Enhanced artificial intelligence for electrochemical sensors in monitoring and removing of azo dyes and food colorant substances

Molecular simulation-based insights into dye pollutant adsorption: A perspective review

Growing concerns about environmental pollution have highlighted the need for efficient and sustainable methods to remove dye contamination from various ecosystems. In this context, computational methods such as molecular dynamics (MD), Monte Carlo (MC) simulations, quantum mechanics (QM) calculations, and machine learning (ML) methods are powerful tools used to study and predict the adsorption processes of dyes on various adsorbents. These methods provide detailed insights into the molecular interactions and mechanisms involved, which can be crucial for designing efficient adsorption systems. MD simulations, detailing molecular arrangements, predict dyes' adsorption behaviour and interaction energies with adsorbents. They simulate the entire adsorption process, including surface diffusion, solvent layer penetration, and physisorption. QM calculations, especially density functional theory (DFT), determine molecular structures and reactivity descriptors, aiding in understanding adsorption mechanisms. They identify stable adsorption configurations and interactions like hydrogen bonding and electrostatic forces. MC simulations predict equilibrium properties and adsorption energies by sampling molecular configurations. ML methods have proven highly effective in predicting and optimizing dye adsorption processes. These models offer significant advantages over traditional methods, including higher accuracy and the ability to handle complex datasets. These methods optimize adsorption conditions, clarify adsorbent functionalization roles, and predict dye removal efficiency under various conditions. This research explores MD, MC, QM, and ML approaches to connect molecular interactions with macroscopic adsorption phenomena. Probing these techniques provides insights into the dynamics and energetics of dye pollutants on adsorption surfaces. The findings will aid in developing and optimizing new materials for dye removal. This review has significant implications for environmental remediation, offering a comprehensive understanding of adsorption at various scales. Merging microscopic data with macroscopic observations enhances knowledge of dye pollutant adsorption, laying the groundwork for efficient, sustainable removal technologies. Addressing the growing challenges of ecosystem protection, this study contributes to a cleaner, more sustainable future.

Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents

Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.

Magnetic ZnFe2O4 composite advances SERS assay for Patent blue V

BACKGROUND

Patent blue V (PbV) an Azo colorant because of its high toxicity to children has been severely limited in food industry. However, frequently the abuse of PbV in some artificial foods is still exposed by media. Current methods for the detection of PbV have to perform tedious pre-processing and the detection sensitivity and speed are required to be further improved.

RESULTS

In this work, we immobilize gold nanoparticles (Au NPs) on the surface of ZnFe2O4 with aid of Inositol hexaphosphate (IP6) to prepare a novel magnetic surface-enhanced Raman scattering (SERS) substrate (designated as ZnFe2O4-IP6-Au NPs) for rapid detection of PbV in beverages. Synergistic effect of magnetic enrichment, magnetic inducing improvement effect (MIIE) and efficient charge transfer (CT) enables ZnFe2O4-IP6-Au NPs-based SERS assay to achieve limit of detection of PbV down to 1.31 × 10-8 mol/L and a concentration linear relationship ranging from 8.6 × 10-4 to 8.6 × 10-8 mol/L. The detection recoveries for PbV in beverages locate in the range from 98.1 to 102.5 %, meaning the feasibility of method. In addition, the presence of IP6 protection greatly improves the storage stability of ZnFe2O4-IP6-Au NPs.

SIGNIFICANCE

ZnFe2O4-IP6-Au NPs substrates with excellent SERS performance could on-site, rapidly and sensitively detect PbV. As a perspective, magnetic-composite-based SERS assay has great scenario in food safety by using portable Raman spectrometer.

Obtaining and characterizing polyphenol extracts based on anthocyanins from Melinis minutiflora inflorescences and Plinia cauliflora fruits and application in gelatins

Anthocyanins are a class of compounds potentially used as food dyes. Thus, this study aimed to obtain and characterize natural extracts from Melinis minutiflora inflorescence (M), Plinia. cauliflora peel (JP) and P. cauliflora peel and seeds (JPS) and apply them as natural food dyes in gelatins. The extracts did not show statistically significant differences in pH values and water activity. The M and JPS extracts showed similar values of anthocyanins and total phenolic compounds and were higher than those from the JP extract. The M and JPS extracts showed a bathochromic effect, which was not observed for the JP extract. The bathochromic effect may indicate a possible complexation of anthocyanins. The color composition analysis revealed that the JP extract has a higher absorbance at a wavelength of 520 nm, indirectly suggesting the presence of more monomeric anthocyanins in its composition. The extract application test in gelatin did not change the texture properties of the gelatins. In addition, our findings revealed that the JPS extract had the best color stability after ten days of analysis, indicating that anthocyanin complexation with the phenolic compounds of P. cauliflora seeds contributed more effectively to anthocyanin stability in the model used.

Intelligent analysis of carbendazim in agricultural products based on a ZSHPC/MWCNT/SPE portable nanosensor combined with machine learning methods

A nano-ZnS-decorated hierarchically porous carbon (ZSHPC) was mixed with MWCNTs to obtain ZSHPC/MWCNT nanocomposites. Then, ZSHPC/MWCNTs were used to modify a screen-printed electrode, and a portable electrochemical detection system combined with machine learning methods was used to investigate carbendazim (CBZ) residues in rice and tea. The electrochemical performance of the constructed electrode showed that the electrode had good electrocatalytic ability, large effective surface area, strong stability and anti-interference ability. Support Vector Machine (SVM), Least Square Support Vector Machine (LS-SVM) and Back Propagation-Artificial Neural Network (BP-ANN) were used to establish the prediction model for CBZ residues in rice and tea, and the traditional linear regression was developed. The investigated results showed that the LS-SVM model had the best prediction performance and the lowest prediction error compared with the traditional linear regression, BP-ANN and SVM models. The R², RMSE, and MAE for the training set samples were 0.9969, 0.3605 and 0.2968, respectively. The R², RMSE, MAE and RPD for the prediction set samples were 0.9924, 0.6190, 0.5360 and 10.3097, respectively. The average recovery range of CBZ in tea and rice was 98.77-109.32% and that of RSD was 0.47-2.58%, indicating that the rapid analysis of CBZ pesticide residues in agricultural products based on a portable electrochemical detection system combined with machine learning was feasible.

A comprehensive correlated analysis of Ra-Doped (ZnO2, ZnO) for optoelectronic applications: a first-principle study

CONTEXT

Zinc oxide (ZnO) exhibits bulk-like behavior and is modified by radium doping to attain favorable electronic properties. The elastic and mechanical response of ZnO₂ is much more favorable than ZnO material. The change in thermal expansion, Debye temperature, free energy, entropy, and specific heat leads it to be a good candidate for thermodynamic applications at low and high temperatures. Optical properties like dielectric function, absorption, refraction, reflection, and refractive index obtained after suitable doping transform the material as optically active. ZnO₂ has low reflectivity and zero absorption below the electronic band gap as compared to ZnO in a wider spectral range. Our analyses on doped ZnO₂ and ZnO make us confident for a wide range of applications in optoelectronic and anti-bacterial treatment in biomedical devices. Especially due to high flexibility and high light transmission, ZnO₂ is an excellent applicant for transparent electrodes.

METHODS

Density functional theory has been employed in consistency with generalized gradient approximation (GGA) with PBEsol to analyze the structural, electronic, elastic, mechanical, thermodynamic, and optical response of pure and Ra-doped (ZnO₂ and ZnO) materials.