The most general motion of a system is a superposition of its normal modes, or eigenstates. For Hermitian system, classical normal mode theory applies. For non-Hermitian systems, presently much progress is done to describe the response of optical micro and nanoresonators in their quasinormal mode basis. We have developed a rigorous modal analysis of nanoresonators with unprecedented generality and report numerical results for the general case of 3D resonators, made of dispersive, anisotropic materials on substrates with guiding layers.
Femtosecond lasers are capable of fabricating uniform periodic nanostructures with a near-wavelength periodicity; however, it is challenging to produce subwavelength nanostructures with large-scale uniformity. Here, we investigate femtosecond laser-induced self-assembly of periodic nanostructures on Si-on-Pt hybrid ultrathin films via photothermal-induced oxidation. The coexistence of scattering light and surface plasmon polaritons on the hybrid films gives rise to a diversity of surface morphologies. Depending on the laser power and sample scanning velocity, beyond the traditional one-dimensional nanogratings that exhibit a near-wavelength periodicity, two types of nanostructures with subdiffraction-limit periodicity while large-scale uniformity are also observed. The first type, occurring at high laser energy and low scanning velocity, is generated by the spatial frequency doubling of the traditional laser-plasmon-interfering nanogratings. It exhibits a periodicity of <λ2. The second type, deep-subwavelength nanostructures, takes place at low pulse energy or low scanning velocity. It is in the form of two-dimensional nanoparticles and has a periodicity of <λ4. The far-field laser-plasmon interference associated with near-field scattering is attributed to the formation of such deep-subwavelength nanostructures, as confirmed by finite-difference time-domain numerical simulations. Our work provides a route toward high-throughput laser fabrication of large-scale deep-subwavelength periodic nanostructures.
The study of neuron morphological classification has important application value to improve the accuracy and efficiency of three-dimensional reconstruction of neurons. However, due to the complex structure of neurons and the existence of global and local self-similarity in morphological distribution, it brings great difficulties to the classification of neuron morphology. Therefore, a new neuronal morphological classification model based on deep residual multiscale convolutional neural network is proposed. Firstly, the overall architecture of the model is based on the fast connection idea of ResNet, which can effectively prevent network model degradation. Secondly, by using the residual connection module, the input information is directly transferred to the output layer through a shortcut, so as to simplify the goal and difficulty of feature learning. Finally, the multi-scale convolution module is combined for feature extraction, and the dilated convolution with different dilation rates is adopted to increase the receiving field to expand the diversity of features, so as to improve the classification accuracy. To verify the effectiveness of the model, experiments are carried out on the neuron morphology classification dataset. The experimental results show that the accuracy, precision, sensitivity and specificity of our method reach 90.11%, 89.63%, 90.77% and 93.27%, respectively. Compared with other classification models (VGG, ResNet, RNN), the proposed model has better classification effect.
Light carries energy and momentum, laying the physical foundation of optical manipulation that has facilitated advances in myriad scientific disciplines, ranging from biochemistry and robotics to quantum physics. Utilizing the momentum of light, optical tweezers have exemplified elegant light–matter interactions in which mechanical and optical momenta can be interchanged, whose effects are the most pronounced on micro and nano objects in fluid suspensions. In solid domains, the same momentum transfer becomes futile in the face of dramatically increased adhesion force. Effective implementation of optical manipulation should thereupon switch to the "energy" channel by involving auxiliary physical fields, which also coincides with the irresistible trend of enriching actuation mechanisms beyond sole reliance on light-momentum-based optical force. From this perspective, this review covers the developments of optical manipulation in schemes of both momentum and energy transfer, and we have correspondingly selected representative techniques to present. Theoretical analyses are provided at the beginning of this review followed by experimental embodiments, with special emphasis on the contrast between mechanisms and the practical realization of optical manipulation in fluid and solid domains.
Electrical engineering management is the basis of scientific construction of power supply industry;it has its own rules.This paper analyzes various factors and steps involved in electrical engineering management so as to ensure the quality of electrical installation work and effectively manage contract for construction,project drawings,inspection and acceptance,and power supply starting.
Abstract As an elementary mode of locomotion, rotation has been ubiquitously demonstrated in macroscopic dimensions, or microscopically realized in levitated systems and hydrodynamic environments. However, it has remained an untouched research topic to achieve regulated rotation on solid surfaces at microscale, wherein friction serves as the dominant yet formidable external force. Here, this gap is bridged through an all‐optical approach. By utilizing pulsed light with an elongated Gaussian profile and twisting it relative to an illuminated object, chiral vortexes are introduced in both the optothermally excited elastic waves and the as‐induced surface friction, endowing the object with a restoring torque. Self‐regulation of the rotor and refueling of the chirality synergistically modulate the rotational motion. On this basis, orientation of the rotor can be adjusted arbitrarily by any specific angle, achieving an angular resolution of rad and rotation speed up to 10 rpm. Furthermore, the composite motion is demonstrated, combining both rotational and translational modes into the light field‐rotor system. The proposed technique extends the capability of optical manipulation on frictional solid surfaces by exploring the relation between the symmetry‐breaking condition and the modes of locomotion, which provides theoretical guidance and practical opportunities for building reconfigurable devices on solid substrates.
Abstract Refractive index (RI) sensors play an important role in various applications including biomedical analysis and food processing industries. However, developing RI sensors with both high resolution and wide linear range remains a great challenge due to the tradeoff between quality ( Q ) factor and free spectral range ( FSR ) of resonance mode. Herein, the optical steelyard principle is presented to address this challenge by synergizing resonances from the Fabry–Perot (FP) cavity and metasurface, integrated in a hybrid configuration form on the end facet of optical fibers. Specifically, the FP resonance acting like the scale beam, offers high resolution while the plasmonic resonance acting like the weight, provides a wide linear range. Featuring asymmetric Fano spectrum due to modal coupling between these two resonances, a high Q value (~ 3829 in liquid) and a sensing resolution (figure of merit) of 2664 RIU −1 are experimentally demonstrated. Meanwhile, a wide RI sensing range (1.330–1.430 in the simulation and 1.3403–1.3757 in the experiment) is realized, corresponding to a spectral shift across several FSR s (four and two FSR s in the simulation and experiment, respectively). The proposed steelyard RI sensing strategy is promising in versatile monitoring applications, e.g., water salinity/turbidity and biomedical reaction process, and could be extended to other types of sensors calling for both high resolution and wide linear range.
Abstract Applying intelligence algorithms to conceive nanoscale meta-devices becomes a flourishing and extremely active scientific topic over the past few years. Inverse design of functional nanostructures is at the heart of this topic, in which artificial intelligence (AI) furnishes various optimization toolboxes to speed up prototyping of photonic layouts with enhanced performance. In this review, we offer a systemic view on recent advancements in nanophotonic components designed by intelligence algorithms, manifesting a development trend from performance optimizations towards inverse creations of novel designs. To illustrate interplays between two fields, AI and photonics, we take meta-atom spectral manipulation as a case study to introduce algorithm operational principles, and subsequently review their manifold usages among a set of popular meta-elements. As arranged from levels of individual optimized piece to practical system, we discuss algorithm-assisted nanophotonic designs to examine their mutual benefits. We further comment on a set of open questions including reasonable applications of advanced algorithms, expensive data issue, and algorithm benchmarking, etc. Overall, we envision mounting photonic-targeted methodologies to substantially push forward functional artificial meta-devices to profit both fields.
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