Engineering thin film semiconductor gas sensors to increase sensitivity and decrease operation temperature

Thin film nanostructured gas sensors typically operate at temperatures above 400°C, but lower temperature operation is highly desirable, especially for remote area field sensing as this reduces significantly power consumption. We have investigated a range of sensor materials based on both pure and doped tungsten oxide (mainly focusing on Fe-doping), deposited using both thermal evaporation and electron-beam evaporation, and using a variety of post-deposition annealing. The films show excellent sensitivity at operating temperatures as low as 150°C for detection of NO2. There is a definite relationship between the sensitivity and the crystallinity and nanostructure obtained through the deposition and heat treatment processes, as well as variations in the conductivity caused both by doping and heat treatmetn. The ultimate goal of this work is to control the sensing properties, including selectivity to specific gases through the engineering of the electronic properties and the nanostructure of the films.

Thermal sensitivity analysis of spacecraft battery

Transient thermal sensitivity is studied for systems that are subjected to conductive heat transfer within themselves and radiative heat transfer with the surrounding environment, including solar heat radiation, The battery in the Indian national communication satellite is one such system for which the studies are conducted with respect to panel conduction, conductance of insulating blanket, power dissipation within the battery, and absorptance and emittance of various elements, Comparison of sensitivities revealed that battery temperature is sensitive to its power dissipation during the beginning of life of the spacecraft, whereas toward the end of life of the spacecraft mission, the effect of absorptance of optical solar reflector is dominating, The influence of optical property values of the multilayer insulation blanket is almost negligible. Among the parameters studied in this analysis, the battery temperature is found to be mast sensitive to emittance of the optical solar reflector.

Methodology for alleviation of voltage excursions in large power systems

This paper presents a method for minimizing the sum of the square of voltage deviations by a least-square minimization technique, and thus improving the voltage profile in a given system by adjusting control variables, such as tap position of transformers, reactive power injection of VAR sources and generator excitations. The control variables and dependent variables are related by a matrix J whose elements are computed as the sensitivity matrix. Linear programming is used to calculate voltage increments that minimize transmission losses. The active and reactive power optimization sub-problems are solved separately taking advantage of the loose coupling between the two problems. The proposed algorithm is applied to IEEE 14-and 30-bus systems and numerical results are presented. The method is computationally fast and promises to be suitable for implementation in real-time dispatch centres.

Supercritical carbon dioxide Brayton cycle for concentrated solar power

Supercritical carbon dioxide based Brayton cycle for possible concentrated solar power applications is investigated and compared with trans- and sub-critical operations of the same fluid. Thermal efficiency, specific work output and magnitude of irreversibility generation are used as some of the performance indicators. While the thermal efficiency increases almost linearly with low side pressure in the sub- and trans-critical cycles, it attains a maximum in the supercritical regime at 85 bar after which there are diminishing returns on increasing the low side pressure. It is also found that supercritical cycle is capable of producing power with a thermal efficiency of >30% even at a lower source temperature (820K) and accounting for foreseeable non-idealities albeit with a higher turbine inlet pressure (similar to 300 bar) which is not matched by a conventional sub-critical cycle even with a high source temperature of 978K. The reasons for lower efficiency than in an ideal cycle are extracted from an irreversibility analysis of components, namely, compressor, regenerator, turbine and gas cooler. Low sensitivity to the source temperature and extremely small volumetric flow rates in the supercritical cycle could offset the drawback of high pressures through a compact system.

A GIS (geographical information system)-based spatial data mining approach for optimal location and capacity planning of distributed biomass power generation facilities: A case study of Tumkur district, India

This paper primarily intends to develop a GIS (geographical information system)-based data mining approach for optimally selecting the locations and determining installed capacities for setting up distributed biomass power generation systems in the context of decentralized energy planning for rural regions. The optimal locations within a cluster of villages are obtained by matching the installed capacity needed with the demand for power, minimizing the cost of transportation of biomass from dispersed sources to power generation system, and cost of distribution of electricity from the power generation system to demand centers or villages. The methodology was validated by using it for developing an optimal plan for implementing distributed biomass-based power systems for meeting the rural electricity needs of Tumkur district in India consisting of 2700 villages. The approach uses a k-medoid clustering algorithm to divide the total region into clusters of villages and locate biomass power generation systems at the medoids. The optimal value of k is determined iteratively by running the algorithm for the entire search space for different values of k along with demand-supply matching constraints. The optimal value of the k is chosen such that it minimizes the total cost of system installation, costs of transportation of biomass, and transmission and distribution. A smaller region, consisting of 293 villages was selected to study the sensitivity of the results to varying demand and supply parameters. The results of clustering are represented on a GIS map for the region.

