Ion-Imprinted Polymer Modified with Carbon Quantum Dots as a Highly Sensitive Copper(II) Ion Probe

Fluorescent chemosensors facilitate the visualization of plant health and their living environment in sustainable agriculture

Globally, 91% of plant production encounters diverse environmental stresses that adversely affect their growth, leading to severe yield losses of 50-60%. In this case, monitoring the connection between the environment and plant health can balance population demands with environmental protection and resource distribution. Fluorescent chemosensors have shown great progress in monitoring the health and environment of plants due to their high sensitivity and biocompatibility. However, to date, no comprehensive analysis and systematic summary of fluorescent chemosensors used in monitoring the correlation between plant health and their environment have been reported. Thus, herein, we summarize the current fluorescent chemosensors ranging from their design strategies to applications in monitoring plant-environment interaction processes. First, we highlight the types of fluorescent chemosensors with design strategies to resolve the bottlenecks encountered in monitoring the health and living environment of plants. In addition, the applications of fluorescent small-molecule, nano and supramolecular chemosensors in the visualization of the health and living environment of plants are discussed. Finally, the major challenges and perspectives in this field are presented. This work will provide guidance for the design of efficient fluorescent chemosensors to monitor plant health, and then promote sustainable agricultural development.

Molecularly Designed Ion-Imprinted Nanoparticles for Real-Time Sensing of Cu(II) Ions Using Quartz Crystal Microbalance

A molecularly designed imprinting method was combined with a gravimetric nanosensor for the real-time detection Cu(II) ions in aqueous solutions without using expensive laboratory devices. Thus, 1:1 and 2:1 mol-ratio-dependent coordination modes between Cu(II), N-methacyloly-L histidine methyl ester (MAH) functional monomer complexes, and their four-fold and six-fold coordinations were calculated by means of density functional theory molecular modeling. Cu(II)-MIP1 and Cu(II)-MIP2 nanoparticles were synthesized in the size range of 80-100 nm and characterized by SEM, AFM and FTIR. Cu(II)-MIP nanoparticles were then conducted to a quartz crystal microbalance sensor for the real-time detection of Cu(II) ions in aqueous solutions. The effects of initial Cu(II) concentration, selectivity, and imprinting efficiency were investigated for the optimization of the nanosensor. Linearity of 99% was obtained in the Cu(II) ion linear concentration range of 0.15-1.57 µM with high sensitivity. The LOD was obtained as 40.7 nM for Cu(II)-MIP2 nanoparticles. The selectivity and the imprinting efficiency of the QCM nanosensor were obtained significantly in the presence of competitive ion samples (Co(II), Ni(II), Zn(II), and Fe(II)). The results are promising for sensing Cu(II) ions as environmental toxicants in water by combining molecularly designed ion-imprinted nanoparticles and a gravimetric sensor.

Carbon Quantum Dots Based Chemosensor Array for Monitoring Multiple Metal Ions

The simultaneous identification of multiple metal ions in water has attracted enormous research interest in the past few decades. We herein describe a novel method for multiple metal ion detection using a carbon quantum dots (CQDs)-based chemosensor array and the CQDs are functionalized with different amino acids (glutamine, histidine, arginine, lysine and proline), which act as sensing elements in the sensor array. Eleven metal ions are successfully identified by the designed chemosensor array, with 100% classification accuracy. Importantly, the proposed method allowed the quantitative prediction of the concentration of individual metal ions in the mixture with the aid of a support vector machine (SVM). The sensor array also enables the qualitative detection of unknown metal ions under the interference of tap water and local river water. Thus, the strategy provides a novel high-throughput approach for the identification of various analytes in complex systems.