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An integrated control strategy is proposed for direct-drive wave energy conversion (DDWEC) systems to address displacement safety constraints and improve the robustness of sensorless position estimation. Under strong wave excitation, buoy displacement may exceed its stroke limit due to conventional amplitude control, leading to mechanical risks. To mitigate this, a displacement-constrained damping regulation law is introduced, incorporating a displacement-dependent correction factor that retains optimal damping within a safe region and increases additional damping smoothly as the displacement approaches its limit. For sensorless operation, a dual-time-scale adaptive amplitude modulation strategy is developed, based on high-frequency square-wave voltage injection. By decoupling the fast position-estimation loop from the slow injection-amplitude adjustment, the demodulated high-frequency current remains within an optimal band, ensuring a high signal-to-noise ratio (SNR) under disturbances and parameter variations. Simulation results show that displacement boundary violations are eliminated, with a 25.7% reduction in peak displacement and only a 7.65% reduction in average captured power. The injection amplitude is adaptively regulated to maintain the demodulated current within the measurement band, enhancing position-estimation stability and accuracy. A fail-safe boundary for extreme sea states (H_s ≈ 2.2 m) is also identified, ensuring robust operation under varying conditions. Full article

(This article belongs to the Special Issue Control and Optimization of Marine Renewable Energy Systems)

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18 pages, 1490 KB

Open AccessArticle

Determinants of Test-to-Reality CO₂ Gaps in European PHEVs: The Limited Role of Battery Capacity

by Maksymilian Mądziel, Paulina Kulasa and Tiziana Campisi

Vehicles 2026, 8(3), 60; https://doi.org/10.3390/vehicles8030060 (registering DOI) - 15 Mar 2026

Abstract

Plug-in hybrid electric vehicles (PHEVs) are expected to reduce fleet CO₂ emissions, but real-world operation often differs markedly from type-approval values. Using European OBFCM data for 457,555 PHEVs (2021–2023) from 14 manufacturers, we quantify the “test-to-reality” CO₂ gap and assess whether traction battery capacity contains an independent signal or mainly reflects vehicle segmentation and in-use behavior. Battery capacity shows only limited standalone explanatory power, while controlling for segment, monitoring year, and manufacturer and incorporating OBFCM-derived usage indicators greatly improves model fit and substantially reduces the apparent battery–gap relationship. We further find strong heterogeneity across vehicle segments, indicating that battery size is not a universal lever of real-world PHEV CO₂ performance. Overall, the results support interpreting battery capacity primarily as a proxy for market positioning and real-world usage (notably charging/engine-dominant operation), highlighting the need to complement type-approval metrics with usage-sensitive indicators when evaluating PHEV compliance in practice. Full article

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34 pages, 32077 KB

Open AccessReview

Rational Design of Hollow Nanostructures: Engineering the Cavity Microenvironment for Advanced Electrocatalysis

by Yong-Gang Sun, Xin Wang, Jian Xiong, Yi-Han Zhang, Jin-Yi Ding, Bo Peng, Yuan Gu, Yi-Cong Xie, Kang-Lin Zhang, Mao Yuan and Xi-Jie Lin

Nanomaterials 2026, 16(6), 360; https://doi.org/10.3390/nano16060360 (registering DOI) - 15 Mar 2026

Abstract

Hollow nanostructures have emerged as a pivotal class of nanomaterials in electrocatalysis, offering intrinsic advantages such as high surface-to-volume ratios, reduced density, and economical utilization of precious metals. However, the prevailing research paradigm has predominantly focused on the external shell characteristics while overlooking the decisive role of the interior cavity microenvironment. This review introduces a novel conceptual framework that positions the rational engineering of the cavity microenvironment—encompassing mass transport dynamics, localized electronic structure modulation, active site exposure, and structural stability—as a unified design principle for next-generation electrocatalysts. We systematically elucidate how precise control over cavity geometry, composition, and interfacial properties can optimize electrocatalytic performance for oxygen reduction (ORR), oxygen evolution (OER), and hydrogen evolution (HER) reactions. By correlating microenvironmental parameters with catalytic metrics, we establish structure–property–performance relationships and highlight recent breakthroughs. Finally, we outline future challenges in achieving atomic-level precision in cavity design, understanding dynamic evolution under operating conditions, and scaling up synthesis for industrial applications. Full article

