Characterizing molecular transformation of dissolved organic matter during high-solid anaerobic digestion of dewatered sludge using ESI FT-ICR MS

Monitoring of cellulose-rich biowaste co-digestion with 3D fluorescence spectroscopy and mass spectrometry-based metabolomics

Anaerobic digestion (AD) is a promising waste management strategy that reduces landfilling while generating biogas. Anaerobic co-digestion involves mixing two or more substrates to enhance the nutrient balance required for microorganism growth and thus improve the degradation. Monitoring AD is crucial for comprehending the biological process, optimizing process stability, and achieving efficient biogas production. In this work, we have used three dimensional excitation emission fluorescence spectroscopy and mass spectrometry metabolomics, two complementary techniques, to monitor the anaerobic co-digestion (AcoD) of cellulose, ash wood or oak wood with food waste. The two approaches were compared together and to the biogas production records. Results of this experiment demonstrated the complementarity of both analytical techniques with the measurement of the biogas production since 3D fluorescence spectroscopy and MS metabolomics revealed the earlier molecular changes occurring in the bioreactors, mainly associated with the hydrolysis step, whereas the biogas production data reflected the biological activity in the last step of the digestion. Moreover, in all cases, the three data sets effectively delineated the differences among the substrates. While the two wood substrates were poorly degradable as they were richer in aromatic compounds, cellulose was highly degradable and was characterized by the production of several glycolipids. Then, the three tested AcoDs resulted in a similar 3D EEM fluorescence and metabolomics profiles, close to the one observed for the AD of food waste alone, indicating that the incorporation of the food waste drove the molecular degradation events in the AcoDs. Substrate-specific differences were appreciated from the biogas production data. The overall results of this research are expected to provide insight into the design of guidelines for monitoring AcoD.

Purification of dredged water by magnetic coagulation: Response surface optimization and dissolved organic matter removal characteristics

In the present study, magnetic coagulation was used to treat dredged water and the response surface method was used to optimize process parameters. The dissolved organic matter (DOM) removal characteristics were characterized by three-dimensional fluorescence spectrometry and ultra-high resolution mass spectrometry. During the magnetic coagulation process, the suspended solids (SS) removal rate increased initially and then decreased under conditions of increasing magnetic powder dosage and stirring rate. After magnetic coagulation and precipitation for 20 min, the contents of SS, ammonia nitrogen, chemical oxygen demand, and total phosphorus in the treated dredged water met the requirements of the discharge standard (GB8978-1996, China). Three-dimensional fluorescence results showed that magnetic coagulation selectively removed fulvic acids and humic acid substances. After magnetic coagulation with precipitation for 10 min and 20 min, the total relative content of lignins, tannins, proteins, lipids, aminosugars, unsaturated hydrocarbons, condensed aromatic structures, and carbohydrates decreased by 26.3% and 39.4%, respectively. After magnetic coagulation, the distribution range of small molecule DOM shifted to the low H/C and high O/C regions. This study provides a novel perspective for studies on the removal of DOM in dredged water by magnetic coagulation. PRACTITIONER POINTS: SS and DOM removal were significantly enhanced by the use of magnetic coagulation. SS removal efficiency was affected by stirring rate and magnetic powder dosage. Magnetic coagulation selectively removed fulvic acids and humic acid substances. DOM molecule shifted to low H/C and high O/C regions after magnetic coagulation.

Synthesis and characterization of titanium dioxide nanoparticles from Bacillus subtilis MTCC 8322 and its application for the removal of methylene blue and orange G dyes under UV light and visible light

