Optical metasurfaces for generating and manipulating optical vortex beams

Optical vortices (OVs) carrying orbital angular momentum (OAM) have attracted considerable interest in the field of optics and photonics owing to their peculiar optical features and extra degree of freedom for carrying information. Although there have been significant efforts to realize OVs using conventional optics, it is limited by large volume, high cost, and lack of design flexibility. Optical metasurfaces have recently attracted tremendous interest due to their unprecedented capability in the manipulation of the amplitude, phase, polarization, and frequency of light at a subwavelength scale. Optical metasurfaces have revolutionized design concepts in photonics, providing a new platform to develop ultrathin optical devices for the realization of OVs at subwavelength resolution. In this article, we will review the recent progress in optical metasurface-based OVs. We provide a comprehensive discussion on the optical manipulation of OVs, including OAM superposition, OAM sorting, OAM multiplexing, OAM holography, and nonlinear metasurfaces for OAM generation and manipulation. The rapid development of metasurface for OVs generation and manipulation will play an important role in many relevant research fields. We expect that metasurface will fuel the continuous progress of wearable and portable consumer electronics and optics where low-cost and miniaturized OAM related systems are in high demand.

Experimental Validation of a Hydrostatic Transmission for Community Wind Turbines

Hydrostatic transmissions are commonly used in heavy-duty equipment for their design flexibility and superior power density. Compared to a conventional wind turbine transmission, a hydrostatic transmission (HST) is a lighter, more reliable, cheaper, continuously variable alternative for a wind turbine. In this paper, for the first time, a validated dynamical model and controlled experiment have been used to analyze the performance of a hydrostatic transmission with a fixed-displacement pump and a variable-displacement motor for community wind turbines. From the dynamics of the HST, a pressure control strategy is designed to maximize the power capture. A hardware-in-the-loop simulation is developed to experimentally validate the performance and efficiency of the HST drive train control in a 60 kW virtual wind turbine environment. The HST turbine is extensively evaluated under steady and time-varying wind on a state-of-the-art power regenerative hydrostatic dynamometer. The proposed controller tracks the optimal tip-speed ratio to maximize power capture.

Additive Manufacturing Feature Taxonomy and Placement of Parts in AM Enclosure

Additive Manufacturing (AM) is a layer-by-layer deposition of material for the production of the desired product. The design flexibility associated with AM is much more when compared to the conventional manufacturing process. To manufacture a part with AM, two things play a critical role: the designing of the part and the other is the placement of the part in the build volume. As already mentioned, design flexibility associated with AM is much more when compared to the conventional manufacturing process. However, to correctly implement the design flexibility, we need a knowledge base at our disposal so that appropriate features can be used for the part production. The AM feature taxonomy forms the backbone of the knowledge base. The taxonomy comprises AM features classified based on different categories, which helps us understand every feature's importance. Talking about the part placement, we know that optimal placement is the key factor that makes the AM process economically feasible.

A Systematic Approach for Developing 3D High-Quality PDMS Microfluidic Chips Based on Micromilling Technology

In recent years, there has been an increased interest in exploring the potential of micro-and mesoscale milling technologies for developing cost-effective microfluidic systems with high design flexibility and a rapid microfabrication process that does not require a cleanroom. Nevertheless, the number of current studies aiming to fully understand and establish the benefits of this technique in developing high-quality microsystems with simple integrability is still limited. In the first part of this study, we define a systematic and adaptable strategy for developing high-quality poly(methyl methacrylate) (PMMA)-based micromilled structures. A case study of the average surface roughness (Ra) minimization of a cuboid column is presented to better illustrate some of the developed strategies. In this example, the Ra of a cuboid column was reduced from 1.68 μm to 0.223 μm by implementing milling optimization and postprocessing steps. In the second part of this paper, new strategies for developing a 3D microsystem were introduced by using a specifically designed negative PMMA master mold for polydimethylsiloxane (PDMS) double-casting prototyping. The reported results in this study demonstrate the robustness of the proposed approach for developing microfluidic structures with high surface quality and structural integrability in a reasonable amount of time.

Design of Sustainable Renewable-Based Utility Plants in the Face of Uncertainty

This work presents the formulation of a two-stage stochastic mixed-integer linear programming (MILP) model to include uncertainty in the design of renewable-based utility plants. The model is based on a superstructure that integrates technologies to process biomass, waste, solar radiation, and wind and considers uncertainty in availability of the renewable resources and on the utility demands. The uncertain parameter space is calculated based on a monthly probability density function for each uncertain parameter and discretized into different levels. It is shown that as uncertainty is considered in the model formulation, design flexibility improves with respect to the deterministic-based designs, although the flexibility is achieved at the expense of higher underused facilities and therefore unused investment cost.