Study on effect of optical wavelength on photo induced strain sensitivity in carbon nanotubes using fiber Bragg grating

In this work, the role of optical wavelength on the photo induced strain in carbon nanotubes (CNT) is probed using a Fiber Bragg Grating (FBG), upon exposure to infrared (IR) (21 mu epsilon mW(-1)) and visible (9 mu epsilon mW(-1)) radiations. The strain sensitivity in CNT is monitored over a smaller range (10(-3) to 10(-9) epsilon) by exposing to a low optical power varying in the range 10(-3) to 10(-6) W. In addition, the wavelength dependent response and recovery periods of CNT under IR (tau(rise) = 150 ms, tau(fall) = 280 ms) and visible (tau(rise) = 1.07 s, tau(fall) = 1.18 s) radiations are evaluated in detail. This study can be further extended to measure the sensitivity of nano-scale photo induced strains in nano materials and opens avenues to control mechanical actuation using various optical wavelengths.

Global Response Sensitivity Analysis of Randomly Excited Dynamic Structures

The response of structural dynamical systems excited by multiple random excitations is considered. Two new procedures for evaluating global response sensitivity measures with respect to the excitation components are proposed. The first procedure is valid for stationary response of linear systems under stationary random excitations and is based on the notion of Hellinger's metric of distance between two power spectral density functions. The second procedure is more generally valid and is based on the l2 norm based distance measure between two probability density functions. Specific cases which admit exact solutions are presented, and solution procedures based on Monte Carlo simulations for more general class of problems are outlined. Illustrations include studies on a parametrically excited linear system and a nonlinear random vibration problem involving moving oscillator-beam system that considers excitations attributable to random support motions and guide-way unevenness. (C) 2015 American Society of Civil Engineers.

Sensitivity map of laser tweezers Raman spectroscopy for single-cell analysis of colorectal cancer

Raman spectroscopy on single, living epithelial cells captured in a laser trap is shown to have diagnostic power over colorectal cancer. This new single-cell technology comprises three major components: primary culture processing of human tissue samples to produce single-cell suspensions, Raman detection on singly trapped cells, and diagnoses of the cells by artificial neural network classifications. it is compared with DNA flow cytometry for similarities and differences. Its advantages over tissue Raman spectroscopy are also discussed. In the actual construction of a diagnostic model for colorectal cancer, real patient data were taken to generate a training set of 320 Raman spectra and, a test set of 80. By incorporating outlier corrections to a conventional binary neural classifier, our network accomplished significantly better predictions than logistic regressions, with sensitivity improved from 77.5% to 86.3% and specificity improved from 81.3% to 86.3% for the training set and moderate improvements for the test set. Most important, the network approach enables a sensitivity map analysis to quantitate the relevance of each Raman band to the normal-to-cancer transform at the cell level. Our technique has direct clinic applications for diagnosing cancers and basic science potential in the study of cell dynamics of carcinogenesis. (C) 2007 Society of Photo-Optical Instrumentation Engineers.

Sensitivity map of laser tweezers Raman spectroscopy for single-cell analysis of colorectal cancer

Raman spectroscopy on single, living epithelial cells captured in a laser trap is shown to have diagnostic power over colorectal cancer. This new single-cell technology comprises three major components: primary culture processing of human tissue samples to produce single-cell suspensions, Raman detection on singly trapped cells, and diagnoses of the cells by artificial neural network classifications. it is compared with DNA flow cytometry for similarities and differences. Its advantages over tissue Raman spectroscopy are also discussed. In the actual construction of a diagnostic model for colorectal cancer, real patient data were taken to generate a training set of 320 Raman spectra and, a test set of 80. By incorporating outlier corrections to a conventional binary neural classifier, our network accomplished significantly better predictions than logistic regressions, with sensitivity improved from 77.5% to 86.3% and specificity improved from 81.3% to 86.3% for the training set and moderate improvements for the test set. Most important, the network approach enables a sensitivity map analysis to quantitate the relevance of each Raman band to the normal-to-cancer transform at the cell level. Our technique has direct clinic applications for diagnosing cancers and basic science potential in the study of cell dynamics of carcinogenesis. (C) 2007 Society of Photo-Optical Instrumentation Engineers.

