Towards outperforming conventional sensor arrays with fabricated individual photonic vapour sensors inspired by Morpho butterflies

A pattern recognition artificial olfactory system based on human olfactory receptors and organic synaptic devices

Neuromorphic sensors, designed to emulate natural sensory systems, hold the promise of revolutionizing data extraction by facilitating rapid and energy-efficient analysis of extensive datasets. However, a challenge lies in accurately distinguishing specific analytes within mixtures of chemically similar compounds using existing neuromorphic chemical sensors. In this study, we present an artificial olfactory system (AOS), developed through the integration of human olfactory receptors (hORs) and artificial synapses. This AOS is engineered by interfacing an hOR-functionalized extended gate with an organic synaptic device. The AOS generates distinct patterns for odorants and mixtures thereof, at the molecular chain length level, attributed to specific hOR-odorant binding affinities. This approach enables precise pattern recognition via training and inference simulations. These findings establish a foundation for the development of high-performance sensor platforms and artificial sensory systems, which are ideal for applications in wearable and implantable devices.

Detection of Volatile Organic Compounds through Spectroscopic Signatures in Nanoporous Fabry-Pérot Optical Microcavities

Increasingly complex modern gas-monitoring scenarios necessitate advanced sensing capabilities to detect and identify a diverse range of gases under varying conditions. There is a rising demand for individual sensors with multiple responses capable of recognizing gases, identifying components in mixtures, and providing stable responses. Inspired by gas sensors employing multivariable response principles, we develop a nanoporous anodic alumina high-order microcavity (NAA-HOμCV) gas sensor with multiple optical outputs for discriminative gas detection. The NAA-HOμCV architecture, formed by a Fabry-Pérot microcavity with distributed Bragg reflector (DBR) mirrors and an extended-length microcavity layer supporting multiple resonant modes, serves as an effective solid-state fingerprint platform for distinguishing volatile organic compound (VOC) gases. Our research reveals that the coupling strength of light into resonant modes and their evolution depend on the thickness of the DBR mirrors and the dimension of the microcavity layer, which allows us to optimize the discriminative sensing capability of the NAA-HOμCV sensor through structural engineering of the microcavity and photonic crystal mirrors. Gas-sensing experiments conducted on the NAA-HOμCV sensor demonstrate real-time discrimination between physiosorbed VOC gases (isopropanol, ethanol, or acetone) in reversible gas sensing. It also achieves superior ppb-level sensing in irreversible gas sensing of model silane molecules. Our study presents promising avenues for designing compact, cost-effective, and highly efficient gas sensors with tailored properties for discriminative gas detection.

Bioinspired Photonic Microchip with Molecularly Imprinted Polymer for Single Recognition of c-Myc Protein in Predictive Medical Diagnostics

Monitoring of trace c-Myc protein as the biomarker of ubiquitous cancers is critical in achieving predictive medical diagnostics. However, qualitative and quantitative detection of c-Myc protein with superior single selectivity and sensitivity is still challenging. Herein, a bioinspired photonic sensing microchip for single recognition of c-Myc protein is outlined with two synergistic aspects involving chemical and physical design criteria. Chemical design uses specific molecularly imprinted polymer (MIP) with exquisite complementarity in its chemical functions and spatial geometries to targeted c-Myc protein, leading to excellent sensitivity and selectivity for single identification. Physical design involves optical geometrical double-reflection polarization rotation and multilayer interference of the fabricated periodic photonic architecture inspired by Papilio palinurus butterfly wings to enhance the spectral diversity of reflectance. Therefore, a one-of-a-kind sensing platform integrates the advantages of MIP and bioinspired photonic structure is demonstrated to actualize distinctive signal conversion and amplification for qualitative and quantitative detection of trace c-Myc protein, accompanied with superior sensitivity (detection limit is 0.014 µg mL-1 ), selectivity, stability, anti-interference ability as well as rapid response/recovery time. This sensor microchip uniquely ventures into the territory of functionally combining bioinspired photonic structure with MIP absorbers, proven promising for prevention or diagnosis of cancers in medical field.

First-Order Individual Gas Sensors as Next Generation Reliable Analytical Instruments

It is conventionally expected that the performance of existing gas sensors may degrade in the field compared to laboratory conditions because (i) a sensor may lose its accuracy in the presence of chemical interferences and (ii) variations of ambient conditions over time may induce sensor-response fluctuations (i.e., drift). Breaking this status quo in poor sensor performance requires understanding the origins of design principles of existing sensors and bringing new principles to sensor designs. Existing gas sensors are single-output (e.g., resistance, electrical current, light intensity, etc.) sensors, also known as zero-order sensors (Karl Booksh and Bruce R. Kowalski, Analytical Chemistry, DOI: 10.1021/ac00087a718). Any zero-order sensor is undesirably affected by variable chemical background and sensor drift that cannot be distinguished from the response to an analyte. To address these limitations, we are developing multivariable gas sensors with independent responses, which are first-order analytical instruments. Here, we demonstrate self-correction against drift in two types of first-order gas sensors that operate in different portions of the electromagnetic spectrum. Our radiofrequency sensors utilize dielectric excitation of semiconducting metal oxide materials on the shoulder of their dielectric relaxation peak and achieve self-correction of the baseline drift by operation at several frequencies. Our photonic sensors utilize nanostructured sensing materials inspired by Morpho butterflies and achieve self-correction of the baseline drift by operation at several wavelengths. These principles of self-correction for drift effects in first-order sensors open opportunities for diverse emerging monitoring applications that cannot afford frequent periodic maintenance that is typical of traditional analytical instruments.

