Smart polymeric temperature sensors – for biological systems

The damaged brain is vulnerable to increase in brain temperature after a severe head injury. Continuous monitoring of intracranial temperature depicts functionality essential to the treatment of brain injury Many innovations have been made in the biomedical industry relying on electronic implants in treating condition such as traumatic brain injury (TBI) or other cerebral diseases. Hence, a methodical and reliable way to measure the temperature is crucial to assess the patient’s situation. In this investigation, an analysis of various approaches to detect the change in the temperature due to resistance, current-voltage characteristics with respect to time has been evaluated. Also, studies describing various materials used in sensors, their working principles and the results anticipated in these discrete procedures are presented. These smart temperature sensors have provided the accuracy and the stability compared to earlier methods used to detect the change in brain temperature since temperature is one of the most important variables in brain monitoring

A 16 x 16 CMOS amperometric microelectrode array for simultaneous electrochemical measurements

There is a requirement for an electrochemical sensor technology capable of making multivariate measurements in environmental, healthcare, and manufacturing applications. Here, we present a new device that is highly parallelized with an excellent bandwidth. For the first time, electrochemical cross-talk for a chip-based sensor is defined and characterized. The new CMOS electrochemical sensor chip is capable of simultaneously taking multiple, independent electroanalytical measurements. The chip is structured as an electrochemical cell microarray, comprised of a microelectrode array connected to embedded self-contained potentiostats. Speed and sensitivity are essential in dynamic variable electrochemical systems. Owing to the parallel function of the system, rapid data collection is possible while maintaining an appropriately low-scan rate. By performing multiple, simultaneous cyclic voltammetry scans in each of the electrochemical cells on the chip surface, we are able to show (with a cell-to-cell pitch of 456 μm) that the signal cross-talk is only 12% between nearest neighbors in a ferrocene rich solution. The system opens up the possibility to use multiple independently controlled electrochemical sensors on a single chip for applications in DNA sensing, medical diagnostics, environmental sensing, the food industry, neuronal sensing, and drug discovery

Smart Platform for Low-Cost MEMS Sensors – Pressure, Flow and Thermal Conductivity

In a technological world that is trending towards smart and autonomous engineering, the collection of quality data is of unrivalled importance. This has led to a huge market demand for the development of low-cost, small and accurate sensors and thus has resulted in significant research into sensors, with the aim of advancing the price/performance ratio in commercial solutions. Micro Electro Mechanical Systems (MEMS) have recently offered an attractive solution to miniaturise and drastically improve the performance of sensors. In this thesis, MEMS technology is exploited to create a multi-sensor technology platform that is used to fabricate several sensing technologies. Piezo-resistive and piezo-electronic pressure sensors are designed, fabricated and tested. Different doping profiles, stress-engineered structures and electronic devices for pressure transduction are investigated, with focus on their sensitivity and non-linearity. A ring is fabricated in the metal layer around the circumference of the membrane that alleviates the effects of over/under etching. This is achieved by creating a new rigid edge of the membrane in the metal layer, which has tighter fabrication tolerances. A piezo-MOSFET is developed and shown to have greater sensitivity than similar state-of-the-art devices. Flow sensors based on a heated tungsten wire are designed, fabricated, tested and substantiated with numerical modelling. Calorimetric and anemometric driving modes are optimised with regards to device structure. Thermodiodes are also used as the temperature transduction devices and are compared to the traditional resistor method and showed to be preferable when further miniaturising the sensor. Thermal conductivity gas sensors based on a heated tungsten resistor are designed, tested and substantiated with numerical modelling. Holes through the membrane are used to improve the sensitivity to measuring carbon dioxide by 270%. Asymmetric holes are utilised to prove a novel method of measuring thermal conductivity in a calorimetric method. Designs improving this new concept are outlined and substantiated with analytical and numerical models. Linear statistical methods and artificial neural networks are used to differentiate flow rate and gas concentration using three on-membrane resistors. With membrane holes, the discrimination between gases in the presence of flow is improved. Neural networks provide a viable solution and show an increase in the accuracy of both flow rate and gas concentration. The main objective of the work in this thesis was to develop low-cost, low-power, small devices capable of high-volume production and monolithic integration using a single smart technology platform for fabrication. The smart technology platform was used to create pressure sensors, flow sensors and thermal conductivity gas sensors. Within each sensing technology, proof-of-concepts and optimisations have been carried out in order to maximise performance whilst using the low-cost, high-volume fabrication process, ultimately helping towards smart and autonomous engineering solutions driven by data

