Colorimetric sensors are widely used as pH indicators, medical diagnostic devices and detection devices. The colorimetric sensor captures the color changes of a chromic chemical (dye) or array of chromic chemicals when exposed to a target substance (analyte). Sensing is typically carried out using the difference in dye color before and after exposure. This approach neglects the kinetic response, that is, the temporal evolution of the dye, which potentially contains additional information. We investigate the importance of the kinetic response by collecting a sequence of images over time. We applied end-to-end learning using three different convolution neural networks (CNN) and a recurrent network. We compared the performance to logistic regression, k-nearest-neighbor and random forest, where these methods only use the difference color from start to end as feature vector. We found that the CNNs were able to extract features from the kinetic response profiles that significantly improves the accuracy of the sensor. Thus, we conclude that the kinetic responses indeed improves the accuracy, which paves the way for new and better chemical sensors based on colorimetric responses.
We present a data-driven machine learning approach to detect drug- and explosives-precursors using colorimetric sensor technology for air-sampling. The sensing technology has been developed in the context of the CRIM-TRACK project. At present a fully- integrated portable prototype for air sampling with disposable sensing chips and automated data acquisition has been developed. The prototype allows for fast, user-friendly sampling, which has made it possible to produce large datasets of colorimetric data for different target analytes in laboratory and simulated real-world application scenarios. To make use of the highly multi-variate data produced from the colorimetric chip a number of machine learning techniques are employed to provide reliable classification of target analytes from confounders found in the air streams. We demonstrate that a data-driven machine learning method using dimensionality reduction in combination with a probabilistic classifier makes it possible to produce informative features and a high detection rate of analytes. Furthermore, the probabilistic machine learning approach provides a means of automatically identifying unreliable measurements that could produce false predictions. The robustness of the colorimetric sensor has been evaluated in a series of experiments focusing on the amphetamine pre-cursor phenylacetone as well as the improvised explosives pre-cursor hydrogen peroxide. The analysis demonstrates that the system is able to detect analytes in clean air and mixed with substances that occur naturally in real-world sampling scenarios. The technology under development in CRIM-TRACK has the potential as an effective tool to control trafficking of illegal drugs, explosive detection, or in other law enforcement applications.
Detection of illegal compounds requires a reliable, selective and sensitive detection device. The successful device features automated target acquisition, identification and signal processing. It is portable, fast, user friendly, sensitive, specific, and cost efficient. LEAs are in need of such technology. CRIM-TRACK is developing a sensing device based on these requirements. We engage highly skilled specialists from research institutions, industry, SMEs and LEAs and rely on a team of end users to benefit maximally from our prototypes. Currently we can detect minute quantities of drugs, explosives and precursors thereof in laboratory settings. Using colorimetric technology we have developed prototypes that employ disposable sensing chips. Ease of operation and intuitive sensor response are highly prioritized features that we implement as we gather data to feed into machine learning. With machine learning our ability to detect threat compounds amidst harmless substances improves. Different end users prefer their equipment optimized for their specific field. In an explosives-detecting scenario, the end user may prefer false positives over false negatives, while the opposite may be true in a drug-detecting scenario. Such decisions will be programmed to match user preference. Sensor output can be as detailed as the sensor allows. The user can be informed of the statistics behind the detection, identities of all detected substances, and quantities thereof. The response can also be simplified to “yes” vs. “no”. The technology under development in CRIM-TRACK will provide custom officers, police and other authorities with an effective tool to control trafficking of illegal drugs and drug precursors.
Guided-mode resonances in structures having periodicity along at least one dimension were widely employed in the last decade in various optical devices. Initially it was shown that at frequencies close to the second order band gap periodic structures can feature total reflection of light due to the guided modes propagating along the surface of the grating. As an application, this allows to substitute a thick multilayer Bragg mirror in VCSELs by a thin grating-based mirror. Most devices utilizing guided-mode resonances were theoretically and numerically investigated with the idealized model of an infinite periodic structure illuminated by a plane wave. To see how grating-based components can perform in real life we take into account two critical factors: the finite size of the grating and the Gaussian shape of the light source replacing a plane wave. These factors can significantly change and impair the performance of filters, mirrors, sensors and other devices operating by the guided-mode resonance effect. We also show experimentally that for some kinds of gratings guided-mode resonances can vanish if the grating is illuminated by extended source, i.e. heated plate in our case, focused on the sample.
KEYWORDS: Explosives, Sensors, Liquids, Explosives detection, Chemical analysis, Molecules, Principal component analysis, RGB color model, Solids, Nose
In the framework of the research project "Xsense" at the Technical University of Denmark (DTU) we are developing a
simple colorimetric sensor array which can be useful in detection of explosives like DNT, TATP, HMX, RDX and
identification of reagents needed for making homemade explosives. The technology is based on an array of chemoselective
compounds immobilized on a solid support. Upon exposure to the analyte in suspicion the colorimetric array
changes color. Each chosen compound reacts chemo-selectively with analytes of interest. A change in a color signature
indicates the presence of unknown explosives and volatile organic compounds (VOCs).
