The Optoelectronic Nose

A dual-functional needle-based VOC sensing platform for rapid vegetable phenotypic classification

Volatile organic compounds (VOCs) are common constituents of fruits, vegetables, and crops, and are closely associated with their quality attributes, such as firmness, sugar level, ripeness, translucency, and pungency levels. While VOCs are vital for assessing vegetable quality and phenotypic classification, traditional detection methods, such as Gas Chromatography-Mass Spectrometry (GC-MS) and Proton Transfer Reaction Mass Spectrometry (PTR-MS) are limited by expensive equipment, complex sample preparation, and slow turnaround time. Additionally, the transient nature of VOCs complicates their detection using these methods. Here, we developed a paper-based colorimetric sensor array combined with needles that could: 1) induce vegetable VOC release in a minimally invasive fashion, and 2) analyze VOCs in situ with a smartphone reader device. The needle sampling device helped release specific VOCs from the studied vegetables that usually require mechanic stimulation, while maintaining the vegetable viability. On the other hand, the colorimetric sensor array was optimized for sulfur compound-based VOCs with a limit of detection (LOD) in the 1-25 ppm range, and classified fourteen different vegetable VOCs, including sulfoxides, sulfides, mercaptans, thiophenes, and aldehydes. By combining principal components analysis (PCA) analysis, the integrated sensor platform proficiently discriminated between four vegetable subtypes originating from two major categories within 2 min of testing time. Additionally, the sensor demonstrates the capability to distinguish between different types of tested fruits and vegetables, including garlic, green pepper, and nectarine. This rapid and minimally invasive sensing technology holds great promise for conducting field-based vegetable quality monitoring.

Quantum-dot-spectrometer-based virtual barcode for the sensitive colorimetric urinalysis

Colorimetric sensing methods are extensively utilized for rapid and sensitive detection of various biomedical and environmental targets, with higher dimensional spectra resulting in more accurate results. Miniature reconstructive spectrometers, as portable colorimetric sensing devices, show promise in capturing high-dimension spectra signals, while facing the challenges of noise-sensitive spectrum reconstruction and complex pre-calibration. To address these issues, we present a virtual barcode method, which is directly based on the utilization of a high-dimension quantum dot (QD) spectrometer intensity vector. Any spectral changes of the analytes can be reflected in the corresponding barcode, without the redundant operation for spectral analysis. We demonstrate the QD barcode method in quantitatively detecting multiple biomarkers in the artificial human urine, including urinary calcium, glucose, nitrite, and creatinine, with lower limits of detection compared to the RGB sensing method (2.4-14.4-fold). To simplify the preparation of the QD spectrometer, we optimize both the number and the spectral distribution of QD filters. Furthermore, an artificial neural network model is also established to achieve 8-fold improved quantitative recognition performance. This QD barcode method can greatly broadens the biosensing application of miniaturized reconstructive spectrometers.

Copper Vacancy-Doped Cu2-xSe Activating Nanozyme Sensor Arrays for Multiprotein Discrimination and Cancer Screening

Given the complexity and interrelated nature of biological activities, acquiring multitarget information is crucial for accurate cancer diagnosis. In this study, we developed a cross-reactive sensor array using peroxidase-like nonstoichiometric copper selenide nanoparticles (Cu2-xSe), which featured varying copper vacancies, for multiplex protein detection and cancer screening. The Cu2-xSe nanoparticles demonstrated outstanding peroxidase-like activity that can be modulated to varying degrees in the presence of multiple proteins with varying isoelectric points and compositions, generating fingerprint outputs. By integrating pattern recognition method principal component analysis with linear discriminant analysis (PCA-LDA), the sensor array effectively discriminated among six proteins and was further applied to cells and clinical samples, attaining 100% accuracy in differentiating cancer patients from healthy individuals, as well as in identifying specific cancer types, namely, liver and prostate cancers. Moreover, as a proof-of-concept, the partial least-squares regression (PLSR) approached the accurate detection of AFP and PSA within the range of 0.1-2.0 μg/L in the complex biological matrix. These results highlighted the practical potential of multitarget sensing and clinical diagnosis by the nanozyme-based chemical nose strategies.