Nonequilibrium sensing of volatile compounds using active and passive analyte delivery

Although sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch, the development of artificial noses is significantly behind their biological counterparts. This largely stems from the sophistication of natural olfaction, which relies on both fluid dynamics within the nasal anatomy and the response patterns of hundreds to thousands of unique molecular-scale receptors. We designed a sensing approach to identify volatiles inspired by the fluid dynamics of the nose, allowing us to extract information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) rather than relying on a large sensor array. By accentuating differences in the nonequilibrium mass-transport dynamics of vapors and training a machine learning algorithm on the sensor output, we clearly identified polar and nonpolar volatile compounds, determined the mixing ratios of binary mixtures, and accurately predicted the boiling point, flash point, vapor pressure, and viscosity of a number of volatile liquids, including several that had not been used for training the model. We further implemented a bioinspired active sniffing approach, in which the analyte delivery was performed in well-controlled 'inhale-exhale' sequences, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. Our results outline a strategy to build accurate and rapid artificial noses for volatile compounds that can provide useful information such as the composition and physical properties of chemicals, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.

A chemiresistive-potentiometric multivariate sensor for discriminative gas detection

Highly efficient gas sensors able to detect and identify hazardous gases are crucial for numerous applications. Array of conventional single-output sensors is currently limited by problems including drift, large size, and high cost. Here, we report a sensor with multiple chemiresistive and potentiometric outputs for discriminative gas detection. Such sensor is applicable to a wide range of semiconducting electrodes and solid electrolytes, which allows to tailor and optimize the sensing pattern by tuning the material combination and conditions. The sensor performance is boosted by equipping a mixed-conducting perovskite electrode with reverse potentiometric polarity. A conceptual sensor with dual sensitive electrodes achieves superior three-dimensional (sub)ppm sensing and discrimination of humidity and seven hazardous gases (2-Ethylhexanol, ethanol, acetone, toluene, ammonia, carbon monoxide, and nitrogen dioxide), and enables accurate and early warning of fire hazards. Our findings offer possibilities to design simple, compact, inexpensive, and highly efficient multivariate gas sensors.

Hydration Activates Dual-Confined Shape-Memory Effects of Cold-Reprogrammable Photonic Crystals

Shape-memory photonic crystals (SMPCs) transform the nanoscale deformation of copolymers into structural color through an undifferentiated response to stimuli; however, activatable selective responses are extremely rare. Herein, activatable dual confined shape-memory effects (CSMEs) derived from the remodeling of the interchain hydrogen bonds (H-bonds) in cold-programmable SMPCs are revealed. The first level is the water-triggered reconstruction of interchain H-bonds, which can activate/lock the collapsed skeleton, showing shape recovery/retention in response to ethanol vapor. The second level is the pressure-induced reorganization of interchain H-bonds that results in the recovered skeleton being locked even when exposed to ethanol vapor or water, while the background porous structure can switch between collapse and recovery. Dual CSMEs result from the Laplace pressure difference and the binding effect of interchain H-bonds in the skeleton according to insights of swelling, in situ deformation tracking, multidimensional infrared spectra, and water wetting/evaporation simulations. The signal interference, source code extraction, and color enhancement of structurally colored patterns can be implemented using CSMEs. This work opens up a new method for fabricating activatable responsive structural color and contributes to the expansion of nanophotonic technology in water printing, erasable watermarks, signal amplifiers, and information coding.

Glasswing-Butterfly-Inspired Multifunctional Scleral Lens and Smartphone Raman Spectrometer for Point-of-Care Tear Biomarker Analysis

Augmenting contact lenses with sensing capabilities requires incorporating multiple functionalities within a diminutive device. Inspired by multifunctional biophotonic nanostructures of glasswing butterflies, a nanostructured scleral lens with enhanced optical, bactericidal, and sensing capabilities is reported. When used in conjunction with a smartphone-integrated Raman spectrometer, the feasibility of point-of-care applications is demonstrated. The bioinspired nanostructures made on parylene films are mounted on the anterior and posterior side of a scleral lens to create a nanostructured lens. Compared to unstructured parylene, nanostructured parylene minimizes glare by 4.3-fold at large viewing angles up to 80^o . When mounted on a scleral lens, the nanostructures block 2.8-fold more ultraviolet (UVA) light while offering 1.1-fold improved transmission in the visible regime. Furthermore, the nanostructures exhibit potent bactericidal activity against Escherichia coli, killing 89% of tested bacteria within 4 h. The same nanostructures, when gold-coated, are used to perform rapid label-free multiplex detection of lysozyme and lactoferrin, the protein biomarkers of the chronic dry eye disease, in whole human tears using drop-coating deposition Raman spectroscopy. The detection of both proteins in whole human tear samples from different subjects using the nanostructured lens produced excellent correlation with commercial enzyme-based assays while simultaneously displaying a 1.5-fold lower standard deviation.

A Family of 2D-MXenes: Synthesis, Properties, and Gas Sensing Applications

Gas sensors, capable of detecting and monitoring trace amounts of gas molecules or volatile organic compounds (VOCs), are in great demand for numerous applications including diagnosing diseases through breath analysis, environmental and personal safety, food and agriculture, and other fields. The continuous emergence of new materials is one of the driving forces for the development of gas sensors. Recently, 2D materials have been gaining huge attention for gas sensing applications, owing to their superior electrical, optical, and mechanical characteristics. Especially for 2D MXenes, high specific area and their rich surface functionalities with tunable electronic structure make them compelling for sensing applications. This Review discusses the latest advancements in the 2D MXenes for gas sensing applications. It starts by briefly explaining the family of MXenes, their synthesis methods, and delamination procedures. Subsequently, it outlines the properties of MXenes. Then it describes the theoretical and experimental aspects of the MXenes-based gas sensors. Discussion is also extended to the relation between sensing performance and the structure, electronic properties, and surface chemistry. Moreover, it highlights the promising potential of these materials in the current gas sensing applications and finally it concludes with the limitations, challenges, and future prospects of 2D MXenes in gas sensing applications.