Over the last decade there has been a huge increase in the green synthesis of nanoparticles. Moreover, there is a continuous increase in harnessing the potential of microorganisms for the development of efficient and biocompatible nanoparticles around the globe. In the present research work, investigators have synthesized TiO2 NPs by harnessing the potential of Bacillus subtilis MTCC 8322 (Gram-positive) bacteria. The formation and confirmation of the TiO2 NPs synthesized by bacteria were carried out by using UV-Vis spectroscopy, Fourier transforms infrared (FT-IR), X-ray diffraction (XRD), scanning electron microscope (SEM), and energy dispersive X-ray spectroscopy (EDX/EDS). The size of the synthesized TiO2 NPs was 80-120 nm which was spherical to irregular in shape as revealed by SEM. FTIR showed the characteristic bands of Ti-O in the range of 400-550 cm-1 and 924 cm-1 while the band at 2930 cm-1 confirmed the association of bacterial biomolecules with the synthesized TiO2 NPs. XRD showed two major peaks; 27.5° (rutile phase) and 45.6° (anatase phase) for the synthesized TiO2 NPs. Finally, the potential of the synthesized TiO2 NPs was assessed as an antibacterial agent and photocatalyst. The remediation of Methylene blue (MB) and Orange G (OG) dyes was carried out under UV- light and visible light for a contact time of 150-240 min respectively. The removal efficiency for 100 ppm MB dye was 25.75% and for OG dye was 72.24% under UV light, while in visible light, the maximum removal percentage for MB and OG dye was 98.85% and 80.43% respectively at 90 min. Moreover, a kinetic study and adsorption isotherm study were carried out for the removal of both dyes, where the pseudo-first-order for MB dye is 263.269 and 475554.176 mg/g for OG dye. The pseudo-second-order kinetics for MB and OG dye were 188.679 and 1666.667 mg/g respectively. In addition to this, the antibacterial activity of TiO2 NPs was assessed against Bacillus subtilis MTCC 8322 (Gram-positive) and Escherichia coli MTCC 8933 (Gram-negative) where the maximum zone of inhibition in Bacillus subtilis MTCC 8322 was about 12 mm, and for E. coli 16 mm.

Screen-printed Sn-doped TiO2 nanoparticles for photocatalytic dye removal from wastewater: A technological perspective

TiO2 is widely used as a photocatalyst with a wide band gap, which limited its application. Ion doping and formulating a high-quality screen-printing paste enhance its features. However, the printability of objects for advanced application seems essential nowadays. In this research, the Sn-doped TiO2 nanoparticles were prepared through a sol-gel method followed by calcination at various temperatures of 450 °C, 550 °C, 650 °C, 750 °C, and 850 °C. Screen-printing pastes were prepared with 18 wt% of the synthesized Sn-doped TiO2 nanoparticles to evaluate photocatalytic activity. Finally, the prepared paste with optimum nanoparticle concentration was screen printed onto the microscope glass slides at various printing times (1, 3, and 5 runs) and annealed at 500 °C temperature to investigate the thickness of printed Sn-doped TiO2 nanoparticles effect. The photocatalytic activity and crystal structure of nano Sn-doped-TiO2 were characterized using photoluminescence (PL) spectroscopy and X-ray diffraction (XRD). Transmission electron microscopy (TEM) and scanning electron microscope (SEM) analyses were conducted to investigate the size and morphology of the prepared nanoparticles, respectively. The highest photocatalytic activity for the degradation of methylene blue was obtained at the calcination temperature of 450 °C.

UV and solar-based photocatalytic degradation of organic pollutants from ceramics industrial wastewater by Fe-doped ZnS nanoparticles

UV and solar-based photocatalytic degradation of 2,4-dichlorophenol (2,4-DCP) as an organic contaminant in ceramics industry wastewater by ZnS and Fe-doped ZnS NPs was the focus of this research. Nanoparticles were prepared using a chemical precipitation process. The cubic, closed-packed structure of undoped ZnS and Fe-doped ZnS NPs was formed in spherical clusters, according to XRD and SEM investigations. According to optical studies, the optical band gaps of pure ZnS and Fe-doped ZnS nanoparticles are 3.35 and 2.51 eV, respectively, and Fe doping increased the number of carriers with high mobility, improved carrier separation and injection efficiency, and increased photocatalytic activity under UV or visible light. Doping of Fe increased the separation of photogenerated electrons and holes and facilitated charge transfer, according to electrochemical impedance spectroscopy investigations. Photocatalytic degradation studies revealed that in the present pure ZnS and Fe-doped ZnS nanoparticles, 100% treatment of 120 mL of 15 mg/L phenolic compound was obtained after 55- and 45-min UV-irradiation, respectively, and complete treatment was attained after 45 and 35-min solar light irradiation, respectively. Because of the synergistic effects of effective surface area, more effective photo-generated electron and hole separation efficiency, and enhanced electron transfer, Fe-doped ZnS demonstrated high photocatalytic degradation performance. The study of Fe-doped ZnS's practical photocatalytic treatment capability for removing 120 mL of 10 mg/L 2,4-DCP solution made from genuine ceramic industrial wastewater revealed Fe-doped ZnS's excellent photocatalytic destruction of 2,4-DCP from real industrial wastewater.

