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In this study, we investigated a novel method of mechanical alignment to enhance the tensile properties of chopped carbon fiber (CCF)/high-density polyethylene (HDPE) composites. By utilizing a novel extruder and take-up system based on the melt spinning technique, we successfully controlled the alignment of CCF at various thicknesses using mechanical tension. This facilitated the production of extruded profiles with controlled tensile conditions. Experimental measurements were conducted to evaluate the tensile strength and modulus of the composites. The results demonstrate the effectiveness of the proposed CCF alignment method, which involves adjusting the thickness of the extruded profiles, in controlling the mechanical properties of the composites. As a result, the ultimate tensile strength increased by 50%, and the tensile modulus increased by 130%. Additionally, we developed a numerical model that predicts the tensile modulus based on randomly generated fibers and the Mori-Tanaka model for property estimation. The fiber orientation distribution was influenced by the fiber shape and manufacturing conditions. Scanning electron microscope (SEM) images revealed that the fibers aligned more along the extrusion direction with increased draw ratio. The orientation distribution of the randomly generated fibers varied significantly based on the fiber length and draw ratio, indicating a narrowed distribution range along the extrusion direction with increased fiber length or draw ratio. These findings demonstrate that the proposed CCF alignment method, which involves changing the thickness of the extruded profiles, can effectively control the mechanical properties of the composite.

The study suggests that GB-CNT/PA6 multiscale hybrid composite can be used to create a network structure with controllable electrical conductivity, making it a promising material for various practical applications. The paper introduces a new method for controlling electrical conductivity of composite materials by creating a segregated network morphology (SNM) using a glass bubble (GB)-carbon nanotube (CNT)/polyamide 6 (PA6) multiscale hybrid composite. Instead of relying solely on CNTs, the addition of GB allows for a more economical process by reducing the required CNT concentration to achieve the desired electrical conductivity. The paper also analyzes the effects of varying GB and CNT content on electrical conductivity based on percolation theory. The results demonstrate an 18.8 times increase in electrical conductivity with the SNM approach. The study proposes that this approach could be used to create composite materials with controllable electrical conductivity, making them suitable for various applications.

Studies on self-sensing system under multiple impacts are limited. Furthermore, real-time prognostics research using electromechanical behavior for impact-damage growth is rare and the impact-damaged area analysis has limited in self-sensing. In this paper, the health state of the carbon-fiber-reinforced plastic samples were monitored in real time utilizing self-sensing data. Damage analysis was conducted through C-scan and cross-sectional analysis, and the results were compared and correlated with those of failure analysis based on real-time electromechanical behavior during multiple impacts. Moreover, the relationship between electromechanical behavior and the impact-damaged area was investigated. The damage propagation during multiple impacts was identified in real time. Furthermore, the electromechanical behavior was predicted to prognosticate the damage propagation in the samples under multiple impacts using a particle filter. The RMSE of the impact-damaged area determined from the predicted electromechanical behavior using real-time prognostics tools was lower than 15 mm2. Moreover, the prediction accuracy according to data acquired was investigated. An advanced condition-based monitoring methodology can monitor current and future health states and damage propagation under 2 J and 3 J of multiple impacts that overcomes the previous self-sensing research. Therefore, this study showed high applicability and guidelines for future self-sensing research fields.

In this study, advanced structural health monitoring (SHM) using a non-destructive self-sensing methodology was proposed for large-sized carbon fiber-reinforced plastic (CFRP). Cyclic point bending tests were performed on three types of CFRPs. The damage severity identification and localization were classified and investigated using four different convolutional neural network (CNN) architectures. Electrical resistance images were used to train each CNN architecture for damage analysis. An optimized CNN architecture for the damage analysis of CFRPs using electrical resistance data was proposed and compared with traditional damage analysis CNN architectures. The applicability of the proposed SHM methodology was verified by analyzing unseen damage in the CFRPs. This study addresses the limitations of previous self-sensing methods by reducing the number of electrodes, which reduces data complexity and increases the sensible area of CFRPs. Thus, this study successfully designed an efficient SHM methodology with a high accuracy of over 90 % for analyzing CFRP damage, including the severity and location, regardless of the type of carbon fiber and stacking sequence of composite structures that showed high applicability.

