Three-dimensional microstructuring of ionogel dielectric layers has been demonstrated to effectively enhance the sensitivity of iontronic sensors. However, conventional fabrication of such microstructures usually via multi-step sacrificial templating suffers from high costs and limited scalability. Herein, we propose a one-step solvent evaporation induced molding strategy that directly yields tapered hollow microcones without any sacrificial templates or post-processing, thus simplifying the manufacturing process. The hollow tapered microcones exhibit high compressibility and progressive deformation, enabling stable compression without sophisticated gradient designs. This deformation behavior ensures stable compression without complex gradient designs, yielding a high sensitivity ( 72.34 kPa−1) across a broad detection range of 0–4000 kPa. The HCSM sensor further exhibits an ultralow detection limit of 7.28 Pa, fast response/recovery (28/35 ms), and excellent stability over 4500 cycles. These capabilities enable wearable monitoring of both macroscopic joint motions and subtle physiological signals. This work provides a simple, scalable strategy for fabricating high-performance, low-cost iontronic sensors without requiring complex microstructural engineering.

We propose a fully ground-based, forward-scattering bistatic Doppler-lidar network for minute-cadence, wide-area horizontal-wind mapping. A narrow-linewidth pulsed transmitter illuminates a range-gated volume, while distributed ground receivers arranged on a ring collect small-angle (5°-15°) forward-scattered returns from the same volume. We develop a unified performance model that couples geometric conditioning with photon statistics, spectral sensitivity, and sky-background radiance. Sensitivity analyses quantify the impacts of receiver number, ring radius, wavelength, background level, and the accumulated pulse number Np = PRF × Tint. The results provide design rules that link uncertainty thresholds to cadence and layout, and they support scalable tiled deployments for large renewable-energy sites and wind corridors.

Hydrogel-based piezoresistive sensors hold great promise for handwriting-enabled human-machine interaction. Accurate detection of weak, transient, and highly similar handwriting pressure signals remains a critical bottleneck for achieving high-fidelity interaction. Herein, a high-performance PAM/SA double-network hydrogel-based sensor is proposed, integrating a synergistic MXene/LM conductive network and tailored microstructures. The MXene/LM synergistic network enhances electrical responsiveness and cyclic stability, while tailored surface microstructures improve low-pressure sensitivity and broaden the linear working range by modulating stress distribution and contact-area evolution. The resulting P-PSML sensor exhibited a high sensitivity of 151.5 kPa−1, an ultralow detection limit of 2.23 Pa, a response time of 115 ms, and reliable electrical stability over 3000 cycles. Furthermore, the sensor captures dynamic electrical signals from 26 handwritten English letters, achieving 97.85% recognition accuracy via a deep learning model. A real-time handwriting signal processing and recognition platform is further built to translate recognition outputs directly into robotic-arm control commands. This work extends handwritten-letter recognition to practical human-machine interaction and provides a feasible sensing strategy for intelligent flexible handwriting interfaces.

Differential Absorption Lidar (DIAL) has emerged as a critical technique for profiling atmospheric ozone, addressing the urgent need to monitor vertical ozone distributions to improve air quality assessments and understand climate impacts. This review systematically examines the fundamental principles, key system architectures, and engineering implementation strategies of ozone DIAL systems, with a focus on practical design strategies and real-world deployment challenges. It first outlines wavelength selection criteria, emphasizing the decisive role of differential absorption cross sections in system sensitivity, while comparing the suitability of fixed-wavelength versus tunable laser sources and evaluating their impact on ultraviolet laser output stability. It then reviews advances in receiver design, including coordinated large- and small-field-of-view configurations and multi-channel filtering mechanisms, highlighting their roles in enhancing near-surface resolution and extending detection range. By analyzing structural diagrams and parameter tables of representative systems such as TROPOZ (Tropospheric Ozone Differential Absorption Lidar), TOPAZ (Tunable Optical Profiler for Aerosol and Ozone), AMOLITE (Autonomous Mobile Ozone LIDAR Instrument for Tropospheric Experiments), and SMOL (Small Mobile Ozone Lidar), this paper synthesizes design strategies and observational capabilities across platforms, evaluating their effectiveness in detecting boundary-layer pollution and monitoring vertical structural evolution. Finally, the review outlines future directions such as system miniaturization and intelligent light-source development, as well as algorithmic integration and the establishment of standardized products and data protocols, aiming to provide a reference framework and technical foundation for optimizing ozone vertical profiling systems.

