Optical tweezers manipulate microscopic objects with light by exchanging momentum and angular momentum between particle and light, generating optical forces and torques. Understanding and predicting them is essential for designing and interpreting experiments. Here, we focus on geometrical optics and optical forces and torques in this regime, and we employ neural networks to calculate them. Using an optically trapped spherical particle as a benchmark, we show that neural networks are faster and more accurate than the calculation with geometrical optics. We demonstrate the effectiveness of our approach in studying the dynamics of systems that are computationally “hard” for traditional computation.
Microengines have shown promise for a variety of applications in nanotechnology, from microfluidics to nanomedicine and targeted drug delivery. However, their precise control over their dynamics is still challenging. We introduce a micro engine that exploits both optical and thermal effects to achieve a high degree of controllability. We find that a gold-silica Janus particle illuminated by a high focused laser beam can be confined at the stationary point where the optical and thermal forces balance. By using circularly polarized light the symmetry between these forces can be broken by transferring angular momentum to the particle, resulting in a tangential force that induces an orbital motion of the particle. We can simultaneously control the velocity and direction of rotation of the particle, changing the ellipticity of the incoming light beam while tuning the radius of the orbit with laser power. We validate our results using a geometrical optics model that incorporates optical force, the absorption of optical power, and the resulting heating of the particle.
Optical forces are often calculated by using geometrical optics to compute the exchange of momentum between particle and light beam. In geometrical optics, the light beam is represented by a certain number of rays. This sets a trade-off between calculation speed and accuracy. Here, we show that using neural networks allows overcoming this limitation, obtaining not only faster but also more accurate simulations. Then, we exploit our neural networks method to study the dynamics of ellipsoidal particles in a double trap, a system that would be computationally impossible otherwise.
Critical Casimir forces emerge between objects, such as colloidal particles, whenever their surfaces spatially confine the fluctuations of the order parameter of a critical liquid used as a solvent. These forces act at short but microscopically large distances between these objects, often reaching hundreds of nanometers. Keeping colloids at such distances is a major experimental challenge, which can be addressed by the means of optical tweezers. Here, we review how optical tweezers have been successfully used to quantitatively study critical Casimir forces acting on particles in suspensions. As we will see, the use of optical tweezers to experimentally study critical Casimir forces can play a crucial role in developing nanotechnologies, representing an innovative way to realize self-assembled devices at the nano- and microscale.
Intracavity optical tweezers have been proven successful for trapping microscopic particles at very low average power intensity – much lower than the one in standard optical tweezers. This feature makes them particularly promising for the study of biological samples. The modeling of such systems, though, requires time-consuming numerical simulations that affect its usability and predictive power. With the help of machine learning, we can overcome the numerical bottleneck – the calculation of optical forces, torques, and losses – reproduce the results in the literature and generalize to the case of counterpropagating-beams intracavity optical trapping.
Even though in most cases optical forces can be calculated semi-analytically, the computation becomes prohibitively slow in problems where the calculation needs to be repeated several times. Starting from a spherical particle in an optical trap, we show how machine learning can be used to improve not only the speed but also the accuracy of the optical force calculations in the geometrical optics approach. This is demonstrated to work efficiently at least up to 9 degrees of freedom, constituting a tool for exploring problems that were out of the scope of the traditional geometrical optics calculation.
Cosmic dust particles are usually collected in space or in the Earth’s stratosphere and deposited on a substrate to be analysed at large terrestrial facilities.
We use Raman tweezers technique for the contacless manypulation of cosmic dust particles, to identify their compositions and to characterize their response to optical forces without any substrate effects, documenting the high potential of this novel technique for space exploration.
FORMA (force reconstruction via maximum-likelihood-estimator analysis) addresses the need to measure the force fields acting on microscopic particles. Compared to alternative established methods, FORMA is faster, simpler, more accurate, and more precise.
Furthermore, FORMA can also measure non-conservative and out-of-equilibrium force fields.
Here, after a brief introduction to FORMA, I will present its use, advantages, and limitations.
I will conclude with the most recent work where we exploit Bayesian inference to expand FORMA's scope of application.
FORMA (force reconstruction via maximum-likelihood-estimator analysis) addresses the need of measuring the force fields acting on microscopic particles. Compared to alternative established methods, FORMA is faster, simpler, more accurate, and more precise. Furthermore, FORMA can also measure non-conservative and out-of-equilibrium force fields. Here, after a brief introduction to FORMA, I will present its use, advantages and limitations. I will conclude with some recent work where we exploit Bayesian inference to expand the scope of application of FORMA.
Sensing photoacoustic waves brings a lot of loss since the detector distance is in the order of millimeters which also leads to noise in the measured signal. To solve this problem, we used an optically trapped silica particle as a transducer in this study. We used two laser sources, one for optical tweezers (976 nm, CW) and a fiber laser for photoacoustic imaging (SHG output: 532 nm, pulsed). The fiber laser was produced in our laboratory whose pulse duration is 8 ns, pulse energy is 10 µJ, and pulse repetition frequency is 65 kHz. The separation between them in the sample plane is 8 µm. The green laser excited several absorbing mediums such as trypan blue, horse hair, black ink and gold thin film. We tracked the position of trapped silica particle (5µm diameter) when the green laser is on and off. We observed dramatic difference between two states. We have validated that this effect is fully photoacoustic by changing the frequency of the green laser with a chopper which led to the exact same frequency when we calculated the Fourier transform of the position distribution of the trapped silica particle. Also, when we change the power of the green laser, the amplitude of the Fourier transformation of the position distribution of the trapped silica particle changes in the same way.
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