Optimum lipid production using agro-industrial wastewater treated microalgae as biofuel substrate

Abundant wastewater discharges from palm oil industries in tropical nations being a valuable resource of biodiesel need proper exploration. Research hinted that such wastewater as economical nutrient source or substrate can support the cultivation of microalgae. In this experiment, we have tested the growth and lipid production of five different microalgal strains in palm oil mill effluent (POME). POME as a biofuel substrate is demonstrated to be lucrative for microalgae-assisted lipids production. POME is rich in macro- and micronutrients can be used as a growth medium for algal growth in order to reduce the growth medium cost and environmental pollutions. Among the five microalgal strains tested, Chlorella sorokiniana revealed optimum biomass and lipid production. The productivity was evaluated in terms of chlorophyll content, growth rate, biomass, and lipid content, which discerned to be 0.099/day, 8.0 mg/L day and 2.68 mg/mg cell dry weight (CDW). Furthermore, in this study, an optimization study was carried out to enhance the microalgae to produce high lipid content using carbon-to-nitrogen ratio and different light/dark periods. The presence of nitrogen combined glucose (with a carbon-to-nitrogen ratio 100:7) as an alternative source to carbon displayed higher lipid production of 2.68 (mg/mg CDW) by C. sorokiniana. This study confirms that 8:16 h light/dark condition at C:TN ratio of 100:7 supported to produce high lipid content of 17 mg lipid/mg CDW. The above results revealed that POME could be a suitable growth media for the alga C. sorokiniana to improve the maximum lipid yield for biofuels production.     

Numerical investigation of effective parameters in convective heat transfer of nanofluids flowing under a laminar flow regime

This article presents a numerical investigation on heat transfer performance and pressure drop of nanofluids flows through a straight circular pipe in a laminar flow regime and constant heat flux boundary condition. Al₂O₃, CuO, carbon nanotube (CNT) and titanate nanotube (TNT) nanoparticles dispersed in water and ethylene glycol/water with particle concentrations ranging between 0 and 6 vol.% were used as working fluids for simulating the heat transfer and flow behaviours of nanofluids. The proposed model has been validated with the available experimental data and correlations. The effects of particle concentrations, particle diameter, particles Brownian motions, Reynolds number, type of the nanoparticles and base fluid on the heat transfer coefficient and pressure drop of nanofluids were determined and discussed in details. The results indicated that the particle volume concentration, Brownian motion and aspect ratio of nanoparticles similar to flow Reynolds number increase the heat transfer coefficient, while the nanoparticle diameter has an opposite effect on the heat transfer coefficient. Finally, the present study provides some considerations for the appropriate choice of the nanofluids for practical applications.     

Multi-objective mixture design and optimisation of steel fiber reinforced UHPC using machine learning algorithms and metaheuristics

Ultra-high-performance concrete (UHPC) is a recent class of concrete with improved durability, rheological and mechanical and durability properties compared to traditional concrete. The production cost of UHPC is considerably high due to a large amount of cement used, and also the high price of other required constituents such as quartz powder, silica fume, fibres and superplasticisers. To achieve specific requirements such as desired production cost, strength and flowability, the proportions of UHPC’s constituents must be well adjusted. The traditional mixture design of concrete requires cumbersome, costly and extensive experimental program. Therefore, mathematical optimisation, design of experiments (DOE) and statistical mixture design (SMD) methods have been used in recent years, particularly for meeting multiple objectives. In traditional methods, simple regression models such as multiple linear regression models are used as objective functions according to the requirements. Once the model is constructed, mathematical programming and simplex algorithms are usually used to find optimal solutions. However, a more flexible procedure enabling the use of high accuracy nonlinear models and defining different scenarios for multi-objective mixture design is required, particularly when it comes to data which are not well structured to fit simple regression models such as multiple linear regression. This paper aims to demonstrate a procedure integrating machine learning (ML) algorithms such as Artificial Neural Networks (ANNs) and Gaussian Process Regression (GPR) to develop high-accuracy models, and a metaheuristic optimisation algorithm called Particle Swarm Optimisation (PSO) algorithm for multi-objective mixture design and optimisation of UHPC reinforced with steel fibers. A reliable experimental dataset is used to develop the models and to justify the final results. The comparison of the obtained results with the experimental results validates the capability of the proposed procedure for multi-objective mixture design and optimisation of steel fiber reinforced UHPC. The proposed procedure not only reduces the efforts in the experimental design of UHPC but also leads to the optimal mixtures when the designer faces strength-flowability-cost paradoxes.