ColorNet: An AI-based framework for pork freshness detection using a colorimetric sensor array

Pork freshness is crucial for flavour, nutrition and consumer health. The current colorimetric sensor array (CSA) detection systems face challenges related to high sensor development costs, low recognition accuracy and limitations in the platform use. Herein, we developed a CSA and ColorNet framework to detect pork freshness. The 53-point CSA was designed by selecting sensitised pH indicators and aldehyde/ketone indicators. To optimize the sensor, the Euclidean distance method was used to identify 24 array points with dyes that exhibited more sensitive responses. The ColorNet captured the color information of pork freshness, allowing real-time detection with a 99.5 % accuracy. For practical deployment and mobile applications, a refined 12-point CSA was developed using gradient activation mapping, maintaining a 99 % recognition rate, which is comparable with the 24-point CSA. The proposed CSA and model ensure consumer health and safety, providing strong technical support for quality monitoring and control in the pork industry.

Freeze-Thaw Imaging for Microorganism Classification Assisted with Artificial Intelligence

Fast and cost-effective microbial classification is crucial for clinical diagnosis, environmental monitoring, and food safety. However, traditional methods encounter challenges including intricate procedures, skilled personnel needs, and sophisticated instrumentations. Here, we propose a cost-effective microbe classification system, also termed freeze-thaw-induced floating pattern of AuNPs (FTFPA), coupled with artificial intelligence, which is capable of identifying microbes at a cost of $0.0023 per sample. Specifically, FTFPA utilizes AuNPs for coincubation with microbes, resulting in distinct patterns upon freeze-thawing due to their weak interaction. These patterns are digitized to train models that distinguish nine microbes in various tasks. The positive sample detection model achieved an F1 score of 0.976 (n = 194), while the multispecies classification task reached a macro F1 score of 0.859 (n = 1728). To address scalability and lightweight requirements across diverse classification scenarios, we categorized microbes based on species classification levels. The macro F1 score of the hierarchical model (n = 5184), order level model (n = 5184), Enterobacteriales level model (n = 2550), and Bacillales level model (n = 1974) was 0.854, 0.907, 0.958, and 0.843. In summary, our method is user-friendly, requiring only simple equipment, is easy to operate, and convenient, providing a platform for microbial identification.

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.

Detection of human salivary stress biomarkers using an easy-to-use array sensor based on fluorescent organic molecules

During stressful conditions, the human body synthesizes catecholamine neurotransmitters such as dopamine and adrenaline, and cortisol. The monitoring of these three molecules levels is crucial for stress management and holds significant medical applications. Here we developed an analytical device that incorporates a biomimetic (tongue-mimic) associated with an optical physico-chemical transducer, able to detect simultaneously cortisol, dopamine and adrenaline in human saliva without pre-treatments, using an array sensor based on fluorescent chemical receptors (BODIPY, Rhodamine, and Naphthylamides) able to interact by non-covalent interaction with cortisol, adrenaline and dopamine, leading to a change of the emission. Calibration, recovery and selectivity have been performed to validate the device. In particular, the linear responses of the array to concentration range of 1 pM-1 mM of the three analytes were demonstrated by PLS analysis, as well as the high selectivity by PLS-DA analysis performed with artificial saliva samples. Analyses in real human saliva samples, compared to the validated analytical methods, demonstrated that our prototype represents the first point-of-care device able to quantify these three analytes in human saliva with one single analysis in a wider concentration range respect to the other standard methods.

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.

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.

A xylenol orange-based pH-sensitive sensor array for identification of bacteria and differentiation of probiotic drinks

The development of straightforward and cost-efficient methods for bacterial identification is very important. In this study, we utilized xylenol orange, metal ions, and diverse bacterial carbon sources to construct a sensor array, achieving precise bacterial identification. Initially, we examined the absorbance variations of xylenol orange with five metal ions (Mn2+, Co2+, Ni2+, Cu2+, and Zn2+) at pH levels ranging from 4 to 7, observing significant changes at 570 nm or 580 nm. Given that bacteria can generate varying amounts of acid in different carbon sources, we employed a blend of xylenol orange and the five metal ions as pH-sensitive probes to characterize bacterial metabolism in three carbon sources, resulting in the development of a five-sensor array that effectively differentiated seven bacteria. Additionally, by utilizing xylenol orange with Co2+, we successfully identified six different bacterial mixtures and five types of probiotic drink products. These findings highlight the potential of our methods for broader practical application in bacterial identification in practical use.

