Array-based "Chemical Nose" Sensing in Diagnostics and Drug Discovery

A Bifunctional "Two-in-One" Array for Simultaneous Diagnosis of Irritable Bowel Syndrome and Identification of Low-FODMAP Diets

Irritable bowel syndrome (IBS) is a globally prevalent functional gastrointestinal disorder frequently misdiagnosed due to overlapping symptoms with other diseases. Currently, there are no rapid and effective diagnostic or therapeutic approaches for IBS. Despite this, low-FODMAP diets (LFDs) have become a major dietary intervention strategy for symptom relief. However, detecting FODMAPs usually relies on chromatographic techniques, which are costly and time-consuming, making it difficult to apply in real-time detection. In this study, we introduce the first dual-functional sensor array capable of rapidly diagnosing IBS and identifying low-FODMAP diets. This six-element array was constructed using nitrophenylboronic acid-modified poly(ethylenimine) coupled with coumarins through dynamic borate ester bonds across a range of pH conditions. Optimized by diverse machine learning algorithms, with the multilayer perceptron (MLP) algorithm proving optimal, the array enabled the simultaneous identification of 12 intestinal bacteria with 99.2% accuracy and the detection of mouse fecal specimens with varying degrees of IBS with 99.8% accuracy within seconds. Furthermore, it allowed for the detection of various FODMAP levels in commercially purchased, brand-named, and differently processed soy milk. The array demonstrates potential for use in both the clinical diagnosis of IBS and the guiding of low-FODMAP diets for patients.

Functionalized Multichannel Fluorescence-Encoded Nanosystem on Erythrocyte-Coated Nanoparticles for Precise Cancer Subtype Discrimination

Rapid and precise cancer subtype discrimination is essential for personalized oncology. Conventional diagnostic methods often lack sufficient accuracy and speed. Here, we introduce a multichannel fluorescence-encoded nanosystem based on erythrocyte-coated polydopamine nanoparticles (PDA@EM), functionalized with multiple resurfaced fluorescent proteins. The fluorescence of these proteins is initially quenched by PDA@EM and restored upon cell addition. This multichannel fluorescence-encoded nanosystem enables highly sensitive "turn-on" fluorescence profiling of cancer cells within 30 min, achieving 100% accuracy in distinguishing various proteins and classifying a wide range of cancer cell lines, including subtypes of oral squamous cell carcinoma (OSCC). Notably, it offers rapid, label-free diagnostics of OSCC malignancy from clinical samples postsurgery. This capability was validated through histopathological and proteomic analyses, which identified protein signatures associated with tumor progression and immune suppression. Overall, our multichannel nanosensor represents an advanced molecular diagnostics platform, paving the way for personalized cancer treatment in clinical oncology.

An albumin fluorescent sensor array discriminates ochratoxins

A sensor array that can distinguish ochratoxins based on the fluorescence of the albumin-ochratoxin complex has been developed. This sensor array enabled the identification of ochratoxins and their mixtures in real food samples.

Machine Learning-Assisted Liquid Crystal Optical Sensor Array Using Cysteine-Functionalized Silver Nanotriangles for Pathogen Detection in Food and Water

The challenge of rapid identification of bacteria in food and water still persists as a major health problem. To tackle this matter, we have developed a single-probe liquid crystal (LC)-based optical sensing platform for the differentiation of five common bacterial strains, including Bacillus cereus, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and S. typhimurium, using cysteine-functionalized silver nanotriangles as signal enhancers. Unique optical patterns were generated from the interaction of the samples with the LC interface and captured by using a camera under polarized light. Pattern recognition was carried out based on image analysis and machine learning (ML) calculations. Among the various ML algorithms trained, Support Vector Machines had the best performance and were able to successfully discern the bacteria with 98.89% accuracy. A linear range of 10-106 CFU mL-1 and detection limits of under 10 CFU mL-1 were attained for all of the strains. The proposed method was tested with water, juice, and milk samples, and prediction accuracies of 95.83, 97.92, and 89.58%, respectively, were obtained. The proposed method offers a simple, cost-efficient solution for bacteria recognition.

Machine learning-assisted pattern recognition and imaging of multiplexed cancer cells via a porphyrin-embedded dendrimer array

Early cancer detection plays a vital role in improving the survival rate of cancer patients, underscoring the importance of developing cancer detection methods. However, it is a great challenge to achieve simple, rapid, and accurate methods for simultaneously discerning various cancers. Herein we developed a 5-element porphyrin-embedded dendrimer-based sensor array, targeting the parallel discrimination of multiple cancers. The porphyrin-embedded dendrimers were modified with various functional groups to generate differentiated interactions with diverse cancer cells, which has been validated by fluorescence responses and laser confocal microscopy imaging. The dual-channel, five-element array, featuring ten signal outputs, achieved 100% accuracy in distinguishing between one human normal cell and six human cancerous cells, as well as in differentiating among mixed cells. Moreover, the screen 6-channel array can accurately distinguish 9 cells from mice and humans in minutes through optimization by multiple machine learning algorithms, including two normal cells and 7 cancerous cells with only 1000 cells, highlighting the significant potential of a porphyrin-embedded dendrimer-based parallel discriminating platform in early cancer diagnosis.

A saccharides regulated fluorescence ratio sensing array for bacterial recognition based on lectin response

Array sensing employs cross-identification among analytes and various sensing units to identify substances or complex systems. This manuscript presents a fluorescence ratio sensing array based on lectin responses for the accurate identification of different bacteria. This strategy uses a saccharide-sensitive polymer as the sensing unit within the sensor. By incorporating various saccharides, it regulates the properties of the single sensing unit at the molecular level, altering its interaction with the analyte. This modulation leads to the generation of multiple distinct detection signals for the target, effectively facilitating the goal of array sensing. This approach streamlines the design and construction of the array sensor, while simultaneously enhancing detection efficiency. Not only does this sensing strategy achieve the differentiation and quantification of various types of lectins, but it also enables the identification of different bacterial species based on their unique lectin response profiles. This research introduces a novel approach that simplifies the construction of array sensors and simultaneously furnishes a potent tool for diagnosing and assessing bacterial infections within clinical settings.

