Fluorescent Polymer Sensor Array for Detection and Discrimination of Explosives in Water

Rapid and On-Scene Chemical Identification of Intact Explosives with Portable Near-Infrared Spectroscopy and Multivariate Data Analysis

There is an ongoing forensic and security need for rapid, on-scene, easy-to-use, non-invasive chemical identification of intact energetic materials at pre-explosion crime scenes. Recent technological advances in instrument miniaturization, wireless transfer and cloud storage of digital data, and multivariate data analysis have created new and very promising options for the use of near-infrared (NIR) spectroscopy in forensic science. This study shows that in addition to drugs of abuse, portable NIR spectroscopy with multivariate data analysis also offers excellent opportunities to identify intact energetic materials and mixtures. NIR is able to characterize a broad range of chemicals of interest in forensic explosive investigations, covering both organic and inorganic compounds. NIR characterization of actual forensic casework samples convincingly shows that this technique can handle the chemical diversity encountered in forensic explosive investigations. The detailed chemical information contained in the 1350-2550 nm NIR reflectance spectrum allows for correct compound identification within a given class of energetic materials, including nitro-aromatics, nitro-amines, nitrate esters, and peroxides. In addition, the detailed characterization of mixtures of energetic materials, such as plastic formulations containing PETN (pentaerythritol tetranitrate) and RDX (trinitro triazinane), is feasible. The results presented illustrate that the NIR spectra of energetic compounds and mixtures are sufficiently selective to prevent false-positive results for a broad range of food-related products, household chemicals, raw materials used for the production of home-made explosives, drugs of abuse, and products that are sometimes used to create hoax improvised explosive devices. However, for frequently encountered pyrotechnic mixtures, such as black powder, flash powder, and smokeless powder, and some basic inorganic raw materials, the application of NIR spectroscopy remains challenging. Another challenge is presented by casework samples of contaminated, aged, and degraded energetic materials or poor-quality HMEs (home-made explosives), for which the spectral signature deviates significantly from the reference spectra, potentially leading to false-negative outcomes.

A Gold Nanoparticle-Based Molecular Self-Assembled Colorimetric Chemosensor Array for Monitoring Multiple Organic Oxyanions

Determination of oxyanions is of paramount importance because of the essential role they play in metabolic processes involved in various aquatic environmental problems. In this investigation, a novel chemical sensor array has been developed by using gold nanoparticles modified with different chain lengths of aminothiols (AET-AuNPs) as sensing elements. The proposed sensor array provides a fingerprint-like response pattern originating from cross-reactive binding events and capable of targeting various anions, including the herbicide glyphosate. In addition, chemometric techniques, linear discrimination analysis (LDA) and the support vector machine (SVM) algorithm were employed for analyte classification and regression/prediction. The obtained sensor array demonstrates a remarkable ability to determine multiple oxyanions in both qualitative and quantitative analysis. The described methodology could be used as a simple, sensitive and fast routine analysis for oxyanions in both laboratory and field settings.

Donor triggered aggregation induced dual emission, mechanochromism and sensing of nitroaromatics in aqueous solution

The title paper describes the photophysics of a series of 2-benzylidenemalononitrile (RDC) derivatives, having donor- π - acceptor (D-π-A) architecture in solution, solid state and in hydrosol. Interestingly, it is observed that by tuning the electronic energy levels via changing donor strength, one can play with the emissive properties to the large extent; from non- fluorescent to fluorescent, to aggregation induced dual fluorescence, to aggregation induced enhanced emission (AIEE). The nature of aggregation is studied by Field Emission Scanning Electron Microscope (FESEM). This aggregation induced emission (AIE) in hydrosol is used to develop a thin layer chromatography (TLC) based simple, easy to use technique to identify the trinitrophenol in aqueous solution as well as can distinguish the three positional nitrophenol derivatives.

Conjugated Polymer Nanoparticles Based Fluorescent Electronic Nose for the Identification of Volatile Compounds

A fluorescence sensing array (or fluorescent electronic nose) is designed on a disposable paper card using 36 sets of soluble conjugated polymeric nanoparticles (SCPNs) as sensors to easily identify wide ranges of volatile analytes, including explosives and toxic industrial chemicals (amines and pungent acids). A 108-dimensional vector obtained from the fluorescent color change in the sensing array is defined and directly treated as an index in a standard chemical library (30 kinds of volatile analytes and a control group). Hierarchical clustering analysis (HCA) and principal component analysis (PCA) indicated the diversity in electronic structures; saturated vapor pressure and miscibility of analytes are keys in differentiating the analytes, with electron-rich arenes and alkylamines enhancing fluorescence and electron-deficient analytes attenuating fluorescence. A support vector machine (SVM) works well to predict an unknown sample, reaching 99.5% accuracy. The excellent fluorescence stability (no fluorescence quenching after being exposed in air for one month) and high sensitivity (emission color changes within minutes when exposed to analytes) suggest that the fluorescent polymer-based electronic nose will play an important role in field detection and identification of a wide spreading of hazardous substances.

