The Optoelectronic Nose

Wearable, Biocompatible, and Dual-Emission Ocular Multisensor Patch for Continuous Profiling of Fluoroquinolone Antibiotics in Tears

The abuse or misuse of antibiotics in clinical and agricultural settings severely endangers human health and ecosystems, which has raised profound concerns for public health worldwide. Trace detection and reliable discrimination of commonly used fluoroquinolone (FQ) antibiotics and their analogues have consequently become urgent to guide the rational use of antibiotic medicines and deliver efficient treatments for associated diseases. Herein, we report a wearable eye patch integrated with a quadruplex nanosensor chip for noninvasive detection and discrimination of primary FQ antibiotics in tears during routine eyedrop treatment. A set of dual-mode fluorescent nanoprobes of red- or green-emitting CdTe quantum dots integrated with lanthanide ions and a sensitizer, adenosine monophosphate, were constructed to provide an enhanced fluorescence up to 45-fold and nanomolar sensitivity toward major FQs owing to the aggregation-regulated antenna effect. The aggregation-driven, CdTe-Ln(III)-based microfluidic sensor chip is highly specific to FQ antibiotics against other non-FQ counterparts or biomolecular interfering species and is able to accurately discriminate nine types of FQ or non-FQ eyedrop suspensions using linear discriminant analysis. The prototyped wearable sensing detector has proven to be biocompatible and nontoxic to human tissues, which integrates the entire optical imaging modules into a miniaturized, smartphone-based platform for field use and reduces the overall assay time to ∼5 min. The practicability of the wearable eye patch was demonstrated through accurate quantification of antibiotics in a bactericidal event and the continuous profiling of FQ residues in tears after using a typical prescription antibiotic eyedrop. This technology provides a useful supplement to the toolbox for on-site and real-time examination and regulation of inappropriate daily drug use that might potentially lead to long-term antibiotic abuse and has great implications in advancing personal healthcare techniques for the regulation of daily medication therapy.

Molecular design and architectonics towards film-based fluorescent sensing

The past few decades have witnessed encouraging progress in the development of high-performance film-based fluorescent sensors (FFSs) for detecting explosives, illicit drugs, chemical warfare agents (CWAs), and hazardous volatile organic chemicals (VOCs), among others. Several FFSs have transitioned from laboratory research to real-world applications, demonstrating their practical relevance. At the heart of FFS technology lies the sensing films, which play a crucial role in determining the analytes and the resulting signals. The selection of sensing fluorophores and the fabrication strategies employed in film construction are key factors that influence the fluorescence properties, active-layer structures, and overall sensing behaviors of these films. This review examines the progress and innovations in the research field of FFSs over the past two decades, focusing on advancements in fluorophore design and active-layer structural engineering. It underscores popular sensing fluorophore scaffolds and the dynamics of excited state processes. Additionally, it delves into six distinct categories of film fabrication technologies and strategies, providing insights into their advantages and limitations. This review further addresses important considerations such as photostability and substrate effects. Concluding with an overview of the field's challenges and prospects, it sheds light on the potential for further development in this burgeoning area.

Extrusion Printing of Surface-Functionalized Metal-Organic Framework Inks for a High-Performance Wearable Volatile Organic Compound Sensor

Wearable sensors hold immense potential for real-time and non-destructive sensing of volatile organic compounds (VOCs), requiring both efficient sensing performance and robust mechanical properties. However, conventional colorimetric sensor arrays, acting as artificial olfactory systems for highly selective VOC profiling, often fail to meet these requirements simultaneously. Here, a high-performance wearable sensor array for VOC visual detection is proposed by extrusion printing of hybrid inks containing surface-functionalized sensing materials. Surface-modified hydrophobic polydimethylsiloxane (PDMS) improves the humidity resistance and VOC sensitivity of PDMS-coated dye/metal-organic frameworks (MOFs) composites. It also enhances their dispersion within liquid PDMS matrix, thereby promoting the hybrid liquid as high-quality extrusion-printing inks. The inks enable direct and precise printing on diverse substrates, forming a uniform and high particle-loading (70 wt%) film. The printed film on a flexible PDMS substrate demonstrates satisfactory flexibility and stretchability while retaining excellent sensing performance from dye/MOFs@PDMS particles. Further, the printed sensor array exhibits enhanced sensitivity to sub-ppm VOC levels, remarkable resistance to high relative humidity (RH) of 90%, and the differentiation ability for eight distinct VOCs. Finally, the wearable sensor proves practical by in situ monitoring of wheat scab-related VOC biomarkers. This study presents a versatile strategy for designing effective wearable gas sensors with widespread applications.

Smartphone-Based Sensing of Cortisol by Functionalized Rhodamine Probes

During a stress condition, the human body synthesizes catecholamine neurotransmitters and specific hormones (called "stress hormones"), the most important of which is cortisol. The monitoring of cortisol levels should be extremely important to control the stress levels, and for this reason, it shows important medical applications. The common analytical methods (HPLC, GC-MS) cannot be used in real life, due to the bulky size of the instruments and the necessity of specialized personnel. Molecular probes solve these problems due to their fast and easy use. The synthesis of new fluorescent rhodamine probes, able to interact by non-covalent interactions with cortisol, the recognition properties in solution as well as in solid state by Strip Test, using a smartphone as detector, are here reported. DFT calculations and FT-IR measurements suggest the formation of supramolecular complexes through hydrogen bonds as main non-covalent interaction. The present study represents one of the first sensor, based on synthetical chemical receptors, able to detect cortisol in a linear range from 1 mM to 1 pM, based on non-covalent molecular recognition and paves the way to the realization of practical point-of-care device for the monitoring of cortisol in real live.

