Extravasation is a common complication during intravenous therapy in which infused fluids leak into the sur-rounding tissues. Timely intervention can prevent severe adverse consequences, but early detection remains an...
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Extravasation is a common complication during intravenous therapy in which infused fluids leak into the sur-rounding tissues. Timely intervention can prevent severe adverse consequences, but early detection remains an unmet clinical need because existing sensors are not sensitive to leakage occurring in small volumes (< 200 mu L) or at deep venipuncture sites. Here, an ultrathin bioimpedance microsensor array that can be integrated on intravenous needles for early and sensitive detection of extravasation is reported. The array comprises eight microelectrodes fabricated on an ultrathin and flexible polyimide substrate as well as functionalized using poly (3,4-ethylenedioxythiophene) and multi-walled carbon nanotubes. Needle integration places the array proximity to venipuncture site, and functional coating significantly reduces interface impedance, both enable the micro -sensors with high sensitivity to detect early extravasation. In vitro and in vivo experiments demonstrate the capability of the microsensors to differentiate various intravenous solutions from different tissue layers as well as identify saline extravasation with detection limit as low as 20 mu L.
Artemisinin is an important frontline antimalarial. Fast, accurate detection of artemisinin in human serum is of importance in monitoring its clinical pharmaceutical effect. In this work, a strategy using microsensor ...
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Artemisinin is an important frontline antimalarial. Fast, accurate detection of artemisinin in human serum is of importance in monitoring its clinical pharmaceutical effect. In this work, a strategy using microsensor array coupled with electrochemiluminescence (ECL) imaging technique was developed for detection of artemisinin. The microsensor array was constructed by integrating a patterned indium tin oxide glass plate with two perforated hydrophobic paper covers. By introducing the reactant of p-aminophenylboronic acid, luminol and artemisinin into the microsensor array, artemisinin would oxidize p-aminophenylboronic acid into p-aminophenol, a product which can efficiently inhibit the ECL of luminol. ECL signals decrease linearly with the increase of artemisinin. Based on the decreased ECL signal, artemisinin can be accurately detected. A good linearity (r = 0.994) was observed for artemisinin detection. The detection sensitivity is 0.48 mu M for artemisinin. The detection selectivity and stability were also investigated. Results show that the present method shows a good selectivity and stability towards artemisinin detection. To evaluate the applicability of the present strategy for detecting artemisinin in real samples, the artemisinin content in human serum and Artemisia annua samples were analyzed. Results demonstrated that the present strategy shows excellent selectivity with high sensitivity towards artemisinin detection in real samples.
The application of a hybrid multivariate curve resolution method, which combines evolving factor analysis (EFA) with alternating least squares (ALS), to the analysis of partially overlapping peaks from vapors measured...
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The application of a hybrid multivariate curve resolution method, which combines evolving factor analysis (EFA) with alternating least squares (ALS), to the analysis of partially overlapping peaks from vapors measured by a microsensor-array gas chromatograph detector is described. The detector comprised an array of four chemiresistors coated with different sorptive thiolate-monolayer-protected gold nanopartide (MPN) films. Three pairs of vapors, the members of which had array response pattern correlation coefficients, rho, ranging from -0.57 to 0.85, were tested at different values of chromatographic resolution, R-s, and relative response ratio, RRR. Composite responses were equivalent to the sums of the responses to the individual components, but differences in peak asymmetry among the sensors in the array led to pattern distortions across the spans of all peaks. With data pre-processing to account for the latter, EFA correctly determined the chemical ranks of the binary composite peaks in 57 of the 63 cases (90%), with most errors observed for the most highly correlated pair. By using calibrated response patterns as inputs for the ALS refinements of EFA-extracted responses, the fidelity of recovered response patterns and elution profiles was sufficiently high to differentiate the composite peak components in 124 of 126 cases (98%) and to quantify them to within +/- 30% of actual values in 95 of 126 (75%) cases. Without such inputs, the corresponding rates were 112 of 126 (89%) and 68 of 126 (54%), respectively. In general, the RRR value was a more important determinant of performance than was the Rs value. The methodology and performance of EFA-ALS in this application are critically assessed. (C) 2014 Elsevier B.V. All rights reserved.
Field-effect transistor (FET) sensors are attractive potentiometric (bio)chemical measurement devices because of their fast response, low output impedance, and potential for miniaturization in standard integrated circ...
