Conversion of Fluorescence Signals into Optical Fingerprints Realizing High-Throughput Discrimination of Anionic Sulfonate Surfactants with Similar Structure Based on a Statistical Strategy and Luminescent Metal–Organic Frameworks

Alizarin complexone modified UiO-66-NH2 as dual-mode colorimetric and fluorescence pH sensor for monitoring perishable food freshness

Food prone to spoilage has a huge food safety hazard, threatening people's health, so early detection of food spoilage is a continuous and urgent need. Herein, we developed a dual-mode response sensor, alizarin complexone@UiO-66-NH2, which can accurately detect pH. The sensor demonstrated significant changes in color from pale yellow to deep pink, while the fluorescence shifted from light blue to blue violet. Moreover, both UV absorption and fluorescence intensity showed a linear correlation with pH raging from 4.5 to 7.5. These results indicate that the sensor effectively responds to pH, making it suitable for detecting the freshness of perishable food. To put this into practice, we integrated the sensor with cellulose-based filter paper to determine the freshness of shrimp and beef, which was proved to be effective in assessing freshness. In the future, it can be combined with intelligent colorimetric and fluorescence instruments to achieve visual detection.

A three-modal fluorescent sensor harnessing diverse luminescent mechanisms for the purpose of segmented Baijiu identification

The classification and verification of segmented Baijiu hold significant importance as they profoundly influence the blending and overall quality of the Baijiu. Our scholarly investigation yielded a fluorescent sensor with three luminescent modes by integrating Tb3+ and RHB into UiO-66. The interplay between carboxyl-containing compounds and RHB/Tb@TLU-2 orchestrates a harmonious molecular association, where the convergence of carboxyl groups with Tb3+ yields a resonating impact on the antenna effect of BDC-SO3-. Furthermore, the acidity and alkalinity of reactants induced a charge transfer interaction between BDC-NH2 and Zr4+ and led to structural changes in RHB/Tb@TLU-2, resulting in observable fluorescence signal variations across the three emission centers. The sensor array successfully identified eight organic acids, achieving an impressive 97.5 % accuracy in discerning segmented Baijiu samples from four Baijiu pits. This meticulous methodology prioritizes simplicity, swiftness, and effectiveness, paving the path for comprehensive segmented Baijiu analysis in the esteemed realm of Brewing production.

Dual-Emission Single Sensing Element-Assembled Fluorescent Sensor Arrays for the Rapid Discrimination of Multiple Surfactants in Environments

Surfactants are considered as typical emerging pollutants, their extensive use of in disinfectants has hugely threatened the ecosystem and human health, particularly during the pandemic of coronavirus disease-19 (COVID-19), whereas the rapid discrimination of multiple surfactants in environments is still a great challenge. Herein, we designed a fluorescent sensor array based on luminescent metal-organic frameworks (UiO-66-NH2@Au NCs) for the specific discrimination of six surfactants (AOS, SDS, SDSO, MES, SDBS, and Tween-20). Wherein, UiO-66-NH2@Au NCs were fabricated by integrating UiO-66-NH2 (2-aminoterephthalic acid-anchored-MOFs based on zirconium ions) with gold nanoclusters (Au NCs), which exhibited a dual-emission features, showing good luminescence. Interestingly, due to the interactions of surfactants and UiO-66-NH2@Au NCs, the surfactants can differentially regulate the fluorescence property of UiO-66-NH2@Au NCs, producing diverse fluorescent "fingerprints", which were further identified by pattern recognition methods. The proposed fluorescence sensor array achieved 100% accuracy in identifying various surfactants and multicomponent mixtures, with the detection limit in the range of 0.0032 to 0.0315 mM for six pollutants, which was successfully employed in the discrimination of surfactants in real environmental waters. More importantly, our findings provided a new avenue in rapid detection of surfactants, rendering a promising technique for environmental monitoring against trace multicontaminants.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Machine learning assisted discrimination and detection of antibiotics by using multicolor microfluidic chemiluminescence detection chip