Three-dimensional photonic crystal optical gas sensor for trace detection and ultrafast response of chemical warfare agent in atmospheric humidity

Chemical warfare agents (CWAs) are toxic that pose a threat to the environment and human health, even trace amounts of CWAs can be fatal. In view of this, there is an urgent need to develop gas sensors for trace detection and ultrafast response of CWAs. Herein, an optical gas sensor has been proposed based on metal-organic frameworks (MOFs) three-dimensional (3D) photonic crystal to detect trace CWAs' simulant (dimethyl methylphosphonate, DMMP) in different atmospheric humidity (RH 20 %, RH 40 %, RH 60 %, RH 80 %). At relative humidity (RH) of 20 %, the sensor shows excellent selectivity of DMMP due to the specific interactions of van der Waals force between UiO-67 and phosphoryl oxygen (OP) group of DMMP (C3H9O3P), the ultrahigh sensitivity (42.7 ppb), ultrafast response (0.5 s) are profit from the ordered superstructure of 3D photonic crystal and its complete photonic bandgap. At higher humidity (RH 40%-80 %), the sensor shows excellent stability, long-term repeatability, and it still keeps ultrahigh sensitivity (12.1 ppb), ultrafast response (0.49 s) for DMMP at RH 80 %. Moreover, an optical gas sensor array has been prepared to solve the problem of cross-sensitive between DMMP and other CWAs at highest humidity (RH ≥ 80 %), the average classification accuracy can reach 98.6 %.

Transparent, flexible, and eco-friendly starch-based films for reversible optoelectronic noses for food spoilage monitoring in smart packaging

A reversible optoelectronic nose is presented consisting of ten acid-base indicators incorporated into a starch-based film, covering a wide pH range. The starch substrate is odorless, biocompatible, flexible, and exhibits high tensile resistance. This optical artificial olfaction system was used to detect the early stages of food decomposition by exposing it to the volatile compounds produced during the spoialge process of three food products (beef, chicken, and pork). A smartphone was used to capture the color changes caused by intermolecular interactions between each dye and the emitted volatiles over time. Digital images were processed to generate a differential color map, which uses the observed color shifts to create a unique signature for each food product. To effectively discriminate among different samples and exposure times, we employed chemometric tools, including hierarchical cluster analysis (HCA) and principal component analysis (PCA). This approach detects food deterioration in a practical, cost-effective, and user-friendly manner, making it suitable for smart packaging. Additionally, the use of starch-based films in the food industry is preferable due to their biocompatibility and biodegradability characteristics.

Scalable Optical Nose Realized with a Chemiresistively Modulated Light-Emitter Array

Biological olfaction relies on a large number of receptors that function as sensors to detect gaseous molecules. It is challenging to realize artificial olfactory systems that contain similarly large numbers of sensory materials. It is shown that combinatorial materials processing with vapor deposition can be used to fabricate large arrays of distinct chemiresistive sensing materials. By combining these with light-emitting diodes, an array of chemiresistively-modulated light-emitting diodes, or ChemLEDs, that permit a simultaneous optical read-out in response to an analyte is obtained. The optical nose uses a common voltage source and ground for all sensing elements and thus eliminates the need for complex wiring of individual sensors. This optical nose contains one hundred ChemLEDs and generates unique light patterns in response to gases and their mixtures. Optical pattern recognition methods enable the quantitative prediction of the corresponding concentrations and compositions, thereby paving the way for massively parallel artificial olfactory systems. ChemLEDs open the possibility to explore demanding gas sensing applications, including in environmental, food quality monitoring, and potentially diagnostic settings.

Multiple-Emitting Luminescent Metal-Organic Framework as an Array-on-a-MOF for Rapid Screening and Discrimination of Nitroaromatics

Fluorescence array technologies have attracted great interest in the sensing field because of their high sensitivity, low cost, and capability of multitarget detection. However, traditional array sensing relies on multiple independent sensors and thus often requires time-consuming and laborious measurement processes. Herein, we introduce a novel fluorescence array strategy of the array-on-a-metal-organic framework (MOF), which integrates multiple array elements into a single MOF matrix to achieve facile sensing and discrimination of multiple target analytes. As a proof-of-concept system, we constructed a luminescent MOF containing three different emitting channels, including a lanthanide ion (europium/Eu3+, red emission), a fluorescent dye (7-hydroxycoumarin-4-acetic acid/HCAA, blue emission), and the MOF itself (UiO-66-type MOF, blue-violet emission). Five structurally similar nitroaromatic compounds (NACs) were chosen as the targets. All three channels of the array-on-a-MOF displayed rapid and stable fluorescence quenching responses to NACs (response equilibrium achieved within 30 s). Different responses were generated for each channel against each NAC due to the various quenching mechanisms, including photoinduced electron transfer, energy competition, and the inner filter effect. Using linear discriminant analysis, the array-on-a-MOF successfully distinguished the five NACs and their mixtures at varying concentrations and demonstrated good sensitivity to quantify individual NACs (detect limit below the advisory concentration in drinking water). Moreover, the array also showed feasibility in the sensing and discrimination of multiple NACs in real water samples. The proposed "array-on-a-MOF" strategy simplifies multitarget discrimination procedures and holds great promise for various sensing applications.