In the conventional Fernald inversion, the boundary value at the calibration point is commonly estimated using a slope-based method. This procedure increases algorithmic complexity and can introduce retrieval errors. Here, we propose an alternative boundary-value determination scheme that exploits the tendency of the Fernald forward-integration equation to diverge. Simulation experiments show that the proposed scheme is more stable than the slope method under atmospheric inhomogeneity and measurement noise. We further applied the method to horizontal lidar scans acquired in Lankao (Henan Province, China), capturing a regional pollution transport and dispersion episode. Together, these results suggest that the method enables real-time monitoring of the horizontal distribution of regional pollutants.

Atmospheric visibility nowcasting is vital for safety-critical operations but remains challenging due to complex atmospheric dynamics. We propose a compact stacking ensemble merging a multilayer perceptron (MLP) and gradient-boosted regression trees (GBRT). The model, trained on seven months of minute-scale resolution data with a variability-adaptive filter to suppress sensor noise, employs cross-validation. Results demonstrate that the ensemble achieves its peak performance in the operationally critical low-visibility regime (V < 5 km). This range is particularly significant as it encompasses the Category I and II (CAT I/II) operational thresholds defined by the World Meteorological Organization (WMO) for aviation and surface transportation safety. In this regime, the ensemble yields an R-2 of 0.82 and an MAE approximate to 385 m, significantly outperforming single learners during rapid weather transitions. Conversely, in the high-visibility regime (V > 20 km), the explanatory power decreases (R-2 of 0.46) due to inherent forward-scattering sensor uncertainties and low aerosol concentrations. Despite these range-specific physical limitations, the model maintains high robustness with narrowly centered residuals. This efficient approach, utilizing cost-effective in situ sensors, is highly suitable for airport and road-weather applications and offers strong potential for multi-site scalability.

The Huaihe River Basin (HRB) experiences pronounced seasonal circulation and recurrent aerosol pollution, yet continuous observations of wind structure above the atmospheric boundary layer remain limited. We integrate rotary Rayleigh Doppler wind lidar (RRDWL) measurements and radiosonde profiles with ERA5 re-analysis, MERRA-2 aerosol optical depth (AOD), surface PM2.5 observations, and selected HYSPLIT trajectories to characterize free-atmospheric wind variability and assess its statistical relationships with regional aerosol conditions. During periods of concurrent observation, RRDWL, radiosonde, and ERA5 profiles consistently capture the dominant features of the wind structure between 11 and 30 km, although agreement weakens near the tropopause. ERA5 data for 2017–2023 reveal pronounced seasonal variability, with stronger upper-tropospheric winds in spring, autumn, and winter than in summer. Enhanced boundary-layer ventilation generally coincides with lower surface PM2.5 concentrations, but the strength and direction of this relationship vary among cities and time periods. The observed probability of elevated-PM2.5 episodes is highest under westerly and northwesterly flow, while selected-event trajectories indicate air-mass pathways originating from, or passing through, western and northern sectors. Seasonal AOD distributions reveal substantial spatial and temporal variability in column-integrated aerosol loading. These results provide a vertically resolved, multi-dataset characterization of atmospheric wind variability over a monsoon-influenced basin and establish an observational framework for distinguishing statistical wind–aerosol relationships from mechanistic interpretations of aerosol transport.