Functionalized luminescent covalent organic frameworks hybrid material as smart nose for the diagnosis of Huanglongbing

Quantitative identification of several volatile organic compounds (VOCs) associated with the same disease provides a strong guarantee of the accurate analysis of the disease. Designing a single luminescent material to interact differently with multiple analytes can generate response patterns with remarkable diversity. Here, a highly green luminescent imine-based 2D COF (TtDFP) is designed and synthesized. TtDFP has ultrasensitive detection performance for trace water in organic solvent. Constructing a ratiometric fluorescence sensor can improve sensitivity for detecting analytes. To contrast the fluorescence signals of Eu³⁺ and COFs in sensing assays, a simple postsynthetic modification (PSM) method is used to introduce Eu³⁺ into TtDFP. The obtained red luminescent hybrid material Eu³⁺@TtDFP EVA film can be a fluorescent nose capable of "sniffing out" and quantifying VOCs (GA and PhA) associated with Huanglongbing (HLB, a devastating disease of citrus) at ppb levels. This work provides a technique of developing functionalized COF hybrid material to facilitate the distinction of various VOCs, which can also be extended to monitor the levels of other VOCs relevant to human health.

Bio-inspired, bimetal ZIF-derived hollow carbon/MXene microstructure aim for superior microwave absorption

Electromagnetic pollution has become an increasingly important problem which has drawbacks to both the accurate operation of the electronic facilities and the safety of human beings. To alleviate and eliminate electromagnetic irradiation, it is inevitable to design microwave absorption materials with desirable absorption intensity and broad effective frequency bandwidth. The combination of carbon-based materials and magnetic materials is an adoptable strategy to perform remarkable microwave absorption performance, while the microstructure should not be ignored. Inspired by the electromagnetic response behaviors of the microstructure from the leafhopper, the hetero-microstructure with hollow void is constructed by adopting bimetal ZIF as the precursor, followed by an interfacial tailoring strategy through electrostatic assembling and calcinating process, which enhances the microwave absorption performance by integrating the merits between the components and the micro-structure. The minimum value of reflection loss achieved -76.40 dB at 7.50 GHz under filler loading of 20% with the thickness of 2.92 mm. Besides, the effective absorption bandwidth could be tailored from 3.55 to 18 GHz among different thicknesses as required. The bio-inspired strategy is validated as a promising method, exhibiting great potential in the designing of the next-generation microwave absorber.

Virtual Sensor Array Based on Piezoelectric Cantilever Resonator for Identification of Volatile Organic Compounds

Piezoelectric cantilever resonator is one of the most promising platforms for real-time sensing of volatile organic compounds (VOCs). However, it has been a great challenge to eliminate the cross-sensitivity of various VOCs for these cantilever-based VOC sensors. Herein, a virtual sensor array (VSA) is proposed on the basis of a sensing layer of GO film deposited onto an AlN piezoelectric cantilever with five groups of top electrodes for identification of various VOCs. Different groups of top electrodes are applied to obtain high amplitudes of multiple resonance peaks for the cantilever, thus achieving low limits of detection (LODs) to VOCs. Frequency shifts of multiple resonant modes and changes of impedance values are taken as the responses of the proposed VSA to VOCs, and these multidimensional responses generate a unique fingerprint for each VOC. On the basis of machine learning algorithms, the proposed VSA can accurately identify different types of VOCs and mixtures with accuracies of 95.8 and 87.5%, respectively. Furthermore, the VSA has successfully been applied to identify the emissions from healthy plants and "plants with late blight" with an accuracy of 89%. The high levels of identifications show great potentials of the VSA for diagnosis of infectious plant diseases by detecting VOC biomarkers.

Sensors for Volatile Organic Compounds

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

Supervised dimension reduction for optical vapor sensing

Detecting and identifying vapors at low concentrations is important for air quality assessment, food quality assurance, and homeland security. Optical vapor sensing using photonic crystals has shown promise for rapid vapor detection and identification. Despite the recent advances of optical sensing using photonic crystals, the data analysis method commonly used in this field has been limited to an unsupervised method called principal component analysis (PCA). In this study, we applied four different supervised dimension reduction methods on differential reflectance spectra data from optical vapor sensing experiments. We found that two of the supervised methods, linear discriminant analysis and least-squares regression PCA, yielded better interclass separation, vapor identification and improved classification accuracy compared to PCA.

Smart Chemical Engineering-Based Lightweight and Miniaturized Attachable Systems for Advanced Drug Delivery and Diagnostics

Smart attachable systems have attracted much attention owing to their capabilities in terms of body performance evaluation, disease diagnostics, and drug delivery. Recent advances in chemical and engineering techniques provide many opportunities to improve device fabrication and applications owing to the advantages of being lightweight and easy to control as well as their battery absence and functional diversity. This review highlights the latest developments in the field of chemical engineering-based lightweight and miniaturized attachable systems, which are mainly inspired by the natural world. Their applications for real-time monitoring, point-of-care sampling, biomarker detection, and controlled release are discussed thoroughly with respect to specific products/prototypes. The perspectives of the field, including persistence guarantee, burden reduction, and personality improvement, are also discussed. It is believed that chemical engineering-based lightweight and miniaturized attachable systems have good potential in both clinical and industrial fields, indicating a large potential to improve human lives in the near future.

