Search

Abstract Polyvinyl chloride (PVC) is the world’s third-most widely manufactured thermoplastic, but has the lowest recycling rate. The development of PVC-like plastics that can be depolymerized back to monomer contributes to a circular plastic economy, but has not been accessed. Here, we develop a series of chemically recyclable plastics from the reversible copolymerization of cyclic anhydride with chloral. The copolymerization is highly efficient through the anionic or cationic mechanism under mild conditions, yielding polyesters with tunable structure and properties from multiple commercial monomers. Notably, these polyesters manifest mechanical properties comparable to PVC and polystyrene. Meanwhile, such polyesters are flame-retardant like PVC due to high chloride content. Of significance, these polyesters can be depolymerized back to starting monomers at high temperatures owing to the reversibility of the copolymerization, leading to a circular economy. Overall, the readily available monomers, simple synthesis, advantageous performance, and practical recyclability make the polymers promising for applications.

In order to obtain a high lift-drag ratio, hypersonic vehicles usually fly with a certain angle of attack. As a potential means to delay hypersonic boundary layer transition at 0° angle of attack, it is not clear how wavy wall affect the deve-lopment of instability waves with angles of attack. In order to study the influence of wavy wall on the development of instability waves in hypersonic boundary layer at small angle of attack, the stability of boundary layer was studied by adopting high-speed infrared camera and high-frequency pressure sensor (PCB) in Mach 6 Ludwieg tube wind tunnel. The development of instability waves along different azimuths and flow directions was analyzed. The experimental results show that the instability waves leading transition at 1° angle of attack are the second-mode waves. Under the condition of 1° angle of attack, the wavy wall has no delay effect on transition at 45° azimuth angle, but has a promotion effect at 90°, 135° and 180° azimuth angles.

Abstract Near-infrared luminophores have many advantages in advanced applications, especially for structures without π-conjugation aromatic rings. However, the fabrication of red clusteroluminogens from nonconjugated polymers is still a big challenge, let alone the near-infrared clusteroluminogens. Here, we develop nonconjugated luminophores with full-spectrum from blue to near-infrared light (470 ~ 780 nm), based on color phenomenon of nonconjugated polyesters synthesized from the amine-initiated copolymerization of epoxides and cyclic anhydrides. We reveal that amines act as initiators attached to polymer chain ends. The formation of various amine-ester complexes in polyesters induces red to near-infrared light, conceptually, amine-ester complexed clusteroluminescence via intra/inter-chain charge transfer. Significantly, emission colors can be easily tuned by the contents and types of amines, microstructures of polyesters, and their concentration. This work provides a low-cost, scalable platform and strategy for the production of high-efficiency, multicolor luminescent materials.

Assessing the quality of maize seedlings is crucial for field management and germplasm evaluation. Traditional methods for evaluating seedling quality mainly rely on manual field surveys, which are not only inefficient but also highly subjective, while large-scale satellite detection often lacks sufficient accuracy. To address these issues, this study proposes an innovative approach that combines the YOLO v8 object detection algorithm with Voronoi spatial analysis to rapidly evaluate maize seedling quality based on high-resolution drone imagery. The YOLO v8 model provides the maize coordinates, which are then used for Voronoi segmentation of the field after applying the Convex Hull difference method. From the generated Voronoi diagram, three key indicators are extracted: Voronoi Polygon Uniformity Index (VPUI), missing seedling rate, and repeated seedling rate to comprehensively evaluate maize seedling quality. The results show that this method effectively extracts the VPUI, missing seedling rate, and repeated seedling rate of maize in the target area. Compared to the traditional plant spacing variation coefficient, VPUI performs better in representing seedling uniformity. Additionally, the R2 for the estimated missing seedling rate and replanting rate based on the Voronoi method were 0.773 and 0.940, respectively. Compared to using the plant spacing method, the R2 increased by 0.09 and 0.544, respectively. The maize seedling quality evaluation method proposed in this study provides technical support for precision maize planting management and is of great significance for improving agricultural production efficiency and reducing labor costs.