Accurate prevention and control of incipient fires in confined spaces place extremely high demands on passive fire-extinguishing devices. However, conventional fire-extinguishing microcapsules are often limited by broad size distributions and the propensity of highly volatile core materials to leak, leading to delayed thermal response and performance degradation. To address this challenge, we developed a novel microfluidic platform integrating 3D-printed templates with non-planar PDMS microchannels, enabling stable and monodisperse (CV = 3.53%) core-shell encapsulation of a highly volatile perfluoro-2-methyl-3-pentanone/heptafluorocyclopentane (PFH/F7A) composite fire suppressant. By establishing a theoretical scaling law based on multiphase-flow shear and mass conservation, we achieved precise decoupled control over microcapsule diameter and shell thickness. Further thermo-fluid-solid coupled mechanical analysis revealed the underlying mechanism of the sub-millisecond "micro-explosion" upon heating, namely, the convergence of an internal vapor-pressure surge induced by core vaporization and the deterministic instability caused by thermal softening of the polymer shell. Using the target geometric parameters for device integration (average diameter of 519.1 μm, coefficient of variation (CV) = 4.87%; shell thickness of 32.6 μm, CV = 6.21%) and the optimal agent ratio (P : F = 5 : 5), we array-integrated the microcapsules into a two-dimensional flexible patch, enabling coordinated quasi-synchronous release of numerous microcapsules at the critical temperature. Macroscopic tests demonstrated that the patch rapidly extinguished an n-heptane pool fire within 5 ± 0.5 s, decisively preventing re-ignition. Moreover, under 300 °C thermal abuse for 18650 lithium-ion batteries, it delayed thermal runaway onset by an average of 355 s and reduced the peak temperature by approximately 199 °C. This work establishes a complete closed loop from the underlying dynamic mechanism to macroscopic device design, providing a new strategy for efficient and adaptive thermal safety protection in complex confined spaces.

Using observations from a single monitoring site in Hefei, eastern China, we developed an enhanced random-forest-gated generalized additive modeling framework (RF–GAM) for visibility nowcasting and particulate-matter (PM2.5, PM10) forecasting. The visibility and local meteorological observations covered 23 May 2024 to 9 April 2025, and the autumn visibility-prediction experiments used the subset from 1 September 2024 to 30 November 2024, whereas the PM task used a longer hourly dataset assembled from Department Of Ecology and Environment of Anhui Province and meteorological records from NOAA’s Climate Data Online (NCEI CDO). The PM modeling dataset contained 37,299 hourly samples from 2 January 2020 to 30 April 2024 after temporal alignment and quality control. In the first stage, a random forest classifier identified atmospheric regimes to mitigate class imbalance; in the second stage, regime-specific GAMs modeled conditional nonlinear responses to key meteorological predictors. For visibility, the stage-1 classifier achieved an AUC of 0.992 for fog identification, and the nowcast attained a tolerance-based accuracy of 84.3

We present a reusable, end-to-end framework that evaluates three incoherent Doppler wind-Lidar frequency discriminators-the Fabry-Perot interferometer (FPI) double-edge, the Fizeau/Michelson fringe imager, and the iodine absorption edge-on a common footing. The model links scene physics (Rayleigh-Brillouin versus narrow Mie spectra), wavelength and background (355/532/1064 nm; day/night), and instrument parameters (FSR, finesse, OPD, laser linewidth/stability) to the Cram & eacute;r-Rao lower bound (CRLB), limit of detection (LOD), bias, and RMSE. Three design rules emerge. (1) FPI valley: a diagonal minimum-LOD valley appears in FSR-finesse space when the effective passband aligns with the Rayleigh-Brillouin (RB) linewidth; near this valley, V-max increases approximately linearly with FSR. (2) Fringe trade-off: increasing OPD steepens the discriminator slope but reduces dynamic range under the |Delta phi| = pi constraint; OPD jitter dominates the bias, with quantitative thresholds of similar to 50-70 nm for |Bias| <2 m s(-1) and similar to 25-40 nm for |Bias| <1 m s(-1) (3) Wavelength/scene selection: at night in molecular-dominated mid-to upper-tropospheric layers the FPI tracks its CRLB most closely; the fringe imager excels in aerosol-rich boundary layers; and iodine at 532 nm is competitive near the surface, offering strong daylight rejection but a narrow linear range. We construct a scenario-design matrix that links sensitivity, dynamic range and measurement availability to receiver architecture. This framework yields transferable guidance for both spaceborne and airborne implementations.

This study focuses on elucidating the enhancement mechanism of lanthanum oxide (La2O3) addition on the tribological and mechanical properties of copper-based friction materials for high-speed railway. Samples with different La2O3/CrFe ratios were prepared via powder metallurgy. Combined with electron backscatter diffraction (EBSD) microstructural analysis, mechanical properties such as hardness and density were evaluated. Tribological experiments based on simulated braking conditions tested the stability of the friction coefficient and wear loss. The research conclusions indicate that 1.0 wt% La2O3 achieves optimal density and hardness through grain refinement and dispersion strengthening. It also shows excellent friction stability and wear resistance within the speed range of 80-350 km/h. Furthermore, La2O3 can regulate the formation of friction films and stabilize contact platforms, resulting in a 34.70% reduction in wear loss under 350 km/h. This research provides an important basis for the design of high-performance copper-based brake pads. It highlights the value of rare earth oxide modification for improving braking safety and efficiency.

