Fluorescent Sensor Array Research Articles

Construction of fluorescent sensor array and three-dimensional microfluidic paper based analytical device for specific identification and visual determination of antibiotics in food.

For antibiotics misuse since the global outbreak of COVID 19, a novel strategy for discriminating and detecting antibiotics is proposed based on the graphene quantum dots with multi-doped heteroatoms including F, N and P (M-GQDs), which exhibit blue emission (419.0nm) under the excitation of 336.0nm. Specifically, the fluorescence of M-GQDs is quenched by tetracyclines (TCs) owing to inner filter effect (IFE) and enhanced by alkane-modified fluoroquinolones (AFQs), which is attributed to restricted conformational rotation based on π-π stacking, hydrogen-bonding and electrostatic interactions. Meanwhile, the electron-accepting property of oxazine ring in oxazine-modified fluoroquinolones (OFQs) increases emission peak at 498.0nm and decreases emission peak at 419.0nm as the color changes from blue to cyan. Moreover, a cascade system integrated with 3D microfluidic paper-based analytical device (3D-μPAD) is applied successfully for visually distinguishing three antibiotics, which shows great potential and versatility of M-GQDs for food safety monitoring.

A review on machine learning-powered fluorescent and colorimetric sensor arrays for bacteria identification.

Biosensors have been widely used for bacteria determinationwith great success. However, the "lock-and-key" methodology used by biosensors to identify bacteria has a significant limitation: it can only detect one species of bacteria. In recent years, optical (fluorescent and colorimetric) sensor arrays are gradually gaining attention from researchers as a new type of biosensor. Theycan acquire multiple features of a target simultaneously, form a feature pattern, and determine the bacteria species with the help of pattern recognition/machine learning algorithms. Previous reviews in this area have focused on the interaction between the sensor array and bacteria or the materials used to make the sensors. This review, on the other hand, will provide researchers with a better understanding of the field by discussing fluorescent and colorimetric sensor arrays based on the mechanism of optical signal generation. These sensor arrays will be compared based on the identified species. Finally, we will discuss the limitations of these sensor arrays and explore possible solutions.

Organic acid-induced triple fluorescent emission carbon quantum dots identification of distilled liquor.

An acid-sensitive carbon dot fluorescence sensing array was investigated for the differentiated recognition of distilled spirits. Due to the electrostatic interactions between CDs and organic acids, organic acids affect the protonation and fluorescence properties of CDs, which in turn modify the CDs triple fluorescence emission. The regular linear variation of the fluorescence sensor was found under acidic conditions (3.0<pH<6.2). A comprehensive study of acids with selectivity, different concentrations (0.1mM, 1mM, 10mM, 40mM), different types (8 species) and mixed acids (formic acid and acetic acid), and good quantification capability for acetic acid (0.01-1mM). Demonstrating good recognition ability of the sensor array for complex analyte. On this basis, the fluorescence sensor array was applied to the classification and recognition of liquors. LDA has realized the identification of 16 kinds of Baijiu and 21 kinds of distilled liquors with an accuracy of 100%.

Machine learning-assisted fluorescence sensor array for qualitative and quantitative analysis of pyrethroid pesticides

The simultaneous detection of multiple residues of pyrethroid pesticides (PPs) on vegetables and fruits is still challenging using traditional nanosensing methods due to the high structural similarity of PPs. In this work, sensor arrays composed of three nanocomposite complexes (rhodamine B-CD@Au, rhodamine 6G-CD@Au, and coumarin 6-CD@Au) were constructed to discriminate between structurally similar PPs. Four PPs, deltamethrin, fenvalerate, cyfluthrin, and fenpropathrin, were successfully discriminated. The ability of these sensor units was derived from the different affinity between receptor/analyte and receptor/dye, as well as the non-linear relationship between fluorescence signal and analyte concentration. Upon multivariate pattern recognition analysis, the array performed high-throughput identification of four PPs in unknown samples with 100% classification accuracy. In addition, good accuracy of predicting concentration using the “stepwise prediction” strategy combined with the machine learning method was achieved.