(This article belongs to the Special Issue Nanomaterials in Electrochemistry: Synthesis and Applications of Electrocatalysts)

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21 pages, 296 KB

Open AccessArticle

Nonlinear Mixed Bi-Skew Jordan-Type Higher Derivations on *-Algebras

by Yimeng Yue, Liang Kong, Fenhong Li and Fugang Chao

Mathematics 2026, 14(6), 997; https://doi.org/10.3390/math14060997 (registering DOI) - 15 Mar 2026

Abstract

Let

A

be a unital *-algebra containing a nontrivial projection. In this paper, we show that every nonlinear mixed bi-skew Jordan-type higher derivation on

A

is an additive higher *-derivation. Moreover, we apply the above result to prime *-algebras, factor von Neumann algebras [...] Read more.

Let

A

be a unital *-algebra containing a nontrivial projection. In this paper, we show that every nonlinear mixed bi-skew Jordan-type higher derivation on

A

is an additive higher *-derivation. Moreover, we apply the above result to prime *-algebras, factor von Neumann algebras and standard operator algebras. Full article

(This article belongs to the Section A: Algebra and Logic)

15 pages, 420 KB

Open AccessArticle

Prevalence and Risk Factors of Aphthous Ulcers Following Periodontal Surgery: A Cross-Sectional Analysis

by Sultan Albeshri, Raed Alrowis, Nouf AlAkeel, Mazen Almobarki, Ibrahim S. Alsanie and Razan Alaqeely

J. Clin. Med. 2026, 15(6), 2237; https://doi.org/10.3390/jcm15062237 (registering DOI) - 15 Mar 2026

Abstract

Background/Objectives: This study aimed to determine the prevalence of aphthous ulcers following periodontal surgery and to identify demographic, behavioral, and clinical predictors of ulcer history before surgery and ulcer development after surgery. Methods: A cross-sectional study was conducted among 227 adult patients undergoing periodontal surgical procedures between November 2024 and May 2025. Demographic, medical, behavioral, and oral health data were collected. Postoperative follow-up at 1 and 2 weeks included a standardized clinical assessment of aphthous ulcers. Statistical analyses included descriptive statistics, chi-square tests, and Chi-squared Automatic Interaction Detection (CHAID) decision tree modeling. Results: Aphthous ulcers developed in 47 patients (20.7%), predominantly within the first postoperative week. CHAID analysis identified age, marital status, and smoking as predictors of preoperative ulcer history (classification accuracy: 73.6%), whereas age and family history predicted postoperative ulcer development (79.4%). Periodontal procedure type was significantly associated with postoperative medication prescription (χ² = 300.45, p < 0.001), suture selection (χ² = 69.19, p = 0.024), and ulcer number (χ² = 48.43, p = 0.031), but not ulcer size or anatomical location. Most ulcers were minor and primarily involved the buccal mucosa. Conclusions: Postoperative aphthous ulceration is a common complication of periodontal surgery, affecting approximately one-fifth of patients. Distinct risk profiles for pre- and post-surgical ulceration highlight the roles of patient-related susceptibility and surgical complexity. These findings support the use of structured risk stratification to guide preoperative counseling and targeted postoperative management. Full article

(This article belongs to the Section Dentistry, Oral Surgery and Oral Medicine)

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36 pages, 1027 KB

Open AccessArticle

Governing Human–AI Co-Evolution: Intelligentization Capability and Dynamic Cognitive Advantage

by Tianchi Lu

Systems 2026, 14(3), 307; https://doi.org/10.3390/systems14030307 (registering DOI) - 15 Mar 2026