Bimodal Array-Based Fluorescence Sensor and Microfluidic Technology for Protein Fingerprinting and Clinical Diagnosis

Proteins play a crucial role in determining disease states in humans, making them prime targets for the development of diagnostic sensors. The developed sensor array is used to investigate global proteomic changes by fingerprinting multifactorial disease states in model urine simulating phenylketonuria and in serum from preeclamptic pregnant women. Here, we report a fluorescence-based chemical sensing array that exploits the host-guest interaction between cucurbit[7]uril (CB[7]) and fluorescent triphenylamine derivatives (TPA) to detect a range of proteins. Using linear discriminant analysis, we identify fluorescence fingerprints of 14 proteins with over 98% accuracy in buffer and human serum. The array is optimized on an automated droplet microfluidic-based platform, for high-throughput sensing with controlled composition and lower sample volumes. This sensor enables the discrimination of proteins in physiological buffer and human serum, with promising applications in disease diagnosis.

A Universal Method for Fingerprinting Multiplexed Bacteria: Evolving Pruned Sensor Arrays via Machine Learning-Driven Combinatorial Group-Specificity Strategy

Array-based sensing technology holds immense potential for discerning the intricacies of biological systems. Nevertheless, developing a universal strategy for simultaneous identification of diverse types of multianalytes and meeting the diagnostic needs of a range of multiclassified clinical diseases poses substantial challenges. Herein, we introduce a combination method for constructing sensor arrays by assembling two types of group-specific elements. Such a method enables the rapid generation of a library of 100 sensing units, each with dual bacterial targeting capabilities. By employing a three-step screening strategy optimized by machine learning algorithms, various optimal five-element arrays were rapidly obtained for diverse clinical infectious models. Moreover, the pruned arrays successfully identified disparate mixing ratios and quantitative detection of clinically prevalent bacterial strains. Optimized through nine multiclassification algorithms, the top-performing multilayer perceptron (MLP) model demonstrated impressive recognition capabilities, achieving 100% accuracy for diagnosing clinical urinary tract infection (UTI) and 99.4% accuracy for clinical sepsis detection in the test models we collected. Such a combinatorial library construction and screening process should be standard and provides insights into successfully generating powerful high-recognition sensor elements and configuring them into highly discriminative mini-sensor arrays.

Expanding the recognition of monosaccharides and glycans: A comprehensive analytical approach using chemical-nose/tongue technology and a comparison to lectin microarrays

Chemical-nose/tongue technologies are emerging as promising analytical tools for glycan analysis. After briefly introducing the importance of glycans and their analytical methods, including the lectin microarray (LMA) as one of the gold standards, the fundamental principles underlying chemical noses/tongues are explained and various applications for monosaccharides and glycans are introduced. Then, the similarities and differences of these two approaches are discussed. While both technologies aim to comprehensively profile biospecimens based on 'interaction patterns' between multiple recognition probes and analytes, each has its own strengths. LMAs excel at specific, targeted analysis based on defined lectin-glycan interactions, whereas chemical nose/tongue offers greater flexibility and expandability in terms of system design, making it well-suited for discovering unknown glycan profiles and detecting broader differences in glycan mixtures. In the future, chemical-nose/tongue technologies may be applied to niche areas in glycan analysis and become powerful tools that complement LMA techniques.

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.

Advances in metal-organic frameworks for optically selective alkaline phosphatase activity monitoring: a perspective

The study of Metal-Organic Frameworks (MOFs) has gained significant momentum due to their remarkable properties, including adjustable pore sizes, extensive surface area, and customizable compositions, which have urged scientists to investigate their applicability in pertinent societal issues such as water absorption, environmental remediation, and sensor technology. MOFs have the ability to transport and detect specific biomolecules, including proteins. One such biomolecule is alkaline phosphatase (ALP) that can be influenced by various diseases and can lead to severe consequences when its regulation is disrupted. The porous nature of MOFs and their tunable nature allows them to selectively adsorb, interact directly or indirectly with ALP. This ultimately influences the electronic and optical properties of the MOF, leading to measurable changes. Early detection and continuous monitoring of ALP play a crucial role in the use of an effective treatment, and recent studies have shown that MOFs are effective in detecting alkaline phosphatases. This manuscript offers a thorough examination of the potential biomedical applications of MOFs for monitoring alkaline phosphatase and envisions possible future trends in this field.

A Glycopolymer Sensor Array That Differentiates Lectins and Bacteria

Identification of bacterial lectins offers an attractive route to the development of new diagnostics, but the design of specific sensors is complicated by the low selectivity of carbohydrate-lectin interactions. Here we describe a glycopolymer-based sensor array which can identify a selection of lectins with similar carbohydrate recognition preferences through a pattern-based approach. Receptors were generated using a polymer scaffold functionalized with an environmentally sensitive fluorophore, along with simple carbohydrate motifs. Exposure to lectins induced changes in the emission profiles of the receptors, enabling the discrimination of analytes using linear discriminant analysis. The resultant algorithm was used for lectin identification across a range of concentrations and within complex mixtures of proteins. The sensor array was shown to discriminate different strains of pathogenic bacteria, demonstrating its potential application as a rapid diagnostic tool to characterize bacterial infections and identify bacterial virulence factors such as production of adhesins and antibiotic resistance.

A Drosophila-inspired intelligent olfactory biomimetic sensing system for gas recognition in complex environments

The olfactory sensory system of Drosophila has several advantages, including low power consumption, high rapidity and high accuracy. Here, we present a biomimetic intelligent olfactory sensing system based on the integration of an 18-channel microelectromechanical system (MEMS) sensor array (16 gas sensors, 1 humidity sensor and 1 temperature sensor), a complementary metal‒oxide‒semiconductor (CMOS) circuit and an olfactory lightweight machine-learning algorithm inspired by Drosophila. This system is an artificial version of the biological olfactory perception system with the capabilities of environmental sensing, multi-signal processing, and odor recognition. The olfactory data are processed and reconstructed by the combination of a shallow neural network and a residual neural network, with the aim to determine the noxious gas information in challenging environments such as high humidity scenarios and partially damaged sensor units. As a result, our electronic olfactory sensing system is capable of achieving comprehensive gas recognition by qualitatively identifying 7 types of gases with an accuracy of 98.5%, reducing the number of parameters and the difficulty of calculation, and quantitatively predicting each gas of 3-5 concentration gradients with an accuracy of 93.2%; thus, these results show superiority of our system in supporting alarm systems in emergency rescue scenarios.

