Copper nanoclusters-based fluorescent sensor array to identify metal ions and dissolved organic matter

A quenched fluorescence sensor array based on bis-lanthanide metal-organic framework for acetaldehyde detection and Baijiu discrimination

Discrimination of segmented Baijiu contributes to stabilizing the quality of products, improving revenue-generating effects. A fluorescence sensor array is constructed based on four fluorescence characteristic peaks of terbium@lanthanum metal-organic framework (Tb@La-MOF). Its fluorescence signal is specifically quenched, when Tb@La-MOF encounters acetaldehyde. Acetaldehyde may inhibit the absorption of energy by the organic ligands in MOF, or/and hydrogen bonding with -COOH on the organic ligand, resulting in energy transfer to Tb(Ⅲ). According to this, the quantitative detection of acetaldehyde is completed with a range of 10-300 μM and the detection limit of 5.5 μM. At the same time, it has been successfully applied to the discrimination of segmented Baijiu. Fifteen segmented from three wine cellars are 100 % discriminated with the combined processing of sensor arrays and analytical methods. Accuracy, simplicity, and low-cost are highlights of this fluorescence sensor array, which has considerable potential for application in detection, production, and food field.

A machine learning-assisted fluorescent sensor array utilizing silver nanoclusters for coffee discrimination

Coffee is a globally consumed commodity of substantial commercial significance. In this study, we constructed a fluorescent sensor array based on two types of polymer templated silver nanoclusters (AgNCs) for the detection of organic acids and coffees. The nanoclusters exhibited different interactions with organic acids and generated unique fluorescence response patterns. By employing principal component analysis (PCA) and random forest (RF) algorithms, the sensor array exhibited good qualitative and quantitative capabilities for organic acids. Then the sensor array was used to distinguish coffees with different processing methods or roast degrees and the recognition accuracy achieved 100%. It could also successfully identify 40 coffee samples from 12 geographical origins. Moreover, it demonstrated another satisfactory performance for the classification of pure coffee samples with their binary and ternary mixtures or other beverages. In summary, we present a novel method for detecting and identifying multiple coffees, which has considerable potential in applications such as quality control and identification of fake blended coffees.

Concurrent manipulation of competitive mechanisms to construct glutathione-stabilized gold nanocluster-based dual-channel molecular classifier for metal ions detection and information steganography

Understanding charge transport in metal ion-mediated glutathione-stabilized gold nanoclusters (GSH-Au NCs) has proved difficult due to the presence of various competitive mechanisms, such as electron transfer (ET) and aggregation induction effect (AIE). In this paper, we present a dual-channel fluorescence (FL) and second-order Rayleigh scattering (SRS) sensing method for high-throughput classification of metal ions, relying on the competition between ET and AIE using GSH-Au NCs. The SRS signals show significant enhancement when Pb2+, Ag+, Al3+, Cu2+, Fe3+, and Hg2+ are present, as a result of the aggregation of GSH-Au NCs. Notably, the fluorescence signal exhibits the opposite trend. The FL intensities of GSH-Au NCs are enhanced by Pb2+, Ag+, and Al3+ through the AIE mechanism, while they are quenched by Cu2+, Fe3+, and Hg2+, which is dominated by the ET mechanism. By employing principal component analysis and hierarchical cluster analysis, these signals are transformed into unique fingerprints and Euclidean distances, respectively, enabling successful distinction of six metal ions and their mixtures with a low detection limit of 30 nM. This new strategy has successfully addressed interference from impurities in the testing of real water samples, demonstrating its strong ability to detect multiple metal ions. Impressively, we have achieved molecular cryptosteganography, which involves encoding, storing, and concealing information by transforming the selective response of GSH-Au NCs to binary strings. This research is anticipated to advance utilization of nanomaterials in logic sensing and information safety, bridging the gap between molecular sensors and information systems.

