Optical sensor arrays for chemical sensing: the optoelectronic nose

Investigation of Polymers as Matrix Materials for Application in Colorimetric Gas Sensors for the Detection of Ammonia

Colorimetric gas sensors are based on a color changing reaction of a sensor dye upon exposure to an analyte. For most sensor applications, the sensor dye must be immobilized in a sensor matrix. The choice of matrix significantly influences the dye's response due to different physical and chemical effects. Ideal matrix materials should be transparent, stable, compatible with the sensor dye, and processable. Polymers are often applied as matrix materials, as they can be easily applied to sensor structures. In this study, we present a method to examine the impact of polymers of different structures and functionalities on sensor dyes. Therefore, 18 polymers are studied in combination with the pH indicator bromocresol green regarding their sensitivity to ammonia. The measurement setup is based on a camera as a detector of the color changing reaction of the sensor materials and allows for the simultaneous measurement of the sensor materials. Furthermore, the response and regeneration time, the stability, and the influence of the environmental parameters humidity and temperature on the colorimetric reaction are investigated. The study demonstrates that polymers as sensor matrices have an influence on the response of sensor dyes, due to their different properties, such as polarity. This has to be considered when choosing a suitable sensor matrix.

Quantum chemical elucidation of the luminescence mechanism in a europium(III) co-doped UiO-66 chemosensor selective to mercury(II)

Lanthanide(III) ions can be incorporated into metal-organic frameworks (MOFs) to form Ln@MOFs through post-synthetic procedures. This makes the MOFs efficient luminescent chemical sensors for detecting trace amounts of heavy metals. In this report, a quantum chemical theoretical protocol has been carried out to elucidate the detection principle of the turn-off luminescence mechanism in a Eu@UiO-66(DPA)-type MOF selective to Hg2+ ions. UiO-66(DPA) is an iso-reticular MOF of UiO-66 constructed from the Zr6-cluster [Zr6(μ3-O)4(μ3-OH)4]12+ and the ligands 1,4-benzenedicarboxylate (BDC) and 2,6-pyridinedicarboxylate (DPA) as linkers. The sensitization and energy transfer (ET) in UiO-66(DPA) doped with Eu3+ were analyzed using multireference ab initio CASSCF/NEVPT2 methods and time-dependent density functional theory (TD-DFT). The cluster model used in the calculations comprises the Z6-cluster/BDC/DPA fragments with the DPA ligand coordinating to Eu3+ or Hg2+ ions. The proposed sensitization pathway involves intersystem crossing from S1(DPA) to T1(DPA), a plausible subsequent energy transfer from T1(DPA) to the 5D1 state of Eu3+, and then vibrational relaxation to the emissive 5D0 state. These results also suggest that the electronic states of the BDC ligand can be strengthened by the population of the T1 electronic states of the DPA antenna via ET. Periodic DFT calculations confirm the electronic state mixture of BDC and DPA linkers in the conduction bands, just above the electronic state of Eu3+ ions, which is in concordance with the proposed Eu3+ sensitization pathways. The assessed optical properties (absorption and emission) of Hg2+@UIO-66(DPA) explain the experimental behavior of this chemosensor when the Hg2+ ion replaces the Eu3+ ion and the luminescence diminishes.

Janus Membrane-Based Wearable Dual-Channel SERS Sensor for Sweat Collection and Monitoring of Lactic Acid and pH Levels

Sweat, as a metabolic byproduct, encompasses a diverse array of molecular information pertinent to our physiological states and overall health. The extraction of minute quantities of sweat, coupled with sensitive monitoring and identification of its internal molecular components, constitutes an effective strategy for assessing bodily conditions. We engineer a Janus membrane utilizing electrospinning techniques for application on human skin to facilitate sweat collection. The hydrophilic layer efficiently extracts trace amounts of sweat secreted by the skin, while the hydrophobic layer maintains a dry contact interface, thereby enhancing the user's comfort during wear. Furthermore, we integrated functional self-assembled nanoparticle layers onto the hydrophilic surface to develop a dual-channel high-sensitivity sensor array capable of measuring pH and lactic acid concentrations. This system adeptly distinguishes between anaerobic exercise and calm state scenarios concerning pH and lactic acid levels.

A smart probe for detection of sugar markers for applications in gastrointestinal barrier dysfunction

Gastrointestinal (GI) barrier dysfunction is an early pathogenic event in many complex diseases. Despite the routine applications of invasive tests, saccharide molecules are used noninvasively for assessing GI tract mucosal barrier function. However, currently available methods for quantification of saccharides molecules are costly and laborious. Simplified, reliable, and high-throughput methods are desired so that GI permeability testing can become routine and widely used. Here, we have developed a one-component system comprising of a naphthyl-pyridine core coupled to a boronic acid receptor, which can be used for early detection of saccharide biomarkers (i.e., lactulose) for applications related to GI barrier dysfunction. For quantitation of lactulose as a model biomarker, we have designed gold nanoparticle decorated surfaces in a highly scalable 96-well format to enable sensitive testing of lactulose within a broad range of concentrations. To tackle current challenges in saccharide biomarker sensing, we developed a hybrid sensing principle integrating two optical modalities (plasmonics and fluorescence) with a synthetic smart-probe (naphthyl-pyridinium) for monitoring GI permeability. This technology can be further developed as an affordable and portable diagnostic tool for GI permeability screening for routine use, facilitating early detection of various diseases affecting the GI tract.

Metal-organic frameworks-based nanozyme sensor array for the discrimination of biogenic amines and detection of histamine

Biogenic amines (BAs) are hazardous substances found in fishery products that are closely associated with fish spoilage and threaten food safety. Traditional chromatographic methods for detecting BAs are expensive, complex, and time-consuming. In this study, we developed a nanozyme-based sensor array to efficiently discriminate between four types of BAs and sensitively detect histamine. Copper-, cerium-, and manganese-based metal-organic frameworks with excellent peroxidase-like activities were employed as sensor elements. Because the catalytic activities of metal-organic frameworks could be modified by different BAs to varying degrees, the sensor array could generate a distinct colorimetric response pattern (fingerprint) for each BA. Based on this principle, the sensor array accurately discriminated BAs over a wide concentration range (10-1000 μM). Histamine could be distinguished down to 1 μM and detected with a detection limit of 4.28 μM within 20 min. In addition, mixtures of BAs, target BAs, interfering substances, and BAs in fishery product samples were well discriminated. Furthermore, our sensor array could also effectively distinguish the freshness of fish samples. This work might offer a useful strategy for the discrimination and detection of BAs and could positively contribute to food safety and public health.