Tuning multispectral fluorescence quantum dot-based identification of short-length amyloid β peptides by applying Cu(II) ions

Currently available methods for detecting amyloid β (Aβ) derivatives are mainly dedicated to determining the long forms Aβ1-42 and Aβ1-40. At the same time, the number of physiologically occurring Aβ analogs is much higher, including those truncated at the N- and C-termini. Their identification using standard methods is challenging due to the structural similarity of various Aβ analogs, but could highly benefit from both biomarkers discovery and pathophysiological studies of Alzheimer's disease. Therefore a "chemical tongue" sensing strategy was employed for the detection of seven Aβ peptide derivatives: Aβ1-16, Aβ4-16, Aβ4-9, Aβ5-16, Aβ5-12, Aβ5-9, Aβ12-16. The proposed sensing system is based on competitive interactions between quantum dots, Cu(II) ions, and Aβ peptides, providing unique fluorescence fingerprints useful for the identification of analytes. After carefully evaluating the Aβ sample preparation protocol, perfect determination of all studied Aβ peptides was achieved using partial least square-discriminant analysis (PLS-DA). The developed PLS-DA models are characterized by excellent accuracy, sensitivity, precision, and specificity of analyte determination, emphasizing the potential of the proposed sensing strategy.

Plasmonic Cross-Reactive Sensing Noses and Tongues

The advancements in the capabilities of artificial sensory technologies, such as electronic/optical noses and tongues, have significantly enhanced their ability to identify complex mixtures of analytes. These improvements are rooted in the evolving manufacturing processes of cross-reactive sensor arrays (CRSAs) and the development of innovative computational methods. The potential applications in early diagnosis, food quality control, environmental monitoring, and more, position CRSAs as an exciting area of research for scientists from diverse backgrounds. Among these, plasmonic CRSAs are particularly noteworthy because they offer enhanced capabilities for remote, fast, and even real-time monitoring, in addition to better portability of instrumentation. Specifically, the synergy between the localized surface plasmon resonance (LSPR) of metallic nanoparticles (NPs) and CRSAs introduces advanced techniques such as LSPR, metal-enhanced fluorescence (MEF), surface-enhanced infrared absorption (SEIRA), surface-enhanced Raman scattering (SERS), and surface-enhanced resonance Raman scattering (SERRS) spectroscopies. This review delves into the importance and versatility of optical-CRSAs, especially those based on plasmonic materials, discussing recent applications and potential new research directions.

Chemical tongues as multipurpose bioanalytical tools for the characterization of complex biological samples

Chemical tongues are emerging powerful bioanalytical tools that mimic the mechanism of the human taste system to recognize the comprehensive characteristics of complex biological samples. By using an array of chromogenic or fluorogenic probes that interact non-specifically with various components in the samples, this tool generates unique colorimetric or fluorescence patterns that reflect the biological composition of a sample. These patterns are then analyzed using multivariate analysis or machine learning to distinguish and classify the samples. This review focuses on our efforts to provide an overview of the fundamental principles of chemical tongues, probe design, and their applications as versatile tools for analyzing proteins, cells, and bacteria in biological samples. Compared to conventional methods that rely on specific targeting (e.g., antibodies or enzymes) or comprehensive omics analyses, chemical tongues offer advantages in terms of cost and the ability to analyze samples without the need for specific biomarkers. The complementary use of chemical tongues and conventional methods is expected to enable a more detailed understanding of biological samples and lead to the elucidation of new biological phenomena.

Mixed host co-assembled systems for broad-scope analyte sensing

Here we report a systems chemistry oriented approach for developing information-rich mixed host chemosensors. We show that co-assembling macrocyclic hosts from different classes, DimerDye sulfonatocalix[4]arenes and cucurbit[n]urils, effectively increases the scope of analyte binding interactions and therefore, sensory outputs. This simple dynamic strategy exploits cross-reactive noncovalent host-host complexation interactions while integrating a reporter dye, thereby producing emergent photophysical responses when an analyte interacts with either host. We first demonstrate the advantages of mixed host co-assembled chemosensors through an increased detection range of hydrophobic, cationic, neutral, and anionic drugs. We then implement mixed host sensors in an array-based platform for the differentiation of illicit drugs, including cannabinoids, benzodiazepine analogs, opiates, anesthetics, amphetamine, and common adulterating substances. Finally, the potential of this approach is applied to profiling real-world multi-component illicit street drug samples, proving to be more effective than classical sensor arrays.

Colorimetric sensing for translational applications: from colorants to mechanisms

Colorimetric sensing offers instant reporting via visible signals. Versus labor-intensive and instrument-dependent detection methods, colorimetric sensors present advantages including short acquisition time, high throughput screening, low cost, portability, and a user-friendly approach. These advantages have driven substantial growth in colorimetric sensors, particularly in point-of-care (POC) diagnostics. Rapid progress in nanotechnology, materials science, microfluidics technology, biomarker discovery, digital technology, and signal pattern analysis has led to a variety of colorimetric reagents and detection mechanisms, which are fundamental to advance colorimetric sensing applications. This review first summarizes the basic components (e.g., color reagents, recognition interactions, and sampling procedures) in the design of a colorimetric sensing system. It then presents the rationale design and typical examples of POC devices, e.g., lateral flow devices, microfluidic paper-based analytical devices, and wearable sensing devices. Two highlighted colorimetric formats are discussed: combinational and activatable systems based on the sensor-array and lock-and-key mechanisms, respectively. Case discussions in colorimetric assays are organized by the analyte identities. Finally, the review presents challenges and perspectives for the design and development of colorimetric detection schemes as well as applications. The goal of this review is to provide a foundational resource for developing colorimetric systems and underscoring the colorants and mechanisms that facilitate the continuing evolution of POC sensors.

Nanotechnology improves the detection of bacteria: Recent advances and future perspectives

Nanotechnology has advanced significantly, particularly in biomedicine, showing promise for nanomaterial applications. Bacterial infections pose persistent public health challenges due to the lack of rapid pathogen detection methods, resulting in antibiotic overuse and bacterial resistance, threatening the human microbiome. Nanotechnology offers a solution through nanoparticle-based materials facilitating early bacterial detection and combating resistance. This study explores recent research on nanoparticle development for controlling microbial infections using various nanotechnology-driven detection methods. These approaches include Surface Plasmon Resonance (SPR) Sensors, Surface-Enhanced Raman Scattering (SERS) Sensors, Optoelectronic-based sensors, Bacteriophage-Based Sensors, and nanotechnology-based aptasensors. These technologies provide precise bacteria detection, enabling targeted treatment and infection prevention. Integrating nanoparticles into detection approaches holds promise for enhancing patient outcomes and mitigating harmful bacteria spread in healthcare settings.

