Pollutants degradation and power generation by photocatalytic fuel cells: A comprehensive review

Abstract Wastewater contains organic compounds (fatty acids, amino acids, and carbohydrates) that have a significant amount of chemical energy. In this regard, the use of wastewater for recovering energy by some appropriate energy conversion technologies can be considered as an appropriate approach to simultaneously achieve the reduction of environmental contamination and increasing supply of energy. The Photocatalytic Fuel Cell (PFC) can provide a new approach in developing technology for simultaneous organic pollutants removal from wastewaters and power generation, but it also has disadvantages, such as requires higher voltage, more cost and complexity. To present a comprehensive vision of the current state of the art, and progress the treatment efficiency and agitate new studies in these fields, this review discussed the study covering PFC aspects, with a focus on the comparison of pollutant degradation, power generation, different photoanode and photocathode materials as well as the application of the Fenton process in PFCs

Comparison of Rhodotorula sp. and Bacillus megaterium in the removal of cadmium ions from liquid effluents

This study compares the capacity of Rhodotorula sp. and Bacillus megaterium for Cd(II) removal considering the influence of operating parameters (pH, biosorbent dosage, contact time, temperature, initial metal concentration in solution). The highest Cd(II) uptake of 14.2 mg/g by Rhodotorula sp. was exhibited at 30°C, when working at pH 6 and with 5 g/l biosorbent dosage, after 48 h of contact time. In these conditions, a removal efficiency of 85% was obtained. Similar outcomes were obtained for B. megaterium (15.1 mg/g, 90%) at 35°C, pH 4 and 3 g/l biosorbent dosage, considered as the optimum set of parameters, equilibrium being achieved for a contact time of 20 min. The possible interaction mechanisms between the biosorbents and Cd(II) were evaluated through point of zero charge (pHpzc), Fourier transform infrared (FTIR), spectroscopy and scanning electron microscopy coupled with energy dispersive X-ray microanalysis (SEM-EDX). Data were modeled using pseudo-first and pseudo-second order kinetic models and Langmuir and Freundlich isotherms models. Further studies considered a modeling approach based on linear regression with Durbin-Watson statistics, while the accuracy and precision of experiments were evaluated by ANOVA.This work was supported by two grants of the Romanian National Authority for Scientific Research, CNCS–UEFISCDI: PN-II-ID-PCE-2011-3-0559, Contract 265/2011 and project number PN-III-P2-2.1-PED-2016-1662, Contract 10/2017 within PNCDI III. The Portuguese team input was performed under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 Programa Operacional Regional do Norte.info:eu-repo/semantics/publishedVersio

Freeze-drying modeling and monitoring using a new neuro-evolutive technique

This paper is focused on the design of a black-box model for the process of freeze-drying of pharmaceuticals. A new methodology based on a self-adaptive differential evolution scheme is combined with a back-propagation algorithm, as local search method, for the simultaneous structural and parametric optimization of the model represented by a neural network. Using the model of the freeze-drying process, both the temperature and the residual ice content in the product vs. time can be determine off-line, given the values of the operating conditions (the temperature of the heating shelf and the pressure in the drying chamber). This makes possible to understand if the maximum temperature allowed by the product is trespassed and when the sublimation drying is complete, thus providing a valuable tool for recipe design and optimization. Besides, the black box model can be applied to monitor the freeze-drying process: in this case, the measurement of product temperature is used as input variable of the neural network in order to provide in-line estimation of the state of the product (temperature and residual amount of ice). Various examples are presented and discussed, thus pointing out the strength of the too

Review of Metaheuristics Inspired from the Animal Kingdom

The search for powerful optimizers has led to the development of a multitude of metaheuristic algorithms inspired from all areas. This work focuses on the animal kingdom as a source of inspiration and performs an extensive, yet not exhaustive, review of the animal inspired metaheuristics proposed in the 2006–2021 period. The review is organized considering the biological classification of living things, with a breakdown of the simulated behavior mechanisms. The centralized data indicated that 61.6% of the animal-based algorithms are inspired from vertebrates and 38.4% from invertebrates. In addition, an analysis of the mechanisms used to ensure diversity was performed. The results obtained showed that the most frequently used mechanisms belong to the niching category

How the COVID-19 pandemic has changed research?