The precise estimation of above-ground biomass (AGB) is imperative for the advancement of breeding programs. Optical variables, such as vegetation indices (VI), have been extensively employed in monitoring AGB. However, the limited robustness of inversion models remains a significant impediment to the widespread application of UAV-based multispectral remote sensing in AGB inversion. In this study, a novel stem–leaf separation strategy for AGB estimation is delineated. Convolutional neural network (CNN) and transfer learning (TL) methodologies are integrated to estimate leaf biomass (LGB) across multiple growth stages, followed by the development of an allometric growth model for estimating stem biomass (SGB). To enhance the precision of LGB inversion, the large-scale remote sensing data and image simulation framework over heterogeneous scenes (LESS) model, which is a three-dimensional (3D) radiative transfer model (RTM), was utilized to simulate a more extensive canopy spectral dataset, characterized by a broad distribution of canopy spectra. The CNN model was pre-trained in order to gain prior knowledge, and this knowledge was transferred to a re-trained model with a subset of field-observed samples. Finally, the allometric growth model was utilized to estimate SGB across various growth stages. To further validate the generalizability, transferability, and predictive capability of the proposed method, field samples from 2022 and 2023 were employed as target tasks. The results demonstrated that the 3D RTM + CNN + TL method outperformed best in LGB estimation, achieving an R² of 0.73 and an RMSE of 72.5 g/m² for the 2022 dataset, and an R² of 0.84 and an RMSE of 56.4 g/m² for the 2023 dataset. In contrast, the PROSAIL method yielded an R² of 0.45 and an RMSE of 134.55 g/m² for the 2022 dataset, and an R² of 0.74 and an RMSE of 61.84 g/m² for the 2023 dataset. The accuracy of LGB inversion was poor when using only field-measured samples to train a CNN model without simulated data, with R² values of 0.30 and 0.74. Overall, learning prior knowledge from the simulated dataset and transferring it to a new model significantly enhanced LGB estimation accuracy and model generalization. Additionally, the allometric growth model’s estimation of SGB resulted in an accuracy of 0.87 and 120.87 g/m² for the 2022 dataset, and 0.74 and 86.87 g/m² for the 2023 dataset, exhibiting satisfactory results. Separate estimation of both LGB and SGB based on stem and leaf separation strategies yielded promising results. This method can be extended to the monitor and inversion of other critical variables.

Abstract Our society is pursuing chemically recyclable polymers to accelerate the green revolution in plastics. Here, we develop a recyclable polyester library from the alternating copolymerization of aldehyde and cyclic anhydride. Although these two monomer sets have little or no thermodynamic driving force for homopolymerization, their copolymerization demonstrates the unexpected alternating characteristics. In addition to readily available monomers, the method is performed under mild conditions, uses common Lewis/Brønsted acids as catalysts, achieves the facile tuning of polyester structure using two distinct monomer sets, and yields 60 polyesters. Interestingly, the copolymerization exhibits the chemical reversibility attributed to its relatively low enthalpy, which makes the resulting polyesters perform closed-loop recycling to monomers at high temperatures. This study provides a modular, efficient, and facile synthesis of recyclable polyesters using sustainable monomers.

