discussion, length-fineness distributions by number count and direct weighing methods have been determined for twenty cotton samples using Suter-Webb sorting techniques. Ramifications of fine ness distributions across length groups on cumulative fiber fineness distribution within a randomly prepared test specimen are presented in the context of breaking load normalization by a fiber fineness indicator.