Self-sensing techniques are restricted to monitoring the various types of damage caused during repeated impact testing, and only a few studies have investigated the prognostics of carbon fiber reinforced plastics (CFRPs); in these studies, the electrical resistance of CFRPs was gauged in real time during multiple-impact testing. Therefore, real-time prognostics and health management using electromechanical behavior data obtained from CFRP structures under repeated impact testing are proposed herein. The health condition of the CFRP is observed in real time during impact testing using mechanical and electromechanical behavior data. Further, the types of failure observed during impact testing are investigated using real-time self-sensing data. Moreover, a particle filter is used for predicting the electromechanical behavior and the remaining number of useful impacts during repeated impact testing conducted using a physics-based prognostics tool. The applicability of the proposed methodology was confirmed by monitoring and predicting impact damage growth on the wind-turbine blade within a 5% prediction error. An advanced-condition-based monitoring technique with the diagnostics and prognostics of the current health state was designed successfully, and an application of the introduced method was demonstrated for industrial use.

Numerous techniques have been developed for the non-destructive evaluation (NDE) of impact damage in fiber reinforced plastics (FRPs), following the increasing demands for their safety and maintenance. Considering the large-scale detection and the vast amount of data involved, machine learning (ML) can be utilized in NDE for damage type analysis and impact damage localization. Furthermore, self-sensing using carbon fiber in FRPs is an emerging technique for NDE that can be combined with ML. In this study, ML was used to design smart FRPs by selecting the fiber type and electrode distance considering the cost and electromechanical sensitivity. Furthermore, a novel algorithm for structural health self-sensing was suggested using an artificial neural network. The developed ML algorithms are advantageous since they do not require a theoretical model when all the factors and the variables of FRPs, such as the maximum absorbed impact energy, maximum impact force, initial electrical resistance, number of electrodes, fiber types, and electrode distance, are to be considered. The algorithm was trained using given input data and the target, and the output could be successfully obtained when new input data were provided. Therefore, the proposed ML algorithms hold great potential and applicability to FRP design and for NDE methods.

Highly sensitive and flexible composite sensors with pressure and temperature sensing abilities are of great importance in human motion monitoring, robotic skins, and automobile seats when checking the boarding status. Several studies have been conducted to improve the temperature-pressure sensitivity; however, they require a complex fabrication process for micro-nanostructures, which are material-dependent. Therefore, there is a need to develop the structural designs to improve the sensing abilities. Herein, we demonstrate a flexible composite with an enhanced pressure and temperature sensing performance. Its structural design consists of a multilayered composite construction with an elastic modulus gradient. Controlled stress concentration and distribution induced by a micropatterned structure between the layers improves its pressure and temperature sensing performance. The proposed composite sensor can monitor a wide range of pressure and temperature stimuli and also has potential applications as an automotive seat sensor for simultaneous human temperature detection and occupant weight sensing.

Functionalized graphene–polymer nanocomposites have gained significant attention for their enhanced mechanical, thermal, and antibacterial properties, but the requirement of multi-step processes or hazardous reducing agents to functionalize graphene limits their current applications. Here, we present a single-step synthesis of thermally reduced graphene oxide (TrGO) based on shellac, which is a low-cost biopolymer that can be employed to produce poly(vinyl alcohol) (PVA)/TrGO nanocomposites (PVA–TrGO). The concentration of TrGO varied from 0.1 to 2.0 wt.%, and the critical concentration of homogeneous TrGO dispersion was observed to be 1.5 wt.%, below which strong interfacial molecular interactions between the TrGO and the PVA matrix resulted in improved thermal and mechanical properties. At 1.5 wt.% filler loading, the tensile strength and modulus of the PVA–TrGO nanocomposite were increased by 98.7% and 97.4%, respectively, while the storage modulus was increased by 69%. Furthermore, the nanocomposite was 96% more effective in preventing bacterial colonization relative to the neat PVA matrix. The present findings indicate that TrGO can be considered a promising material for potential applications in biomedical devices.

