Nanotechnology is gaining enormous attention as the most dynamic research area in science and technology. It involves the synthesis and applications of nanomaterials in diverse fields including medical, agriculture, textiles, food technology, cosmetics, aerospace, electronics, etc. Silver nanoparticles (AgNPs) have been extensively used in such applications due to their excellent physicochemical, antibacterial, and biological properties. The use of plant extract as a biological reactor is one of the most promising solutions for the synthesis of AgNPs because this process overcomes the drawbacks of physical and chemical methods. This review article summarizes the plant‐mediated synthesis process, the probable reaction mechanism, and the colorimetric sensing applications of AgNPs. Plant‐mediated synthesis parameters largely affect the surface plasmon resonance (SPR) characteristic due to the changes in the size and shape of AgNPs. These changes in the size and shape of plant‐mediated AgNPs are elaborately discussed here by analyzing the surface plasmon resonance characteristics. Furthermore, this article also highlights the promising applications of plant‐mediated AgNPs in sensing applications regarding the detection of mercury, hydrogen peroxide, lead, and glucose. Finally, it describes the future perspective of plant‐mediated AgNPs for the development of green chemistry.

Jute is a bio-degradable, agro-renewable, and widely available lingo cellulosic fiber having high tensile strength and initial modulus, moisture regain, good sound, and heat insulation properties. For these unique properties and eco-friendly nature of jute fibers, jute-based products are now widely used in many sectors such as packaging, home textiles, agro textiles, build textiles, and so forth. The diversified applications of jute products create an excellent opportunity to mitigate the negative environmental effect of petroleum-based products. For producing the best quality jute products, the main prerequisite is to ensure the jute yarn quality that can be defined by the load at break (L.B), strain at break (S.B), tenacity at break (T.B), and tensile modulus (T.M). However, good quality yarn production by considering these parameters is quite difficult because these parameters follow a non-linear relationship. Therefore, it is essential to build up a model that can cover this entire inconsistent pattern and forecast the yarn quality accurately. That is why, in this study, a laboratory-based research work was performed to develop a fuzzy model to predict the quality of jute yarn considering L.B, S.B, T.B, and T.M as input parameters. For this purpose, 173 tex (5 lb/spindle) and 241 tex (7 lb/spindle) were produced, and then L.B, S.B, T.B and T.M values were measured. Using this measured value, a fuzzy model was developed to determine the optimum L.B, S.B, T.B, and T.M to produce the best quality jute yarn. In our proposed fuzzy model, for 173 tex and 241 tex yarn count, the mean relative error was found to be 1.46% (Triangular membership) and 1.48% (Gaussian membership), respectively, and the correlation coefficient was 0.93 for both triangular and gaussian membership function. This result validated the effectiveness of the proposed fuzzy model for an industrial application. The developed fuzzy model may help a spinner to produce the best quality jute yarn.