Abstract

This research addresses a structural cybernetic anomaly within strategic management precipitated by the integration of artificial intelligence into the organizational core. Traditional paradigms, specifically the resource-based view and the dynamic capabilities framework, operate under closed-system, first-order cybernetic assumptions that fail to capture the dissipative nature of algorithmic agents. By conceptualizing the enterprise as a complex adaptive system operating far from thermodynamic equilibrium, this study introduces the theory of dynamic cognitive advantage. Grounded in second-order cybernetics, the framework posits that competitive differentiation emerges from the historical, recursive, structural coupling of human semantic intent and machine syntactic processing. This research formalizes this co-evolutionary dynamic utilizing coupled non-linear differential equations and time decay integrals. Furthermore, it operationalizes the central mechanism of this capability—the cognitive flywheel—and proposes a fractal governance architecture to mitigate systemic vulnerabilities such as automation bias. To transition these propositions into management science, a proposed mixed-methods empirical research agenda is presented. It outlines a future partial least squares–structural equation modeling (PLS-SEM) approach to test the mediating role of the cognitive flywheel and the moderating effect of fractal governance on organizational resilience. This research provides a mathematically formalized, empirically testable architecture for navigating the artificial intelligence economy. Full article

(This article belongs to the Section Complex Systems and Cybernetics)

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26 pages, 4823 KB

Open AccessArticle

Remote Tower Air Traffic Controller Multimodal Fatigue Detection

by Weijun Pan, Dajiang Song, Ruihan Liang, Zirui Yin and Boyuan Han

Sensors 2026, 26(6), 1856; https://doi.org/10.3390/s26061856 (registering DOI) - 15 Mar 2026

Abstract

Remote tower (rTWR) operations are reshaping air traffic control but introduce significant human-factor risks, notably cognitive fatigue induced by prolonged screen-based visual surveillance. To mitigate these risks in a safety-critical domain where missed detections can be catastrophic, we propose a non-intrusive, multimodal fatigue detection framework fusing ocular and cardiac signals. A high-fidelity simulation study with 36 controllers was conducted to collect eye-tracking and electrocardiogram (ECG) data, from which a 12-dimensional feature vector—integrating gaze entropy and heart rate variability (HRV)—was extracted. Addressing the severe class imbalance and scarcity of fatigue samples in physiological data, we developed a cost-sensitive XGBoost classifier combining SMOTE oversampling with a dynamically weighted loss function. Experimental results show that the proposed framework performed well under mixed-subject evaluation and improved sensitivity to fatigue events. Although a marked performance drop was observed under LOSO evaluation, personalized calibration partially alleviated this limitation, indicating the potential of the framework for real-time fatigue monitoring in remote tower operations. Full article

(This article belongs to the Section Physical Sensors)

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22 pages, 6635 KB

Open AccessArticle

EdgeGeoDiff: A Novel Two-Stage Diffusion Approach for Precipitation Downscaling with Edge Details and Geographical Priors

by Shiji Zhang, Chenghong Zhang, Tao Wu, Tao Zou and Yuanchang Dong

Sensors 2026, 26(6), 1857; https://doi.org/10.3390/s26061857 (registering DOI) - 15 Mar 2026

Abstract

Precipitation downscaling aims to enhance coarse-resolution data to higher resolutions. Due to the similarity between downscaling and super-resolution (SR), deep learning-based SR approaches have been increasingly adopted in this domain. However, single-image super-resolution (SISR) methods applied to precipitation data face two main challenges: weak high-frequency signals and highly skewed distributions in precipitation datasets, which often lead to overly smooth reconstructions, failure to capture precipitation extremes, and loss of fine-scale variability with predictions biased toward mean values. To address these issues, we propose EdgeGeoDiff, a two-stage diffusion model for precipitation downscaling that leverages both edge information and geographical priors (e.g., terrain-related factors such as elevation). In the first stage, a residual network reconstructs an initial high-resolution precipitation field with preliminary structural details. In the second stage, edge features extracted using the Laplacian operator, together with geographical priors, guide a diffusion model to generate residuals that enhance fine-scale precipitation structures. Experimental results on real-world precipitation datasets show that EdgeGeoDiff effectively reconstructs fine-scale details while preserving large-scale patterns and outperforms conventional SISR methods in terms of its RMSE, PSNR, SSIM, and CSI, particularly demonstrating superior performance in the high-frequency region of the spectrum. Full article

(This article belongs to the Topic Applied Computer Vision and Pattern Recognition: 2nd Edition)

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24 pages, 1253 KB

Open AccessArticle

A Reinforcement Learning-Based Framework for Tariff-Aware Load Shifting in Energy-Intensive Manufacturing

by Jersson X. Leon-Medina, Mario Eduardo González Niño, Claudia Patricia Siachoque Celys, Bernardo Umbarila Suarez and Francesc Pozo

