Nondestructive Detection of Sunflower Seed Vigor and Moisture Content Based on Hyperspectral Imaging and Chemometrics.

Sunflower is an important crop, and the vitality and moisture content of sunflower seeds have an important influence on the sunflower's planting and yield. By employing hyperspectral technology, the spectral characteristics of sunflower seeds within the wavelength range of 384-1034 nm were carefully analyzed with the aim of achieving effective prediction of seed vitality and moisture content. Firstly, the original hyperspectral data were subjected to preprocessing techniques such as Savitzky-Golay smoothing, standard normal variable correction (SNV), and multiplicative scatter correction (MSC) to effectively reduce noise interference, ensuring the accuracy and reliability of the data. Subsequently, principal component analysis (PCA), extreme gradient boosting (XGBoost), and stacked autoencoders (SAE) were utilized to extract key feature bands, enhancing the interpretability and predictive performance of the data. During the modeling phase, random forests (RFs) and LightGBM algorithms were separately employed to construct classification models for seed vitality and prediction models for moisture content. The experimental results demonstrated that the SG-SAE-LightGBM model exhibited outstanding performance in the classification task of sunflower seed vitality, achieving an accuracy rate of 98.65%. Meanwhile, the SNV-XGBoost-LightGBM model showed remarkable achievement in moisture content prediction, with a coefficient of determination (R2) of 0.9715 and root mean square error (RMSE) of 0.8349. In conclusion, this study confirms that the fusion of hyperspectral technology and multivariate data analysis algorithms enables the accurate and rapid assessment of sunflower seed vitality and moisture content, providing robust tools and theoretical support for seed quality evaluation and agricultural production practices. Furthermore, this research not only expands the application of hyperspectral technology in unraveling the intrinsic vitality characteristics of sunflower seeds but also possesses significant theoretical and practical value.

Inversion of heavy metal content in soil using hyperspectral characteristic bands-based machine learning method.

The global concern regarding the adverse effects of heavy metal pollution in soil has grown significantly. Accurate prediction of heavy metal content in soil is crucial for environmental protection. This study proposes an inversion analysis method for heavy metals (As, Cd, Cr, Cu, Ni, Pb) in soil based on hyperspectral and machine learning algorithms for 21 soil reference materials from multiple provinces in China. On this basis, an integrated learning model called Stacked RF (the base model is XGBoost, LightGBM, CatBoost, and the meta-model is RF) was established to perform soil heavy metal inversion. Specifically, three popular algorithms were initially employed to preprocess the spectral data, then Random Forest (RF) was used to select the best feature bands to reduce the impact of noise, finally Stacking and four basic machine learning algorithms were used to establish comparisons and analysis of inversion model. Compared with traditional machine learning methods, the stacking model showcases enhanced stability and superior accuracy. Research results indicate that machine learning algorithms, especially ensemble learning models, have better inversion effects on heavy metals in soil. Overall, the MF-RF-Stacking model performed best in the inversion of the six heavy metals. The research results will provide a new perspective on the ensemble learning model method for soil heavy metal content inversion using data of hyperspectral characteristic bands collected from soil reference materials.

Free light-shape focusing in extreme-ultraviolet radiation with self-evolutionary photon sieves.

Extreme-ultraviolet (EUV) radiation is a promising tool, not only for probing microscopic activities but also for processing nanoscale structures and performing high-resolution imaging. In this study, we demonstrate an innovative method to generate free light-shape focusing with self-evolutionary photon sieves under a single-shot coherent EUV laser; this includes vortex focus shaping, array focusing, and structured-light shaping. The results demonstrate that self-evolutionary photon sieves, consisting of a large number of specific pinholes fabricated on a piece of Si3N4 membrane, are capable of freely regulating an EUV light field, for which high-performance focusing elements are extremely lacking, let alone free light-shape focusing. Our proposed versatile photon sieves are a key breakthrough in focusing technology in the EUV region and pave the way for high-resolution soft X-ray microscopy, spectroscopy in materials science, shorter lithography, and attosecond metrology in next-generation synchrotron radiation and free-electron lasers.

Porous Carbonaceous Aerogels Composed of Multiscale Carbon-Based Units for High-Performance Microwave Absorption.

