4.4 kW Nd:YAG slab amplifier with a high repetition frequency and high beam quality.

Using the self-developed fused indium wetting technology and planar waveguide, the uniform heat dissipation of the slab crystal and uniform pumping of the pump light were achieved, respectively. Based on the master oscillator power amplification (MOPA) scheme, the power was then amplified when the seed light source passed through the Nd:YAG slab crystal three times. Additionally, the image transfer system that we added to the amplified optical path achieved high beam quality. Finally, we obtained a rectangular pulsed laser with an output average power of 4461 W, a repetition frequency of 20 kHz, a pulse width of 62 ns, an optical-to-optical conversion efficiency of 26.8%, and a beam quality of ß x=7.0 and ß y=7.7.

Environmentally Sustainable and Multifunctional Chrome-like Coatings Having No Chromium Designed with Reinforcement Learning.

Decorative chrome plating (DCP) continues to be ubiquitous in creating highly appealing metal finishings and coatings, beating out other organic dye-based finishes. However, the hazardous chrome plating process is fraught with adverse health effects for the workers involved and causes significant environmental damage. In this work, we present a multilayer thin film structure to mimic the chrome appearance. To find a design efficiently, we employ a reinforcement learning (RL) algorithm to perform an automatic inverse design. This results in structures composed of environmentally friendly materials that not only have the chrome color but can also achieve additional functions beyond decoration. As an example, one structure is designed to have high transmission in the radio frequency regime, a property that general metals cannot have, which can broaden the decorative chrome applications to include microwave operating devices. The experimental structures are fabricated by physical vapor deposition to demonstrate the indistinguishable chrome color and validate the effectiveness of the RL inverse design approach.

Coal Wall Spalling Mechanism and Grouting Reinforcement Technology of Large Mining Height Working Face.

To control the problem of coal wall spalling in large mining height working faces subject to mining, considering the Duanwang Mine 150505 fully mechanized working face, the mechanism of coal wall spalling in working faces was investigated by theoretical analysis, numerical simulation and field experiment. Based on analysis of coal wall spalling in the working face, a new grouting material was developed. The stress and plastic zone changes affecting the coal wall, before and after grouting in the working face, were analyzed using numerical simulation and surrounding rock grouting reinforcement technology was proposed for application around the new grouting material. The results showed that: (1) serious spalling of the 150505 working face was caused by the large mining height, fault influence and low roof strength, and (2) the new nano-composite low temperature polymer materials used have characteristics of rapid reaction, low polymerization temperature, adjustable setting time, high strength and environmental protection. Based on analysis of the working face coal wall spalling problem, grouting reinforcement technology based on new materials was proposed. Industrial tests were carried out on the working face. Field monitoring showed that the stability of the working face coal wall was significantly enhanced and that rib spalling was significantly improved after comprehensive anti-rib-spalling grouting measures were adopted. These results provide a basis for rib spalling control of working faces under similar conditions.

Dynamic prediction of work status for workers with occupational injuries: assessing the value of longitudinal observations.

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.

NEUTRON: Neural particle swarm optimization for material-aware inverse design of structural color.

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.

Cation-Vacancy-Enriched Nickel Phosphide for Efficient Electrosynthesis of Hydrogen Peroxides.

Electrocatalytic hydrogen peroxide (H2 O2 ) synthesis via the two-electron oxygen reduction reaction (2e ORR) pathway is becoming increasingly important due to the green production process. Here, cationic vacancies on nickel phosphide, as a proof-of-concept to regulate the catalyst's physicochemical properties, are introduced for efficient H2 O2 electrosynthesis. The as-fabricated Ni cationic vacancies (VNi )-enriched Ni2- x P-VNi electrocatalyst exhibits remarkable 2e ORR performance with H2 O2 molar fraction of >95% and Faradaic efficiencies of >90% in all pH conditions under a wide range of applied potentials. Impressively, the as-created VNi possesses superb long-term durability for over 50 h, suppassing all the recently reported catalysts for H2 O2 electrosynthesis. Operando X-ray absorption near-edge spectroscopy (XANES) and synchrotron Fourier transform infrared (SR-FTIR) combining theoretical calculations reveal that the excellent catalytic performance originates from the VNi -induced geometric and electronic structural optimization, thus promoting oxygen adsorption to the 2e ORR favored "end-on" configuration. It is believed that the demonstrated cation vacancy engineering is an effective strategy toward creating active heterogeneous catalysts with atomic precision.

Surface plasmon polariton laser based on a metallic trench Fabry-Perot resonator.

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.