International audienceThe coronavirus disease pandemic (COVID-19) started in 2019 and induced long-lasting effects on many aspects of life. Every one of us felt how the quiet existence was transformedinto a chaotic state full of uncertainties, doubts, and fear for one’s safety. This led to many societal changes, some influenced by objective facts and events, others by human risk perception and behavior modifications. Although risk perception tends to be biased and the responses of individuals to the perceived threat are very different, jumping from lack of precautions and a false feeling of security to unnecessary scares and stigmatization of risks groups will impact human activities in all areas for many years to come. Here, we review the positive and negative outcomes of the pandemic on academia and scientific enterprises

A review on mycotoxins detection techniques in edible oils

Worldwide, contamination of mycotoxins in certain aliments (including cereals, edible oil, unprocessed milk, pistachios, ginger, and other products) has, in recent years, raised serious concerns. Due to their toxicity, the maximum acceptable levels for mycotoxins are standardised and checking their occurrence in foodstuffs is essential to assure food safety and customer protection. In terms of mycotoxins detection techniques, having a series of advantages such as high sensitivity, rapidity, ease of use, and simultaneous identification of several mycotoxins, immunoassay-based methods have been significantly developed. Furthermore, on-site detection and chromatography-based techniques prepare sensitivity, accuracy, and selectivity in the determination of mycotoxins. Also, some new and modified mycotoxin compounds are identified by tandem mass spectrometric detectors. In order to organise the knowledge status, improve detection efficiency and motivate new research, this review focuses on research published in on mycotoxins detection techniques in the edible oil

The Prediction of Peritoneal Carcinomatosis in Patients with Colorectal Cancer Using Machine Learning

The incidence of colon, rectal, and colorectal cancer is very high, and diagnosis is often made in the advanced stages of the disease. In cases where peritoneal carcinomatosis is limited, patients can benefit from newer treatment options if the disease is promptly identified, and they are referred to specialized centers. Therefore, an essential diagnostic benefit would be identifying those factors that could lead to early diagnosis. A retrospective study was performed using patient data gathered from 2010 to 2020. The collected data were represented by routine blood tests subjected to stringent inclusion and exclusion criteria. In order to determine the presence or absence of peritoneal carcinomatosis in colorectal cancer patients, three types of machine learning approaches were applied: a neuro-evolutive methodology based on artificial neural network (ANN), support vector machines (SVM), and random forests (RF), all combined with differential evolution (DE). The optimizer (DE in our case) determined the internal and structural parameters that defined the ANN, SVM, and RF in their optimal form. The RF strategy obtained the best accuracy in the testing phase (0.75). Using this RF model, a sensitivity analysis was applied to determine the influence of each parameter on the presence or absence of peritoneal carcinomatosis

Gallic Acid Reactive Extraction with and without 1-Octanol as Phase Modifier: Experimental and Modeling

Gallic acid (GA) is a naturally occurring phenolic acid that can be found in the leaves, roots, flowers, or stems of a wide variety of plant species. It has a broad range of uses in the food and pharmaceutical industries. The objective of this research is to investigate the GA reactive extraction process employing dichloromethane and n-heptane as solvents, 1-octanol as a phase-modifier, and Amberlite LA-2 as an amine extractant dissolved in the organic phase. The separation yield and distribution coefficient data were discussed, along with the analysis of the extraction conditions and the extraction mechanism. Dichloromethane employed as the solvent, 80 g/L Amberlite LA2 used as the extractant, and 10% phase modifier were determined to be the ideal conditions for the reactive extraction onto a biphasic organic-aqueous system. Statistical regression and artificial neural networks (ANNs) established with the differential evolution (DE) algorithm were also used to model and optimize the process

Graphene-based membrane techniques for heavy metal removal: A critical review

As good quality water is becoming increasingly scarce, efficient methods for water recovery, recycling, and reuse must be developed to ensure a reliable water supply. This endeavor may take into account other water supplies such as sea and wastewater. Due to its unique structure and excellent physical and chemical properties, graphene and its derivatives are appealing for a variety of applications, including pollutant removal and water desalination. The graphene-based membranes (GBMs) displayed extremely high molecular separation and mass-transport properties, as well as antifouling properties that are not found in the existing state-of-art commercial membranes. As such, there is a huge potential for technology disruption. The present paper reviews the latest developments, discoveries, and prospective applications related to graphene-based membranes with an in-depth focus on heavy metal removal. The review offers a summary and outlook on the opportunities and challenges in this arising field. It was observed that GBM techniques are capable of producing large, leak-free, and fouling-free membranes that have a promising potential for the removal of hazardous pollutants from water.Scopu

Folic Acid Ionic-Liquids-Based Separation: Extraction and Modelling

Folic acid (vitamin B9) is an essential micronutrient for human health. It can be obtained using different biological pathways as a competitive option for chemical synthesis, but the price of its separation is the key obstacle preventing the implementation of biological methods on a broad scale. Published studies have confirmed that ionic liquids can be used to separate organic compounds. In this article, we investigated folic acid separation by analyzing 5 ionic liquids (CYPHOS IL103, CYPHOS IL104, [HMIM][PF6], [BMIM][PF6], [OMIM][PF6]) and 3 organic solvents (heptane, chloroform, and octanol) as the extraction medium. The best obtained results indicated that ionic liquids are potentially valuable for the recovery of vitamin B9 from diluted aqueous solutions as fermentation broths; the efficiency of the process reached 99.56% for 120 g/L CYPHOS IL103 dissolved in heptane and pH 4 of the aqueous folic acid solution. Artificial Neural Networks (ANNs) were combined with Grey Wolf Optimizer (GWO) for modelling the process, considering its characteristics