On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform

Synthesizing the best material globally is challenging; it needs to know what and how much the best ingredient composition should be for satisfying multiple figures of merit simultaneously. Traditional one-variable-at-a-time methods are inefficient; the design-build-test-learn (DBTL) method could achieve the optimal composition from only a handful of ingredients. A vast design space needs to be explored to discover the possible global optimal composition for on-demand materials synthesis. This research developed a hypothesis-guided DBTL (H-DBTL) method combined with robots to expand the dimensions of the search space, thereby achieving a better global optimal performance. First, this study engineered the search space with knowledge-aware chemical descriptors and customized multiobjective functions to fulfill on-demand research objectives. To verify this concept, this novel method was used to optimize colorimetric ammonia sensors across a vast design space of as high as 19 variables, achieving two remarkable optimization goals within 1 week: first, a sensing array was developed for ammonia quantification of a wide dynamic range, from 0.5 to 500 ppm; second, a new state-of-the-art detection limit of 50 ppb was reached. This work demonstrates that the H-DBTL approach, combined with a robot, develops a novel paradigm for the on-demand optimization of functional materials.

Gas-responsive two-dimensional metal-organic framework composites for trace visualization of volatile organic compounds

Highly sensitive and specific identification of complex volatile organic compound mixtures has always been a huge challenge in the field of gas detection. To address this issue, the gas-responsive two-dimensional metal-organic framework (MOF) composites have been designed for fabricating a colorimetric sensor arrays for extremely sensitive detection of volatile organic compounds (VOCs). The physically exfoliated MOF nanosheets Zn2(bim)4 with large surface area and abundant unsaturated active sites were used for loading various dyes to form dye/Zn2(bim)4 composites. Due to the protective effect on dye activity and preconcentration for VOCs, the dye/Zn2(bim)4 composites-based colorimetric sensor arrays showed significantly enhanced sensitivity compared with the corresponding dyes for the detection of various VOCs. The mechanical flexibility of the dye/MOF nanosheets endowed the excellent film-forming properties on various substrates for fabricating the colorimetric sensor arrays. Besides owing to the hydrophobic property and the protection of the Zn2(bim)4 nanosheets, the dye/Zn2(bim)4 sensor arrays exhibited excellent anti-interference including humidity and temperature influence. On the basis of the fantastic properties of dye/Zn2(bim)4 composites for VOCs detection, the dye/Zn2(bim)4 sensor arrays were applied for the early perception of the plant disease late blight via ultra-sensitive and highly specific sensing the VOCs released from the infected plants.

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.

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

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

Rapid Diagnosis of Urinary Tract Cancers on a LEGO-Inspired Detection Platform via Chemiresistive Profiling of Volatile Metabolites

Rapid and in situ profiling of volatile metabolites from body fluids represents a new trend in cancer diagnosis and classification in the early stages. We report herein an on-chip strategy that combines an array of conductive nanosensors with a chaotic gas micromixer for real-time monitoring of volatiles from urine and for accurate diagnosis and classification of urinary tract cancers. By integrating a class of LEGO-inspired microchambers immobilized with MXene-based sensing nanofilms and zigzag microfluidic gas channels, it enables the intensive intermingling of volatile organic chemicals with sensor elements that tremendously facilitate their ion-dipole interactions for molecular recognition. Aided with an all-in-one, point-of-care platform and an effective machine-learning algorithm, healthy or diseased samples from subpopulations (i.e., tumor subtypes, staging, lymph node metastasis, and distant metastasis) of urinary tract cancers can be reliably fingerprinted in a few minutes with high sensitivity and specificity. The developed detection platform has proven to be a noninvasive supplement to the liquid biopsies available for facile screening of urinary tract cancers, which holds great potential for large-scale personalized healthcare in low-resource areas.

A Reaction-Based Optical Fingerprinting Strategy for the Recognition of Fat-Soluble Samples: Discrimination of Motor Oils

Optical "fingerprints" are widely used for chemometrics-assisted recognition of samples of different types. An emerging trend in this area is the transition from obtaining "static" spectral data to reactions analyzed over time. Indicator reactions are usually carried out in aqueous solutions; in this study, we developed reactions that proceed in an organic solvent, thereby making it possible to recognize fat-soluble samples. In this capacity, we used 5W40, 10W40, and 5W30 motor oils from four manufacturers, with six samples in total. The procedure involved mixing a dye, sample, and reagents (HNO3, HCl, or tert-butyl hydroperoxide) in an ethanolic solution in a 96-well plate and measuring absorbance or near-infrared fluorescence intensity every several minutes for 20-55 min. The obtained photographic images were processed by linear discriminant analysis (LDA) and the k-nearest neighbors algorithm (kNN). Discrimination accuracy was evaluated by a validation procedure. A reaction of oxidation of a dye by nitric acid allowed us to recognize all six samples with 100% accuracy for LDA. Merging of data from the four reactions that did not provide complete discrimination ensured an accuracy of 93% for kNN. The newly developed indicator systems have good prospects for the discrimination of other fat-soluble samples. Overall, the results confirm the viability of the kinetics-based discrimination strategy.