This paper presents a scattering-based technique for object singulation in a cluttered environment. An analytical model-based control scattering approach is necessary for controlled object singulation. Controlled scattering implies achieving the desired distances between objects after collision. However, current analytical approaches are limited due to insufficient information of the physical environment properties, such as the coefficient of restitution, coefficient of friction, and masses of objects. In this paper, this limitation is overcome by introducing a technique to learn these parameters from unlabeled videos. For the analytical model, an impulse-based approach is used. A virtual world simulator is designed based on a dynamic model and the estimated physical properties of all objects in the environment. Experiments are performed in a virtual world until the targeted scattering pattern is achieved. The targeted scattering pattern implies that all objects are singulated. Finally, the desired input from the virtual world is fed to the robot manipulator to perform real-world scattering.

Two novel Cu2+ sensors, 1 and 2, bearing naphthalimide and a DPA moiety were synthesized to study copper accumulation in organelles by selective Cu2+ sensing. The ER-selective Cu2+ sensor 1 that we developed serves as a valuable tool for understanding the subcellular compartmentalization and roles of copper ions in physiology and pathophysiology. [ABSTRACT FROM AUTHOR]

We report a study on manufacturing and characterization of a platform material for high-performance lightweight bipolar plates for fuel cells based on nanocomposites consisting of carbon nanotubes (CNTs) and exfoliated graphite nanoplatelets (xGnPs). The experiments were designed and performed in three steps. In the preexperimental stage, xGnP-epoxy composite samples were prepared at various xGnP weight percentages to determine the maximum processable nanofiller concentration. The main part of the experiment employed the statistics-based design of experiments (DOE) methodology to identify improved processing conditions and CNT: xGnP ratio for minimized electrical resistivity. In the postexperimental stage, optimized combinations of material and processing parameters were investigated. With the aid of a reactive diluent, 20wt.% was determined to the be maximum processable carbon nanomaterial content in the epoxy. The DOE analyses revealed that the CNT: xGnP ratio is the most dominant factor that governs the electrical properties, and its implications in relation to CNT-xGnP interactions and microstructure are elucidated. In addition, samples fabricated near the optimized condition revealed that there exists an optimal CNT: xGnP ratio at which the electrical performance can be maximized. The electrical and mechanical properties of optimal samples suggest that CNT-xGnP hybrid nanocomposites can serve as an alternative material platform for affordable, lightweight bipolar plates. [ABSTRACT FROM AUTHOR]

The purpose of the study was to compare and analyze the effects of two instructional methods instructor-led and simulation-based instructions--on engineering students achievements and course interests in a manufacturing engineering class. Twenty-nine undergraduate students participated in the study, and repeated measures were employed to collect multiple sets of data. The study showed no significant differences in the means of achievement and interests. The results are discussed in conjunction with the data tables. [ABSTRACT FROM AUTHOR]

Design of hierarchical micro-nanostructured zinc oxide nanorods (ZnO NRs) grown on microscale curvature surfaces is suggested for the effective contact area changes between the top and bottom triboelectric layers under mechanical stimuli. The microscale structures of carbon-fiber-reinforced plastics (CFRPs) by means of simple fabrication methods are preserved. By combining micro and nanostructures, they make it possible to be a spacer-free, ultrathin, and highly sensitive triboelectric sensors (TESs). The flexible TES shows a high-pressure sensitivity and bending sensing capability which enables the detection of repeatedly increasing bending because this structure shows the enhanced contact area changes owing to the effective soft spreading layer of flexible CFRPs. In addition, these CFRP-based TESs can detect tensile strains and impact forces, which suggest potential applications as self-powered structural sensors. [ABSTRACT FROM AUTHOR]