A universal array platform for ultrasensitive, high-throughput and microvolume detection of heavy metal, nucleic acid and bacteria based on photonic crystals combined with DNA nanomachine

The development of a universal, sensitive, and rapid assay platform to achieve detections of heavy metal, nucleic acid and bacteria is of great significance but it also faces a thorny challenge. Herein, a novel and universal array platform was developed by combining photonic crystals (PCs) and DNA nanomachine. The developed array platform integrated the physical and biological signal amplification ability of PCs and DNA nanomachine, resulting in ultrasensitive detections of Hg²⁺, DNA, and Shigella sonnei with limits of detection (LODs) of 22.1 ppt, 31.6 fM, and 9 CFU/mL, respectively. More importantly, by utilizing a microplate reader as signal output device, the array achieved high-throughput scanning (96 samples/3 min) with only 2 μL loading sample, which is advantageous for the detection of infectious dangerous targets. In addition, the PCs array could be obtained easily and rapidly based on self-assembly of colloidal nanospheres, and the DNA nanomachine was operated with enzyme-free and time-saving features. Benefiting from these merits, the proposed PCs array offered a powerful universal platform for large-scale detection of various analytes in the fields of pollution monitoring, epidemic control, and public health.

Wide-Gamut Biomimetic Structural Colors from Interference-Assisted Two-Photon Polymerization

Two-photon polymerization (TPP) is an emerging direct laser writing technique for the fabrication of structural colors. However, its coloration ability is suppressed as the vertical resolution is up to several microns. To solve this issue, an interference-assisted TPP technique was employed. Laser interference at a highly reflective interface produced the periodic energy redistribution along the vertical direction, turning the laser voxel into multilayer structures and confirming this technology as a facile and robust method for precise control of its vertical feature size. Biomimetic structural colors (BSCs) inspired from the ridge-lamella configurations in the Morph butterflies were fabricated using this improved TPP technique. The coloration mechanisms of the multilayer interference from the lamella layers, the thin-film interference from the fusion of multilayers, and the hybrid situations were systematically studied. These BSC colors were grouped as pixel palettes with various TPP parameters corresponding to each other, and they spanned almost the entire standard red-green-blue color space. Moreover, under optimized conditions, it was possible to fabricate a 1 cm² area within 2.5 h. These features make interference-assisted TPP an ideal coloration method for practical applications, such as display, decoration, sensing, and so on.

Field-Effect Sensors Using Biomaterials for Chemical Sensing

After millions of years of evolution, biological chemical sensing systems (i.e., olfactory and taste systems) have become very powerful natural systems which show extreme high performances in detecting and discriminating various chemical substances. Creating field-effect sensors using biomaterials that are able to detect specific target chemical substances with high sensitivity would have broad applications in many areas, ranging from biomedicine and environments to the food industry, but this has proved extremely challenging. Over decades of intense research, field-effect sensors using biomaterials for chemical sensing have achieved significant progress and have shown promising prospects and potential applications. This review will summarize the most recent advances in the development of field-effect sensors using biomaterials for chemical sensing with an emphasis on those using functional biomaterials as sensing elements such as olfactory and taste cells and receptors. Firstly, unique principles and approaches for the development of these field-effect sensors using biomaterials will be introduced. Then, the major types of field-effect sensors using biomaterials will be presented, which includes field-effect transistor (FET), light-addressable potentiometric sensor (LAPS), and capacitive electrolyte–insulator–semiconductor (EIS) sensors. Finally, the current limitations, main challenges and future trends of field-effect sensors using biomaterials for chemical sensing will be proposed and discussed.

Virtual Sensor Array Based on Butterworth-Van Dyke Equivalent Model of QCM for Selective Detection of Volatile Organic Compounds

Recently virtual sensor arrays (VSAs) have been developed to improve the selectivity of volatile organic compound (VOC) sensors. However, most reported VSAs rely on detecting single property change of the sensing material after their exposure to VOCs, thus resulting in a loss of much valuable information. In this work, we propose a VSA with the high dimensionality of outputs based on a quartz crystal microbalance (QCM) and a sensing layer of MXene. Changes in both mechanical and electrical properties of the MXene film are utilized in the detection of the VOCs. We take the changes of parameters of the Butterworth-van Dyke model for the QCM-based sensor operated at multiple harmonics as the responses of the VSA to various VOCs. The dimensionality of the VSA's responses has been expanded to four independent outputs, and the responses to the VOCs have shown good linearity in multidimensional space. The response and recovery times are 16 and 54 s, respectively. Based on machine learning algorithms, the proposed VSA accurately identifies different VOCs and mixtures, as well as quantifies the targeted VOC in complex backgrounds (with an accuracy of 90.6%). Moreover, we demonstrate the capacity of the VSA to identify "patients with diabetic ketosis" from volunteers with an accuracy of 95%, based on the detection of their exhaled breath. The QCM-based VSA shows great potential for detecting VOC biomarkers in human breath for disease diagnosis.

Biomimetic Chip Enhanced Time-Gated Luminescent CRISPR-Cas12a Biosensors under Functional DNA Regulation

Despite that the currently discovered CRISPR-Cas12a system is beneficial for improving the detection accuracy and design flexibility of luminescent biosensors, there are still challenges to extend target species and strengthen adaptability in complicated biological media. To conquer these obstacles, we present here some useful strategies. For the former, the limitation to nucleic acids assay is broken through by introducing a simple functional DNA regulation pathway to activate the unique trans-cleavage effect of this CRISPR system, under which the expected biosensors are capable of effectively transducing a protein (employing dual aptamers) and a metal ion (employing DNAzyme). For the latter, a time-gated luminescence resonance energy transfer imaging manner using a long-persistent nanophosphor as the energy donor is performed to completely eliminate the background interference and a nature-inspired biomimetic periodic chip constructed by photonic crystals is further combined to enhance the persistent luminescence. In line with the above efforts, the improved CRISPR-Cas12a luminescent biosensor not only exhibits a sound analysis performance toward the model targets (carcinoembryonic antigen and Na⁺) but also owns a strong anti-interference feature to actualize accurate sensing in human plasma samples, offering a new and applicative analytical tool for laboratory medicine.

