Artificial intelligence-based material discovery for clean energy future

Artificial intelligence (AI)-assisted materials design and discovery methods can come to the aid of global concerns for introducing new efficient materials in different applications. Also, a sustainable clean future requires a transition to a low-carbon economy that is material-intensive. AI-assisted methods advent as inexpensive and accelerated methods in the design of new materials for clean energies. Herein, the emerging research area of AI-assisted material discovery with a focus on developing clean energies is discussed. The applications, advantages, and challenges of using AI in material discovery are discussed and the future perspective of using AI in clean energy is studied. This perspective paves the way for a better understanding of the future of AI applications in clean energies

An accurate PSO-GA based neural network to model growth of carbon nanotubes

By combining particle swarm optimization (PSO) and genetic algorithms (GA) this paper offers an innovative algorithm to train artificial neural networks (ANNs) for the purpose of calculating the experimental growth parameters of CNTs. The paper explores experimentally obtaining data to train ANNs, as a method to reduce simulation time while ensuring the precision of formal physics models. The results are compared with conventional particle swarm optimization based neural network (CPSONN) and Levenberg–Marquardt (LM) techniques. The results show that PSOGANN can be successfully utilized for modeling the experimental parameters that are critical for the growth of CNTs

Cupula-Inspired Hyaluronic Acid-Based Hydrogel Encapsulation to Form Biomimetic MEMS Flow Sensors

Blind cavefishes are known to detect objects through hydrodynamic vision enabled by arrays of biological flow sensors called neuromasts. This work demonstrates the development of a MEMS artificial neuromast sensor that features a 3D polymer hair cell that extends into the ambient flow. The hair cell is monolithically fabricated at the center of a 2 µm thick silicon membrane that is photo-patterned with a full-bridge bias circuit. Ambient flow variations exert a drag force on the hair cell, which causes a displacement of the sensing membrane. This in turn leads to the resistance imbalance in the bridge circuit generating a voltage output. Inspired by the biological neuromast, a biomimetic synthetic hydrogel cupula is incorporated on the hair cell. The morphology, swelling behavior, porosity and mechanical properties of the hyaluronic acid hydrogel are characterized through rheology and nanoindentation techniques. The sensitivity enhancement in the sensor output due to the material and mechanical contributions of the micro-porous hydrogel cupula is investigated through experiments.Singapore. National Research Foundation (Campus for Research Excellence and Technological Enterprise programme

From Biological Cilia to Artificial Flow Sensors: Biomimetic Soft Polymer Nanosensors with High Sensing Performance

We report the development of a new class of miniature all-polymer flow sensors that closely mimic the intricate morphology of the mechanosensory ciliary bundles in biological hair cells. An artificial ciliary bundle is achieved by fabricating bundled polydimethylsiloxane (PDMS) micro-pillars with graded heights and electrospinning polyvinylidenefluoride (PVDF) piezoelectric nanofiber tip links. The piezoelectric nature of a single nanofiber tip link is confirmed by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). Rheology and nanoindentation experiments are used to ensure that the viscous properties of the hyaluronic acid (HA)-based hydrogel are close to the biological cupula. A dome-shaped HA hydrogel cupula that encapsulates the artificial hair cell bundle is formed through precision drop-casting and swelling processes. Fluid drag force actuates the hydrogel cupula and deflects the micro-pillar bundle, stretching the nanofibers and generating electric charges. Functioning with principles analogous to the hair bundles, the sensors achieve a sensitivity and threshold detection limit of 300 mV/(m/s) and 8 μm/s, respectively. These self-powered, sensitive, flexible, biocompatibale and miniaturized sensors can find extensive applications in navigation and maneuvering of underwater robots, artificial hearing systems, biomedical and microfluidic devices.Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology)Singapore-MIT Alliance for Research and Technology (SMART) (Innovation Grants ING148079- ENG

Materials discovery of ion-selective membranes using artificial intelligence

Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering

Modeling of TiC-N thin film coating process on drills using particle swarm optimization algorithm

The prediction of maximum hardness in thin-film coating on high speed cutting drills is an essential prerequisite for developing drilling and it is depended on many factors such as ion bombard time, sub layer temperature, work and chamber pressure. This paper proposes the estimation of hardness of titanium nitride carbide (TIC-N) thin-film layers as protective of high speed cutting drills using Improved Particle Swarm Optimization-based Neural Network (PSONN). Based on the obtained experimental data during the process of chemical vapor deposition (CVD) and physical vapor deposition (PVD), the modeling of the coating variables for achieving the maximum hardness of titanium thin-film layers is performed. By comparison the experimental results with model estimation the accuracy of the system was approximately 97.47% acquired while back propagation (BP) had 95.5% precision.7 page(s

Paper-based membraneless hydrogen peroxide fuel cell prepared by micro-fabrication

A paper-based membraneless single-compartment hydrogen peroxide power source prepared by micro-electromechanical systems (MEMS) technology is reported. The cell utilizes hydrogen peroxide as both fuel and oxidant in a low volume cell fabricated on paper. The fabrication method used is a simple method where precise, small-sized patterns are produced which include the hydrophilic paper bounded by hydrophobic resin. Open circuit potentials of 0.61 V and 0.32 V are achieved for the cells fabricated with Prussian Blue as the cathode and aluminium/nickel as the anode materials, respectively. The power produced by the cells is 0.81 mW cm⁻² at 0.26 V and 0.38 mW cm⁻² at 0.14 V, respectively, even after the cell is bent or distorted. Such a fuel cell provides an easily fabricated, environmentally friendly, flexible and cost saving power source. The cell may be integrated within a self-sustained diagnostic system to provide the on-demand power for future bio-sensing applications.4 page(s

The Selection of milling parameters by the PSO-based neural network modeling method

During the past decade, polymer nanocomposites have emerged relatively as a new and rapidly developing class of composite materials and attracted considerable investment in research and development worldwide. An increase in the desire for personalized products has led to the requirement of the direct machining of polymers for personalized products. In this work, the effect of cutting parameters (spindle speed and feed rate) and nanoclay (NC) content on machinability properties of polyamide-6/nanoclay (PA-6/NC) nanocomposites was studied by using high speed steel end mill. This paper also presents a novel approach for modeling cutting forces and surface roughness in milling PA-6/NC nanocomposite materials, by using particle swarm optimization-based neural network (PSONN) and the training capacity of PSONN is compared to that of the conventional neural network. In this regard, advantages of the statistical experimental algorithm technique, experimental measurements artificial neural network and particle swarm optimization algorithm, are exploited in an integrated manner. The results indicate that the nanoclay content on PA-6 significantly decreases the cutting forces, but does not have a considerable effect on surface roughness. Also the obtained results for modeling cutting forces and surface roughness have shown very good training capacity of the proposed PSONN algorithm in comparison to that of a conventional neural network.12 page(s