Integrating Humidity-Resistant and Colorimetric COF-on-MOF Sensors with Artificial Intelligence Assisted Data Analysis for Visualization of Volatile Organic Compounds Sensing

Direct visualization and monitoring of volatile organic compounds (VOCs) sensing processes via portable colorimetric sensors are highly desired but challenging targets. The key challenge resides in the development of efficient sensing systems with high sensitivity, selectivity, humidity resistance, and profuse color change. Herein, a strategy is reported for the direct visualization of VOCs sensing by mimicking human olfactory function and integrating colorimetric COF-on-MOF sensors with artificial intelligence (AI)-assisted data analysis techniques. The Dye@Zeolitic Imidazolate Framework@Covalent Organic Framework (Dye@ZIF-8@COF) sensor takes advantage of the highly porous structure of MOF core and hydrophobic nature of the COF shell, enabling highly sensitive colorimetric sensing of trace number of VOCs. The Dye@ZIF-8@COF sensor exhibits exceptional sensitivity to VOCs at sub-parts per million levels and demonstrates excellent humidity resistance (under 20-90% relative humidity), showing great promise for practical applications. Importantly, AI-assisted information fusion and perceptual analysis greatly promote the accuracy of the VOCs sensing processes, enabling direct visualization and classification of seven stages of matcha drying processes with a superior accuracy of 95.74%. This work paves the way for the direct visualization of sensing processes of VOCs via the integration of advanced humidity-resistant sensing materials and AI-assisted data analyzing techniques.

Impact of Molecule-Molecule Interactions When Discerning Low-Concentration Hazardous Gas Mixtures

Gas sensor arrays are versatile and powerful tools for gas detection and analysis, enabling a wide range of applications across numerous industries. Critically, the accuracy and reliability of these arrays depend on the distinct gas sensing behavior or selectivity of the individual component gas sensors. However, studies of such arrays often consider only overly idealized scenarios, and the interaction between gas molecules is not typically considered in such studies. Here, based on first-principles calculations and direct experimental demonstrations, we show that interactions between gas molecules at the surface can play a significant role. We found that NO2 and NH3 molecules can be expected to align together to form dimers due to the strong interaction between NH3 and NO2 at the Fermi level, which enhances the adsorption capability and sensitivity of MoS2. Compared with the gas sensing performance of MoS2 for either NO2 or NH3 alone, a faster response is observed for sensing the NO2 and NH3 gas mixtures. Enhanced sensitivity, however, is achieved only at lower carrier densities with an appropriate concentration ratio between NO2 and NH3. These results not only provide evidence of the pronounced effect of gas molecule interactions but also suggest an approach for discerning gases.

Combinatorial Library-Screened Array for Rapid Clinical Sperm Quality Assessment

Nonspecific interactions are ubiquitous and important in biology; however, they are rarely valued or employed in the field of clinical disease diagnosis. Relying on nonspecific cross-interactions, sensor arrays have shown great potential in distinguishing mixtures or nuanced compounds. However, developing generic strategies for constructing effective arrays tailored to compositionally complicated and highly individualized clinical biospecimens remains challenging. Here, we introduce a combinatorial chemistry-screened array strategy, leveraging four-component Ugi reactions to achieve the rapid synthesis of hundreds of structurally diverse sensing elements. Moreover, effective sensor arrays can thus be built by rapidly screening sensors against diverse analytes. Next, we demonstrate the practical applicability of this array by clinical sperm quality assessment, given the current lack of well-established clinical rapid detection techniques. A library of 192 structurally diverse sensor elements was synthesized. Following screening, a pruned 14-element array was successfully constructed, achieving 94.2% accuracy in distinguishing between healthy individuals and four types of abnormal sperm samples from patients within 1 min. This universal strategy avoids complex sensor design and greatly improves the efficiency of sensor array construction, offering a new way of designing effective arrays.

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

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

Frontiers in laccase nanozymes-enabled colorimetric sensing: A review

In recent years, nanozyme-based analytics have become popular. Among these, laccase nanozyme-based colorimetric sensors have emerged as simple and rapid colorimetric detection methods for various analytes, effectively addressing natural enzymes' stability and high-cost limitations. Laccase nanozymes are nanomaterials that exhibit inherent laccase enzyme-like activity. They can oxidize phenolic compounds to generate a coloured product, independently or with a chromogenic agent. This chromogenic reaction provides the basis for developing simple and robust colorimetric assays for various analytes, enabling rapid and point-of-need analytical decision-making in food safety, clinical diagnostics, and environmental monitoring. This review article provides a concise overview of laccase nanozymes, including their classification and catalytic mechanisms. The article mainly discusses colorimetric and dual-mode detection methods and outlines various strategies to enhance the colorimetric sensing performance of laccase nanozymes. Additionally, the article highlights future research directions that could further improve laccase nanozyme-enabled colorimetric sensing. We hope this work will enhance the field's understanding and help future researchers identify gaps in developing simple, low-cost colorimetric sensors.

Advances and opportunities of hydrogel-based artificial olfactory colorimetric systems for food safety detection: A review

BACKGROUND

Food safety has attracted increasing attention in recent years. Harmful gases often produced during food storage have devastating effects on human health and ecosystems, and identifying and detecting them is essential. To date, many traditional methods have been used to monitor the freshness of food products. These methods have high precision and sensitivity, but their application to daily life is limited because they are time-consuming and require complex instruments. Therefore, the development of simple, rapid, and low-cost methods for evaluating food freshness is essential.

RESULTS

Hydrogel-based colorimetric artificial olfactory systems have attracted a great deal of attention and have been widely applied in the field of food safety testing in recent years. The system has the advantages of a low detection limit, portability, onsite monitoring, high precision and rapid detection. This superiority makes the colorimetric hydrogel gas sensor a strong contender for improving food safety detection. This paper reviews the application of a hydrogel-based colorimetric artificial olfactory system for detecting hazardous gases for food safety purposes. The main sections of this review succinctly discuss the construction principles and operational mechanisms of the colorimetric artificial olfactory system. Additionally, it delves into the selection and preparation methodologies of hydrogel materials. This paper focuses on advancements in hydrogel-based colorimetric artificial olfactory systems for detecting gases, such as ammonia, biogenic amines, and H2S that are relevant to food safety. Furthermore, this work outlines the challenges encountered by this artificial olfactory system and proposes potential solutions to overcome them.