Gold Nanocluster-Based Self-Assembly Fluorescence Microbeads for Sensor Array Discrimination of Multicomponent Metal Ions

Benefiting from easy visualization and simultaneous detection of multiple targets, fluorescence microbeads are commonly used as fluorescence-sensing elements to detect pollutants in the environment. However, the application of fluorescence microbead-based sensor arrays is still limited because fluorescence dyes always suffer from self-quenching, photobleaching, and spectral overlap. Herein, three kinds of gold nanoclusters (Au NCs) were assembled with polystyrene microspheres (PS NPs) by electrostatic interaction to prepare fluorescence microbeads (PS-Au NCs), developing a sensor array for the simultaneous analysis of multiple metal ions. In this work, different PS-Au NCs showed an enhancing or quenching fluorescence response to various metal ions, owing to distinct binding capacities. Combined with the recognition algorithm from linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA), this sensor assay could realize single-component and multicomponent qualitative detection for 8 kinds of heavy metal ions (HMIs) including Cu2+, Co2+, Pb2+, Hg2+, and Ce3+. Particularly, the large surface area of PS NPs could provide a direct reaction microenvironment to improve the efficiency of the detection process. Meanwhile, the fluorescence property of Au NCs could also be enhanced by a partially effective aggregation-induced emission (AIE) effect to give better fluorescence signal output. Under optimal conditions, 8 kinds of heavy metals and their multicomponent mixtures could be identified at concentrations as low as 0.62 μM. Meanwhile, the analytical performance of this sensor assay in water samples was also verified, meeting the requirement of actual analysis. This study provides a great potential and practical example of single-batch, multicomponent identification for HMIs.

Fluorescent metal nanoclusters: From luminescence mechanism to applications in enzyme activity assays

Metal nanoclusters (MNCs) have outstanding fluorescence property and biocompatibility, which show widespread applications in biological analysis. Particularly, evaluation of enzyme activity with the fluorescent MNCs has been developed rapidly within the past several years. In this review, we first introduced the fluorescent mechanism of mono- and bi-metallic nanoclusters, respectively, whose interesting luminescence properties are mainly resulted from electron transfer between the lowest unoccupied molecular orbital (LUMO) and highest occupied molecular orbital (HOMO) energy levels. Meanwhile, the charge migration within the structure occurs through ligand-metal charge transfer (LMCT) or ligand-metal-metal charge transfer (LMMCT). On such foundation, diverse enzyme activities were rigorously evaluated, including three transferases and nine hydrolases, in turn harvesting rapid research progresses within past 5 years. Finally, we summarized the design strategies for evaluating enzyme activity with the MNCs, presented the major issues and challenges remained in the relevant research, coupled by showing some improvement measures. This review will attract researchers dedicated to the studies of the MNCs and provide some constructive insights for their further applications in enzyme analysis.

Nanotechnology-based optical sensors for Baijiu quality and safety control

Baijiu quality and safety have received considerable attention owing to the gradual increase in its consumption. However, owing to the unique and complex process of Baijiu production, issues leading to quality and safety concerns may occur during the manufacturing process. Therefore, establishing appropriate analytical methods is necessary for Baijiu quality assurance and process control. Nanomaterial (NM)-based optical sensing techniques have garnered widespread interest because of their unique advantages. However, comprehensive studies on nano-optical sensing technology for quality and safety control of Baijiu are lacking. In this review, we systematically summarize NM-based optical sensor applications for the accurate detection and quantification of analytes closely related to Baijiu quality and safety. Furthermore, we evaluate the sensing mechanisms for each application. Finally, we discuss the challenges nanotechnology poses for Baijiu analysis and future trends. Overall, nanotechnological approaches provide a potentially useful alternative for simplifying Baijiu analysis and improving final product quality and safety.

"Turn-on" fluorescence sensing for sensitively detecting Cr(VI) via a guest exchange process in Cu NCs@MIL-101 composites

Copper nanoclusters (Cu NCs) are a new fluorescent material that is often used for determining metal ions, but most sensing systems are based on the "turn-off" model. Here, a "turn-on" model of fluorescence sensing for the detection of Cr(VI) was developed based on Cu NCs@MIL-101 composites. The Cu NCs@MIL-101 composites were synthesized from a simple mixture of Cu NCs and MIL-101(Cr), in which the Cu NCs were uniformly distributed in MIL-101(Cr). Notably, the fluorescence intensity of Cu NCs@MIL-101 was significantly weakened due to the internal filtration effect (IFE) of MIL-101. When Cr(VI) was introduced, the fluorescence of Cu NCs@MIL-101 was recovered by the guest exchange process between Cr(VI) and the Cu NCs, which overcame the IFE of Cu NCs@MIL-101. Based on this, a "turn-on" fluorescence probe was successfully constructed for the quantitative detection of Cr(VI) with two linear ranges of 0.05-1 μM and 1-20 μM, and a low detection limit of 0.05 μM. The proposed fluorescence probe possessed excellent selectivity and anti-interference ability, and was successfully applied for the detection of Cr(VI) in real water samples with satisfactory results. This study provides a new approach for the analytical application of Cu NCs.