Operando surface optical nanometrology reveals diazonium salts' visible photografting mechanism

High resolution and quantitative surface modification through photografting is a highly desirable strategy towards the preparation of smart surfaces, enabling chemical functions to be precisely located onto specific regions of inert surfaces. Although promising, the mechanisms leading to direct (without the use of any additive) photoactivation of diazonium salts using visible wavelengths are poorly understood, precluding the generalization of popular diazonium-based electrografting strategies into high resolution photografting ones. In this paper, we employ quantitative phase imaging as a nanometrology tool for evaluating the local grafting rate with diffraction-limited resolution and nanometric precision. By carefully measuring the surface modification kinetics under a range of different conditions, we reveal the reaction mechanism while evaluating the influence of key parameters, such as the power density, the radical precursor concentration and the presence of side reactions.

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.

Carbocyanine-Based Optical Sensor Array for the Discrimination of Proteins and Rennet Samples Using Hypochlorite Oxidation

Optical sensor arrays are widely used in obtaining fingerprints of samples, allowing for solutions of recognition and identification problems. An approach to extending the functionality of the sensor arrays is using a kinetic factor by conducting indicator reactions that proceed at measurable rates. In this study, we propose a method for the discrimination of proteins based on their oxidation by sodium hypochlorite with the formation of the products, which, in turn, feature oxidation properties. As reducing agents to visualize these products, carbocyanine dyes IR-783 and Cy5.5-COOH are added to the reaction mixture at pH 5.3, and different spectral characteristics are registered every several minutes (absorbance in the visible region and fluorescence under excitation by UV (254 and 365 nm) and red light). The intensities of the photographic images of the 96-well plate are processed by principal component analysis (PCA) and linear discriminant analysis (LDA). Six model proteins (bovine and human serum albumins, γ-globulin, lysozyme, pepsin, and proteinase K) and 10 rennet samples (mixtures of chymosin and pepsin from different manufacturers) are recognized by the proposed method. The method is rapid and simple and uses only commercially available reagents.

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

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

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

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

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

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

Single-Probe-Based Colorimetric and Photothermal Dual-Mode Identification of Multiple Bacteria

Effective identification of multiple pathogenic bacteria in unknown samples is important for disease prevention and control but remains a challenge yet. A single-mode array-based sensing approach is simple and sensitive, but it usually relies on the use of multiple cross-reactive receptors to construct sensor arrays, which is cumbersome and insufficiently accurate. Here, we developed a sensor array with colorimetric and photothermal dual mode of differentiating multiple pathogenic bacteria. The sensor array was based on boronic acid-functionalized Au-Fe₃O₄ nanoparticles (BA-GMNPs), which not only possess localized surface plasmon resonance properties, showing a burgundy color similar to that of AuNPs, but also exhibit mild superparamagnetism, allowing for the differentiation of bacteria before and after binding to the nanoparticles. Immobilization of BA-GMNPs on the bacterial cell surface by covalent bonding would diminish NaCl-induced assembly of BA-GMNPs. Different BA-GMNPs@bacterial complexes differed in their ability to resist assembly and produced different colorimetric and photothermal response signals. A unique molecular fingerprint of each bacterium was obtained by linear discriminant analysis of the response patterns, demonstrating an effective differentiation among the six species studied. Compared with single-mode sensing arrays based on multiple receptors, this method only requires the preparation of a single nanomaterial, which produces two signal outputs for the identification of multiple bacteria with better differentiation. It can distinguish not only multiple pathogenic bacteria but also Gram-negative and Gram-positive bacteria, and, more importantly, it can perform preliminary discrimination of unknown samples.

A Multichannel Fluorescent Tongue for Amyloid-β Aggregates Detection

Attention has been paid to the early diagnosis of Alzheimer's disease, due to the maximum benefit acquired from the early-stage intervention and treatment. However, the sensing techniques primarily depended upon for neuroimaging and immunological assays for the detection of AD biomarkers are expensive, time-consuming and instrument dependent. Here, we developed a multichannel fluorescent tongue consisting of four fluorescent dyes and GO through electrostatic and π-π interaction. The array distinguished multiple aggregation states of 1 µM Aβ40/Aβ42 with 100% prediction accuracy via 10-channel signal outputs, illustrating the rationality of the array design. Screening vital sensor elements for the simplified sensor array and the optimization of sensing system was achieved by machine learning algorithms. Moreover, our sensing tongue was able to detect the aggregation states of Aβ40/Aβ42 in serum, demonstrating the great potential of multichannel array in diagnosing the Alzheimer's diseases.

