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Cost-effective water cleaning approaches using improved Treatment technologies, for instance based on catalytic processes with high activity catalysts, are urgently needed. The aim of our study was to synthesize efficient Fenton-like photo-catalysts for rapid degradation of persistent organic micropollutants in aqueous medium. Iron-based nanomaterials were chemically synthesized through simple procedures by immobilization of either iron(II) oxalate (FeO) or iron(III) citrate (FeC) on magnetite (M) nanoparticles stabilized with polyethylene glycol (PEG). Various investigation techniques were performed in order to characterize the freshly prepared catalysts. By applying advanced oxidation processes, the effect of catalyst dosage, hydrogen peroxide concentration and UV-A light exposure were examined for Bisphenol A (BPA) conversion, at laboratory scale, in mild conditions. The obtained results revealed that BPA degradation was rapidly enhanced in the presence of low-concentration H2O2, as well as under UV-A light, and is highly dependent on the surface characteristics of the catalyst. Complete photo-degradation of BPA was achieved over the M/PEG/FeO catalyst in less than 15 minutes. Based on the catalytic performance, a hierarchy of the tested catalysts was established: M/PEG/FeO > M/PEG/FeC > M/PEG. The results of cytotoxicity assay using MCF-7 cells indicated that the aqueous samples after treatment are less cytotoxic.
Singlet oxygen generation potential of two novel free-base-porphyrin photocatalysts was investigated. The freebase-porphyrin-sensitized Fe3O4 magnetic nanoparticles (MNPs) were tested for the degradation of the model pollutant Bisphenol A (BPA) in aqueous solution, for the first time. MNPs with either cubic or spherical shape were synthesized using the sonochemical approach, followed by sensitizing with photoactive 4,4′,4′′,4′′′- (Porphine-5,10,15,20-tetrayl)tetrakis(benzoic acid) (TCPP). The resulted photocatalysts were characterized in detail by scanning and transmission electron microscopy, Brunauer–Emmett–Teller analysis, spectral techniques and vibrating sample magnetometry. The electron spin resonance experiments have confirmed the high activity of the photocatalysts through the efficient formation of singlet oxygen in solution. The optimum operational parameters for BPA degradation were established as follows: 1.0 μmol L−1 BPA, 1.0 g L−1 of photocatalyst, 100 μmol L−1 H2O2, under UVA irradiation. In these conditions, the results for both photocatalysts revealed that after only 10 min of reaction, over 64% and ca. 90% of BPA have been removed from solution in the absence and presence of H2O2, respectively. Whereas after 60 minutes of treatment, only 24% of BPA in real wastewater effluent samples were removed under UVA irradiation in the absence of H2O2, showing the high complexity of real wastewater. Moreover, both photocatalysts were successfully used for BPA removal in three consecutive runs, without significant loss of catalytic features.
The mechanism of action of zirconium permanent modifiers on graphite surfaces was investigated in order to understand its influence on the analytical signal in atomic and molecular absorption spectrometry (AAS/MAS). For this, the molecule formation of CaF was studied, which is used for the indirect analytical determination of fluorine in high-resolution continuum source graphite furnace molecular absorption spectrometry (HR-CS-GFMAS). The kinetics of this reaction was established by monitoring its molecular spectrum at different atomisation temperatures. An Arrhenius plot showed a pseudo-first order reaction with respect to fluorine (n = 1). An intermediate state was isolated, and its structure was elucidated by spectroscopic methods: scanning electron microscopy with energy dispersive X-ray spectroscopy (SEMEDX), X-ray photoelectron spectroscopy (XPS), X-ray absorption spectroscopy (XANES and EXAFS), and Raman microspectroscopy. We propose here a mechanism, where ZrO2 acts as a heterogeneous catalyst: after a pyrolytic step, an intermediate state of ZrO(OCaF) is activated, and at higher temperatures, CaF(g) is released from the zirconium-coated graphite surface. No evidence of the formation of zirconium carbide was found. Consequently, as the CaF formation is catalysed by a heterogeneous catalyst, surface modifications with ZrO2 nanoparticles and ZrO xerogels were investigated in order to increase the surface area. Their influence was evaluated in the molecule formation of CaF, CaCl, CaBr, and CaI. Graphite furnace modification with zirconium oxide nanoparticles proves to be the best choice for fluorine analysis with a signal enhancement of more than eleven times with respect a non-coated graphite furnace. However, the influence of zirconium modifications in the analytical signals of Cl, and I is lower than the F signals or even negative in case of the Br. Understanding zirconium modifiers as heterogeneous catalysts offers a new perspective to AAS and MAS, and reveals the potential of surface analytical methods for development of improved permanent modifiers and graphite furnace coatings.