ROS-Responsive Fluorescent Sensor Array for Precise Diagnosis of Cancer via pH-Controlled Multicolor Gold Nanoclusters.

Intracellular reactive oxygen species (ROS) are closely associated with cancer cell types. Therefore, ROS-based pattern recognition is a promising strategy for precise diagnosis of cancer, but such a possibility has never been reported yet. Herein, we proposed an ROS-responsive fluorescent sensor array based on pH-controlled histidine-templated gold nanoclusters (AuNCs@His) to distinguish cancer cell types and their proliferation states. In this strategy, three types of AuNCs@His with diverse fluorescence profiles were first synthesized by only adjusting the pH value. Upon the addition of various ROS, fluorescence quenching of three types of AuNCs@His occurred with different degrees, thereby forming unique optical "fingerprints", which were well-clustered into several separated groups without overlap by principal component analysis (PCA). The sensing mechanism was attributable to the oxidation of AuNCs@His by ROS, as revealed by X-ray photoemission spectroscopy, Fourier transform infrared spectroscopy, 1H nuclear magnetic resonance spectroscopy, and electrospray ionization mass spectrometry. Based on the ROS-responsive sensing pattern, cancer cell types were successfully differentiated via PCA with 100% accuracy. Additionally, the proposed sensor array exhibited excellent performance in distinguishing the proliferation states of cancer cells, which was supported by the results of the Ki-67 immunohistochemistry assay. Overall, the ROS-responsive fluorescent sensor array can serve as a promising tool for precise diagnosis of cancer, indicating great potential for clinical application.

Array-based sensing and discrimination of segmented Baijiu using organic molecules-regulated PEI@Ag NPs@Ln as fluorescent probes.

Quickly discriminating different segmented Baijiu can directly control its grade and indirectly affect the quality of the finished Baijiu. A fluorescence sensor array was constructed based on PEI-terminated silver nanoparticles and lanthanide metal ions (PEI@Ag NPs@Ln). Ag NPs were stably dispersed in the PEI-woven network, initially accompanied by excellent fluorescence signals. Organic molecules disrupted the PEI structure and dragged the Ag NPs out. The free Ag NPs sintered or aggregated with the diffusion, resulting in fluorescence quenching. The three lanthanide ions speed up the process, allowing different organic molecules to exhibit more distinct signals. Thus, this sensor was used to map 11 organic molecules' fingerprints and to discriminate segmented Baijiu. The whole process takes only 2min. With the assistance of pattern recognition, segmented Baijiu from three cellars were successfully discriminated. Fast, effective and simple are highlights, which opens up its practical application potential in the detection field.

Detection of plant pathogenic fungi by a fluorescent sensor array

Detection and identification of plant pathogenic fungi have relevant implications for both food safety and human health, and require fast and easy-to handle diagnostic techniques. In this context, we report the design and development of a fluorescent array-based sensor, containing different organic receptors, able to detect selectively Penicillium italicum, Alternaria alternata and Fusarium sacchari. The detection of these pathogenic fungi can be performed using a smartphone, leading to the first example of detection of microscopic fungal plant pathogens by fluorescent array using a smartphone as detector. This study represents a proof-of-concept for the realization of practical sensoristic devices able to detect pathogens in plant products and food or in environmental samples, in order to guarantee food and human health security.

A butterfly-shaped ESIPT dye for pattern recognition of metal ions

Accurate monitoring of versatile metal ions in complicated environmental or biological samples is meaningful for avoiding heavy metal poisoning accidents. To reduce the production cost of a metal ion sensor array, we intended to develop solvent directed fluorescent sensor arrays on the basis of a single dye molecule. Herein, two fluorescent ESIPT dyes named SC1 and SC2 were designed by incorporating different numbers of benzothiazole units as the metal ion binding site. It was discovered that SC1 was highly selective toward Zn2+ in all tested solvents, while a solvent directed cross-responsive sensing behavior was observed for SC2. Therefore a three-component sensor array SC2SA was readily constructed by simply dissolving SC2 in common solvents including ethanol, DMF and DMSO. The sensor array was determined to be accurate for semi-quantitative discrimination and detection of Zn2+, Cd2+, Mg2+, Fe2+ and Fe3+ in diluted serum media. Moreover, sensor array SC2SA was also successfully applied for differentiating Zn2+/Cd2+ and Fe2+/Fe3+ in pure and mixed forms and reliable identification of unknown Zn2+, Cd2+ and Mg2+ in diluted serum samples.