Quantifying the effect of maize tassel on canopy reflectance is essential for creating a tasseling progress monitoring index, aiding precision agriculture monitoring, and understanding vegetation canopy radiative transfer. Traditional field measurements often struggle to detect the subtle reflectance differences caused by tassels due to complex environmental factors and challenges in controlling variables. The three-dimensional (3D) radiative transfer model offers a reliable method to study this relationship by accurately simulating interactions between solar radiation and canopy structure. This study used the LESS (large-scale remote sensing data and image simulation framework) model to analyze the impact of maize tassels on visible and near-infrared reflectance in heterogeneous 3D scenes by modifying the structural and optical properties of canopy components. We also examined the anisotropic characteristics of tassel effects on canopy reflectance and explored the mechanisms behind these effects based on the quantified contributions of the optical properties of canopy components. The results showed that (1) the effect of tassels under different planting densities mainly manifests in the near-infrared band of the canopy spectrum, with a variation magnitude of ±0.04. In contrast, the impact of tassels on different leaf area index (LAI) shows a smaller response difference, with a magnitude of ±0.01. As tassels change from green to gray during growth, their effect on reducing canopy reflectance increases. (2) The effect of maize tassel on canopy reflectance varied with spectral bands and showed an obvious directional effect. In the red band at the same sun position, the difference in tassel effect caused by the observed zenith angle on canopy reflectance reaches 200%, while in the near-infrared band, the difference is as high as 400%. The hotspot effect of the canopy has a significant weakening effect on the shadow effect of the tassel. (3) The non-transmittance optical properties of maize tassels reduce canopy reflectance, while their high reflectance increases it. Thus, the dual effects of tassels create a game in canopy reflectance, with the final outcome mainly depending on the sensitivity of the canopy spectrum to transmittance. This study demonstrates the potential of using 3D radiative transfer models to quantify the effects of crop fine structure on canopy reflectance and provides some insights for optimizing crop structure and implementing precision agriculture management (such as selective breeding of crop optimal plant type).

Abstract Polyesters with both cyclic topology and chemical recyclability are attractive. Here, the alternating copolymerization of cyclic anhydride and o‐phthalaldehyde to synthesize a series of cyclic and recyclable polyesters are reported for the first time. Besides readily available monomers, the copolymerization is carried out at 25 °C, uses common Lewis/Brønsted acids as catalysts, and achieves high yields within 1 h. The resulting polyesters possess well‐defined alternating sequences, high‐purity cyclic topology, and tunable structures using distinct two monomer sets. Of interest, the copolymerization manifests obvious chemical reversibility as revealed by kinetic and thermodynamic studies, making the unprecedented polyesters easy to recycle to their distinct two monomers in a closed loop at high temperatures. This work furnishes a facile and efficient method to synthesize cyclic polyesters with closed‐loop recyclability.

This work presents a comprehensive analysis of the ballistic response of Q235 steel/6061-T6 aluminum bilayer plates fabricated via explosive welding, with a special focus placed on elucidating the mechanisms of ballistic resistance enhancement due to the bonded interface. For comparison purposes, unbonded Q235 steel/6061-T6 aluminum bilayer plates (e.g. being stacked together or spaced with an air gap) were also investigated. Ballistic tests were carried out to compare the residual velocities and failure behaviours of the welded and unwelded bilayer plates. Computational models were built in LS/DYNA and validated with the experiment. Experimental results showed that the ballistic limit velocity of the explosively-welded plate is 13.4% and 9.4% higher than those of the in-contact plate and air-gapped plate. Simulation results suggested that the enhanced ballistic resistance was attributed to its welded interface. On the one hand, the welded interface allowed effective stress attenuation within the steel layer leading to a greater damage tolerance and more energy dissipation during the prolonged response time. On the other hand, when deforming together with the steel layer, the welded interface enforced a uniform indentation on the central region of the aluminum substrate leading to greater energy absorption due to compression and thinning of the material.

