Faster and More Accurate Geometrical-Optics Optical Force Calculation Using Neural Networks

Ciriza, David Bronte; Magazzù, Alessandro; Callegari, Agnese; Barbosa, Gunther; Neves, Antônio Augusto; Iatı̀, Maria Antonia; Volpe, Giovanni; Maragó, Onofrio M.

doi:10.1021/acsphotonics.2c01565

Cited by 5 publications

(8 citation statements)

References 36 publications

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“…The interaction of the Janus particle with the focused Gaussian optical beam is described in the geometrical optics approximation: , the beam is represented by an appropriate set of rays that, impinging on the Janus particle surface, are reflected, transmitted, and, when hitting the gold-coated spherical cap, also partially absorbed, see Figure S2. While each ray is undergoing this infinite series of scattering events, it exchanges linear and angular momentum with the particle and therefore applies an optical force and torque.…”

Section: Methodsmentioning

confidence: 99%

Optically Driven Janus Microengine with Full Orbital Motion Control

Bronte Ciriza,

Callegari,

Donato

et al. 2023

ACS Photonics

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Microengines have shown promise for a variety of applications in nanotechnology, microfluidics, and nanomedicine, including targeted drug delivery, microscale pumping, and environmental remediation. However, achieving precise control over their dynamics remains a significant challenge. In this study, we introduce a microengine that exploits both optical and thermal effects to achieve a high degree of controllability. We find that in the presence of a strongly focused light beam, a gold-silica Janus particle becomes confined at the stationary point where the optical and thermal forces balance. By using circularly polarized light, we can transfer angular momentum to the particle, breaking the symmetry between the two forces and resulting in a tangential force that drives directed orbital motion. 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. Our experimental results are validated using a geometrical optics phenomenological model that considers the optical force, the absorption of optical power, and the resulting heating of the particle. The demonstrated enhanced flexibility in the control of microengines opens up new possibilities for their utilization in a wide range of applications, including microscale transport, sensing, and actuation.

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Section: Methodsmentioning

confidence: 99%

Optically Driven Janus Microengine with Full Orbital Motion Control

Bronte Ciriza,

Callegari,

Donato

et al. 2023

ACS Photonics

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“…Backed by their ability to learn from previous examples in order to make new predictions, NN are contributing to biology [29], food sensing control [17], and even to containment of epidemics [35]. In fact, NN have recently been demonstrated as an useful technique to increase both the speed [32] and the accuracy [9] of optical forces calculations when compared to GO, allowing the study of more complex systems through Brownian dynamics simulations. While these previous works consider spheres [32] and ellipsoids [9], there is no evident reason to remain constrained to these relatively simple shapes.…”

Section: /16mentioning

confidence: 99%

“…In fact, NN have recently been demonstrated as an useful technique to increase both the speed [32] and the accuracy [9] of optical forces calculations when compared to GO, allowing the study of more complex systems through Brownian dynamics simulations. While these previous works consider spheres [32] and ellipsoids [9], there is no evident reason to remain constrained to these relatively simple shapes. Indeed, the computation time saving that could be achieved by using NN for force calculation of particles with more complex shapes makes this a particularly attractive application.…”

Section: /16mentioning

confidence: 99%

Modelling red blood cell optical trapping by machine learning improved geometrical optics calculations

Tognato

Ciriza

Maragó

et al. 2023

Preprint

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Optically trapping red blood cells allows to explore their biophysical properties, which are affected in many diseases. However, because of their nonspherical shape, the numerical calculation of the optical forces is slow, limiting the range of situations that can be explored. Here we train a neural network that improves both the accuracy and the speed of the calculation and we employ it to simulate the motion of a red blood cell under different beam configurations. We found that by fixing two beams and controlling the position of a third, it is possible to control the tilting of the cell. We anticipate this work to be a promising approach to study the trapping of complex shaped and inhomogeneous biological materials, where the possible photodamage imposes restrictions in the beam power.

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“…Backed by their ability to learn from previous examples in order to make new predictions, NN are contributing to biology [ 24 ], food sensing control [ 25 ], and even to containment of epidemics [ 26 ]. In fact, NN have recently been demonstrated as an useful technique to increase both the speed [ 27 ] and the accuracy [ 28 ] of optical forces calculations when compared to GO, allowing the study of more complex systems through Brownian dynamics simulations. While these previous works consider spheres [ 27 ] and ellipsoids [ 28 ], there is no evident reason to remain constrained to these relatively simple shapes.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, NN have recently been demonstrated as an useful technique to increase both the speed [ 27 ] and the accuracy [ 28 ] of optical forces calculations when compared to GO, allowing the study of more complex systems through Brownian dynamics simulations. While these previous works consider spheres [ 27 ] and ellipsoids [ 28 ], there is no evident reason to remain constrained to these relatively simple shapes. Indeed, the computation time saving that could be achieved by using NN for force calculation of particles with more complex shapes makes this a particularly attractive application.…”

Section: Introductionmentioning

confidence: 99%

Modelling red blood cell optical trapping by machine learning improved geometrical optics calculations

Tognato¹,

Ciriza²,

Maragó³

et al. 2023

Biomed. Opt. Express

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Optically trapping red blood cells allows for the exploration of their biophysical properties, which are affected in many diseases. However, because of their nonspherical shape, the numerical calculation of the optical forces is slow, limiting the range of situations that can be explored. Here we train a neural network that improves both the accuracy and the speed of the calculation and we employ it to simulate the motion of a red blood cell under different beam configurations. We found that by fixing two beams and controlling the position of a third, it is possible to control the tilting of the cell. We anticipate this work to be a promising approach to study the trapping of complex shaped and inhomogeneous biological materials, where the possible photodamage imposes restrictions in the beam power.

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Faster and More Accurate Geometrical-Optics Optical Force Calculation Using Neural Networks

Cited by 5 publications

References 36 publications

Optically Driven Janus Microengine with Full Orbital Motion Control

Optically Driven Janus Microengine with Full Orbital Motion Control

Modelling red blood cell optical trapping by machine learning improved geometrical optics calculations

Modelling red blood cell optical trapping by machine learning improved geometrical optics calculations

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