A novel 2D Eu-MOF as a dual-functional fluorescence sensor for detection of benzaldehyde and Fe3

Lanthanide metal-organic frameworks (Ln-MOFs) have unique advantages in sensing due to their excellent optical properties. In this study, we synthesized a dicarboxylic acid ligand with amide groups and successfully synthesized a novel two-dimensional (2D) MOF with the molecular formula C42H31EuN4O10 (Eu-MOF) by a solvothermal method. Single-crystal X-ray diffraction showed that amide groups are exposed on the outside of the two-dimensional coordination layer, with the possibility of recognizing specific molecules through hydrogen bonding interactions. The ligand's "antenna effect" enables Eu-MOF to emit a strong luminescence characterized by the "f-f" transition. Further studies have revealed that Eu-MOF could be used as a bifunctional fluorescent probe for the selective detection of benzaldehyde and Fe3+. The sensing mechanism has been analyzed in detail through powder X-ray diffraction (PXRD) analysis, UV-vis spectroscopy, fluorescence lifetime measurement, and density functional (DFT) theory calculation. This design and research can provide a reference for subsequent related work.

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.

A Polycaprolactone-Capped ZnO Quantum Dots-Based Fluorometric Sensor for the Detection of Fe3+ Ions in Seawater

Fe3+ ion plays a very active role in life, agriculture, and industry. Human health and the environment are seriously affected by the abnormal presence or excess of this cation. Therefore, the development of a fast, reliable, sensitive, and simple fluorescent probe to detect this cation is crucial. In the present paper, polycaprolactone-capped zinc oxide quantum dots were prepared for the determination of Fe3+ ions. The proposed fluorescent chemosensor exhibited a fluorometric and strong quenching effect toward Fe3+ ions at two wavelengths (303 and 602 nm). The limit of detection (LOD) was calculated as 0.410, and 0.333µM at the mentioned wavelengths. Also, the binding stoichiometric ratio was calculated as 1:1 by Job's plot. The findings indicated that the PCL@ZnO colorimetric chemosensor could be successfully applied with reliable, and good accuracy for the detection of Fe3+ ions in real seawater samples.

Ensuring food safety by artificial intelligence-enhanced nanosensor arrays

Current analytical methods utilized for food safety inspection requires improvement in terms of their cost-efficiency, speed of detection, and ease of use. Sensor array technology has emerged as a food safety assessment method that applies multiple cross-reactive sensors to identify specific targets via pattern recognition. When the sensor arrays are fabricated with nanomaterials, the binding affinity of analytes to the sensors and the response of sensor arrays can be remarkably enhanced, thereby making the detection process more rapid, sensitive, and accurate. Data analysis is vital in converting the signals from sensor arrays into meaningful information regarding the analytes. As the sensor arrays can generate complex, high-dimensional data in response to analytes, they require the use of machine learning algorithms to reduce the dimensionality of the data to gain more reliable outcomes. Moreover, the advances in handheld smart devices have made it easier to read and analyze the sensor array signals, with the advantages of convenience, portability, and efficiency. While facing some challenges, the integration of artificial intelligence with nanosensor arrays holds promise for enhancing food safety monitoring.

A fluorescent sensor array based on antibiotic-stabilized metal nanoclusters for the multiplex detection of bacteria

To address the need for facile, rapid detection of pathogens in water supplies, a fluorescent sensing array platform based on antibiotic-stabilized metal nanoclusters was developed for the multiplex detection of pathogens. Using five common antibiotics, eight different nanoclusters (NCs) were synthesized including ampicillin stabilized copper NCs, cefepime stabilized gold and copper NCs, kanamycin stabilized gold and copper NCs, lysozyme stabilized gold NCs, and vancomycin stabilized gold/silver and copper NCs. Based on the different interaction of each NC with the bacteria strains, unique patterns were generated. Various machine learning algorithms were employed for pattern discernment, among which the artificial neural networks proved to have the highest performance, with an accuracy of 100%. The developed prediction model performed well on an independent test dataset and on real samples gathered from drinking water, tap water and the Anzali Lagoon water, with prediction accuracy of 96.88% and 95.14%, respectively. This work demonstrates how generic antibiotics can be implemented for NC synthesis and used as recognition elements for pathogen detection. Furthermore, it displays how merging machine learning techniques can elevate sensitivity of analytical devices.

