Active rotational and translational microrheology beyond the linear spring regime

Active particle tracking microrheometers have the potential to perform accurate broad-band measurements of viscoelasticity within microscopic systems. Generally, their largest possible precision is limited by Brownian motion and low frequency changes to the system. The signal to noise ratio is usually improved by increasing the size of the driven motion compared to the Brownian as well as averaging over repeated measurements. New theory is presented here which gives the complex shear modulus when the motion of a spherical particle is driven by non-linear forces. In some scenarios error can be further reduced by applying a variable transformation which linearises the equation of motion. This allows normalisation which eliminates low frequency drift in the particle's equilibrium position. Using this method will easily increase the signal strength enough to significantly reduce the measurement time for the same error. Thus the method is more conducive to measuring viscoelasticity in slowly changing microscopic systems, such as a living cell.Comment: 9 pages, 2 figure

Energy, momentum and propagation of non-paraxial high-order Gaussian beams in the presence of an aperture

Non-paraxial theories of wave propagation are essential to model the interaction of highly focused light with matter. Here we investigate the energy, momentum and propagation of the Laguerre–, Hermite– and Ince–Gaussian solutions (LG, HG, and IG) of the paraxial wave equation in an apertured non-paraxial regime. We investigate the far-field relationships between the LG, HG, and IG solutions and the vector spherical wave function (VSWF) solutions of the vector Helmholtz wave equation. We investigate the convergence of the VSWF and the various Gaussian solutions in the presence of an aperture. Finally, we investigate the differences in linear and angular momentum evaluated in the paraxial and non-paraxial regimes. The non-paraxial model we develop can be applied to calculations of the focusing of high-order Gaussian modes in high-resolution microscopes. We find that the addition of an aperture in high numerical aperture optical systems does not greatly affect far-field properties except when the beam is significantly clipped by an aperture. Diffraction from apertures causes large distortions in the near-field and will influence light–matter interactions. The method is not limited to a particular solution of the paraxial wave equation. Our model is constructed in a formalism that is commonly used in scattering calculations. It is thus applicable to optical trapping and other optical investigations of matter

Approximate and exact modeling of optical trapping

Approximate methods such a Rayleigh scattering and geometric optics have been widely used for the calculation of forces in optical tweezers. We investigate their applicability and usefulness, comparing results using these approximate methods with exact calculations. © 2010 SPIE

Not just energy, but momentum and angular momentum too: Mechanical effects in scattering

We review the transport and transfer of momentum and angular momentum by electromagnetic waves, and applications of the mechanical effects of scattering

Strong transient flows generated by thermoplasmonic bubble nucleation

The challenge of inducing and controlling localized fluid flows for generic force actuation and for achieving efficient mass transport in microfluidics is key to the development of next generation miniaturized systems for chemistry and life sciences. Here we demonstrate a methodology for the robust generation and precise quantification of extremely strong flow transients driven by vapor bubble nucleation on spatially isolated plasmonic nanoantennas excited by light. The system is capable of producing peak flow speeds of the order mm/s at modulation rates up to 100 Hz in water, thus allowing for a variety of high-throughput applications. Analysis of flow dynamics and fluid viscosity dependence indicate that the transient originates in the rapid bubble expansion that follows nucleation rather than being strictly thermocapillary in nature.Comment: Main Text: 11 pages, 4 figures Supporting Information: 9 pages, 8 figures. Revised manuscript: further details about experiment and analysis, corrected minor analysis error, further clarification of physical understandin

Optical tweezers toolbox: better, faster, cheaper; choose all three

Numerical computation of optical tweezers is one path to understanding the subtleties of their underlying mechanism—electromagnetic scattering. Electromagnetic scattering models of optical trapping can be used to find the properties of the optical forces and torques acting on trapped particles. These kinds of calculations can assist in predicting the outcomes of particular trapping configurations. Experimentally, looking at the parameter space is time consuming and in most cases unfruitful. Theoretically, the same limitations exist but are easier to troubleshoot and manage. Towards this end a new more usable optical tweezers toolbox has been written. Understanding of the underlying theory has been improved, as well as the regimes of applicability of the methods available to the toolbox. Here we discus the physical principles and carry out numerical comparisons of performance of the old toolbox with the new one and the reduced (but portable) code

Deep learning in light-matter interactions

The deep-learning revolution is providing enticing new opportunities to manipulate and harness light at all scales. By building models of light-matter interactions from large experimental or simulated datasets, deep learning has already improved the design of nanophotonic devices and the acquisition and analysis of experimental data, even in situations where the underlying theory is not sufficiently established or too complex to be of practical use. Beyond these early success stories, deep learning also poses several challenges. Most importantly, deep learning works as a black box, making it difficult to understand and interpret its results and reliability, especially when training on incomplete datasets or dealing with data generated by adversarial approaches. Here, after an overview of how deep learning is currently employed in photonics, we discuss the emerging opportunities and challenges, shining light on how deep learning advances photonics

Optical tweezers escape forces

With suitable calibration, optical tweezers can be used to measure forces. If the maximum force that can be exerted is of interest, calibration can be performed using viscous drag to remove a particle from the trap, typically by moving the stage. The stage velocity required to remove the particle then gives the escape force. However, the escape force can vary by up to 30% or more, depending on the particle trajectory. This can have significant quantitative impact on measurements. We describe the variation of escape force and escape trajectory, using both experimental measurements and simulations, and discuss implications for experimental measurement of forces