The fabrication of multicolor chemiluminescence (CL) sensing chip for the discrimination and detection of multianalytes remains a great challenge. Herein, machine learning assisted multicolor microfluidic CL detection chip for the identification and concentration prediction of antibiotics was presented. Firstly, a three-channel microfluidic CL detection chip was fabricated. The three detection zones of the microfluidic detection chip were modified with CL catalyst Co(II) and different CL reagents including luminol, luminol mixed with fluorescein, and luminol mixed with phloxine B, respectively. Strong blue, green and pink-purple colored light emissions can be generated from the three detection zones in the presence of H2O2 solution. The three multicolor CL emissions show different degrees of reduce in intensity and change in color in the presence of different antibiotics, including diethylstilbestro (DES), metronidazole (MNZ), kanamycin (KAN), isoniazide (INH), and ceftiofur sodium (CS), resulting in distinct fingerprint-like response patterns. The red (R), green (G), blue (B) and gray scale values of the three multicolor light emissions were extracted and ten characteristic sensing parameters were chosen to obtain multicolor CL response database. Then, machine learning assisted data analysis were carried out. The five antibiotics can be facilely classified by using principal component analysis (PCA) and hierarchical clustering analysis (HCA), and further quantified by using deep neural networks (DNN) algorithm. Good results were obtained for identification of binary antibiotic mixtures, spiked antibiotics in water samples, and unknown antibiotic samples. Satisfied results were obtained for concentration prediction of antibiotics. This work provides a simple machine learning assisted and multicolor microfluidic CL detection chip based CL sensing strategy for discrimination and quantitative detection of multiple analytes.

Aptamers dimerization inspired biomimetic clamp assay towards impedimetric SARS-CoV-2 antigen detection

Antigen-detecting rapid diagnostic testing (Ag-RDT) has contributed to containing the spread of SARS-CoV-2 variants of concern (VOCs). In this study, we proposed a biomimetic clamp assay for impedimetric SARS-CoV-2 nucleocapsid protein (Np) detection. The DNA biomimetic clamp (DNA-BC) is formed by a pair of Np aptamers connected via a T₂₀ spacer. The 5'- terminal of the DNA-BC is phosphate-modified and then anchored on the surface of the screen-printed gold electrode, which has been pre-coated with Au@UiO-66-NH₂. The integrated DNA-material sensing biochip is fabricated through the strong Zr-O-P bonds to form a clamp-type impedimetric aptasensor. It is demonstrated that the aptasensor could achieve Np detection in one step within 11 min and shows pronounced sensitivity with a detection limit of 0.31 pg mL^-1. Above all, the aptasensor displays great specificity and stability under physiological conditions as well as various water environments. It is a potentially promising strategy to exploit reliable Ag-RDT products to confront the ongoing epidemic.

Recent Progress in Sensor Arrays: From Construction Principles of Sensing Elements to Applications

The traditional sensors are designed based on the "lock-and-key" strategy with high selectivity and specificity for detecting specific analytes, which however are not suitable for detecting multiple analytes simultaneously. With the help of pattern recognition technologies, the sensor arrays excel in distinguishing subtle changes caused by multitarget analytes with similar structures in a complex system. To construct a sensor array, the multiple sensing elements are undoubtedly indispensable units that will selectively interact with targets to generate the unique "fingerprints" based on the distinct responses, enabling the identification among various analytes through pattern recognition methods. This comprehensive review mainly focuses on the construction strategies and principles of sensing elements, as well as the applications of sensor array for identification and detection of target analytes in a wide range of fields. Furthermore, the present challenges and further perspectives of sensor arrays are discussed in detail.

A simple array integrating machine learning for identification of flavonoids in red wines

Bioactive flavonoids, the major ingredients of red wines, have been proven to prevent atherosclerosis and cardiovascular disease due to their anti-inflammatory and anti-oxidant activity. However, flavonoids have proven challenging to identify, even when multiple approaches are combined. Hereby, a simple array was constructed to detect flavonoids by employing phenylboronic acid modified perylene diimide derivatives (PDIs). Through multiple non-specific interactions (hydrophilic, hydrophobic, charged, aromatic, hydrogen-bonded and reversible covalent interactions) with flavonoids, the fluorescence of PDIs can be modulated, and variations in intensity can be used to create fingerprints of flavonoids. This array successfully discriminated 14 flavonoids of diverse structures and concentrations with 100% accuracy, based on patterns in fluorescence intensity modulation, via optimized machine learning algorithms. As a result, this array demonstrated the parallel detection of 8 different types and origins of red wines with a high accuracy, revealing the excellent potential of the sensor array in food mixtures detection.

NH3 Plasma Functionalization of UiO-66-NH2 for Highly Enhanced Selective Fluorescence Detection of U(VI) in Water

