Flow-through vs flow-over: analysis of transport and binding in nanohole array plasmonic biosensors

We quantify the efficacy of flow-through nanohole sensing, as compared to the established flow-over format, through scaling analysis and numerical simulation. Nanohole arrays represent a growing niche within surface plasmon resonance-based sensing methods, and employing the nanoholes as nanochannels can enhance transport and analytical response. The additional benefit offered by flow-through operation is, however, a complex function of operating parameters and application-specific binding chemistry. Compared here are flow-over sensors and flow-through nanohole array sensors with equivalent sensing area, where the nanohole array sensing area is taken as the inner-walls of the nanoholes. The footprints of the sensors are similar (e.g., a square 20 μm wide flow-over sensor has an equivalent sensing area as a square 30 μm wide array of 300 nm diameter nanoholes with 450 nm periodicity in a 100 nm thick gold film). Considering transport alone, an analysis here shows that given equivalent sensing area and flow rate the flow-through nanohole format enables greatly increased flux of analytes to the sensing surface (e.g., 40-fold for the case of Q = 10 nL/min). Including both transport and binding kinetics, a computational model, validated by experimental data, provides guidelines for performance as a function of binding time constant, analyte diffusivity, and running parameters. For common binding kinetics and analytes, flow-through nanohole arrays offer ～10-fold improvement in response time, with a maximum of 20-fold improvement for small biomolecules with rapid kinetics.     

Volatile organic compound detection using nanostructured copolymers

Regioregular polythiophene-based conductive copolymers with highly crystalline nanostructures are shown to hold considerable promise as the active layer in volatile organic compound (VOC) chemresistor sensors. While the regioregular polythiophene polymer chain provides a charge conduction path, its chemical sensing selectivity and sensitivity can be altered either by incorporating a second polymer to form a block copolymer or by making a random copolymer of polythiophene with different alkyl side chains. The copolymers were exposed to a variety of VOC vapors, and the electrical conductivity of these copolymers increased or decreased depending upon the polymer composition and the specific analytes. Measurements were made at room temperature, and the responses were found to be fast and appeared to be completely reversible. Using various copolymers of polythiophene in a sensor array can provide much better discrimination to various analytes than existing solid state sensors. Our data strongly indicate that several sensing mechanisms are at play simultaneously, and we briefly discuss some of them.     

Rapid analyte recognition in a device based on optical sensors and the olfactory system

We report here the development of a new vapor sensing device that is designed as an array of optically based chemosensors providing input to a pattern recognition system incorporating artificial neural networks. Distributed sensors providing inputs to an integrative circuit is a principle derived from studies of the vertebrate olfactory system. In the present device, primary chemosensing input is provided by an array of fiber-optic sensors. The individual fiber sensors, which are broadly yet differentially responsive, were constructed by immobilizing molecules of the fluorescent indicator dye Nile Red in polymer matrices of varying polarity, hydrophobicity, pore size, elasticity, and swelling tendency, creating unique sensing regions that interact differently with vapor molecules. The fluorescent signals obtained from each fiber sensor in response to 2-s applications of different analyte vapors have unique temporal characteristics. Using signals from the fiber array as inputs, artificial neural networks were trained to identify both single analytes and binary mixtures, as well as relative concentrations. Networks trained with integrated response data from the array or with temporal data from a single fiber made numerous errors in analyte identification across concentrations. However, when trained with temporal information from the fiber array, networks using "name" or "characteristic" output codes performed well in identifying test analytes.     

Quantification of quaternary mixtures of low alcohols in water: temporal-resolved measurements with microporous and hyperbranched polymer sensors for reduction of sensor number

The focus of this study is the quantification of multianalyte mixtures in water by the use of sensor arrays based on polymer layers. Reflectometric interference spectroscopy is used as a optical sensor system for temporal-resolved measurements of the interaction kinetics of analytes in water with the polymer layers. The principles and widespread possibilities of this approach are demonstrated using the quantification of quaternary aqueous mixtures of low alcohols from methanol up to 1-butanol. The sensitive layers consist of two hyperbranched polyesters and one microporous polyimide. Different time-dependent sensor signals are evaluated by artificial neural networks. Because the kinetics of sorption and desorption of the analytes differ significantly, the number of sensors needed for a quantification of analytes in mixtures can be reduced. A feature extraction allows identification of the most important differences of kinetic patterns of the analytes and allows improvement of the multivariate calibration. It is shown that a quantification of quaternary mixtures of methanol, ethanol, 1-propanol, and 1-butanol is possible on the basis of only two polymer sensors.     