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.

Freeze-Thaw-Induced Patterning of Extracellular Vesicles with Artificial Intelligence for Breast Cancers Identifications

Extracellular vesicles (EVs) play a crucial role in the occurrence and progression of cancer. The efficient isolation and analysis of EVs for early cancer diagnosis and prognosis have gained significant attention. In this study, for the first time, a rapid and visually detectable method termed freeze-thaw-induced floating patterns of gold nanoparticles (FTFPA) is proposed, which surpasses current state-of-the-art technologies by achieving a 100 fold improvement in the limit of detection of EVs. Notably, it allows for multi-dimensional visualizations of EVs through site-specific oligonucleotide incorporation. This capability empowers FTFPA to accurately identify EVs derived from subtypes of breast cancers with artificial intelligence algorithms. Intriguingly, learning the freezing-thawing-microstructures of EVs with a random forest algorithm is not only able to distinguish their original cell lines (with an accuracy of 95.56%), but also succeed in processing clinical samples (n = 156) to identify EVs by their healthy donors, breast lump and breast cancer subtypes (Luminal A, Triple-negative breast cancer, and Luminal B) with an accuracy of 83.33%. Therefore, this AI-empowered micro-visualization method establishes a rapid and precise point-of-care platform that is applicable to both fundamental research and clinical settings.

Scalable Photonic Nose Development through Corona Phase Molecular Recognition

Breath sensors promise early disease diagnosis through noninvasive, rapid analysis, but have struggled to reach clinical use due to challenges in scalability and multivariate data extraction. The current breath sensor design necessitates various channel materials and surface functionalization methods, which delays the process. Additionally, the limited options for channel materials that provide optimum sensitivity and selectivity further restrict the array size to a maximum of only 10 to 20 channels. To address these limitations, we propose a breath sensing array design process based on Corona Phase Molecular Recognition (CoPhMoRe), which enables the creation of an expansive library of nanoparticle interfaces and broad fingerprints for multiple analytes in the breath. Although CoPhMoRe has predominantly been utilized for liquid-phase sensing, its recent application to gas-phase sensing has shown significant potential for breath sensing. We introduce the recent demonstrations in the field and present the concept of a CoPhMoRe-based photonic-nose sensor array, leveraging fluorescent nanomaterials such as near-infrared single-walled carbon nanotubes. Additionally, we identified four critical milestones for translating CoPhMoRe into breath sensors for practical clinical applications.

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.

Hydrogel-Based Microdroplet Ensembles Encapsulating Multiplexed EXPAR Assays for Trichromic Digital Profiling of MicroRNAs and in-Depth Classification of Primary Urethral Cancers

The primary challenge in microarray-based biological analysis lies in achieving the sensitive and specific detection of single-molecule targets while ensuring high reproducibility. A user-friendly digital imaging platform has been developed for the encoded trichromic profiling of circulating microRNAs (miRNAs). This platform replaces the traditional exponential polymerase amplification reaction (EXPAR) conducted on the microliter scale with a system that confines the amplification process within thousands of femtoliter-sized microdroplet reactors, cross-linked from tetra-armed poly(ethylene glycol) acrylate (Tetra-PEGA) and poly(ethylene glycol) dithiol (HS-PEG-SH), thus offering significant advantages, including minimal sample input, enhanced reactivity, and simplified analytical procedures. The quantitative analysis relies on digital counting of fluorescently positive microdroplets, each containing an individual miRNA sequence. This approach significantly reduces nonspecific amplification and improves sensitivity by over 2 orders of magnitude. The system has shown great potential in differentiating between subtypes of primary urethral carcinoma, suggesting its practical application in routine cancer diagnostics through simple urinalysis.