The performance of a laser detection system is commonly summarized by a limited set of instrumental figures of merit, including detection range, sensitivity, spatial or spectral resolution, ranging precision, and temporal response [...]

Cobalt is a promising alternative to copper as interconnect metal when semiconductor technology advances to 5 nm nodes and below. Co needs to be polished but the polishing mechanism is not clear enough. This study employs AFM to investigate the microscopic removal mechanism of Co CMP in an air environment, using Si, SiO2, and diamond probes as abrasives. The microstructure and chemical state of the scribed Co surface were analyzed using Hertz contact theory, AFM, SEM, XPS, EDS, and HRTEM. The results demonstrate that the probe material and applied load significantly affect the wear depth and width. The depth obtained by SiO2, Si, and diamond probes is about 1, 7, and 70 nm, respectively, under the same load. The Si and diamond probes primarily remove material through mechanical plowing, whereas the SiO2 probe combines mechanical plowing with chemical reactions. XPS analyses revealed that the SiO2 probe facilitated the formation of more Co3O4 on the Co surface. HRTEM results showed a Co oxide film (similar to 1.5 nm thickness) and an amorphous Co layer (similar to 15 nm thickness) in the scribed region. The oxide film enhanced adhesion, forming a protective layer that hindered further material removal and reduced friction. This study provides theoretical support for optimizing Co CMP processes, which is crucial to next-generation integrated circuits.

Despite improved sensitivity of iontronic pressure sensors with microstructures, structural compressibility and stability issues hinder achieving exceptional sensitivity across a wide pressure range. Herein, the interplay between ion concentration, mechanical properties, structural geometry, and aspect ratio (AR) on the sensitivity of lithium bis(trifluoromethanesulfonyl) imide/thermoplastic polyurethane (LiTFSI/TPU) ionogel is delved into. The results indicate that cones exhibit superior compressibility compared to pyramids and hemispheres, manifesting in an enhanced sensitivity toward the LiTFSI/TPU ionogel. Subsequently, by strategically combining cones with varying ARs, a harmonious balance between structural stability and compressibility is achieved, culminating in the fabrication of hierarchical iontronic flexible sensors (HIFS). Remarkably, HIFS-III with a three-level hierarchical conical microstructure demonstrates a preeminent sensitivity of 127.65 kPa-1 within similar to 500 kPa. Even within the ultrabroad pressure range of 1500-3000 kPa, the sensitivity remains exceeding 10 kPa-1. Furthermore, HIFS-III boasts swift response and relaxation times (similar to 11 and 18 ms, respectively), a low detection limit (similar to 6.35 Pa), as well as remarkable durability (15,000 cycles). The exceptional sensing capabilities of HIFS-III underscore its emergence as a promising high-performance sensing and feedback solution tailored for applications in human-machine interaction and e-skin.

Co is expected to replace Cu as the next generation of interconnect metal material and plays an integral role in the field of integrated circuits (IC). However, the reaction mechanism behind Co chemical mechanical polishing is not yet clear. To fill these gaps, the following explorations have been conducted. Reactive force field molecular dynamics (ReaxFF MD) and X-ray photoelectron spectroscopy (XPS) were employed to investigate the atomistic reaction and removal underlying the cobalt (Co) chemical mechanical polishing (CMP) in an aqueous (H2O) environment. It demonstrates the adsorption of H2O onto the Co surface, resulting in the formation of Co-OH, Co-H2O, and other reaction products. H2O molecules also undergo reactions with the abrasive. Furthermore, the OH-terminated surface of the diamond abrasive can chemically interact with the Co substrate, resulting in the formation of C-O-Co bridge bonds. Additionally, the polishing process generates C-Co bonds. Both bonds contribute to the removal of Co atoms, although the former has a relatively smaller effect. A smaller gap between C and Co corresponds to a greater number of C-Co bonds and a faster attainment of dynamic equilibrium. Moreover, as the gap decreases, the normal and frictional force increase within a certain range. However, beyond a certain limit, the friction force decreases. This study aids in uncovering the atomic behavior at the interface during Co CMP and understanding the polishing mechanism.

