A ternary hybrid material composed of Ni nanoparticles (NPs), TiO NPs, and poly(L-lysine) (Ply) was used as a sensing material. It was electrodeposited in situ onto a commercial 433-MHz surface acoustic wave (SAW) resonator to construct a Ni-TiO-Ply/SAW sensor. The Ni-TiO-Ply sensing layer fully covered the resonant cavity of the SAW resonator. As the sensing layer completely covers the interdigital transducer and piezoelectric substrate, the sensing area is significantly increased, and the resonator is protected from damage or contamination. To detect the level of dopamine (DA) in serum, the fabrication of the Ni-TiO-Ply sensing layer, distributions of various components in the sensing layer, and responses of the SAW biosensor to DA were investigated in detail. In addition, an electric field-assisted liquid-phase oxidation technique was developed for loading analytes onto the SAW sensors. After optimizing the pH value and L-lysine content of the sensing layer electrolyte and the pH value of the DA solution, the SAW biosensor responded to DA with a linear concentration range of 1 to 1000 nM, sensitivity of 5.77 MHz nM cm, and limit of detection of 0.067 nM. Moreover, the sensor exhibited good selectivity, reproducibility, and stability at ambient temperature.Graphical abstract.
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http://dx.doi.org/10.1007/s00604-020-04635-7 | DOI Listing |
ACS Nano
January 2025
CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China, Hefei, Anhui 230027, PR China.
Flexible on-skin electronics present tremendous popularity in intelligent electronic skins (e-skins), healthcare monitoring, and human-machine interfaces. However, the reported e-skins can hardly provide high permeability, good stretchability, and large sensitivity and are limited in long-term stability and efficient recyclability when worn on the human body. Herein, inspired from the human skin, a permeable, stretchable, and recyclable cellulose aerogel-based electronic system is developed by sandwiching a screen-printed silver sensing layer between a biocompatible CNF/HPC/PVA (cellulose nanofiber/hydroxypropyl cellulose/poly(vinyl alcohol)) aerogel hypodermis layer and a permeable polyurethane layer as the epidermis layer.
View Article and Find Full Text PDFACS Nano
January 2025
State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
The exponential growth of the Internet of Things (IoTs) has led to the widespread deployment of millions of sensors, crucial for the sensing layer's perception capabilities. In particular, there is a strong interest in intelligent photonic sensing. However, the current photonic sensing device and chip typically offer limited functionality, and the devices providing their power take up excessive amounts of space.
View Article and Find Full Text PDFJ Colloid Interface Sci
January 2025
Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037 China; College of Chemical Engineering, Jiangsu Key Lab for the Chemistry & Utilization of Agricultural and Forest Biomass, Nanjing Forestry University, Nanjing 210037 China. Electronic address:
Poly(N-isopropylacrylamide) (PNIPAM) composite hydrogels have recently emerged as promising candidates for soft hydrogel actuators. However, developing a facile and fast method to obtain multifunctional PNIPAM hydrogel actuators with simulating biological versatility remains a major challenge. Herein, we developed a fast-redox initiation system to prepare PNIPAM/sodium carboxymethyl cellulose (CMC)/TCT MXene nanocomposite hydrogel with multidirectional actuating behaviors and improved mechanical properties.
View Article and Find Full Text PDFISA Trans
January 2025
Department of Electronics and Telecommunication, C. V. Raman Global University, Bhubaneswar 752054, Odisha, India. Electronic address:
Early and highly accurate detection of rapidly damaging deadly disease like Acute Lymphoblastic Leukemia (ALL) is essential for providing appropriate treatment to save valuable lives. Recent development in deep learning, particularly transfer learning, is gaining a preferred trend of research in medical image processing because of their admirable performance, even with small datasets. It inspires us to develop a novel deep learning-based leukemia detection system in which an efficient and lightweight MobileNetV2 is used in conjunction with ShuffleNet to boost discrimination ability and enhance the receptive field via convolution layer succession.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Automation, Southeast University, Nanjing 210096, China.
Transferring knowledge learned from standard GelSight sensors to other visuotactile sensors is appealing for reducing data collection and annotation. However, such cross-sensor transfer is challenging due to the differences between sensors in internal light sources, imaging effects, and elastomer properties. By understanding the data collected from each type of visuotactile sensors as domains, we propose a few-sample-driven style-to-content unsupervised domain adaptation method to reduce cross-sensor domain gaps.
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