Optimization of wide-band linear arrays

An optimization method is proposed for linear arrays to be used in ultrasound systems under wide-band operation. A fast algorithm, the threshold accepting, has been utilized to determine the element positions and weight coefficients of a linear array that generates a desired beam pattern. To reduce the computational burden in the optimization procedure, an efficient numerical routine for the beam pattern evaluation has been implemented. We address the optimization problem of both dense and sparse wideband arrays. In the first case, the goal is to minimize the side-lobe energy by varying the element weights; we compare the optimized beam pattern with that obtained with classical shading functions, showing that better results can be achieved with a wide-band optimization. We also consider the optimization of the layout (positions and weights) of a sparse linear array to achieve a desired beam pattern with a fixed or minimum number of array elements. The comparison of the proposed method with a narrow-band optimization algorithm is presented, showing that better performances (about -7 dB further reduction of the side-lobe level) can be achieved with a wide-band sparse array optimization. Further numerical simulations are given, showing that the proposed method yields better results than wideband sparse random arrays and periodic arrays with the same aperture width     

Chemical sensors for portable, handheld field instruments

A review of three commonly used classes of chemical sensor technologies as applicable to implementation in portable, handheld field instruments is presented. Solid-state gas and chemical sensors have long been heralded as the solution to a wide variety of portable chemical sensing system applications. However, advances in optical sensing technology have reduced the size of supporting infrastructure to be competitive with their solid-state counterparts. Optical, solid-state, and hybrid arrays of sensors have application for portable instruments, but issues of insufficient selectivity and sensitivity continue to hamper the widespread introduction of these miniaturized sensors for solving chemical sensing problems in environments outside the laboratory. In this article, we evaluate three of the major classes of compact chemical sensors for portable applications: (solid-state) chemiresistors, (solid-state) CHEMFETs, and (optical) surface plasmon resonance sensors (SPR). These sensors are evaluated and reviewed, according to the current state of research, in terms of their ability to operate at low-power, small-size, and relatively low-cost in environments, with numerous interferents and variable ambient conditions     

Protein sensing and cell discrimination using a sensor array based on nanomaterial-assisted chemiluminescence

Cross-reactive sensor arrays, known as "chemical noses", offer an alternative to time-consuming analytical methods. Here, we report a sensor array based on nanomaterial-assisted chemiluminescence (CL) for protein sensing and cell discrimination. We have found that the CL efficiencies are improved to varied degrees for a given protein or cell line on catalytic nanomaterials. Distinct CL response patterns as "fingerprints" can be obtained on the array and then identified through linear discriminant analysis (LDA). The sensing of 12 kinds of proteins at three concentrations, as well as 12 types of human cell lines among normal, cancerous, and metastatic, has been performed. Compared with most fluorescent or colorimetric approaches which rely on the strong interaction between analytes and sensing elements, our array offers the advantage of both sensitivity and reversibility.     

Neuromorphic Processing for Optical Microbead Arrays: Dimensionality Reduction and Contrast Enhancement

This paper presents a neuromorphic approach for sensor-based machine olfaction that combines a portable chemical detection system based on microbead array technology with a biologically inspired model of signal processing in the olfactory bulb. The sensor array contains hundreds of microbeads coated with solvatochromic dyes adsorbed in, or covalently attached on, the matrix of various microspheres. When exposed to odors, each bead sensor responds with corresponding intensity changes, spectral shifts, and time-dependent variations associated with the fluorescent sensors. The bead array responses are subsequently processed using a model of olfactory circuits that capture the following two functions: chemotopic convergence of receptor neurons and center on-off surround lateral interactions. The first circuit performs dimensionality reduction, transforming the high-dimensional microbead array response into an organized spatial pattern (i.e., an odor image). The second circuit enhances the contrast of these spatial patterns, improving the separability of odors. The model is validated on an experimental dataset containing the responses of a large array of microbead sensors to five different analytes. Our results indicate that the model is able to significantly improve the separability between odor patterns, compared to that available from the raw sensor response     