Dual-Emission Carbonized Polymer Dots Combined with Metal Ions as a Single-Component Fluorescence Sensor Array for Pattern Recognition of Glycosaminoglycans

Dual-Emission Carbonized Polymer Dots Combined with Metal Ions as a Single-Component Fluorescence Sensor Array for Pattern Recognition of Glycosaminoglycans

Supramolecular Fluorescence Sensor Array Based on Cucurbit[8]uril Complexes Used for the Detection of Multiplex Quaternary Ammonium Pesticides.

The simultaneous detection of multiple quaternary ammonium pesticides (QAPs) in water is a challenge due to their high solubility in water and similar structures. In this paper, we have developed a quadruple-channel supramolecular fluorescence sensor array for the simultaneous analysis of five QAPs, including paraquat (PQ), diquat (DQ), difenzoquat (DFQ), mepiquat (MQ), and chlormequat (CQ). Not only were QAP samples of different concentrations (10, 50, and 300 μM) in water distinguished with 100% accuracy but also single QAP and binary QAP mixed samples (DFQ-DQ) were sensitively quantified. Our experimental interference study confirmed that the developed array has good anti-interference ability. The array can quickly identify five QAPs in river and tap water samples. In addition, it also qualitatively detected QAP residues in Chinese cabbage and wheat seedlings extract. This array has rich output signals, low cost, easy preparation, and simple technology, demonstrating great potential in environmental analysis.

Gold Nanocluster-Based Fluorescent Sensor Array for Antibiotic Sensing and Identification

Antibiotic contamination has become a serious global problem due to abuse and misuse. Therefore, it is important to develop an efficient detection method to monitor the rational use of antibiotics. In this study, fluorescent gold nanoclusters with 11-mercaptoundecanoic acid as ligands (MUA-AuNCs) were synthesized by a one-step method firstly. Rare earth ions (Re3+) can enhance the fluorescence of MUA-AuNCs through inducing the aggregation of MUA-AuNCs, but antibiotics decrease the fluorescence of the Re3+-MUA-AuNCs to different degrees through coordination with Re3+ and competitive absorption with AuNCs. Therefore, a sensor array was obtained on the basis of the above mechanism, which can detect and discriminate six different antibiotics with a detection range from 40 to 300 μM. A 100% correct classification was achieved. The fluorescent sensor array showed high selectivity for tetracycline antibiotics and good anti-interference performance was demonstrated. Combined with pattern recognition methods, the proposed sensor array can be used for the discrimination of different antibiotics and binary antibiotic mixtures. Furthermore, the excellent performance of this sensor array in quantitation and blind sample recognition further validates its potential for practical applications.

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.

Enzyme-Linked Immunosorbent Assay-Based Microarray on a Chip for Bioaerosol Sensing: Toward Sensitive and Multiplexed Profiling of Foodborne Allergens.

