“…Evenly covering the fabric with SWCNT is important because SWCNT plays a role in creating electronic properties for the non-conductive fabric, thanks to its nano-sized electrical interface with a diameter of only a few nanometers. 28 Figure 7.(a) The morphology of the pristine PET/SP sample, (b), (c) The morphology of the SWCNT-coated PET/SP sample.…”
Section: Resultsmentioning
confidence: 99%
“…Evenly covering the fabric with SWCNT is important because SWCNT plays a role in creating electronic properties for the non-conductive fabric, thanks to its nano-sized electrical interface with a diameter of only a few nanometers. 28 Simultaneously, the authors examined the correlation between the coating times, which is the time that the fabric was soaked in SWCNT, and the resistance of the coated samples. Specifically, two PET/SP samples with the same dimension of 10mm width by 100mm length were dipped in the SWCNT liquid for less than 5 min (denoted as sample 1) and 15 min (denoted as sample 2).…”
Section: Morphology Of the Textile Sensormentioning
In this study, the authors proposed a method to fabricate a resistive stretch textile sensor from polyester spandex (PET/SP) fabric and commercial single-walled carbon nanotube (SWCNT). In addition, we designed and trained a one-dimension convolutional neural network to classify four resistance workouts, which employed data acquired from the proposed sensor as the input. To figure out the most appropriate PET/SP sample for the deep learning application, we investigated morphologies and characterization of three samples in distinct conditions of the coating process. Data acquired from the proposed sensor illustrated the significant difference between activated and non-activated muscle groups in each specific exercise. With the PET/SP sample which met the requirements of the application, after 100 epochs, the deep learning model achieved 97.2% training accuracy and 90% test accuracy. This study demonstrates that the SWCNT-coated PET/SP stretch textile sensor can be utilized effectively to track the activity of forearm muscles during resistance training. Other than that, the proposed 1D-CNN, with the advantage of training time and computational cost, is able to classify time series data with high performance and thus can be applied widely in various deep learning applications, especially in the healthcare and sports industries.
“…Evenly covering the fabric with SWCNT is important because SWCNT plays a role in creating electronic properties for the non-conductive fabric, thanks to its nano-sized electrical interface with a diameter of only a few nanometers. 28 Figure 7.(a) The morphology of the pristine PET/SP sample, (b), (c) The morphology of the SWCNT-coated PET/SP sample.…”
Section: Resultsmentioning
confidence: 99%
“…Evenly covering the fabric with SWCNT is important because SWCNT plays a role in creating electronic properties for the non-conductive fabric, thanks to its nano-sized electrical interface with a diameter of only a few nanometers. 28 Simultaneously, the authors examined the correlation between the coating times, which is the time that the fabric was soaked in SWCNT, and the resistance of the coated samples. Specifically, two PET/SP samples with the same dimension of 10mm width by 100mm length were dipped in the SWCNT liquid for less than 5 min (denoted as sample 1) and 15 min (denoted as sample 2).…”
Section: Morphology Of the Textile Sensormentioning
In this study, the authors proposed a method to fabricate a resistive stretch textile sensor from polyester spandex (PET/SP) fabric and commercial single-walled carbon nanotube (SWCNT). In addition, we designed and trained a one-dimension convolutional neural network to classify four resistance workouts, which employed data acquired from the proposed sensor as the input. To figure out the most appropriate PET/SP sample for the deep learning application, we investigated morphologies and characterization of three samples in distinct conditions of the coating process. Data acquired from the proposed sensor illustrated the significant difference between activated and non-activated muscle groups in each specific exercise. With the PET/SP sample which met the requirements of the application, after 100 epochs, the deep learning model achieved 97.2% training accuracy and 90% test accuracy. This study demonstrates that the SWCNT-coated PET/SP stretch textile sensor can be utilized effectively to track the activity of forearm muscles during resistance training. Other than that, the proposed 1D-CNN, with the advantage of training time and computational cost, is able to classify time series data with high performance and thus can be applied widely in various deep learning applications, especially in the healthcare and sports industries.