Radioactive U(VI) in nuclear wastewater is a global environmental pollutant that poses a great threat to human health. Therefore, it is of great significance to develop a U(VI) sensor with desirable sensitivity and selectivity. Inspired by electron-donating group modification for enhancement of binding affinity toward U(VI), we report an amine group functionalization of UiO-66-NH₂, using a low-cost, environmentally friendly, and low-temperature NH₃ plasma technique as a fluorescence switching nanoprobe for highly sensitive and selective detection of U(VI). The resulting amine-functionalized UiO-66-NH₂ (LTP@UiO-66-NH₂) shows dramatically enhanced fluorescence emission and selective sensitivity for U(VI) on the basis of the quenching effect. The quenching efficiency increases from 58 to 80% with the same U(VI) concentration (17.63 μM) after NH₃ plasma functionalization. As a result, the LTP@UiO-66-NH₂ has the best K_sv (1.81 × 10⁵ M^-1, 298 K) and among the lowest LODs (0.08 μM, 19.04 ppb) compared with those reported in the literature. Intraday and interday precision and application in real environment experiments indicate stable and accurate U(VI) detection performance. Fluorescence lifetime and temperature-dependent detection experiments reveal that the quenching mechanism belongs to the static quenching interaction. The highly selective fluorescence detection is attributed to the selective binding of U(VI) by the rich functionalized amine groups of LTP@UiO-66-NH₂. This work provides an efficient fluorescence probe for highly sensitive U(VI) detection in water, and a new strategy of tailored plasma functionalization for developing a practical MOF sensor platform for enhanced fluorescence emission, sensitivity, and selectivity for detecting trace amounts of radioactive species in the environment.

CsPbBr3 and CsPbBr3/SiO2 Nanocrystals as a Fluorescence Sensing Platform for High-Throughput Identification of Multiple Thiophene Sulfides

Air pollution is a serious problem. Refractory thiophene sulfides, which cause air pollution, bring great challenges to their rapid and accurate identification. In this work, we propose a fluorescent sensor array based on two perovskite nanocrystals (CsPbBr₃ NCs and CsPbBr₃/SiO₂ NCs) to distinguish different thiophene sulfides. The hydrogen bonding force between the thiophenics of thiophene sulfides and the amino groups of the perovskite NCs results in the weakening of the fluorescence signals of the perovskite NCs. The diverse interactions between thiophene sulfides and two perovskite NCs provide rich information, which can be obtained on the sensor array and identified by linear discriminant analysis. Five thiophene sulfides (i.e., benzothiophene, dibenzothiophene, 2-methylbenzothiophene, 3-methylthiophene, and thiophene) were discriminated by the sensor array at concentrations of 10-50 ppm. The effectiveness of the sensor array was further verified in the discrimination of blinded samples, in which all 10 samples were correctly identified. In addition, it is gratifying that even binary mixtures of thiophene sulfides could be distinguished by the proposed sensor array.

Lab-on-a-ZnO-Submicron-Particle Sensor Array for Monitoring AD upon Cd2+ Exposure with CSF Tau441% as an Effective Hallmark

In this study, based on the posttreatment strategy, blue-color-emissive ZnO submicron particles (B-ZnO SMPs) and red-color-emissive ZnO submicron particles (R-ZnO SMPs) were obtained from rationally designed Zn-infinite coordination polymer (ICP) precursors. After modification of thiol-containing aptamers, diverse spectral changes in the ultraviolet and visible regions of B- and R-ZnO SMPs toward different tau species were explored to construct a lab-on-a-ZnO-submicron-particle sensor array. Assisted by principal component analysis (PCA), the unique fingerprints of the sensor array enabled the simultaneous differentiation and quantitative detection of different tau species (tau381, tau410, and tau441) for the first time. Furthermore, the dynamic changes of tau441% (the ratio of the two most reported representative 4R isoform (full-length tau441) and 3R isoform (tau381)) in cerebrospinal fluid (CSF) during the Alzheimer's disease (AD) onset of Cd²⁺-exposed rats could also be monitored by the lab-on-a-ZnO-submicron-particle sensor array, which was supposed to be an effective hallmark and highly correlated with the formation of neurofibrillary tangles (NFTs). This study not only provides a further insight into the involvement of subchronic Cd²⁺ exposure in the tau etiology of AD but also offers more comprehensive and effective information about the asymptomatic stage of AD upon environmental risk, which has potential applications in the early diagnosis and therapy.

Screening and Discrimination of Perfluoroalkyl Substances in Aqueous Solution Using a Luminescent Metal-Organic Framework Sensor Array

The extensive production and large-scale use of perfluoroalkyl substances (PFASs) have raised their presence in aquatic environments worldwide. Thus, the facile and reliable screening of PFASs in aqueous systems is of great significance. Herein, we designed a novel fluorescent sensor array for the rapid screening and discrimination of multiple PFASs in water. The sensor array comprised three highly stable zirconium porphyrinic luminescent metal-organic frameworks (i.e., PCNs) with different topological structures. The sensing mechanism was based on the static fluorescence quenching of PCNs by PFASs upon their adsorptive interactions. The fluorescence response patterns were characteristic for each PFAS because of their different adsorption affinities toward different PCNs. Through the interpretation of response patterns by statistical methods, the proposed PCN array successfully discriminated six different kinds of PFASs, each PFAS at different concentrations and PFAS mixtures at different molar ratios. The practicability of this array was further verified by effectively discriminating PFASs in two real water samples. Remarkably, the PCN sensors exhibited a very short response time toward PFASs (within 10 s) due to the ordered pore structure allowing fast PFAS diffusion. This study not only provides a facile method for rapid PFAS screening in waters but also broadens the application of luminescent metal-organic frameworks and array techniques in sensing fields.