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Department of Electrical EngineeringA Sensor system is advanced along sensor technologies are developed. The performance improvement of sensor system can be expected by using the internet of things (IoT) communication technology and artificial neural network (ANN) for data processing and computation. Sensors or systems exchanged the data through this wireless connectivity, and various systems and applications are possible to implement by utilizing the advanced technologies. And the collected data is computed using by the ANN and the efficiency of system can be also improved. Gas monitoring system is widely need from the daily life to hazardous workplace. Harmful gas can cause a respiratory disease and some gas include cancer-causing component. Even though it may cause dangerous situation due to explosion. There are various kinds of hazardous gas and its characteristics that effect on human body are different each gas. The optimal design of gas monitoring system is necessary due to each gas has different criteria such as the permissible concentration and exposure time. Therefore, in this thesis, conventional sensor system configuration, operation, and limitation are described and gas monitoring system with wireless connectivity and neural network is proposed to improve the overall efficiency. As I already mentioned above, dangerous concentration and permissible exposure time are different depending on gas types. During the gas monitoring, gas concentration is lower than a permissible level in most of case. Thus, the gas monitoring is enough with low resolution for saving the power consumption in this situation. When detecting the gas, the high-resolution is required for the accurate concentration detecting. If the gas type is varied in the above situation, the amount of calculation increases exponentially. Therefore, in the conventional systems, target specifications are decided by the highest requirement in the whole situation, and it occurs increasing the cost and complexity of readout integrated circuit (ROIC) and system. In order to optimize the specification, the ANN and adaptive ROIC are utilized to compute the complex situation and huge data processing. Thus, gas monitoring system with learning-based algorithm is proposed to improve its efficiency. In order to optimize the operation depending on situation, dual-mode ROIC that monitoring mode and precision mode is implemented. If the present gas concentration is decided to safe, monitoring mode is operated with minimal detecting accuracy for saving the power consumption. The precision mode is switched when the high-resolution or hazardous situation are detected. The additional calibration circuits are necessary for the high-resolution implementation, and it has more power consumption and design complexity. A high-resolution Analog-to-digital converter (ADC) is kind of challenges to design with efficiency way. Therefore, in order to reduce the effective resolution of ADC and power consumption, zooming correlated double sampling (CDS) circuit and prediction successive approximation register (SAR) ADC are proposed for performance optimization into precision mode. A Microelectromechanical systems (MEMS) based gas sensor has high-integration and high sensitivity, but the calibration is needed to improve its low selectivity. Conventionally, principle component analysis (PCA) is used to classify the gas types, but this method has lower accuracy in some case and hard to verify in real-time. Alternatively, ANN is powerful algorithm to accurate sensing through collecting the data and training procedure and it can be verified the gas type and concentration in real-time. ROIC was fabricated in complementary metal-oxide-semiconductor (CMOS) 180-nm process and then the efficiency of the system with adaptive ROIC and ANN algorithm was experimentally verified into gas monitoring system prototype. Also, Bluetooth supports wireless connectivity to PC and mobile and pattern recognition and prediction code for SAR ADC is performed in MATLAB. Real-time gas information is monitored by Android-based application in smartphone. The dual-mode operation, optimization of performance and prediction code are adjusted with microcontroller unit (MCU). Monitoring mode is improved by x2.6 of figure-of-merits (FoM) that compared with previous resistive interface.clos

