ABSTRACT
The electrical conductivity of blood is a crucial physiological parameter with diverse applications in medical diagnostics. Here, a novel approach utilizing a portable millifluidic nanogenerator lab-on-a-chip device for measuring blood conductivity at low frequencies, is introduced. The proposed device employs blood as a conductive substance within its built-in triboelectric nanogenerator system. The voltage generated by this blood-based nanogenerator device is analyzed to determine the electrical conductivity of the blood sample. The self-powering functionality of the device eliminates the need for complex embedded electronics and external electrodes. Experimental results using simulated body fluid and human blood plasma demonstrate the device's efficacy in detecting variations in conductivity related to changes in electrolyte concentrations. Furthermore, artificial intelligence models are used to analyze the generated voltage patterns and to estimate the blood electrical conductivity. The models exhibit high accuracy in predicting conductivity based solely on the device-generated voltage. The 3D-printed, disposable design of the device enhances portability and usability, providing a point-of-care solution for rapid blood conductivity assessment. A comparative analysis with traditional conductivity measurement methods highlights the advantages of the proposed device in terms of simplicity, portability, and adaptability for various applications beyond blood analysis.
Subject(s)
Electric Conductivity , Lab-On-A-Chip Devices , Nanotechnology , Humans , Nanotechnology/instrumentation , Equipment DesignABSTRACT
OBJECTIVES: This study aimed to establish an artificial intelligence (AI) system to classify vertical level differences between vocal folds during vocalization and to evaluate the accuracy of the classification. METHODS: We designed models with different depths between the right and left vocal folds using an excised canine larynx. Video files for the data set were obtained using a high-speed camera system and a color complementary metal oxide semiconductor camera with global shutter. The data sets were divided into training, validation, and testing. We used 20,000 images for building the model and 8000 images for testing. To perform deep learning multiclass classification and to estimate the vertical level difference, we introduced DenseNet121-ConvLSTM. RESULTS: The model was trained several times using different numbers of epochs. We achieved the most optimal results at 100 epochs, and the batch size used during training was 16. The proposed DenseNet121-ConvLSTM model achieved classification accuracies of 99.5% and 88.0% for training and testing, respectively. After verification using an external data set, the overall accuracy, precision, recall, and f1-score were 90.8%, 91.6%, 90.9%, and 91.2%, respectively. CONCLUSIONS: The newly developed AI system may be an easy and accurate method for classifying superior and inferior vertical level differences between vocal folds. Thus, this AI system can be applied and may help in the assessment of vertical level differences in patients with unilateral vocal fold paralysis.
ABSTRACT
The rapid advancement of electrical and telecommunication facilities has resulted in increasing requirements for the development of electromagnetic interference (EMI) shielding composites. Accordingly, an experimental study was conducted to evaluate the EMI shielding performance of carbon nanomaterial (CNM)-embedded carbon-fiber-reinforced polymer (CFRP) or glass-fiber-reinforced polymer (GFRP) composites. Nine combinations of CNMs and carbon or glass fibers were used to fabricate the composites. The synergistic effects of CNMs on the EMI shielding performance were systematically investigated. The results indicated that plate-type CNMs (i.e., graphene and graphite nanoplatelets) have more prominent effects than fiber-type CNMs (carbon nanofibers). The composites fabricated with CFRP afforded higher EMI shielding than the GFRP-based composites. Among the eighteen samples, 3% CNT-GNP in CFRP composites, which included plate-typed CNM, exhibited the best EMI shielding performances, showing 38.6 dB at 0.7 GHz. This study helps understand the shielding performance of CNM-embedded CFRP and GFRP composites in electrical and telecommunication facilities.
ABSTRACT
Magneto-rheological gel (MRG) has been the subject of recent research due to its versatile applications. Especially, the magneto-induced electrical properties of MRGs under different levels of magnetic field enables them to be used as magneto-sensors. However, conventional MRG shows a low level of electrical conductivity, complicating its use in sensor applications. In this regard, in the present study, the carbon nanotube (CNT) and graphene oxide (GO) are added to fabricate new types of MRG. Herein, four different MRG samples were fabricated with reference to an amount of CNT and GO. The microstructural images of carbonyl iron powder (CIP)-based chain structures with CNT and GO were observed using SEM images. Then, their magneto-induced electrical impedances were investigated under four levels of magnetic field (i.e., 0, 50, 100, and 150 mT) and input frequencies (1, 2, 5, and 10 Hz). Based on the experimental results, three electrical models, including first-order series and parallel, and first- and half-order complex models, were proposed, and their accuracy was examined, showing the highest accuracy when first- and half-order complex models were used. The simulated results indicated that the incorporation of both CNT and GO can improve the magneto-induced electrical sensitivity; thus, it can be concluded that MRG with CNT and GO can be a possible method to be used in magneto-sensor applications.