Nanofibrous PANI-based conductive polymers for trace gas analysis

Electrospun nanofibres have been confirmed to be very good candidates for ultra-sensitive gas sensors since they greatly improve surface area to volume ratios of coatings, which in turn affect two additional and crucial features for sensors: high sensitivity and fast response time. Electrospinning is a simple method for the deposition of long (up to several centimetres) nanofibres, aligned or non-woven, directly onto suitable transducers. Such a structured layer may have better properties than a compact film, providing faster adsorption and minimising some bulk effects (i.e. long diffusion-desorption time, analyte entrapment, etc.). Electrospun conductive polymers (CPs) have been specifically investigated for developing smart sensors whose electrical properties change upon interactions with the analytes. Polyaniline is one of the most interesting CPs for gas sensing, because of its conductive features, when doped, as well as its thermal stability and sensing performance. The sensing mechanisms are different, depending on the nature of both PANi and the targeting analytes. Thus, various blends of polyaniline and insulating host polymers have been planned, prepared, deposited and studied to optimise the properties of sensors consequent to the combination of the electrical conductivity of CP and of the physical properties of the host polymer. Host polymer carriers cause great modifications to the topology of the interacting surface (diameter and length of the fibres, roughness, porosity, presence of beads and grains, non-woven framework and branched junctions, adhesion, etc.), in addition to the different affinity to the analytes tested. However, they enable electrodes to function over a wider dynamic range of gas or vapour concentrations. The polymer features have been also characterised over a range of water vapour concentrations and temperatures.     

Ordered‐Assembly Conductive Nanowires Array with Tunable Polymeric Structure for Specific Organic Vapor Detection

An artificial organic vapor sensor based on a finite number of 1D nanowires arrays can provide a strategy to allow classification and identification of different analytes with high efficiency, but fabricating a 1D nanowires array is challenging. Here, a coaxial Ag/polymer nanowires array is prepared as an organic vapor sensor with specific recognition, using a strategy combining superwettability‐based nanofabrication and polymeric swelling‐induced resistance change. Such organic vapor sensor containing commercial polymers can successfully classify and identify various organic vapors with good separation efficiency. An Ag/polymer nanowires array with synthetic polyethersulfone polymers is also fabricated, through molecular structure modification of the polymers, to distinguish the similar organic vapors of methanol and ethanol. Theoretical simulation results demonstrate introduction of specific molecular interaction between the designed polymers and organic vapors can improve the specific recognition performance of the sensors.     

Evaluation of multitransducer arrays for the determination of organic vapor mixtures

A study of vapor recognition and quantification by polymer-coated multitransducer (MT) arrays is described. The primary data set consists of experimentally derived sensitivities for 11 organic vapors obtained from 15 microsensors comprising five cantilever, capacitor, and calorimeter devices coated with five different sorptive-polymer films. These are used in Monte Carlo simulations coupled with principal component regression models to assess expected performance. Recognition rates for individual vapors and for vapor mixtures of up to four components are estimated for single-transducer (ST) arrays of up to five sensors and MT arrays of up to 15 sensors. Recognition rates are not significantly improved by including more than five sensors in an MT array for any specific analysis, regardless of difficulty. Optimal MT arrays consistently outperform optimal ST arrays of similar size, and with judiciously selected 5-sensor MT arrays, one-third of all possible ternary vapor mixtures are reliably discriminated from their individual components and binary component mixtures, whereas none are reliably determined with any of the ST arrays. Quaternary mixtures could not be analyzed effectively with any of the arrays. A "universal" MT array consisting of eight sensors is defined, which provides the best possible performance for all analytical scenarios. Accurate quantification is predicted for correctly identified vapors.     

Dielectrophoresis‐Assisted Integration of 1024 Carbon Nanotube Sensors into a CMOS Microsystem

Carbon‐nanotube (CNT)‐based sensors offer the potential to detect single‐molecule events and picomolar analyte concentrations. An important step toward applications of such nanosensors is their integration in large arrays. The availability of large arrays would enable multiplexed and parallel sensing, and the simultaneously obtained sensor signals would facilitate statistical analysis. A reliable method to fabricate an array of 1024 CNT‐based sensors on a fully processed complementary‐metal‐oxide‐semiconductor microsystem is presented. A high‐yield process for the deposition of CNTs from a suspension by means of liquid‐coupled floating‐electrode dielectrophoresis (DEP), which yielded 80% of the sensor devices featuring between one and five CNTs, is developed. The mechanism of floating‐electrode DEP on full arrays and individual devices to understand its self‐limiting behavior is studied. The resistance distributions across the array of CNT devices with respect to different DEP parameters are characterized. The CNT devices are then operated as liquid‐gated CNT field‐effect‐transistors (LG‐CNTFET) in liquid environment. Current dependency to the gate voltage of up to two orders of magnitude is recorded. Finally, the sensors are validated by studying the pH dependency of the LG‐CNTFET conductance and it is demonstrated that 73% of the CNT sensors of a given microsystem show a resistance decrease upon increasing the pH value.     

