Jiangsu Changjiang Electronics Technology (China)

The escalating global e-waste crisis and demand for sustainable electronics drive the urgent need for high-performance bio-based polymers. Poly(lactic acid) (PLA), a leading bio-based polyester, suffers from inherent brittleness and limited shape adaptability, hindering its application in green electronics. Herein, we report a fully bio-based, tough PLA blend with excellent room-temperature shape adaptability (RTSA) via melt blending with a low-molecular-weight bio-based polyester (l-BPE). Owing to poor compatibility and low molecular weight, the l-BPE disperses as multiscale domains, effectively toughening PLA via a multiple microcracking mechanism while preserving PLA's high glass transition temperature (Tg). The PLA/l-BPE20 achieves a toughness of 71.9 ± 5.6 MJ/m3, a tensile strength of 39.5 ± 1.1 MPa, and excellent RTSA with high shape fixation rates (>99% in tensile mode, >82% in bending mode). The RTSA stems from the synergistic effect of high Tg-restricted chain mobility, oriented chains at microcracks, and low l-BPE resilience. As a wire protective layer, the blend maintains circuit functionality in complex deformed shapes (spiral, W-shape) without cracking or entropy-driven recovery, outperforming rigid PLA and flexible PBAT-based layers. This work provides a facile incompatible-blending strategy to tough PLA and endow RTSA, enabling high-performance bio-based materials for green electronics.

Abstract Firstly, based on the principle of a six-axis manipulator, a complete machine for the detection of Complex curved surface workpiece surface structure is designed. After modular analysis, the motion parameters and structural parameters of each mechanism are determined, and its three-dimensional drawing is completed in SolidWorks software; Secondly, the ZMC network motion control system is used to complete its trajectory planning, establish the main steps and application technology of surface material detection, and carry out interference detection; Finally, the mechanical properties of the 3D model are analyzed in SolidWorks-Simulation module. The simulation results show that the shape variable of the cantilever beam is not more than 0.1010 m, the amplitude is not more than 0.0611 m, and the maximum deformation of the arc pendulum table is 0.8152 m. The stability of the whole machine is remarkable; The stresses of the left and right cantilevers are 20.9 MPa and 26.5 MPa, respectively, and are evenly distributed, which improves the inherent defects of uneven stress and excessive local load of the traditional six-axis manipulator.

The spatial variation and transfer characteristics of heavy metals (HMs) in the soil-rice system are important for revealing HM pollution in rice production areas and guaranteeing the safety of rice products. In this research, 95 pairs of topsoil (0-20cm) and their corresponding rice samples from a production area of southeastern China were collected. The pollution status, spatial variation characteristics, and HMs transfer models were studied using geostatistical analysis, health-risk assessment, and principal component analysis. Results indicated that the mean total concentrations of cadmium (Cd), copper (Cu), lead (Pb), zinc (Zn) and nickel (Ni) were 0.21, 28.65, 27.02, 38.50, and 98.75 mg/kg, respectively. Cadmium posed the highest potential ecological risk, although the overall regional risk remained moderate. The maximum Cd concentration in rice grains exceeded the national food-safety limit (GB2762-2017) by threefold, indicating a tangible risk of Cd accumulation and associated health impacts for local consumers. Children exhibited a higher exposure risk to HMs than adults, especially for Cd and Cu. The spatial distributions of HMs in rice were similar to those in soils, reflecting that the concentration of HMs in soil affected the accumulation of HMs in rice. The spatial distribution patterns of enrichment index revealed stronger transfer capacities for Ni, Zn, and Cd in the western region, with contamination hotspots for multiple HMs identified in the north-central zone. Soil physicochemical properties were shown to significantly govern HM transfer.

In target state estimation using sensor networks, conventional methods often face difficulties. These include nonlinear target dynamics, high communication overhead, and weak robustness against disturbances. This paper introduces a distributed estimation framework with heterogeneous filters. It combines the strengths of different filters to suit various operating conditions. The framework builds a multi-layered cluster of nodes. These nodes include standard Kalman filters (KF), extended Kalman filters (EKF), and particle filters (PF). Within each homogeneous filter group, the system applies a covariance-based fusion strategy. Between heterogeneous groups, it uses an adaptive fusion method driven by residuals. To reduce communication load, the framework avoids transmitting raw data. It also introduces a node activation mechanism based on trajectory nonlinearity. This mechanism decides which filter groups should run at each time. Simulation results show clear improvements. The method enhances accuracy and robustness. It also lowers computation and communication costs. These results show promise for real-world use and large-scale deployment.

The relentless miniaturization of consumer electronics drives the demand for high-performance micro-camera modules, where imaging quality is critically dependent on precise lens-sensor alignment. Conventional methods for assessing optical performance, such as spatial frequency response (SFR) measurements, are accurate but inherently offline and slow, creating a bottleneck for mass production. This work introduces a rapid, in-line inspection methodology that leverages computational vision to predict imaging quality through the quantification of lens centration error. We establish a statistical baseline using traditional SFR evaluation and then employ a telecentric imaging system to extract geometric features and measure assembly misalignments. A linear regression model reveals a significant negative correlation between the measured centration error and the SFR value. To validate industrial applicability, we conducted an online inspection of 300 consecutively manufactured modules. The results showed that modules with good concentricity, defined as having an alignment error of 25 micrometers or less, exhibited SFR values that were on average 15% higher than those of poorly aligned modules. The ability to correlate structural precision with functional optical output provides a scalable and efficient pathway for quality assurance in the production of next-generation devices, including integrated photonic circuits and metasurfaces, bridging a critical gap between macro-scale assembly and nanoscale precision requirements.

Remote sensing semantic segmentation is crucial for extracting detailed land surface information, enabling applications such as environmental monitoring, land use planning, and resource assessment. In recent years, advancements in artificial intelligence have spurred the development of automatic remote sensing semantic segmentation methods. However, the existing semantic segmentation methods focus on distinguishing spectral characteristics of different objects while ignoring the differences in the elevation of the different targets. This results in land cover misclassification in complex scenarios involving shadow occlusion and spectral confusion. In this paper, we introduce a depth prompting two-dimensional (2D) remote sensing semantic segmentation framework (DepthSeg). It automatically models depth/height information from 2D remote sensing images and integrates it into the semantic segmentation framework to mitigate the effects of spectral confusion and shadow occlusion. During the feature extraction phase of DepthSeg, we introduce a lightweight adapter to enable cost-effective fine-tuning of the large-parameter vision transformer encoder pre-trained by natural images. In the depth prompting phase, we propose a depth prompter to model depth/height features explicitly. In the semantic prediction phase, we introduce a semantic classification decoder that couples the depth prompts with high-dimensional land-cover features, enabling accurate extraction of land-cover types. Experiments on the LiuZhou dataset validate the advantages of the DepthSeg framework in land cover mapping tasks. Detailed ablation studies further highlight the significance of the depth prompts in remote sensing semantic segmentation.