ABSTRACT
Highly flexible and stretchable sensors are becoming increasingly widespread due to their versatile applicability in human/robot monitoring sensors. Conductive polymeric composites have been regarded as potential candidates for such sensors, and carbon nanotubes (CNTs) are widely used to fabricate such composites. In the present study, CNT-embedded high flexible sensors were fabricated using a facile three-roll milling method, which mitigates the drawbacks of the conventional fabrication methods. CNTs content varied between 0.5 and 4.0 wt.%, and the percolation threshold range was obtained via conductivity/resistivity values of the fabricated sensors. Following this, the electrical stability of the sensors was examined against the various DC and AC signals. Furthermore, the fabricated sensors were stretched up to 500% strain, and their sensitivity against varying strain amplitudes was investigated in terms of the change in resistance and gauge factors. Lastly, the fabricated sensors were applied to human fingers for monitoring finger bending and releasing motions to validate their potential applications. The experimental results indicated that these sensors have a percolation threshold of around 2% CNTs content, and the sensors fabricated with 2 to 4% CNTs content showed measurable resistance changes against the applied strain amplitudes of 50-500%. Among these sensors, the sensor with 2% CNTs content showed the highest sensitivity in the studied strain range, exhibiting a resistance change and gauge factor of about 90% and 1.79 against 50% strain amplitude and about 18,500% and 37.07 against 500% strain amplitude, respectively. All these sensors also showed high sensitivity for finger motion detection, showing a resistance change of between 22 and 69%.
ABSTRACT
The conductive polymeric composites incorporating carbon nanotube (CNT) and carbonyl iron powder (CIP) have attracted much attention for various sensor applications. In this paper, a comprehensive study of the magneto-sensing property of a CNT-CIP embedded polymer composite is conducted to implement the composite as magneto-sensors. Thus, this study experimentally investigated the magneto-sensing performances of CNT-doped polymeric composites with the addition of CIP in terms of electrical conductivity, sensitivity, repeatability, and response time. First, the CNT-CIP clusters were manufactured and their interactions were analyzed with the zeta potential measurement and SEM observation. Then, the CNT-CIP clusters were embedded into the polymeric composites for the magneto-sensing evaluations. Experiments showed that the CNT contents in the range of percolation threshold (i.e., 0.5% and 0.75%) are optimal values for sensor applications. The addition of CNT 0.5% and 0.75% resulted in a high sensitivity of 7% and a faster response time within 400 ms. Experiment evaluation confirmed a high potential of implementing CNT-CIP composite as magneto-sensors.
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In this study, hybridized carbon nanomaterials (CNMs), such as carbon nanotubes (CNTs)-graphene, CNT-carbon nanofibers (CNFs), or CNT-graphite nanoplatelet (GNP) materials were embedded in glass-fiber-reinforced plastic (GFRP) or carbon-fiber-reinforced plastic (CFRP) composites to obtain electrical/piezoresistive sensing characteristics that surpass those of composites with only one type of CNM. In addition, to quantitatively assess their sensing characteristics, the materials were evaluated in terms of gauge factor, peak shift, and R-squared values. The electrical property results showed that the GFRP samples containing only CNTs or both CNTs and graphene exhibited higher electrical conductivity values than those of other composite samples. By evaluating piezoresistive sensing characteristics, the CNT-CNF GFRP composites showed the highest gauge factor values, followed by the CNT-graphene GFRP and CNT-only GFRP composites. These results are explained by the excluded volume theory. The peak shift and R-squared value results signified that the CNT-graphene GFRP composites exhibited the best sensing characteristics. Thus, the CNT-graphene GFRP composites would be the most feasible for use as FRP composite sensors.
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Flexible electronic devices have gained significant interest due to their different potential applications. Herein, we report highly flexible, stretchable, and sensitive sensors made of sprayed CNT layer, sandwiched between two polymer layers. A facile fabrication process was employed in which the CNT solution was directly sprayed onto a patterned bottom polymer layer, above which a second polymer layer was casted to get a sandwiched composite structure. Varying amounts of CNT solution (i.e., 10, 25, 40, 70, and 100 mL) were sprayed to get conductive CNT layers of different thicknesses/densities. The physical characteristics of the conductive CNT layers were studied through SEM and optical images. The starting electrical resistance values (without strain) as well as the changes in electrical resistance against human body motions were monitored. The synthesized samples exhibited good response against finger and wrist bending. The conductivity of the samples increased with increase of CNT solution volume while the sensitivity followed the inverse relation, suggesting that the sensors with controlled sensitivity could be fabricated for targeted strain ranges using the proposed method.
ABSTRACT
Magnetorheological gel (MRG) is a smart material that can change its stiffness property by external magnetic field and has been applied as a smart rubber in suppressing vibration. Recent studies show that the electrical resistance of MRG also can be affected with external magnetic field. Thus, this study aimed to conduct analysis on MRG resistance variation due to external magnetic field with DC and AC input voltage. With an DC input voltage, the resistance change due to magnetic field was modeled. In addition, the capacitance variation of the material was observed. The impedance of MRG due to AC input voltage was analyzed and was observed that the impedance of MRG was affected by both the magnetic field and the input frequency. With the experiment data, the impedance modeling of MRG in frequency domain was derived. Based on experiment results, the performance and limitation of MRG as a magnetometer sensor are discussed.