DNA Framework-Based Programmable Atom-Like Nanoparticles for Non-Coding RNA Recognition and Differentiation of Cancer Cells

The cooperative diagnosis of non-coding RNAs (ncRNAs) can accurately reflect the state of cell differentiation and classification, laying the foundation of precision medicine. However, there are still challenges in simultaneous analyses of multiple ncRNAs and the integration of biomarker data for cell typing. In this study, DNA framework-based programmable atom-like nanoparticles (PANs) are designed to develop molecular classifiers for intra-cellular imaging of multiple ncRNAs associated with cell differentiation. The PANs-based molecular classifier facilitates signal amplification through the catalytic hairpin assembly. The interaction between PAN reporters and ncRNAs enables high-fidelity conversion of ncRNAs expression level into binding events, and the assessment of in situ ncRNAs levels via measurement of the fluorescent signal changes of PAN reporters. Compared to non-amplified methods, the detection limits of PANs are reduced by four orders of magnitude. Using human gastric cancer cell lines as a model system, the PANs-based molecular classifier demonstrates its capacity to measure multiple ncRNAs in living cells and assesses the degree of cell differentiation. This approach can serve as a universal strategy for the classification of cancer cells during malignant transformation and tumor progression.

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.

Hymenaea courbaril resin-mediated gold nanoparticles as catalysts in organic dyes degradation and sensors in pharmaceutical pollutants

In this study, green synthesis of gold nanoparticles (AuNPs) using aqueous extract from Hymenaea courbaril resin (HCR) is reported. The successful formation, functional group involvement, size, and morphology of the subject H. courbaril resin mediated gold nanoparticles (HCRAuNPs) were confirmed by Ultra Violet-Visible (UV-vis) spectroscopy, Fourier-Transform Infrared spectroscopy (FTIR), and Transmission Electron Microscopy (TEM) techniques. Stable and high yield of HCRAuNPs was formed in 1:15 (aqueous solution: salt solution) reacted in sunlight as indicated by the visual colour change and appearance of surface Plasmon resonance (SPR) at 560 nm. From the FT-IR results, the phenolic hydroxyl (-OH) functional group was found to be involved in synthesis and stabilization of nanoparticles. The TEM analysis showed that the particles are highly dispersed and spherical in shape with average size of 17.5 nm. The synthesized HCRAuNPs showed significant degradation potential against organic dyes, including methylene blue (MB, 85 %), methyl orange (MO, 90 %), congo red (CR, 83 %), and para nitrophenol (PNP, 76 %) up to 180 min. The nanoparticles also demonstrated the effective detection of pharmaceutical pollutants, including amoxicillin, levofloxacin, and azithromycin in aqueous environment as observable changes in color and UV-Vis spectral graph.

Food for Thought: Optical Sensor Arrays and Machine Learning for the Food and Beverage Industry

Arrays of cross-reactive sensors, combined with statistical or machine learning analysis of their multivariate outputs, have enabled the holistic analysis of complex samples in biomedicine, environmental science, and consumer products. Comparisons are frequently made to the mammalian nose or tongue and this perspective examines the role of sensing arrays in analyzing food and beverages for quality, veracity, and safety. I focus on optical sensor arrays as low-cost, easy-to-measure tools for use in the field, on the factory floor, or even by the consumer. Novel materials and approaches are highlighted and challenges in the research field are discussed, including sample processing/handling and access to significant sample sets to train and test arrays to tackle real issues in the industry. Finally, I examine whether the comparison of sensing arrays to noses and tongues is helpful in an industry defined by human taste.

MoS2-Based Sensor Array for Accurate Identification of Cancer Cells with Ensemble-Modified Aptamers

In this work, we present an array-based chemical nose sensor that utilizes a set of ensemble-modified aptamer (EMAmer) probes to sense subtle physicochemical changes on the cell surface for cancer cell identification. The EMAmer probes are engineered by domain-selective incorporation of different types and/or copies of positively charged functional groups into DNA scaffolds, and their differential interactions with cancer cells can be transduced through competitive adsorption of fluorophore-labeled EMAmer probes loaded on MoS2 nanosheets. We demonstrate that this MoS2-EMAmer-based sensor array enables rapid and effective discrimination among six types of cancer cells and their mixtures with a concentration of 104 cells within 60 min, achieving a 94.4% accuracy in identifying blinded unknown cell samples. The established MoS2-EMAmer sensing platform is anticipated to show significant promise in the advancement of cancer diagnostics.

Development of Optical Differential Sensing Based on Nanomaterials for Biological Analysis

The discrimination and recognition of biological targets, such as proteins, cells, and bacteria, are of utmost importance in various fields of biological research and production. These include areas like biological medicine, clinical diagnosis, and microbiology analysis. In order to efficiently and cost-effectively identify a specific target from a wide range of possibilities, researchers have developed a technique called differential sensing. Unlike traditional "lock-and-key" sensors that rely on specific interactions between receptors and analytes, differential sensing makes use of cross-reactive receptors. These sensors offer less specificity but can cross-react with a wide range of analytes to produce a large amount of data. Many pattern recognition strategies have been developed and have shown promising results in identifying complex analytes. To create advanced sensor arrays for higher analysis efficiency and larger recognizing range, various nanomaterials have been utilized as sensing probes. These nanomaterials possess distinct molecular affinities, optical/electrical properties, and biological compatibility, and are conveniently functionalized. In this review, our focus is on recently reported optical sensor arrays that utilize nanomaterials to discriminate bioanalytes, including proteins, cells, and bacteria.

Metal Ion-Regulated Fluorescent Sensor Array Based on Gold Nanoclusters for Physiological Phosphate Sensing

The detection of physiological phosphates (PPs) is of great importance due to their essential roles in numerous biological processes, but the efficient detection of different PPs simultaneously remains challenging. In this work, we propose a fluorescence sensor array for detecting PPs based on metal-ion-regulated gold nanoclusters (AuNCs) via an indicator-displacement assay. Zn2+ and Eu3+ are selected to assemble with two different AuNCs, resulting in quenching or enhancing their fluorescence. Based on the competitive interaction of metal ions with AuNCs and PPs, the fluorescence of AuNCs will be recovered owing to the disassembly of AuNC-metal ion ensembles. Depending on different PPs' distinct fluorescence responses, a four-channel sensor array was established. The array not only exhibits good discrimination capability for eight kinds of PPs (i.e., ATP, ADP, AMP, GTP, CTP, UTP, PPi, and Pi) via linear discriminant analysis but also enables quantitative detection of single phosphate (e.g., ATP) in the presence of interfering PPs mixtures. Moreover, potential application of the present sensor array for the discrimination of different PPs in real samples (e.g., cell lysates and serum) was successfully demonstrated with a good performance. This work illustrates the great potential of a metal ion-regulated sensor array as a new and efficient sensing platform for differential sensing of phosphates as well as other disease-related biomolecules.