Microcellular sensing media with ternary transparency states for fast and intuitive identification of unknown liquids

Rapid, accurate, and intuitive detection of unknown liquids is greatly important for various fields such as food and drink safety, management of chemical hazards, manufacturing process monitoring, and so on. Here, we demonstrate a highly responsive and selective transparency-switching medium for on-site, visual identification of various liquids. The light scattering–based sensing medium, which is designed to be composed of polymeric interphase voids and hollow nanoparticles, provides an extremely large transmittance window (>95%) with outstanding selectivity and versatility. This sensing medium features ternary transparency states (transparent, semitransparent, and opaque) when immersed in liquids depending on liquid-polymer interactions and diffusion kinetics. Several different types of these transparency-changing media can be configured into an arrayed platform to discriminate a wide variety of liquids and also quantify their mixing ratios. The outstanding versatility and user friendliness of the sensing platform allow the development of a practical tool for discrimination of diverse organic liquids.

Direct laser writing of vapour-responsive photonic arrays

Using direct laser writing, arrays of optically responsive ionogel structures were fabricated. To demonstrate their responsive nature, visible colour changes in the presence of different solvent vapours were investigated. This represents a new departure for photonic structural colouration, in which the fabricating structure shows a programmable, controllable, and dynamic stimuli response.

A flexible virtual sensor array based on laser-induced graphene and MXene for detecting volatile organic compounds in human breath

Detecting volatile organic compounds (VOCs) in human breath is critical for the early diagnosis of diseases. Good selectivity of VOC sensors is crucial for the accurate analysis of VOC biomarkers in human breath, which consists of more than 200 types of VOCs. In this paper, a flexible virtual sensor array (FVSA) was proposed based on a sensing layer of MXene and laser-induced graphene interdigital electrodes (LIG-IDEs) for detecting VOCs in exhaled human breath. The fabrication of LIG-IDEs avoids the costly and complicated procedures required for the preparation of traditional IDEs. The FVSA's responses of multiple parameters help build a unique fingerprint for each VOC, without a need for changing the temperature of the sensing element, which is commonly used in the VSA of semiconductor VOC sensors. Based on machine learning algorithms, we have achieved highly precise recognition of different VOCs and mixtures and accurate prediction (accuracy of 89.1%) of the objective VOC's concentration in variable backgrounds using this proposed FVSA. Moreover, a blind analysis validates the capacity of the FVSA to identify alcohol content in human breath with an accuracy of 88.9% using breath samples from volunteers before and after alcohol consumption. These results show that the proposed FVSA is promising for the detection of VOC biomarkers in human exhaled breath and early diagnosis of diseases.

Bio-inspired Ag nanovilli-based sandwich-type SERS aptasensor for ultrasensitive and selective detection of 25-hydroxy vitamin D3

Vitamin D has been identified as an essential biomarker for various diseases such as rheumatoid arthritis, cancer, and cardiovascular diseases. Recently, many reports have demonstrated a potential link between vitamin D and systemic infections, including coronavirus disease 2019. The villi of the small intestine increase the surface area of the intestinal walls, demonstrating exceptionally efficient absorption of nutrients in the lumen and adding digestive secretions. In this study, based on the villi structure, we developed a bio-inspired silver nanovilli-based sandwich-type surface enhanced Raman scattering aptasensor for the ultrasensitive and selective detection of 25-hydroxy vitamin D₃. The densely packed nanovilli structure enhanced the Raman signal, forming hotspots owing to its large surface area. Using experiments and electromagnetic simulations, we optimized the nanovilli structure as a SERS sensor. The sandwich-type aptasensor was designed using an aptamer and 4-Phenyl-1,2,4-triazoline-3,5-dione-methylene blue complex. The nanovilli-based aptasensor could sensitively detect various concentrations of 25-hydroxy vitamin D₃, ranging from those found in deficient to excess conditions. The detection limit of the nanovilli-based sandwich-type aptasensor for 25-hydroxy vitamin D₃ was 0.001 ng/mL, which is much lower than the deficiency concentration, and was detectable even in the human serum. In addition, our proposed sensor exhibited good repeatability (17.76%) and reproducibility (7.47%). Moreover, the nanovilli-based sandwich-type SERS aptasensor could selectively distinguish 25-hydroxy vitamin D₃ from other vitamins. The silver nanovilli-based sandwich-type surface enhanced Raman scattering aptasensor opens a new avenue for the development of a bio-inspired vitamin-sensing platform.

Form Birefringence in Resonant Transducers for the Selective Monitoring of VOCs under Ambient Conditions

In this work, we have developed a new kind of nanocolumnar birefringent Bragg microcavity (BBM) that, tailored by oblique angle deposition, behaves as a selective transducer of volatile organic compounds (VOCs). Unlike the atomic lattice origin of birefringence in anisotropic single crystals, in the BBM, it stems from an anisotropic self-organization at the nanoscale of the voids and structural elements of the layers. The optical adsorption isotherms recorded upon exposure of these nanostructured systems to water vapor and VOCs have revealed a rich yet unexplored phenomenology linked to their optical activity that provides both capacity for vapor identification and partial pressure determination. This photonic response has been reproduced with a theoretical model accounting for the evolution of the form birefringence of the individual layers upon vapor condensation in nanopores and internanocolumnar voids. BBMs that repel water vapor but are accessible to VOCs have been also developed through grafting of their internal surfaces with perfluorooctyltriethoxysilane molecules. These nanostructured photonic systems are proposed for the development of transducers that, operating under environmental conditions, may respond specifically to VOCs without any influence by the degree of humidity of the medium.