     

Antimicrobial Ionic Liquid-Based Materials for Biomedical Applications

Excessive and unwarranted administration of antibiotics has invigorated the evolution of multidrug-resistant microbes. There is, therefore, an urgent need for advanced active compounds. Ionic liquids with short-lived ion-pair structures are highly tunable and have diverse applications. Apart from their unique physicochemical features, the newly discovered biological activities of ionic liquids have fascinated biochemists, microbiologists, and medical scientists. In particular, their antimicrobial properties have opened new vistas in overcoming the current challenges associated with combating antibiotic-resistant pathogens. Discussions regarding ionic liquid derivatives in monomeric and polymeric forms with antimicrobial activities are presented here. The antimicrobial mechanism of ionic liquids and parameters that affect their antimicrobial activities, such as chain length, cation/anion type, cation density, and polymerization, are considered. The potential applications of ionic liquids in the biomedical arena, including regenerative medicine, biosensing, and drug/biomolecule delivery, are presented to stimulate the scientific community to further improve the antimicrobial efficacy of ionic liquids.     

Enhanced photoelectrochemical water splitting using zinc selenide/graphitic carbon nitride type-II heterojunction interface

The objective of this research is to construct a type-II heterojunction interface for effective photoelectrochemical (PEC) water splitting for hydrogen generation. A series of ZnSe/g-C₃N₄ heterojunctions is prepared by ultrasonication procedure and tested for PEC water splitting for the first time. The successful formation of ZnSe/g-C₃N₄ is confirmed by phase, morphological and optical analysis. Linear sweep voltammetry of 0.05 ZG (0.05% ZnSe/g-C₃N₄) showed a six-fold higher photocurrent density of 500 μA than g-C₃N₄. These results are supported by the Tafel slopes and PL (photoluminescence spectroscopy) studies by showing the smallest slope and lesser electron-hole recombination for 0.05 ZG. Increased lifetime of 107 ms and a higher donor density of 3.6 × 10¹⁹ cm^?3 for 0.05 ZG is observed. The smallest semicircle for 0.05 ZG in EIS implies the least charge transfer resistance among the prepared heterojunctions. All the results comply with each other showing the successful formation of type-II heterojunction for enhanced PEC water splitting.     

Mechanochemical Adhesion and Plasticity in Multifiber Hydrogel Networks

The extracellular matrix (ECM) has force-responsive (i.e., mechanochemical) properties that enable adaptation to mechanical loading through changes in fibrous network structure and interfiber bonding. Imparting such properties into synthetic fibrous materials will allow reinforcement under mechanical load, the potential for material self-adhesion, and the general mimicking of ECM. Multifiber hydrogel networks are developed through the electrospinning of multiple fibrous hydrogel populations, where fibers contain complementary chemical moieties (e.g., aldehyde and hydrazide groups) that form covalent bonds within minutes when brought into contact under mechanical load. These fiber interactions lead to microscale anisotropy, as well as increased material stiffness and plastic deformation. Macroscale structures (e.g., tubes and layered scaffolds) are fabricated from these materials through interfiber bonding and adhesion when placed into contact while maintaining a microscale fibrous architecture. The design principles for engineering plasticity described can be applied to numerous material systems to introduce unique properties, from textiles to biomedical applications.     

Measurement of local strain: machine vision and finite element method

Journal of Mechanical Science and Technology - Determining the material deformation behavior including local strain data is very useful for analyzing the forming processes. In order to measure...     