We are working towards the selection of compounds that undergo color changes in the presence of explosives and
VOCs, as well as the development of an immobilization method for the molecules. Digital imaging of the colorimetric
array before and after exposure to the analytes creates a color difference map which gives a unique fingerprint for each
explosive and VOCs. Such sensing technology can be used for screening relevant explosives in a complex background as
well as to distinguish mixtures of volatile organic compounds distributed in gas and liquid phases. This sensor array is
inexpensive, and can potentially be produced as single use disposable.
In this work protective "sight glasses" for infrared gas sensors showing a sub-wavelength nanostructure with random
patterns have been fabricated by reactive ion etching (RIE) in an easy and comparable cheap single step mask-less
process. By an organic coating, the intrinsic water repellent property of the surface could be enhanced, shown by contact
angle and roll-off angle measurements. The "self-cleaning" surface property and chemical robustness towards aggressive
environments are demonstrated. FT-IR spectroscopy concerning the optical properties of these nanostructured silicon
windows revealed a stable anti-reflective "moth-eye" effect in certain wavelength ranges owing to the nanostructures.
Realizing that no one sensing principle is perfect we set out to combine four fundamentally different sensing principles
into one device. The reasoning is that each sensor will complement the others and provide redundancy under various
environmental conditions. As each sensor can be fabricated using microfabrication the inherent advantages associated
with MEMS technologies such as low fabrication costs and small device size allows us to integrate the four sensors into
one portable device at a low cost.
In an effort to produce a handheld explosives sensor the Xsense project has been initiated at the Technical University of
Denmark in collaboration with a number of partners. Using micro- and nano technological approaches it will be
attempted to integrate four detection principles into a single device. At the end of the project, the consortium aims at
having delivered a sensor platform consisting of four independent detector principles capable of detecting concentrations
of TNT at sub parts-per-billion (ppb) concentrations and with a false positive rate less than 1 parts-per-thousand. The
specificity, sensitivity and reliability are ensured by the use of clever data processing , surface functionalisation and
nanostructured sensors and sensor surfaces.
KEYWORDS: Sensors, Explosives, Bioalcohols, Explosives detection, Color difference, RGB color model, Statistical analysis, Chemical analysis, Molecules, Principal component analysis
In the framework of the research project 'Xsense' at the Technical University of Denmark (DTU) we
are developing a simple colorimetric sensor array which can be useful in detection of explosives like DNT and
TNT, and identification of volatile organic compounds in the presence of water vapor in air. The technology is
based on an array of chemo-responsive dyes immobilized on a solid support. Upon exposure to the analyte in
suspicion the dye array changes color. Each chosen dye reacts chemo selectively with analytes of interest. A
change in a color signature indicates the presence of unknown explosives and volatile organic compounds
(VOCs).
We are working towards the selection of dyes that undergo color changes in the presence of explosives
and VOCs, as well as the development of an immobilization method for the molecules. Digital imaging of the
dye array before and after exposure to the analytes creates a color difference map which gives a unique
fingerprint for each explosive and volatile organic compound. Such sensing technology can be used to screen for
relevant explosives in a complex background as well as to distinguish mixtures of volatile organic compounds
distributed in gas phase. This sensor array is inexpensive, and can potentially be produced as single use
disposable.
Dye doped polymer photonic crystal band edge lasers are applied for evanescent wave sensing of cells. The lasers
are rectangular shaped slab waveguides of dye doped polymer on a glass substrate, where a photonic crystal
is formed by 100 nm deep air-holes in the surface of the 375 nm high waveguides. The lasers are fabricated
by combined nanoimprint and photolithography (CNP) in Ormocore hybrid polymer doped with the laser dye
Pyrromethene 597. The lasers emit in the chip plane at a wavelength around 595 nm when pumped with 5 ns
pulses from a compact frequency doubled Nd:YAG laser. We investigate the sensitivity of photonic crystal band-edge
lasers to partial coverage with HeLa cells. The lasers are chemically activated with a flexible UV activated
anthraquinone based linker molecule, which enables selective binding of cells and molecules. When measuring in
Phosphate Buffered Saline (PBS), which has a refractive index close to that of the cells, the emission wavelength
depends linearly on the cell density on the sensor surface. Our results demonstrate that nanostructured hybrid
polymer lasers, which are cheap to fabricate and very simple to operate, can be selectively chemically activated
with UV sensitive photolinkers for further bioanalytical applications. This opens the possibility to functionalize
arrays of optofluidic laser sensors with different bio-recognition molecules for multiplexed sensing. The linear
relationship between cell coverage and wavelength indicates that the slight refractive index perturbation from
the partial coverage of the sensor influences the entire optical mode, rather than breaking down the photonic
crystal feedback.
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