Spectroelectrochemical Sensing Based on Multimode Selectivity Simultaneously Achievable in a Single Device. 5. Simulation of Sensor Response for Different Excitation Potential Waveforms

2000 ◽  
Vol 72 (22) ◽  
pp. 5567-5575 ◽  
Author(s):  
Andrew F. Slaterbeck ◽  
Michael L. Stegemiller ◽  
Carl J. Seliskar ◽  
Thomas H. Ridgway ◽  
William R. Heineman
Keyword(s):  
Sensor Response ◽  
Excitation Potential ◽  
Spectroelectrochemical Sensing

scholarly journals 6-Axis Stress Tensor Sensor Using Multifaceted Silicon Piezoresistors

Micromachines ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 279
Author(s):  
Kentaro Noda ◽  
Jian Sun ◽  
Isao Shimoyama
Keyword(s):  
Stress Tensor ◽  
Elastic Body ◽  
Building Materials ◽  
Sensor Response ◽  
Stress Change ◽  
Sensor Chip ◽  
Naked Eye ◽  
Highly Sensitive ◽  
Wide Range

A tensor sensor can be used to measure deformations in an object that are not visible to the naked eye by detecting the stress change inside the object. Such sensors have a wide range of application. For example, a tensor sensor can be used to predict fatigue in building materials by detecting the stress change inside the materials, thereby preventing accidents. In this case, a sensor of small size that can measure all nine components of the tensor is required. In this study, a tensor sensor consisting of highly sensitive piezoresistive beams and a cantilever to measure all of the tensor components was developed using MEMS processes. The designed sensor had dimensions of 2.0 mm by 2.0 mm by 0.3 mm (length by width by thickness). The sensor chip was embedded in a 15 mm3 cubic polydimethylsiloxane (PDMS) (polydimethylsiloxane) elastic body and then calibrated to verify the sensor response to the stress tensor. We demonstrated that 6-axis normal and shear Cauchy stresses with 5 kPa in magnitudes can be measured by using the fabricated sensor.

Optimization of sensor response functions for colorimetry of reflective and emissive objects

1996 ◽  
Vol 5 (3) ◽  
pp. 507-517 ◽  
Author(s):  
M. Wolski ◽  
C.a. Bouman ◽  
J.P. Allebach ◽  
E. Walowit
Keyword(s):  
Sensor Response ◽  
Response Functions

Hysteresis correction of tactile sensor response with a generalized Prandtl–Ishlinskii model

2012 ◽  
Vol 18 (7-8) ◽  
pp. 1127-1138 ◽  
Author(s):  
José A. Sánchez-Durán ◽  
Óscar Oballe-Peinado ◽  
Julián Castellanos-Ramos ◽  
Fernando Vidal-Verdú
Keyword(s):  
Tactile Sensor ◽  
Sensor Response

scholarly journals Preparation of single walled carbon nanotube-pyrene 3D hybrid nanomaterial and its sensor response to ammonia

2018 ◽  
Vol 256 ◽  
pp. 853-860 ◽  
Author(s):  
Ahmet Şenocak ◽  
Cem Göl ◽  
Tamara V. Basova ◽  
Erhan Demirbaş ◽  
Mahmut Durmuş ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Sensor Response ◽  
Single Walled Carbon Nanotube ◽  
Hybrid Nanomaterial ◽  
Single Walled Carbon

Crystalline structure, defects and gas sensor response to NO2 and H2S of tungsten trioxide nanopowders

2003 ◽  
Vol 93 (1-3) ◽  
pp. 475-485 ◽  
Author(s):  
I. Jiménez ◽  
J. Arbiol ◽  
G. Dezanneau ◽  
A. Cornet ◽  
J.R. Morante
Keyword(s):  
Gas Sensor ◽  
Crystalline Structure ◽  
Tungsten Trioxide ◽  
Sensor Response ◽  
Structure Defects

scholarly journals Short-Time Fourier Transform and Decision Tree-Based Pattern Recognition for Gas Identification Using Temperature Modulated Microhotplate Gas Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Aixiang He ◽  
Jun Yu ◽  
Guangfen Wei ◽  
Yi Chen ◽  
Hao Wu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Fourier Transform ◽  
Decision Tree ◽  
Gas Sensors ◽  
Operating Temperature ◽  
Ambient Air ◽  
Sensor Response ◽  
Short Time Fourier Transform ◽  
Short Time ◽  
Different Gases

Because the sensor response is dependent on its operating temperature, modulated temperature operation is usually applied in gas sensors for the identification of different gases. In this paper, the modulated operating temperature of microhotplate gas sensors combined with a feature extraction method based on Short-Time Fourier Transform (STFT) is introduced. Because the gas concentration in the ambient air usually has high fluctuation, STFT is applied to extract transient features from time-frequency domain, and the relationship between the STFT spectrum and sensor response is further explored. Because of the low thermal time constant, the sufficient discriminatory information of different gases is preserved in the envelope of the response curve. Feature information tends to be contained in the lower frequencies, but not at higher frequencies. Therefore, features are extracted from the STFT amplitude values at the frequencies ranging from 0 Hz to the fundamental frequency to accomplish the identification task. These lower frequency features are extracted and further processed by decision tree-based pattern recognition. The proposed method shows high classification capability by the analysis of different concentration of carbon monoxide, methane, and ethanol.

scholarly journals LTCC Packaged Ring Oscillator Based Sensor for Evaluation of Cell Proliferation

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3346 ◽  
Author(s):  
Joni Kilpijärvi ◽  
Niina Halonen ◽  
Maciej Sobocinski ◽  
Antti Hassinen ◽  
Bathiya Senevirathna ◽  
...  
Keyword(s):  
Frequency Shift ◽  
Flip Chip ◽  
Effective Permittivity ◽  
Complementary Metal Oxide Semiconductor ◽  
Cellular Adhesion ◽  
Sensor Response ◽  
Lung Epithelial Cells ◽  
Ring Oscillator ◽  
Oxide Semiconductor ◽  
Chip Surface

A complementary metal-oxide-semiconductor (CMOS) chip biosensor was developed for cell viability monitoring based on an array of capacitance sensors utilizing a ring oscillator. The chip was packaged in a low temperature co-fired ceramic (LTCC) module with a flip chip bonding technique. A microcontroller operates the chip, while the whole measurement system was controlled by PC. The developed biosensor was applied for measurement of the proliferation stage of adherent cells where the sensor response depends on the ratio between healthy, viable and multiplying cells, which adhere onto the chip surface, and necrotic or apoptotic cells, which detach from the chip surface. This change in cellular adhesion caused a change in the effective permittivity in the vicinity of the sensor element, which was sensed as a change in oscillation frequency of the ring oscillator. The sensor was tested with human lung epithelial cells (BEAS-2B) during cell addition, proliferation and migration, and finally detachment induced by trypsin protease treatment. The difference in sensor response with and without cells was measured as a frequency shift in the scale of 1.1 MHz from the base frequency of 57.2 MHz. Moreover, the number of cells in the sensor vicinity was directly proportional to the frequency shift.

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