The real-world crowns of broadleaf tree species feature green leaves surrounding branches, resulting in leaf spatial aggregation effect in the crown. However, the impact of such leaf spatial aggregation on chlorophyll content retrieval has not yet been determined. This study investigated the effect of leaf spatial aggregation on chlorophyll content retrieval in two distinct apple orchards with open canopies. The “PROSPECT + LESS” model was used for canopy reflectance simulation, and 25 hyperspectral vegetation indices (VIs) were analyzed to identify universal VIs for various leaf aggregations. Sensitivity analysis was conducted to evaluate the impact of leaf aggregation on the relationships between VIs and chlorophyll content. An artificial neural network regression algorithm was used to retrieve chlorophyll content by reversing the radiative transfer model (RTMs). The results show that leaf aggregation significantly affects the relationships between VIs and chlorophyll content as a result of the variability in the ratio of photosynthetic vegetation pixels to background pixels captured by the sensor at the top of the canopy. TCARI/OSAVI was found to be resistant to confounding factors (e.g., leaf area index and dry matter content) and maintained stable relationships with chlorophyll content. Leaf spatial aggregation had a significant impact on chlorophyll content retrieval, especially when leaves were highly aggregated. In such cases, the spectral variation driven by the photosynthetic vegetation was masked by the background, leading to a large divergence between simulated and observed spectra. Low to moderate levels of leaf aggregation, on the other hand, provided accurate chlorophyll content retrieval in both apple orchards (R2 = 0.49 to 0.67). In conclusion, when using 3D RTMs to retrieve chlorophyll content, it is recommended to configure low to moderate levels of leaf aggregation to ensure high accuracy and efficiency.

This study investigates the effect of polyurea coating on the ballistic resistance of polyurea-steel laminate plates. Ballistic tests are carried out to compare the residual velocities and failure modes of the monolithic steel plate and polyurea-steel laminate plates on an equal mass basis. Experimental results show that polyurea coating could lead to an enhanced ballistic resistance of the laminate plate (with 5.2% greater ballistic limit velocity), but only when applied at the impact-receiving face. Finite element simulations are performed to provide insights into the penetration process and the energy partitioning to reveal the enhancing mechanisms of the frontal polyurea coating. Excellent agreement was obtained between the numerical simulations and the experimental results. Simulation results suggest that the frontal polyurea-coating has a cushioning effect to alleviate and redistribute the total contact force acting on the substrate steel layer, which helps to spread the local impact to a far-field response. The polyurea-coating on the impact-receiving face provides additional ballistic resistance by promoting greater energy dissipation of the steel layer through global deformation.

Land-use maps are thematic materials reflecting the current situation, geographical diversity, and classification of land use and are an important scientific foundation that can assist decision-makers in adjusting land-use structures, agricultural zoning, regional planning, and territorial improvement according to local conditions. Spectral reflectance and radar signatures of time series are important in distinguishing land-use types. However, their impact on the accuracy of land-use mapping and decision making remains unclear. Also, the many spatial and temporal heterogeneous landscapes in southern Xinjiang limit the accuracy of existing land-use classification products. Therefore, our objective herein is to develop reliable land-use products for the highly heterogeneous environment of the southern Xinjiang Uygur Autonomous Region using the freely available public Sentinel image datasets. Specifically, to determine the effect of temporal features on classification, several classification scenarios with different temporal features were developed using multi-temporal Sentinel-1, Sentinel-2, and terrain data in order to assess the importance, contribution, and impact of different temporal features (spectral and radar) on land-use classification models and determine the optimal time for land-use classification. Furthermore, to determine the optimal method and parameters suitable for local land-use classification research, we evaluated and compared the performance of three decision-tree-related classifiers (classification and regression tree, random forest, and gradient tree boost) with respect to classifying land use. Yielding the highest average overall accuracy (95%), kappa (95%), and F1 score (98%), we determined that the gradient tree boost model was the most suitable for land-use classification. Of the four individual periods, the image features in autumn (25 September to 5 November) were the most accurate for all three classifiers in relation to identifying land-use classes. The results also show that the inclusion of multi-temporal image features consistently improves the classification of land-use products, with pre-summer (28 May–20 June) images providing the most significant improvement (the average OA, kappa, and F1 score of all the classifiers were improved by 6%, 7%, and 3%, respectively) and fall images the least (the average OA, kappa, and F1 score of all the classifiers were improved by 2%, 3%, and 2%, respectively). Overall, these analyses of how classifiers and image features affect land-use maps provide a reference for similar land-use classifications in highly heterogeneous areas. Moreover, these products are designed to describe the highly heterogeneous environments in the study area, for example, identifying pear trees that affect local economic development, and allow for the accurate mapping of alpine wetlands in the northwest.

