Chemometric study on the TiO2-photocatalytic degradation of nitrilotriacetic acid

A Gas Sensors Detection System for Real-Time Monitoring of Changes in Volatile Organic Compounds during Oolong Tea Processing

The oxidation step in Oolong tea processing significantly influences its final flavor and aroma. In this study, a gas sensors detection system based on 13 metal oxide semiconductors with strong stability and sensitivity to the aroma during the Oolong tea oxidation production is proposed. The gas sensors detection system consists of a gas path, a signal acquisition module, and a signal processing module. The characteristic response signals of the sensor exhibit rapid release of volatile organic compounds (VOCs) such as aldehydes, alcohols, and olefins during oxidative production. Furthermore, principal component analysis (PCA) is used to extract the features of the collected signals. Then, three classical recognition models and two convolutional neural network (CNN) deep learning models were established, including linear discriminant analysis (LDA), k-nearest neighbors (KNN), back-propagation neural network (BP-ANN), LeNet5, and AlexNet. The results indicate that the BP-ANN model achieved optimal recognition performance with a 3-4-1 topology at pc = 3 with accuracy rates for the calibration and prediction of 94.16% and 94.11%, respectively. Therefore, the proposed gas sensors detection system can effectively differentiate between the distinct stages of the Oolong tea oxidation process. This work can improve the stability of Oolong tea products and facilitate the automation of the oxidation process. The detection system is capable of long-term online real-time monitoring of the processing process.

A Novel Machine Learning Model to Predict the Photo-Degradation Performance of Different Photocatalysts on a Variety of Water Contaminants

This paper describes an innovative machine learning (ML) model to predict the performance of different metal oxide photocatalysts on a wide range of contaminants. The molecular structures of metal oxide photocatalysts are encoded with a crystal graph convolution neural network (CGCNN). The structure of organic compounds is encoded via digital molecular fingerprints (MF). The encoded features of the photocatalysts and contaminants are input to an artificial neural network (ANN), named as CGCNN-MF-ANN model. The CGCNN-MF-ANN model has achieved a very good prediction of the photocatalytic degradation rate constants by different photocatalysts over a wide range of organic contaminants. The effects of the data training strategy on the ML model performance are compared. The effects of different factors on photocatalytic degradation performance are further evaluated by feature importance analyses. Examples are illustrated on the use of this novel ML model for optimal photocatalyst selection and for assessing other types of photocatalysts for different environmental applications.

A generalized predictive model for TiO2-Catalyzed photo-degradation rate constants of water contaminants through artificial neural network

Titanium dioxide (TiO₂) is a well-known photocatalyst in the applications of water contaminant treatment. Traditionally, the kinetics of photo-degradation rates are obtained from experiments, which consumes enormous labor and experimental investments. Here, a generalized predictive model was developed for prediction of the photo-degradation rate constants of organic contaminants in the presence of TiO₂ nanoparticles and ultraviolet irradiation in aqueous solution. This model combines an artificial neural network (ANN) with a variety of factors that affect the photo-degradation performance, i.e., ultraviolet intensity, TiO₂ dosage, organic contaminant type and initial concentration in water, and initial pH of the solution. The molecular fingerprints (MF) were used to interpret the organic contaminants as binary vectors, a format that is machine-readable in computational linguistics. A dataset of 446 data points for training and testing was collected from the literature. This predictive model shows a good accuracy with a root mean square error (RMSE) of 0.173.

New insight into monitoring degradation products during the TiO2-photocatalysis process by multivariate molecular spectroscopy

This study focuses on the feasibility of a spectroscopic multivariate method for monitoring the concentration of phenol and its main degradation products during heterogeneous photocatalysis. Phenolic compounds were chosen as model to evaluate the degradation process due to their toxicity and persistence in the environment and also their well-known degradation pathway. The predictive capability of the multivariate method developed by partial least squares regression (PLSR) over the spectral range of 200-350 nm was satisfactory, allowing mean predicted errors below 5.0 % in the simultaneous determination of the target compounds using six latent variables and smoothing spectra. Suitable results were reported for the simultaneous determination of hydroquinone, resorcinol, pyrocatechol, and p-benzoquinone in accordance to the chromatographic method. Characteristics such as simplicity, low cost, and fast data acquisition are remarkable in this procedure, which makes it appropriate for this type of analytical control.

