Detection Of Chemical Oxygen Demand Research Articles

An integrated smartphone-based electrochemical detection system for highly sensitive and on-site detection of chemical oxygen demand by copper-cobalt bimetallic oxide-modified electrode.

Aportable and integrated electrochemical detection system has been constructed for on-site and real-time detection of chemical oxygen demand (COD). The system mainly consists of four parts: (i) sensing electrode with a copper-cobalt bimetallic oxide (CuCoOx)-modified screen-printed electrode; (ii) an integrated electrochemical detector for the conversion, amplification, and transmission of weak signals; (iii) a smartphone installed with a self-developed Android application (APP) for issuing commands, receiving, and displaying detection results; and (iv) a 3D-printed microfluidic cell for the continuous input of water samples. Benefiting from the superior catalytic capability of CuCoOx, the developed system shows a high detection sensitivity with 0.335 μA/(mg/L) and a low detection limit of 5.957 mg/L for COD determination and possessing high anti-interference ability to chloride ions. Moreover, this system presents good consistency with the traditional dichromate method in COD detection of actual water samples. Due to the advantages of cost effectiveness, portability, and point-of-care testing, the system shows great potential for water quality monitoring, especially inresource-limited remote areas.

Highly sensitive electrochemical determination of chemical oxygen demand by carbon‐capsulated CuOx derived from Cu foam supported Cu‐MOF

AbstractEfficient detection of chemical oxygen demand (COD) is crucial for effective pollution prevention. Traditional Cu‐based electrodes, widely utilized for COD sensors suffer from issues related to low activity and stability. This study introduced a novel approach by employing a copper foam‐supported metal‐organic frameworks (Cu‐MOF), synthesized through a solvothermal method, which is subsequently pyrolyzed to yield a carbon‐capsulated CuOx/Cu foam electrode. Cyclic voltammetry analysis demonstrated that the carbon‐capsulated CuOx/Cu foam electrode exhibited superior redox activity, notably generating an increased amount of Cu(III) species. This enhancement significantly contributed to the electrocatalytic oxidation of organic compounds. The developed electrode demonstrated a wide linear detection range of 5–600 ppm, with a low detection limit of 0.96 ppm (S/N=3) for COD sensing. Notably, the sensor exhibited excellent anti‐interference capabilities, desirable reproducibility, and stability. The proposed method was successfully applied to determine COD in real water samples. Comparative analysis with the standard potassium dichromate method revealed high accuracy and a low relative error (2.89 %–6.72 %). This innovative approach holds promise for rapid and accurate COD detection, presenting a valuable contribution to environmental monitoring and water quality assessment.

Sample Generation Method Based on Variational Modal Decomposition and Generative Adversarial Network (VMD-GAN) for Chemical Oxygen Demand (COD) Detection Using Ultraviolet Visible Spectroscopy.

Ultraviolet visible spectroscopy can realize the detection of chemical oxygen demand (COD), especially for low concentration levels due to its high sensitivity, but the issue of insufficient real water sample data has always been a challenge owing to the low probability of occurrence of actual water pollution events. However, in existing methods, generated absorption spectra do not conform to actual situations as the former neglect the actual spectral characteristics. On the other hand, the diversity and complexity are restricted because the information in one-dimensional data is not enough for direct spectral generation. This study proposed a spectral sample generation method based on the variational modal decomposition and generative adversarial network (VMD-GAN). First, the VMD algorithm was utilized to separate principal components and residuals of absorption spectra. Among them, the GAN was used to generate new principal components to ensure that the major spectral characteristics of actual water samples are not lost. The corresponding residuals were then obtained by adjusting the parameters of a three-order Gaussian fitting function, which is more beneficial than the direct use of GAN in the aspect of diversity and complexity. Based on the spectral reconstruction with new principal components and residuals, various absorption spectra were generated more coincident with actual situations. Finally, the effectiveness of this method was evaluated by establishing regression models and predicting COD for actual water samples. In all, the insufficient water sample data can be expanded for a better performance in modeling and analysis of water pollution using the proposed method.

A straightforward approach utilizing an exponential model to compensate for turbidity in chemical oxygen demand measurements using UV-vis spectrometry.