SIGNIFICANCE AND NOVELTY

The study presented in this paper demonstrates the potential application of a hydrogel-based colorimetric artificial olfactory system for food safety detection, and complements the previous review of colorimetric hydrogel gas sensors for the detection of spoilage gases in food products and serves as a reference for further advanced research and applications in this field.

Use of a Novel Passive E-Nose to Monitor Fermentable Prebiotic Fiber Consumption

We developed a home-based electronic nose (E-Nose) to passively monitor volatile organic compounds (VOCs) emitted following bowel movements and assessed its validity by correlating the output with prebiotic fiber intake. Healthy, non-overweight participants followed a three-week protocol which included the following: (1) installing the E-Nose in their bathroom; (2) activating the device following each bowel movement; (3) recording their dietary intake; (4) consuming a fiber bar (RiteCarbs) containing a blend of 10 g of prebiotic fiber daily during weeks two and three; and (5) submit stool specimens at the beginning and end of the study for 16S rRNA gene sequencing and analysis. Participants' fecal microbiome displayed significantly increased relative abundance of putative total SCFA-producing genera (p = 0.0323) [total acetate-producing genera (p = 0.0214), total butyrate-producing genera (p = 0.0131)] and decreased Gram-negative proinflammatory genera (p = 0.0468). Prebiotic intervention significantly increased the participants' fiber intake (p = 0.0152), E-Nose Min/Max (p = 0.0339), and area over the curve in VOC-to-fiber output (p = 0.0044). Increased fiber intake was negatively associated (R2 = 0.53, p = 0.026) with decreased relative abundance of putative Gram-negative proinflammatory genera. This proof-of-concept study demonstrates that a prototype E-Nose can noninvasively detect a direct connection between fiber intake and VOC outputs in a home-based environment.

Optofluidic paper-based analytical device for discriminative detection of organic substances via digital color coding

Developing a portable yet affordable method for the discrimination of chemical substances with good sensitivity and selectivity is essential for on-site visual detection of unknown substances. Herein, we propose an optofluidic paper-based analytical device (PAD) that consists of a macromolecule-driven flow (MDF) gate and photonic crystal (PhC) coding units, enabling portable and scalable detection and discrimination of various organic chemical, mimicking the olfactory system. The MDF gate is designed for precise flow control of liquid analytes, which depends on intermolecular interactions between the polymer at the MDF gate and the liquid analytes. Subsequently, the PhC coding unit allows for visualizing the result obtained from the MDF gate and generating differential optical patterns. We fabricate an optofluidic PAD by integrating two coding units into a three-dimensional (3D) microfluidic paper within a 3D-printed cartridge. The optofluidic PADs clearly distinguish 11 organic chemicals with digital readout of pattern recognition from colorimetric signals. We believe that our optofluidic coding strategy mimicking the olfactory system opens up a wide range of potential applications in colorimetric monitoring of chemicals observed in environment.

Multiplexed bacterial recognition based on "All-in-One" semiconducting polymer dots sensor and machine learning

The accurate discrimination of bacterial infection is imperative for precise clinical diagnosis and treatment. Here, this work presents a simplified sensor array utilizing "All-in-One" Pdots for efficient discrimination of diverse bacterial samples. The "All-in-One" Pdots sensor (AOPS) were synthesized using three components that exhibit fluorescence resonance energy transfer (FRET) effect, facilitating the efficient integration of multiple discrimination channels to generate specific fluorescence response patterns through a single detection under single-wavelength excitation. Additionally, machine learning techniques were employed to visually represent the fluorescence response patterns of AOPS upon exposure to bacterial metabolites derived from diverse bacterial species. The as-prepared sensor platform demonstrated excellent performance in analyzing eight common bacteria, drug-resistant strains, mixed bacterial samples, bacterial biofilms and real samples, presenting significant potential in the identification of complex samples for bacterial analysis.

Coordination Chemistry of Mixed-Donor Pyridine-Containing Macrocyclic Ligands: From Optical to Redox Chemosensors for Heavy Metal Ions

2,8-Dithia-5-aza-2,6-pyridinophane (L1) has been used as a receptor unit in the construction of the conjugated redox chemosensor 5-ferrocenylmethyl-2,8-dithia-5-aza-2,6-pyridinophane (L3). In order to further explore the coordination chemistry of L1, and comparatively, that of its structural analogue 2,11-dithia-5,8-diaza-2,6-pyridinophane (L2), featuring two secondary nitrogen atoms in the macrocyclic unit, the crystal structures of the new synthesised complexes [Pb(L1)(ClO4)2]·½CH3CN, [Cu(L2)](ClO4)2·CH3CN and [Cd(L2)(NO3)]NO3 were determined by X-ray diffraction analysis. The electrochemical response of L3 towards the metal ions Cu2+, Zn2+, Cd2+, Hg2+, and Pb2+ was investigated by cyclic voltammetry (CV) in CH2Cl2/CH3CN 0.25:1 (v/v) mixture. Upon addition to L3 of increasing amounts of the aforementioned metal cations, the wave corresponding to the Fc+/Fc redox couple of the un-complexed L3 was gradually replaced by a new reversible wave at more positive potentials and corresponding to the Fc+/Fc redox couple of the complexed ligand. The maximum anodic shift of the ferrocene oxidation wave is observed in the presence of Pb2+ (230 mV), to which corresponds a reaction coupling efficiency (RCE) value as large as 7.9 × 103. The response selectivity of L3 is discussed in reference to the optical selectivity observed for conjugated chemosensors featuring L1 as receptor unit and different fluorogenic fragments as signalling units.

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

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

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.