MEASUREMENT AND MODELING OF HUMIDITY SENSORS

Humidity measurement has been increasingly important in many industries and process control applications. This thesis research focus mainly on humidity sensor calibration and characterization. The humidity sensor instrumentation is briefly described. The testing infrastructure was designed for sensor data acquisition, in order to compensate the humidity sensor’s temperature coefficient, temperature chambers using Peltier elements are used to achieve easy-controllable stable temperatures. The sensor characterization falls into a multivariate interpolation problem. Neuron networks is tried for non-linear data fitting, but in the circumstance of limited training data, an innovative algorithm was developed to utilize shape preserving polynomials in multiple planes in this kind of multivariate interpolation problems

Integrated microfluidics, heaters, and electronic sensors for Lab-on-a-Chip applications

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.Includes bibliographical references (leaves 123-125).Microfluidics, microfabricated suspended heaters and electronic field effect sensors have been successfully integrated on a single device chip. This integration enables spatial cycling of as little as 11nL of reagents over different thermally isolated temperature zones, to be coupled with the field effect sensing capabilities, for label-free detection of biomolecules such as DNA. The microfluidic valves provide control over reagent flow, and flow rates of up to 1.8nLs⁻¹ have been demonstrated with the on-chip pumps. Initial characterization of the suspended heaters was successfully carried out using thermochromic crystals. Functionality of the heaters was shown and a rough calibration was obtained. The subsequent implementation of temperature measurement using fluorescent dyes, enabled real-time spatial temperature mapping. This method demonstrated the capability of monitoring fluid temperatures in microfluidic channels with 5ÌC accuracy at 2[mu]m² resolution. Thermal isolation of the suspended heaters was clearly observed from the steep gradients in the spatial temperature profiles captured. Finally, localized boiling of water in the microfluidic channels was achieved, with only 30mW supplied to the heaters. In order to evaluate the sensors, tests were carried out to determine its sensitivity to surface charge. Buffer solutions of different pH were injected, and the sensors have been able to measure pH values ranging from 2.2 - 7.4 and demonstrate sensitivity of up to 38.8mV per pH unit change. Highly charged poly-electrolytes were also investigated as model systems to validate sensor detection of charged biomolecules.(cont.) The adsorption and layer-by-layer deposition of multiple poly-electrolyte layers to the sensor surface have been successfully detected. This device paves the way for future integration of multiple microfluidic compo- nents, for lab-on-a-chip applications.by Tzu Liang Loh.S.M

An investigation into spike-based neuromorphic approaches for artificial olfactory systems

The implementation of neuromorphic methods has delivered promising results for vision and auditory sensors. These methods focus on mimicking the neuro-biological architecture to generate and process spike-based information with minimal power consumption. With increasing interest in developing low-power and robust chemical sensors, the application of neuromorphic engineering concepts for electronic noses has provided an impetus for research focusing on improving these instruments. While conventional e-noses apply computationally expensive and power-consuming data-processing strategies, neuromorphic olfactory sensors implement the biological olfaction principles found in humans and insects to simplify the handling of multivariate sensory data by generating and processing spike-based information. Over the last decade, research on neuromorphic olfaction has established the capability of these sensors to tackle problems that plague the current e-nose implementations such as drift, response time, portability, power consumption and size. This article brings together the key contributions in neuromorphic olfaction and identifies future research directions to develop near-real-time olfactory sensors that can be implemented for a range of applications such as biosecurity and environmental monitoring. Furthermore, we aim to expose the computational parallels between neuromorphic olfaction and gustation for future research focusing on the correlation of these senses