A Programmable Plasmonic Gas Microsystem for Detecting Arbitrarily Combinated Volatile Organic Compounds (VOCs) with Ultrahigh Resolution

For gas sensors, the ultrasensitive and highly selective detection of multiple components is of great significance in a wide range of applications extending from environment to healthcare, which is still a long-term challenge due to the single sensing mechanism of most sensors. Here, we combine the advantages of microfluidic chips and surface-enhanced Raman spectroscopy (SERS) spectra to fabricate a smart single-chip for simultaneously detecting an arbitrary combination of VOCs that incorporates different detection units, working on either a physisorption or chemisorption mechanism. Full integration of microfluidic and multiplex nanostructure components on one chip permits programmable design for sensing multifarious volatile compounds, and enables on-chip signal amplifications with increased reproducibility. As a proof-of-principle experiment, we demonstrate the simultaneous identification of 9 different gases that belong to aromatic compounds, aldehydes, ketones, or sulfides in one mixture, with high sensitivity (ppb level), high selectivity, and high robustness (error ∼8%). We further evaluated the application of our universal gas sensor in two scenarios including indoor air pollution monitoring and exhaled breath-based disease diagnosis. We expect that our design will improve the various practical applications of gas sensors.

Smartphone-Assisted Sensing of Trinitrotoluene by Optical Array

Here we report the design and fabrication of an array-based sensor, containing functionalized Carbon Dots, Bodipy's and Naphthalimide probes, that shows high fluorescence emissions and sensitivity in the presence of low amounts of TNT explosive. In particular, we have fabricated the first sensor device based on an optical array for the detection of TNT in real samples by using a smartphone as detector. The possibility to use a common smartphone as detector leads to a prototype that can be also used in a real-life field application. The key benefit lies in the possibility of even a nonspecialist operator in the field to simply collect and send data (photos) to the trained artificial intelligence server for rapid diagnosis but also directly to the bomb disposal unit for expert evaluation. This new array sensor contains seven different fluorescent probes that are able to interact via noncovalent interactions with TNT. The interaction of each probe with TNT has been tested in solution by fluorescence titrations. The solid device has been tested in terms of selectivity and linearity toward TNT concentration. Tests performed with other explosives and other nitrogen-based analytes demonstrate the high selectivity for TNT molecules, thus supporting the reliability of this sensor. In addition, TNT can be detected in the range of 98 ng∼985 μg, with a clear different response of each probe to the different amounts of TNT.

Humidity-Independent Artificial Olfactory Array Enabled by Hydrophobic Core-Shell Dye/MOFs@COFs Composites for Plant Disease Diagnosis

As a class of important artificial olfactory system, the colorimetric sensor array possesses great potential for commercialization due to its cost-effectiveness and portability. However, when applied to practical applications, the humidity interference from ambient environment and dissatisfactory sensitivity for trace target VOCs are largely unsolved problems. To overcome the problems, we developed a series of dye/MOFs@COFs gas-sensing materials with core-shell structure using a hydrophobization strategy by encapsulation of dye/metal-organic frameworks (MOFs) into hydrophobic covalent organic frameworks (COFs). Benefiting from the hydrophobic property of the COF shell, the dye/MOFs@COFs composites were endowed with excellent humidity-resistance even under 100% relative humidity (RH). Moreover, due to the uniform distribution of dyes on the porous MOFs, the dye/MOFs@COFs sensors also exhibited improved sensitivity at the sub-ppm level, compared with conventional dye sensors. On basis of the excellent humidity-resistance and improved sensitivity, an artificial olfactory array based on dye/MOFs@COFs composites was proven to be a successful practical application in early and accurate detection of wheat scab (1 day after inoculation) by monitoring its released VOC markers. The synthetic strategy for core-shell dye/MOFs@COFs is applicable to a wide range of colorimetric sensor arrays, endowing them with excellent humidity-resistance and sensitivity for the feasibility of practical applications.

Nonplanar porphyrins: synthesis, properties, and unique functionalities

Porphyrins are variously substituted tetrapyrrolic macrocycles, with wide-ranging biological and chemical applications derived from metal chelation in the core and the 18π aromatic surface. Under suitable conditions, the porphyrin framework can deform significantly from regular planar shape, owing to steric overload on the porphyrin periphery or steric repulsion in the core, among other structure modulation strategies. Adopting this nonplanar porphyrin architecture allows guest molecules to interact directly with an exposed core, with guest-responsive and photoactive electronic states of the porphyrin allowing energy, information, atom and electron transfer within and between these species. This functionality can be incorporated and tuned by decoration of functional groups and electronic modifications, with individual deformation profiles adapted to specific key sensing and catalysis applications. Nonplanar porphyrins are assisting breakthroughs in molecular recognition, organo- and photoredox catalysis; simultaneously bio-inspired and distinctly synthetic, these molecules offer a new dimension in shape-responsive host-guest chemistry. In this review, we have summarized the synthetic methods and design aspects of nonplanar porphyrin formation, key properties, structure and functionality of the nonplanar aromatic framework, and the scope and utility of this emerging class towards outstanding scientific, industrial and environmental issues.

