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

Photonic crystal gas sensors based on metal-organic frameworks and polymers

A photonic crystal (PC) is an optical microstructure with an adjustable dielectric constant. The PC sensor was deemed a powerful tool for gas molecule detection due to its excellent sensitivity, stability, online use and tailorable optical performance. The detection signals are generated by monitoring the changes of the photonic band gap when the sensing behavior occurs. Recently, many efforts have been devoted to improving the PC sensor's detection performance and reducing technical costs by selecting and refining functional materials. In this case, metal-organic frameworks (MOFs) with a large specific surface, tunable structural properties and polymers with unique swelling properties have attracted increasingly attention. In this review, a systematic review of PC gas sensors based on MOFs and polymers was carried out for the first time. Firstly, the optical properties and gas sensing mechanism of PCs were briefly summarized. Secondly, a detailed discussion of the structural properties and rapid preparation methods of distributed Bragg reflectors (DBRs), opals and inverse opals (IOPCs) was presented. Thirdly, the recent advances in MOF, polymer and MOF/polymer-based PC sensors over the past few years were summarized. It should be noted that the sensitivity and selectivity enhancement strategy by appropriate material species selection, organic ligand functionalization, metal-ion doping, diverse functional material arrays, and multi-component compounding were analyzed in detail. Finally, prospects on PC gas sensors are given in terms of preparation methods, material functionalization and future applications.

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.

Biochemical Methanol Gas Sensor (MeOH Bio-Sniffer) for Non-Invasive Assessment of Intestinal Flora from Breath Methanol

Methanol (MeOH) in exhaled breath has potential for non-invasive assessment of intestinal flora. In this study, we have developed a biochemical gas sensor (bio-sniffer) for MeOH in the gas phase using fluorometry and a cascade reaction with two enzymes, alcohol oxidase (AOD) and formaldehyde dehydrogenase (FALDH). In the cascade reaction, oxidation of MeOH was initially catalyzed by AOD to produce formaldehyde, and then this formaldehyde was successively oxidized via FALDH catalysis together with reduction of oxidized form of β-nicotinamide adenine dinucleotide (NAD+). As a result of the cascade reaction, reduced form of NAD (NADH) was produced, and MeOH vapor was measured by detecting autofluorescence of NADH. In the development of the MeOH bio-sniffer, three conditions were optimized: selecting a suitable FALDH for better discrimination of MeOH from ethanol in the cascade reaction; buffer pH that maximizes the cascade reaction; and materials and methods to prevent leaking of NAD+ solution from an AOD-FALDH membrane. The dynamic range of the constructed MeOH bio-sniffer was 0.32-20 ppm, which encompassed the MeOH concentration in exhaled breath of healthy people. The measurement of exhaled breath of a healthy subject showed a similar sensorgram to the standard MeOH vapor. These results suggest that the MeOH bio-sniffer exploiting the cascade reaction will become a powerful tool for the non-invasive intestinal flora testing.

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.

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.

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.