Statistical investigation on the role of supporting electrolytes during NTA degradation on BDD anodes

This work reported a comparative study on the electrochemical incineration of nitrilotriacetic acid (NTA) in the presence of different supporting electrolytes (Na2SO4 and NaCl). Galvanostatic electrolyses were conducted in an undivided electrochemical cell containing boron-doped diamond (BDD) anode and platinum cathode. Initial solution pH, flow rate, applied current density, and supporting electrolyte concentration were selected as variables, besides the mineralization efficiency of NTA that was selected as response. Central composite rotatable design and response surface methodology were employed here to examine the statistical significance of the selected variables, as well as to determine the optimal conditions of the degradation process. Under the same operating conditions, two regression models were thus constructed to illustrate the differing impact of supporting electrolytes in BDD anode cells. The kinetics for NTA degradation followed different reaction orders for the two scenarios (in the absence and presence of NaCl), indicating the complex interaction between hydroxyl radicals and active chlorine. Despite this, the experimental results demonstrated that effective mineralization of NTA might also be achieved in the presence of chlorides (of lower concentrations). Besides, in the case of chlorides, the average mass transfer coefficient of the system was found to be strongly dependent on the initial solution pH. Lastly, a plausible reaction sequence concerning the electrolytic oxidation of NTA in chloride media was also proposed.

Chemometric study on the electrochemical incineration of nitrilotriacetic acid using platinum and boron-doped diamond anode

This study investigated the electrochemical incineration of nitrilotriacetic acid (NTA) at boron-doped diamond (BDD) and platinum (Pt) anodes. Trials were performed in the presence of sulfate electrolyte media under recirculation mode. The parameters that influence the degradation efficiency were investigated, including applied current density, flow rate, supporting electrolyte concentration and reaction time. To reduce the number of experiments, the system had been managed under chemometric technique named Doehlert matrix. As a consequence, the mineralization of NTA demonstrated similar behavior upon operating parameters on these two anodes. Further kinetic study indicated that the degradations followed pseudo-first-order reactions for both BDD and Pt anodes, and the reaction rate constant of the former was found to be higher than that of the latter. Such difference could be interpreted by results from fractal analysis. In addition, a reaction sequence for NTA mineralization considering all the detected intermediates was also proposed.

MIL-53(Fe) as a highly efficient bifunctional photocatalyst for the simultaneous reduction of Cr(VI) and oxidation of dyes

A bifunctional photocatalyst-Fe-benzenedicarboxylate (MIL-53(Fe)) has been synthesized successfully via a facile solvothermal method. The resulting MIL-53(Fe) photocatalyst exhibited an excellent visible light (λ≥ 420nm) photocatalytic activity for the reduction of Cr(VI), the reduction rate have reached about 100% after 40min of visible light irradiation, which has been more efficient than that of N-doped TiO2 (85%) under identical experimental conditions. Further experimental results have revealed that the photocatalytic activity of MIL-53(Fe) for the reduction of Cr(VI) can be drastically affected by the pH value of the reaction solution, the hole scavenger and atmosphere. Moreover, MIL-53(Fe) has exhibited considerable photocatalytic activity in the mixed systems (Cr(VI)/dyes). After 6h of visible light illumination, the reduction ratio of Cr(VI) and the degradation ratio of dyes have been exceed 60% and 80%, respectively. More significantly, the synergistic effect can also be found during the process of photocatalytic treatment of Cr(VI) contained wastewater under the same photocatalytic reaction conditions, which makes it a potential candidate for environmental restoration. Finally, a possible reaction mechanism has also been investigated in detail.

Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion

Instrumental test of food quality using perception sensors instead of human panel test is attracting massive attention recently. A novel cross-perception multi-sensors data fusion imitating multiple mammal perception was proposed for the instrumental test in this work. First, three mimic sensors of electronic eye, electronic nose and electronic tongue were used in sequence for data acquisition of rice wine samples. Then all data from the three different sensors were preprocessed and merged. Next, three cross-perception variables i.e., color, aroma and taste, were constructed using principal components analysis (PCA) and multiple linear regression (MLR) which were used as the input of models. MLR, back-propagation artificial neural network (BPANN) and support vector machine (SVM) were comparatively used for modeling, and the instrumental test was achieved for the comprehensive quality of samples. Results showed the proposed cross-perception multi-sensors data fusion presented obvious superiority to the traditional data fusion methodologies, also achieved a high correlation coefficient (>90%) with the human panel test results. This work demonstrated that the instrumental test based on the cross-perception multi-sensors data fusion can actually mimic the human test behavior, therefore is of great significance to ensure the quality of products and decrease the loss of the manufacturers.

Photocatalytic reduction of vanadium(V) in TiO₂ suspension: chemometric optimization and application to wastewaters

The photocatalytic reduction of V(V) to V(IV) over TiO₂ in aqueous solution is presented. Experiments were undertaken on air-equilibrated water spiked with V(V) (0.6-20 mgL(-1)), under UV-A or solar light. A chemometric study was performed to optimize the reduction yield, by considering the most important variables recognized to affect the photocatalytic process. Among pH, irradiation time and catalyst concentration, the two latter proved to be determinant. The good yields achieved (up to 98%), along with the possibility of working in aerated solution, make this procedure simple, rapid and efficient. Although a deep insight on the photochemical mechanisms was beyond our scope, the role of electron donors was investigated, proving the efficiency of 2-propanol, citric acid and formic acid in the acceleration and improvement of V(V) conversion. After irradiation, total vanadium and aqueous V(V) and V(IV) after solid-phase separation on Chelex-100 resin, were determined by inductively coupled plasma optical emission spectroscopy (ICP-OES). The procedure was applied to contaminated wastewaters, combining remediation and possible vanadium recovery as V(IV).

Photocatalytic degradation using design of experiments: a review and example of the Congo red degradation

The use of chemometric methods such as response surface methodology (RSM) based on statistical design of experiments (DOEs) is becoming increasingly widespread in several sciences such as analytical chemistry, engineering and environmental chemistry. Applied catalysis, is certainly not the exception. It is clear that photocatalytic processes mated with chemometric experimental design play a crucial role in the ability of reaching the optimum of the catalytic reactions. The present article reviews the major applications of RSM in modern experimental design combined with photocatalytic degradation processes. Moreover, the theoretical principles and designs that enable to obtain a polynomial regression equation, which expresses the influence of process parameters on the response are thoroughly discussed. An original experimental work, the photocatalytic degradation of the dye Congo red (CR) using TiO(2) suspensions and H(2)O(2), in natural surface water (river water) is comprehensively described as a case study, in order to provide sufficient guidelines to deal with this subject, in a rational and integrated way.

Determination of free amino acid content in Radix Pseudostellariae using near infrared (NIR) spectroscopy and different multivariate calibrations

Near infrared (NIR) spectroscopy combined with multivariate calibration was attempted to analyze free amino acid content of Radix Pseudostellariae. The original spectra of Pseudostellariae samples in wavelength range of 10000-4000 cm(-1) were acquired. Partial least squares (PLS), kernel PLS (k-PLS), back propagation neural network (BP-NN), and support vector regression (SVR) algorithms were performed comparatively to develop calibration models. Some parameters of the calibration models were optimized by cross-validation. The performance of BP-NN model was better than PLS, k-PLS, and SVR models. The root mean square error of prediction (RMSEP) and the correlation coefficient (R) of BP-NN model were 0.687 and 0.889 in prediction set respectively. Results showed that NIR spectroscopy combined with multivariate calibration has significant potential in quantitative analysis of free amino acid content in Radix Pseudostellariae.