Current deep learning-based change detection approaches mostly produce convincing results by introducing attention mechanisms to traditional convolutional networks. However, given the limitation of the receptive field, convolution-based methods fall short of fully modelling global context and capturing long-range dependencies, thus insufficient in discriminating pseudo changes. Transformers have an efficient global spatio-temporal modelling capability, which is beneficial for the feature representation of changes of interest. However, the lack of detailed information may cause the transformer to locate the boundaries of changed regions inaccurately. Therefore, in this article, a hybrid CNN-transformer architecture named CTCANet, combining the strengths of convolutional networks, transformer, and attention mechanisms, is proposed for high-resolution bi-temporal remote sensing image change detection. To obtain high-level feature representations that reveal changes of interest, CTCANet utilizes tokenizer to embed the features of each image extracted by convolutional network into a sequence of tokens, and the transformer module to model global spatio-temporal context in token space. The optimal bi-temporal information fusion approach is explored here. Subsequently, the reconstructed features carrying deep abstract information are fed to the cascaded decoder to aggregate with features containing shallow fine-grained information, through skip connections. Such an aggregation empowers our model to maintain the completeness of changes and accurately locate small targets. Moreover, the integration of the convolutional block attention module enables the smoothing of semantic gaps between heterogeneous features and the accentuation of relevant changes in both the channel and spatial domains, resulting in more impressive outcomes. The performance of the proposed CTCANet surpasses that of recent certain state-of-the-art methods, as evidenced by experimental results on two publicly accessible datasets, LEVIR-CD and SYSU-CD.

Ear height (EH) and ear–plant height ratio (ER) are important agronomic traits in maize that directly affect nutrient utilization efficiency and lodging resistance and ultimately relate to maize yield. However, challenges in executing large-scale EH and ER measurements severely limit maize breeding programs. In this paper, we propose a novel, simple method for field monitoring of EH and ER based on the relationship between ear position and vertical leaf area profile. The vertical leaf area profile was estimated from Terrestrial Laser Scanner (TLS) and Drone Laser Scanner (DLS) data by applying the voxel-based point cloud method. The method was validated using two years of data collected from 128 field plots. The main factors affecting the accuracy were investigated, including the LiDAR platform, voxel size, and point cloud density. The EH using TLS data yielded R2 = 0.59 and RMSE = 16.90 cm for 2019, R2 = 0.39 and RMSE = 18.40 cm for 2021. In contrast, the EH using DLS data had an R2 = 0.54 and RMSE = 18.00 cm for 2019, R2 = 0.46 and RMSE = 26.50 cm for 2021 when the planting density was 67,500 plants/ha and below. The ER estimated using 2019 TLS data has R2 = 0.45 and RMSE = 0.06. In summary, this paper proposed a simple method for measuring maize EH and ER in the field, the results will also offer insights into the structure-related traits of maize cultivars, further aiding selection in molecular breeding.

Organic–inorganic nanocomposites for photothermal therapy of cancers emerged as a promising strategy against malignant tumors. However, it is still a big challenge to develop a nanocomposite system that can maximize the synergistic photo-thermal therapy effect as well as preserve high stability for simultaneous delivery of the chemotherapeutic drugs and photo-thermal agents. Here, we have exploited an organic liposome containing inorganic core for co-loading the aggregates of bovine serum albumin (BSA), indocyanine green (ICG), and doxorubicin (DOX), abbreviated as BID-liposomal nanocomposites. The three kinds of substances were aggregated in the core of liposomal nanocomposites through hydrophobic and electrostatic interactions. In vitro characterization shows that BID-liposomal nanocomposites were spherical nanoparticles with size of 30–50 nm and good storage stability. Moreover, BID-Liposomal nanocomposites illustrate the strongest cytotoxicity among all the formulations against murine 4T1 tumor cells. In breast cancer-bearing mouse models, BID liposomes lead to significant improvements in tumor inhibition effects with no obvious toxicity. Therefore, the BID-liposomal nanoparticle is believed to be a promising strategy for chemo-photo-thermal therapy against cancers.

