Assessing the efficiency of photocatalytic removal of alizarin red using copper doped zinc oxide nanostructures by combining SERS optical detection

A facile synthesis of water-soluble copper nanoclusters as label-free fluorescent probes for rapid, selective and sensitive determination of alizarin red

Copper nanoclusters (FA@CuNCs) emitting blue fluorescence were successfully developed via a one-pot technique. In this method, the copper chloride, folic acid and hydrazine hydrate were applied as a precursor, protective agent and reducing agent, respectively. The absorption, fluorescence excitation and emission spectra of FA@CuNCs were carried out by using ultraviolet-visible and fluorescence spectrometry, respectively. The morphology, particle size, functional groups, oxidation states of elements of FA@CuNCs were discussed via using transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR) and X-ray photoelectron spectroscopy (XPS). The stability of FA@CuNCs was studied under various conditions, such as storage time at 25 ℃, ultraviolet radiation time, sodium chloride solutione and pH. The FA@CuNCs displayed blue fluorescence under the excitation wavelength of 361 nm, and the fluorescence quantum yield was 7.45 %. As a result of the inner filter effect, the alizarin red could significantly weaken the blue fluorescence of FA@CuNCs. Thus, the as-prepared FA@CuNCs could be utilized as fluorescence nanosensors for the trace determination of alizarin red. This platform suggested an excellent linear range for alizarin red varying from 0.5 to 200 μM with a fitting coefficient of 0.9955. The detection limit was calculated to be 0.064 μM in the light of the 3b/k (b and k refer to the standard deviation and slope of fitted curve, respectively). Furthermore, the as-developed FA@CuNCs could be used to detect the alizarin red in real samples and for the sensing of temperature.

Smart optical sensing of multiple antibiotic residues from wastewater effluents with ensured specificity using SERS assisted with multivariate analysis

Surface-enhanced Raman spectroscopy offers great potential for rapid and highly sensitive detection of pharmaceuticals from environmental sources. Herein, we investigated the feasibility of label-free sensing of antibiotic residues from wastewater effluents with high specificity by combining with multivariate analysis. Highly ordered silver nanoarrays with ∼34 nm roughness have been fabricated using a cost-effective electroless deposition technique. As-fabricated Ag arrays showed superior LSPR effects with an enhancement factor of 8 × 107. Excellent reproducibility has also been noticed with RSD values within 11%, whilst the sensor showed good stability and reusability characteristics for being used as a low-cost and reusable sensor. SERS studies demonstrated that antibiotics-spiked wastewater effluents can be detected with high efficiency in a label-free method. The molecular fingerprint bands of antibiotics such as sulfamethoxazole, sulfadiazine, and ciprofloxacin were well analyzed in effluent, tap, and deionized water. It has been found that antibiotics can be detected near picomolar levels; meanwhile, liquid chromatography-mass spectrometry (LC-MS) exhibited a detection limit within nanomolar concentrations only. Furthermore, the specificity of SERS sensing has been further analyzed using a multivariate analysis method, principal component analysis followed by linear discriminant analysis (PCA-LDA); which showed prominent discrimination to distinguish each antibiotic residue from wastewater effluents. The current study presented the potential of Ag nanoarray sensors for rapid, highly specific, and cost-effective analysis of pharmaceutical products for environmental remediation applications.

Integrated Ozonation Ni-NiO/Carbon/g-C3N4 Nanocomposite-Mediated Catalytic Decomposition of Organic Contaminants in Wastewater under Visible Light

Developing a hybrid process for wastewater purification is of utmost importance to make conventional methods more efficient and faster. Herein, an effective visible light-active nickel-nickel oxide/carbon/graphitic carbon nitride (Ni-NiO/C/g-C3N4)-based nanocatalyst was developed. A hybrid process based on ozonation and Ni-NiO/C/g-C3N4 visible light photocatalysis was applied to decolourize the Congo red (CR), Alizarin Red S (ARS), and real dairy industry wastewater. The synthesized catalyst was characterized using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), Χ-ray powder diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and UV-Vis diffuse reflectance spectrophotometry (UV-Vis DRS). The factors affecting the catalytic process were evaluated, including contact time, solution pH, initial dye concentration, etc. The degradation rate of CR and ARS was compared between the photocatalysis, ozonation, and integrated photocatalytic ozonation (PC/O3) methods. The results showed 100% degradation of CR and ARS within 5 min and 40 min, respectively, by integrated PC/O3. The reusability of the modified catalyst was evaluated, and four successive regenerations were achieved. The modified Ni-NiO/C/g-C3N4 composite could be considered an effective, fast, and reusable catalyst in an integrated PC/O3 process for the complete decolourization of wastewater.