Recent Progress in Amine Gas Sensors for Food Quality Monitoring: Novel Architectures for Sensing Materials and Systems

The increasing demand for food production has necessitated the development of sensitive and reliable methods of analysis, which allow for the optimization of storage and distribution while ensuring food safety. Methods to quantify and monitor volatile and biogenic amines are key to minimizing the waste of high-protein foods and to enable the safe consumption of fresh products. Novel materials and device designs have allowed the development of portable and reliable sensors that make use of different transduction methods for amine detection and food quality monitoring. Herein, we review the past decade's advances in volatile amine sensors for food quality monitoring. First, the role of volatile and biogenic amines as a food-quality index is presented. Moreover, a comprehensive overview of the distinct amine gas sensors is provided according to the transduction method, operation strategies, and distinct materials (e.g., metal oxide semiconductors, conjugated polymers, carbon nanotubes, graphene and its derivatives, transition metal dichalcogenides, metal organic frameworks, MXenes, quantum dots, and dyes, among others) employed in each case. These include chemoresistive, fluorometric, colorimetric, and microgravimetric sensors. Emphasis is also given to sensor arrays that record the food quality fingerprints and wireless devices that operate as radiofrequency identification (RFID) tags. Finally, challenges and future opportunities on the development of new amine sensors are presented aiming to encourage further research and technological development of reliable, integrated, and remotely accessible devices for food-quality monitoring.

Artificial olfactory sensor technology that mimics the olfactory mechanism: a comprehensive review

Artificial olfactory sensors that recognize patterns transmitted by olfactory receptors are emerging as a technology for monitoring volatile organic compounds. Advances in statistical processing methods and data processing technology have made it possible to classify patterns in sensor arrays. Moreover, biomimetic olfactory recognition sensors in the form of pattern recognition have been developed. Deep learning and artificial intelligence technologies have enabled the classification of pattern data from more sensor arrays, and improved artificial olfactory sensor technology is being developed with the introduction of artificial neural networks. An example of an artificial olfactory sensor is the electronic nose. It is an array of various types of sensors, such as metal oxides, electrochemical sensors, surface acoustic waves, quartz crystal microbalances, organic dyes, colorimetric sensors, conductive polymers, and mass spectrometers. It can be tailored depending on the operating environment and the performance requirements of the artificial olfactory sensor. This review compiles artificial olfactory sensor technology based on olfactory mechanisms. We introduce the mechanisms of artificial olfactory sensors and examples used in food quality and stability assessment, environmental monitoring, and diagnostics. Although current artificial olfactory sensor technology has several limitations and there is limited commercialization owing to reliability and standardization issues, there is considerable potential for developing this technology. Artificial olfactory sensors are expected to be widely used in advanced pattern recognition and learning technologies, along with advanced sensor technology in the future.

One-Component Multichannel Sensor Array for Rapid Identification of Bacteria

Bacterial infections routinely cause serious problems to public health. To mitigate the impact of bacterial infections, sensing systems are urgently required for the detection and subsequent epidemiological control of pathogenic organisms. Most conventional approaches are time-consuming and highly instrument- and professional operator-dependent. Here, we developed a novel one-component multichannel array constructed with complex systems made from three modified polyethyleneimine as well as negatively charged graphene oxide, which provided an information-rich multimode response to successfully identify 10 bacteria within minutes via electrostatic interactions and hydrophobic interactions. Furthermore, the concentration of bacteria (from OD₆₀₀ = 0.025 to 1) and the ratio of mixed bacteria were successfully achieved with our smart sensing system. Our designed sensor array also exhibited huge potential in biological samples, such as in urine (OD₆₀₀ = 0.125, 94% accuracy). The way to construct a sensor array with minimal sensor element with abundant signal outputs tremendously saves cost and time, providing a powerful tool for the diagnosis and assessment of bacterial infections in the clinic.

Paper-based colorimetric sensor array for the rapid and on-site discrimination of green tea samples based on the flavonoid composition

Green tea is a worldwide appreciated food product with Chinese production estimated to reach over 3m tons in 2027 and with many valuable health effects. The development of analytical methods to discriminate among green tea samples is induced by economic benefits and to avoid deliberate origin mislabeling and adulteration. In this study, we present a paper-based colorimetric sensor array comprised of six ordinary reagents tailored for the discrimination of green tea extracts of different brands according to differences in the composition of flavonoids. The colorimetric array was rationally designed based on indicators that differentially react with a variety of flavonoids via specific functional groups. 4 μL of each reagent was impregnated onto the paper surface followed by the addition of the green tea extract. After 1 minute, digital images were acquired using a smartphone and the color changes were employed to build differential maps with a unique fingerprint for each green tea sample. Moreover, principal component analysis (PCA) and hierarchical component analysis (HCA) were employed to successfully discriminate among the samples, enabling the origin and adulteration identification of the samples. Therefore, this study provides a simple, effective, low-cost, and portable method for quick discrimination and quality control of green tea samples.

Hydrophobic and Rapid-Response Sensor Inks: Array-Based Fingerprinting of Perfumes

Counterfeited perfumes mixed with inexpensive additives for commercial purposes pose a great threat to cosmetic market competition and human health. Herein, a 24-element, solid-state colorimetric sensor array employing chemo-responsive dye inks for accurate discrimination of a variety of fragrance bases and "sniffing out" real perfumes from adulterated samples was first reported. The physiochemical robustness and gas response kinetics of the sensor array were optimized with the streamlined design of the channel geometry and hydrophobic modification of the sensor substrate. A unique and distinguishable color change profile was obtained within 2 min exposure of diluted vapor that enabled clear fingerprinting of chemically similar perfume samples. Four commercial perfume products were successfully distinguished and categorized according to their similarity to relevant perfume bases using chemometric methods including hierarchical clustering and principal component analysis. The sensor array also allows the discrimination of ethanol-diluted fragrance bases from the pristine sample, revealing its potential for quality assurance of perfumes and other cosmetics. Such easy-to-use, disposable, and miniaturized chemical sensing detectors therefore prove exceptionally valuable for fast analysis of luxuries such as perfumes and other industrial products with complex chemical compositions.

