Simultaneous degradation and separation of antibiotics in sewage effluent by photocatalytic nanofiltration membrane in a continuous dynamic process.

Bifunctional photocatalytic nanofiltration (PNF) membrane is increasingly concerned in practical micro-polluted water purification, but there are still several bottlenecks that inhibit its practicality. In this context, the feasibility of a novel metal-free and visible light-responsive surface-anchored PNF membrane for simultaneously removing target antibiotics in real sewage effluent in a continuous dynamic process was explored. The results showed that the optimal PNF-4 membrane was expectedly consisted of an inside tight sub-nanopore structured separation layer and an outside thinner, smoother, super hydrophilic mesoporous degradation layer, respectively. Consequently, the activated PNF-4 membrane could synergistically reduce trimethoprim and sulfamethoxazole concentrations to below two orders of magnitude, accompanying with almost constant high water permeability, suggesting that the hydrophilic modification of the mesoporous degradation layer basically offsets its inherent hydraulic resistance. Also, after repeating the fouling-physical rinsing process three times lasted for 78 h, only sporadic adherent contaminants remained onto the top surface, together with the minimal total and irreversible fouling ratios (as low as 7.2% and 1.2%, respectively), strongly demonstrated that PNF-4 membrane displayed good self-cleaning performance. Undoubtedly, this will significantly reduce its potential cleaning frequency and maintenance cost in long-term operation. Meanwhile, the acute and chronic biotoxicities of its permeate to Virbrio qinghaiensis sp. -67 were also reduced sharply to 2.22% and 0.45%, respectively. All of these evidences suggest that the dual functions of PNF-4 membrane are synergetic in an uninterrupted permeating process. It will provide useful insights for continuously enhancing the practicality and effectiveness of PNF membrane in actual micro-polluted water purification scenarios.

Adsorption and fouling behaviors of customized nanocomposite membrane to trace pharmaceutically active compounds under multiple influent matrices.

Rare information is available on fouling behavior of customized nanofiltration (NF) membrane evoked by pharmaceutically active compounds (PhACs) under real multiple influent matrices pretreated by ultrafiltration module beforehand. To this end, a novel tight NF membrane with excellent perm-selectivity and antiadhesion was fabricated and used to assess its separation performance/mechanism and fouling behavior to a broad range of small molecular PhACs in the context. The adsorption ratio results revealed that the affinities between five selected PhACs and the customized nanocomposite membrane surface were all much weaker (below 5.5%) than the solute-solute interacting forces (between 23.6 and 83.2%), whether for natural or synthetic complex micropollutants. The predominant membrane fouling could be interpreted by the incomplete blocking model in the permeation of both influent conditions. For neat nanocomposite membrane, the order of critical factors important on separation mechanism was electrostatic effect, adsorption and steric hindrance. The fouling layer seemed to act as a secondary separating layer for those negatively charged or hydrophilic PhACs, but showed the cake enhanced concentration polarization effect for the neutral and hydrophobic ones. This study provides valuable insights for defining PhACs fate and NF membrane fouling behavior to fit increasingly stringent criteria for wastewater treatment.

Frequency-Temporal Disagreement Adaptation for Robotic Terrain Classification via Vibration in a Dynamic Environment.

The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate the vibration-based terrain classification (VTC) in a dynamic environment, and propose a novel learning framework, named DyVTC, which tackles online-collected unlabeled data with concept drift. In the DyVTC framework, the exterior disagreement (ex-disagreement) and interior disagreement (in-disagreement) are proposed novely based on the feature diversity and intrinsic temporal correlation, respectively. Such a disagreement mechanism is utilized to design a pseudo-labeling algorithm, which shows its compelling advantages in extracting key samples and labeling; and consequently, the classification accuracy could be retrieved by incremental learning in a changing environment. Since two sets of features are extracted from frequency and time domain to generate disagreements, we also name the proposed method feature-temporal disagreement adaptation (FTDA). The real-world experiment shows that the proposed DyVTC could reach an accuracy of 89.5%, but the traditional time- and frequency-domain terrain classification methods could only reach 48.8% and 71.5%, respectively, in a dynamic environment.