The predicting power of models for eutrophication

Restoration of water-bodies from eutrophication has proved to be extremely difficult. Mathematical models have been used extensively to provide guidance for management decisions. The aim of this paper is to elucidate important problems of using models for predicting environmental changes. First, the necessity for a proper uncertainty assessment of the model, upon calibration, has not been widely recognized. Predictions must not be a single time trajectory; they should be a band, expressing system uncertainty and natural variability. Availability of this information may alter the decision to be taken. Second, even with well-calibrated models, there is no guarantee they will give correct projections in situations where the model is used to predict the effects of measures designed to bring the system into an entirely different ”operating point”, as is typically the case in eutrophication abatement. The concept of educated speculation is introduced to partially overcome this difficulty. Lake Veluwe is used as a case to illustrate the point. Third, as questions become more detailed, such as ”what about expected algal composition”, there is a greater probability of running into fundamental problems that are associated with predicting the behaviour of complex non-linear systems. Some of these systems show extreme initial condition sensitivity and even, perhaps, chaotic behaviour, and are therefore fundamentally unpredictable.

Compliance verification methodology for renewable generation integration. Application to island power grids

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High sensitive NO2 gas sensor with low power consumption using selectively grown ZnO nanorods.

The noble gas sensor using multiple ZnO nanorods was fabricated with CMOS compatible process and sol-gel growth method on selective area and gas response characteristics to NO2 gas of the sensor device were investigated. We confirmed the sensors had high sensitive response denoted by the sensitivity of several tens for NO2 gas sensing and also showed pretty low power consumption close to 20 mW even though the recovery of resistance come up to almost the initial value.

Sensitivity analysis and optimization of the nuclear fuel cycle

A sensitivity study has been conducted to assess the robustness of the conclusions presented in the MIT Fuel Cycle Study. The Once Through Cycle (OTC) is considered as the base-line case, while advanced technologies with fuel recycling characterize the alternative fuel cycles. The options include limited recycling in LWRs and full recycling in fast reactors and in high conversion LWRs. Fast reactor technologies studied include both oxide and metal fueled reactors. The analysis allowed optimization of the fast reactor conversion ratio with respect to desired fuel cycle performance characteristics. The following parameters were found to significantly affect the performance of recycling technologies and their penetration over time: Capacity Factors of the fuel cycle facilities, Spent Fuel Cooling Time, Thermal Reprocessing Introduction Date, and incore and Out-of-core TRU Inventory Requirements for recycling technology. An optimization scheme of the nuclear fuel cycle is proposed. Optimization criteria and metrics of interest for different stakeholders in the fuel cycle (economics, waste management, environmental impact, etc.) are utilized for two different optimization techniques (linear and stochastic). Preliminary results covering single and multi-variable and single and multi-objective optimization demonstrate the viability of the optimization scheme.

A high temperature and low power SOI CMOS MEMS based thermal conductivity gas sensor

The design, 3D FEM modelling and measurement results of a novel high temperature, low power SOI CMOS MEMS thermal conductivity gas sensor are presented here. The sensor consists of a circular membrane with an embedded tungsten micro-heater. The high sensing capability is based on the temperature sensitivity of the resistive heating element. The sensor was fabricated at a commercial foundry using a 1 μm process and measures only 1×1 mm 2. The circular membrane has a 600 μm diameter while the heating element has a 320 μm diameter. Measurement results show that for a constant power consumption of 75 mW the heater temperature was 562.4°C in air, 565.9°C in N2, 592.5°C for 1 % H2 in Ar and 599.5°C in Ar. © 2013 IEEE.