Multi-functional silver nanoprism-titanium dioxide hybrid nanoarrays for trace-level SERS sensing and photocatalytic removal of hazardous organic pollutants

Owing to the excellent optoelectronic properties of metal nanoparticle-semiconductor interfaces; hybrid substrates with superior catalytic and sensing properties can be designed. In the present study, we have attempted to evaluate anisotropic silver nanoprisms (SNP) functionalized titanium dioxide (TiO₂) particles for multifunctional applications such as SERS sensing and photocatalytic decomposition of hazardous organic pollutants. Hierarchical TiO₂/SNP hybrid arrays have been fabricated via facile and low-cost casting techniques. The structural, compositional, and optical characteristics of TiO₂/SNP hybrid arrays were well elucidated and correlated to SERS activities. SERS studies revealed that TiO₂/SNP nanoarrays possess almost 288 times enhancement compared to bare TiO₂ substrates and 2.6 times enhancement than pristine SNP. The fabricated nanoarrays demonstrated detection limits down to 10^-12 M concentration levels and lower spot-to-spot variability of ∼ 11%. The photocatalytic studies showed that almost 94 and 86% of rhodamine B and methylene blue were decomposed within 90 min of visible light exposure. Besides, two times enhancement in photocatalytic activities of TiO₂/SNP hybrid substrates was also observed than bare TiO₂. The highest photocatalytic activity was exhibited by SNP to TiO₂ molar ratio of 1.5 × 10^-3. The electrochemical surface area and the interfacial electron-transfer resistance were increased with the increment in TiO₂/SNP composite load from 3 to 7 wt%. Differential Pulse Voltammetry (DPV) analysis revealed a higher RhB degradation potential of TiO₂/SNP arrays than SNP or TiO₂. The synthesized hybrids exhibited excellent reusability without any significant deterioration in photocatalytic properties over five successive cycles. TiO₂/SNP hybrid arrays were proved to be multiple platforms for sensing and degrading hazardous pollutants for environmental applications.

Quantitative Analysis of Acetone in Transformer Oil Based on ZnO NPs@Ag NWs SERS Substrates Combined with a Stoichiometric Model

Acetone is an essential indicator for determining the aging of transformer insulation. Rapid, sensitive, and accurate quantification of acetone in transformer oil is highly significant in assessing the aging of oil-paper insulation systems. In this study, silver nanowires modified with small zinc oxide nanoparticles (ZnO NPs@Ag NWs) were excellent surface-enhanced Raman scattering (SERS) substrates and efficiently and sensitively detected acetone in transformer oil. Stoichiometric models such as multiple linear regression (MLR) models and partial least square regressions (PLS) were investigated to quantify acetone in transformer oil and compared with commonly used univariate linear regressions (ULR). PLS combined with a preprocessing algorithm provided the best prediction model, with a correlation coefficient of 0.998251 for the calibration set, 0.997678 for the predictive set, a root mean square error in the calibration set (RMSECV = 0.12596 mg/g), and a prediction set (RMSEP = 0.11408 mg/g). For an acetone solution of 0.003 mg/g, the mean absolute percentage error (MAPE) was the lowest among the three quantitative models. For a concentration of 7.29 mg/g, the MAPE was 1.60%. This method achieved limits of quantification and detections of 0.003 mg/g and 1 μg/g, respectively. In general, these results suggested that ZnO NPs@Ag NWs as SERS substrates coupled with PLS simply and accurately quantified trace acetone concentrations in transformer oil.