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

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

A substituent-modified new salicylaldehyde-diphenyl-azine based AIEgen: A promising skeleton for copper ion sensor

In this study, we have reported a novel 4-bromo-salicylaldehyde-diphenyl-azine (B-1), a new member of salicylaldehyde-diphenyl-azine (SDPA) family known for its excellent sensing properties. In contrast to the previously reported AIEgens, we found that the bromo-substitution at the 4th position of the salicylaldehyde moiety blue-shifted the emission by 10 and 15 nm as compared to the unsubstituted (Tong et.al 2017) and Bromo at the 5th position (Jain et.al 2023) respectively. Moreover, B-1 crystallizes instantly as the cooling process starts, which was not observed in the previously reported scaffolds. The sensing investigation again demonstrated the precise and ultrasensitive behavior of B-1 for copper ions. B-1 has a very low LOD value i.e. 29.2 x 10-8 M with a high association constant and binds with copper ion in 2:1 mode. This time we also analyzed the practical applicability in the solid phase using cotton swabs and performed the real-time estimation of copper ions in water and biological samples like urine and blood serum. The excellent percentage recovery and the RSD value suggest the precision of the experiments. Further, we also perform the sensing in living cancer HeLa cells. Altogether, we found that the SDPA skeleton is precise and ultrasensitive for copper ions and versatile which can be used variously to detect copper ions in the real world. This research will surely help in developing new specific skeleton-based AIEgens with desirable emission properties and precise applications in the future.

Cu2Cl(OH)3 nanozyme-based colorimetric sensor array for phosphates discrimination and disease identification

The identification of phosphates holds significant importance in many physiological processes and disease diagnosis, and traditional detection techniques struggle to simultaneously detect and distinguish phosphates. The complexity of synthesizing sensing units restricts the construction of sensor arrays as well. In this study, a bifunctional dicopper chloride trihydroxide (Cu2Cl(OH)3) nanozyme with conspicuous laccase- and peroxidase-like activities has been synthesized in basic deep eutectic solvents (DES). Exploiting the various regulatory impacts of multiple phosphates on the dual-enzyme mimicking activities, the sensor array based on the laccase mimic and peroxidase mimic properties of Cu2Cl(OH)3 was designed, which has been successfully harnessed for the identification of eight phosphates (ATP, ADP, AMP, PPi, Pi, GTP, GDP, and GMP). This approach streamlines the creation of sensor arrays. Besides, the three simulated actual samples (healthy individuals, moderately ill patients, and severely ill patients) have been accurately distinguished. This work makes a substantial contribution to enhancing the highly effective construction of array channels and promoting discrimination of phosphates in intricate samples.

Recent advances in photoluminescent fluorescent probe technology for food flavor compounds analysis

The real-time, precise qualitative and quantitative sensing of food flavor compounds is crucial for ensuring food safety, quality, and consumer acceptance. As indicators for food flavor labeling, it is vital to delve deep into the specific ingredient and content of food flavor compounds to assess the food flavor quality, but still facing huge challenges. Photoluminescent fluorescent probe technology, with fast detection and high sensitivity, has shown immense potentials in detecting food flavor compounds. In this review, the classification and optical sensing mechanism of photoluminescent fluorescent probe technology are described in detail. Besides, challenges in applying photoluminescent fluorescent probe technology to analyze food flavor compounds are outlined to indicate future research directions. We hope this review can provide an insight for the applications of photoluminescent fluorescent probe technology in the evaluation of food flavor quality in future.

Exploring the synergistic effect of aggregation and hydrogen bonding: a fluorescent probe for dual sensing of phytic acid and uric acid

We synthesized an unoxidized bis-indolyl methane (BIM) derivative (probe 1) comprising of tetraphenylethylene (TPE) as the signalling moiety. The amphiphilic probe could form self-assembled nanoscopic aggregates in the aqueous medium. The emission of 1 in non-polar solvents originates from the LE state, while in polar solvents, it is dominated by TICT. Moreover, probe 1 exhibited a 'turn-on' fluorescence response for both uric acid (with a blue shift in emission maxima) and phytic acid (with a red shift in emission maxima). Therefore, the present system provides an exceptional opportunity to distinguish between phytic acid and uric acid by considering two different emission channels. Mechanistic investigations revealed that both H-bonding and electrostatic interactions between the probe and analytes could effectively cause restricted intramolecular rotations, leading to a turn-on response. Additionally, in the case of phytic acid, larger aggregates were observed with prominent CT characteristics. The change in the extent of charge transfer interaction in the formed adducts resulted in distinct fluorescence responses with phytic acid and uric acid. Furthermore, we explored the applicability of the present system in the screening of real-life samples, such as uric acid in urine samples and phytic acid in grains. The LOD for phytic acid and uric acid was found to be ∼5.48 nM and 10.4 nM, respectively. The quantitative nature of the system was confirmed, showing promising results in terms of recovery values (between 95.6% and 104.2%) and detection limits. Additionally, we also employed handy paper strips for the on-site monitoring of phytic acid and uric acid, thereby eliminating the need for complex instrumentation or trained technicians.

Small molecule modulation of protein corona for deep plasma proteome profiling

The protein corona formed on nanoparticles (NPs) has potential as a valuable diagnostic tool for improving plasma proteome coverage. Here, we show that spiking small molecules, including metabolites, lipids, vitamins, and nutrients into plasma can induce diverse protein corona patterns on otherwise identical NPs, significantly enhancing the depth of plasma proteome profiling. The protein coronas on polystyrene NPs when exposed to plasma treated with an array of small molecules allows for the detection of 1793 proteins marking an 8.25-fold increase in the number of quantified proteins compared to plasma alone (218 proteins) and a 2.63-fold increase relative to the untreated protein corona (681 proteins). Furthermore, we discovered that adding 1000 µg/ml phosphatidylcholine could singularly enable the detection of 897 proteins. At this specific concentration, phosphatidylcholine selectively depletes the four most abundant plasma proteins, including albumin, thus reducing the dynamic range of plasma proteome and enabling the detection of proteins with lower abundance. Employing an optimized data-independent acquisition approach, the inclusion of phosphatidylcholine leads to the detection of 1436 proteins in a single plasma sample. Our molecular dynamics results reveal that phosphatidylcholine interacts with albumin via hydrophobic interactions, H-bonds, and water bridges. The addition of phosphatidylcholine also enables the detection of 337 additional proteoforms compared to untreated protein corona using a top-down proteomics approach. Given the critical role of plasma proteomics in biomarker discovery and disease monitoring, we anticipate the widespread adoption of this methodology for the identification and clinical translation of biomarkers.