Intelligent Packaging for Real-Time Monitoring of Food-Quality: Current and Future Developments

Food packaging encompasses the topical role of preserving food, hence, extending the shelf-life, while ensuring the highest quality and safety along the production chain as well as during storage. Intelligent food packaging further develops the functions of traditional packages by introducing the capability of continuously monitoring food quality during the whole chain to assess and reduce the insurgence of food-borne disease and food waste. To this purpose, several sensing systems based on different food quality indicators have been proposed in recent years, but commercial applications remain a challenge. This review provides a critical summary of responsive systems employed in the real-time monitoring of food quality and preservation state. First, food quality indicators are briefly presented, and subsequently, their exploitation to fabricate intelligent packaging based on responsive materials is discussed. Finally, current challenges and future trends are reviewed to highlight the importance of concentrating efforts on developing new functional solutions.

Point-of-care test system for detection of immunoglobulin-G and -M against nucleocapsid protein and spike glycoprotein of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) epidemic continues to ravage the world. In epidemic control, dealing with a large number of samples is a huge challenge. In this study, a point-of-care test (POCT) system was successfully developed and applied for rapid and accurate detection of immunoglobulin-G and -M against nucleocapsid protein (anti-N IgG/IgM) and receptor-binding domain in spike glycoprotein (anti-S-RBD IgG/IgM) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Any one of the IgG/IgM found in a sample was identified as positive. The POCT system contains colloidal gold-based lateral flow immunoassay test strips, homemade portable reader, and certified reference materials, which detected anti-N and anti-S-RBD IgG/IgM objectively in serum within 15 min. Receiver operating characteristic curve analysis was used to determine the optimal cutoff values, sensitivity, and specificity. It exhibited equal to or better performances than four approved commercial kits. Results of the system and chemiluminescence immunoassay kit detecting 108 suspicious samples had high consistency with kappa coefficient at 0.804 (P < 0.001). Besides, the levels and alterations of the IgG/IgM in an inpatient were primarily investigated by the POCT system. Those results suggested the POCT system possess the potential to contribute to rapid and accurate serological diagnosis and epidemiological survey of COVID-19.

Butterfly wing architectures inspire sensor and energy applications

Natural biological systems are constantly developing efficient mechanisms to counter adverse effects of increasing human population and depleting energy resources. Their intelligent mechanisms are characterized by the ability to detect changes in the environment, store and evaluate information, and respond to external stimuli. Bio-inspired replication into man-made functional materials guarantees enhancement of characteristics and performance. Specifically, butterfly architectures have inspired the fabrication of sensor and energy materials by replicating their unique micro/nanostructures, light-trapping mechanisms and selective responses to external stimuli. These bio-inspired sensor and energy materials have shown improved performance in harnessing renewable energy, environmental remediation and health monitoring. Therefore, this review highlights recent progress reported on the classification of butterfly wing scale architectures and explores several bio-inspired sensor and energy applications.

The evolution of structural colour in butterflies

Butterflies display some of the most striking examples of structural colour in nature. These colours originate from cuticular scales that cover the wing surface, which have evolved a diverse suite of optical nanostructures capable of manipulating light. In this review we explore recent advances in the evolution of structural colour in butterflies. We discuss new insights into the underlying genetics and development of the structural colours in various nanostructure types. Improvements in -omic and imaging technologies have been paramount to these new advances and have permitted an increased appreciation of their development and evolution.

Bio-inspired gas sensing: boosting performance with sensor optimization guided by "machine learning"

The performance of existing gas sensors often degrades in field conditions because of the loss of measurement accuracy in the presence of interferences. Thus, new sensing approaches are required with improved sensor selectivity. We are developing a new generation of gas sensors, known as multivariable sensors, that have several independent responses for multi-gas detection with a single sensor. In this study, we analyze the capabilities of natural and fabricated photonic three-dimensional (3-D) nanostructures as sensors for the detection of different gaseous species, such as vapors and non-condensable gases. We employed bare Morpho butterfly wing scales to control their gas selectivity with different illumination angles. Next, we chemically functionalized Morpho butterfly wing scales with a fluorinated silane to boost the response of these nanostructures to the vapors of interest and to suppress the response to ambient humidity. Further, we followed our previously developed design rules for sensing nanostructures and fabricated bioinspired inorganic 3-D nanostructures to achieve functionality beyond natural Morpho scales. These fabricated nanostructures have embedded catalytically active gold nanoparticles to operate at high temperatures of ≈300 °C for the detection of gases for solid oxide fuel cell (SOFC) applications. Our performance advances in the detection of multiple gaseous species with specific nanostructure designs were achieved by coupling the spectral responses of these nanostructures with machine learning (a.k.a. multivariate analysis, chemometrics) tools. Our newly acquired knowledge from studies of these natural and fabricated inorganic nanostructures coupled with machine learning data analytics allowed us to advance our design rules for sensing nanostructures toward the required gas selectivity for numerous gas monitoring scenarios at room and high temperatures for industrial, environmental, and other applications.

Microfluidics in Gas Sensing and Artificial Olfaction

Rapid, real-time, and non-invasive identification of volatile organic compounds (VOCs) and gases is an increasingly relevant field, with applications in areas such as healthcare, agriculture, or industry. Ideal characteristics of VOC and gas sensing devices used for artificial olfaction include portability and affordability, low power consumption, fast response, high selectivity, and sensitivity. Microfluidics meets all these requirements and allows for in situ operation and small sample amounts, providing many advantages compared to conventional methods using sophisticated apparatus such as gas chromatography and mass spectrometry. This review covers the work accomplished so far regarding microfluidic devices for gas sensing and artificial olfaction. Systems utilizing electrical and optical transduction, as well as several system designs engineered throughout the years are summarized, and future perspectives in the field are discussed.