Antimicrobial Agent Functional Gold Nanocluster-Mediated Multichannel Sensor Array for Bacteria Sensing

Urinary tract infections (UTIs) have greatly affected human health in recent years. Accurate and rapid diagnosis of UTIs can enable a more effective treatment. Herein, we developed a multichannel sensor array for efficient identification of bacteria based on three antimicrobial agents (vancomycin, lysozyme, and bacitracin) functional gold nanoclusters (AuNCs). In this sensor, the fluorescence intensity of the three AuNCs was quenched to varying degrees by the bacterial species, providing a unique fingerprint for different bacteria. With this sensing platform, seven pathogenic bacteria, different concentrations of the same bacteria, and even bacterial mixtures were successfully differentiated. Furthermore, UTIs can be accurately identified with our sensors in ∼30 min with 100% classification accuracy. The proposed sensing systems offer a rapid, high-throughput, and reliable sensing platform for the diagnosis of UTIs.

Quantum dots-based "chemical tongue" for the discrimination of short-length Aβ peptides

A "chemical tongue" is proposed based on thiomalic acid-capped quantum dots (QDs) with signal enrichment provided by excitation-emission matrix (EEM) fluorescence spectroscopy for the determination of close structural analogs-short-length amyloid β (Aβ) peptides related to Alzheimer's disease. Excellent discrimination is obtained by principal component analysis (PCA) for seven derivatives: Aβ1-16, Aβ4-16, Aβ4-9, Aβ5-16, Aβ5-12, Aβ5-9, Aβ12-16. Detection of Aβ4-16, Aβ4-16, and Aβ5-9 in binary and ternary mixtures performed by QDs-based chemical tongue using partial least squares-discriminant analysis (PLS-DA) provided perfect 100% accuracy for the two studied peptides (Aβ4-16 and Aβ4-16), while for the third one (Aβ5-9) it was slightly lower (97.9%). Successful detection of Aβ4-16 at 1 pmol/mL (1.6 ng/mL) suggests that the detection limit of the proposed method for short-length Aβ peptides can span nanomolar concentrations. This result is highly promising for the development of simple and efficient methods for sequence recognition in short-length peptides and better understanding of mechanisms at the QD-analyte interface.

Multilocus Distance-Regulated Sensor Array for Recognition of Polyphenols via Machine Learning and Indicator Displacement Assay

Developing new strategies to construct sensor arrays that can effectively distinguish multiple natural components with similar structures in mixtures is an exceptionally challenging task. Here, we propose a new multilocus distance-modulated indicator displacement assay (IDA) strategy for constructing a sensor array, incorporating machine learning optimization to identify polyphenols. An 8-element array, comprising two fluorophores and their six dynamic covalent complexes (C1-C6) formed by pairing two fluorophores with three distinct distance-regulated quenchers, has been constructed. Polyphenols with diverse spatial arrangements and combinatorial forms compete with the fluorophores by forming pseudocycles with quenchers within the complexes, leading to varying degrees of fluorescence recovery. The array accurately and effectively distinguished four tea polyphenols and 16 tea varieties, thereby demonstrating the broad applicability of the multilocus distance-modulated IDA array in detecting polyhydroxy foods and natural medicines.

Modern Potentiometric Biosensing Based on Non-Equilibrium Measurement Techniques

Modern potentiometric sensors based on polymeric membrane ion-selective electrodes (ISEs) have achieved new breakthroughs in sensitivity, selectivity, and stability and have extended applications in environmental surveillance, medical diagnostics, and industrial analysis. Moreover, nonclassical potentiometry shows promise for many applications and opens up new opportunities for potentiometric biosensing. Here, we aim to provide a concept to summarize advances over the past decade in the development of potentiometric biosensors with polymeric membrane ISEs. This Concept article articulates sensing mechanisms based on non-equilibrium measurement techniques. In particular, we emphasize new trends in potentiometric biosensing based on attractive dynamic approaches. Representative examples are selected to illustrate key applications under zero-current conditions and stimulus-controlled modes. More importantly, fruitful information obtained from non-equilibrium measurements with dynamic responses can be useful for artificial intelligence (AI). The combination of ISEs with advanced AI techniques for effective data processing is also discussed. We hope that this Concept will illustrate the great possibilities offered by non-equilibrium measurement techniques and AI in potentiometric biosensing and encourage further innovations in this exciting field.

Molecular Recognition with Macrocyclic Receptors for Application in Precision Medicine

Macrocyclic receptors can serve as alternatives to natural recognition systems as recognition tools. They provide effectively preorganized cavities to encapsulate guests via host-guest interactions, thereby affecting the physiochemical properties of the guests. Macrocyclic receptors exhibit chemical and thermal stabilities higher than those of natural receptors and thus are expected to resist degradation inside the body. This reduces the risk of harmful degradation byproducts and ensures optimal levels of effectiveness. Macrocyclic receptors have precise molecular weights and well-defined structures; this ensures their batch-to-batch reproducibility, which is critical for ensuring quality and effectiveness levels. Moreover, macrocyclic receptors exhibit broad modification tunabilities, rendering them adaptable to various guests. Molecular recognition is the basis of numerous biological processes. Macrocyclic receptors may display considerable potential for application in diagnosing and treating diseases, depending on the host-guest recognition of bioactive molecules. However, the binding affinities and selectivities of macrocyclic receptors toward bioactive molecules are generally insufficient, which may lead to problems such as low diagnosis accuracies, off-target leaking, and interference with normal functions. Therefore, addressing the challenge of the strong and specific complexation of bioactive molecules and macrocyclic receptors is imperative.To overcome this challenge, we proposed the innovative strategies of longitudinal cavity extension and coassembled heteromultivalent recognition for application in the recognition of small molecules and biomacromolecules, respectively. The deepened cavity provides a stronger hydrophobic effect and a larger interaction area while maintaining the framework rigidity. By coassembling two macrocyclic amphiphiles into one ensemble, we achieved the desired heteromultivalent recognition. This strategy affords the necessary binding properties while preventing the requirement of tedious steps and site mismatch in covalent synthesis. Using these two strategies, we achieved specific and strong binding of macrocyclic receptors to various bioactive molecules including biomarkers, drugs, and disease-related peptides/proteins. We then applied these macrocyclic receptor-based recognition systems in biosensing and bioimaging, drug delivery, and therapeutics.In this Account, we summarize the strategies we used in the recognition of small molecules and biomacromolecules. Thereafter, we discuss their applications in precision medicine, involving the (1) sensing of biomarkers and imaging of lesion sites, which are critical in the early screening of diseases and accurate diagnoses; (2) precise loading and targeted delivery of drugs, which are crucial in improving their therapeutic efficacies and reducing their side effects; and (3) capture and removal of disease-related biomacromolecules, which are significant for precise intervention in life processes. Finally, we propose recommendations for the further development of macrocyclic receptor-based recognition systems in biomedicine. Macrocyclic receptors exhibit considerable potential for research, and continued investigation may not only expand the applications of supramolecular chemistry but also open novel avenues for the development of precision medicine.

