In the traditional optical traps, the displacement of the trapped particle is usually detected by measuring the forward scattered (FS) light and back-scattered (BS) light. However, in the dual-beam fiber-optic traps, it is difficult to using the FS light and BS light to achieve the displacement detection. As mentioned in our previous work, the side-scattered light of the trapped particle can also be used for displacement detection and achieve a large linear range of the displacement detection. In this paper, we furtherly use a four-fiber bundle, rather than traditional quadrant photodiode (QPD) to collect the side-scattered light from trapped particle in the dual-beam fiber-optic trap. The power of the collected light in the cores of the fiber bundle is monitored by multiple detectors, and later operated for differential signals to denote the displacement of the particle. To achieve better displacement detection performance, we have analyzed the influence of core radius r, numerical aperture NA, and core interval Δ based on the simulations using a geometrical optics model. The results show that, high sensitivity and large linear range can be realized by optimizing these parameters. The proposed detection method is easy to integrate into a dual-beam fiber-optic trap, which provides a pathway for optical trapping systems with all fiber devises.
KEYWORDS: Filtering (signal processing), Particles, Electronic filtering, Signal processing, Optical filters, Optical tweezers, Systems modeling, Motion models, Interference (communication), Signal to noise ratio
The cooling and quantum control of the optically trapped particles is a hot topic in quantum frontier research. One of the key steps is using Kalman filter to extract the particle’s motion from noisy signals. Time delays of the Kalman filters are found in the process of signal extraction. Here the particle displacements based on the parameters of actual optical trapping systems are simulated, and the time delays of the Kalman filtering process are observed by changing the oscillation periods and the relaxation time for stabilization. The results indicate that Kalman filtering can effectively compress the noises in the displacement signal and thus improve the signal-to-noise ratio. Furthermore, as smaller the signal frequency is, larger time delays are observed in the process. It shows that the time delays should be noticed and compensated. Meanwhile, it is shown that the consuming time for signal stabilizations in the filtering process and the phase of the original signal, neither of which affects the filtering effect. These simulation results are our initial explorations for the cooling of optically trapped particles in vacuum. It would provide possible help to deal with the delay mismatch resulted from Kalman filtering and for the cooling of the optically trapped particles.
Optical tweezers (OTs) are an important tool for the viscosity measurements in microrheology, and passive techniques have the features of being simple and need no external force generations. Current passive methods using OT always first calibrate the potential stiffness and then do parameter fittings to obtain the viscosity. Here, we introduced and demonstrated a passive viscosity estimation method for low-viscous microfluids using OTs without stiffness calibration and parameter fitting. By Brownian trajectory tracking of single trapped bead, the viscosity coefficients of water and NaCl solutions are quickly obtained with small deviations (typical <10 % ) from the reference values. Besides, we introduce estimations for the commonly used voltage-to-displacement conversion factor, and the consistency check between the estimations and calibrations is used to represent the estimation quality. The whole process is very convenient for automatic processing. Further matrix operations are proposed and tested, which are expected to be integrated with holographic OTs and optical fiber traps for distributed multidimensional measurement.
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