Woody and herbaceous wastes for the remediation of polluted waters of wetlands

Plants wastes play an important role during water remediation in wetlands. Plant waste is made into biochar, which is usually used directly or as a water biofilter to remove pollutants. While, the water remediation effect of combination for biochar from woody and herbaceous wastes coupling with substrate types in CWs have not been fully explored. To explore the water remediation effect of combination for biochar coupling with substrate on pH, Turbidity, COD, NH₄⁺-N, TN and TP, four plant configuration modes combining seven woody plants and eight herbaceous plants (Plants A, Plants B, Plants C, Plants D) were coupled with three substrate types (Substrate 1, Substrate 2, Substrate 3) as 12 experiment groups, using water detection methods and significant differences test (LSD) to analyze. Results showed: (1) Compared to Substrate 3, Substrate 1 and Substrate 2 removed significantly higher in pollutants concentration (p < 0.05); (2) NH₄⁺-N final concentration in Plants C and Plants D were both significantly lower than Plants A and Plants B coupling with Substrate 1 and Substrate 2 (p < 0.05). The TN final concentration of Plants C was significantly lower than Plants A in Substrate 1 (p < 0.05), and Plants A's turbidity was significantly lower than Plants C and Plants D's in Substrate 2 (p < 0.05); (3) The pollutants removal of group A1, A2, B1, B2, C1, C2, D1 and D2 were significantly higher than other experiment groups (p < 0.05). Group A2, B2, C1 and D1 had the best water remediation effect and better stability of plant community. Findings in this study will be beneficial for remediating polluted water and building sustainable wetlands.

Efficient cobalt hydroxide nanosheets for enhanced electrochemical sensing of Hg (II) ion

A sensitive electrochemical device was suggested via the modification of a simple graphite rod electrode (GRE) with cobalt hydroxide (Co(OH)₂) nanosheets. After closed circuit process on the modified electrode, the anodic stripping voltammetry (ASV) technique was used for measuring of Hg(II). In optimal experimental conditions, the suggested assay depicted a linear response over a broad range in the range 0.25-30 μg L^-1, with the lowest detection limit of 0.07 μg L^-1. Besides good selectivity, the sensor also indicated excellent reproducibility with a relative standard deviation (RSD) value of 2.9%. Moreover, the Co(OH)₂-GRE showed satisfactory sensing performance in real water samples with appropriate recovery values (96.0-102.5%). Additionally, possible interfering cations were examined, but no significant interference was found. By taking some merits such high sensitivity, remarkable selectivity and good precision, this strategy is expected to provide an efficient protocol for the electrochemical measuring of toxic Hg(II) in environmental matrices.

Application of CNN and ANN in assessment the effect of chemical components of biological nanomaterials in treatment of infection of inner ear and environmental sustainability

Nanoparticles (NPs) are a promising alternative to antibiotics for targeting microorganisms, especially in the case of difficult-to-treat bacterial illnesses. Antibacterial coatings for medical equipment, materials for infection prevention and healing, bacterial detection systems for medical diagnostics, and antibacterial immunizations are potential applications of nanotechnology. Infections in the ear, which can result in hearing loss, are extremely difficult to cure. The use of nanoparticles to enhance the efficacy of antimicrobial medicines is a potential option. Various types of inorganic, lipid-based, and polymeric nanoparticles have been produced and shown beneficial for the controlled administration of medication. This article focuses on the use of polymeric nanoparticles to treat frequent bacterial diseases in the human body. Using machine learning models such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), this 28-day study evaluates the efficacy of nanoparticle therapy. An innovative application of advanced CNNs, such as Dense Net, for the automatic detection of middle ear infections is reported. Three thousand oto-endoscopic images (OEIs) were categorized as normal, chronic otitis media (COM), and otitis media with effusion (OME). Comparing middle ear effusions to OEIs, CNN models achieved a classification accuracy of 95%, indicating great promise for the automated identification of middle ear infections. The hybrid CNN-ANN model attained an overall accuracy of more than 0.90 percent, with a sensitivity of 95 percent and a specificity of 100 percent in distinguishing earwax from illness, and provided nearly perfect measures of 0.99 percent. Nanoparticles are a promising treatment for difficult-to-treat bacterial diseases, such as ear infections. The application of machine learning models, such as ANNs and CNNs, can improve the efficacy of nanoparticle therapy, especially for the automated detection of middle ear infections. Polymeric nanoparticles, in particular, have shown efficacy in treating common bacterial infections in children, indicating great promise for future treatments.