Smart Gas Sensors: Recent Developments and Future Prospective

Gas sensor is an indispensable part of modern society with wide applications in environmental monitoring, healthcare, food industry, public safety, etc. With the development of sensor technology, wireless communication, smart monitoring terminal, cloud storage/computing technology, and artificial intelligence, smart gas sensors represent the future of gas sensing due to their merits of real-time multifunctional monitoring, early warning function, and intelligent and automated feature. Various electronic and optoelectronic gas sensors have been developed for high-performance smart gas analysis. With the development of smart terminals and the maturity of integrated technology, flexible and wearable gas sensors play an increasing role in gas analysis. This review highlights recent advances of smart gas sensors in diverse applications. The structural components and fundamental principles of electronic and optoelectronic gas sensors are described, and flexible and wearable gas sensor devices are highlighted. Moreover, sensor array with artificial intelligence algorithms and smart gas sensors in "Internet of Things" paradigm are introduced. Finally, the challenges and perspectives of smart gas sensors are discussed regarding the future need of gas sensors for smart city and healthy living.

Colorimetric sensor array for identifying antioxidants based on pyrolysis-free synthesis of Fe-N/C single-atom nanozymes

Iron-anchored nitrogen/doped carbon single-atom nanozymes (Fe-N/C), which possess homogeneous active sites and adjustable catalytic environment, represent an exemplary model for investigating the structure-function relationship and catalytic activity. However, the development of pyrolysis-free synthesis technique for Fe-N/C with adjustable enzyme-mimicking activity still presents a significant challenge. Herein, Fe-N/C anchored three carrier morphologies were created via a pyrolysis-free approach by covalent organic polymers. The peroxidase-like activity of these Fe-N/C nanozymes was regulated via the pores of the anchored carrier, resulting in varying electron transfer efficiency due to disparities in contact efficacy between substrates and catalytic sites within diverse microenvironments. Additionally, a colorimetric sensor array for identifying antioxidants was developed: (1) the Fe-N/C catalytically oxidized two substrates TMB and ABTS, respectively; (2) the development of a colorimetric sensor array utilizing oxTMB and oxABTS as sensing channels enabled accurate discrimination of antioxidants such as ascorbic acid (AsA), glutathione (GSH), cysteine (Cys), gallic acid (GA), and caffeic acid (CA). Subsequently, the sensor array underwent rigorous testing to validate its performance, including assessment of antioxidant mixtures and individual antioxidants at varying concentrations, as well as target antioxidants and interfering substances. In general, the present study offered valuable insights into the active origin and rational design of nanozyme materials, and highlighting their potential applications in food analysis.

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.

Olfactory Visualization Sensing Array Made with CelluMOFs to Predict Fruit Ripeness Using Deep Learning

Developing a colorimetry-based artificial scent screening system (i.e., an olfactory visual sensing system) with high sensitivity and accurate pattern recognition for detecting fruit ripeness remains challenging. In this work, we construct a flexible dye/CelluMOFs-based sensor array with improved sensitivity for on-site detection of characteristic gases of fruits and integrate a densely connected convolutional network (DenseNet) into the sensor array, enabling it to recognize unique scent fingerprints and categorize the ripeness of fruits. In the system, CelluMOFs are synthesized through in situ growth of γ-cyclodextrin metal-organic frameworks (γ-CD-MOFs) on flexible fiber filter paper to fabricate a uniform, flexible and porous dye/CelluMOFs sensitive membrane. Compared to the pristine filter paper, the CelluMOFs exhibit increased porosity with a 62 times higher specific surface area and a 3-fold increase in dye loading capacity after 12 h of adsorption. The prepared dye/CelluMOFs sensing film shows outstanding mechanical and detection stability with negligible deviation after 100 cycles of rubbing. The colorimetric visualization arrays with multiple colorimetric dye/CelluMOFs chips, enable the sensitive recognition and detection of nine kinds of characteristic fruit odors and achieve a high response at 8-1500 ppm of trans-2-hexenal, showcasing remarkably low gas detection thresholds. On the basis of the ppm-level limit of detection with high sensitivity, the fabricated colorimetric sensor arrays are typically used for in situ assessment of fruit ripeness by integrating DenseNet. This approach achieves a satisfactory classification accuracy of 99.09% on the validation set, enabling high-precision prediction of fruit ripeness levels.

A simple Ag-MoS2 hybrid nanozyme-based sensor array for colorimetric identification of biothiols and cancer cells

The intracellular levels of biothiols are associated with various diseases including cancer, and biothiols are regarded as tumor biomarker. Due to the similarity of the molecular structure of biothiols, the development of simple, rapid, efficient, and sensitive colorimetric sensor arrays holds great promise for clinical cancer diagnosis. Here, we developed a simple Ag-MoS2 hybrid nanozyme-based sensor array for colorimetric identification of biothiols and cancer cells. The novel Ag-MoS2 nanoprobe was synthesized in a simple and efficient way through the in situ self-reduction reaction between MoS2 and noble metal precursor. Benefiting from to the formation of heterogeneous metal structures, the peroxidase (POD)-like catalytic activity of the synthesized Ag-MoS2 hybrid nanocomposites is significantly enhanced compared to MoS2 alone. Moreover, the catalytic activity of Ag-MoS2 nanozyme was correlated with the pH of the reaction solution and the inhibitory effects of the three biothiols on the nanozyme-triggered chromogenic system differed in the specific pH environments. Therefore, each sensing unit of this electronic tongue generated differential colorimetric fingerprints of different biothiols. After principal component analysis (PCA), the developed novel colorimetric sensor array can accurately discriminate biothiols between different types, various concentrations, and different mixture proportions. Further, the sensor array was used for the colorimetric identification of real serum and cellular samples, demonstrating its great potential in tumor diagnostic applications.

The Performance of Partial Least Squares Methods in Virtual Nanosensor Array-Multiple Metal Ions Sensing Based on Multispectral Fluorescence of Quantum Dots

The design of chemical sensors and probes is usually based on selective receptors for individual analytes, however, many analytical tasks are dedicated to multi-analyte sensing or recognizing properties of the sample related to more than one analyte. While it is possible to simultaneously use multiple sensors/receptors in such cases, multi-responsive probes could be an attractive alternative. In this work, we use thiomalic acid-capped CdTe quantum dots as a multiple-response receptor for the detection and quantification of six heavy metal cations: Ag(I), Cd(II), Co(II), Cu(II), Ni(II), and Pb(II) at micromolar concentration levels. Multiplexing is realized via multispectral fluorescence (so-called virtual sensor array). For such a sensing strategy, the effective decoding of the excitation-emission spectrum is essential. Herein, we show how various parameters of chemometric analysis by the Partial Least Squares method, such as preprocessing type and data structure, influence the performance of discrimination and quantification of the heavy metals. The established models are characterized by respective performance metrics (accuracy, sensitivity, precision, specificity/RMSE, a, b, R2) determined for both train and test sets in replicates, to obtain reliable and repeatable results.