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Field-effect transistor (FET) sensors are attractive potentiometric (bio)chemical measurement devices because of their fast response, low output impedance, and potential for miniaturization in standard integrated circuit manufacturing technologies. Yet the wide adoption of these sensors for real-world applications is still limited, mainly due to temporal drift and cross-sensitivities that introduce considerable error in the measurements. In this paper, we demonstrate that such non-idealities can be corrected by joint use of an array of FET sensors - selective to target and major interfering ions - with machine learning (ML) methods in order to accurately predict ion concentrations continuously and in the field. We studied the predictive performance of linear regression (LR), support vector regression (SVR), and state-of-art deep neural networks (DNNs) when monitoring pH from combinatorial H+, Na+, and K+ ion-sensitive FET (ISFET) sequences of readings collected over a period of 90 consecutive days in real water quality assessment conditions. The proposed ML algorithms were trained against reference online measurements obtained from a commercial pH sensor. Results show a greater capability of DNNs to provide precise pH monitoring for longer than a week, achieving a relative root-mean-square error reduction of 73% over standard two-point sensor calibration methods.
This paper presents an experimental study of an accurate temperature-monitoring method using an embedded thin-film microsensor array for laser-assisted polymer bonding for MEMS packaging. The work is carried out using...
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This paper presents an experimental study of an accurate temperature-monitoring method using an embedded thin-film microsensor array for laser-assisted polymer bonding for MEMS packaging. The work is carried out using a fiber-coupled diode laser system and benzocyclobutene polymer as the bonding material. Beam-forming optical elements are used to generate top-hat and frame-shaped beam profiles. Platinum-based sensor arrays are fabricated using sputtering and ion-beam etching methods. Peripheral sensors are embedded at the interface between the polymer sealing ring on the top (capping) substrate and the sensor substrate in the bonding process. The embedded peripheral sensors allow precise monitoring of the temperature profile of the polymer track in the laser-assisted thermal curing process for substrate bonding. The sensor at the center of the array can monitor the temperature that would be experienced by a MEMS device in a manufacturing environment. Results show that accurate temperature monitoring can be obtained using the embedded sensor array. A lower temperature than that required for bonding is seen at the center of the bottom (device) substrate. This is a highly desirable effect for packaging of temperature-sensitive devices. In addition, the effects of substrate material and arrangement of heat dissipation on the resultant temperature profiles have been investigated. [2009-0207]
The capabilities of a chemiresistive microsensor array for detecting and identifying trace target analytes were examined under a simulated Martian atmosphere. The simulated environment included low oxygen content (0.1...
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The capabilities of a chemiresistive microsensor array for detecting and identifying trace target analytes were examined under a simulated Martian atmosphere. The simulated environment included low oxygen content (0.15%) balanced by carbon dioxide, low pressure (7.5 hPa) and temperature (199.1 K), and trace levels of up to three target molecules presented simultaneously. The target molecules were selected from four possible analytes (methane, hydrogen, ethane and sulfur dioxide), presented to the sensor array at multiple concentrations of 200 nmol/mol or less. Signals from four elements of a 16-element microsensor array were employed in the data analyses described. Each element used a different sensing film and was operated with a dynamic temperature program tuned to the background environment. The rich data streams collected from the microsensor array as it was exposed to the complex mixtures and carbon dioxide-based background were analyzed using two approaches: Linear Discriminant Analysis (LDA) and Partial Least Squares Discriminant Analysis (PLS-DA). Analysis of the data by LDA was used for initial assessment of the data streams, and indicated that the data from the microsensor array provided sufficient information to identify, and potentially quantify, each of the target analytes. Further analysis showed that it was possible to separate the methane from the other analytes, as demonstrated after analyzing the data by PLS-DA. Furthermore, the models developed using PLS-DA on one day were able to discriminate the analytes on other days. The success rate was qualitatively dependent on the length of time between the day on which the model was trained and the day on which the validation data were acquired. The work demonstrates the potential of this microsensor array approach to be further developed as a low mass, low-power-consumption screening tool for space exploration. Published by Elsevier B.V.
A hybrid multivariate curve resolution method that combines evolving factor analysis (EFA) with alternating least squares (ALS) is applied to simulated partially overlapping binary gas chromatographic (GC) peaks from ...