Food allergy has become a growing health concern that may impair life quality and even cause life-threatening outcomes. Accidental and continuous exposure to allergenic bioaerosols has a substantially negative impact on the respiratory health of patients. Traditional analytical methodologies for food allergens are restricted by strong reliance on bulk instrumentation and skilled personnel, particularly in low-resource settings. In this study, a fluorescent sensor array based on the enzyme-linked immunosorbent assay performed on a herringbone-shaped microfluidic chip (ELISA-HB-chip) was designed for dynamically sensitive and multiplexed quantification of foodborne allergens in aerosols that originated from liquid food extracts. Due to the high surface area of aerosol particles and sufficient mixing of immunological reagents using a herringbone micromixer, the detection sensitivity was improved by over an order of magnitude compared to traditional allergen detection in the aqueous phase. Through fluorescence imaging of multiple regions on the ELISA-HB-chip, four important foodborne allergens, namely, ovalbumin, ovomucoid, lysozyme, and tropomyosin, could be simultaneously monitored without any cross-reactivity, and the limits of detection for these allergenic species were determined to be 7.8, 1.2, 4.2, and 0.31 ng/mL, respectively. Combining with a 3D printed and portable fluorescence microscope, this platform exhibited an excellent field-deployable capacity for quick and accurate determination of allergens in the aerosol state from spiked buffer solutions, thus displaying the practicality for food safety screening at cooking or food processing sites where patients are potentially under exposure to allergenic bioaerosols that escaped from food matrices or extracts.

Fluorescence sensor array of a multiplexing probe with three/four excitations/emissions for rapid and highly sensitive discrimination of foodborne pathogenic bacteria

Development of sensitive, accurate yet simplified fluorescence sensor array is crucial for rapid, multi-bacterial, on-site sensing applications. A fluorescence sensor array based on a multiplexing fluorescence probe (MFP, excitations/emissions with 278 nm/558 nm, 290 nm/364 nm, 362 nm/592 and 616 nm) was constructed. The positive MFP equipped with rich quinoline and pyridine groups can combine with the negative charge groups and electron-rich groups of bacterial surfaces, which causes the symbolic signal response in the sensing channels (four emissions and three excitations). The sensor array displayed sensitive response to bacteria even at a lower level (102 cfu mL−1) and high identification accuracy (100%) for drug-resistant bacteria, inactivated bacteria, and blends of bacteria. Furthermore, the developed sensor array demonstrated a high-throughput bacterial identification (92.1% accuracy) in real samples (pork and milk) with high sensitivity, rapid response, easy-to-operate, and independence of specific recognition elements, which would brighten the development of fluorescence sensor array.

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.

Supramolecular fluorescence sensor array based on calixarenes for discrimination of nucleotides and their phosphate derivatives

ABSTRACT Nucleotides and their derivatives are vital biomarkers that play essential roles in biochemical reactions and influence processes of metabolism and anabolism. However, discriminating between various structurally similar nucleotides and their phosphate derivatives using convenient means still poses a challenge. Here, we designed a supramolecular fluorescence sensor array constructed with four different calixarenes loaded with dyes to discriminate between nucleotides and their phosphate derivatives. Based on the differences in the ability of calixarenes to recognise various nucleotides and nucleotide phosphates, a unique fluorescence response pattern is generated for each analyte. With the assistance of linear discriminant analysis, the discrimination of target analytes is effectively achieved. Subsequent assays involving various concentrations of nucleoside triphosphates and mixtures of nucleotide phosphates have also verified the potential of this sensor array based on calixarenes in the investigation of nucleotides.

An Ingestible Pill With CMOS Fluorescence Sensor Array, Bi-Directional Wireless Interface and Packaged Optics for in-Vivo Bio-Molecular Sensing.

The article presents for the first time a pill-based ingestible electronics with CMOS integrated multiplexed fluorescence bio-molecular sensor arrays, bi-directional wireless communication and packaged optics in a FDA-approved capsule for in-vivo bio-molecular sensing. The silicon chip integrates both the sensor array, and the ultra-low-power (ULP) wireless system that allows offloading sensor computing to an external base station that can reconfigure the sensor measurement time, and its dynamic range, allowing optimized high sensitivity measurement under low power consumption. The integrated receiver achieves -59 dBm receiver sensitivity dissipating 121 µW of power. The integrated transmitter operates in a dual mode FSK/OOK delivering -15 dBm of power. The 15-pixel fluorescence sensor array follows an electronic-optic co-design methodology and integrates the nano-optical filters with integrated sub-wavelength metal layers that achieves high extinction ratio (39 dB), thereby eliminating the need for bulky external optical filters. The chip integrates photo-detection circuitry and on-chip 10-bit digitation, and achieves measured sensitivity of 1.6 attomoles of fluorescence labels on surface, and between 100 pM to 1 nM of target DNA detection limit per pixel. The complete package includes a CMOS fluorescent sensor chip with integrated filter, a prototyped UV LED and optical waveguide, functionalized bioslip, off-chip power management and Tx/Rx antenna that fits in a standard FDA approved capsule size 000.

