Integrated sequential fuzzy logic search models for simulating wastewater treatment plants missing influent parameters

Aiming at achieving optimum operations of wastewater treatment plant (WWTP) processes, it is essential to develop accurate predictive models for expected influent pollutant loads. To overcome limited timespan data and model complexity, this study is devoted to propose an integration of inputs' sequential search with the adaptive neuro-fuzzy inference system (ANFIS) for reducing the modelling complexity. The input data included nine influent parameters measured bi-weekly for 12 years at a WWTP, South Africa (SA). The sequential search process was used for input optimization to select the most representative inputs in modelling four parameters. The obtained results indicated a strong correlation with R² and NSE above 0.85 for the four targeted influent parameters. After validating the developed models using different criteria, the missing records were predicted throughout the study period. The integration of sequential search input optimization and ANFIS modelling was able to provide a high performance in modelling WWTP datasets.     

3D Printing of Small-Scale Soft Robots with Programmable Magnetization

Soft magnetic structures having a non-uniform magnetization profile can achieve multimodal locomotion that is helpful to operate in confined spaces. However, incorporating such magnetic anisotropy into their body is not straightforward. Existing methods are either limited in the anisotropic profiles they can achieve or too cumbersome and time-consuming to produce. Herein, a 3D printing method allowing to incorporate magnetic anisotropy directly into the printed soft structure is demonstrated. This offers at the same time a simple and time-efficient magnetic soft robot prototyping strategy. The proposed process involves orienting the magnetized particles in the magnetic ink used in the 3D printer by a custom electromagnetic coil system acting onto the particles while printing. The resulting structures are extensively characterized to confirm the validity of the process. The extent of orientation is determined to be between 92% and 99%. A few examples of remotely actuated small-scale soft robots that are printed through this method are also demonstrated. Just like 3D printing gives the freedom to print a large number of variations in shapes, the proposed method also gives the freedom to incorporate an extensive range of magnetic anisotropies.     

Understanding the Diffusion-Dominated Properties of MOF-Derived Ni–Co–Se/C on CuO Scaffold Electrode using Experimental and First Principle Study

Batteries and supercapacitors continue to be one of the most researched topics in the class of energy storage devices. The continuous development of battery and supercapacitor cell components has shown promising development throughout the years—from slabs of pure metal to porous and tailored structures of metal-based active materials. In this direction, metal–organic frameworks (MOFs) serve great advantages in improving the properties and structure of the derived metal-based active materials. This research provides a novel electrode material, Ni–Co–Se/C@CuO, derived from Ni–Co-MOF integrated with pre-oxidized Cu mesh. The superior electrochemical performance of Ni–Co–Se/C@CuO over Ni–Co-MOF@CuO is evident through its higher specific capacity, lower resistivity, richer redox activity, and more favorable diffusion-dominated storage mechanism. When assembled as a hybrid supercapacitor (HSC), the hybrid device using rGO and Ni–Co–Se/C@CuO as electrodes exhibits a high energy density of 42 W h kg⁻¹ at a power density of 2 kW kg⁻¹, and maintains its capacity retention even after 20 000 cycles. The improved capacity performance is also evaluated using first-principle investigations, revealing that the unique and preserved heterostructure of Ni–Co–Se/C@CuO portrays enhanced metallic properties. Such evaluation of novel electrodes with superior properties may benefit next-generation electrodes for supercapacitor devices.