Differentiation of chemical components in a binary solvent vapor mixture using carbon/polymer composite-based chemiresistors

We demonstrate a "universal solvent sensor" constructed from a small array of carbon/polymer composite chemiresistors that respond to solvents spanning a wide range of Hildebrand solubility parameters. Conductive carbon particles provide electrical continuity in these composite films. When the polymer matrix absorbs solvent vapors, the composite film swells, the average separation between carbon particles increases, and an increase in film resistance results, as some of the conduction pathways are broken. The adverse effects of contact resistance at high solvent concentrations are reported. Solvent vapors including isooctane, ethanol, diisopropylmethylphosphonate (DIMP), and water are correctly identified ("classified") using three chemiresistors, their composite coatings chosen to span the full range of solubility parameters. With the same three sensors, binary mixtures of solvent vapor and water vapor are correctly classified; following classification, two sensors suffice to determine the concentrations of both vapor components. Poly(ethylenevinyl acetate) and poly(vinyl alcohol) (PVA) are two such polymers that are used to classify binary mixtures of DIMP with water vapor; the PVA/carbon particle composite films are sensitive to less than 0.25% relative humidity. The Sandia-developed visual-empirical region of influence (VERI) technique is used as a method of pattern recognition to classify the solvents and mixtures and to distinguish them from water vapor. In many cases, the response of a given composite sensing film to a binary mixture deviates significantly from the sum of the responses to the isolated vapor components at the same concentrations. While these nonlinearities pose significant difficulty for (primarily) linear methods such as principal component analysis, VERI handles both linear and nonlinear data with equal ease. In the present study, the maximum speciation accuracy is achieved by an array containing three or four sensor elements, with the addition of more sensors resulting in a measurable accuracy decrease.     

Design of wide-band arrays for low side-lobe level beam patterns by simulated annealing

We address the window design problem of linear arrays to be used in wide-band beamforming systems. During last decades, stochastic optimization algorithms have proven to be very efficient in optimal beamforming synthesis, but most research interest has focused on linear and two-dimensional arrays under narrow-band excitation. In the present paper, we introduce a general approach, based on the simulated annealing optimization algorithm, to design uniformly spaced wide-band arrays that generate low side-lobe level beam patterns. The effectiveness of different beam pattern formulations is evaluated within the proposed framework; we also consider three different methods to control the side-lobe level, yielding different array performances. Moreover, we investigate the influence of the frequency bandwidth on the optimized beam patterns.     

Properties of the beampattern of weight- and layout-optimizedsparse arrays

Theory for random arrays predicts a mean sidelobe level given by the inverse of the number of elements. In practice, however, the sidelobe level fluctuates much around this mean. In this paper two optimization methods for thinned arrays are given: one is for optimizing the weights of each element, and the other one optimizes both the layout and the weights. The weight optimization algorithm is based on linear programming and minimizes the peak sidelobe level for a given beamwidth. It is used to investigate the conditions for finding thinned arrays with peak sidelobe level at or below the inverse of the number of elements. With optimization of the weights of a randomly thinned array, it is possible to come quite close and even below this value, especially for 1D arrays. Even for 2D sparse arrays a large reduction in peak sidelobe level is achieved. Even better solutions are found when the thinning pattern is optimized also. This requires an algorithm that uses mixed integer linear programming. In this case solutions, with lower peak sidelobe level than the inverse number of elements can be found both in the 1D and the 2D cases     

Large‐Area Integrated Triboelectric Sensor Array for Wireless Static and Dynamic Pressure Detection and Mapping

Large‐area flexible pressure sensors are of paramount importance for various future applications, such as electronic skin, human–machine interfacing, and health‐monitoring devices. Here, a self‐powered and large‐area integrated triboelectric sensor array (ITSA) based on coupling a triboelectric sensor array and an array chip of CD4066 through a traditional connection is reported. Enabled by a simple and cost‐effective fabrication process, the size of the ITSA can be scaled up to 38 × 38 cm². In addition, unlike previously proposed triboelectric sensors arrays, which can only react to the dynamic interaction, this ITSA is able to detect static and dynamic pressure. Moreover, through integrating the ITSA with a signal processing circuit, a complete wireless sensing system is present. Diverse applications of the system are demonstrated in detail, including detecting pressure, identifying position, tracking trajectory, and recognizing the profile of external contact objects. Thus, the ITSA in this work opens a new route in the direction of large‐area, self‐powered, and wireless triboelectric sensing systems.     