Nanostructured Carbon Nitrides for CO2 Capture and Conversion

Carbon nitride (CN), a 2D material composed of only carbon (C) and nitrogen (N), which are linked by strong covalent bonds, has been used as a metal-devoid and visible-light-active photocatalyst owing to its magnificent optoelectronic and physicochemical properties including suitable bandgap, adjustable energy-band positions, tailor-made surface functionalities, low cost, metal-free nature, and high thermal, chemical, and mechanical stabilities. CN-based materials possess a lot of advantages over conventional metal-based inorganic photocatalysts including ease of synthesis and processing, versatile functionalization or doping, flexibility for surface engineering, low cost, sustainability, and recyclability without any leaching of toxic metals from photocorrosion. Carbon nitrides and their hybrid materials have emerged as attractive candidates for CO₂ capture and its reduction into clean and green low-carbon fuels and valuable chemical feedstock by using sustainable and intermittent renewable energy sources of sunlight and electricity through the heterogeneous photo(electro)catalysis. Here, the latest research results in this field are summarized, including implementation of novel functionalized nanostructured CNs and their hybrid heterostructures in meeting the stringent requirements to raise the efficiency of the CO₂ reduction process by using state-of-the-art photocatalysis, electrocatalysis, photoelectrocatalysis, and feedstock reactions. The research in this field is primarily focused on advancement in the synthesis of nanostructured and functionalized CN-based hybrid heterostructured materials. More importantly, the recent past has seen a surge in studies focusing significantly on exploring the mechanism of their application perspectives, which include the behavior of the materials for the absorption of light, charge separation, and pathways for the transport of CO₂ during the reduction process.     

Outage performance of downlink communications in cognitive-based two-tier networks: cooperative and non-cooperative femtocells

Femtocell deployment, which is a promising approach to the coverage and capacity improvement of indoor communications, suffers from cross-tier interference. Therefore to make the femtocell technology practical this issue needs to be addressed appropriately. One serious type of cross-tier interference occurs in downlink communication, in which a macrocell user is located far from its macro base station. In this setup, the communication of the adjacent femto access points with their users makes the macrocell user experience a low SINR. This paper considers this scenario and shows how cognitive-enabled femto access points can cope with cross-tier interference. More precisely, we compute the outage probability of macro users in a two-tier network when femto access points use the energy detection-based spectrum sensing technique to find the unoccupied frequency subband. To improve the outage probability of macro users, we also study the effectiveness of cooperation among neighbor femto access points. In all cases, the analytical expressions are validated by computer simulations which confirm the accuracy of the used approximations.

     

Performance analysis of two‐way wireless‐powered Amplify‐and‐Forward relaying in the presence of co‐channel interference

In this paper, we investigate the performance assessment of a bidirectional relaying system using energy harvesting techniques. We assume independent and nonidentically distributed (i.n.i.d.) Nakagami‐m fading channels where the amplify‐and‐forward relay is subject to co‐channel interference (CCI) due to transmissions of other transmitters. Two different scenarios, namely, scenario I and scenario II are evaluated. In scenario I, both end‐sources provide the required energy for the relay, whereas the relay also harvests energy from the co‐channel interferes. Then, in the first phase of cooperation, both end‐sources send the information to the relay, and after amplifying the received signal, relay transfers information to the appropriate destination in the second time‐slot. In the scenario II, both end‐sources harvest energy from the relay. After that, the information cooperative transmission is done similar to the first scenario. For both considered scenarios, tight closed‐form expressions of outage probability, symbol error probability, ergodic capacity, and throughput are obtained at arbitrary signal‐to‐noise‐ratios (SNRs). To get more insights, simplified high SNR results for both scenarios are also deduced where the diversity orders are obtained. Monte Carlo simulation results are presented to validate the correctness of our proposed analysis. Our results explicitly demonstrate that the first scenario has a better performance than the second one in the medium and high SNR region, whereas the second scenario outperforms the first one in the low SNR regime.