Selective sensing of 2,4,6-trinitrophenol (TNP) in aqueous media with "aggregation-induced emission enhancement" (AIEE)-active iridium(iii) complexes

A series of new phosphorescent cyclometalated iridium(iii) complexes which possess aggregation-induced emission enhancement (AIEE) detect 2,4,6-trinitrophenol (TNP) selectively with high quenching constants in aqueous media. The sensing mechanism was systematically investigated by mass spectrometry, ¹H and ¹⁹F NMR spectroscopy. X-ray crystal structure analysis reveals an O-HO interaction between TNP and the ancillary ligand which explains the high selectivity for TNP compared to other nitro-aromatics.

Supramolecular fluorescent sensor array for simultaneous qualitative and quantitative analysis of quaternary ammonium herbicides

A supramolecular fluorescent sensor array was firstly used to simultaneously qualitatively and quantitatively analyze quaternary ammonium herbicides.

Dual in vitro and in silico analysis of thiacalix[4]arene dinaphthalene sulfonate for the sensing of 4-nitrotoluene and 2,3-dinitrotoluene

A newly synthesized thiacalix[4]arene dinaphthalene sulfonate, i.e., TCDNS, has been used as a fluorescence quencher for 2,3-DNT as well as 4-NT. The complex was analyzed by ESI-MS and ¹H-NMR titration, which has been further confirmed by the in silico study.

Graphene Nanoplatelet-Polymer Chemiresistive Sensor Arrays for the Detection and Discrimination of Chemical Warfare Agent Simulants

A cross-reactive array of semiselective chemiresistive sensors made of polymer-graphene nanoplatelet (GNP) composite coated electrodes was examined for detection and discrimination of chemical warfare agents (CWA). The arrays employ a set of chemically diverse polymers to generate a unique response signature for multiple CWA simulants and background interferents. The developed sensors' signal remains consistent after repeated exposures to multiple analytes for up to 5 days with a similar signal magnitude across different replicate sensors with the same polymer-GNP coating. An array of 12 sensors each coated with a different polymer-GNP mixture was exposed 100 times to a cycle of single analyte vapors consisting of 5 chemically similar CWA simulants and 8 common background interferents. The collected data was vector normalized to reduce concentration dependency, z-scored to account for baseline drift and signal-to-noise ratio, and Kalman filtered to reduce noise. The processed data was dimensionally reduced with principal component analysis and analyzed with four different machine learning algorithms to evaluate discrimination capabilities. For 5 similarly structured CWA simulants alone 100% classification accuracy was achieved. For all analytes tested 99% classification accuracy was achieved demonstrating the CWA discrimination capabilities of the developed system. The novel sensor fabrication methods and data processing techniques are attractive for development of sensor platforms for discrimination of CWA and other classes of chemical vapors.

Reliability of Aerosol Jet Printed Fluorescence Quenching Sensor Arrays for the Identification and Quantification of Explosive Vapors

One of the primary challenges in explosive detection using fluorescence quenching is the identification and quantification of detected targets. In this work, we explore the reliability of aerosol jet printed sensor arrays for the discrimination of nitroaromatic traces using linear discriminant analysis (LDA). We varied the amount of the deposited material by controlling the printer's shutter to investigate the impact on the detection reliability. For a twofold variation of the amount of the deposited material, we report excellent classification rates between 81 and 96% for the discrimination of nitrobenzene, 1,3-dinitrobenzene, and 2,4-dinitrotoluene at 1, 3, and 10 parts per billion in air, respectively. Our results close to the detection limits indicate a remarkable identification and quantification of explosive trace vapors because of high control of the printing process. This work demonstrates the high potential of digitally printed fluorescence quenching sensor arrays and the excellent capabilities of LDA as a simple supervised statistical learning technique.

Selective chemiluminescence method for the determination of trinitrotoluene based on molecularly imprinted polymer-capped ZnO quantum dots

A novel molecularly imprinted polymer (MIP) based chemiluminescence (CL) assay is described for the determination of TNT in environmental samples.

A field-deployed surface plasmon resonance (SPR) sensor for RDX quantification in environmental waters

A field-deployable surface plasmon resonance (SPR) sensor is reported for the detection RDX at ppb concentration in environmental samples.

Ratiometric fluorescence and mesoporous structure dual signal amplification for sensitive and selective detection of TNT based on MIP@QD fluorescence sensors

MIP@QD fluorescence sensors combined with ratiometric fluorescence techniques and mesoporous silica materials for highly sensitive detection of TNT.

