Intelligent computing for the dynamics of entropy optimized nanofluidic system under impacts of MHD along thick surface

This article examines entropy production (EP) of magneto-hydrodynamics viscous fluid flow model (MHD-VFFM) subject to a variable thickness surface with heat sink/source effect by utilizing the intelligent computing paradigm via artificial Levenberg–Marquardt back propagated neural networks (ALM-BPNNs). The governing partial differential equations (PDEs) of MHD-VFFM are transformed into ODEs by applying suitable similarity transformations. The reference dataset is obtained from Adam numerical solver by the variation of Hartmann number (Ha), thickness parameter [Formula: see text], power index ([Formula: see text], thermophoresis parameter (Nt), Brinkman number (Br), Lewis number (Le) and Brownian diffusion parameter (Nb) for all scenarios of proposed ALM-BPNN. The reference data samples arbitrary selected for training/testing/validation are used to find and analyze the approximated solutions of proposed ALM-BPNNs as well as comparison with reference results. The excellent performance of ALM-BPNN is consistently endorsed by Mean Squared Error (MSE) convergence curves, regression index and error histogram analysis. Intelligent computing based investigation suggests that the rise in values of Ha declines the velocity of the fluid motion but converse trend is seen for growing values of [Formula: see text]. The rising values of Ha, Nt and Br improve the heat transfer but converse trend is seen for growing values of [Formula: see text]. The inclining values of Nt incline the mass transfer but it shows reverse behavior for escalating values of Le. The inclining values of Br incline the EP.

Covalent organic framework nanofluidic membrane as a platform for highly sensitive bionic thermosensation

Thermal sensation, which is the conversion of a temperature stimulus into a biological response, is the basis of the fundamental physiological processes that occur ubiquitously in all organisms from bacteria to mammals. Significant efforts have been devoted to fabricating artificial membranes that can mimic the delicate functions of nature; however, the design of a bionic thermometer remains in its infancy. Herein, we report a nanofluidic membrane based on an ionic covalent organic framework (COF) that is capable of intelligently monitoring temperature variations and expressing it in the form of continuous potential differences. The high density of the charged sites present in the sub-nanochannels renders superior permselectivity to the resulting nanofluidic system, leading to a high thermosensation sensitivity of 1.27 mV K−1, thereby outperforming any known natural system. The potential applicability of the developed system is illustrated by its excellent tolerance toward a broad range of salt concentrations, wide working temperatures, synchronous response to temperature stimulation, and long-term ultrastability. Therefore, our study pioneers a way to explore COFs for mimicking the sophisticated signaling system observed in the nature.

Experimental study on application characteristics of zeolite/water stuffing for the nanofluidic packer rubber

Aiming at sealing failure problem of packer rubber during well testing and completion, a new type of “nanofluidic packer rubber” is developed. The nanofluidic packer rubber is composed of honeycomb matrix skeleton encapsulating nanofluidic system as stuffing. Taking ZSM-5 zeolite/water nanofluidic system as an example stuffing for the nanofluidic packer rubber, the application properties are studied by means of experiment. Ten loading/unloading cycles are carried out on the pretreated zeolite/water stuffing at different loading rates and system temperatures on a pressure-volume characteristic test bench. The impact law of loading rate and system temperature on repeatable practicability, pressure threshold, and deformation capacity of the stuffing are obtained and the influence mechanisms are discussed. Results show that the zeolite/water stuffing works stable and repeatable after the first three loading/unloading cycles. The loading rate has lifting effects on throughput capacity, pressure threshold and deformation capacity when system temperature is under 75°C. With the increase of system temperature, pressure threshold decrease, and throughput capacity and deformation capacity increase. All the application characteristics found in zeolite/water stuffing are favorable for improving the working performance of packer rubber. This work provides theoretical and data support for the application of the nanofluidic packer rubber.

pH-regulated thermo-driven nanofluidics for nanoconfined mass transport and energy conversion

A thermo-driven nanofluidic system was developed to extract waste heat based on directed ionic transport.