Ironless Inductive Position Sensor for Harsh Magnetic Environments

Linear Variable Differential Transformers (LVDTs) are widely used for high-precision and high-accuracy linear position sensing in harsh environments, such as the LHC collimators at CERN. These sensors guarantee theoretically infinite resolution and long lifetimes thanks to contactless sensing. Furthermore, they offer very good robustness and ruggedness, as well as micrometer uncertainty over a range of centimeters when proper conditioning techniques are used (such as the three-parameter Sine-Fit algorithm). They can also be suitable for radioactive environments. Nevertheless, an external DC/slowly-varying magnetic field can seriously affect the LVDT reading, leading to position drifts of hundreds of micrometers, often unacceptable in high-accuracy applications. The effect is due to the presence of non-linear ferromagnetic materials in the sensor’s structure. A detailed Finite Element model of an LVDT is first proposed in order to study and characterize the phenomenon. The model itself becomes a powerful design tool for possible countermeasures to the interference effect. In particular, a combination of magnetic shielding and DC polarization is proposed to reduce the drift due to the external field. Nevertheless, such solutions cannot lead to complete immunity, given the unavoidable presence of magnetic materials in the sensor. Taking the CERN application as a starting point, this thesis aims at conceiving, modelling and characterizing a valid alternative to LVDTs for harsh magnetic environments, which would guarantee magnetic-field-immune position sensing while keeping all the advantageous properties of LVDTs. The Ironless Inductive Position Sensor (I2PS) is an air-cored structure made of 5 coaxial coils. The position sensing is achieved by spatially-variable magnetic fluxes, which give rise to position-dependent coil voltages, just as for LVDTs. The complete electromagnetic model of the sensor is proposed, showing the working principle and demonstrating the magnetic-field immunity from a theoretical viewpoint. In addition, a high-frequency electromagnetic analysis is performed, in order to model the skin and proximity effects in the conductors and foresee their impact on the sensor’s functioning. The models are validated with FEM simulations and experimental measurements. The thermal behaviour of the sensor is also investigated and an effective compensation algorithm is proposed to cancel the temperature-dependence of the position reading. In addition, a smart real-time reading algorithm is proposed in order to significantly reduce the estimation error of standard three-parameter Sine-Fit algorithms when an additional sinusoidal signal is present on the main waveform. Finally, a generic optimization procedure is proposed in order to maximize the performances of the sensor in terms of sensitivity. Taking this procedure as a guideline, an actual I2PS optimized prototype is designed and manufactured, having the specifications of the LHC collimators application as a reference. The optimized prototype shows immunity to external ramped and sinusoidal fields, as expected. In addition, it is used for the experimental validation of the models and the reading techniques, which demonstrate their effectiveness

Advanced instrumentation: Technology database enhancement, volume 4, appendix G

The purpose of this task was to add to the McDonnell Douglas Space Systems Company's Sensors Database, including providing additional information on the instruments and sensors applicable to physical/chemical Environmental Control and Life Support System (P/C ECLSS) or Closed Ecological Life Support System (CELSS) which were not previously included. The Sensors Database was reviewed in order to determine the types of data required, define the data categories, and develop an understanding of the data record structure. An assessment of the MDSSC Sensors Database identified limitations and problems in the database. Guidelines and solutions were developed to address these limitations and problems in order that the requirements of the task could be fulfilled

Carbon-Based Nanomaterials for (Bio)Sensors Development

Carbon-based nanomaterials have been increasingly used in sensors and biosensors design due to their advantageous intrinsic properties, which include, but are not limited to, high electrical and thermal conductivity, chemical stability, optical properties, large specific surface, biocompatibility, and easy functionalization. The most commonly applied carbonaceous nanomaterials are carbon nanotubes (single- or multi-walled nanotubes) and graphene, but promising data have been also reported for (bio)sensors based on carbon quantum dots and nanocomposites, among others. The incorporation of carbon-based nanomaterials, independent of the detection scheme and developed platform type (optical, chemical, and biological, etc.), has a major beneficial effect on the (bio)sensor sensitivity, specificity, and overall performance. As a consequence, carbon-based nanomaterials have been promoting a revolution in the field of (bio)sensors with the development of increasingly sensitive devices. This Special Issue presents original research data and review articles that focus on (experimental or theoretical) advances, challenges, and outlooks concerning the preparation, characterization, and application of carbon-based nanomaterials for (bio)sensor development