Chiral Iron Oxide Supraparticles Enable Enantiomer-Dependent Tumor-Targeted Magnetic Resonance Imaging

The chemical, physical and biological effects of chiral nanomaterials have inspired general interest and demonstrated important advantages in fundamental science. Here, chiral iron oxide supraparticles (Fe3 O4 SPs) modified by chiral penicillamine (Pen) molecules with g-factor of ≈2 × 10-3 at 415 nm are fabricated, and these SPs act as high-quality magnetic resonance imaging (MRI) contrast agents. Therein, the transverse relaxation efficiency and T2 -MRI results demonstrated chiral Fe3 O4 SPs have a r2 relaxivity of 157.39 ± 2.34 mM-1 ·S-1 for D-Fe3 O4 SPs and 136.21 ± 1.26 mM-1 ·S-1 for L-Fe3 O4 SPs due to enhanced electronic transition dipole moment for D-Fe3 O4 SPs compared with L-Fe3 O4 SPs. The in vivo MRI results show that D-Fe3 O4 SPs exhibit two-fold lower contrast ratio than L-Fe3 O4 SPs, which enhances targeted enrichment in tumor tissue, such as prostate cancer, melanoma and brain glioma tumors. Notably, it is found that D-Fe3 O4 SPs have 7.7-fold higher affinity for the tumor cell surface receptor cluster-of-differentiation 47 (CD47) than L-Fe3 O4 SPs. These findings uncover that chiral Fe3 O4 SPs act as a highly effective MRI contrast agent for targeting and imaging broad tumors, thus accelerating the practical application of chiral nanomaterials and deepening the understanding of chirality in biological and non-biological environments.

A summary of calixarene-based fluorescent sensors developed during the past five years

Calixarenes are "chalice like" phenol-based macrocycles that are one of the most fascinating studied scaffolds in supramolecular chemistry. Their preorganized nonpolar cavities and ion binding sites, and their well-defined conformations all lay important foundations for forming host-guest complexes. Conjugation of calixarene scaffolds with various fluorophores at either upper or lower rims has led to the development of smart fluorescent probes for inorganic molecules or ions, aliphatic or aromatic compounds, biomolecules, temperature and hypoxia, even multi-component traditional Chinese medicine (TCM). Moreover, significant advancements have been made for biological applications. This review critically summarizes the recent advances made in these areas.

Adaptive Supramolecular Networks: Emergent Sensing from Complex Systems

Molecular differentiation by supramolecular sensors is typically achieved through sensor arrays, relying on the pattern recognition responses of large panels of isolated sensing elements. Here we report a new one-pot systems chemistry approach to differential sensing in biological solutions. We constructed an adaptive network of three cross-assembling sensor elements with diverse analyte-binding and photophysical properties. This robust sensing approach exploits complex interconnected sensor-sensor and sensor-analyte equilibria, producing emergent supramolecular and photophysical responses unique to each analyte. We characterize the basic mechanisms by which an adaptive network responds to analytes. The inherently data-rich responses of an adaptive network discriminate among very closely related proteins and protein mixtures without relying on designed protein recognition elements. We show that a single adaptive sensing solution provides better analyte discrimination using fewer response observations than a sensor array built from the same components. We also show the network's ability to adapt and respond to changing biological solutions over time.

Fluorescent Investigation of Proteins Using DNA-Synthetic Ligand Conjugates

The unfathomable role that fluorescence detection plays in the life sciences has prompted the development of countless fluorescent labels, sensors, and analytical techniques that can be used to detect and image proteins or investigate their properties. Motivated by the demand for simple-to-produce, modular, and versatile fluorescent tools to study proteins, many research groups have harnessed the advantages of oligodeoxynucleotides (ODNs) for scaffolding such probes. Tight control over the valency and position of protein binders and fluorescent dyes decorating the polynucleotide chain and the ability to predict molecular architectures through self-assembly, inherent solubility, and stability are, in a nutshell, the important properties of DNA probes. This paper reviews the progress in developing DNA-based, fluorescent sensors or labels that navigate toward their protein targets through small-molecule (SM) or peptide ligands. By describing the design, operating principles, and applications of such systems, we aim to highlight the versatility and modularity of this approach and the ability to use ODN-SM or ODN-peptide conjugates for various applications such as protein modification, labeling, and imaging, as well as for biomarker detection, protein surface characterization, and the investigation of multivalency.

Molecules that Generate Fingerprints: A New Class of Fluorescent Sensors for Chemical Biology, Medical Diagnosis, and Cryptography