Machine Learning-Assisted Sensor Array Based on Poly(amidoamine) (PAMAM) Dendrimers for Diagnosing Alzheimer's Disease

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder, and the early diagnosis of AD remains challenging. Here we have developed a fluorescent sensor array composed of three modified polyamidoamine dendrimers. Proteins of various properties were differentiated via this array with 100% accuracy, proving the rationality of the array's design. The mechanism of the fluorescence response was discussed. Furthermore, the robust three-element array enables parallel detection of multiple Aβ40/Aβ42 aggregates (0.5 μM) in diverse interferents, serum media, and cerebrospinal fluid (CSF) with high accuracy, through machine learning algorithms, demonstrating the tremendous potential of the sensor array in Alzheimer's disease diagnosis.

Sensors for Volatile Organic Compounds

This paper provides an overview of recent developments in the field of volatile organic compound (VOC) sensors, which are finding uses in healthcare, safety, environmental monitoring, food and agriculture, oil industry, and other fields. It starts by briefly explaining the basics of VOC sensing and reviewing the currently available and quickly progressing VOC sensing approaches. It then discusses the main trends in materials' design with special attention to nanostructuring and nanohybridization. Emerging sensing materials and strategies are highlighted and their involvement in the different types of sensing technologies is discussed, including optical, electrical, and gravimetric sensors. The review also provides detailed discussions about the main limitations of the field and offers potential solutions. The status of the field and suggestions of promising directions for future development are summarized.

Data Fusion Approaches for the Characterization of Musts and Wines Based on Biogenic Amine and Elemental Composition

Samples from various winemaking stages of the production of sparkling wines using different grape varieties were characterized based on the profile of biogenic amines (BAs) and the elemental composition. Liquid chromatography with fluorescence detection (HPLC-FLD) combined with precolumn derivatization with dansyl chloride was used to quantify BAs, while inductively coupled plasma (ICP) techniques were applied to determine a wide range of elements. Musts, base wines, and sparkling wines were analyzed accordingly, and the resulting data were subjected to further chemometric studies to try to extract information on oenological practices, product quality, and varieties. Although good descriptive models were obtained when considering each type of data separately, the performance of data fusion approaches was assessed as well. In this regard, low-level and mid-level approaches were evaluated, and from the results, it was concluded that more comprehensive models can be obtained when joining data of different natures.

Fe-N-C single-atom nanozymes based sensor array for dual signal selective determination of antioxidants

Machine learning algorithms as a powerful tool can efficiently utilize and process large quantities of data generated by high-throughput experiments in various fields. In this work, we used a general ionic salt-assisted synthesis method to prepare oxidase-like Fe-N-C SANs. The possible reason for the excellent enzyme-mimicking activity and affinity of Fe-N-C SANs was further verified by density functional theory calculations. Due to the remarkable oxidase-mimicking activity, the prepared Fe-N-C SANs were used to detect ascorbic acid (AA) with a detection limit of 0.5 μM. Based on the machine learning algorithms, we successfully distinguished six antioxidants (ascorbic acid, glutathione, L-cysteine, dithiothreitol, uric acid, and dopamine) with the same concentration by either one kind of Fe-N-C SANs or three kinds of different Fe-N-C SANs. The usefulness of the Fe-N-C SANs sensor arrays was further validated by the hierarchal cluster analysis, where they also can be correctly identified. More importantly, a SANs-based digital-image colorimetric sensor array has also been successfully constructed and thereby achieved visual and informative colorimetric analysis for practical samples out of the lab. This work not only provides a design synthesis method to prepare SANs but also combines machine learning algorithms with SANs sensors to identify analytes with similar properties, which can further expand to the detection of proteins and cells related to diseases in the future.

Carbocyanine-Based Fluorescent and Colorimetric Sensor Array for the Discrimination of Medicinal Compounds

Array-based optical sensing is an efficient technique for the determination and discrimination of small organic molecules. This study is aimed at the development of a simple and rapid strategy for obtaining an optical response from a wide range of low-molecular-weight organic compounds. We have suggested a colorimetric and fluorimetric sensing platform based on the combination of two response mechanisms using carbocyanine dyes: aggregation and oxidation. In the first one, the analyte forms ternary aggregates with an oppositely charged surfactant wherein the dye is solubilized in the hydrophobic domains of the surfactant accompanied with fluorescent enhancement. The second mechanism is based on the effect of the analyte on the catalytic reaction rate of dye oxidation with H2O2 in the presence of a metal ion (Cu2+, Pd2+), which entails fluorescence waning and color change. The reaction mixture in a 96-well plate is photographed in visible light (colorimetry) and the near-IR region under red light excitation (fluorimetry). In this proof-of-concept study, we demonstrated the feasibility of discrimination of nine medicinal compounds using principal component analysis: four cephalosporins (ceftriaxone, cefazolin, ceftazidime, cefotaxime), three phenothiazines (promethazine, promazine, chlorpromazine), and two penicillins (benzylpenicillin, ampicillin) in an aqueous solution and in the presence of turkey meat extract. The suggested platform allows simple and rapid recognition of analytes of various nature without using spectral equipment, except for a photo camera.