Sensors 2026, 26(6), 1858; https://doi.org/10.3390/s26061858 (registering DOI) - 15 Mar 2026

Abstract

Optimizing energy-intensive manufacturing under time-varying electricity tariffs requires scheduling strategies that reduce cost without compromising operational feasibility. This study is grounded in readily available industrial sensing: we exclusively use time-series measurements of aggregated active power and energy at the main distribution board of a quicklime production plant. We propose a tariff-aware load-shifting framework in which a Proximal Policy Optimization (PPO) reinforcement learning agent is trained in a custom Gymnasium environment to apply discrete consumption scaling actions constrained to 80–125% of a baseline profile during the operating shift (08:00–16:00), explicitly accounting for demand-charge exposure in the TOU peak window (13:00–15:00). The reward design combines instantaneous electricity cost with cumulative energy-tracking penalties and terms associated with operational constraints. Multi-day validation over

N = 30

working days shows consistent economic benefits, with a median total cost reduction on the order of 10% (narrow IQR) driven by reduced peak-window energy and demand peaks. However, the script-based binary compliance indicators (viol_energy, viol_prod_min) reveal deviations from the energy-balance criterion and occasional minimum-production shortfalls under the tolerances used, highlighting the cost–production trade-off and the need for stricter constraint handling for industrial deployment. In addition, we benchmark against dynamic programming (DP), an alternative RL policy (DQN), and a greedy heuristic (GREEDY), comparing cost; operational performance; and, when applicable, computational efficiency, which positions PPO as a competitive alternative among the considered methods. Overall, this work demonstrates how learning-based decision making can be coupled with real-world industrial sensing infrastructures, providing a data-driven tariff-aware scheduling layer for industrial energy management under practical constraints. Full article

(This article belongs to the Special Issue AI-Driven Analytics and Intelligent Sensing for Industrial Systems)

20 pages, 752 KB

Open AccessArticle

Numerical Investigation of the Hydrodynamic and Aerodynamic Responses of NREL 5 MW Monopile and Jacket Wind Turbines to the Draupner Wave

by Leila Mokhberioskouei, Barış Namlı and Cihan Bayındır

J. Mar. Sci. Eng. 2026, 14(6), 551; https://doi.org/10.3390/jmse14060551 (registering DOI) - 15 Mar 2026

Abstract

Offshore wind energy is an attractive renewable energy source due to its advantages. However, the chaotic marine environment makes the analysis of offshore wind energy extremely difficult. Furthermore, studying the behavior of wind turbines under rare and hazardous natural events such as rogue waves is crucial for the safety and operation of wind turbines and the platforms mounted on them. Therefore, this study numerically investigates the aerodynamic, hydrodynamic, and structural properties of the National Renewable Energy Laboratory (NREL) 5 MW wind turbines under the effect of the Draupner wave, the first marine rogue wave ever recorded. To this end, the geometric and structural information of the NREL 5 MW wind turbines mounted on monopile and jacket platforms is explained. The characteristics of the Draupner wave and the variations in its wave height time series are investigated. The recorded wave height time series values are imported into the QBlade program, and the dynamics of NREL 5MW monopile and jacket wind turbines are simulated. Based on the simulation data, the aerodynamic, hydrodynamic, and structural properties of these structures are examined and analyzed. The results demonstrate that Draupner waves have a significant effect on the aerodynamic, hydrodynamic, and structural parameters of the wind turbines. These parameters are observed to reach their highest values, particularly between the 250th and 280th seconds, when the Draupner wave height reaches its peak. Our findings indicate that the jacket structure experienced higher total forces due to its larger wetted surface area and geometric complexity, while the monopile foundation showed higher inertial loading in the X-direction because of its larger added mass. Additionally, we observed that total aerodynamic power generation is significantly affected by the passage of the Draupner rogue wave. We discuss our findings and their limitations. This numerical study is intended to be a milestone for researchers working on the structural health of offshore wind turbines and platforms under the effect of rogue waves. Full article

(This article belongs to the Special Issue Hydrodynamic Response to the Effect of Current Loads on Floating Offshore Platform)