ConspectusFluorescent molecular sensors, often referred to as "turn-on" or "turn-off" fluorescent probes, are synthetic agents that change their fluorescence signal in response to analyte binding. Although these sensors have become powerful analytical tools in a wide range of research fields, they are generally limited to detecting only one or a few analytes. Pattern-generating fluorescent probes, which can generate unique identification (ID) fingerprints for different analytes, have recently emerged as a new class of luminescent sensors that can address this limitation. A unique characteristic of these probes, termed ID-probes, is that they integrate the qualities of conventional small-molecule-based fluorescent sensors and cross-reactive sensor arrays (often referred to as chemical, optical, or electronic noses/tongues). On the one hand, ID-probes can discriminate between various analytes and their combinations, akin to array-based analytical devices. On the other hand, their minute size enables them to analyze small-volume samples, track dynamic changes in a single solution, and operate in the microscopic world, which the macroscopic arrays cannot access.Here, we describe the principles underlying the ID-probe technology, as well as provide an overview of different ID-probes that have been developed to date and the ways they can be applied to a wide range of research fields. We describe, for example, ID-probes that can identify combinations of protein biomarkers in biofluids and in living cells, screen for several protein inhibitors simultaneously, analyze the content of Aβ aggregates, as well as ensure the quality of small-molecule and biological drugs. These examples highlight the relevance of this technology to medical diagnosis, bioassay development, cell and chemical biology, and pharmaceutical quality assurance, among others. ID-probes that can authorize users and protect secret data are also presented and the mechanisms that enable them to hide (steganography), encrypt (cryptography), and prevent access to (password protection) information are discussed.The versatility of this technology is further demonstrated by describing two types of probes: unimolecular ID-probes and self-assembled ID-probes. Probes from the first type can operate inside living cells, be recycled, and their initial patterns can be more easily obtained in a reproducible manner. The second type of probes can be readily modified and optimized, allowing one to prepare various different probes from a much wider range of fluorescent reporters and supramolecular recognition elements. Taken together, these developments indicate that the ID-probe sensing methodology is generally applicable, and that such probes can better characterize analyte mixtures or process chemically encoded information than can the conventional fluorescent molecular sensors. We therefore hope that this review will inspire the development of new types of pattern-generating probes, which would extend the fluorescence molecular toolbox currently used in the analytical sciences.

Au/Mn nanodot platform for in vivo CT/MRI/FI multimodal bioimaging and photothermal therapy against tongue cancer

Surgical resection is the main method for oral tongue squamous cell carcinoma (OTSCC) treatment. However, the oral physiological function and aesthetics may be seriously damaged during the operation with a high risk of recurrence. Therefore, it is important to develop an alternative strategy with precise guidance for OTSCC treatment. Herein, multifunctional Au/Mn nanodots (NDs) are designed and synthesized. They can perform multimodal bioimaging, including computed tomography (CT) and magnetic resonance imaging (MRI) simultaneously, and exhibit bright near-infrared fluorescence imaging (FI) for navigation, and even integrate photothermal therapy (PTT) property. The localization of OTSCC relies on visual and tactile cues of surgeons while lacking noninvasive pretreament labeling and guidance. Au/Mn NDs provide CT/MRI imaging, giving two means of accurate positioning pretherapy. Meanwhile, the fluorescence of the Au/Mn NDs in the near-infrared region (NIR) is beneficial for noninvasive labeling and intuitive observation with the naked eye to determine the tumor boundary during PTT. Further, Au/Mn NDs showed excellent results in ablating tumors in vivo. Above all, the Au/Mn NDs provide a key platform for multimodal bioimaging and PTT in a single nanoagent, which demonstrated attractive performance for OTSCC treatment.

Machine-learning-assisted SERS nanosensor platform toward chemical fingerprinting of Baijiu flavors

A novel fingerprinting platform for multiplex detection of flavor molecules in Baijiu was developed by using a surface-enhanced Raman scattering (SERS) nanosensor array in combination with machine learning. The SERS sensors were constructed by core-shell Fe₃O₄@Ag nanoparticles modified with molecules carrying end-groups of hydroxyl, pyridyl, methyl, and amino, respectively, which interacted with flavors and led to changes in the sensors' spectra. All the Raman spectra acquired from the nanosensor array contacting with the sample were concatenated into a single SERS super-spectrum, representing the flavor fingerprint which was recognized through machine learning. Principal component analysis, support vector machine, and partial least squares were utilized to build classification and quantitation models for predictive analyses. The SERS nanosensor array was successfully used for fingerprinting ten typical flavors in Baijiu including four esters, three alcohols, and three acids, with an accuracy of 100%, linear detection ranges over two orders of magnitude, and limits of detection ranging from 3.45 × 10^-3 mg/L of phenylethyl acetate to 1.21 × 10^-2 mg/L of ethyl hexanoate. It was also demonstrated that satisfactory accuracies (recoveries) ranging from 96.2 to 104% and relative standard deviations ranging from 0.65 to 2.78% were obtained for the simultaneous quantification of 3-methylbutyl acetate and phenylethyl acetate in eighteen Baijiu samples of three flavor types including sauce flavor, strong flavor, and light flavor. Compared with the existing detection techniques, this chemical fingerprinting platform is easy to use, highly sensitive, and can perform multiplex detection, which has great potential for practical applications.

Identification of Proteins Using Supramolecular Gold Nanoparticle-Dye Sensor Arrays

The rapid detection of proteins is very important in the early diagnosis of diseases. Gold nanoparticles (AuNPs) can be engineered to bind biomolecules efficiently and differentially. Cross-reactive sensor arrays have high sensitivity for sensing proteins using differential interactions between sensor elements and bioanalytes. A new sensor array was fabricated using surface-charged AuNPs with dyes supramolecularly encapsulated into the AuNP monolayer. The fluorescence of dyes is partially quenched by the AuNPs and can be restored or further quenched due to the differential interactions between AuNPs with proteins. This sensing system enables the discrimination of proteins in both buffer and human serum, providing a potential tool for real-world disease diagnostics.

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.

Iron oxides based nanozyme sensor arrays for the detection of active substances in licorice

With the increasing applications of traditional Chinese medicines worldwide, authenticity identification and quality control are significant for them to go global. Licorice is a kind of medicinal material with various functions and wide applications. In this work, colorimetric sensor arrays based on iron oxide nanozymes were constructed to discriminate active indicators in licorice. Fe₂O₃, Fe₃O₄, and His-Fe₃O₄ nanoparticles were synthesized by a hydrothermal method, possessing excellent peroxidase-like activity that can catalyze the oxidation of 3,3',5,5' -tetramethylbenzidine (TMB) in the presence of H₂O₂ to produce a blue product. When licorice active substances were introduced in the reaction system, they showed competitive effect on peroxidase-mimicking activity of nanozymes, resulting in inhibitory effect on the oxidation of TMB. Based on this principle, four licorice active substances including glycyrrhizic acid, liquiritin, licochalcone A, and isolicoflavonol with the concentration ranging from 1 μM to 200 μM were successfully discriminated by the proposed sensor arrays. This work supplies a low cost, rapid and accurate method for multiplex discrimination of active substances to guarantee the authenticity and quality of licorice, which is also expected to be applied to distinguish other substances.

