Cross-reactive chemical sensor arrays

A novel ratiometric fluorescent sensor array based on the copper clusters hydrogels coupling of DNA

Hydrogels with DNA possess flexible and designable 3D cross-linked polymer networks, which generate numerous specific stimulus-response characteristics through reasonable design for the potential sensor array applications. Unfortunately, the complementary fragments of specific nucleotide sequences that form cross-links in the hydrogels with DNA often come across these problems of instability and high-cost, leading to a serious impediment to further application. Herein, we construct a novel ratiometric fluorescence sensor array to discriminate and quantify metal ions based on the hydrogels with DNA. The dual-emission hydrogels with DNA were synthesized by the self-assembly reaction of agarose, low-cost nonspecific sequence double-stranded DNA, and optically active copper nanoclusters (Cu NCs) with aggregation-induced emission (AIE) effect. Interestingly, different metal ions could precisely regulate the skeleton network of the hydrogels with DNA, leading to the change of their skeleton network and thus showing the distinguishing FL responses of the hydrogels with DNA. As a proof-of-concept demonstration, the ratiometric fluorescence sensor array was used to discriminate four metal ions (Pb2+, Co2+, Ni2+, and Cr(VI)) at multiple concentrations and metal ion mixtures. It exhibited a good linearity in quantitative analysis and reproducibility. Such a simple and high-sensitivity sensor array has been successfully applied to the high-through discrimination of toxic metal ions in environmental and serum samples.

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.

Dithiothreitol-functionalized perovskite-based visual sensing array capable of distinguishing food oils

At present, the combination of fingerprint recognition methods and environmentally friendly and economical analytical instruments is becoming increasingly important in the food industry. Herein, a dithiothreitol (DTT)-functionalized CsPbBr3-based colorimetric sensor array is developed for qualitatively differentiating multiple food oils. In this sensor array composition, two types of iodides (octadecylammonium iodide (ODAI) and ZnI2) are used as recognition elements, and CsPbBr3 is used as a signal probe for the sensor array. Different food oils oxidize iodides differently, resulting in different amounts of remaining iodides. Halogen ion exchange occurs between the remaining iodides and CsPbBr3, leading to different colors observed under ultraviolet light, enabling a unique fingerprint for each food oil. A total of five food oils exhibit their unique colorimetric array's response patterns and were successfully differentiated by linear discriminant analysis (LDA), realizing 100% classification accuracy.

Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi-Sensor System with Foundation Models

Combining chemical sensor arrays with machine learning enables designing intelligent systems to perform complex sensing tasks and unveil properties that are not directly accessible through conventional analytical chemistry. However, personalized and portable sensor systems are typically unsuitable for the generation of extensive data sets, thereby limiting the ability to train large models in the chemical sensing realm. Foundation models have demonstrated unprecedented zero-shot learning capabilities on various data structures and modalities, in particular for language and vision. Transfer learning from such models is explored by providing a framework to create effective data representations for chemical sensors and ultimately describe a novel, generalizable approach for AI-assisted chemical sensing. The translation of signals produced by remarkably simple and portable multi-sensor systems into visual fingerprints of liquid samples under test is demonstrated, and it is illustrated that how a pipeline incorporating pretrained vision models yields> 95 % $>95\%$ average classification accuracy in four unrelated chemical sensing tasks with limited domain-specific training measurements. This approach matches or outperforms expert-curated sensor signal features, thereby providing a generalization of data processing for ultimate ease-of-use and broad applicability to enable interpretation of multi-signal outputs for generic sensing applications.

Selective Identification of Hazardous Gases Using Flexible, Room-Temperature Operable Sensor Array Based on Reduced Graphene Oxide and Metal Oxide Nanoparticles via Machine Learning

Selective detection and monitoring of hazardous gases with similar properties are highly desirable to ensure human safety. The development of flexible and room-temperature (RT) operable chemiresistive gas sensors provides an excellent opportunity to create wearable devices for detecting hazardous gases surrounding us. However, chemiresistive gas sensors typically suffer from poor selectivity and zero-cross selectivity toward similar types of gases. Herein, a flexible, RT operable chemiresistive gas sensors array is designed, featuring reduced graphene oxide (rGO) and rGO decorated with zinc oxide (ZnO), titanium dioxide (TiO2), and tin dioxide (SnO2) nanoparticles (NPs) on a flexible polyimide (PI) substrate. The sensor array consists of four different sensing layers capable of the selective identification of various hazardous gases such as NO2, NO, and SO2 using machine learning (ML). The gas sensor array exhibits a stable response even when mechanically deformed or exposed to high humidity (up to 60%). Each gas sensor, due to the different metal oxide NPs, shows unique responses in terms of sensitivity, responsiveness, response time, and recovery time to different gases. Consequently, the sensor array generates distinct response patterns that effectively differentiate between the target gases. By leveraging these distinctive recovery patterns and employing a data fusion approach in ML, specific concentrations of target gases can be distinguished. Using ML with fused array sensing data, the training and test accuracies achieved were 98.20 and 97.70%, respectively. This innovative combination of sensor arrays and ML offers significant potential for selective gas detection in environmental monitoring and personal safety applications.

Portable alkaloid discrimination via nanozyme-mediated colorimetric paper-based sensor array integrated with smartphone detection

A paper-based colorimetric sensor array mediated by a novel nanozyme (CuCo2O4) was developed using a screen-printing technology. The aim was to facilitate the identification of different kinds of alkaloids. Typically, three chromogenic substrates (3,3',5,5'-tetramethylbenzidine, 2,2'-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid), and o-phenylenediamine) were selected as sensing elements, which can be catalyzed by a CuCo2O4 nanozyme with peroxidase-like activity to yield corresponding oxidized products, thereby inducing color changes. Owing to the varying inhibitory ability of different alkaloids on acetylcholinesterase (AChE), a decrease in choline (Ch) concentration occurs and subsequently results in the restoration of color within the units of sensor array. Color data can be transformed into hue information with a smartphone. The above color variations generated a unique "fingerprint" pattern on five alkaloids (berberine, palmatine, jatrorrhizine, eserine, and harmane), which can be successfully discriminated through linear discriminant analysis in the range 0.2 to 20 µM. Furthermore, the sensor arrays allowed successful discrimination of the above five alkaloids in Chinese herbal medicine samples and recognition of 22 blind samples. This work presents a novel nanozyme-based paper sensor array, which is a user-friendly and reliable platform for probing different alkaloids. In addition, the developed sensing strategy enables the identification of AChE-related diseases, positively contributing to the screening available of AD-associated drugs.

