The development of biomedical materials with biocompatibility, especially cytocompatibility, is the frontal research field for material science, biology, medicine, pharmacology and related interdisciplines. We have successfully synthesized a new biomedical material, PNIPAM-g-P(NIPAMco-St) (PNNS) core-shell nanoparticles, and investigated its thermosensitive and fluorescent properties. In order to evaluate the cytocompatibility of the PNNS nanoparticles, the effect of the PNNS nanoparticles on the human ether-àgo-go-related gene (hERG) K(+) channel in HEK-293 cells was investigated for the first time with the inverted fluorescence microscope and the whole-cell patch-clamp technique. The PNNS nanoparticles can be adsorbed on the surface of the cell membrane of HEK-293 cells, and cannot change the structure of HEK-293 cells. The low concentration of the PNNS nanoparticles can slightly inhibit the stable and tail current of the hERG K(+) channel, left-shift the activation curve of the hERG K(+) channel and decrease the deactivation time constant (τ)of the hERG K(+) channel. However, in the presence of the high concentration of the PNNS nanoparticles, the changes mentioned above gradually return to the level in the absence of the PNNS nanoparticles. These results indicated that the PNNS nanoparticles can not damage the cells. Thus, the PNNS nanoparticles have a good cytocompatibility and might be applied as a biomedical material.
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http://dx.doi.org/10.1163/092050611X587529 | DOI Listing |
ACS Nano
October 2024
The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matu̅, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
As growth in global demand for computing power continues to outpace ongoing improvements in transistor-based hardware, novel computing solutions are required. One promising approach employs stochastic nanoscale devices to accelerate probabilistic computing algorithms. Percolating Networks of Nanoparticles (PNNs) exhibit stochastic spiking, which is of particular interest as it meets criteria for criticality which is associated with a range of computational advantages.
View Article and Find Full Text PDFAdv Mater
July 2024
The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matū, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand.
The complex self-assembled network of neurons and synapses that comprises the biological brain enables natural information processing with remarkable efficiency. Percolating networks of nanoparticles (PNNs) are complex self-assembled nanoscale systems that have been shown to possess many promising brain-like attributes and which are therefore appealing systems for neuromorphic computation. Here experiments are performed that show that PNNs can be utilized as physical reservoirs within a nanoelectronic reservoir computing framework and demonstrate successful computation for several benchmark tasks (chaotic time series prediction, nonlinear transformation, and memory capacity).
View Article and Find Full Text PDFNano Lett
November 2023
The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matu̅, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
The biological brain is a highly efficient computational system in which information processing is performed via electrical spikes. Neuromorphic computing systems that work on similar principles could support the development of the next generation of artificial intelligence and, in particular, enable low-power edge computing. Percolating networks of nanoparticles (PNNs) have previously been shown to exhibit critical spiking behavior, with promise for highly efficient natural computation.
View Article and Find Full Text PDFNanoscale
June 2023
The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand.
Reservoir computing (RC) has attracted significant interest as a framework for the implementation of novel neuromorphic computing architectures. Previously attention has been focussed on software-based reservoirs, where it has been demonstrated that reservoir topology plays a role in task performance, and functional advantage has been attributed to small-world and scale-free connectivity. However in hardware systems, such as electronic memristor networks, the mechanisms responsible for the reservoir dynamics are very different and the role of reservoir topology is largely unknown.
View Article and Find Full Text PDFLangmuir
February 2017
BK21 PLUS Centre for Advanced Chemical Technology, Department of Polymer Science and Engineering, Pusan National University, Pusan 609-735, Korea.
Hyper-cross-linked polynaphthalene nanoparticles (PNNs) capable of catalyzing the degradation of organic pollutants upon exposure to visible light have been developed. The nascent and metal-free PNNs with a porous structure, high specific surface area, and narrow bandgap are chemically and thermally stable in the catalytic system, which make it promising as a kind of excellent photocatalytic material compared to conventional photocatalysts. The photocatalytic activity of the as-obtained PNNs exhibits remarkable photocatalytic performance for the degradation of rhodamine B (RhB) and methyl blue (MB) under the irradiation of visible light.
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