An ion-coordination hydrogel based sensor array for point-of-care identification and removal of multiple tetracyclines

Misuse and overuse of tetracycline antibiotics (TCs) brings serious issues to ecological environment, food safety and human health. It is urgent to develop unique platform for high efficient identification and removal of TCs. In the present investigation, an effective and simple fluorescence sensor array was constructed based on the interaction between metal ions (Eu3+ and Al3+) and antibiotics. Benefiting from the different affinities between the ions and TCs, the sensor array can identify TCs from other antibiotics, which also can further differentiating four kinds of TCs (OTC, CTC, TC and DOX) from each other via linear discriminant analysis (LDA) technique. Meanwhile, the sensor array performed well in quantitative analysis of single TC antibiotic and differentiation of TCs mixtures. More interestingly, Eu3+ and Al3+-doped sodium alginate/polyvinyl alcohol hydrogel beads (SA/Eu/PVA and SA/Al/PVA) were further constructed, which can not only identify the TCs but simultaneously remove the antibiotics with high efficiency. The investigation provided an instructive way for rapid detection and environment protection.

Machine Learning-Assisted Nanoenzyme/Bioenzyme Dual-Coupled Array for Rapid Detection of Amyloids.

Array-based sensing methods offer significant advantages in the simultaneous detection of multiple amyloid biomarkers and thus have great potential for diagnosing early-stage Alzheimer's disease. Yet, detecting low concentrations of amyloids remains exceptionally challenging. Here, we have developed a fluorescent sensor array based on the dual coupling of a nanoenzyme (AuNPs) and bioenzyme (horseradish peroxidase) to detect amyloids. Various ss-DNAs were bound to the nanoenzyme for regulating enzymatic activity and recognizing amyloids. A simplified sensor array was generated from a screening model via machine learning algorithms and achieved signal amplification through a two-step enzymatic reaction. As a result, our sensing system could discriminate the aggregation species and aggregation kinetics at 200 nM with 100% accuracy. Moreover, AD model mice and healthy mice were distinguished with 100% accuracy through the sensor array, providing a powerful sensing platform for diagnosing AD.

Luminescent lanthanide metallogel as a sensor array to efficiently discriminate various saccharides

By loading 3-nitrophenylboronic acid (3-NPBA) which can react with saccharide, the fluorescence lanthanide metallogel can emit fluorescence signals at 398, 490 and 546 nm. Thus, a three-dimensional responsive fluorescence sensor array (H6L/Tb3+/3-NPBA) was prepared to recognize saccharides. Due to the typical pH responsiveness of the fluorescence behavior of the sensor array, it is suitable for H+ generation systems, such as the reaction of 3-NPBA with cis-diol of saccharides. The response of fluorescence signal intensity to 15 different saccharides was measured, and the qualitative discrimination of those saccharides was investigated by principal component analysis (PCA). The results showed that PCA elliptic clusters did not overlap, even though the concentration of the saccharides was as low as 3 mmol/L, and the discrimination rate of each saccharide reached 100 %. The “double blind” experiment indicated that the sensor system has good ability to discriminate unknown component samples. Furthermore, the discrimination rate of fructose/glucose mixtures with varied mole ratios also reached 100 %. The method of Euclidean distance confirmed that there was a good linear correlation between PCA elliptic clusters of fructose and its concentration, which could realize the quantitative analysis of fructose. The results of PCA indicated that the elliptic clusters of tea drinks with various sugars and commercially available sugar-containing drinks were loosely distributed, which further confirmed that the sensor system can effectively discriminate the saccharides under the interference of complex components.