Multistep Array-Based Sensing of Bioanalytes Using Modified MoS2, Fluorescence Proteins, and Cucurbituril

One pot sensor by multiplexing in the array is an attractive system for rapid discrimination of multiple analytes. Multiplexing can be achieved in two ways, i.e., using multiple signal transducers or adding sequential agents to the sensor media. Herein, we have used a combination of both multichannel and sequential ON-OFF strategies for the discrimination of different bioanalytes. The sensor array was constructed by implementing positively charged MoS2 as a receptor and different fluorescent proteins possessing distinguishable emission profiles as signal transducers. The sensing setup was constructed with the interaction between oppositely charged MoS2 and the host-guest combination between a cationic headgroup of MoS2 and Cucurbit [7] uril (CB7) to alter the fluorescence of signal transducers in situ noncovalently. Electrodynamic analysis and optical assays suggest that the electrostatic interaction played a major role in the modulation of the fluorescence outcomes in the array. Both cationic and anionic proteins were discriminated at a 50 nM concentration. The detection limit of the sensor array by using β-gal protein was found to be 1 nM. The sensor array was further implemented for the discrimination of normal and diseased cell lines and lysates, which indicates the versatile detection ability of this reported sensor array.

Multicolorimetric Sensor Array Based on Silver Metallization of Gold Nanorods for Discriminating Dopaminergic Agents

Dopaminergic agents are compounds that modulate dopamine-related activity in the brain and peripheral nerves within the pathways on both sides of the blood-brain barrier. Atypical levels of them can precipitate a multitude of neurological disorders, whose timely diagnosis signifies not only stopping the advancement of the illness but also surmounting it. A silver metallized gold nanorod (AuNRs) conditional sensor array, designed to detect dopaminergic agents for assessing nervous system disorders, yielded significant results in simultaneous detection and discrimination of Benserazide (Benz), Levodopa (L-DOPA), and Carbidopa (Carb). The array was composed of two different concentrations of silver ions as sensor elements (SEs), which generated unique signatures indicative of the presence of reductive target analytes, triggered by the incongruent formation of the Au@Ag core-shell, causing visual and fingerprint colorimetric patterns. Generating diverse responses is the key to the functionality of array-based sensing, which facilitated achieving spectral and color variation originating from the blue shift of AuNRs longitudinal localized surface plasmon resonance (LLSPR) in the extinction spectrum. Also, employing a smartphone camera enables clear visual discrimination across an extensive concentration span. Pattern recognition through linear discriminant analysis (LDA) underscored the robust discrimination accuracies of this sensor, along with quantification by means of partial least-squares regression (PLSR), affirming its potential for practical applications. Notably, the array demonstrated high sensitivity in detecting varied concentrations of target analytes, even in commercial drug samples. The sensor responses exhibited a linear correlation with the concentrations of Benz, L-DOPA, and Carb ranging from 1.59 to 100.0, 5.26 to 100.0, and 5.32 to 100.0 μmol L-1, respectively, and the minimum detectable concentrations for Benz, L-DOPA, and Carb were measured at 0.53, 1.75, and 1.77 μmol L-1, respectively. The implemented machine-learning-empowered array-based sensor represents advancements in dopaminergic agent tracing and naked eye detection.

Multipressure Sampling for Improving the Performance of MOF-based Electronic Noses

Metal-organic frameworks (MOFs) are a promising class of porous materials for the design of gas sensing arrays, which are often called electronic noses. Due to their chemical and structural tunability, MOFs are a highly diverse class of materials that align well with the similarly diverse class of volatile organic compounds (VOCs) of interest in many gas detection applications. In principle, by choosing the right combination of cross-sensitive MOFs, layered on appropriate signal transducers, one can design an array that yields detailed information about the composition of a complex gas mixture. However, despite the vast number of MOFs from which one can choose, gas sensing arrays that rely too heavily on distinct chemistries can be impractical from the cost and complexity perspective. On the other hand, it is difficult for small arrays to have the desired selectivity and sensitivity for challenging sensing applications, such as detecting weakly adsorbing gases with weak signals, or conversely, strongly adsorbing gases that readily saturate MOF pores. In this work, we employed gas adsorption simulations to explore the use of a variable pressure sensing array as a means of improving both sensitivity and selectivity as well as increasing the information content provided by each array. We studied nine different MOFs (HKUST-1, IRMOF-1, MgMOF-74, MOF-177, MOF-801, NU-100, NU-125, UiO-66, and ZIF-8) and four different gas mixtures, each containing nitrogen, oxygen, carbon dioxide, and exactly one of the hydrogen, methane, hydrogen sulfide, or benzene. We found that by lowering the pressure, we can limit the saturation of MOFs, and by raising the pressure, we can concentrate weakly adsorbing gases, in both cases, improving gas detection with the resulting arrays. In many cases, changing the system pressure yielded a better improvement in performance (as measured by the Kullback-Liebler divergence of gas composition probability distributions) than including additional MOFs. We thus demonstrated and quantified how sensing at multiple pressures can increase information content and cross-sensitivity in MOF-based arrays while limiting the number of unique materials needed in the device.

Fingerprinting DNAzyme Cross-Reactivity for Pattern-Based Detection of Heavy Metals

Heavy metal contamination in food and water is a major public health concern because heavy metals are toxic in minute amounts. DNAzyme sensors are emerging as a promising tool for rapid onsite detection of heavy metals, which can aid in minimizing exposure. However, DNAzyme activity toward its target metal is not absolute and has cross-reactivity with similar metals, which is a major challenge in the wide-scale application of DNAzyme sensors for environmental monitoring. To address this, we constructed a four DNAzyme array (17E, GR-5, EtNA, and NaA43) and used a pattern-based readout to improve sensor accuracy. We measured cross-reactivity between three metal cofactors (Pb2+, Ca2+, and Na+) and common interferents (Mg2+, Zn2+, Mn2+, UO22+, Li+, K+, and Ag+) and then used t-SNE analysis to identify and quantify the metal ion. We further showed that this method can be used for distinguishing mixtures of metals and detecting Pb2+ in environmental soil samples at micromolar concentrations.

Paper-based optical sensor arrays for simultaneous detection of multi-targets in aqueous media: A review

Sensor arrays, which draw inspiration from the mammalian olfactory system, are fundamental concepts in high-throughput analysis based on pattern recognition. Although numerous optical sensor arrays for various targets in aqueous media have demonstrated their diverse applications in a wide range of research fields, practical device platforms for on-site analysis have not been satisfactorily established. The significant limitations of these sensor arrays lie in their solution-based platforms, which require stationary spectrophotometers to record the optical responses in chemical sensing. To address this, this review focuses on paper substrates as device components for solid-state sensor arrays. Paper-based sensor arrays (PSADs) embedded with multiple detection sites having cross-reactivity allow rapid and simultaneous chemical sensing using portable recording apparatuses and powerful data-processing techniques. The applicability of office printing technologies has promoted the realization of PSADs in real-world scenarios, including environmental monitoring, healthcare diagnostics, food safety, and other relevant fields. In this review, we discuss the methodologies of device fabrication and imaging analysis technologies for pattern recognition-driven chemical sensing in aqueous media.