Ratiometric fluorescence detection of dopamine based on copper nanoclusters and carbon dots

Nanoclusters for fluorescence detection are generally comprised of rare and expensive noble metals, and the nanoclusters based on more affordable transition metal have attracted increasing attention. This study designed a ratiometric fluorescent probe to detect dopamine (DA), an important neurotransmitter. With carbon dots encapsulated within silica (CDs@SiO2) as the reference, the emitted reference signal was almost unchanged due to the protection of inert silicon shell. Meanwhile, copper nanoclusters modified with 3-aminophenyl boronic acid (APBA-GSH-CuNCs) provided the sensing signal, in which the phenylboric acid could specifically recognize the cis-diol structure of DA, and caused the fluorescence quenching by photoinduced electron transfer. This dual emission ratiometric fluorescent probe exhibited high sensitivity and anti-interference, and was able to selectively responded to DA with a linear range of 0-1.4 mM, the detection limit of 5.6 nM, and the sensitivity of 815 mM-1. Furthermore, the probe successfully detected DA in human serum samples, yielding recoveries ranging from 92.5% to 102.7%. Overall, this study highlights the promising potential of this ratiometric probe for detecting DA.

Glutathione S-transferase templated copper nanoclusters as a fluorescent probe for turn-on sensing of chlorotetracycline

Hereby, facile-green copper nanoclusters templated by glutathione S-transferase (GST-CuNCs) have been innovatively synthesized via a simple one-pot stirring method at room temperature. The as-prepared nanoclusters exhibited uniform size with satisfactory fluorescence intensity, good stability and low cytotoxicity. Significantly, the fluorescence of the obtained GST-CuNCs could be considerably enhanced by the addition of chlorotetracycline (CTC) rather than other analogues of CTC, which was ascribed to the aggregation-induced enhancement caused by the interaction between CTC and GST. The enhanced fluorescence intensity demonstrated a good linear correlation with the CTC concentration in the range of 30-120 μM (R2 = 0.99517), and the low detection limit was 69.7 nM. Furthermore, the proposed approach showed favorable selectivity and anti-interference toward CTC among prevalent ions and amino acids. Additionally, this nanoprobe was also applied to the quantitative detection of CTC in serum samples with satisfactory outcomes, which demonstrated excellent prospects for practical applications.

Machine learning-assisted photoluminescent sensor array based on gold nanoclusters for the discrimination of antibiotics with test paper

Antibiotic residues accumulation in the environment endangers ecosystems and human health. There is an urgent need for a facile and efficient strategy to detect antibiotics. Here, we report a photoluminescent sensor array based on protein-stabilized gold nanoclusters (AuNCs) for the detection of two families of antibiotics, tetracyclines and quinolones. The nanoclusters were synthesized with bovine serum albumin (BSA) and ovalbumin (OVA), respectively. They had different interactions with seven kinds of antibiotics and exhibited diverse photoluminescence (PL) responses, which were analyzed by linear discriminant analysis and ExtraTrees algorithms. The sensor array performed well in both classification and quantification of seven antibiotics. And the quantitative results of all antibiotics obtained R2 of no less than 0.99 at 0-100 μM when using suitable regression models. Additionally, the sensor array was able to distinguish antibiotic mixtures and multiple interfering substances, and it also kept 100% classification accuracy in river water samples. Moreover, test paper assisted by a smartphone was applied for quick detection of antibiotics, with good performance in both HEPES buffer and river water. These studies reveal great potential for the point-of-use analysis of antibiotics in environmental monitoring.

Spatio-temporal characterization of dissolved organic matter in karst rivers disturbed by acid mine drainage and its correlation with metal ions

