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Basics of Image Sensors
Published in Junichi Nakamura, Image Sensors and Signal Processing for Digital Still Cameras, 2017
where q is the elementary charge (1.60218 × 1019C). Obviously, Equation 3.6 represents the “input referred” conversion gain, and it is not measured directly. The “output referred” conversion gain is obtained by multiplying the voltage gain from the charge detection node to the output and is given by
Charge Measurement
Published in John G. Webster, Halit Eren, Measurement, Instrumentation, and Sensors Handbook, 2017
Saps Buchman, John T. Mester, T.J. Sumner
Electric charge, a basic property of elementary particles, is defined by convention as negative for the electron and positive for the proton. In 1910, Robert Andrews Millikan (1868–1953) demonstrated the quantization and determined the value of the elementary charge by measuring the motion of small charged droplets in an adjustable electric field. The SI unit of charge, the coulomb (C), is defined in terms of base SI units as
Accurate Scanning of Magnetic Fields
Published in Krzysztof Iniewski, Optical, Acoustic, Magnetic, and Mechanical Sensor Technologies, 2017
Hendrik Husstedt, Udo Ausserlechner, Manfred Kaltenbacher
The first term in Equation 8.1 describes the electrostatic force F→el and the second term the magnetic force F→mag. It is assumed that the charge transfer consists only of electrons that are charged with the negative elementary charge Q = −e0. Without any magnetic field, there is only an electrical field in the direction of the current density which corresponds to the y direction (see Figure 8.1). With an external magnetic field, the magnetic part of the Lorentz force causes a force pushing the electrons in the x direction. This separation of charges generates an additional electrical field in the x direction which is called the Hall field, E→H. In the steady state, the electrical force from the Hall field completely compensates for the magnetic part of the Lorentz force: −e0(v→×B→)−e0E→H=0.
Numerical modeling of the performance of high flow DMAs to classify sub-2 nm particles
Published in Aerosol Science and Technology, 2019
Huang Zhang, Girish Sharma, Yang Wang, Shuiqing Li, Pratim Biswas
Here, our simulation conditions for the flow rates (q = 9 lpm and Q = 136 lpm) and the particle mobility sizes are assumed to be same as the experimental case 1 and 3 by Wang et al. (2014). The particles are assumed to carry a single elementary charge. Figure 6a shows the transfer function with increasing applied voltage to classify particles of different dp. It can be observed that the larger particles need a larger applied voltage for classification. The transfer functions are triangular in shape with the appearance of tails on both sides, and rounding of the top, due to the high diffusion coefficient of sub-2 nm particles. In Figure 6b, the simulation peak voltages, at which the transfer function is maximum for each particle diameter, are compared with the experimental data (Wang et al. 2014). The changing trend of the simulation and experimental data agrees well, and the relative errors between the simulation and the experimental voltages are below 5.7%. Two mechanisms may explain this difference. First, the direction of the aerosol flow through its inlet slit may not be perfectly axisymmetric in the experiments, but our model assumes the aerosol flow to be symmetric in two-dimensional computational domain. Second, the flow rate in experiment was not measured directly, but was calculated based on the mobility values of monodisperse organic ions (Ude and de la Mora 2005). The difference of the gas properties used in the experiment and simulation may also contribute to this small deviation.