Intelligent food packaging for smart sensing of food safety

In this contemporary era, with over 8 billion people worldwide, ensuring food safety has become more critical than ever. To address this concern, the introduction of intelligent packaging marks a significant breakthrough. Essentially, this innovation tackles the challenge of rapid deterioration in perishable foods, which is vital to the well-being of communities and food safety. Unlike traditional methods that primarily emphasize shelf-life extension, intelligent packaging goes further by incorporating advanced sensing technologies to detect signs of spoilage and contamination in real-time, such as changes in temperature, oxygen levels, carbon dioxide levels, humidity, and the presence of harmful microorganisms. The innovation can rely on various packaging materials like plastics, metals, papers, or biodegradable polymers, combined with sophisticated sensing techniques such as colorimetric sensors, time-temperature indicators, radio-frequency identification tags, electronic noses, or biosensors. Together, these elements form a dynamic and tailored packaging system. This system not only protects food from spoilage but also offers stakeholders immediate and adequate information about food quality. Moreover, the real-world application on seafood, meat, dairy, fruits, and vegetables demonstrates the feasibility of using intelligent packaging to significantly enhance the safety and shelf life of a wide variety of perishable goods. By adopting intelligent packaging for smart sensing solutions, both the food industry and consumers can significantly reduce health risks linked with contamination and reduce unnecessary food waste. This underscores the crucial role of intelligent packaging in modern food safety and distribution systems, showcasing an effective fusion of technology, safety, and sustainability efforts aimed at nourishing a rapidly growing global population.

Calixarene-Based Supramolecular Sensor Array for Pesticide Discrimination

The identification and detection of pesticides is crucial to protecting both the environment and human health. However, it can be challenging to conveniently and rapidly differentiate between different types of pesticides. We developed a supramolecular fluorescent sensor array, in which calixarenes with broad-spectrum encapsulation capacity served as recognition receptors. The sensor array exhibits distinct fluorescence change patterns for seven tested pesticides, encompassing herbicides, insecticides, and fungicides. With a reaction time of just three minutes, the sensor array proves to be a rapid and efficient tool for the discrimination of pesticides. Furthermore, this supramolecular sensing approach can be easily extended to enable real-time and on-site visual detection of varying concentrations of imazalil using a smartphone with a color scanning application. This work not only provides a simple and effective method for pesticide identification and quantification, but also offers a versatile and advantageous platform for the recognition of other analytes in relevant fields.

Emerging trends in metal oxide-based electronic noses for healthcare applications: a review

An electronic nose (E-nose) is a technology fundamentally inspired by the human nose, designed to detect, recognize, and differentiate specific odors or volatile components in complex and chaotic environments. Comprising an array of sensors with meticulously designed nanostructured architectures, E-noses translate the chemical information captured by these sensors into useful metrics using complex pattern recognition algorithms. E-noses can significantly enhance the quality of life by offering preventive point-of-care devices for medical diagnostics through breath analysis, and by monitoring and tracking hazardous and toxic gases in the environment. They are increasingly being used in defense and surveillance, medical diagnostics, agriculture, environmental monitoring, and product validation and authentication. The major challenge in developing a reliable E-nose involves miniaturization and low power consumption. Various sensing materials are employed to address these issues. This review presents the key advancements over the last decade in E-nose technology, specifically focusing on chemiresistive metal oxide sensing materials. It discusses their sensing mechanisms, integration into portable E-noses, and various data analysis techniques. Additionally, we review the primary metal oxide-based E-noses for disease detection through breath analysis. Finally, we address the major challenges and issues in developing and implementing a portable metal oxide-based E-nose.

Paper-based fluorescence sensor array with functionalized carbon quantum dots for bacterial discrimination using a machine learning algorithm

The rapid discrimination of bacteria is currently an emerging trend in the fields of food safety, medical detection, and environmental observation. Traditional methods often require lengthy culturing processes, specialized analytical equipment, and bacterial recognition receptors. In response to this need, we have developed a paper-based fluorescence sensor array platform for identifying different bacteria. The sensor array is based on three unique carbon quantum dots (CQDs) as sensing units, each modified with a different antibiotic (polymyxin B, ampicillin, and gentamicin). These antibiotic-modified CQDs can aggregate on the bacterial surface, triggering aggregation-induced fluorescence quenching. The sensor array exhibits varying fluorescent responses to different bacterial species. To achieve low-cost and portable detection, CQDs were formulated into fluorescent ink and used with an inkjet printer to manufacture paper-based sensor arrays. A smartphone was used to collect the responses generated by the bacteria and platform. Diverse machine learning algorithms were utilized to discriminate bacterial types. Our findings showcase the platform's remarkable capability to differentiate among five bacterial strains, within a detection range spanning from 1.0 × 103 CFU/mL to 1.0 × 107 CFU/mL. Its practicality is further validated through the accurate identification of blind bacterial samples. With its cost-effectiveness, ease of fabrication, and high degree of integration, this platform holds significant promise for on-site detection of diverse bacteria.

Advances in flame synthesis of nano-scale architectures for chemical, biomolecular, plasmonic, and light sensing

Flame spray pyrolysis (FSP), a key technique under the broader category of flame aerosol synthesis, is being increasingly explored for the design of advanced miniaturized sensor architectures with applications including chemical, biomolecular, plasmonic, and light sensing. This review provides an overview of the advantages of FSP for the fabrication of nanostructured materials for sensing, delving into synthesis strategies and material structures that meet the increasing demands for miniaturized sensor devices. We focus on the fundamentals of FSP, discussing reactor configurations and how process parameters such as precursor compositions, flow rates, and temperature influence nanoparticle characteristics and their sensing performance. A detailed analysis of nanostructures, compositions, and morphologies made by FSP and their applications in chemical, chemiresistive, plasmonic, biosensing, and light sensing is presented. This review identifies the challenges and opportunities of FSP, exploring current limitations and potential improvements for industrial translation. We conclude by highlighting future research directions aiming to establish guidelines for the flame-based design of nano-scale sensing architectures.