“…The biosensor performance is primarily based on the specific binding affinity of the analyte, how dense their coating is on transducer surface and orientation of bioreceptor after immobilization, in terms of ensuring the integrity of the binding and retaining their biological activity, to allow considerable electronic interaction between the biomolecule/analyte and transducer [21]. The overall efficiency of the transducer and bioreceptor is critically related to the detection outcome with respect to response time, signal-tonoise ratio, sensitivity and selectivity of a system [22][23][24][25][26][27][28]. The AD biomarkers using the biosensors are detected (i) using electrochemical techniques such as square wave voltammetry (SWV), electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and differential pulse voltammetry (DPV); (ii) using optical techniques such as surface plasmon resonance (SPR) and laser surface plasmon resonance (LSPR); and (iii) using microscopic techniques like scanning electron microscopy (SEM); (iv) colorimetric and fluorometric technique [29].…”
The current review focuses towards the advancements made in the past decade in the field of nanotechnology for the early Alzheimer’s disease (AD) diagnosis. This review includes the application of nanomaterials and nanosensors for the early detection of the main AD biomarkers (amyloid beta, phosphorylated tau, apolipoprotein E4 allele or APOE4, microRNAs, cholesterol, hydrogen peroxide etc) in biological fluids, to detect the biomarkers at a very low concentration ranging in pico, femto and even atto molar concentrations. The field of drug development has always aimed and is constantly working on developing disease modifying drugs, but these drugs will only succeed when given in the early disease stages. Thus, developing efficient diagnostic tools is of vital importance. Various nanomaterials such as liposomes; dendrimers; polymeric nanoparticles; coordination polymers; inorganic nanoparticles such as silica, manganese oxide, zinc oxide, iron oxide, super paramagnetic iron oxides; quantum dots, silver nanoparticles, gold nanoparticles, and carbon based nanostructures (carbon nanotubes, graphene oxide, nanofibres, nanodiamonds, carbon dots); Up-conversion nanoparticles; 2D nanomaterials; and radioactive nanoprobes have been used in constructing and improving efficiency of nano-sensors for AD biosensing at an early stage of diagnosis.
“…Among the wide range of analytical tools, electrochemical sensors have shown significant advantages over other techniques (i.e., HPLC, spectrophotometry, colorimetry and chemiluminescence) due to their ability to provide cheap, fast, easy-to-use and in-line measurements [11]. An effective approach to improving the sensitivity and electrooxidation kinetics of electrochemical sensors is to employ nanostructured catalysts [12,13]. Furthermore, multiple studies have now shown a clear size-dependence of the catalytic activity that may briefly be ascribed to surface-enhancement and quantum effects [14,15], with detectable amounts ranging from 1-2 nM [16,17] up to 3 µM [18].…”
Section: Introductionmentioning
confidence: 99%
“…32Å in the 𝐵𝑖 𝑂 cluster and Bi(1)-Bi(8) = 3.3Å in the 𝐵𝑖 𝑂 𝑁𝑂 cluster; 𝑏 is the distance C(12)-C(15) = 2.79Å in the 𝐵𝑖 𝑂 cluster and C(27)-C(28) = 2.79Å in the 𝐵𝑖 𝑂 𝑁𝑂 cluster; R is the mean distance between Bi(30)-C(12) and Bi(27)-C(15) in the 𝐵𝑖 𝑂 cluster, and it is equal to 3.62Å; in 𝐵𝑖 𝑂 𝑁𝑂 , R is the mean…”
In this work, novel platforms for paracetamol sensing were developed by the deposition of Bi2O3, Bi5O7NO3 and their heterostructures onto screen-printed carbon-paste electrodes. An easy and scalable solid state synthesis route was employed, and by setting the calcination temperatures at 500 °C and 525 °C we induced the formation of heterostructures of Bi2O3 and Bi5O7NO3. Cyclic voltammetry measurements highlighted that the heterostructure produced at 500 °C provided a significant enhancement in performance compared to the monophases of Bi2O and Bi5O7NO3, respectively. That heterostructure showed a mean peak-to-peak separation Ep of 411 mV and a sensitivity increment of up to 70% compared to bare electrodes. A computational study was also performed in order to evaluate the geometrical and kinetic parameters of representative clusters of bismuth oxide and subnitrate when they interact with paracetamol.
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