Real Time Sensing of Soil Potassium Levels Using Zinc Oxide-Multiwall Carbon Nanotube-Based Sensors

Carbon nanotubes in plant dynamics: Unravelling multifaceted roles and phytotoxic implications

Carbon nanotubes (CNTs) have emerged as a promising frontier in plant science owing to their unique physicochemical properties and versatile applications. CNTs enhance stress tolerance by improving water dynamics and nutrient uptake and activating defence mechanisms against abiotic and biotic stresses. They can be taken up by roots and translocated within the plant, impacting water retention, nutrient assimilation, and photosynthesis. CNTs have shown promise in modulating plant-microbe interactions, influencing symbiotic relationships and mitigating the detrimental effects of phytopathogens. CNTs have demonstrated the ability to modulate gene expression in plants, offering a powerful tool for targeted genetic modifications. The integration of CNTs as sensing elements in plants has opened new avenues for real-time monitoring of environmental conditions and early detection of stress-induced changes. In the realm of agrochemicals, CNTs have been explored for their potential as carriers for targeted delivery of nutrients, pesticides, and other bioactive compounds. CNTs have the potential to demonstrate phytotoxic effects, detrimentally influencing both the growth and developmental processes of plants. Phytotoxicity is characterized by induction of oxidative stress, impairment of cellular integrity, disruption of photosynthetic processes, perturbation of nutrient homeostasis, and alterations in gene expression. This review aims to provide a comprehensive overview of the current state of knowledge regarding the multifaceted roles of CNTs in plant physiology, emphasizing their potential applications and addressing the existing challenges in translating this knowledge into sustainable agricultural practices.

Development of electrochemical sensors for quick detection of environmental (soil and water) NPK ions

All over the world, technology is becoming more and more prevalent in agriculture. Different types of instruments are already being used in this sector. For the time being, every farmer is trying to produce more crops on a piece of land. Eventually, soil loses its nutrients; however, to grow more crops, farmers use more fertilizers without knowing the proper conditions of the soil in real time. To overcome this issue, many scientists have recently focused on developing electrochemical sensors to detect macronutrients, i.e., nitrogen (N), phosphorus (P), and potassium (K), in soil or water rapidly. In this review, we focus mainly on the recent developments in electrochemical sensors used for the detection of nutrients (NPK) in different types of samples. As it is outlined, the use of smart and portable electrochemical sensors can be helpful for the reduction of excess fertilizer and can play a vital role in maintaining suitable conditions in soils and water. We are optimistic that this review can guide researchers in the development of a portable and suitable NPK detection system for soil nutrients.

Machine learning-assisted ammonium detection using zinc oxide/multi-walled carbon nanotube composite based impedance sensors

We report a machine learning approach to accurately correlate the impedance variations in zinc oxide/multi walled carbon nanotube nanocomposite (F-MWCNT/ZnO-NFs) to NH4+ ions concentrations. Impedance response of F-MWCNT/ZnO-NFs nanocomposites with varying ZnO:MWCNT compositions were evaluated for its sensitivity and selectivity to NH4+ ions in the presence of structurally similar analytes. A decision-making model was built, trained and tested using important features of the impedance response of F-MWCNT/ZnO-NF to varying NH4+ concentrations. Different algorithms such as kNN, random forest, neural network, Naïve Bayes and logistic regression are compared and discussed. ML analysis have led to identify the most prominent features of an impedance spectrum that can be used as the ML predictors to estimate the real concentration of NH4+ ion levels. The proposed NH4+ sensor along with the decision-making model can identify and operate at specific operating frequencies to continuously collect the most relevant information from a system.