Bio-Inspired Functional Materials for Environmental Applications

With the global population expected to reach 9.7 billion by 2050, there is an urgent need for advanced materials that can address existing and developing environmental issues. Many current synthesis processes are environmentally unfriendly and often lack control over size, shape, and phase of resulting materials. Based on knowledge from biological synthesis and assembly processes, as well as their resulting functions (e.g., photosynthesis, self-healing, anti-fouling, etc.), researchers are now beginning to leverage these biological blueprints to advance bio-inspired pathways for functional materials for water treatment, air purification and sensing. The result has been the development of novel materials that demonstrate enhanced performance and address sustainability. Here, an overview of the progress and potential of bio-inspired methods toward functional materials for environmental applications is provided. The challenges and opportunities for this rapidly expanding field and aim to provide a valuable resource for researchers and engineers interested in developing sustainable and efficient processes and technologies is discussed.

Sorption Kinetic Parameters from Nanomechanical Sensing for Discrimination of 2-Nonenal from Saturated Aldehydes

Nanomechanical sensors have gained significant attention as promising platforms for artificial olfaction. Since sorption kinetic parameters that can be estimated from the sensing signals of nanomechanical sensors reflect the chemical and physicochemical interactions between the odorant and receptor material, the parameters can be utilized for the direct discrimination of each odorant. In this study, we demonstrated the discrimination of 20 vapors, including hydrocarbons, alcohols, organic acids, ketones, and aldehydes, which are reported as human body odor components, using the parameters extracted in the analytical solution of nanomechanical sensors based on sorption kinetics with viscoelastic behaviors. By using one of the specific nanomechanical sensors─membrane-type surface stress sensor─as a sensing unit, we successfully discriminated trans-2-nonenal known as an aging marker from other saturated aldehydes along with quantifying their concentrations.

Data-centric artificial olfactory system based on the eigengraph

Recent studies of electronic nose system tend to waste significant amount of important data in odor identification. Until now, the sensitivity-oriented data composition has made it difficult to discover meaningful data to apply artificial intelligence in terms of in-depth analysis for odor attributes specifying the identities of gas molecules, ultimately resulting in hindering the advancement of the artificial olfactory technology. Here, we realize a data-centric approach to implement standardized artificial olfactory systems inspired by human olfactory mechanisms by formally defining and utilizing the concept of Eigengraph in electrochemisty. The implicit odor attributes of the eigengraphs were mathematically substantialized as the Fourier transform-based Mel-Frequency Cepstral Coefficient feature vectors. Their effectiveness and applicability in deep learning processes for gas classification have been clearly demonstrated through experiments on complex mixed gases and automobile exhaust gases. We suggest that our findings can be widely applied as source technologies to develop standardized artificial olfactory systems.

Nanoarchitectonics of highly dispersed polythiophene on paper for accurate quantitative detection of metal ions

π-Conjugated polymers such as polythiophene provide intramolecular wire effects upon analyte capture, which contribute to sensitive detection in chemical sensing. However, inherent aggregation-induced quenching causes difficulty in fluorescent chemical sensing in the solid state. Herein, we propose a solid-state fluorescent chemosensor array device made of a paper substrate (PCSAD) for the qualitative and quantitative detection of metal ions. A polythiophene derivative modified by dipicolylamine moieties (1poly), which shows optical changes upon the addition of target metal ions (i.e., Cu2+, Cd2+, Ni2+, Co2+, Pb2+, Zn2+, and Hg2+), was highly dispersed on the paper substrate using office apparatus. In this regard, morphological observation of the PCSAD after printing of 1poly suggested the contribution of the fiber structures of the paper substrate to the homogeneous dispersion of 1poly ink to suppress aggregation-induced quenching. The optical changes in the PCSAD upon the addition of metal ions was rapidly recorded using a smartphone, which was further applied to imaging analysis and pattern recognition techniques for high-throughput sensing. Indeed, the printed PCSAD embedded with 1poly achieved the accurate detection of metal ions at ppm levels contained in river water. The limit of detection of the PCSAD-based sensing system using a smartphone (48 ppb for Cu2+ ions) is comparable to that of a solution-based sensing system using a stationary spectrophotometer (16 ppb for Cu2+ ions). Therefore, the methodology based on a combination of a paper-based sensor array and a π-conjugated polymer will be a promising approach for solid-state fluorescent chemosensors.

A single recognition unit-based virtual sensor Array: Applying 3D fluorescence spectroscopy to inner filter effect-based sensing

A convenient, fast, low-cost detection and discrimination method is demanded for environmental monitoring but still it remains more technological challenges. Herein, we demonstrate that the inner filter effect (IFE), in combination with three-dimensional fluorescence spectroscopy, can offer a virtual sensor array (VSA) as apropersolution. And with the aid of pattern recognition techniques, it is feasible to recognize compounds with structural similarities economically and effectively. In this study, with the help of visual clustering plots of principal component analysis (PCA), a prediction model based on hierarchical strategy was made using support vector machine (SVM) method for the qualitative profiling of aromatic pollutants. The VSA was constructed by a single metal-organic framework (MOF) recognition unit (MOF-74 (Zn)) with the excitation wavelength as external regulatory factors. Pattern characteristics of four aromatics with very similar structures (phenylamine, chlorobenzene, nitrobenzene, and phenol), both single analyte and binary mixtures, were acquired. The primary constituents of multi-dimensional spectral signals were subsequently extracted and fed into a vector machine to construct a prediction model through 10-fold cross-validation optimization, resulting in a classification accuracy of 100% for single analytes and 96% for mixtures. Quantitative research has shown that, except for chlorobenzene, all three other analytes can be predicted in concentration within an acceptable error range, and the mixture can be predicted proportionally. Moreover, the VSA can be used to distinguish these pollutants in tap and river water also. We propose for the first time a new tack for the construction of VSA in a general manner, namely using three-dimensional full range fluorescence scanning for IFE based sensing to get multiple times of information resulting from different weak interaction between analyte and sensor for decision-making.

Enantioselective and Chemoselective Optical Detection of Chiral Organic Compounds without Resorting to Chromatography

Enantiorecognition and resolution are of essential importance in many diverse areas of science. Whenever there arises a need to analyze/investigate enantiomers in different situations chromatography stands up in our minds immediately. Nevertheless, chemoselective and enantioselective recognition/discrimination (without going for separation) constitutes a different perception and requirement. The techniques using chiroptical sensing cause detection based on molecular interactions induced in different manners. Enantioselective sensing of monosaccharides in γ-cyclodextrin assembly and by diboronic acid based fluorescent sensors, application of bi-naphthol and H8 BINOL based sensors and dendrimers, metal-to-ligand charge transfer transitions in CD, exciton-coupled circular dichroism, surface enhanced Raman spectroscopy, and enantioselective indicator displacement sensor arrays for enantioselective recognition/detection of chiral organic compounds, such as amines, amino acids/alcohols, and hydroxycarboxylic acids have been discussed in progressive manner with mechanistic explanations, wherever available. Besides, the chiroptical vs LC approach has been discussed. The present paper is focused on certain different non-chromatographic optical techniques and aims to extend an understanding and a view to consider such techniques which have been successful in selective detection, and determination of absolute configuration and enantiomeric excess, (without resorting to separation vis-à-vis LC) and that have potential use in high-throughput chiral assay and combinatorial search for asymmetric catalysts and reagents.