Additive Combination of Spectra Reflected from Porous Silicon and Carbon/Porous Silicon Rugate Filters to Improve Vapor Selectivity

Selectivity remains a challenge for rapid optical vapor sensing via light reflected from porous silicon photonic crystals. This work highlights a method to increase optical vapor selectivity of porous silicon rugate filters by analyzing additive spectra from two rugate filter substrates with different functionalities, an oxidized and carbonized surface. Individually, both porous silicon rugate filters demonstrated sensitivity but not selectivity toward the vapor analytes. However, differences in peak shift trends between the two substrates suggested differences in vapor affinities for the surfaces. By adding the two spectra, improvements to selectivity relative to the individual surfaces were observed even at low vapor pressures and for analytes of similar polarity, refractive index, and concentration. These results are expected to contribute toward optical vapor selectivity improvements in one-dimensional porous silicon photonic crystals.

Stability and Selective Vapor Sensing of Structurally Colored Lepidopteran Wings Under Humid Conditions

Biological photonic nanoarchitectures are capable of rapidly and chemically selectively sensing volatile organic compounds due to changing color when exposed to such vapors. Here, stability and the vapor sensing properties of butterfly and moth wings were investigated by optical spectroscopy in the presence of water vapor. It was shown that repeated 30 s vapor exposures over 50 min did not change the resulting optical response signal in a time-dependent manner, and after 5-min exposures the sensor preserved its initial properties. Time-dependent response signals were shown to be species-specific, and by using five test substances they were also shown to be substance-specific. The latter was also evaluated using principal component analysis, which showed that the time-dependent optical responses can be used for real-time analysis of the vapors. It was demonstrated that the capability to detect volatile organic compounds was preserved in the presence of water vapor: high-intensity color change signals with short response times were measured in 25% relative humidity, similar to the one-component case; therefore, our results can contribute to the development of biological photonic nanoarchitecture-based vapor detectors for real-world applications, like living and working environments.

Bioinspired 2D Nanomaterials for Sustainable Applications

The increasing demand for constructing ecological civilization and promoting socially sustainable development has encouraged scientists to develop bioinspired materials with required properties and functions. By bringing science and nature together, plenty of novel materials with extraordinary properties can be created by learning the best from natural species. In combination with the exceptional features of 2D nanomaterials, bioinspired 2D nanomaterials and technologies have delivered significant achievements. Here, the progress over the past decade in bioinspired 2D photonic structures, energy nanomaterials, and superwetting materials, is summarized, together with the challenges and opportunities in developing bioinspired materials for sustainable energy and environmental technologies.

Vapor detection through dynamic process of molecule desorption from butterfly wings

This work explores an alternative vapor sensing mechanism through analyzing dynamic desorption process from butterfly wings for the differentiation of both individual and mixed vapors quantitatively. Morpho butterfly wings have been used in differentiating individual vapors, but it is challenging to use them for the differentiation of mixed vapor quantitatively. This paper demonstrates the use of Morpho butterfly wings for the sensitive and selective detection of closely related vapors in mixtures. Principal components analysis (PCA) is used to process the reflectance spectra of the wing scales during dynamic desorption of different vapors. With the desorption-based detection mechanism, individual vapors with different concentrations and mixed vapors with different mixing ratios can be differentiated using the butterfly wing based sensors. Both the original butterfly wings and butterfly wings with surface modification show the capability in distinguishing vapors in mixtures, which may offer a guideline for further improving selectivity and sensitivity of bioinspired sensors.

An electronic nose using a single graphene FET and machine learning for water, methanol, and ethanol

The poor gas selectivity problem has been a long-standing issue for miniaturized chemical-resistor gas sensors. The electronic nose (e-nose) was proposed in the 1980s to tackle the selectivity issue, but it required top-down chemical functionalization processes to deposit multiple functional materials. Here, we report a novel gas-sensing scheme using a single graphene field-effect transistor (GFET) and machine learning to realize gas selectivity under particular conditions by combining the unique properties of the GFET and e-nose concept. Instead of using multiple functional materials, the gas-sensing conductivity profiles of a GFET are recorded and decoupled into four distinctive physical properties and projected onto a feature space as 4D output vectors and classified to differentiated target gases by using machine-learning analyses. Our single-GFET approach coupled with trained pattern recognition algorithms was able to classify water, methanol, and ethanol vapors with high accuracy quantitatively when they were tested individually. Furthermore, the gas-sensing patterns of methanol were qualitatively distinguished from those of water vapor in a binary mixture condition, suggesting that the proposed scheme is capable of differentiating a gas from the realistic scenario of an ambient environment with background humidity. As such, this work offers a new class of gas-sensing schemes using a single GFET without multiple functional materials toward miniaturized e-noses.

Nanomaterial-based gas sensors used for breath diagnosis

Gas-sensing applications commonly use nanomaterials (NMs) because of their unique physicochemical properties, including a high surface-to-volume ratio, enormous number of active sites, controllable morphology, and potential for miniaturisation.

Vapor Selectivity of a Natural Photonic Crystal to Binary and Tertiary Mixtures Containing Chemical Warfare Agent Simulants

Vapor sensing via light reflected from photonic crystals has been increasingly studied as a means to rapidly identify analytes, though few studies have characterized vapor mixtures or chemical warfare agent simulants via this technique. In this work, light reflected from the natural photonic crystals found within the wing scales of the Morpho didius butterfly was analyzed after exposure to binary and tertiary mixtures containing dimethyl methylphosphonate, a nerve agent simulant, and dichloropentane, a mustard gas simulant. Distinguishable spectra were generated with concentrations tested as low as 30 ppm and 60 ppm for dimethyl methylphosphonate and dichloropentane, respectively. Individual vapors, as well as mixtures, yielded unique responses over a range of concentrations, though the response of binary and tertiary mixtures was not always found to be additive. Thus, while selective and sensitive to vapor mixtures containing chemical warfare agent simulants, this technique presents challenges to identifying these simulants at a sensitivity level appropriate for their toxicity.