Structural engineering is definitely a promising and effective approach to develop excellent microwave absorbing materials with quantities of advantages. Especially, when carbon materials act as the constituents, the fabricated absorbers are available to gain more prominent absorption performance. However, extra high conductivities and the widespread aggregations and stacking of low-dimensional carbon materials always detrimentally affect the impedance matching and weaken the attenuation capacity, inevitably confining their further absorption applications. Herein, by introducing the amorphous chiral carbon nanocoils to overcome the challenges and achieve the strategies of structure optimization and multicomponent recombination, the reduced graphene oxide/carbon nanocoil/carbon nanotube aerogels were successfully synthesized by a successive hydrothermal method and freeze-drying strategy. The as-obtained aerogels possess a porous architecture that contribute to the extraordinary impedance matching and multiple reflections, which integrate the multifarious dielectric loss mechanisms of diverse carbon materials simultaneously. Benefiting from the tricomponent synergistic effect, the ultralight aerogels reach an outstanding microwave absorption property with an extremely low filler content of only 6 wt %. This work provides a helpful approach to design hierarchical absorbers consisted by multidimensional carbon materials for fantastic microwave absorption.

Surface Enhancement Effects of Tiny SnO2 Nanoparticle Modification on α-Fe2O3 for Room-Temperature NH3 Sensing.

The development of a gas sensor capable of detecting ammonia with high selectivity and rapid response at room temperature has consistently posed a formidable challenge. To address this issue, the present study utilized a one-step solvothermal method to co-assemble α-Fe2O3 and SnO2 by evenly covering SnO2 nanoparticles on the surface of α-Fe2O3. By controlling the morphology and Fe/Sn mole ratio of the composite, the as-prepared sample exhibits high-performance detection of NH3. At room temperature conditions, a gas sensor composed of α-Fe2O3@3%SnO2 demonstrates a rapid response time of 14 s and a notable sensitivity of 83.9% when detecting 100 ppm ammonia. Experiments and density functional theory (DFT) calculations suggest that the adsorption capacity of α-Fe2O3 to ammonia is enhanced by the surface effect provided by SnO2. Meanwhile, the existence of SnO2 tailors the pore structure and effective surface area of α-Fe2O3, creating multiple channels for the diffusion and adsorption of ammonia molecules. Additionally, an N-N heterostructure is formed between α-Fe2O3 and SnO2, which enhances the potential energy barrier and improves the ammonia sensing performance. Demonstration experiments have proved that the sensor shows significant advantages over commercial sensors in the process of ammonia detection in agricultural facilities. This work provides new insights into the perspectives on ammonia detection at room temperature.

ASFL-YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family.

Introduction: Insect pests from the family Papilionidae (IPPs) are a seasonal threat to citrus orchards, causing damage to young leaves, affecting canopy formation and fruiting. Existing pest detection models used by orchard plant protection equipment lack a balance between inference speed and accuracy. Methods: To address this issue, we propose an adaptive spatial feature fusion and lightweight detection model for IPPs, called ASFL-YOLOX. Our model includes several optimizations, such as the use of the Tanh-Softplus activation function, integration of the efficient channel attention mechanism, adoption of the adaptive spatial feature fusion module, and implementation of the soft Dlou non-maximum suppression algorithm. We also propose a structured pruning curation technique to eliminate unnecessary connections and network parameters. Results: Experimental results demonstrate that ASFL-YOLOX outperforms previous models in terms of inference speed and accuracy. Our model shows an increase in inference speed by 29 FPS compared to YOLOv7-x, a higher mAP of approximately 10% than YOLOv7-tiny, and a faster inference frame rate on embedded platforms compared to SSD300 and Faster R-CNN. We compressed the model parameters of ASFL-YOLOX by 88.97%, reducing the number of floating point operations per second from 141.90G to 30.87G while achieving an mAP higher than 95%. Discussion: Our model can accurately and quickly detect fruit tree pest stress in unstructured orchards and is suitable for transplantation to embedded systems. This can provide technical support for pest identification and localization systems for orchard plant protection equipment.

Study on detection method of microplastics in farmland soil based on hyperspectral imaging technology.