Zn(II)-Dipicolylamine-Attached Amphiphilic Polythiophene for Quantitative Pattern Recognition of Oxyanions in Mixtures

Herein, we propose a novel amphiphilic polythiophene-based chemosensor functionalized with a Zn(II)-dipicolylamine side chain (1poly  ⋅ Zn) for the pattern recognition of oxyanions. Optical changes in amphiphilic 1poly  ⋅ Zn can be induced by the formation of a random coil from a backbone-planarized structure upon the addition of target oxyanions, which results in blueshifts in the UV-vis absorption spectra and turn-on-type fluorescence responses. Dynamic behavior in a polythiophene wire and/or among wires could be a driving force for obtaining visible color changes, while the molecular wire effect is dominant in obtaining fluorescence sensor responses. Notably, the magnitude of optical changes in 1poly  ⋅ Zn has depended on differences in properties of oxyanions, such as their binding affinity, hydrophilicity, and molecular geometry. Thus, various colorimetric and fluorescence response patterns of 1poly  ⋅ Zn to oxyanions were obtained, albeit using a single chemosensor. A constructed information-rich dataset was applied to pattern recognition for the simultaneous group categorization of phosphate and carboxylate groups and the prediction of similar structural oxyanions at a different order of concentrations in their mixture solutions.

Universal Single-Step Approach to the Immobilization of Cyclodextrins in a Supercritical Medium for Capturing Drug, Dye, and Metal Nanoclusters

By utilizing nanoreactor-like structures, the immobilization of macromolecules such as calixarenes and cyclodextrins (CD) with bucket-like structures provides new possibilities for engineered surface-molecule systems. The practical use of any molecular system depends on the availability of a universal procedure for immobilizing molecules with torus-like structures on various surfaces while maintaining identical operating parameters. There are currently several steps, including toxic solvent-based approaches using modified β-CD to covalently attach to surfaces with multistep reactions. However, the existing multistep process results in molecular orientation, restricts the accessibility of the hydrophobic barrel of β-CD's for practical use, and is effectively unable to use the surfaces immobilized with β-CD for a variety of applications. In this study, it was demonstrated that β-CD attached to the oxide-based semiconductor and metal surfaces through a condensation reaction between the hydroxyl-terminated oxide-based semiconductor/metal oxide and β-CD in supercritical carbon dioxide (SCCO₂) as a medium. The primary benefit of SCCO₂-assisted grafting of unmodified β-CD on various oxide-based metal and semiconductor surfaces is that it is a simple, efficient, one-step process and that it is ligand-free, scalable, substrate-independent, and uses minimal energy. Various physical microscopy and chemical spectroscopic methods were used to analyze the grafted β-CD oligomers. The application of the grafted β-CD films was demonstrated by the immobilization of rhodamine B (RhB), a dye, and dopamine, a drug. The in situ nucleation and growth of silver nanoclusters (AgNCs) in the molecular systems were studied for antibacterial and tribological properties by utilizing the guest-host interaction ability of β-CD.

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.

Visual Sensor Arrays for Distinction of Phenolic Acids Based on Two Single-Atom Nanozymes

Although great achievements have been made in the study of artificial enzymes, the design of nanozymes with high catalytic activities of natural enzymes and the further establishment of sensitive biosensors still remain challenging. Here, two nanozymes, i.e., ZnCoFe three-atom nanozyme (TAzyme) and Sn single-atom nanozyme (SAzyme)/Ti₃C₂T_x, are developed, which show peroxidase-like catalytic activities by catalyzing the reaction of hydrogen peroxide (H₂O₂), 4-aminoantipyrine (4-AAP), and phenolic acids to generate colorimetric reactions. The involvement of different phenolic acids leads to the generation of different color products. These subtle color-variation profiles between these phenolic acids prompt us to exploit an electronic tongue based on the two nanozymes to distinguish phenolic acids. Data interpretation by the pattern recognition method, such as linear discriminant analysis (LDA), displays good clustering separation of six different phenolic acids at concentrations of 0.1 μM to 1 mM, validating the effectiveness of the colorimetric nanozyme sensor array.

Artificial Olfactory Signal Modulation for Detection in Changing Environments

Animals have evolved to sense in complex environments through both modulation behavior including sniffing as well as sophisticated neural processing including memory and neuromodulation. Here, we explore thermal modulation of chemically diverse sensor arrays, where response patterns are based on partitioning of odorants across the array. The differential response patterns contain information about the chemical nature of the odorant for identification. By transitioning away from well-defined concentration modulation, traditionally used in the field, to thermal modulation, it is possible to capture both diagnostic patterns as well as intensity information in complex environments. This performance is demonstrated with carbon-black based, chemically diverse sensor arrays, that are thermally modulated with light at 25 mHz exposed to different analytes of varying concentrations.

A short review article on conjugated polymers

This article provides a brief review of conjugated polymers and the various typical polymerization reactions exploited by the community to synthesise different conjugated polyelectrolytes with varied conjugated backbone systems. We further discuss with detailed emphasises the mechanism involved such as photo-induced electron transfer, resonance energy transfer, and intra-molecular charge transfer in the detection or sensing of various analytes. Owing to their excellent photo-physical properties, facile synthesis, ease of functionalization, good biocompatibility, optical stability, high quantum yield, and strong fluorescence emission. Conjugated polymers have been explored for wide applications such as chemical and biological sensors, drug delivery and drug screening, cancer therapeutics and imaging. As such we believe it will be a timely review article for the community.

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.

AIE-doped Poly(Ionic Liquid) Photonic Spheres for the Discrimination of Psychoactive Substances

Drugs of abuse has drawn intense attention due to increasing concerns to public health and safety. The construction of a sensing platform with the capability to identify them remains a big challenge because of the limitations of synthetic complexity, sensing scope and receptor extendibility. Here a kind of poly(ionic liquid) (PIL) photonic crystal spheres doped with aggregation-induced emission (AIE) luminogens was developed. As diverse noncovalent interactions involve in PIL moieties, the single sphere shows different binding affinity to a broad range of psychoactive substances. Furthermore, the dual-channel signals arising from photonic crystal structures and sensitive AIE-luminogens provide high-dimensional information for discriminative detection of targets, even for molecules with slight structural differences. More importantly, such single sphere sensing platform could be flexibly customized through ion-exchange, showing great extendibility to fabricate high-efficiency/high-throughput sensing arrays without tedious synthesis.