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18 pages, 7585 KB

Open AccessArticle

Design and Characterization of a Bench-Top Ludwieg Tube for Aerodynamic Measurements via Simultaneous Quantification of Mach Number and Velocity

by Boris S. Leonov, Richard Q. Binzley, Nathan G. Phillips, Roman Rosser, Farhan Siddiqui, Arthur Dogariu and Richard B. Miles

Fluids 2026, 11(3), 80; https://doi.org/10.3390/fluids11030080 (registering DOI) - 15 Mar 2026

Abstract

This article presents the design and detailed characterization of a new supersonic wind tunnel at the Aerospace Laboratory for Lasers, ElectroMagnetics, and Optics of Texas A&M University, tailored for optical diagnostic development and sub-scale fundamental compressible fluid dynamics research. A Ludwieg tube tunnel architecture was selected due to its robustness, versatility, and low operational costs. The tunnel consists of a 50-foot-long driver tube constructed from modular Tri-Clamp spools, a Mach 4 nozzle with 3 in. exit diameter configured as a free jet, and a fast-acting valve with 14 ms opening time for high-duty-cycle operation. Such construction proved to be a robust, compact, and affordable solution for academic applications. Characterization methods consisted of simultaneous high-speed dot-schlieren, total and static pressure measurements, and femtosecond laser electronic excitation tagging. Average flow velocity for the first steady-state test time was measured via FLEET at (668.0 ± 5.7) m/s. The Mach number was calculated based on the angles of the attached oblique shocks formed near the 30° cone model. Calculated Mach number was repeatable from run to run and had small oscillations near the average value of 3.96 ± 0.03. Based on the simultaneously measured velocity and Mach number, the static temperature was calculated to be between (68.6 ± 0.3) K and (66.3 ± 0.3) K throughout the 400 ms test time, completely defining the thermodynamic state of the generated freestream flow. Full article

(This article belongs to the Special Issue High-Speed Processes in Continuous Media)

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20 pages, 2758 KB

Open AccessArticle

A Dynamic Risk Assessment System for Expressway Lane-Changing: Integrating Bayesian Networks and Markov Chains Under High-Density Traffic

by Quantao Yang and Peikun Li

Systems 2026, 14(3), 306; https://doi.org/10.3390/systems14030306 (registering DOI) - 15 Mar 2026

Abstract

In high-density expressway environments, lane-changing (LC) maneuvers act as stochastic perturbations that compromise the hydrodynamic stability of traffic flow, leading to safety hazards and operational delays. While existing literature has extensively modeled crash severity in static complex environments (e.g., tunnels and mountainous terrains), there remains a critical deficiency in quantifying the dynamic, systemic risks induced by LC maneuvers under saturation conditions. To address this gap, this study proposes a novel Systemic Risk Assessment Framework. First, a Hidden Markov Model (HMM) is employed to decode the latent state transitions of following vehicles, quantifying the systemic consequence of LC maneuvers as “operational delay” based on traffic wave theory. Second, a Bayesian Network (BN) is constructed to infer the causal probability of risk, integrating geometric proxies such as insertion angle with kinematic variables. Validated with real-world trajectory data, the model achieves high accuracy in identifying risk accumulation precursors. This research contributes to the field of transportation systems by shifting the risk paradigm from static collision prediction to dynamic system reliability analysis, offering theoretical support for Connected and Autonomous Vehicle (CAV) decision logic. Full article

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26 pages, 5614 KB

Open AccessArticle

Experimental and Simulation Study on Liquid Entrainment in the Gas Cyclone–Liquid Jet Absorption Separator

by Liang Ma, Yang Su, Anlin Liu, Zhisheng Zhao, Junhong Wu, Xiaoxu Duan and Yuting Zhang

Processes 2026, 14(6), 929; https://doi.org/10.3390/pr14060929 (registering DOI) - 15 Mar 2026

