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This review discusses the important concept of cotton fiber friction at both the macro-and nanoscale. First, the technological importance of fiber friction and its role in fiber breakage during fiber processing is discussed. Next, previous studies on frictional properties of cotton fibers are reviewed and different experimental procedures to measure friction between fibers or against another surface are evaluated. Friction models developed to explain friction process during various experimental procedures are considered and their limitations are discussed. Since interpretation of friction processes at the macroscale can be challenging (mainly due to difficulties in analyzing the multiple asperities in contact), a separate section is devoted to surveying studies on the emerging field of single-asperity friction experiments with atomic force microscope (AFM). Special attention is given to studies on nanoscale frictional characteristics of rough viscoelastic surfaces (e.g., plant cuticular biopolymers and cotton fibers). Due to the close relationship between friction and adhesion hysteresis at the nanoscale, adhesion studies with AFM on viscoelastic surfaces are also reviewed. Lastly, recommendations are made for future research in the field of frictional properties of cotton fibers.

Throughout the past ten years, comprehensive understanding of fundamental and applied research has focused on functional coating and specifically on microencapsulaion. In this study, weak polycation poly(allylamine hydrochloride) and strong polyanion poly(sodium styrene sulfonate) were used for fabrication of nano film through layer by layer technique on the surface of disperse dye particles. Then micron-sized particles were surrounded by poly(urea formaldehyde) using in-situ polymerization. Chemical structure, surface morphology, and size distribution of these novel microcapsules were characterized by Fourier transform infrared spectrometry, differential scanning calorimetry, optical microscopy, and scanning electronic microscopy. Size and surface morphology of the microcapsules can be optimized by selecting proper weight ratio of urea to formaldehyde and core to shell material type, and amount of surfactant and agitation rate. This technology demonstrated good capability in several applications in textile industry, such as dying fabrics because of saving huge amount of water and showing slow-release property of dye without using dye assistant agents.

The surface topography and nanomechanical attributes of two samples of cotton fibers, namely, A and B, were characterized with various operation modes of an Atomic Force Microscope (AFM). The surface topography and friction images of the fibers were obtained in contact mode. The nanomechanical properties images—i.e., adhesion and deformation—were obtained in force tapping mode. The results indicate that the surface nanomechanical and nanoscale frictional properties of the fibers vary significantly between two samples. The plots of friction versus normal force of the fibers’ surface from both samples are fitted to the equation of single-asperity, adhesion-controlled friction. Nevertheless, within the range of the applied normal force, the friction curves of sample A surfaces show a characteristic transition phase. That is, under low normal forces, the friction curves closely conform with the Hertzian component of friction; after the transition takes place at higher normal forces, the friction curves follow Amontons’ law of friction. We demonstrated that the transition phase corresponds to a state at which the cuticle layer molecules are displaced from the fibers’ surface. The average adhesion force of the samples is consistent with the average friction signal strength collected under low normal forces.

Artificial Intelligence is dominated by Artificial Neural Networks (ANNs). Currently, the Batch Gradient Descent (BGD) is the only solution to train ANN weights when dealing with large datasets. In this article, a modification to the BGD is proposed which significantly reduces the training time and improves the convergence. The modification, called Instance Eliminating Back Propagation (IEBP), eliminates correctly-predicted-instances from the Back Propagation. The speedup is due to the elimination of unnecessary matrix multiplication operations from the Back Propagation. The proposed modification does not add any training hyperparameter to the existing ones and reduces the memory consumption during the training.