Microplastics (MPs) in farming soils can have a substantial impact on soil ecology and agricultural productivity, as well as affecting human health and the food chain cycle. As a result, it is vital to study MPs detection technologies that are rapid, efficient, and accurate in agriculture soils. This study investigated the classification and detection of MPs using hyperspectral imaging (HSI) technology and a machine learning methodology. To begin, the hyperspectral data was preprocessed using SG convolution smoothing and Z-score normalization. Second, the feature variables were extracted from the preprocessed spectral data using bootstrapping soft shrinkage, model adaptive space shrinkage, principal component analysis, isometric mapping (Isomap), genetic algorithm, successive projections algorithm (SPA), and uninformative variable elimination. Finally, three support vector machine (SVM), back propagation neural network (BPNN), and one-dimensional convolutional neural network (1D-CNN) models were developed to classify and detect three microplastic polymers: polyethylene, polypropylene, and polyvinyl chloride, as well as their combinations. According to the experimental results, the best approaches based on three models were Isomap-SVM, Isomap-BPNN, and SPA-1D-CNN. Among them, the accuracy, precision, recall and F1_score of Isomap-SVM were 0.9385, 0.9433, 0.9385 and 0.9388, respectively. The accuracy, precision, recall and F1_score of Isomap-BPNN were 0.9414, 0.9427, 0.9414 and 0.9414, respectively, while the accuracy, precision, recall and F1_score of SPA-1D-CNN were 0.9500, 0.9515, 0.9500 and 0.9500, respectively. When their classification accuracy was compared, SPA-1D-CNN had the best classification performance, with a classification accuracy of 0.9500. The findings of this study shown that the SPA-1D-CNN based on HSI technology can efficiently and accurately identify MPs in farmland soils, providing theoretical backing as well as technical means for real-time detection of MPs in farmland soils.

Hierarchical and Porous Structures of Carbon Nanotubes-Anchored MOF Derivatives Bridged by Carbon Nanocoils as Lightweight and Broadband Microwave Absorbers.

High-performance microwave absorption (MA) materials have attracted more and more attention because they can effectively prevent microwave radiation and interference from electronic devices. Herein, a new type of MA composite is constructed by introducing carbon nanotubes (CNTs)-anchored metal-organic framework derivatives (MOFDs) into a conductive carbon nanocoil (CNC) network, denoted as CNC/CNT-MOFD. The CNC/MOFD shows a wide effective absorption band of 6.7 GHz under a filling ratio of only 9% in wax-matrix. This is attributed to the hierarchical and porous structures of MOFD bridged by the uniformly dispersed conductive CNC network and the cross-polarization induced by the 3D spiral CNCs. Besides, the as-grown 1D CNTs improve space utilization, porosity, and polarization loss of the composites, resulting in the increase of imaginary permittivity, which further realizes impedance matching and energy attenuation. The Ni nanoparticles in layers of MOFD and at the tips of CNTs generate magnetic loss, promoting the low-frequency absorption ability. Resultantly, RCS values of the optimized composite in all tested theta (θ) ranges are less than -25 dB m2 at 9.5 GHz, effectively reducing the probability of the target detected by the radar.

Detection of peanut seed vigor based on hyperspectral imaging and chemometrics.

Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R2 was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor.

Environmental pseudotargeted metabolomics: A high throughput and wide coverage method for metabolic profiling of 1000-year paddy soil chronosequences.

Pseudotargeted metabolomics is achieved by introducing an algorithm designed to choose ions for selected ion monitoring from identified metabolites. This method integrates the advantages of both untargeted and targeted metabolomics. In this study, environmental pseudotargeted metabolomics was established to analyze the soil metabolites, based on microwave assisted derivatization followed by gas chromatography-mass spectrometry analysis. The method development included the optimization of extraction factors and derivatization conditions, evaluation of silylation reagent types and matrix-dependent behaviors. Under the optimal conditions, the microwave oximation and silylation were completed in 5 min and 9 min. A total of 184 metabolites from 26 chemical classifications were identified in soil matrices. The method validation demonstrated excellent performance in terms of linearity (correlation coefficient > 0.99), repeatability (relative standard deviation (RSD) < 20 %), reproducibility (RSD < 25 %), stability (relative difference < 10 % within 18 h), and sensitivity (16-110 times higher signal-to-noise ratio). This developed method was applied to characterize the metabolite compositions and metabolic profiling in a 1000-year paddy soil chronosequence. The relative abundance of trehalose was highest in 6-(40.3 %), 60-(55.8 %), 300-(67.7 %)and 1000-(61.7 %)years paddy soil, respectively, but long-chain fatty acids were most abundant in marine sediment (57.4 %). Forty-two characteristic metabolites were considered as primarily responsible for discriminating and characterizing the paddy soil chronosequences development and seven major metabolic pathways were altered. In addition, GC-MS metabolite profile presented better discriminating power in paddy soil ecosystem changes than phospholipid fatty acids (PLFAs). Overall, environmental pseudotargeted metabolomics can provide a high throughout and wide coverage approach for performing metabolic profiling in the soil research.