Identification of Pristine and Protein Corona Coated Micro- and Nanoplastic Particles with a Colorimetric Sensor Array

A colorimetric sensor array has been developed to differentiate various micro- and nanoplastic particles (MNPs), both pristine and those coated with a protein corona, in buffered water. This array utilizes five distinct cross-reactive chemo-responsive dyes, which exhibit changes in visible optical absorbance upon interaction with MNPs. Although no single dye responds exclusively to either pristine or protein-corona-coated MNPs, the collective shifts in color across all dyes create a unique molecular fingerprint for each type of MNP. This method demonstrates high sensitivity, capable of detecting MNPs of various sizes (50 nm, 100 nm, and 2 μm) and differentiating them from controls at concentrations as low as 10 ng/mL using standard chemometric techniques, ensuring accurate results without error. Additionally, the method can effectively distinguish between pristine and protein-corona-coated polystyrene MNPs. This colorimetric approach offers a rapid, cost-effective, and accurate method for monitoring MNP pollution and assessing their prior interactions with biological systems.

2-hydroxy-1- Naphthaldehyde Based Colorimetric Probe for the Simultaneous Detection of Bivalent Copper and Nickel with High Sensitivity and Selectivity

A neoteric colorimetric probe based on 2-hydroxy-1-naphthaldehyde (PMB3) was designed and synthesized for the real-time as well as on-site naked-eye detection of Cu2+/Ni2+ ions. Various physicochemical methods were employed to characterize the probe, and its colorimetric response to different metal ions was meticulously investigated. The probe, PMB3, exhibited a sensitive colorimetric response to Cu2+/Ni2+ ions among other competing metal ions, culminating in a prominent colour change from colourless to yellow. The stoichiometry of the ligand metal complexes was ascertained to be in a 1:1 ratio using Job's plot analysis, which was further corroborated by ESI-MS data. With detection limits of 4.56 µM for Cu2+ and 2.68 µM for Ni2+, the method was effectively extended to real sample analysis, ensuring propitious results that closely aligned with the actual values.

Fast and accurate identification of pathogenic bacteria using excitation-emission spectroscopy and machine learning

Fast and reliable identification of pathogenic bacteria is of upmost importance to human health and safety. Methods that are currently used in clinical practice are often time consuming, require expensive equipment, trained personnel, and therefore have limited applications in low resource environments. Molecular identification methods address some of these shortcomings. At the same time, they often use antibodies, their fragments, or other biomolecules as recognition units, which makes such tests specific to a particular target. In contrast, array-based methods use a combination of reporters that are not specific to a single pathogen. These methods provide a more data-rich and universal response that can be used for identification of a variety of bacteria of interest. In this report, we demonstrate the application of the excitation-emission spectroscopy of an environmentally sensitive fluorescent dye for identification of pathogenic bacterial species. 2-(4'-Dimethylamino)-3-hydroxyflavone (DMAF) interacts with the bacterial cell envelope resulting in a distinct spectral response that is unique to each bacterial species. The dynamics of dye-bacteria interaction were thoroughly investigated, and the limits of detection and identification were determined. Neural network classification algorithm was used for pattern recognition analysis and classification of spectral data. The sensor successfully discriminated between eight representative pathogenic bacteria, achieving a classification accuracy of 85.8% at the species level and 98.3% at the Gram status level. The proposed method based on excitation-emission spectroscopy of an environmentally sensitive fluorescent dye is a powerful and versatile diagnostic tool with high accuracy in identification of bacterial pathogens.

A novel immunoassay technique using principal component analysis for enhanced detection of emerging viral variants

Rapid diagnostics are critical infectious disease tools that are designed to detect a known biomarker using antibodies specific to that biomarker. However, a way to detect unknown disease variants has not yet been achieved in a paper test format. We describe here a route to make an adaptable paper immunoassay that can detect an unknown biomarker, demonstrating it on SARS-CoV-2 variants. The immunoassay repurposes cross reactive antibodies raised against the alpha variant. Gold nanoparticles of two different colors conjugated to two different antibodies create a colorimetric signal, and machine learning of the resulting colorimetric pattern is used to train the assay to discriminate between variants of alpha and Omicron BA.5. By using principal component analysis, the colorimetric test patterns can pick up and discriminate an unknown variant that it has not encountered before, Omicron BA.1. The test has an accuracy of 100% and a potential calculated discriminatory power of 900. We show that it can be used adaptively and that it can be used to pick up emerging variants without the need to raise new antibodies.

Multioxide combinatorial libraries: fusing synthetic approaches and additive technologies for highly orthogonal electronic noses

This study evaluates the performance advancement of electronic noses, on-chip engineered multisensor systems, exploiting a combinatorial approach. We analyze a spectrum of metal oxide semiconductor materials produced by individual methods of liquid-phase synthesis and a combination of chemical deposition and sol-gel methods with hydrothermal treatment. These methods are demonstrated to enable obtaining a fairly wide range of nanomaterials that differ significantly in chemical composition, crystal structure, and morphological features. While synthesis routes foster diversity in material properties, microplotter printing ensures targeted precision in making on-chip arrays for evaluation of a combinatorial selectivity concept in the task of organic vapor, like alcohol homologs, acetone, and benzene, classification. The synthesized nanomaterials demonstrate a high chemiresistive response, with a limit of detection beyond ppm level. A specific combination of materials is demonstrated to be relevant when the number of sensors is low; however, such importance diminishes with an increase in the number of sensors. We show that on-chip material combinations could favor selectivity to a specific analyte, disregarding the others. Hence, modern synthesis methods and printing protocols supported by combinatorial analysis might pave the way for fabricating on-chip orthogonal multisensor systems.

