Electrochemical carbon dioxide (CO2) reduction (CO2RR) in acidic conditions not only enables high CO2 utilization but also reduces the formation of interfacial (bi)carbonate. However, the acidic environment tends to favor the competing hydrogen evolution reaction (HER), which lowers the overall energy efficiency of CO2 reduction. Here, we use axial oxygen coordination to tune the spin state of iron-nitrogen-carbon sites, shifting from the low-spin (LS, t2g 5 eg 0) to the medium-spin (MS, t2g 4 eg 1) state. Experimental results and theoretical simulations show that this medium-spin structure results in spin-electron filling of the sigma & lowast; orbital, weakening the interfacial attraction of H3O+, significantly inhibiting HER, and reducing the *CO desorption energy; thus, CO2RR performance in acidic media is greatly improved. The designed Fe-N4O structure achieves a mass activity of 76.17 A mgFe -1 and CO current densities of approximately 335 mA cm-2 in acidic conditions, far exceeding those of Fe-N4 (7.86 mA cm-2). Meanwhile, the catalyst reaches a high j CO of 324.55 mA cm-2, 80.97% CO2 utilization efficiency, and an energy efficiency of 36.89% in a self-designed proton-exchange-membrane porous-solid-electrolyte reactor. This work highlights the spin-manipulation mechanism for enhancing acidic CO2RR performance.

To enhance the efficiency of flue gas purification from straw combustion, a combined approach using the ammonia method and electrostatic charged spray was investigated. This study investigated the charging characteristics of atomized droplets and their impact on flue gas purification. The results show that the charge-to-mass ratio of droplets increases and then decreases as charging voltages increase. At a constant voltage, the ratio increases with higher ammonia concentrations and shows a gradual increase with higher spray pressures. For flue gases from three common straw combustion sources, the average dust removal rate at 8 kV was 2.5 to 3 times higher than at 0 kV. Under the 8 kV condition with a 10% ammonia solution, the NO removal rate was approximately 4.7 times, and the NO2 removal rate was 2.8 times compared to water alone. Particulate matter, NO, and NO2 removal rates were 61.2%, 88.6%, and 88.1%, respectively, at a spray pressure of 0.5 MPa, 8 kV charging voltage, and 10% ammonia concentration. This study provides an experimental foundation for developing high-efficiency flue gas purification systems for straw combustion.

To address the operational demands of irregular farmland with fixed obstacles, this study proposes a full-coverage path planning framework that integrates UAV-based 3D perception and angle-adaptive optimization. First, digital orthophoto maps (DOMs) and digital elevation models (DEMs) were reconstructed from low-altitude aerial imagery. The feasible working region was constructed by shrinking field boundaries inward and dilating obstacle boundaries outward. This ensured sufficient safety margins for machinery operation. Next, segmentation angles were scanned from 0° to 180° to minimize the number and irregularity of sub-regions; then a two-level simulation search was performed over 0° to 360° to optimize the working direction for each sub-region. For each sub-region, the optimal working direction was selected based on four criteria: the number of turns, travel distance, coverage redundancy, and planning time. Between sub-regions, a closed-loop interconnection path was generated using eight-directional A* search combined with polyline simplification, arc fitting, Chaikin subdivision, and B-spline smoothing. Simulation results showed that a 78° segmentation yielded four regular sub-regions, achieving 99.97% coverage while reducing the number of turns, travel distance, and planning time by up to 70.42%, 23.17%, and 85.6%. This framework accounts for field heterogeneity and turning radius constraints, effectively mitigating path redundancy in conventional fixed-angle methods. This framework enables general deployment in agricultural field operations and facilitates extensions toward collaborative and energy-optimized task planning.

Wheat plays a crucial role in global food security; however, in recent years, Fusarium Head Blight (FHB) has severely impacted both wheat yield and quality. Strong oxidative free radicals, with high oxidation potential and rapid reaction rates, offer an effective approach for pollutant degradation and microbial inactivation. In this study, the control effect of strong oxidizing radicals on FHB was evaluated by comparing the untreated control group (JM23), which was infected with FHB, to the experimental group (FG06), which was treated with strong oxidizing radicals following FHB infection. The results show that FG06 achieved a control effectiveness of 87.87%. The study also assessed grain characteristics and milling quality. Statistical analysis revealed that FG06 had a slightly lower flour extraction rate (71.24%) compared to the control wheat (JM23), but it exhibited competitive flour whiteness (81.30) and a gluten index of 85.50%. The dough stability at 10 min was 27.00 FE, while several gelatinization parameters were significantly lower than JM23. However, FG06 had higher protein content (10.94%), flour protein content (10.70%), ash content (0.58%), wet gluten content (28.70%), dry gluten content (9.40%), and sedimentation value (73.00 mL), all significantly higher than those of JM23. Additionally, FG06 had a gelatinization temperature of 68.61 °C, similar to JM23. Overall, Strong oxidizing radicals as an alternative to conventional pesticides not only effectively controls FHB but also maintains or even enhances wheat milling and processing quality, promoting more sustainable agricultural practices.