Research on non-destructive testing of hotpot oil quality by fluorescence hyperspectral technology combined with machine learning.

Eating repeatedly used hotpot oil will cause serious harm to human health. In order to realize rapid non-destructive testing of hotpot oil quality, a modeling experiment method of fluorescence hyperspectral technology combined with machine learning algorithm was proposed. Five preprocessing algorithms were used to preprocess the original spectral data, which realized data denoising and reduces the influence of baseline drift and tilt. The feature bands extracted from the spectral data showed that the best feature bands for the two-classification model and the six-classification model were concentrated between 469 and 962 nm and 534-809 nm, respectively. Using the PCA algorithm to visualize the spectral data, the results showed the distribution of the six types of samples intuitively, and indicated that the data could be classified. Based on the modeling analysis of the feature bands, the results showed that the best two-classification models and the best six-classification models were MF-RF-RF and MF-XGBoost-LGB models, respectively, and the classification accuracy reached 100 %. Compared with the traditional model, the error was greatly reduced, and the calculation time was also saved. This study confirmed that fluorescence hyperspectral technology combined with machine learning algorithm could effectively realize the detection of reused hotpot oil.

Design of an efficient combined multipoint picking scheme for tea buds.

Herein, a combined multipoint picking scheme was proposed, and the sizes of the end of the bud picker were selectively designed. Firstly, the end of the bud picker was abstracted as a fixed-size picking box, and it was assumed that the tea buds in the picking box have a certain probability of being picked. Then, the picking box coverage and the greedy algorithm were designed to make as few numbers of picking box set as possible to cover all buds to reduce the numbers of picking. Furthermore, the Graham algorithm and the minimum bounding box were applied to fine-tune the footholds of each picking box in the optimal coverage picking box set, so that the buds were concentrated in the middle of the picking boxes as much as possible. Moreover, the geometric center of each picking box was taken as a picking point, and the ant colony algorithm was used to optimize the picking path of the end of the bud picker. Finally, by analyzing the influence of several parameters on the picking performance of the end of the bud picker, the optimal sizes of the picking box were calculated successfully under different conditions. The experimental results showed that the average picking numbers of the combined multipoint picking scheme were reduced by 31.44%, the shortest picking path was decreased by 11.10%, and the average consumed time was reduced by 50.92% compared to the single-point picking scheme. We believe that the proposed scheme can provide key technical support for the subsequent design of intelligent bud-picking robots.

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models.

Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.

Enhanced mass transfer of pulsed vacuum pressure pickling and changes in quality of sour bamboo shoots.

Sour bamboo shoot is a traditional Chinese fermented vegetable food. The traditional pickling method of sour bamboo shoots has the disadvantages of being time-consuming, inhomogeneous, and difficult to control. Pulsed vacuum pressure pickling (PVPP) technology uses pulsed vacuum pressure to enhance the pickling efficiency significantly. To demonstrate the effects of salt content and PVPP technical parameters on the fermentation of bamboo shoots, the sample salinity, pH value, color, crunchiness and chewiness, nitrite content, and lactic acid bacteria content during the pickling process were investigated. The salt content inside the bamboo shoots gradually increased to the equilibrium point during the pickling process. The pickling efficiency of bamboo shoots under PVPP technology increased by 34.1% compared to the traditional control groups. Meanwhile, the uniform salt distribution under PVPP technology also obtained better performance in comparison with the traditional groups. The pH value declined slowly from 5.96 to 3.70 with the extension of pickling time and sour flavor accumulated progressively. No significant differences were found in the color values (L *, a *, and b *) and the crunchiness of the bamboo shoot under different salt solution concentrations, vacuum pressure, and pulsation frequency ratio conditions. Colony-forming unit of lactic acid bacteria (CFU of LAB) decreased, to begin with, and then increased until the 6th day, followed by a declining trend in volatility. The nitrate content of bamboo shoots samples under PVPP treatments did not exceed the safety standard (<20 mg/kg) during the whole fermentation process, which proves the safety of PVPP technology. In conclusion, PVPP technology can safely replace the traditional method with better quality performance. The optimal PVPP processing conditions (vacuum pressure 60 kPa, 10 min vacuum pressure time vs. 4 min atmospheric pressure time, salt solution concentration 6%) have been recommended for pickling bamboo shoots with high product quality.