Bioinspired structural color sensors based on responsive soft materials

Structural colors in nature have inspired the design of diverse photonic structures, which can interact with light via interference, diffraction or scattering. Among them, responsive soft material-involved photonic structures uniquely feature large volumetric changes upon external stimuli. The volumetric changes result in peak/valley shift of reflection spectra and perceptible color changes, providing responsive soft material-based structural color systems capability of serving as sensors for detecting chemical and biological analytes. Synthetic polymers and some natural materials are the most studied and utilized responsive soft materials for constructing structural color sensors, by tuning the thickness and morphology of formed films, or incorporating them into template structures, or their self-assembling. In this review article, structural colors in nature are firstly introduced, followed by discussing recent developments of promising responsive soft material-based structural color sensors, including the design of structural color sensors based on synthetic polymers and natural materials, as well as their applications for chemical sensing, biosensing, and multi-analyte sensing with sensor arrays. For specific sensing of chemicals and biomolecules, the sensing performance is evaluated in terms of detection range, sensitivity, response time, and selectivity. For multi-analyte sensing, cross-reactive structural sensor arrays based on simply a single soft material will be shown capable of discriminating various series of similar compounds. The future development of structural color sensors is also proposed and discussed.     

High-throughput nanohole array based system to monitor multiple binding events in real time

We have developed an integrated label-free, real-time sensing system that is able to monitor multiple biomolecular binding events based on the changes in the intensity of extraordinary optical transmission (EOT) through nanohole arrays. The core of the system is a sensing chip containing multiple nanohole arrays embedded within an optically thick gold film, where each array functions as an independent sensor. Each array is a square array containing 10 x 10 nanoholes (150 nm in diameter), occupying a total area of 3.3 mum x 3.3 mum. The integrated system includes a laser light source, a temperature-regulated flow cell encasing the sensing chip, motorized optics, and a charge-coupled detector (CCD) camera. For demonstration purposes, sensing chips containing 25 nanohole arrays were studied for their use in multiplexed detection, although the sensing chip could be easily populated to contain up to 20 164 nanohole arrays within its 0.64 cm2 sensing area. Using this system, we successfully recorded 25 separate binding curves between glutathione S-transferase (GST) and anti-GST simultaneously in real time with good sensitivity. The system responds to binding events in a concentration-dependent manner, showing a sharp linear response to anti-GST at concentrations ranging from 13 to 290 nM. The EOT intensity-based approach also enables the system to monitor multiple bindings simultaneously and continuously, offering a temporal resolution on milliseconds scale that is decided only by the camera speed and exposure time. The small footprint of the sensing arrays combined with the EOT intensity-based approach enables the system to resolve binding events that occurred on nanohole sensing arrays spaced 96 mum apart, with a reasonable prediction of resolving binding events spaced 56 mum apart. This work represents a new direction that implements nanohole arrays and EOT intensity to meet high-throughput, spatial and temporal resolution, and sensitivity requirements in drug discovery and proteomics studies.     

Vapor recognition with small arrays of polymer-coated microsensors. A comprehensive analysis.

A comprehensive analysis of vapor recognition as a function of the number of sensors in a vapor-sensor array is presented. Responses to 16 organic vapors collected from six polymer-coated surface acoustic wave (SAW) sensors were used in Monte Carlo simulations coupled with pattern recognition analyses to derive statistical estimates of vapor recognition rates as a function of the number of sensors in the array (< or = 6), the polymer sensor coatings employed, and the number and concentration of vapors being analyzed. Results indicate that as few as two sensors can recognize individual vapors from a set of 16 possibilities with < 6% average recognition error, as long as the vapor concentrations are > 5 x LOD for the array. At lower concentrations, a minimum of three sensors is required, but arrays of 3-6 sensors provide comparable results. Analyses also revealed that individual-vapor recognition hinges more on the similarity of the vapor response patterns than on the total number of possible vapors considered. Vapor mixtures were also analyzed for specific 2-, 3-, 4-, 5-, and 6-vapor subsets where all possible combinations of vapors within each subset were considered simultaneously. Excellent recognition rates were obtainable for mixtures of up to four vapors using the same number of sensors as vapors in the subset. Lower recognition rates were generally observed for mixtures that included structurally homologous vapors. Acceptable recognition rates could not be obtained for the 5- and 6-vapor subsets examined, due, apparently, to the large number of vapor combinations considered (i.e., 31 and 63, respectively). Importantly, increasing the number of sensors in the array did not improve performance significantly for any of the mixture analyses, suggesting that for SAW sensors and other sensors whose responses rely on equilibrium vapor-polymer partitioning, large arrays are not necessary for accurate vapor recognition and quantification.