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Recent years have witnessed a rapid development in the field of structural coloration, colors generated from the interaction of nanostructures with light. Compared to conventional color generation based on pigments and dyes, structural color generation exhibits unique advantages in terms of spatial resolution, operational stability, environmental friendliness, and multiple functionality. Here, we discuss recent development in structural coloration based on layered thin films and optical metasurfaces. This review first presents fundamentals of color science and introduces a few popular color spaces used for color evaluation. Then, it elaborates on representative physical mechanisms for structural color generation, including Fabry–Pérot resonance, photonic crystal resonance, guided mode resonance, plasmon resonance, and Mie resonance. Optimization methods for efficient structure parameter searching, fabrication techniques for large-scale and low-cost manufacturing, as well as device designs for dynamic displaying are discussed subsequently. In the end, the review surveys diverse applications of structural colors in various areas such as printing, sensing, and advanced photovoltaics.

Summary Single-atom catalysts (SACs) have the advantages of both homogeneous and heterogeneous catalysts, which show promising application potentials in many renewable energy-conversion technologies and critical industrial processes. In particular, carbon-supported SACs (CS-SACs) are of great interest because of their maximal atom utilization (∼100%), unique physicochemical structure, and beneficial synergistic effects between active catalytic sites and carbon substrates. In this review, we offer a critical overview of the unique advantages of CS-SACs related to their material designs, catalytic activities, and potential application areas. The state-of-the-art design and synthesis of CS-SACs are described under the framework of bottom-up and top-down approaches. We also comprehensively summarize recent advances in developing CS-SACs for important electrochemical reactions, i.e., oxygen reduction reaction, hydrogen evolution reaction, oxygen evolution reaction, CO2 reduction reaction, nitrogen reduction reaction, serving as bi-/multi-functional electrocatalysts, and usages in supercapacitors and batteries. Lastly, the critical challenges and future opportunities in this emerging field are highlighted.

Numerical optimization methods for optical coating designs are often time-consuming. In this work, we show that combining machine learning with optimization can improve efficiency and even lead to better designs.

Objective Occupational injuries (OIs) cause an immense burden on the US population. Prediction models help focus resources on those at greatest risk of a delayed return to work (RTW). RTW depends on factors that develop over time; however, existing methods only utilize information collected at the time of injury. We investigate the performance benefits of dynamically estimating RTW, using longitudinal observations of diagnoses and treatments collected beyond the time of initial injury. Materials and Methods We characterize the difference in predictive performance between an approach that uses information collected at the time of initial injury (baseline model) and a proposed approach that uses longitudinal information collected over the course of the patient’s recovery period (proposed model). To control the comparison, both models use the same deep learning architecture and differ only in the information used. We utilize a large longitudinal observation dataset of OI claims and compare the performance of the two approaches in terms of daily prediction of future work state (working vs not working). The performance of these two approaches was assessed in terms of the area under the receiver operator characteristic curve (AUROC) and expected calibration error (ECE). Results After subsampling and applying inclusion criteria, our final dataset covered 294 103 OIs, which were split evenly between train, development, and test datasets (1/3, 1/3, 1/3). In terms of discriminative performance on the test dataset, the proposed model had an AUROC of 0.728 (90% confidence interval: 0.723, 0.734) versus the baseline’s 0.591 (0.585, 0.598). The proposed model had an ECE of 0.004 (0.003, 0.005) versus the baseline’s 0.016 (0.009, 0.018). Conclusion The longitudinal approach outperforms current practice and shows potential for leveraging observational data to dynamically update predictions of RTW in the setting of OI. This approach may enable physicians and workers’ compensation programs to manage large populations of injured workers more effectively.

Designing optical structures for generating structural colors is challenging because of the complex relationship between the optical structures and the color perceived by human eyes. Machine learning-based approaches have been developed to expedite this design process. However, existing methods solely focus on structural parameters of the optical design, which could lead to suboptimal color generation because of the inability to optimize the selection of materials. To address this issue, an approach known as Neural Particle Swarm Optimization is proposed in this paper. The proposed method achieves high design accuracy and efficiency on two structural color design tasks; the first task is designing environment-friendly alternatives to chrome coatings, and the second task concerns reconstructing pictures with multilayer optical thin films. Several designs that could replace chrome coatings have been discovered; pictures with more than 200,000 pixels and thousands of unique colors can be accurately reconstructed in a few hours.

Electrocatalytic hydrogen peroxide (H

A superconducting nanowire acting as a single-photon detector and as a microwave delay line is used to demonstrate an imaging device at the single-photon level with sub-20-µm spatial resolution and 50-ps temporal resolution.

A metallic trench Fabry-Perot resonator achieves room temperature lasing of surface plasmons with a record-narrow linewidth., Recent years have witnessed a growing interest in the development of small-footprint lasers for potential applications in small-volume sensing and on-chip optical communications. Surface plasmons—electromagnetic modes evanescently confined to metal-dielectric interfaces—offer an effective route to achieving lasing at nanometer-scale dimensions when resonantly amplified in contact with a gain medium. We achieve narrow-linewidth visible-frequency lasing at room temperature by leveraging surface plasmons propagating in an open Fabry-Perot cavity formed by a flat metal surface coated with a subwavelength-thick layer of optically pumped gain medium and orthogonally bound by a pair of flat metal sidewalls. We show how the lasing threshold and linewidth can be lowered by incorporating a low-profile tapered grating on the cavity floor to couple the excitation beam into a pump surface plasmon polariton providing a strong modal overlap with the gain medium. Low-perturbation transmission-configuration sampling of the lasing plasmon mode is achieved via an evanescently coupled recessed nanoslit, opening the way to high–figure of merit refractive index sensing of analytes interacting with the open metallic trench.

In many settings, it is important that a model be capable of providing reasons for its predictions (i.e., the model must be interpretable). However, the model's reasoning may not conform with well-established knowledge. In such cases, while interpretable, the model lacks \textit{credibility}. In this work, we formally define credibility in the linear setting and focus on techniques for learning models that are both accurate and credible. In particular, we propose a regularization penalty, expert yielded estimates (EYE), that incorporates expert knowledge about well-known relationships among covariates and the outcome of interest. We give both theoretical and empirical results comparing our proposed method to several other regularization techniques. Across a range of settings, experiments on both synthetic and real data show that models learned using the EYE penalty are significantly more credible than those learned using other penalties. Applied to a large-scale patient risk stratification task, our proposed technique results in a model whose top features overlap significantly with known clinical risk factors, while still achieving good predictive performance.

Nature Photonics 11, 247–251 (2017); published online 27 March 2017; corrected after print 14 August 2017. In the version of this Article originally published, in Fig. 3c, in the y axis label, the units '(c.p.s.)' should not have been included; the label should have read “Dark counts”. This has now been corrected in the online versions of the Article.

Optical multi-layer thin films are widely used in optical and energy applications requiring photonic designs. Engineers often design such structures based on their physical intuition. However, solely relying on human experts can be time-consuming and may lead to sub-optimal designs, especially when the design space is large. In this work, we frame the multi-layer optical design task as a sequence generation problem. A deep sequence generation network is proposed for efficiently generating optical layer sequences. We train the deep sequence generation network with proximal policy optimization to generate multi-layer structures with desired properties. The proposed method is applied to two energy applications. Our algorithm successfully discovered high-performance designs, outperforming structures designed by human experts in task 1, and a state-of-the-art memetic algorithm in task 2., Comment: 15 pages, 10 figures, poster presentation at RL4RealLife 2020