Simpler Procedure and Improved Performance for Pathogenic Bacteria Analysis with a Paper-Based Ratiometric Fluorescent Sensor Array

Bacterial infections are the leading cause of morbidity and mortality in the world, particularly due to a delay in treatment and misidentification of the bacterial species causing the infection. Therefore, rapid and accurate identification of these pathogens has been of prime importance. The conventional diagnostic techniques include microbiological, biochemical, and genetic analyses, which are time-consuming, require large sample volumes, expensive equipment, reagents, and trained personnel. In response, we have now developed a paper-based ratiometric fluorescent sensor array. Environment-sensitive fluorescent dyes (3-hydroxyflavone derivatives) pre-adsorbed on paper microzone plates fabricated using photolithography, upon interaction with bacterial cell envelopes, generate unique fluorescence response patterns. The stability and reproducibility of the sensor array response were thoroughly investigated, and the analysis procedure was refined for optimal performance. Using neural networks for response pattern analysis, the sensor was able to identify 16 bacterial species and recognize their Gram status with an accuracy rate greater than 90%. The paper-based sensor was stable for up to 6 months after fabrication and required 30 times lower dye and sample volumes as compared to the analogous solution-based sensor. Therefore, this approach opens avenues to a state-of-the-art diagnostic tool that can be potentially translated into clinical applications in low-resource environments.

Multiplexed Analysis of Photochemical Oxidants Using a Nanoparticle-Based Optoelectronic Nose

Photochemical pollutants pose a substantial threat to human health in both outdoor and indoor environments. Herein, we prepare a class of gold nanoparticle-based colorimetric sensor arrays on optimized hydrophobic substrates using a simple pin-printing method for accurate identification and quantification of various gas-phase oxidants, as these microdetectors are low cost, sensitive, and easy to fabricate. For an array of AuNP sensors modified with various thiol-terminated ligands, a unique and distinguishable change in color (i.e., red, green, and blue response patterns) was obtained for each specific pollutant for molecular fingerprinting. Remarkable discrimination among 15 gases at a fairly low vapor concentration (i.e., 500 ppb) was illustrated using standard chemometric methods. Using digital imaging, the AuNP colorimetric sensor array offers ultrasensitive dosimetric identification of gas-phase oxidants relevant to outdoor and indoor air pollution, with limits of detection generally at sub-ppb levels for 2 h measurement. As a practical application, the sensor array is able to predict the overall air quality in indoor office environments over 24 h. Such sensor array based on chemically induced sintering of nanoparticles has significant implications for the development of nanosensors used in continuous monitoring of potential airborne pollutants at low concentrations from a large number of locations in a cost-effective manner.

Pattern-based colorimetric sensor array to monitor food spoilage using automated high-throughput analysis

Despite the existing rapid and reliable analytical methods for determining biogenic amine in food matrices, recently special efforts have been devoted for development of portable and inexpensive devices for discrimination of biogenic amines (BAs) in food products to achieve onsite detection of food-spoilage. Thus, in this context, a field deployable cross-reactive sensor array and a field-portable array reader has been developed for determination of food quality. The sensor array consisting of metal complexes (C1 - C11) of single azophenol dye-based receptor generated a unique visible response on interaction with different amines (A1 - A7). Further, the colorimetric pattern and discrimination efficacy of the sensor array was evaluated using multivariate statistical techniques such as principal component analysis and linear discriminant analysis. Motivated by outstanding discriminatory power of sensor array, titration experiment was performed with BAs, and colorimetric response of array was linearly corelated to concentrations of BAs such as tryptamine and spermine with R² values of 0.9596 and 0.967 respectively. Finally, for practical utility and the field analysis, a portable reader was developed and utilized for quantification of biogenic amines in meat and cottage cheese samples spiked with spermine and tryptamine up to the concentrations of 40 μM; therefore, apparently proving the potential applicability of the designed sensing method for food quality monitoring.

Disposable Paper-Based Biosensors for the Point-of-Care Detection of Hazardous Contaminations-A Review

The fast detection of trace amounts of hazardous contaminations can prevent serious damage to the environment. Paper-based sensors offer a new perspective on the world of analytical methods, overcoming previous limitations by fabricating a simple device with valuable benefits such as flexibility, biocompatibility, disposability, biodegradability, easy operation, large surface-to-volume ratio, and cost-effectiveness. Depending on the performance type, the device can be used to analyze the analyte in the liquid or vapor phase. For liquid samples, various structures (including a dipstick, as well as microfluidic and lateral flow) have been constructed. Paper-based 3D sensors are prepared by gluing and folding different layers of a piece of paper, being more user-friendly, due to the combination of several preparation methods, the integration of different sensor elements, and the connection between two methods of detection in a small set. Paper sensors can be used in chromatographic, electrochemical, and colorimetric processes, depending on the type of transducer. Additionally, in recent years, the applicability of these sensors has been investigated in various applications, such as food and water quality, environmental monitoring, disease diagnosis, and medical sciences. Here, we review the development (from 2010 to 2021) of paper methods in the field of the detection and determination of toxic substances.