Metal-Mediated Polydopamine Nanoparticles-DNA Nanomachine Coupling Electrochemical Conversion of Metal-Organic Frameworks for Ultrasensitive MicroRNA Sensing

The development of a robust sensing platform with an efficient probe assembly, and ingenious signal conversion is of great significance for bioanalytical application. In this work, a multipedal polydopamine nanoparticles-DNA (PDANs-DNA) nanomachine coupling electrochemical-driven metal-organic frameworks (MOFs) conversion-enabled biosensing platform was constructed. The PDANs-DNA nanomachine was designed based on Ca²⁺-mediated DNA adsorption and target-triggered catalytic hairpin assembly on PDANs, which not only maintained the DNA immobilization simplicity but also possessed a high walking efficiency. PDANs-DNA nanomachine could walk fast on the electrode via multiple legs under exonuclease III driving, resulting in the formation of DNA dendrimers through two hairpins assembly. The MOFs (Fe-MIL-88-NH₂) probe was decorated on the DNA dendrimers to act as a porous metal precursor and converted into electroactive Prussian Blue by a controlled electrochemical approach, which was a facile, simple, and room-temperature approach compared with the commonly employed MOFs conversion methods. Using microRNA-21 (miRNA-21) as the model target, the proposed biosensor achieved miRNA-21 detection ranging from 10 aM to 10 pM with the detection limit of 5.8 aM. The proposed strategy presented a highly efficient walking platform with the ingenious electrochemical conversion of MOFs, providing more options for the design of an electrochemical platform and holding potential applications in clinical analysis and disease diagnosis.

pH-Regulated H4TCPE@Eu/AMP ICP Sensor Array and Its Fingerprinting on Test Papers: Toward Point-of-Use Systematic Analysis of Environmental Antibiotics

In this work, 1,1,2,2-tetra(4-carboxylphenyl)ethylene (H₄TCPE) was selected as the guest and incorporated into a Eu/AMP ICP host to establish a "lab-on-an-AIE@Ln/ICP" sensor array for identifying and sensing environmental antibiotics simultaneously. First, on the basis of a theoretical study of the antenna effect and reductive photoinduced charge transfer between the as-prepared H₄TCPE@Eu/AMP ICPs and antibiotics, respectively, the response from the sensitized time-resolved fluorescence of the host and the unique aggregation-induced emission (AIE) of the guest were selected as the main sensing elements for the sensor array. With the regulation of pH, the diverse fluorescence responses for antibiotics with either structural differences (flumequine, oxytetracycline, and sulfadiazine) or structural similarities (oxytetracycline, tetracycline, and doxycycline) were recorded and processed by principal component analysis; systematic analysis of environmental antibiotics was therefore realized. Encouraged by the superior anti-aggregation-caused quenching effect of H₄TCPE@Eu/AMP ICPs on the test strip, the distinct fluorescence color changes of the "lab-on-an-AIE@Ln/ICP" sensor array were further explored with the aid of smartphones. The fingerprinting pattern of the sensor array on test paper eventually holds great potential for the point-of-use systematic analysis of environmental antibiotics even in complicated real samples.

Lanthanide-functionalized metal-organic frameworks based ratiometric fluorescent sensor array for identification and determination of antibiotics

Antibiotics have made great contributions to the improvement of human health and life quality. However, the current abuse of antibiotics not only has a serious impact on the environment, but also endangers people's health. For this reason, the simultaneous identification and accurate determination of as many antibiotics in the environment, food and organisms as possible is critical. Herein, a ratiometric fluorescent sensor array based on Eu³⁺ and Tb³⁺ co-doped metal-organic frameworks (MOFs) was fabricated. Benefiting from the sensitization of the organic ligands to Eu³⁺ and Tb³⁺, the reaction of MOFs with various antibiotics resulted in different responses to the ratio of fluorescent intensity at 545 nm and 616 nm (F₅₄₅/F₆₁₆). After these responses were differentiated by principal component analysis (PCA), totally eight kinds of 25 antibiotics were well distinguished with the existence of interfering substances. The proposed sensor array exhibited high accuracy (98%) for the identification of 48 unknown samples in water and outstanding quantitative ability for the mixture of antibiotics. Finally, the practicability of the sensor array for the analysis of real samples was proved. In this strategy, we have not only provided an efficient way for the comprehensive identification and determination of antibiotics, but also promised new opportunities for the development of ratiometric signal based sensor array.