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A hybrid multivariate curve resolution method that combines evolving factor analysis (EFA) with alternating least squares (ALS) is applied to simulated partially overlapping binary gas chromatographic (GC) peaks from a microsensor array detector. Extended disjoint principal component regression is then used to relate the results of EFA-ALS to vapor recognition probabilities. The application of this methodology to such data is illustrated and the performance is evaluated. Responses to a set of organic vapors obtained from a portable CC with a detector consisting of an array of four nanoparticle-coated chemiresistors (CR) are used to derive the absolute and relative sensitivity values for the modeling and simulations performed. From these, seven vapor pairs spanning a range of pattern similarity are selected and modeled as Gaussian peaks whose magnitudes and degrees of overlap are varied by simulation. Performance is assessed as a function of the response pattern similarity, chromatographic resolution, signal-to-noise ratio, and the relative response ratio of the composite peak constituents. Overall, despite the low dimensionality of the array data, EFA-ALS provides an effective means of extracting information about co-eluting components from the GC-microsensor array system, and the array provides sufficient diversity of responses to identify those components in most cases, provided that the relative response ratio is <20:1. (C) 2009 Elsevier B.V. All rights reserved.
We report a conductometric nanoparticle biosensor array to address the significant variation of electrical property in nanomaterial biosensors due to the random network nature of nanoparticle thin-film. Indium oxide a...
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We report a conductometric nanoparticle biosensor array to address the significant variation of electrical property in nanomaterial biosensors due to the random network nature of nanoparticle thin-film. Indium oxide and silica nanoparticles (SNP) are assembled selectively on the multi-site channel area of the resistors using layer-by-layer self-assembly. To demonstrate enzymatic biosensing capability, glucose oxidase is immobilized on the SNP layer for glucose detection. The packaged sensor chip onto a ceramic pin grid array is tested using syringe pump driven feed and multi-channel I-V measurement system. It is successfully demonstrated that glucose is detected in many different sensing sites within a chip, leading to concentration dependent currents. The sensitivity has been found to be dependent on the channel length of the resistor, 4-12 nA/mM for channel lengths of 5-20 mu m, while the apparent Michaelis-Menten constant is 20 mM. By using sensor array, analytical data could be obtained with a single step of sample solution feeding. This work sheds light on the applicability of the developed nanoparticle microsensor array to multi-analyte sensors, novel bioassay platforms, and sensing components in a lab-on-a-chip.
This paper describes the application of microsphere vapor sensing arrays to the detection of ignitable liquid (IL) vapors as both pure vapors and as residues (ILRs) on simulated fire debris samples. The temporal fluor...
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This paper describes the application of microsphere vapor sensing arrays to the detection of ignitable liquid (IL) vapors as both pure vapors and as residues (ILRs) on simulated fire debris samples. The temporal fluorescence response profile of the microsphere array generated a reproducible pattern unique to each analyte that could be used to classify subsequent sensor responses. This system, together with a support vector machine pattern recognition algorithm, was used to address several different IL and ILR classification scenarios. High classification accuracy (98%) was maintained over more than 200 vapor responses and the array was able to identify ILs when presented to the pattern classification algorithm within a dataset containing 11 other volatile compounds. Both burned and unburned IL treated samples were classified correctly greater than 97% of the time. These results indicate that microsphere vapor sensing arrays may be useful for the rapid identification of ILs and ILRs.
We have developed an 8-ch capillary-based dispensing workstation with a variable capillary pitch mechanism. The capillary intervals can be varied from 1 to 9 mm to dispense different solutions simultaneously at an arb...
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We have developed an 8-ch capillary-based dispensing workstation with a variable capillary pitch mechanism. The capillary intervals can be varied from 1 to 9 mm to dispense different solutions simultaneously at an arbitrary dispensing pitch, allowing direct dispensing from microplates to integrated analytical systems. To evaluate the precision of its dispensing performance, droplets of Rhodamine G dye were dispensed onto glass slides and the values of the optical volume were analyzed. The error in the dispensed volume proved to be 0.54 nL when dispensing 20 nL. In dispensing small volumes, the volume error for this workstation was found to be about 100-fold less than that seen in conventional dispensers. Even highly viscous solutions containing 50% glycerol could be dispensed with precision. Rapid dispensing was also achieved. Moreover, the application of the workstation to preparing addressable 8 x 12 microsensor array chips was demonstrated, providing an independent and reproducible spot array.
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