SEGMENTATION OF ADDITIVE MANUFACTURING DEFECTS USING U-NET

Additive manufacturing (AM) provides design flexibility and allows rapid fabrications of parts with complex geometries. The presence of internal defects, however, can lead to deficit performance of the fabricated part. X-ray Computed Tomography (XCT) is a non-destructive inspection technique often used for AM parts. Although defects within AM specimens can be identified and segmented by manually thresholding the XCT images, the process can be tedious and inefficient, and the segmentation results can be ambiguous. The variation in the shapes and appearances of defects also poses difficulty in accurately segmenting defects. This paper describes an automatic defect segmentation method using U-Net based deep convolutional neural network (CNN) architectures. Several models of U-Net variants are trained and validated on an AM XCT image dataset containing pores and cracks, achieving a best mean intersection over union (IOU) value of 0.993. Performance of various U-Net models is compared and analyzed. Specific to AM porosity segmentation with XCT images, several techniques in data augmentation and model development are introduced. This work demonstrates that U-Net can be effectively applied for automatic segmentation of AM porosity from XCT images with high accuracy. The method can potentially help improve quality control of AM parts in an industry setting.

Size-Adjustable Nano-Drug Delivery Systems for Enhanced Tumor Retention and Penetration

Over the past decades, nano-drug delivery systems have shown great potential in improving tumor treatment. And the controllability and design flexibility of nanoparticles endow them a broad development space. The particle size is one of the most important factors affecting the potency of nano-drug delivery systems. Large-size (100–200 nm) nanoparticles are more conducive to long circulation and tumor retention, but have poor tumor penetration; small-size (<50 nm) nanoparticles can deeply penetrate tumor but are easily cleared. Most of the current fixed-size nanoparticles are difficult to balance the retention and penetration, while the proposal of size-adjustable nano-drug delivery systems offers a solution to this paradox. Many endogenous and exogenous stimuli, such as acidic pH, upregulated enzymes, temperature, light, catalysts, redox conditions, and reactive oxygen species, can trigger the in situ transformation of nanoparticles based on protonation, hydrolysis, click reaction, phase transition, photoisomerization, redox reaction, etc. In this review, we summarize the principles and applications of stimuli-responsive size-adjustable strategies, including size-enlargement strategies and size-shrinkage strategies. We also propose the challenges faced by size-adjustable nano-drug delivery systems, hoping to promote the development of this strategy.

Multiobjective optimization design of hybrid composite laminates for vibration using sequential permutation table method

Due to the large variable design space in optimization problems of composite laminates, it remains one of the challenging tasks to develop efficient optimization design methods to improve the design flexibility and efficiency. This work presents a sequential permutation table (SPT) method for the multiobjective optimization design of two-material hybrid composite laminates with simply supported boundary conditions, which maximizes the fundamental frequency and minimizes the cost/weight. Based on the vibration analysis of hybrid composite laminates, the approximate linear regularity of the square of fundamental frequency is derived, and two best ply orientations for the two materials are identified, respectively. By assigning one best ply orientation with maximum fundamental frequency at respective stacking positions, and using another best ply orientation to replace plies in the stacking sequence from the mid-plane to the outermost can lead to the optimum. Two multiobjective optimization problems are employed to verify the SPT method, results are compared with those obtained by heuristic algorithms. The obtained better solutions demonstrate the effectiveness and efficiency of the SPT method and its potentials for optimal design of hybrid composite laminates.

Development of a New Temporary Attachment Technique for Detecting Debonding of a Composite Structure Using Impedance Based Non-Destructive Testing Method

To date, the application of composite materials has been used throughout the globe due to its advantages, such as corrosion resistance, high strength, design flexibility, and light weight. However, the joining of composite materials is usually achieved with adhesives, where debonding of parts can cause unexpected failure. Thus, detecting and locating defects due to impact or fatigue stresses at an early stage is crucial to ensure safety. Various non-destructive testing (NDT) techniques have been used to detect defects in composite structures, where this study proposes an improved approach of using one of the NDT techniques to detect and locate debonding of glass fiber epoxy plates. Here, the electromechanical impedance (EMI) technique is used with a new way of detecting defects using a movable device. This idea could reduce the overall cost of the monitoring system as the conventional EMI technique requires one to permanently attach a large number of piezoelectric transducers when monitoring large structures. The performance of the proposed idea is tested against another temporary attachment method to investigate the possibility of using the new idea for monitoring debonding in composite structures.

Microwave Dielectric Property Retrieval from Open-Ended Coaxial Probe Response with Deep Learning

In the manuscript, we propose a new technique for determination of Debye parameters, representing the dielectric properties of materials, from the reflection coefficient response of open-ended coaxial probes. The method retrieves the Debye parameters using a deep learning model designed through utilization of numerically generated data. Unlike real data, using synthetically generated input and output data for training purposes provides representation of a wide variety of materials with rapid data generation. Furthermore, the proposed method provides design flexibility and can be applied to any desired probe with intended dimensions and material. Next, we experimentally verified the designed deep learning model using measured reflection coefficients when the probe was terminated with five different standard liquids, four mixtures,and a gel-like material.and compared the results with the literature. Obtained mean percent relative error was ranging from 1.21±0.06 to 10.89±0.08. Our work also presents a large-scale statistical verification of the proposed dielectric property retrieval technique.