The efficiency, relatively low cost and eco-friendly nature of hydrogen peroxide-assisted photocatalysis treatment procedures are significant advantages over conventional techniques for wastewater remediation. Herein, we evaluate the behaviour of g-cyclodextrin (g-CD) immobilised on either bare or chitosan (CS)–functionalised Fe3O4 nanoparticles, for photodegrading Bisphenol A (BPA) in ultrapure water and in real wastewater samples. The BPA removal efficiencies with Fe3O4/g-CD and Fe3O4/CS/g-CD were compared with those of Fe3O4/b-CD, and were monitored under UVA irradiation at near-neutral pH. The addition of H2O2 at low concentrations (15 mmol L-1) significantly increased BPA photodegradation in the presence of each nanocomposite. The highest catalytic activity was shown by both Fe3O4/g-CD and Fe3O4/CS/g-CD nanocomposites (,60 and 27%BPA removal in ultrapure water and real wastewater effluent, respectively). Our findings reveal the superior performance of g-CD-functionalised Fe3O4 relative to that of Fe3O4/b-CD. The use of CD-based nanocomposites as photocatalytic materials could be an attractive option in the pre- or post-treatment stage of wastewaters by advanced oxidation processes before or after biological treatment.
In this contribution different ways are explored with the aim to generate suitable training data for ‘non-ideal’ samples using various approaches, e.g., computer-generated images or unsupervised learning algorithms such as generative adversarial networks (GANs). We used these data to train simple CNNs to produce segmentation masks of SEM images and tested the trained networks on real SEM images of complex nanoparticle samples. The novel use of CNN for the automated analysis of the size of nanoparticles of complex shape and with a high degree of agglomeration has proved to be a promising tool for the evaluation of particle size distribution on a large number of constituent particles. Further development and validation of the preliminary model, respectively larger training and validation data sets are necessary.
In this contribution different ways are explored with the aim to generate suitable training data for ‘non-ideal’ samples using various approaches, e.g., computer-generated images or unsupervised learning algorithms such as generative adversarial networks (GANs). We used these data to train simple CNNs to produce segmentation masks of SEM images and tested the trained networks on real SEM images of complex nanoparticle samples. The novel use of CNN for the automated analysis of the size of nanoparticles of complex shape and with a high degree of agglomeration has proved to be a promising tool for the evaluation of particle size distribution on a large number of constituent particles. Further development and validation of the preliminary model, respectively larger training and validation data sets are necessary.
Electrochemical methods offer great promise in meeting the demand for user-friendly on-site devices for Monitoring important parameters. The food industry often runs own lab procedures, for example, for mycotoxin analysis, but it is a major goal to simplify analysis, linking analytical methods with smart technologies. Enzyme-linked immunosorbent assays, with photometric detection of 3,3’,5,5’-tetramethylbenzidine (TMB),form a good basis for sensitive detection. To provide a straightforward approach for the miniaturization of the detectionstep, we have studied the pitfalls of the electrochemical TMB detection. By cyclic voltammetry it was found that the TMB electrochemistry is strongly dependent on the pH and the electrode material. A stable electrode response to TMB could be achieved at pH 1 on gold electrodes. We created a smartphonebased, electrochemical, immunomagnetic assay for the detection of ochratoxin A in real samples, providing a solid basis forsensing of further analytes.
Luminescent solar concentrators (LSC) allow to obtain renewable energy from building integrated photovoltaic systems. As promising efficient and long-term stable LSC fluorophores semiconductor nanocrystals like Quantum dots (QDs) with size and composition tunable optoelectronic properties have recently emerged. The most popular II/VI or IV/VI semiconductor QDs contain, however, potentially hazardous cadmium or lead ions, which is a bottleneck for commercial applications. A simple aqueous based, microwaveassisted synthesis for environmentally friendly and highly emissive AgInS2/ ZnS QDs is developed using 3-mercaptopropionic acid (MPA) and glutathione (GSH) and their incorporation into polylaurylmethacrylate (PLMA) polymer slabs integrable in LSC devices (10.4 × 10.4 × 0.2 cm3, G = 12.98).
With this simple approach, optical power efficiencies (OPE) of 3.8% and 3.6% and optical quantum efficiencies (OQE) of 24.1% and 27.4% are obtained, which are among the highest values yet reported.
A particle roughness analysis tool, based on electron microscopy images (SEM and TEM). The influence of various parameters on the calculated roughness was also investigated: the setting of the proper threshold, accelerating voltage, etc. The samples were gradually tilted to extend imaging information of more than only one projection. Furthermore, the measurement uncertainty of the profile roughness of particles associated to various orientations was estimated.
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from scanning electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. We compare the segmentation masks obtained with our suggested workflow qualitatively and quantitatively to state-of-the-art methods using various metrics. Finally, we used the segmentation masks for automatically extracting particle size distributions from the SEM images of TiO2 particles, which were in excellent agreement with particle size distributions obtained manually but could be obtained in a fraction of the time.