Functionalized graphene-based electrochemical array sensors for the identification of distinct conformational states of Amyloid Beta in Alzheimer's disease

Aβ oligomers have been widely accepted as significant biomarkers for Alzheimer's disease (AD) detection, monitoring, and therapy since they are highly correlated with AD development. In this work, an electrochemical array-based sensing platform was successfully built using a group of functionalized graphene with different physicochemical features. Since the electro-insulated Aβ peptide species severely interfered with the electron transport on the electrode surface, the presence of Aβ led to a significant change in the electrochemical impedance signal. The resulting variety of the impedance was then classified and processed by linear discriminant analysis. The constructed sensing platform can discriminate different Aβ forms, the mixture of various Aβ forms, and different ratios of Aβ42 to Aβ40 with 100% accuracy by only the combination of dual probes. Furthermore, it also exhibited excellent performance for screening Aβ inhibitors and metal chelators. The strategy utilizes the infinitesimal general discrepancy instead of specific biomarker recognition, exhibiting the advantage of no requirement to know the exact information about the specific ligand and receptor in advance, which is promising to be widened for the other biosensing detection fields.

Explainable Deep Learning-Assisted Photochromic Sensor for β-Lactam Antibiotic Identification

Photochromic sensors have the advantages of diverse isomers for multi-analysis, providing more sensing information and possessing more recognition units and more sensitivity to external stimulations, but they present enormous complexity with various stimulations as well. Deep learning (DL) algorithms contribute a huge advantage at analyzing nonlinear and multidimensional data, but they suffer from nontransparent inner networks, "black-boxes". In this work, we employed the explainable DL approach to process and explicate photochromic sensing. Spirooxazine metallic complexes were adopted to prepare a multi-state analysis array for β-Lactams identification and quantitation. A dataset of 2520 unduplicated fluorescence intensity images was collected for convolutional neural network (CNN) operation. The method clearly discriminated six β-Lactams with 97.98% prediction accuracy and allowed rapid quantification with a concentration range from 1 to 100 mg/L. The photochromic sensing mechanism was verified via molecular simulation and class activation mapping, which explicated how the CNN model assesses the importance of photochromic sensor states and makes a discrimination decision. The explainable DL-assisted analysis method establishes an end-to-end strategy to ascertain and verify the complicated sensing mechanism for device optimization and even new scientific discovery.

Detection and differentiation of per- and polyfluoroalkyl substances (PFAS) in water using a fluorescent imprint-and-report sensor array

Widespread industrial use of per- and polyfluoroalkyl substances (PFAS) as surfactants has led to global contamination of water sources with these persistent, highly stable chemicals. As a result, humans and wildlife are regularly exposed to PFAS, which have been shown to bioaccumulate and cause adverse health effects. Methods for detecting PFAS in water are currently limited and primarily utilize mass spectrometry (MS), which is time-consuming and requires expensive instrumentation. Thus, new methods are needed to rapidly and reliably assess the pollution level of water sources. While some fluorescent PFAS sensors exist, they typically function in high nanomolar or micromolar concentration ranges and focus on sensing only 1-2 individual PFAS. Our work aims to address this problem by developing a fluorescent sensor for both individual PFAS, as well as complex PFAS mixtures, and demonstrate its functionality in tap water samples. Here we show that dynamic combinatorial libraries (DCLs) with simple building blocks can be templated with a fluorophore and subsequently used as sensors to form an array that differentially detects each PFAS species and various mixtures thereof. Our method is a high-throughput analysis technique that allows many samples to be analyzed simultaneously with a plate reader. This is one of the first examples of a fluorescent PFAS sensor array that functions at low nanomolar concentrations, and herein we report its use for the rapid detection of PFAS contamination in water.

Ensemble Modified Aptamer Based Pattern Recognition for Adaptive Target Identification

The difficulty of the molecular design and chemical synthesis of artificial sensing receptors restricts their diagnostic and proteomic applications. Herein, we report a concept of "ensemble modified aptamers" (EMAmers) that exploits the collective recognition abilities of a small set of protein-like side-chain-modified nucleic acid ligands for discriminative identification of molecular or cellular targets. Different types and numbers of hydrophobic functional groups were incorporated at designated positions on nucleic acid scaffolds to mimic amino acid side chains. We successfully assayed 18 EMAmer probes with differential binding affinities to seven proteins. We constructed an EMAmer-based chemical nose sensor and demonstrated its application in blinded unknown protein identification, giving a 92.9% accuracy. Additionally, the sensor is generalizable to the detection of blinded unknown bacterial and cellular samples, which enabled identification accuracies of 96.3% and 94.8%, respectively. This sensing platform offers a discriminative means for adaptive target identification and holds great potential for diverse applications.

Recent Advances in Colorimetric Sensors Based on Gold Nanoparticles for Pathogen Detection

Infectious pathogens cause severe threats to public health due to their frightening infectivity and lethal capacity. Rapid and accurate detection of pathogens is of great significance for preventing their infection. Gold nanoparticles have drawn considerable attention in colorimetric biosensing during the past decades due to their unique physicochemical properties. Colorimetric diagnosis platforms based on functionalized AuNPs are emerging as a promising pathogen-analysis technique with the merits of high sensitivity, low-cost, and easy operation. This review summarizes the recent development in this field. We first introduce the significance of detecting pathogens and the characteristics of gold nanoparticles. Four types of colorimetric strategies, including the application of indirect target-mediated aggregation, chromogenic substrate-mediated catalytic activity, point-of-care testing (POCT) devices, and machine learning-assisted colorimetric sensor arrays, are systematically introduced. In particular, three biomolecule-functionalized AuNP-based colorimetric sensors are described in detail. Finally, we conclude by presenting our subjective views on the present challenges and some appropriate suggestions for future research directions of colorimetric sensors.

Applications of Synthetic Receptors in Bioanalysis and Drug Transport

Synthetic receptors are powerful tools for molecular recognition. They can bind to guests with high selectivity and affinity, and their structures are tunable and diversified. These features, plus the relatively low cost and high simplicity in synthesis and modification, support the feasibility of array-based molecular analysis with synthetic receptors for improved selectivity in the recognition of a wide range of targets. More attractively, host-guest interaction is reversible and guest displacement allows biocompatible and gentle release of the host-bound molecules, simplifying the stimulation designs needed to control analyte sensing, enrichment, and transportation. Here, we highlight a few recent advancements in using synthetic receptors for molecular analysis and manipulation, with the focus on macrocyclic receptors and their applications in displacement sensing, separation, imaging, and drug transport.