State-of-the-art progress of metal-organic framework-based electrochemical and optical sensing platforms for determination of bisphenol A as an endocrine disruptor

A novel pencil graphite electrode modified with an iron-based conductive metal-organic framework exhibited good ability in simultaneous sensing bisphenol A and bisphenol S

Bisphenol A (BPA) and its substitute bisphenol S (BPS) are desirable materials widely used in manufacturing plastic products but can pose carcinogenic risks to humans. A new conductive iron-based metal-organic framework (Fe-HHTP)-modified pencil graphite electrode (PGE) for electrochemically sensing BPA and BPS was prepared and fully characterized by SEM, TEM, FT-IR, XRD, and XPS. Results showed that the optimal conditions for preparing Fe-HHTP/PGE were a pH of 6.5, a Fe-HHTP concentration of 2 mg·mL-1, a deposition potential of 0 V, and a deposition time of 100 s. The Fe-HHTP/PGE prepared under such conditions harbored a significant electrocatalytic activity with a detection limit of 0.8 nM for BPA and 1.7 nM for BPS (S/N = 3). Correspondingly, the electrochemical response current was linearly correlated to BPA and BPS, ranging from 0.01 to 100 μM. Fe-HHTP/PGE also obtained satisfactory recoveries by 93.8-102.1% and 96.0-101.3% for detecting BPA and BPS in plastic food packaging samples. Our work has provided a novel electrochemical tool to simultaneously detect BPA and BPS in food packaging samples and environmental matrixes.

Three-Dimensional Electrochemical Sensors for Food Safety Applications

Considering the increasing concern for food safety, electrochemical methods for detecting specific ingredients in the food are currently the most efficient method due to their low cost, fast response signal, high sensitivity, and ease of use. The detection efficiency of electrochemical sensors is determined by the electrode materials' electrochemical characteristics. Among them, three-dimensional (3D) electrodes have unique advantages in electronic transfer, adsorption capacity and exposure of active sites for energy storage, novel materials, and electrochemical sensing. Therefore, this review begins by outlining the benefits and drawbacks of 3D electrodes compared to other materials before going into more detail about how 3D materials are synthesized. Next, different types of 3D electrodes are outlined together with common modification techniques for enhancing electrochemical performance. After this, a demonstration of 3D electrochemical sensors for food safety applications, such as detecting components, additives, emerging pollutants, and bacteria in food, was given. Finally, improvement measures and development directions of electrodes with 3D electrochemical sensors are discussed. We think that this review will help with the creation of new 3D electrodes and offer fresh perspectives on how to achieve extremely sensitive electrochemical detection in the area of food safety.

MOF-Based Mycotoxin Nanosensors for Food Quality and Safety Assessment through Electrochemical and Optical Methods

Mycotoxins in food are hazardous for animal and human health, resulting in food waste and exacerbating the critical global food security situation. In addition, they affect commerce, particularly the incomes of rural farmers. The grave consequences of these contaminants require a comprehensive strategy for their elimination to preserve consumer safety and regulatory compliance. Therefore, developing a policy framework and control strategy for these contaminants is essential to improve food safety. In this context, sensing approaches based on metal-organic frameworks (MOF) offer a unique tool for the quick and effective detection of pathogenic microorganisms, heavy metals, prohibited food additives, persistent organic pollutants (POPs), toxins, veterinary medications, and pesticide residues. This review focuses on the rapid screening of MOF-based sensors to examine food safety by describing the main features and characteristics of MOF-based nanocomposites. In addition, the main prospects of MOF-based sensors are highlighted in this paper. MOF-based sensing approaches can be advantageous for assessing food safety owing to their mobility, affordability, dependability, sensitivity, and stability. We believe this report will assist readers in comprehending the impacts of food jeopardy exposure, the implications on health, and the usage of metal-organic frameworks for detecting and sensing nourishment risks.

Machine learning approaches for predicting arsenic adsorption from water using porous metal-organic frameworks

Arsenic in drinking water is a serious threat for human health due to its toxic nature and therefore, its eliminating is highly necessary. In this study, the ability of different novel and robust machine learning (ML) approaches, including Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Decision Tree, and Random Forest was implemented to predict the adsorptive removal of arsenate [As(V)] from wastewater over 13 different metal–organic frameworks (MOFs). A large experimental dataset was collected under various conditions. The adsorbent dosage, contact time, initial arsenic concentration, adsorbent surface area, temperature, solution pH, and the presence of anions were considered as input variables, and adsorptive removal of As(V) was selected as the output of the models. The developed models were evaluated using various statistical criteria. The obtained results indicated that the LightGBM model provided the most accurate and reliable response to predict As(V) adsorption by MOFs and possesses R², RMSE, STD, and AAPRE (%) of 0.9958, 2.0688, 0.0628, and 2.88, respectively. The expected trends of As(V) removal with increasing initial concentration, solution pH, temperature, and coexistence of anions were predicted reasonably by the LightGBM model. Sensitivity analysis revealed that the adsorption process adversely relates to the initial As(V) concentration and directly depends on the MOFs surface area and dosage. This study proves that ML approaches are capable to manage complicated problems with large datasets and can be affordable alternatives for expensive and time-consuming experimental wastewater treatment processes.

Recent Advances in Early Diagnosis of Viruses Associated with Gastroenteritis by Biosensors

Gastroenteritis, as one of the main worldwide health challenges, especially in children, leads to 3–6 million deaths annually and causes nearly 20% of the total deaths of children aged ˂5 years, of which ~1.5 million gastroenteritis deaths occur in developing nations. Viruses are the main causative agent (~70%) of gastroenteritis episodes and their specific and early diagnosis via laboratory assays is very helpful for having successful antiviral therapy and reduction in infection burden. Regarding this importance, the present literature is the first review of updated improvements in the employing of different types of biosensors such as electrochemical, optical, and piezoelectric for sensitive, simple, cheap, rapid, and specific diagnosis of human gastroenteritis viruses. The Introduction section is a general discussion about the importance of viral gastroenteritis, types of viruses that cause gastroenteritis, and reasons for the combination of conventional diagnostic tests with biosensors for fast detection of viruses associated with gastroenteritis. Following the current laboratory detection tests for human gastroenteritis viruses and their limitations (with subsections: Electron Microscope (EM), Cell Culture, Immunoassay, and Molecular Techniques), structural features and significant aspects of various biosensing methods are discussed in the Biosensor section. In the next sections, basic information on viruses causing gastroenteritis and recent developments for fabrication and testing of different biosensors for each virus detection are covered, and the prospect of future developments in designing different biosensing platforms for gastroenteritis virus detection is discussed in the Conclusion and Future Directions section as well.