SPR imaging based electronic tongue via landscape images for complex mixture analysis

Electronic noses/tongues (eN/eT) have emerged as promising alternatives for analysis of complex mixtures in the domain of food and beverage quality control. We have recently developed an electronic tongue by combining surface plasmon resonance imaging (SPRi) with an array of non-specific and cross-reactive receptors prepared by simply mixing two small molecules in varying and controlled proportions and allowing the mixtures to self-assemble on the SPRi prism surface. The obtained eT generated novel and unique 2D continuous evolution profiles (CEPs) and 3D continuous evolution landscapes (CELs) based on which the differentiation of complex mixtures such as red wine, beer and milk were successful. The preliminary experiments performed for monitoring the deterioration of UHT milk demonstrated its potential for quality control applications. Furthermore, the eT exhibited good repeatability and stability, capable of operating after a minimum storage period of 5 months.

Dummy molecularly imprinted polymers-capped CdTe quantum dots for the fluorescent sensing of 2,4,6-trinitrotoluene

Molecularly imprinted polymers (MIPs) with trinitrophenol (TNP) as a dummy template molecule capped with CdTe quantum dots (QDs) were prepared using 3-aminopropyltriethoxy silane (APTES) as the functional monomer and tetraethoxysilane (TEOS) as the cross linker through a seed-growth method via a sol-gel process (i.e., DMIP@QDs) for the sensing of 2,4,6-trinitrotoluene (TNT) on the basis of electron-transfer-induced fluorescence quenching. With the presence and increase of TNT in sample solutions, a Meisenheimer complex was formed between TNT and the primary amino groups on the surface of the QDs. The energy of the QDs was transferred to the complex, resulting in the quenching of the QDs and thus decreasing the fluorescence intensity, which allowed the TNT to be sensed optically. DMIP@QDs generated a significantly reduced fluorescent intensity within less than 10 min upon binding TNT. The fluorescence-quenching fractions of the sensor presented a satisfactory linearity with TNT concentrations in the range of 0.8-30 μM, and its limit of detection could reach 0.28 μM. The sensor exhibited distinguished selectivity and a high binding affinity to TNT over its possibly competing molecules of 2,4-dinitrophenol (DNP), 4-nitrophenol (4-NP), phenol, and dinitrotoluene (DNT) because there are more nitro groups in TNT and therefore a stronger electron-withdrawing ability and because it has a high similarity in shape and volume to TNP. The sensor was successfully applied to determine the amount of TNT in soil samples, and the average recoveries of TNT at three spiking levels ranged from 90.3 to 97.8% with relative standard deviations below 5.12%. The results provided an effective way to develop sensors for the rapid recognition and determination of hazardous materials from complex matrices.

Highly selective and sensitive fluorescent paper sensor for nitroaromatic explosive detection

Rapid, sensitive, and selective detection of explosives such as 2,4,6-trinitrotoluene (TNT) and 2,4,6-trinitrophenol (TNP), especially using a facile paper sensor, is in high demand for homeland security and public safety. Although many strategies have been successfully developed for the detection of TNT, it is not easy to differentiate the influence from TNP. Also, few methods were demonstrated for the selective detection of TNP. In this work, via a facile and versatile method, 8-hydroxyquinoline aluminum (Alq(3))-based bluish green fluorescent composite nanospheres were successfully synthesized through self-assembly under vigorous stirring and ultrasonic treatment. These polymer-coated nanocomposites are not only water-stable but also highly luminescent. Based on the dramatic and selective fluorescence quenching of the nanocomposites via adding TNP into the aqueous solution, a sensitive and robust platform was developed for visual detection of TNP in the mixture of nitroaromatics including TNT, 2,4-dinitrotoluene (DNT), and nitrobenzene (NB). Meanwhile, the fluorescence intensity is proportional to the concentration of TNP in the range of 0.05-7.0 μg/mL with the 3σ limit of detection of 32.3 ng/mL. By handwriting or finger printing with TNP solution as ink on the filter paper soaked with the fluorescent nanocomposites, the bluish green fluorescence was instantly and dramatically quenched and the dark patterns were left on the paper. Therefore, a convenient and rapid paper sensor for TNP-selective detection was fabricated.

Luminescence sensors applied to water analysis of organic pollutants--an update

The development of chemical sensors for environmental analysis based on fluorescence, phosphorescence and chemiluminescence signals continues to be a dynamic topic within the sensor field. This review covers the fundamentals of this type of sensors, and an update on recent works devoted to quantifying organic pollutants in environmental waters, focusing on advances since about 2005. Among the wide variety of these contaminants, special attention has been paid polycyclic aromatic hydrocarbons, pesticides, explosives and emerging organic pollutants. The potential of coupling optical sensors with multivariate calibration methods in order to improve the selectivity is also discussed.