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.

Fast detection of penicillium rot and the conservation status of packaged citrus fruit using an optical array sensor

A novel optical array sensor designed to detect the conservation status of citrus fruit as well as contamination of ripened fruits by green mold incited by the fungus Penicillium digitatum is reported here. The device demonstrates high sensitivity, specificity, and cost-effectiveness, making it suitable for integration into the citrus fruit supply chain, including production and packaging systems.

Expanding the Toolbox of Oxidants: Controllable Etching of Ultrasmall Au Nanoparticles toward Tailorable NIR-II Luminescence and Ligand-Mediated Biodistribution and Clearance

Oxidant-driven and controllable etching of small-sized nanoparticles (NPs, d < 3 nm) and tailorable modulation of their optical properties are challenging due to the high reactivity and complicated surface chemistry. Herein, we present a facile strategy for highly controllable oxidative etching of ultrasmall AuNPs and tailorable modulation of luminescence. The proper choice of a moderate oxidant, ClO-, could not only selectively etch the Au(I)-thiolate motifs from the nanoparticle surface at the subnanometer scale but also retained a stable metallic core structure without aggregation, which impressively prompted the wide-range luminescent switching from the visible to second near-infrared (NIR-II) region. The resultant oxidized AuNPs displayed highly luminescent NIR-II emission with a quantum yield of 3.0%, excellent monodispersed stability, ideal biocompatibility, and tunable shielding effects against protein adsorption. With those outstanding features, oxidized AuNPs could be utilized as nanoprobes for long-lasting and in vivo bioimaging of associated metabolic behaviors with distinguishable organ-specific targeting capabilities and ligand-mediated kinetics in nanoparticle clearance. These findings expand the toolbox of oxidants for the controllable synthesis of NIR-II nanoprobes and open up a path for exploring diverse ligand interactions on ultrasmall AuNPs with organs or tissues that might advance their monitoring applications for a wide range of clinically important diseases.

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.

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 Metal-Organic Framework-Based Colorimetric Sensor Array for Transcutaneous CO2 Monitoring via Lensless Imaging

Transcutaneous carbon dioxide (TcPCO2) monitoring provides a non-invasive alternative to measuring arterial carbon dioxide (PaCO2), making it valuable for various applications, such as sleep diagnostics and neonatal care. However, traditional transcutaneous monitors are bulky, expensive, and pose risks such as skin burns. To address these limitations, we have introduced a compact, cost-effective CMOS imager-based sensor for TcPCO2 detection by utilizing colorimetric reactions with metal-organic framework (MOF)-based nano-hybrid materials. The sensor, with a colorimetric sensing array fabricated on an ultrathin PDMS membrane and then adhered to the CMOS imager surface, can record real-time sensing data through image processing without the need for additional optical components, which significantly reduces the sensor's size. Our system shows impressive sensitivity and selectivity, with a low detection limit of 26 ppm, a broad detection range of 0-2% CO2, and strong resistance to interference from common skin gases. Feasibility tests on human subjects demonstrate the potential of this MOF-CMOS imager-based colorimetric sensor for clinical applications. Additionally, its compact design and responsiveness make it suitable for sports and exercise settings, offering valuable insights into respiratory function and performance. The sensing system's compact size, low cost, and reversible and highly sensitive TcPCO2 monitoring capability make it ideal for integration into wearable devices for remote health tracking.

Plasmonic Cross-Reactive Sensing Noses and Tongues

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

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.

A Supramolecular Fluorescent Sensor Array Composed of Conjugated Fluorophores and Cucurbit[7]uril for Bacterial Recognition

Bacterial infections have emerged as a significant contributor to global mortality and morbidity rates. Herein, we introduce a dual fluorescence "turn-on" supramolecular sensor array composed of three assembled complexes (C1-C3), formed from three positively charged fluorophores (A1-A3) and one cucurbit[7]uril (CB[7]). The ability of this three-element array to simultaneously recognize 10 bacterial species within just 30 s was remarkable, boasting an impressive 100% accuracy. Additionally, the array excelled at distinguishing among various bacterial mixtures and enabled the quantitative detection of common bacterial strains. Notably, it has been skillfully applied to differentiate 10 bacterial samples in urine, achieving excellent differentiation and showcasing promising potential for medical diagnostic applications.