Nanosensor-Enabled Detection and Identification of Intracellular Bacterial Infections in Macrophages

Opportunistic bacterial pathogens can evade the immune response by residing and reproducing within host immune cells, including macrophages. These intracellular infections provide reservoirs for pathogens that enhance the progression of infections and inhibit therapeutic strategies. Current sensing strategies for intracellular infections generally use immunosensing of specific biomarkers on the cell surface or polymerase chain reaction (PCR) of the corresponding nucleic acids, making detection difficult, time-consuming, and challenging to generalize. Intracellular infections can induce changes in macrophage glycosylation, providing a potential strategy for signature-based detection of intracellular infections. We report here the detection of bacterial infection in macrophages using a boronic acid (BA)-based pH-responsive polymer sensor array engineered to distinguish mammalian cell phenotypes by their cell surface glycosylation signatures. The sensor was able to discriminate between different infecting bacteria in minutes, providing a promising tool for diagnostic and screening applications.

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.

Photonic Solutions for Challenges in Sensing

Sensing technologies support timely and critical decisions to save precious resources in healthcare, veterinary care, food safety, and environmental protection. However, the design of sensors demands strict technical characteristics for real-world applications. In this Viewpoint, we discuss the main challenges to tackle in the sensing field and how photonics represents a valuable tool in this sphere.

Ensuring food safety by artificial intelligence-enhanced nanosensor arrays

Current analytical methods utilized for food safety inspection requires improvement in terms of their cost-efficiency, speed of detection, and ease of use. Sensor array technology has emerged as a food safety assessment method that applies multiple cross-reactive sensors to identify specific targets via pattern recognition. When the sensor arrays are fabricated with nanomaterials, the binding affinity of analytes to the sensors and the response of sensor arrays can be remarkably enhanced, thereby making the detection process more rapid, sensitive, and accurate. Data analysis is vital in converting the signals from sensor arrays into meaningful information regarding the analytes. As the sensor arrays can generate complex, high-dimensional data in response to analytes, they require the use of machine learning algorithms to reduce the dimensionality of the data to gain more reliable outcomes. Moreover, the advances in handheld smart devices have made it easier to read and analyze the sensor array signals, with the advantages of convenience, portability, and efficiency. While facing some challenges, the integration of artificial intelligence with nanosensor arrays holds promise for enhancing food safety monitoring.

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.

Wearable sensor supports in-situ and continuous monitoring of plant health in precision agriculture era

Plant health is intricately linked to crop quality, food security and agricultural productivity. Obtaining accurate plant health information is of paramount importance in the realm of precision agriculture. Wearable sensors offer an exceptional avenue for investigating plant health status and fundamental plant science, as they enable real-time and continuous in-situ monitoring of physiological biomarkers. However, a comprehensive overview that integrates and critically assesses wearable plant sensors across various facets, including their fundamental elements, classification, design, sensing mechanism, fabrication, characterization and application, remains elusive. In this study, we provide a meticulous description and systematic synthesis of recent research progress in wearable sensor properties, technology and their application in monitoring plant health information. This work endeavours to serve as a guiding resource for the utilization of wearable plant sensors, empowering the advancement of plant health within the precision agriculture paradigm.

Detection of Maize Mold Based on a Nanocomposite Colorimetric Sensor Array under Different Substrates

This study developed a novel nanocomposite colorimetric sensor array (CSA) to distinguish between fresh and moldy maize. First, the headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) method was used to analyze volatile organic compounds (VOCs) in fresh and moldy maize samples. Then, principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to identify 2-methylbutyric acid and undecane as key VOCs associated with moldy maize. Furthermore, colorimetric sensitive dyes modified with different nanoparticles were employed to enhance the dye properties used in the nanocomposite CSA analysis of key VOCs. This study focused on synthesizing four types of nanoparticles: polystyrene acrylic (PSA), porous silica nanospheres (PSNs), zeolitic imidazolate framework-8 (ZIF-8), and ZIF-8 after etching. Additionally, three types of substrates, qualitative filter paper, polyvinylidene fluoride film, and thin-layer chromatography silica gel, were comparatively used to fabricate nanocomposite CSA combining with linear discriminant analysis (LDA) and K-nearest neighbor (KNN) models for real sample detection. All moldy maize samples were correctly identified and prepared to characterize the properties of the CSA. Through initial testing and nanoenhancement of the chosen dyes, four nanocomposite colorimetric sensitive dyes were confirmed. The accuracy rates for LDA and KNN models in this study reached 100%. This work shows great potential for grain quality control using CSA methods.

Modeling and optimization of the ratio of fluorophores: a step towards enhancing the sensitivity of ratiometric probes

In the ratiometric fluorescent (RF) strategy, the selection of fluorophores and their respective ratios helps to create visual quantitative detection of target analytes. This study presents a framework for optimizing ratiometric probes, employing both two-component and three-component RF designs. For this purpose, in a two-component ratiometric nanoprobe designed for detecting methyl parathion (MP), an organophosphate pesticide, yellow-emissive thioglycolic acid-capped CdTe quantum dots (Y-QDs) (analyte-responsive), and blue-emissive carbon dots (CDs) (internal reference) were utilized. Mathematical polynomial equations modeled the emission profiles of CDs and Y-QDs in the absence of MP, as well as the emission colors of Y-QDs in the presence of MP separately. In other two-/three-component examples, the detection of dopamine hydrochloride (DA) was investigated using an RF design based on blue-emissive carbon dots (B-CDs) (internal reference) and N-acetyl L-cysteine functionalized CdTe quantum dots with red/green emission colors (R-QDs/G-QDs) (analyte-responsive). The colors of binary/ternary mixtures in the absence and presence of MP/DA were predicted using fitted equations and additive color theory. Finally, the Euclidean distance method in the normalized CIE XYZ color space calculated the distance between predicted colors, with the maximum distance defining the real-optimal concentration of fluorophores. This strategy offers a more efficient and precise method for determining optimal probe concentrations compared to a trial-and-error approach. The model's effectiveness was confirmed through experimental validation, affirming its efficacy.

A novel colorimetric and fluorometric dual-signal identification of organics and Baijiu based on nanozymes with peroxidase-like activity

Nanozymes were nanomaterials with enzymatic properties. They had diverse functions, adjustable catalytic activity, high stability, and easy large-scale production, attracting interest in biosensing. However, nanozymes were scarcely applied in Baijiu identification. Herein, a colorimetric and fluorometric dual-signal determination mediated by a nanozyme-H2O2-TMB system was developed for the first time to identify organics and Baijiu. Since the diverse peroxidase-like activity of nanozymes, resulted in different degrees of oxidized TMB. Based on this, 21 organics were identified qualitatively and quantitatively by colorimetric method with a rapid response (<12 min), broad linearity (0.0005-35 mM), and low detection limits (a minimum of 30 nM for glutaric acids). Furthermore, the fluorometric method exhibited excellent potential for accurate determination of organics, with detection ranges of 2-200 µmol/L (LOD: 0.22 µmol/L) for l-ascorbic acid and 2-300 µmol/L (LOD: 0.59 µmol/L) for guaiacol. Finally, the sensor was successfully applied to identify fake Baijiu and Baijiu from 16 different brands.

