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... from 4th cycle and takes 12 cycles to determine the 12-bit output. The last cycle is the used to latch the output and reset the capacitor array and then a new conversion cycle begins the centre of the aperture. The electric field is highly distributed around the aperture, which non-linear distribution can induce a dielectric force on particle. Fig. 2. Effect of particle size and translocation style on resistance change (a) The relative resistance change with respect to various particle size (b) The relative resistance change of two particles with different size stick together to flow through the sensing channel (c) The relative resistance change of two particles at different ...
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Let n1; n2;: :: ; nd be positive integers and H be the numerical semigroup generated by n1; n2;: :: ; nd . Let A := k[H] := k[tⁿ¹ ; tⁿ² ;: :: ; tnd ] ≅ = k[x1; x2;: :: ; xd]/I be the numerical semigroup ring of H over k: In this paper we give a condition (*) that implies that the minimal number of generators of the defining ideal I is bounded expli...
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Citations
... The mathematical expression obtained defines the count loss that occurs and can be factored into the data to give a more accurate result (Princen and Kwolek, 1965). Newer commercial devices contain threshold comparators that only allow pulses through that are equal or greater than the value predefined by the user for a cell type, thereby allowing processing of a heterogeneous cell population (Guo et al., 2012). Data can then be obtained on these pulse parameters (e.g., height and width of pulse), which can be converted to the required information such as cell size and distribution. ...
Cell identification and enumeration are essential procedures within clinical and research laboratories. For over 150 years, quantitative investigation of body fluids such as counts of various blood cells has been an important tool for diagnostic analysis. With the current evolution of point-of-care diagnostics and precision medicine, cheap and precise cell counting technologies are in demand. This article reviews the timeline and recent notable advancements in cell counting that have occurred as a result of improvements in sensing including optical and electrical technology, enhancements in image processing capabilities, and contributions of micro and nanotechnologies. Cell enumeration methods have evolved from the use of manual counting using a hemocytometer to automated cell counters capable of providing reliable counts with high precision and throughput. These developments have been enabled by the use of precision engineering, micro and nanotechnology approaches, automation and multivariate data analysis. Commercially available automated cell counters can be broadly classified into three categories based on the principle of detection namely, electrical impedance, optical analysis and image analysis. These technologies have many common scientific uses, such as hematological analysis, urine analysis and bacterial enumeration. In addition to commercially available technologies, future technological trends using lab-on-a-chip devices have been discussed in detail. Lab-on-a-chip platforms utilize the existing three detection technologies with innovative design changes utilizing advanced nano/microfabrication to produce customized devices suited to specific applications.
The electrical sensing zone (ESZ) particle counting method has been in use for decades. It records a change in resistance when a particle flows in a conducting fluid from a large reservoir through a narrow aperture into another reservoir, where the flow direction is aligned with an applied electric field and the particle is assumed to be much less conductive than the fluid. The measured resistance of the entire system goes through a peak as the particle flows through the aperture, at the center of the aperture, and the height of the peak is assumed to be a measure of particle volume. In this paper, the sensitivity of the ESZ particle counting method is shown to be directly related to the particle intrinsic conductivity, which depends only on particle shape and conductivity relative to the matrix fluid. The intrinsic conductivity is the main parameter that influences the change in conductivity, in the dilute limit, when a particle is added to a conducting matrix. A simple finite element model of an ESZ particle counter, built from cubic voxels, along with a sphere (calibration particle), cube, and ellipsoid of equal volumes, are used to show how particle shape affects the ESZ result. Even though the specific counter geometry can affect the resistance seen, it is shown that the intrinsic conductivity still explains the main influence on the resistance results in these numerical experiments, within certain geometric bounds. Finally, if the particle and fluid conductivity are close to each other, the measurement errors caused by particle shapes that are different from the calibration particle can be in principle be largely eliminated by exploiting the shape-independence of the intrinsic conductivity near this condition, which is analytically known.