Detection of Trace Amounts of Water in Organic Solvents by DNA-Based Nanomechanical Sensors

The detection of trace amounts of water in organic solvents is of great importance in the field of chemistry and in the industry. Karl Fischer titration is known as a classic method and is widely used for detecting trace amounts of water; however, it has some limitations in terms of rapid and direct detection because of its time-consuming sample preparation and specific equipment requirements. Here, we found that a DNA-based nanomechanical sensor exhibits high sensitivity and selectivity to water vapor, leading to the detection and quantification of trace amounts of water in organic solvents as low as 12 ppm in THF, with a ppb level of LoD through their vapors. Since the present method is simple and rapid, it can be an alternative technique to the conventional Karl Fischer titration.

Recent Advances in Nanomechanical Membrane-Type Surface Stress Sensors towards Artificial Olfaction

Nanomechanical sensors have gained significant attention as powerful tools for detecting, distinguishing, and identifying target analytes, especially odors that are composed of a complex mixture of gaseous molecules. Nanomechanical sensors and their arrays are a promising platform for artificial olfaction in combination with data processing technologies, including machine learning techniques. This paper reviews the background of nanomechanical sensors, especially conventional cantilever-type sensors. Then, we focus on one of the optimized structures for static mode operation, a nanomechanical Membrane-type Surface stress Sensor (MSS), and discuss recent advances in MSS and their applications towards artificial olfaction.

Chiral Recognition with Broad Selective Sensor Arrays

The detection and discrimination of chiral analytes has always been a topical theme in food and pharmaceutical industries and environmental monitoring, especially when dealing with chiral drugs and pesticides, whose enantiomeric nature assessment is of crucial importance. The typical approach matches novel chiral receptors designed ad hoc for the discrimination of a target enantiomer with emerging nanotechnologies. The massive synthetic efforts requested and the difficulty of analyzing complex matrices warrant the ever-growing exploitation of sensor array as an alternative route, using a limited number of chiral or both chiral and achiral sensors for the stereoselective identification and dosing of chiral compounds. This review aims to illustrate a little-explored winning strategy in chiral sensing based on sensor arrays. This strategy mimics the functioning of natural olfactory systems that perceive some couples of enantiomeric compounds as distinctive odors (i.e., using an array of a considerable number of broad selective receptors). Thus, fundamental concepts related to the working principle of sensor arrays and the role of data analysis techniques and models have been briefly presented. After the discussion of existing examples in the literature using arrays for discriminating enantiomers and, in some cases, determining the enantiomeric excess, the remaining challenges and future directions are outlined for researchers interested in chiral sensing applications.

Rapid Fluorescence Sensor Guided Detection of Urinary Tract Bacterial Infections

Introduction

Urinary tract infections (UTI) are one of the most serious human bacterial infections affecting millions of people every year. Therefore, simple and reliable identification of the urinary tract pathogenic bacteria within a few minutes would be of great significance for diagnosis and treatment of clinical patients with UTIs. In this study, the fluorescence sensor was reported to guide the detection of urinary tract bacterial infections rapidly.

Methods

The Ami-AuNPs-DNAs sensor was fabricated by the amino-modified Au nanoparticles (Ami-AuNPs) and six DNAs signal molecules, which bound to the urinary tract pathogenic bacteria and generated corresponding response signals. Further, based on the collected response signals, identification was performed by principal component analysis (PCA) and linear discriminant analysis (LDA). The Ami-AuNPs and Ami-AuNPs-DNAs were characterized by transmission electron microscopy, UV−vis absorption spectrum, Fourier transform infrared spectrum, dynamic light scattering and zeta potentials. Thereafter, the Ami-AuNPs-DNAs sensor was used to discriminate and identify five kinds of urinary tract pathogenic bacteria. Moreover, the quantitative analysis performance towards individual bacteria at different concentrations were also evaluated.

Results

The Ami-AuNPs-DNAs sensor were synthesized successfully in terms of spherical, well-dispersed and uniform in size, which could well discriminate five main urinary tract pathogenic bacteria with unique fingerprint-like patterns and was sufficiently sensitive to determine individual bacteria with a detection limit to 1×10⁷ cfu/mL. Furthermore, the sensor had also been successfully applied to identify bacteria in urine samples collected from clinical UTIs.

Conclusion

The developed fluorescence sensor could be applied to rapid and accurate discrimination of urinary tract pathogenic bacteria and holds great promise for the diagnosis of the disease caused by bacterial infection.

A facile way to construct sensor array library via supramolecular chemistry for discriminating complex systems

Differential sensing, which discriminates analytes via pattern recognition by sensor arrays, plays an important role in our understanding of many chemical and biological systems. However, it remains challenging to develop new methods to build a sensor unit library without incurring a high workload of synthesis. Herein, we propose a supramolecular approach to construct a sensor unit library by taking full advantage of recognition and assembly. Ten sensor arrays are developed by replacing the building block combinations, adjusting the ratio between system components, and changing the environment. Using proteins as model analytes, we examine the discriminative abilities of these supramolecular sensor arrays. Then the practical applicability for discriminating complex analytes is further demonstrated using honey as an example. This sensor array construction strategy is simple, tunable, and capable of developing many sensor units with as few syntheses as possible.

The Application of In Situ Methods to Monitor VOC Concentrations in Urban Areas—A Bibliometric Analysis and Measuring Solution Review

Urbanisation development affects urban vegetation both directly and indirectly. Since this process usually involves a dramatic change in land use, it is seen as likely to cause ecological pressure on local ecosystems. All forms of human activity, including urbanisation of areas close to residential buildings, significantly impact air quality. This study aims to identify and characterise different measurement solutions of VOCs, allowing the quantification of total and selective compounds in a direct at source (in situ) manner. Portable devices for direct testing can generally be divided into detectors, chromatographs, and electronic noses. They differ in parameters such as operating principle, sensitivity, measurement range, response time, and selectivity. Direct research allows us to obtain measurement results in a short time, which is essential from the point of view of immediate reaction in the case of high concentrations of tested compounds and the possibility of ensuring the well-being of people. The paper also attempts to compare solutions and devices available on the market and assess their application.