Colorimetric sensor array for versatile detection and discrimination of model analytes with environmental relevance

In the current work, a rapid, simple, low-cost, and sensitive smartphone-based colorimetric sensor array coupled with pattern-recognition methods was proposed for the determination and differentiation of some organic and inorganic bases (i.e., OH-, CO32-, PO43-, NH3, ClO-, diethanolamine, triethanolamine) as model compounds. The sensing system has been designed based on color-sensitive dyes (Fuchsine, Giemsa, Thionine, and CoCl2) which were used as sensor elements. The color changes of a sensor array were observed by the naked eye. The color patterns were recorded using digital imaging in a three-dimensional (red, green, and blue) space and quantitatively analyzed with color calibration techniques. Distinctive colorimetric patterns for target bases via linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA) were observed. The results indicated that the analytes related to each class (at the different concentration levels in the range of 0.001-1.0 mol L-1) were clustered together in the canonical discriminant plot and HCA dendrogram with high sensitivity and an overall precision of 85%. Furthermore, the first function factor of LDA correlated with the concentration of each target analyte in a correlation coefficient (R2) range of 0.864-0.996. These described procedures based on the colorimetric sensor array technique could be a promising candidate for practical applications in package technology and facile detection of pollutants.

Surveillance of pathogenic bacteria on a food matrix using machine-learning-enabled paper chromogenic arrays

Global food systems can benefit significantly from continuous monitoring of microbial food safety, a task for which tedious operations, destructive sampling, and the inability to monitor multiple pathogens remain challenging. This study reports significant improvements to a paper chromogenic array sensor - machine learning (PCA-ML) methodology sensing concentrations of volatile organic compounds (VOCs) emitted on a species-specific basis by pathogens by streamlining dye selection, sensor fabrication, database construction, and machine learning and validation. This approach enables noncontact, time-dependent, simultaneous monitoring of multiple pathogens (Listeria monocytogenes, Salmonella, and E. coli O157:H7) at levels as low as 1 log CFU/g with over 90% accuracy. The report provides theoretical and practical frameworks demonstrating that chromogenic response, including limits of detection, depends on time integrals of VOC concentrations. The paper also discusses the potential for implementing PCA-ML in the food supply chain for different food matrices and pathogens, with species- and strain-specific identification.

Protein Corona Sensor Array Nanosystem for Detection of Coronary Artery Disease

Coronary artery disease (CAD) is the most common type of heart disease and represents the leading cause of death in both men and women worldwide. Early detection of CAD is crucial for decreasing mortality, prolonging survival, and improving patient quality of life. Herein, a non-invasive is described, nanoparticle-based diagnostic technology which takes advantages of proteomic changes in the nano-bio interface for CAD detection. Nanoparticles (NPs) exposed to biological fluids adsorb on their surface a layer of proteins, the "protein corona" (PC). Pathological changes that alter the plasma proteome can directly result in changes in the PC. By forming disease-specific PCs on six NPs with varying physicochemical properties, a PC-based sensor array is developed for detection of CAD using specific PC pattern recognition. While the PC of a single NP may not provide the required specificity, it is reasoned that multivariate PCs across NPs with different surface chemistries, can provide the desirable information to selectively discriminate the condition under investigation. The results suggest that such an approach can detect CAD with an accuracy of 92.84%, a sensitivity of 87.5%, and a specificity of 82.5%. These new findings demonstrate the potential of PC-based sensor array detection systems for clinical use.

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

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

Ratiometric Imaging of Catecholamine Neurotransmitters with Nanosensors

Neurotransmitters are important signaling molecules in the brain and are relevant in many diseases. Measuring them with high spatial and temporal resolutions in biological systems is challenging. Here, we develop a ratiometric fluorescent sensor/probe for catecholamine neurotransmitters on the basis of near-infrared (NIR) semiconducting single wall carbon nanotubes (SWCNTs). Phenylboronic acid (PBA)-based quantum defects are incorporated into them to interact selectively with catechol moieties. These PBA-SWCNTs are further modified with poly(ethylene glycol) phospholipids (PEG-PL) for biocompatibility. Catecholamines, including dopamine, do not affect the intrinsic E11 fluorescence (990 nm) of these (PEG-PL-PBA-SWCNT) sensors. In contrast, the defect-related E11* emission (1130 nm) decreases by up to 35%. Furthermore, this dual functionalization allows tuning selectivity by changing the charge of the PEG polymer. These sensors are not taken up by cells, which is beneficial for extracellular imaging, and they are functional in brain slices. In summary, we use dual functionalization of SWCNTs to create a ratiometric biosensor for dopamine.

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.

Machine Learning-Assisted Janus Colorimetric Face Mask for Breath Ammonia Analysis

Artificial olfactory systems have been widely used in medical fields such as in the analysis of volatile organic compounds (VOCs) in human exhaled breath. However, there is still an urgent demand for a portable, accurate breath VOC analysis system for the healthcare industry. In this work, we proposed a Janus colorimetric face mask (JCFM) for the comfortable evaluation of breath ammonia levels by combining the machine learning K-nearest neighbor (K-NN) algorithm. Such a Janus fabric is designed for the unidirectional penetration of exhaled moisture, which can reduce stickiness and ensure facial dryness and comfort. Four different pH indicators on the colorimetric array serve as recognition elements that cross-react with ammonia, capturing the optical fingerprint information on breath ammonia by mimicking the sophisticated olfactory structure of mammals. The Euclidean distance (ED) is used to quantitatively describe the ammonia concentration between 1 ppm and 10 ppm, indicating that there is a linear relationship between the ammonia concentration and the ED response (R2 = 0.988). The K-NN algorithm based on RGB response features aids in the analysis of the target ammonia level and achieves a prediction accuracy of 96%. This study integrates colorimetry, Janus design, and machine learning to present a wearable and portable sensing system for breath ammonia analysis.