Graphene oxide/cellulose nanofibril composite: A high-performance catalyst for the fabrication of an electrochemical sensor for quantification of p-nitrophenol, a hazardous water pollutant

A critical review on metal organic frameworks (MOFs)-based sensors for foodborne pathogenic bacteria detection

The pervasive threat of foodborne pathogenic bacteria necessitates advancements in rapid and reliable detection methods. Traditional approaches suffer from significant limitations including prolonged processing times, limited sensitivity and specificity. This review comprehensively examines the integration of metal organic frameworks (MOFs) with sensor technologies for the enhanced detection of foodborne pathogens. MOFs, with their unique properties such as high porosity, tunable pore sizes, and ease of functionalization, offer new avenues for sensor enhancement. This paper provides a comprehensive analysis of recent developments in MOFs-based sensors, particularly focusing on electrochemical, fluorescence, colorimetric, and surface-enhanced Raman spectroscopy sensors. We have provided a detailed introduction for the operational principles of these sensors, highlighting the role of MOFs play in enhancing their performance. Comparative analyses demonstrate MOFs' superior capabilities in enhancing signal response, reducing response time, and expanding detection limits. This review culminates in presenting MOFs as transformative materials in the detection of foodborne pathogens, paving the way for their broader application in ensuring food safety.

A sensitive PnpR-based biosensor for p-nitrophenol detection

A common aromatic and phenolic pollutant, p-nitrophenol (PNP), is widely used in various industry and has serious risk to the environmental health. Biosensors have been extensively employed as an alternative technology for pollutants monitoring. By mining the new sensing elements, more specific biosensors could be characterized for highly sensitive detection. Herein, the PnpR transcription factor was identified to activate the transcription of pnpC1 by binding to PpnpC1 promoter region in P. putida DLL-E4, and PNP was recognized as its specific inducer. The PnpR-based biosensor for detection of PNP was developed, demonstrating adequate sensitivity in a liquid solution with satisfactory specificity. The biosensor was optimized by adopting a transcriptional amplifier, which increased the maximum output by 149-fold, and improved the detection limit by 100-fold, from 1 mg/L to 10 μg/L. These biosensors had a linear range of 5-80 mg/L and 0.01-1.0 mg/L for PNP determination, respectively. Then, the agarose gel entrapment-based biosensor was constructed and allowed a good of PNP detection in the range of 5-60 mg/L in M9 solid agar within 70 min, and a detection sensitive of 16.8 mg/kg in soil. The good performance of the biosensor suggested its potential application of high-efficient and on-site detection in environmental matrices.

Polyimide-multiwalled carbon nanotubes composite as electrochemical sensing platform for the simultaneous detection of nitrophenol isomers

Developing novel electrode materials plays a crucial role in enhancing the electrochemical sensing performance of chemically modified electrodes. This research presents a composite electrode material based on polyimide incorporated with multiwalled carbon nanotubes (PI-MWCNT) for the simultaneous detection of three nitrophenol isomers (NPs). First, the composite was prepared and characterized using microscopies, spectroscopic techniques, and electrochemical experiments. The results indicated that the PI-MWCNT exhibited porosity and roughness, which facilitated the enhancement of its sensing performance. Afterward, the detection capabilities of PI-MWCNT towards NPs were evaluated through voltammetry experiments under optimal conditions. The differential pulse voltammetry (DPV) curves revealed three distinct anodic peaks in the NPs solution, with linear ranges of 1-300 μM for 2-NP, 0.25-250 μM for 3-NP, and 0.25-400 μM for 4-NP. The limits of detection (LOD) were 0.50 μM for both 2-NP and 3-NP, and 0.64 μM for 4-NP. Furthermore, the proposed electrode material was successfully applied to real samples, achieving recovery rates ranging from 92.9% to 106%. This study could contribute to the development of more efficient and sensitive electrochemical sensors.

Predictive modeling for the adsorptive and photocatalytic removal of phenolic contaminants from water using artificial neural networks

Numerous harmful phenolic contaminants are discharged into water that pose a serious threat to environment where two of the most important purification methodologies for the mitigation of phenolic contaminants are adsorption and photocatalysis. Besides cost, each process has drawbacks in terms of productivity, environmental impact, sludge creation, and the development of harmful by-products. To overcome these limitations, the modeling and optimization of water treatment methods is required. Artificial Intelligence (AI) is employed for the interpretation of treatment-based processes due to powerful learning, simplicity, high estimation accuracy, effectiveness, and improvement of process efficiency where artificial neural networks (ANNs) are most frequently employed for predicting and analyzing the efficiency of processes applied for the mitigation of these phenolic contaminants from water. ANNs are superior to conventional linear regression models because the latter are incapable of dealing with non-linear systems. ANNs can also reduce the operational cost of treating phenol-contaminated water. A correlation coefficient of >0.99 can be achieved using ANN with enhanced phenol mitigation percentage accuracy generally ranging from 80 % to 99.99 %. Using ANN optimization, the maximum phenol mitigation efficiencies achieved were 99.99 % for phenol, 99.93 % for bisphenol A, 99.6 % for nonylphenol, 97.1 % for 2-nitrophenol, 96.6 % for 4-chlorophenol and 90 % for 2,6-dichlorophenol. In numerous ANN models, Levenberg-Marquardt backpropagation algorithm for training was employed using MATLAB software. This study overviews their employment and application for optimization and modeling of removal processes and explicitly discusses the important input and output parameters necessary for better performance of the system. The comparison of ANNs with other AI techniques revealed that ANNs have better predictability for mitigation of most of the phenolic contaminants. Furthermore, several challenges and future prospects have also been discussed.

Hybrid Nanomaterials: A Brief Overview of Versatile Solutions for Sensor Technology in Healthcare and Environmental Applications

The integration of nanomaterials into sensor technologies not only poses challenges but also opens up promising prospects for future research. These challenges include assessing the toxicity of nanomaterials, scalability issues, and the seamless integration of these materials into existing infrastructures. Future development opportunities lie in creating multifunctional nanocomposites and environmentally friendly nanomaterials. Crucial to this process is collaboration between universities, industry, and regulatory authorities to establish standardization in this evolving field. Our perspective favours using screen-printed sensors that employ nanocomposites with high electrochemical conductivity. This approach not only offers cost-effective production methods but also allows for customizable designs. Furthermore, incorporating hybrids based on carbon-based nanomaterials and functionalized Mxene significantly enhances sensor performance. These high electrochemical conductivity sensors are portable, rapid, and well-suited for on-site environmental monitoring, seamlessly aligning with Internet of Things (IoT) platforms for developing intelligent systems. Simultaneously, advances in electrochemical sensor technology are actively working to elevate sensitivity through integrating nanotechnology, miniaturization, and innovative electrode designs. This comprehensive approach aims to unlock the full potential of sensor technologies, catering to diverse applications ranging from healthcare to environmental monitoring. This review aims to summarise the latest trends in using hybrid nanomaterial-based sensors, explicitly focusing on their application in detecting environmental contaminants.