Analyses of lung cancer-derived volatiles in exhaled breath and in vitro models

Lung cancer is one of the leading causes of cancer incidence and cancer-related deaths in the world. Early diagnosis of pulmonary tumors results in improved survival compared to diagnosis with more advanced disease, yet early disease is not reliably indicated by symptoms. Despite of the improved testing and monitoring techniques for lung cancer in the past decades, most diagnostic tests, such as sputum cytology or tissue biopsies, are invasive and risky, rendering them unfeasible for large population screening. The non-invasive analysis of exhaled breath has gained attentions as an innovative screening method to measure chemical alterations within the human volatilome profile as a result of oncogenesis. More importantly, volatile organic compounds (VOCs) have been correlated to the pathophysiology of disease since the source of volatile compounds relies mostly on endogenous metabolic processes that are altered as a result of disease onset. Therefore, studying VOCs emitted from human breath may assist lung cancer diagnosis, treatment monitoring, and other surveillance of this devastating disease. In this mini review, we evaluated recent human studies that have attempted to identify lung cancer-derived volatiles in exhaled breath of patients. We also examined reported volatiles in cell cultures of lung cancer to better understand the origins of cancer-associated VOCs. We highlight the metabolic processes of lung cancer that could be responsible for the endogenous synthesis of these VOCs and pinpoint the protein-encoding genes involved in these pathways. Finally, we highlight the potential value of a breath test in lung cancer and propose prominent areas for future research required for the incorporation of VOCs-based testing into clinical settings.

Multi-responsive luminescent coordination polymer nanosheets for selective detection of nitroaromatics

Sensitive sensing of nitroaromatic compounds (NACs) is realized by using luminescent lanthanum-tricarboxytriphenylamine (La-TCA) nanosheets fabricated by a top-down sonication assisted strategy. The accessible Lewis base sites and electron-rich fluorophores on the surface of the La-TCA nanosheets enable them to interact with electron-deficient NACs, delivering multi-responsive behaviours (emission intensity quenching, wavelength red-shift and valley) for hydroxyl group NACs.

Printed 384-Well Microtiter Plate on Paper for Fluorescent Chemosensor Array in Food Analysis

We propose a printed 384-well microtiter paper-based fluorescent chemosensor array device (384-well microtiter PCAD) to simultaneously categorize and discriminate saccharides and sulfur-containing amino acids for food analysis. The 384-well microtiter PCAD required 1 μL/4 mm 2 of each well can allow high-throughput sensing. The device embedded with self-assembled fluorescence chemosensors displayed a fingerprint-like response pattern for targets, the image of which was rapidly captured by a portable digital camera. Indeed, the paper-based chemosensor array system combined with imaging analysis and pattern recognition techniques successfully not only categorized saccharides and sulfur-containing amino acids but also classified mono- and disaccharide groups. Furthermore, the quantitative detectability of the printed device was revealed by a spike recovery test for fructose and glutathione in a diluted freshly made tomato juice. We believe that the 384-well microtiter PCAD using the imaging analysis system will be a powerful sensor for multi-analytes at several categorized groups in real samples.

Gas sensing based on metal-organic frameworks: Concepts, functions, and developments

Effective detection of pollutant gases is vital for protection of natural environment and human health. There is an increasing demand for sensing devices that are equipped with high sensitivity, fast response/recovery speed, and remarkable selectivity. Particularly, attention is given to the designability of sensing materials with porous structures. Among diverse kinds of porous materials, metal-organic frameworks (MOFs) exhibit high porosity, high degree of crystallinity and exceptional chemical activity. Their strong host-guest interactions with guest molecules facilitate the application of MOFs in adsorption, catalysis and sensing systems. In particular, the tailorable framework/composition and potential for post-synthetic modification of MOFs endow them with widely promising application in gas sensing devices. In this review, we outlined the fundamental aspects and applications of MOFs for gas sensors, and discussed various techniques of monitoring gases based on MOFs as functional materials. Insights and perspectives for further challenges faced by MOFs are discussed in the end.

Monosystem Discriminative Sensor toward Inorganic Anions via Incorporating Three Different Luminescent Channels in Metal-Organic Frameworks

Because there are great demands of distinguishing multiple chemically similar analytes, chemical sensors for multivariate analyses have been developed rapidly in the past few decades. However, designing luminescent discriminative sensors based on a monosystem has been a challenge until now. In this work, we first develop a triemitting luminescent discriminative platform named RGB@TLU-2 with three different emission centers: blue-emitting center (BDC-NH₂), green-emitting (Tb@BDC-SO₃^-), and red-emitting center (rhodamine B, RhB). The different luminescent mechanisms (ligand emission, LMET emission, guest emission) in these emission centers endow RGB@TLU-2 with high cross-reactivity, which is essential for discriminating applications. To balance the three luminescent centers, all variables in the synthesis process are optimized carefully. Surprisingly, the RGB@TLU-2 shows a variety of luminescent response patterns when immersed into 12 inorganic anions. Two unsupervised multidimensional analysis methods, (principal component analysis and hierarchical cluster analysis), are used to explore the relationship between these anions. On the basis of the luminescent response of analytes, 5 response modes are obtained and 12 inorganic anions are classified into 6 groups. The sensing mechanisms are discussed in detail. Detection limits of typical anions Cr₂O₇^2-, PO₄^3-, ClO^-, and NO₂^- are calculated as 2.895 × 10^-8, 6.353 × 10^-6, 1.134 × 10^-5, and 4.56 × 10^-4 mol/L, respectively. Furthermore, the RGB@TLU-2 also shows the ability to distinguish 4 (Fe³⁺, Fe²⁺, Cu²⁺ and Cr³⁺) of 12 metal ions and 3 (Trp, Pro, and Arg) of 11 amino acids.

Electronic Noses and Their Applications for Sensory and Analytical Measurements in the Waste Management Plants-A Review

Waste management plants are one of the most important sources of odorants that may cause odor nuisance. The monitoring of processes involved in the waste treatment and disposal as well as the assessment of odor impact in the vicinity of this type of facilities require two different but complementary approaches: analytical and sensory. The purpose of this work is to present these two approaches. Among sensory techniques dynamic and field olfactometry are considered, whereas analytical methodologies are represented by gas chromatography–mass spectrometry (GC-MS), single gas sensors and electronic noses (EN). The latter are the core of this paper and are discussed in details. Since the design of multi-sensor arrays and the development of machine learning algorithms are the most challenging parts of the EN construction a special attention is given to the recent advancements in the sensitive layers development and current challenges in data processing. The review takes also into account relatively new EN systems based on mass spectrometry and flash gas chromatography technologies. Numerous examples of applications of the EN devices to the sensory and analytical measurements in the waste management plants are given in order to summarize efforts of scientists on development of these instruments for constant monitoring of chosen waste treatment processes (composting, anaerobic digestion, biofiltration) and assessment of odor nuisance associated with these facilities.