Highly Invisible Photonic Crystal Patterns Encrypted in an Inverse Opaline Macroporous Polyurethane Film for Anti-Counterfeiting Applications

Invisible photonic crystal (PC) pattern with encrypted and discoverable information is potentially useful for anti-counterfeiting labels, but it is still a big challenge to realize strict invisibility, fast response, and convenient triggering. Here, a new kind of soaking-revealed invisible PC pattern is fabricated by the regional coating of "ethylene glycol-ethanol" ink on a collapsed inverse opaline macroporous polyurethane (IOM-PU) film, followed by a quick thermal treatment. During the above process, wet heating retains the collapsed but recoverable IOM structure, but dry heating disables the recovery of ordered IOM structure due to the adhesion of macropore walls, which render the "pattern" and the "background" with different optical responses to the solvent. In the dry state, the pattern was invisible because both the collapsed IOM-PU film and the adhesive PU film are colorless and transparent. Once the sample is soaked in ethanol-water mixtures, the invisible pattern appears immediately because only the "wet-heated" region recovers the ordered macroporous structure and shows color, which forms a significant contrast in color to the "dry-heated" region. Compared to the previously invisible PC pattern, the current material has many superior properties, such as high invisibility, large color contrast in showing, excellent recyclability, and good toughness in bending and stretching.

Iridescence-controlled and flexibly tunable retroreflective structural color film for smart displays

Structural color materials, which use nano- or microstructures to reflect specific wavelengths of ambient white light, have drawn much attention owing to their wide applications ranging from optoelectronics, coatings, to energy-efficient reflective displays. Although various structural color materials based on specular or diffuse reflection have been demonstrated, neither efficient retroreflective structural colors nor iridescent and non-iridescent colors to different observers simultaneously were reported by existing artificial or natural structural color materials. Here, we show that by partially embedding a monolayer of polymer microspheres on the sticky side of a transparent tape, the spontaneously formed interferometric structure on the surface of air-cushioned microspheres can lead to unique structural colors that remain non-iridescent under coaxial illumination and viewing conditions, but appear iridescent under noncoaxial illumination and viewing conditions. Our findings demonstrate a smart, energy-efficient, and tunable retroreflective structural color material that is especially suitable for nighttime traffic safety and advertisement display applications.

Natural spider silk as a photonics component for humidity sensing

Biological microfibers are remarkable materials with diversity in their chemistry, structure and functions that provide a range of solutions for photonic structures. Here we proposed and demonstrated a humidity detection technique for spectral tuning of whispering gallery modes (WGMs) in a cylindrical microresonator formed by a piece of spider egg sac silk (SpEss) from Araneus Ventricosus. We launched a supercontinuum laser into the SpEss via a tapered single-mode fiber to excite WGMs. When the ambient humidity changed, the profile diameter and effective refractive index of the SpEss changed, which caused the WGM resonant dips to shift. The experimental results showed that when the relative humidity (RH) changed from 20% to 75% RH, the average testing sensitivity of the proposed sensor was 389.1 pm/%RH and the maximum testing sensitivity was 606.7 pm/%RH in the range of 60% to 75% RH. Also, the proposed SpEss-based humidity sensor showed a fast response time of 494 ms and good repeatability with fluctuations less than 8% compared with the initial test values. The SpEss-based sensor expanded the application of spider silk as a biodegradable and biocompatible material in biochemical sensing.

Optical Detection of Vapor Mixtures Using Structurally Colored Butterfly and Moth Wings

Photonic nanoarchitectures in the wing scales of butterflies and moths are capable of fast and chemically selective vapor sensing due to changing color when volatile vapors are introduced to the surrounding atmosphere. This process is based on the capillary condensation of the vapors, which results in the conformal change of the chitin-air nanoarchitectures and leads to a vapor-specific optical response. Here, we investigated the optical responses of the wing scales of several butterfly and moth species when mixtures of different volatile vapors were applied to the surrounding atmosphere. We found that the optical responses for the different vapor mixtures fell between the optical responses of the two pure solvents in all the investigated specimens. The detailed evaluation, using principal component analysis, showed that the butterfly-wing-based sensor material is capable of differentiating between vapor mixtures as the structural color response was found to be characteristic for each of them.

Detection and Discrimination of Volatile Organic Compounds using a Single Film Bulk Acoustic Wave Resonator with Temperature Modulation as a Multiparameter Virtual Sensor Array

This paper describes the detection and discrimination of volatile organic compounds (VOCs) using an e-nose system based on a multiparameter virtual sensor array (VSA), which consists of a single-chip temperature-compensated film bulk acoustic wave resonator (TC-FBAR) coated with 20-bilayer self-assembled poly(sodium 4-styrenesulfonate)/poly(diallyldimethylammonium chloride) thin films. The high-frequency and microscale FBAR multiparameter VSA was realized by temperature modulation, which can greatly reduce the cost and complexity compared to those of a traditional e-nose system and can allow it to operate at different temperatures. The discrimination effect depends on the synergy of temperature modulation and the sensing material. For proof-of-concept validation purposes, the TC-FBAR was exposed to six different VOC vapors at six different gas partial pressures by real-time VOC static detection and dynamic detection. The resulting frequency shifts and impedance responses were measured at different temperatures and evaluated using principal component analysis and linear discriminant analysis, which revealed that all analytes can be distinguished and classified with more than 97% accuracy. To the best of our knowledge, this report is the first on an FBAR multiparameter VSA based on temperature modulation, and the proposed novel VSA shows great potential as a compact and promising e-nose system integrated in commercial electronic products.