Transparent conductive oxides (TCOs) are widely used in optoelectronic devices such as flat-panel displays and solar cells. A significant optical property of TCOs is their band gap, which determines the spectral range of the transparency of the material. In this study, a tunable band gap range from 3.35 eV to 3.53 eV is achieved for zinc oxide (ZnO) nanocrystals (NCs) films synthesized by nonthermal plasmas through the removal of surface groups using atomic layer deposition (ALD) coating of Al2O3 and intense pulsed light (IPL) photo-doping. The Al2O3 coating is found to be necessary for band gap tuning, as it protects ZnO NCs from interactions with the ambient and prevents the formation of electron traps. With respect to the solar spectrum, the 0.18 eV band gap shift would allow ~4.1% more photons to pass through the transparent layer, for instance, into a CH3NH3PbX3 solar cell beneath. The mechanism of band gap tuning via photo-doping appears to be related to a combination of the Burstein–Moss (BM) and band gap renormalization (BGN) effects due to the significant number of electrons released from trap states after the removal of hydroxyl groups. The BM effect shifts the conduction band edge and enlarges the band gap, while the BGN effect narrows the band gap.

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

The branches of fruit trees provide support for the growth of leaves, buds, flowers, fruits, and other organs. The number and length of branches guarantee the normal growth, flowering, and fruiting of fruit trees and are thus important indicators of tree growth and yield. However, due to their low height and the high number of branches, the precise management of fruit trees lacks a theoretical basis and data support. In this paper, we introduce a method for extracting topological and structural information on fruit tree branches based on LiDAR (Light Detection and Ranging) point clouds and proved its feasibility for the study of fruit tree branches. The results show that based on Terrestrial Laser Scanning (TLS), the relative errors of branch length and number are 7.43% and 12% for first-order branches, and 16.75% and 9.67% for second-order branches. The accuracy of total branch information can reach 15.34% and 2.89%. We also evaluated the potential of backpack-LiDAR by comparing field measurements and quantitative structural models (QSMs) evaluations of 10 sample trees. This comparison shows that in addition to the first-order branch information, the information about other orders of branches is underestimated to varying degrees. The root means square error (RMSE) of the length and number of the first-order branches were 3.91 and 1.30 m, and the relative root means square error (NRMSE) was 14.62% and 11.96%, respectively. Our work represents the first automated classification of fruit tree branches, which can be used in support of precise fruit tree pruning, quantitative forecast of yield, evaluation of fruit tree growth, and the modern management of orchards.

For a consistent numerical method to be practically useful, it is widely accepted that it must preserve the asymptotic stability of the original continuous problem. However, in this study, we show that it may lead to unreliable numerical solutions in long time simulation even if a classical numerical method gives a larger stability region than that of the original continuous problem. Some numerical experiments on the reaction-diffusion equations with delay are presented to confirm our findings. Finally, some open problems on the subject are proposed.

This paper presents a class of new numerical methods for nonlinear functional-integrodifferential equations, which are derived by an adaptation of Pouzet-Runge-Kutta methods originally introduced for standard Volterra integrodifferential equations. Based on the nonclassical Lipschitz condition, analytical and numerical stability is studied and some novel stability criteria are obtained. Numerical experiments further illustrate the theoretical results and the effectiveness of the methods. In the end, a comparison between the presented methods and the existed related methods is given.