Rapid and sensitive screening of multiple polycyclic aromatic hydrocarbons by a reusable fluorescent sensor array

Simple and rapid sensing of polycyclic aromatic hydrocarbons (PAHs) remains a great technical challenge due to their chemical stability and structural similarity. Here, a simple, sensitive and cost-effective sensing strategy is proposed to detect multiple PAHs by utilizing the inner filter effect (IFE) and a reusable fluorescent sensor array consisting of four polyvinyl alcohol (PVA) composite carbon quantum dots (CDs) film sensors. The CDs/PVA films have a wide and tunable excitation range, which provide sufficient spectral overlap with PAHs and ensure the efficient occurrence of IFE. Under different excitations, the diverse UV absorption capacities of PAHs resulted in diverse spectral responses, enabling a unique chemical fingerprint for each PAH. Upon multivariate pattern recognition analysis, the array rendered high-throughput discrimination and sensitive quantification of 16 priority PAHs with 100% classification accuracy and detection limit as low as 57 nM. Moreover, the rapid and accurate screening of multiple environmental samples were also realized with the results consistent with high-performance liquid chromatography. This IFE-based reusable array is readily prepared, green and feasible, which exhibits great potential in environmental analysis and brings an advanced strategy to high-throughput sensing of more pollutants with similar structures and lack of recognition sites.

Surface Chemistry during Atomic Layer Deposition of Pt Studied with Vibrational Sum-Frequency Generation

A detailed understanding of the growth of noble metals by atomic layer deposition (ALD) is key for various applications of these materials in catalysis and nanoelectronics. The Pt ALD process using MeCpPtMe3 and O2 gas as reactants serves as a model system for the ALD processes of noble metals in general. The surface chemistry of this process was studied by in situ vibrational broadband sum-frequency generation (BB-SFG) spectroscopy, and the results are placed in the context of a literature overview of the reaction mechanism. The BB-SFG experiments provided direct evidence for the presence of CH3 groups on the Pt surface after precursor chemisorption at 250 °C. Strong evidence was found for the presence of a C=C containing complex (e.g., the form of Cp species) and for partial dehydrogenation of the surface species during the precursor half-cycle. The reaction kinetics of the precursor half-cycle were followed at 250 °C, showing that the C=C coverage saturated before the saturation of CH3. This complex behavior points to the competition of multiple surface reactions, also reflected in the temperature dependence of the reaction mechanism. The CH3 saturation coverage decreased significantly with temperature, while the C=C coverage remained constant after precursor chemisorption on the Pt surface for temperatures from 80 to 300 °C. These SFG results have resulted in a better understanding of the Pt ALD process and also highlight the surface chemistry during thin-film growth as a promising field of study for the BB-SFG community.

A colorimetric chemical tongue detects and distinguishes between multiple analytes

The very photoinitiators used to polymerize hydrogels can react with analytes to produce distinctive colorimetric profiles.

Lessons in Organic Fluorescent Probe Discovery

Fluorescent probes have gained profound use in biotechnology, drug discovery, medical diagnostics, molecular and cell biology. The development of methods for the translation of fluorophores into fluorescent probes continues to be a robust field for medicinal chemists and chemical biologists, alike. Access to new experimental designs has enabled molecular diversification and led to the identification of new approaches to probe discovery. This review provides a synopsis of the recent lessons in modern fluorescent probe discovery.

Fluorescence quenching based detection of nitroaromatics using luminescent triphenylamine carboxylic acids

Detection of nitroaromatics employing greener techniques has been one of the most active research fields in chemistry. A series of triphenylamine (TPA) functionalized carboxylic acids were synthesized and characterized using various spectroscopic techniques including single-crystal X-ray diffraction analysis. The interaction of carboxylic acid-decorated TPAs with nitroaromatic compounds was photophysically explored using absorption and emission spectroscopy. Stern–Volmer plot accounts for the appreciable quenching constant of the TPA-acids. Density functional theory calculations were carried out to study the new compounds' frontier molecular orbital energy levels and the possible interactions with picrate anion and revealed an unusual charge transfer interaction between acids and picrate anion. The contact mode detection shows the TPA-acids can be used as dip-strip sensors for picric acid detection.

Impact of a nanomixture of carbon black and clay on the mechanical properties of a series of irradiated natural rubber/butyl rubber blend

A series of natural rubber/butyl rubber NR/IIR blend loaded with N660 carbon black (CB) and triethoxy vinyl silane treated clay nanoparticles (TCNPs) were prepared using gamma irradiation in the presence of a polyfunctional monomer, trimethylolpropane triacrylate (TMPTA). The effect of incorporating different contents of N660 CB and five parts per hundred of rubber (phr) of treated clay on the mechanical properties of the prepared nanocomposites has been investigated. The addition of TCNP to CB/rubber composites markedly increase their tensile strength due to the increase of the cross-link density. These results indicated that the TCNP may be enclosed or trapped in the occluded structure of CB. The effect of CB and the TCNP content on the tensile strength (σ), elongation at break (ε b, %), and modulus of elasticity (E, MPa) of natural rubber/butyl rubber NR/IIR blend have been investigated. The incorporation of 5 phr of TCNP into 30 phr CB-loaded NR/IIR composites results in the increased tensile strength value by about 60%. Finally, theoretical models were used to interpret the experimental results.

Non-injective gas sensor arrays: identifying undetectable composition changes

Metal-organic frameworks (MOFs) are nanoporous materials with good prospects as recognition elements for gas sensors owing to their adsorptive sensitivity and selectivity. A gravimetric, MOF-based sensor functions by measuring the mass of gas adsorbed in a MOF. Changes in the gas composition are expected to produce detectable changes in the mass of gas adsorbed in the MOF. In practical settings, multiple components of the gas adsorb into the MOF and contribute to the sensor response. As a result, there are typically many distinct gas compositions that produce the same single-sensor response. The response vector of a gas sensor array places multiple constraints on the gas composition. Still, if the number of degrees of freedom in the gas composition is greater than the number of MOFs in the sensor array, the map from gas compositions to response vectors will be non-injective (many-to-one). Here, we outline a mathematical method to determine undetectable changes in gas composition to which non-injective gas sensor arrays are unresponsive. This is important for understanding their limitations and vulnerabilities. We focus on gravimetric, MOF-based gas sensor arrays. Our method relies on a mixed-gas adsorption model in the MOFs comprising the sensor array, which gives the mass of gas adsorbed in each MOF as a function of the gas composition. The singular value decomposition of the Jacobian matrix of the adsorption model uncovers (i) the unresponsive directions and (ii) the responsive directions, ranked by sensitivity, in gas composition space. We illustrate the identification of unresponsive subspaces and ranked responsive directions for gas sensor arrays based on Co-MOF-74 and HKUST-1 aimed at quantitative sensing of CH₄/N₂/CO₂/C₂H₆mixtures relevant to natural gas sensing.