MOF-derived AuNS/LDH with high adsorption ability for surface enhanced Raman spectroscopy detection.

The sensitivity of surface enhanced Raman spectroscopy (SERS) depends on the construction of "hot spots" and the number of analyte molecules adsorbed onto the substrates. Herein, we have constructed a kind of SERS substrate based on gold nanostars (Au NSs) coated with nickel-cobalt layered double hydroxide (LDH) using a zeolitic imidazolate skeleton as sacrificial template via nickel ions etching. LDH was used as the absorption medium for target molecules, and concurrently prevented Au NSs from agglomeration to improve stability and uniformity of the substrate. Meanwhile, encapsulated Au NSs were used as the enhancement medium for Raman detection. The porous LDH shell around the Au NSs promoted the target molecules to approach the Au NSs, which was certified by the experimental results of UV-Vis absorption and simulation analysis using the density functional theory. The detection of Rhodamine 6G solution with a concentration of 10-9 M was realized by the AuNS/LDH, and the relative standard deviation of Raman signals was less than 10%. Therefore, this work provides a new idea and a suitable structure to improve SERS signal intensity by introducing adsorption medium into the SERS substrate.

[Effect of pH on the Abundance and Community Structure of Comammox Nitrospira in Paddy Soils].

Soil pH is recognized as an important environmental factor in determining the niche differentiation for ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA) communities. Species of comammox, a single microorganism capable of the complete oxidation of ammonia to nitrate, have recently been discovered. Metagenomic analysis and quantitative PCR showed that Comammox Nitrospira were found in a wide range of environments, including soil. Comammox bacteria are differentiated into one of two clades (A and B) based on the phylogeny of genes encoding the α-subunit of ammonia monooxygenase genes (amoA). However, all discovered Comammox Nitrospira strains have been isolated and cultured in aquatic ecosystems, including N. inopinata, N. nitrosa, and N. nitrificans, all belonging to clade A. Currently, Comammox Nitrospira has not been obtained from soil environments, which limits our understanding of soil Comammox Nitrospira. Here we hypothesized that, as AOA and AOB, the ecological site of Comammox Nitrospira may also be affected by pH. Therefore, soil samples with differing pH were collected, and the abundances and community structures were studied to elucidate the mechanism of pH effect on the distributions and community compositions of Comammox Nitrospira in soil. Quantitative PCR of comammox clade A and clade B amoA genes in DNA extracts were performed using QuantStudio TM6 Flex Real-Time PCR Systems. The community compositions for Comammox Nitrospira were studied by the cloning libraries of amoA genes method. The results showed that the abundance of Comammox clade A amoA gene in acidic paddy soil was two orders of magnitude higher than that in neutral paddy soil (P<0.05), and the abundance of Comammox clade B in acidic paddy soil was significantly higher than that in neutral paddy soil (P<0.05); the abundance of Comammox clade A amoA gene in acidic paddy soil was 60 times higher than that of clade B, whereas the abundance ratio of Comammox clade A and clade B amoA genes in neutral paddy soil was about two times higher. These results indicated that soil pH significantly affected the abundance of Comammox Nitrospira. The results of cloning and sequencing showed that the Comammox in neutral paddy soil was mainly N. inopinata, which belonged to clade A; no strain belonging to clade B was annotated. Comammox clade A in acidic paddy soil was mainly Composed of N. inopinata and N. nitrosa, and clade B was mainly uncultured bacterium (FN395328). The results indicated that soil pH was an important factor in shaping Comammox Nitrospira community structure. Comammox Nitrospira were detected in all soil samples, and Comammox clade A had a preference for acidic environments. It seemed that species from N. nitrosa possessed the ecological niche of low pH environments, whereas species from N. inopinata preferred to live in neutral environments. In conclusion, pH had a significant effect on the abundance and community structure of Comammox Nitrospira, which was one of the important factors affecting the niche differentiation of Comammox Nitrospira.