Recently, ultraviolet-visible (UV-vis) absorption spectrometry has garnered considerable attention because it enables real-time and unpolluted detection of chemical oxygen demand (COD) and plays a crucial role in the early warning of emerging organic contaminants. However, the accuracy of detection is inevitably constrained by the co-absorption of organic pollutants and turbidity at UV wavelengths. To ensure accurate detection of COD, it is necessary to directly subtract the absorbance caused by turbidity from the overlaid spectrum using the principle of superposition. The absorbance of COD is confined to the UV range, whereas that of turbidity extends across the entire UV-vis spectrum. Therefore, based on its visible absorbance, the UV absorbance of turbidity can be predicted. In this way, the compensation for turbidity is achieved by subtracting the predicted absorbance from the overlaid spectrum. Herein, a straightforward yet robust exponential model was employed based on this principle to predict the corresponding absorbance of turbidity at UV wavelengths. The model was used to analyze the overlaid absorption spectra of synthetic water samples containing COD and turbidity. The partial least squares (PLS) method was employed to predict the COD concentrations in synthetic water samples based on the compensated spectra, and the corresponding root mean square error (RMSE) values were recorded. The results indicated that the processed spectra yielded a considerably lower RMSE value (9.51) than the unprocessed spectra (29.9). Furthermore, the exponential model outperformed existing turbidity compensation models, including the Lambert-Beer law-based model (RMSE = 12.53) and multiple-scattering cluster method (RMSE = 79.34). Several wastewater samples were also analyzed to evaluate the applicability of the exponential model to natural water. UV analysis yielded undesirable results owing to filtration procedures. However, the consistency between the compensated spectra and filtered wastewater samples in the visible range demonstrated that the model is applicable to natural water. Therefore, this proposed method is advantageous for improving the accuracy of COD measurement in turbid water.

Rapid detection of chemical oxygen demand, pH value, total nitrogen, total phosphorus, and ammonia nitrogen in biogas slurry by near infrared spectroscopy.

Effective treatment of sewage requires accurate measurement of important water quality parameters, such as chemical oxygen demand (COD), pH value, total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH3-N). Traditional detection techniques can result in secondary contamination and are time- and labor-intensive. Near infrared spectroscopy was used in this study to create a model of these parameters of pig manure anaerobic fermentation sewage. The models' viability for quickly estimating the aforementioned water quality characteristics was reviewed, and the models' performance in predicting the results of several samples (biogas slurry, supernatant, and biogas residue) was contrasted. By analyzing the near infrared spectrograms with a spectral range of 4000 cm-1 and 12 500 cm-1 and using second derivative (SD), Savitzky-Golay smoothing (SG) and standard normal variable (SNV) to preprocess the spectra, partial least squares (PLS) was selected to establish the prediction model. The results showed that the effect of the NIR model constructed from the supernatant was better than that of biogas slurry and biogas residue. The determination coefficients for COD, pH value, NH3-N and TN were 0.69, 0.87, 0.81, and 0.94, respectively. This study could provide reference for on-line monitoring of wastewater in the future.

Water chemical oxygen demand prediction model based on the CNN and ultraviolet-visible spectroscopy

Excessive levels of organic matter in water threaten ecological safety and endanger human health. As the water resource environment is deteriorating, accurate and rapid determination of water quality parameters has become a current research hotspot. In recent years, the ultraviolet spectrometry method has been more widely used in the detection of chemical oxygen demand (COD), which is convenient and without chemical reagents. However, this method tends to use absorbance at 254 nm to measure COD. It has a good detection effect when the composition of pollutants is single, but in real life, the complex composition of pollutants will seriously affect the accuracy of measurement. Therefore, a COD prediction model based on ultraviolet-visible (UV-Vis) spectrometry and the convolutional neural network (CNN) is proposed. Compared with other traditional COD prediction models, this model makes full use of the absorbance of all ultraviolet and visible wavelengths, avoiding the information loss caused by using specific wavelengths. Meanwhile, this model is constructed based on the shallow CNN, using convolutional layers with different step lengths instead of the traditional pooling layers, which reduces computation and enhances the capture of spectral feature peaks. Additionally, with the powerful feature extraction capability of the CNN, this model reduces the reliance on pre-processing methods and improves the utilization of spectral information. Experiments have shown that our model has better fitting results and accuracy than other traditional COD prediction models such as the principal component analysis (PCA), partial least squares regression (PLSR), and backpropagation (BP) neural network. This study provides a better solution for improving the accuracy of UV-Vis water quality COD detection, which is conducive to real-time monitoring of the water quality, providing data support of water pollution and its development trend for the government’s water resource protection policy and promoting biodiversity development.