Dissolved organic matter (DOM) is widely present in surface water environments and plays a critical role in the biogeochemical cycling of metal ions. Metal ions in acid mine drainage (AMD) have seriously polluted karst surface water environments, but few studies have explored interactions between DOM and metal ions in AMD-disturbed karst rivers. Here, the composition and sources of DOM in AMD-disturbed karst rivers were investigated by fluorescence excitation-emission spectroscopy combined with parallel factor analysis. In addition, correlations between metal ions and other factors (DOM components, total dissolved carbon (TDC) and pH) were determined using structural equation modeling (SEM). Results showed that there were evident differences in the seasonal distribution of TDC and metal ion concentrations in AMD-disturbed karst rivers. The concentrations of DOC, dissolved inorganic carbon (DIC), and metal ions were generally higher in the dry season than in the wet season, with Fe and Mn pollution being the most pronounced. The DOM in AMD contained two types of protein-like substances that were mainly from autochthonous inputs, while DOM in AMD-disturbed karst rivers contained two additional types of humic-like substances from both autochthonous and allochthonous inputs. The SEM results showed that the influence of DOM components on the distribution of metal ions was greater than that of TDC and pH. Among the DOM components, the influence of humic-like substances was greater than that of protein-like substances. Additionally, DOM and TDC had direct positive effects on metal ions, while pH had a direct negative effect on these. These results further elucidated the geochemical interactions between DOM and metal ions in AMD-disturbed karst rivers, which will assist in the pollution prevention of metal ions in AMD.

Multiple fluorescence polymer dots-based differential array sensors for highly efficient heavy metal ions detection

Water pollution caused by harmful heavy metal ions (HMIs) can significantly impact aquatic ecosystems and pose a high risk to human health. In this work, equipped with ultra-high fluorescence brightness, efficient energy transfer, and environmentally friendly performance, polymer dots (Pdots) were employed to construct a pattern recognition fluorescent HMIs detection platform. A single-channel unary Pdots differential sensing array was first developed to identify multiple HMIs with 100% classification accuracy. Then an "all-in-one" multiple Förster resonance energy transfer (FRET) Pdots differential sensing platform was constructed to discriminate HMIs in the artificial polluted water samples and actual water samples, exhibiting high classification accuracy in distinguishing HMIs. The proposed strategy leverages the compounded cumulative differential variation of diverse sensing channels for analytes, which is anticipated to find extensive applications in other fields for detection purposes.

Identification of multi-component metal ion mixtures in complex systems using fluorescence sensor arrays

The growing co-contamination of multiple metal ions seriously influences human health due to their synergistic and additive toxicological effects, whereas the rapid discrimination of multiple heavy metal ions in complex aquatic systems remains a major challenge. Herein, a high- throughput fluorescence sensor array was fabricated based on three gold nanoclusters (GSH-Au NCs, OVA-Au NCs, and BSA-Au NCs) for the direct identification and quantification of seven heavy metal ions (Pb²⁺, Fe³⁺, Cu²⁺, Co²⁺, Ag⁺, Hg²⁺ and As³⁺) from environmental waters without sample pretreatment other than filtration. At the detection system, three gold nanoclusters with various ligands possessed distinct binding capacities against metal ions and induced aggregation-induced fluorescence enhancement and quenching, resulting in a unique pattern of fluorescence variations. Meanwhile, integrated the collected fluorescence fingerprints with linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA), a discrete database was obtained for the accurate recognition and sensitive detection of metal ions. Under the optimized conditions, the limit of detection (LOD) of the proposed fluorescence sensor array for metal ions detection at nM concentration level along with a satisfactory accuracy. Importantly, our study indicated that the fluorescence sensor array could be widely used as a general platform in environmental monitoring against multiple targets at low concentrations.

Tree-based machine learning models assisted fluorescent sensor array for detection of metal ions based on silver nanocluster probe

The growing concern over heavy metal pollution and its impact on the environment and human health has led to a proliferation of research on the detection and differentiation of heavy metal ions. A novel fluorescent sensor array utilizing only one single Ag-nanoclusters (Ag NCs) was developed for the efficient detection of six metal ions. The Ag NCs probe was prepared by using poly(methyl vinyl ether-alt-maleic acid) (PMVEM) as the ligand and has different fluorescence properties in water and dimethyl sulfoxide (DMSO). The interaction between metal ions and Ag NCs resulted in a characteristic fluorescence variation pattern which was subsequently analyzed using various tree-based machine learning models. We have compared different combinations of classification models and pre-processing methods of which the K-Nearest Neighbors Classifier with the first five linear discriminants has the highest accuracy. Through the integration of concentration models within a tree-based pipeline optimization framework, six unique concentration regression models were selected for each metal ion. In addition, the developed sensor array could identify metal ions in binary mixtures. And it still kept high accuracy for the classification of six target metal ions in river water. In conclusion, the proposed framework was found to be effective in the detection of heavy metal ions in environmental samples, thus providing a promising approach for addressing heavy metal pollution.