Rapid Fingerprinting of Urinary Volatile Metabolites and Point-of-Care Diagnosis of Phenylketonuria on a Patterned Nanorod Sensor Array with Multiplexed Surface-Enhanced Raman Scattering Readouts

Phenylketonuria (PKU) is one of the most common genetic metabolic diseases, especially among newborns. Traditional clinical examination of newborn blood samples for PKU is invasive, laborious, and limited to hospitals and healthcare facilities. We reported herein a SERS-based sensor array with three thiophenolic nanoreceptors built on a patterned nanorod vertical array for rapid and inexpensive detection of characteristic volatile biomarkers indicative of PKU in the urine and accurate classification of newborn baby patients all performed on a hand-held SERS spectrophotometer. The well-ordered array was generated from the volatility-driven assembly of gold nanorods (AuNRs) into an upright and closely packed hexagonal configuration. The uniformly distributed nanowells between AuNRs offered an intense and aspect-ratio-dependent plasmonic field for the molecular enhancement of SERS outputs. The SERS-based detector was integrated into a test chip for regular monitoring of volatile phenylketone bodies in the spiked solution or patients' urine within 5 min, allowing the quantification of a wide variety of normal or abnormal metabolites at their physiologically relevant concentration range. The detection limits for common biomarkers of PKU, including phenylpyruvic acid, 4-hydroxyphenylacetic acid, and phenylacetic acid, were at a few μM and well below the diagnostic thresholds. Moreover, the volatile headspace mixtures from a given urine sample could be fingerprinted by the sensor array and discriminated using machine-learning algorithms. Ultimately, the discrimination of baby patients among 26 cases of mild and classic PKU phenotypes and 17 cases of healthy volunteers could be realized with an overall accuracy of 97%. This hand-held SERS platform plays a pivotal role in advancing healthcare applications in quick screening of neonatal PKU through a facile urinary vapor test.

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 %.

Customizable Colorimetric Sensor Array via a High-Throughput Robot for Mitigation of Humidity Interference in Gas Sensing

One challenge for gas sensors is humidity interference, as dynamic humidity conditions can cause unpredictable fluctuations in the response signal to analytes, increasing quantitative detection errors. Here, we introduce a concept: Select humidity sensors from a pool to compensate for the humidity signal for each gas sensor. In contrast to traditional methods that extremely suppress the humidity response, the sensor pool allows for more accurate gas quantification across a broader range of application scenarios by supplying customized, high-dimensional humidity response data as extrinsic compensation. As a proof-of-concept, mitigation of humidity interference in colorimetric gas quantification was achieved in three steps. First, across a ten-dimensional variable space, an algorithm-driven high-throughput experimental robot discovered multiple local optimum regions where colorimetric humidity sensing formulations exhibited high evaluations on sensitivity, reversibility, response time, and color change extent for 10-90% relative humidity (RH) in room temperature (25 °C). Second, from the local optimum regions, 91 sensing formulations with diverse variables were selected to construct a parent colorimetric humidity sensor array as the sensor pool for humidity signal compensation. Third, the quasi-optimal sensor subarrays were identified as customized humidity signal compensation solutions for different gas sensing scenarios across an approximately full dynamic range of humidity (10-90% RH) using an ingenious combination optimization strategy, and two accurate quantitative detections were attained: one with a mean absolute percentage error (MAPE) reduction from 4.4 to 0.75% and the other from 5.48 to 1.37%. Moreover, the parent sensor array's excellent humidity selectivity was validated against 10 gases. This work demonstrates the feasibility and superiority of robot-assisted construction of a customizable parent colorimetric sensor array to mitigate humidity interference in gas quantification.

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.

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.

Intelligent food packaging for smart sensing of food safety

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

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.

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.

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.