Detection of chemical oxygen demand in water based on UV absorption spectroscopy and PSO-LSSVM algorithm

Detection of chemical oxygen demand in water based on UV absorption spectroscopy and PSO-LSSVM algorithm

Rapid Potentiometric Detection of Chemical Oxygen Demand Using a Portable Self-Powered Sensor Chip.

Chemical oxygen demand (COD) is an important indicator of organic pollutants in water bodies. Most of the present testing methods have the disadvantages of having complicated steps, being time-consuming, and using toxic and hazardous substances. In this work, rapid potentiometric detection of chemical oxygen demand (COD) using a portable self-powered sensor chip was successfully developed. The indium tin oxide (ITO) electrode was etched by laser, and the photocatalytic materials TiO2/CuS and Pt were modified onto the photoanode and the cathode to prepare the sensor chip. Based on the principle of photocatalytic degradation, organic pollutants can be oxidized by TiO2/CuS, and the concentration will affect the generated voltage. The quantitative detection of COD in the range of 0.05-50 mg/L can be rapidly achieved within 5 min by a miniature device. Besides good portability and sensitivity, the proposed sensor also has the advantages of environmental friendliness and ease of use, which is an ideal choice for the on-site detection of water pollution.

Graphene oxide orientated by a magnetic field and application in sensitive detection of chemical oxygen demand

An upright GO (UGO) modified screen-printed electrode was prepared with the help of the external magnetic field for improving its electrochemical performance. The ratio of GO: Nafion and the magnetic field intensity on the properties of UGO were examined by scanning electron microscope, cyclic voltammetry and electrochemical impedance spectroscopy. The magnetic field intensity does not influence the electron transfer kinetics but increase the number of active sites and therefore enhance the electroactive surface area. In addition, the UGO electrode that was electrodeposited Ni nanoparticles (denotes as Ni NPs/UGO modified electrode) display excellent oxidation towards glycine using chronoamperometry. The Ni NPs/UGO modified electrode indicated an excellent performance for electrochemical COD (chemical oxide demand) analysis with a linear detection range of 0.1–400 mg/L and a lower detection limit of 0.02 mg/L. Moreover, this Ni NPs/UGO modified electrode can be applied to the rapid determination of COD in general real water samples. The results were in agreement with those obtained by using the standard method (ISO 6060).

基于紫外-荧光多光谱融合的水质化学需氧量检测

基于紫外-荧光多光谱融合的水质化学需氧量检测

Visible light photoelectrochemical properties of a hydrogenated TiO2 nanorod film and its application in the detection of chemical oxygen demand

Hydrogenated TiO2 nanorod arrays as a photoelectrochemical sensor exhibit good stability and reproducibility for online COD monitoring using visible light.

Amperometric sensing of chemical oxygen demand at glassy carbon and silicon electrodes modified with boron-doped diamond

A boron-doped diamond (BDD) sensor is proposed for effective detection of chemical oxygen demand (COD) by means of amperometric technique. Boron-doped diamond thin films, acting as active sensors, were deposited on both silicon wafer and glassy carbon (GC) substrates by microwave plasma assisted chemical vapour deposition. SEM micrographs showed that BDD–Si displays triangle-faceted crystallites ca. 0.5–3μm in size, while BDD–GC has triangle-faceted crystallites ranging from 0.5 to 3μm and also a small amount of square-faceted grains 0.5–1μm in size. The structure of BDD was confirmed by broad Raman bands centred at 483cm−1 and 1216cm−1. Cyclic voltammograms were measured in tetrabutylammonium perchlorate/dimethyl sulfoxide solution to determine chemical oxygen demand by amperometric technique. The reduction of oxygen at boron-doped diamond predominantly involves the one electron reduction of oxygen to superoxide. The reduction of oxygen on BDD–Si and BDD–GC was found to be quasi-reversible (ΔE=59−100mV). The lowest detection limit was about 0.9mgl−1. Two different types of electrochemical behaviour were observed at BDD–Si and BDD–GC electrodes which indicates a complexity of electroreduction of oxygen on the BDD surface.

