The aggregation of variably charged nanoparticles is usually induced by the changes in internal and external conditions, such as solution temperature, pH, particle size, van der Waals force, and electrostatic repulsion among particles. In order to explore the effect of pH on the aggregation of variable charge nanoparticles, this paper proposed an extended model based on the 3D on-lattice Cluster–Cluster Aggregation (CCA) model. The extended model successfully established the relationship between pH and sticking probability, and used Smoluchowski theory to calculate the aggregation rate of nanoparticles. The simulation results showed that: (1) the change of the aggregation rate of the variable charge nanoparticles with pH conforms to the Gaussian distribution, (2) the initial particle concentration has a significant effect on the aggregation rate of the nanoparticles, and (3) pH can affect the competition between van der Waals force and electrostatic repulsion between particles, thereby affecting the degree of openness of clusters. The research demonstrated the extended CCA model is valuable in studying the aggregation of the variably charged nanoparticles via transforming the corresponding influence factors into the influence on the sticking probability.
Exploring bioelectroanalysis and bioelectrocatalysis in non-aqueous systems are essential for bridging the gap between laboratorial and industrial scale. Bioelectrodes based on carbon nanomaterials, such as carbon nanotubes and graphene, have been designed and fabricated with biocompatible surface functionalities. This review presents recent advances in regulation of a biocompatible microenvironment of enzyme electrodes in non-aqueous systems. We summarize the modification strategies to facilitate electron transfer and promote enrichment of hydrophobic analytes. We focus on the mining and modification for robust oxidoreductases from extremophiles to explore the biosensors in extreme conditions. Challenges and future prospects for bioelectrodes in non-aqueous systems are discussed.
Metal–organic frames (MOFs) have recently been used to support redox enzymes for highly sensitive and selective chemical sensors for small biomolecules such as oxygen (O2), hydrogen peroxide (H2O2), etc. However, most MOFs are insulative and their three-dimensional (3D) porous structures hinder the electron transfer pathway between the current collector and the redox enzyme molecules. In order to facilitate electron transfer, here we adopt two-dimensional (2D) metal–organic layers (MOLs) to support the HRP molecules in the detection of H2O2. The correlation between the current response and the H2O2 concentration presents a linear range from 7.5 μM to 1500 μM with a detection limit of 0.87 μM (S/N = 3). The sensitivity, reproducibility, and stability of the enzyme sensor are promoted due to the facilitated electron transfer.
The aggregation of variably charged nanoparticles is usually induced by the changes in internal and external conditions, such as solution temperature, pH, particle size, van der Waals force, and electrostatic repulsion among particles. In order to explore the effect of pH on the aggregation of variable charge nanoparticles, this paper proposed an extended model based on the 3D on-lattice Cluster-Cluster Aggregation (CCA) model. The extended model successfully established the relationship between pH and sticking probability, and used Smoluchowski theory to calculate the aggregation rate of nanoparticles. The simulation results showed that: (1) the change of the aggregation rate of the variable charge nanoparticles with pH conforms to the Gaussian distribution, (2) the initial particle concentration has a significant effect on the aggregation rate of the nanoparticles, and (3) pH can affect the competition between van der Waals force and electrostatic repulsion between particles, thereby affecting the degree of openness of clusters. The research demonstrated the extended CCA model is valuable in studying the aggregation of the variably charged nanoparticles via transforming the corresponding influence factors into the influence on the sticking probability.
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