Advances in Ultra-small Fluorescence Nanoprobes for Detection of Metal Ions, Drugs, Pesticides and Biomarkers

Identification of trace level chemical species (drugs, pesticides, metal ions and biomarkers) plays key role in environmental monitoring. Recently, fluorescence assay has shown significant advances in detecting of trace level drugs, pesticides, metal ions and biomarkers in real samples. Ultra-small nanostructure materials (metal nanoclusters (NCs), quantum dots (QDs) and carbon dots (CDs)) have been integrated with fluorescence spectrometer for sensitive and selective analysis of trace level target analytes in various samples including environmental and biological samples. This review summarizes the properties of metal NCs and ligand chemistry for the fabrication of metal NCs. We also briefly summarized the synthetic routes for the preparation of QDs and CDs. Advances of ultra-small fluorescent nanosensors (NCs, QDs and CDs) for sensing of metal ions, drugs, pesticides and biomarkers in various sample matrices are briefly discussed. Additionally, we discuss the recent challenges and future perspectives of ultra-small materials as fluorescent sensors for assaying of wide variety of target analytes in real samples.

Polyethyleneimine-capped copper nanoclusters for detection and discrimination of 2,4,6-trinitrotoluene and 2,4,6-trinitrophenol

The detection and discrimination of 2,4,6-trinitrotoluene (TNT) and 2,4,6-trinitrophenol (TNP) from analogues are of great importance to global security and are full of challenges in the field of trace sensing. Here, benefitting from the strong electrophilicity of TNT, a sensing strategy is established by synthesizing polyethyleneimine capped copper nanoclusters (PEI-Cu NCs) with abundant -NH₂ groups. By carefully controlling the size and structure of PEI-Cu NCs, Förster resonance energy transfer (FRET) from PEI-Cu NCs to the Meisenheimer complex occurs resulting from their spectral overlap when detecting TNT, while, due to the energy level match of TNP with PEI-Cu NCs, as well as the strong affinity between its -OH and -NH₂ in PEI-Cu NCs, photo-induced electron transfer (PET) is feasibly expected. As a result, TNT and TNP could be detected from 26 types of analogues and cations with a limit of detection (LOD) of 26.57 and 12.82 nM, respectively. Besides, owing to the brown color of the Meisenheimer complex, the discrimination of TNT and TNP could be additionally realized by colorimetric detection. We expect that the proposed methodology would not only shine light on the detection and discrimination of TNT and TNP that mitigate against public security concerns, but also pave a way for the deep understanding of FRET and PET related fluorescence quenching mechanisms from the aspect of controllable sensing material design and synthesis.

Novel NBN-Embedded Polymers and Their Application as Fluorescent Probes in Fe3+ and Cr3+ Detection

The isosteric replacement of C═C by B–N units in conjugated organic systems has recently attracted tremendous interest due to its desirable optical, electronic and sensory properties. Compared with BN-, NBN- and BNB-doped polycyclic aromatic hydrocarbons, NBN-embedded polymers are poised to expand the diversity and functionality of olefin polymers, but this new class of materials remain underexplored. Herein, a series of polymers with BNB-doped π-system as a pendant group were synthesized by reversible addition-fragmentation chain transfer (RAFT) polymerization from NBN-containing vinyl monomers, which was prepared via intermolecular dehydration reaction between boronic acid and diamine moieties in one pot. Poly{2-(4-Vinylphenyl)-2,3-dihydro-1H-naphtho[1,8-de][1,3,2]diazaborinine} (P1), poly{N-(4-(1H-naphtho[1,8-de][1,3,2]diazaborinin-2(3H)-yl)phenyl)acrylamide} (P2) and poly{N-(4-(1H-benzo[d][1,3,2]diazaborol-2(3H)-yl)phenyl)acrylamide} (P3) were successfully synthesized. Their structure, photophysical properties and application in metal ion detection were investigated. Three polymers exhibit obvious solvatochromic fluorescence. As fluorescent sensors for the detection of Fe³⁺ and Cr³⁺, P1 and P2 show excellent selectivity and sensitivity. The limit of detection (LOD) achieved by Fe³⁺ is 7.30 nM, and the LOD achieved by Cr³⁺ is 14.69 nM, which indicates the great potential of these NBN-embedded polymers as metal fluorescence sensors.