Low-level wind shear poses a significant hazard to aviation, especially at airports located on high plateaus and surrounded by complex terrain. In this study, we present a comprehensive analysis integrating Doppler Lidar and radiosonde measurements collected at the Xining Caojiapu Airport, situated on the northeastern Tibetan Plateau, during June 2022. The results indicate a remarkably high frequency of severe wind shear events (|Δv| ≥ 6 m/s), with an overall occurrence rate of 34% during the observation period. These events are predominantly confined to two distinct atmospheric layers: just above the surface and near the top of the convective boundary layer. The diurnal cycle of wind shear is closely associated with boundary-layer dynamics, exhibiting sharp increases after sunrise and pronounced peaks around midday, coinciding with enhanced turbulent mixing and surface heating. Case analyses further reveal that the most intense shear episodes occur at strong thermal inversions, where momentum decoupling produces thin, critical interfaces conducive to turbulence generation. In contrast, well-mixed convective conditions result in more distributed but persistent shear throughout the lower atmosphere. Diagnostic profiles of atmospheric stratification and dynamic instability, characterized by the Brunt–Väisälä frequency and Richardson number, elucidate the intricate interplay between thermal structure and vertical wind gradients. Collectively, these findings provide a robust quantitative basis for improving wind shear risk assessments and early warning systems at airports in mountainous regions, while offering new insights into the complex interactions between turbulence and atmospheric stratification.

Lidar technology is pivotal for detecting and monitoring the atmospheric environment. However, maintaining optical path stability in complex environments poses significant challenges, especially regarding adaptability and cost efficiency. This study proposes a tunable optical alignment method that is applied to the Rotating Rayleigh Doppler Wind Lidar (RRDWL) to enable precise detection of mid-to-upper atmospheric wind fields. Building on the conventional echo signal strength method, this approach calibrates the signal strength using cloud information and the signal-to-noise ratio (SNR), enabling stratified and tunable optical alignment. Experimental results indicate that the optimized RRDWL achieves a maximum detection height increase from 42 km to nearly 51 km. Additionally, the average horizontal wind speed error at 30 km decreases from 11.3 m/s to 4.4 m/s, with a minimum error of approximately 1 m/s. These findings confirm that the proposed method enhances the effectiveness and reliability of the Lidar system under complex operational and diverse weather conditions. Furthermore, it improves detection performance and provides robust support for applications in related fields.

Conductive hydrogels inherently integrate flexibility, stretchability, and biocompatibility, ideal for air-writing applications. However, conventional systems face an inherent sensitivity-range tradeoff due to competing electrical-mechanical requirements. Herein, we present a bioinspired PAM/SA/CNTs/LM-Ag (polyacrylamide/sodium alginate/carbon nanotubes/Galinstan, LM-silver) hydrogel microcrack sensor featuring an innovative microcrack architecture and optimized conductive substrate design. The pre-stretching induced controllable microcracks in the conductive Ag layer swiftly propagate under low strain, abruptly disrupting conductive pathways and yielding a pronounced resistance variation. Meanwhile, the inherent fluidity and superior conductivity of uniformly dispersed LM particles in both the conductive layer and hydrogel matrix synergistically reduce the sensor's initial resistance, significantly enhancing its strain responsiveness and conferring exceptional sensitivity (GF = 438.47 +/- 27.85). Furthermore, the mutual bridging of 1D CNTs and stretchable LM particles within the hydrogel matrix effectively sustains conductive pathways under large strains, enabling an exceptionally broad detection range (900 %). Leveraging its multifaceted performances, including remarkable elongation capacity, mechanical resilience, electrical conductivity, interfacial adhesion, and ultra-sensitive strain response, the device demonstrates precise biomechanical tracking capabilities. This enables real-time robotic hand control through gesture replication. Furthermore, when coupled with a one-dimensional convolutional neural network (1D-CNN), the system attains 96.73 % character recognition accuracy in air-writing applications, underscoring its potential for advanced human-machine interaction systems.