The lymphatic system is critical for maintaining the homeostasis of lipids and interstitial fluid and regulating the immune cell development and functions. Developmental anomaly-induced lymphatic dysfunction is associated with various pathological conditions, including lymphedema, inflammation, and cancer. Most lymphatic endothelial cells (LECs) are derived from a subset of endothelial cells in the cardinal vein. However, recent studies have reported that the developmental origin of LECs is heterogeneous. Multiple regulatory mechanisms, including those mediated by signaling pathways, transcription factors, and epigenetic pathways, are involved in lymphatic development and functions. Recent studies have demonstrated that the epigenetic regulation of transcription is critical for embryonic LEC development and functions. In addition to the chromatin structures, epigenetic modifications may modulate transcriptional signatures during the development or differentiation of LECs. Therefore, the understanding of the epigenetic mechanisms involved in the development and function of the lymphatic system can aid in the management of various congenital or acquired lymphatic disorders. Future studies must determine the role of other epigenetic factors and changes in mammalian lymphatic development and function. Here, the recent findings on key factors involved in the development of the lymphatic system and their epigenetic regulation, LEC origins from different organs, and lymphatic diseases are reviewed.

Owing to the development of medical technology, devices to assess resonance (hypernasality and hyponasality) which result from conditions such as cleft palate and brain injury are being studied. In general, nasometric instruments are used to support clinical judgments of these disorders. For conventional separation-type nasometric instruments, there is an acoustic feedback effect between oral and nasal sounds. Recently, a mask-type nasometric instrument was developed for acoustic feedback insensitivity, but it has not yet been popularized. In this study, we analyzed the acoustic characteristics of the mask-type structure according to existing nasometric instruments. We evaluated the acoustic collection characteristics of the structure through the lumped-element model with an electromechanical-equivalent circuit. The analysis confirmed that the optimum area of the acoustic hole was obtained and a closed-type mask structure could be designed. In addition, we obtained voice data from a healthy control group and examined significant differences in the structure of the separation-type and mask-type nasometric instruments. Consequently, we confirmed a significant difference in nasalance according to the acoustic collection structure of the nasometric instruments.

In step with the development of Industry 4.0, research on automatic operation technology and components related to automobiles is continuously being conducted. In particular, the torque angle sensor (TAS) module of the steering wheel system is considered to be a core technology owing to its precise angle, torque sensing, and high-speed signal processing. In the case of conventional TAS modules, in addition to the complicated gear structure, there is an error in angle detection due to the backlash between the main and sub-gear. In this paper, we propose a multi-track encoder-based vehicle steering system, which is incorporated with a TAS module structure that minimizes the number of components and the angle detection error of the module compared with existing TAS modules. We also fabricated and tested an angle detection signal processing board and evaluated it on a test stand. As a result, we could confirm its excellent performance of an average deviation of 0.4° and applicability to actual vehicles by evaluating its electromagnetic interference (EMI) environmental reliability. The ultimate goal of the TAS module is to detect the target steering angle with minimal computation by the steering or main electronic control unit (ECU) to meet the needs of the rapidly growing vehicle technology. The verified angle detection module can be applied to an actual steering system in accordance with the mentioned technical requirements.

We present theoretical and experimental studies on the effects of platelet-like filler orientation on the mechanical properties of melt-spun exfoliated graphite nanoplatelet(xGnP)-nylon 6(PA6) composite fibers. In numerical studies, the Mori–Tanaka micromechanics model was employed to formulate analytical models to predict the mechanical properties of xGnP-PA6 composite fibers with varying xGnP orientations in a three-dimensional spatial domain. Simulation results showed that the predicted properties of xGnP-PA6 composite fibers were highly affected by xGnP orientation and were correlated with the measured properties of composite fibers treated with varying draw ratios. The tensile moduli of composite fibers at varying xGnP contents showed significant improvements, which is attributed to the drawing-induced alignment of PA6 molecular chains as well as the alignment of xGnPs. Both as-received and acid-treated xGnPs were incorporated in PA6, and mechanical test results suggested that acid-treated xGnPs provide stronger interfacial bonding with PA6. [ABSTRACT FROM AUTHOR]