Controlled fabrication of core-shell γ-Fe2O3@C-Reduced graphene oxide composites with tunable interfacial structure for highly efficient microwave absorption.

The design of a high-performance microwave absorbing material is highly dependent on the synergistic structural design of heterostructure and the appropriate material compositions. Herein, a series of composites of reduced graphene oxide (RGO) and core-shell structured γ-Fe2O3@C nanoparticles have been achieved by a hydrothermal and in-situ chemical vapor deposition (CVD) method. In particular, the structure of the carbon layer, including its graphitization and thickness, can be controlled by optimizing the CVD conditions, which is beneficial to tailor the impedance matching and dielectric loss. The rationally designed RGO/γ-Fe2O3@C composite has multiple electromagnetic dissipation mechanisms. The effective absorption ranges of an optimal sample at a filling rate of 20% can cover 100% X-band and 98% Ku-band at thicknesses of 3.0 mm and 2.2 mm, respectively. This finding suggested that the controllable fabrication of core-shell heterostructures could be viable approach to upgrade the microwave absorption performance of transition metal oxides.

Surface modification of helical carbon nanocoil (CNC) with N-doped and Co-anchored carbon layer for efficient microwave absorption.

Surface modification and composition control for nanomaterials are effective strategies for designing high-performance microwave absorbing materials (MWAMs). Herein, we have successfully fabricated Co-anchored and N-doped carbon layers on the surfaces of helical carbon nanocoils (CNCs) by wet chemical and pyrolysis methods, denoted as Co@N-Carbon/CNCs. It is found that pure CNCs show a very good microwave absorption performance under a filling ratio of only 6%, which is attributed to the uniformly dispersed conductive network and the cross polarization induced by the unique chiral and spiral morphology. The coating of N-doped carbon layers on CNCs further enriches polarization losses and the uniform anchoring of Co nanoparticles in these layers generates magnetic losses, which enhance the absorption ability and improve the low frequency performance. As compared with the pure CNCs-filling samples, the optimized Co@N-Carbon/CNCs-2.4 enhances the absorption capacity in the lower frequency range under the same thickness, and realizes the decreased thickness from 3.2 to 2.8 mm in the same X band, as well as the decreased thickness from 2.2 to 1.9 mm in the Ku band. Resultantly, a specific effective absorption wave value of 22 GHz g-1 mm-1 has been achieved, which enlightens the synthesis of ultrathin and light high-performance MWAMs.

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging.

The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the R p 2 , R c 2 and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the R p 2 , R c 2 , and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality.

[Analysis of the Traits of Nitrogen Metabolism Pathways for Several Forest Soils in Eastern China].

Nitrogen metabolism pathways mediated by microorganisms play an important role in maintaining the structure and functional stability of soil ecosystems. Clarifying the relationships between microbial communities and nitrogen metabolism pathways can expand our understanding of nitrogen metabolism pathways at a microscopic level. However, the horizontal gene transfer of microorganisms means that taxonomy-based methods cannot be easily applied. A growing number of studies have shown that functional traits affect community construction and ecosystem functions. Using methods based on functional traits to study soil microbial communities can, therefore, better characterize nitrogen metabolism pathways. Here, five typical forest soils in China, namely black soil(Harbin, Heilongjiang), dark-brown earth(Changbaishan, Jilin), yellow-brown earth(Wuhan, Hubei), red earth(Fuzhou, Fujian), and humid-thermo ferralitic soil(Ledong, Hainan), were selected to study the traits of nitrogen metabolism pathways using metagenomic technology combined with the trait-based methods. The studied nitrogen metabolism pathways were ammonia assimilation, nitrate dissimilatory reduction, nitrate assimilatory reduction, denitrification, nitrification, nitrogen fixation, and anaerobic ammonia oxidation. The results showed that bacteria dominated the metagenomic library, accounting for 98.02% of all the sequences. Across all domains, the most common pathway was ammonia assimilation. For example, an average of 2830 ammonia assimilation pathway genes were detected for every million annotated bacterial sequences. In comparison, nitrogen fixation and anaerobic ammonia oxidation were the least detected pathways, accounting for 28.3 and 10.7 per million sequences, respectively. Different microorganisms can participate in a same nitrogen metabolism pathway, and the community structure of different soils was variable. The five typical forest soils in China show the same microbial nitrogen metabolism pathway traits; however, the community structure of the microorganisms mediating these processes was found to vary.