Infections with bacterial or fungal biofilms have emerged as a major public heath concern because biofilm-growing cells are highly resistant to both antibiotics and host immune defenses. This review focuses on the progress in understanding the mechanisms of biofilm-specific antimicrobial resistance and in developing innovative therapeutic measures based on novel antibiofilm agents.

Recent work has shown that poly(3-hexylthiophene) (P3HT) and the surface-functionalized fullerene 1-(3-methyloxycarbonyl)propyl(1-phenyl[6,6])C(61) (PCBM) are much more miscible than originally thought, and the evidence of this miscibility requires a return to understanding the optimal morphology and structure of organic photovoltaic active layers. This manuscript describes the results of experiments that were designed to provide quantitative thermodynamic information on the miscibility, interdiffusion, and depth profile of P3HT : PCBM thin films that are formed by thermally annealing initial bilayers. It is found that the resultant thin films consist of a 'bulk' layer that is not influenced by the air or substrate surface. The composition of PCBM in this 'bulk' layer increases with increased PCBM loading in the original bilayer until the 'bulk' layer contains 22 vol% PCBM. The introduction of additional PCBM into the sample does not increase the amount of PCBM dispersed in this 'bulk' layer. This observation is interpreted to indicate that the miscibility limit of PCBM in P3HT is 22 vol%, while the precise characterization of the depth profiles in these films shows that the PCBM selectively segregates to the silicon and near air surface. The selective segregation of the PCBM near the air surface is ascribed to an entropic driving force.

Neuromorphic computing inspired by the neural systems in human brain will overcome the issue of independent information processing and storage. An artificial synaptic device as a basic unit of a neuromorphic computing system can perform signal processing with low power consumption, and exploring different types of synaptic transistors is essential to provide suitable artificial synaptic devices for artificial intelligence. Hence, for the first time, an electret-based synaptic transistor (EST) is presented, which successfully shows synaptic behaviors including excitatory/inhibitory postsynaptic current, paired-pulse facilitation/depression, long-term memory, and high-pass filtering. Moreover, a neuromorphic computing simulation based on our EST is performed using the handwritten artificial neural network, which exhibits an excellent recognition accuracy (85.88%) after 120 learning epochs, higher than most reported organic synaptic transistors and close to the ideal accuracy (92.11%). Such a novel synaptic device enriches the diversity of synaptic transistors, laying the foundation for the diversified development of the next generation of neuromorphic computing systems.

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