Abstract

Liquid entrainment presents a significant challenge in wet flue gas desulfurization systems, leading to downstream corrosion and secondary pollution. This study systematically investigates the characteristics of liquid entrainment and pressure drop in a gas cyclone–liquid jet absorption separator (GLAS) through both experimental and simulation methods. The effects of inlet gas flow rate (Q_G), absorbent flow rate (Q_L), overflow pipe insertion depth, and the presence of a liquid-guiding cover (LGC) were evaluated. The results revealed that liquid entrainment initially increased and then decreased with rising Q_G, Q_L, and insertion depth of overflow pipe, given the competing effects of turbulent jet breakup and centrifugal separation. To mitigate liquid entrainment, a novel LGC was introduced at the overflow pipe outlet. This intervention resulted in a reduction in liquid entrainment by up to 23.9%, achieved through physical interception and inertial impaction, while maintaining the difference value of pressure drop of less than 302 Pa. The numerical simulations further analyzed the gas–liquid two-phase distributions in GLAS under various operating conditions, with results that align well with experimental observations. These findings offer valuable insights for mitigating liquid entrainment in GLAS and optimizing its industrial applications. Full article

(This article belongs to the Section Separation Processes)

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36 pages, 5742 KB

Open AccessArticle

EEDC: Energy-Efficient Distance-Controlled Clustering for Bottleneck Avoidance in Wireless Sensor Networks

by Ahmad Abuashour, Yahia Jazyah and Naser Zaeri

IoT 2026, 7(1), 29; https://doi.org/10.3390/iot7010029 (registering DOI) - 15 Mar 2026

Abstract

Wireless Sensor Networks (WSNs) commonly employ clustering to improve scalability and energy efficiency; however, cluster heads (CHs) located near the base station (BS) often suffer from excessive relay traffic, leading to rapid energy depletion and reduced network lifetime. This article proposes an Energy-Efficient Distance-Controlled Clustering (EEDC) scheme that adjusts CH density and transmission power according to each node’s distance from the BS. In EEDC, a higher number of CHs is deployed near the BS to balance forwarding loads, while fewer CHs are selected in distant regions to conserve energy. Additionally, CHs adapt their transmission power to enable distance-proportional communication. A mathematical model is developed to analyze the relationship between CH distribution, transmission power, and overall energy consumption. Performance evaluation is conducted through simulations and compared with LEACH, HEED, DEEC, SEP, and EECS. The results show that EEDC improves the stability period by up to 42%, extends network lifetime by 23%, increases average residual energy by 13–29%, enhances throughput by 16–44%, and achieves 23–61% higher packet delivery efficiency. Moreover, cumulative CH energy consumption is reduced by 5–21%, leading to more balanced energy distribution. These findings indicate that distance-controlled CH selection and adaptive transmission power effectively alleviate the BS energy bottleneck and enhance the energy efficiency and operational longevity of clustered WSNs. Full article

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17 pages, 3835 KB

Open AccessArticle

Fabrication of Wear-Resistant and Anti-Reflection Surfaces Based on Armor-Protected Nanocone Structures

by Haoyu Tian, Jianxun Chen, Jiaheng Bi, Haotian Guo, Cheng Lei and Ruirui Li

Micromachines 2026, 17(3), 360; https://doi.org/10.3390/mi17030360 (registering DOI) - 15 Mar 2026

Abstract

Antireflection surfaces play an indispensable role in modern optics, with extensive applications covering optical windows and other precision optical components. The fabrication of anti-reflection surfaces frequently relies on micro/nano-structuring technologies. However, the fabricated micro/nanostructures typically experience performance degradation in transmission enhancement caused by abrasion during operation. To address this problem, we designed and fabricated a double-sided nanocone structure shielded by a protective armor layer. This armor layer efficiently prevents surface mechanical wear and preserves the nanocone structures, leading to almost constant transmittance of the anti-reflection surface even after abrasion. The anti-reflection surface was fabricated by first patterning a square grid armor on one side of fused silica via photolithography, followed by the preparation of an etching mask and nanocone structures using reactive ion etching (RIE). Nanocones were then fabricated on the opposite side of the substrate, finally forming the double-sided nanocone structure. The fabricated armor-protected double-sided nanocone structure exhibited an increase in the average transmittance from 93.43% to 98.31% within the wavelength range of 800–1200 nm. After abrasion testing under 10 MPa pressure, the nanocones under the protective armor showed almost no damage, and the average transmittance remained at approximately 97.85%, demonstrating the outstanding mechanical robustness of the proposed design. Full article

(This article belongs to the Special Issue Micro/Nanomanufacturing and Cross-Scale Fabrication: Methods, Systems, and Applications)

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