Determination of Chemical Oxygen Demand (COD) at Boron-doped Diamond (BDD) Sensor by Means of Amperometric Technique

A boron-doped diamond (BDD) sensor was proposed for effective detection of chemical oxygen demand (COD) by means of amperometric technique. Thin boron doped diamond active sensor layer was deposited on Si wafer and glassy carbon substrate by Microwave Plasma Enhanced Chemical Vapor Deposition (MW PE CVD). The structure of BDD was confirmed by Raman spectra analysis. Broad Raman bands centered at 482 cm-1 and 1219 cm-1 are typical for boron-doped diamond layers. The BDD deposition onto the glassy carbon gives the maximum oxygen reduction peak current with the lowest background signal. The reduction of O2 on boron-doped diamond electrode was studied in DMSO solution The reduction of oxygen at boron-doped diamond predominantly involves the 1ereduction of oxygen to superoxide. The lowest COD detection limit was about 0.3 mg/l.

Electrochemical Tuning the Activity of Nickel Nanoparticle and Application in Sensitive Detection of Chemical Oxygen Demand

Nickel nanoparticles were in situ prepared on the surface of glassy carbon electrode via electrochemical reduction using NiSO4 as the precursor. The shape of nickel nanoparticles was successfully controlled by variation of reduction potential, deposition time, pH value, and concentration of Ni2+. The resulting nickel nanoparticles exhibited high activity to the electrochemical oxidation of glycine, and greatly increased the oxidation current of glycine. As a result, the electrochemical activity of nickel nanoparticles toward the oxidation of glycine was easily tuned. The application of nickel nanoparticles in the sensitive and rapid detection of chemical oxygen demand (COD) was studied. Based on the greatly enhanced oxidation current signal of glycine on the surface of nickel nanoparticles, a novel electrochemical method was developed for the detection of COD. The linearity is from 0.24 to 480 mg L–1, and the limit of detection is as low as 0.14 mg L–1. Finally, nickel nanoparticle-modified electrode was used to detect COD values of different water samples, and the results were tested using conventional dichromate method.

A nano-copper electrochemical sensor for sensitive detection of chemical oxygen demand

Copper nanoparticle (nano-Cu) was electrodeposited on the surface of Cu disk electrode under −1 V for 60 s, and then used to construct an electrochemical sensor for chemical oxygen demand (COD). The electrochemical oxidation behavior of glycine, a standard compound for evaluating the COD, was investigated. The potential shifts negatively, and the current increases greatly at the surface of nano-Cu, indicating remarkable enhancement effect on the detection of COD. The analytical conditions such as electrolyte, deposition potential, deposition time and detected potential were studied. As a result, a sensitive, simple and rapid electroanalytical method was developed for COD using amperometric detection. The linear range is from 4.8 to 600 mg L −1, and the limit of detection is as low as 3.6 mg L −1. Moreover, this method exhibits high tolerance level to chloride ion, and 0.02 M chloride ion has no influence. Finally, the sensor was used to detect the COD values of different water samples, and the results were testified by the standard dichromate method.

Photocatalytic degradation of organic pollutants and detection of chemical oxygen demand by fluorescence methods

TiO 2 photocatalyst was used to degrade organic pollutants in aqueous solutions and the detection of chemical oxygen demand was studied by fluorescence method. It was found that TiO 2 dosage, initial solution pH, initial concentration electron acceptor Ce 4+, the time of UV light irradiation and the temperature of solution affected the degradation of the organic pollutants. The optimum conditions were obtained at amounts of TiO 2 1.0 g l −1, pH 0.38, Ce 4+ 6.0 mmol l −1, time 10 min and temperature 80 °C, respectively. The produced Ce 3+ was determined by fluorescence method, with the maximum excitation and emission wavelengths at 264.8 and 362.1 nm, respectively. The fluorescence intensity was linear with COD in 0∼100.0 mg l −1. The limit of detection was 0.9 mg l −1. This method was applied in detection of COD in samples of lake water and simulate sewage.