With the development of the agro-processing industry, the efficient cryogenic treatment and resource utilization of porcine bile—a high-value byproduct—has received increasing attention. This study investigates the dynamic behaviour and freezing characteristics of porcine bile droplets upon impact on cold substrates under varying conditions of surface temperature (−10 °C to −20 °C) and impact velocity (0.18–0.59 m/s). The effects of droplet size, dimensionless numbers (Weber, Reynolds, Bond, Ohnesorge, and Prandtl), and thermal gradients were systematically analyzed. A thermoelectric cooling substrate combined with high-speed imaging was used to quantitatively characterize the spreading ratio, retraction ratio, and freezing time of droplets. The results show that the maximum spreading ratio increases with higher impact velocity but decreases with lower substrate temperature. Lower substrate temperatures significantly shorten the freezing time, with a maximum reduction of up to 45%, particularly for smaller droplets. Droplets with high Weber numbers (We > 3) form flattened ice layers with preserved retraction patterns, while those with low Weber numbers (We < 1) generate smooth, hemispherical ice caps. For the first time, the thermophysical properties of porcine bile were incorporated into the framework of droplet impact dynamics on cryogenic surfaces. The findings reveal multiscale freezing mechanisms of biological fluids at low temperatures and provide a theoretical basis for optimizing processes such as freeze-drying and cryogenic sterilization in agro-product processing.

The leaf area index (LAI) is a critical biophysical parameter that reflects crop growth conditions and the canopy photosynthetic potential, serving as a cornerstone in precision agriculture and dynamic crop monitoring. However, traditional LAI estimation methods relying on single-source remote sensing data and often suffer from insufficient accuracy in high-density vegetation scenarios, limiting their capacity to reflect crop growth variability comprehensively. To overcome these limitations, this study introduces an innovative multi-source feature fusion framework utilizing unmanned aerial vehicle (UAV) multispectral imagery for precise LAI estimation in winter wheat. RGB and multispectral datasets were collected across seven different growth stages (from regreening to grain filling) in 2024. Through the extraction of color attributes, spatial structural information, and eight representative vegetation indices (VIs), a robust multi-source dataset was developed to integrate diverse data types. A convolutional neural network (CNN)-based feature extraction backbone, paired with a multi-source feature fusion network (MSF-FusionNet), was designed to effectively combine spectral and spatial information from both RGB and multispectral imagery. The experimental results revealed that the proposed method achieved superior estimation performance compared to single-source models, with an R2 of 0.8745 and RMSE of 0.5461, improving the R2 by 36.67% and 5.54% over the RGB and VI models, respectively. Notably, the fusion method enhanced the accuracy during critical growth phases, such as the regreening and jointing stages. Compared to traditional machine learning techniques, the proposed framework exceeded the performance of the XGBoost model, with the R2 rising by 4.51% and the RMSE dropping by 12.24%. Furthermore, our method facilitated the creation of LAI spatial distribution maps across key growth stages, accurately depicting the spatial heterogeneity and temporal dynamics in the field. These results highlight the efficacy and potential of integrating UAV multi-source data fusion with deep learning for precise LAI estimation in winter wheat, offering significant insights for crop growth evaluation and precision agricultural management.

Accurately monitoring coupled water–nitrogen stress is critical for wheat (Triticum aestivum L.) productivity under climate change. This study developed a machine learning framework utilizing multimodal leaf electrophysiological signals––intrinsic resistance, impedance, capacitive reactance, inductive reactance, and capacitance––to decouple water and nitrogen stress signatures in wheat. A parallel modelling strategy was implemented employing Gradient Boosting, Random Forest, and Ridge Regression, selecting the optimal algorithm per feature based on predictive performance. Controlled pot experiments revealed IZ as the paramount biomarker across leaf positions, indicating its sensitivity to ion flux perturbations under abiotic stress. Crucially, algorithm-feature specificity was identified: Ridge Regression excelled in modeling linear responses due to its superior noise suppression, while GB effectively captured nonlinear dynamics. Flag leaves during reproductive stages provided significantly more stable predictions compared to vegetative third leaves, aligning with their physiological primacy as source organs. This framework offers a robust, non-invasive approach for real-time water and nitrogen stress diagnostics in precision agriculture.

Electrochemical carbon dioxide reduction reaction (CO2RR) under strongly acidic conditions enables high CO2 utilization. However, especially in proton exchange membrane (PEM) electrode assembly reactors, achieving selective CO2RR in such environments remains challenging due to uncontrolled interfacial water diffusion at high current densities. Here, we develop a nickel-based heterogeneous molecular electrocatalyst (NiPc-NH2/CNT-SHP) featuring amino (-NH2) functional groups and grafted long-chain hydrophobic molecules. Under acidic conditions, -NH2 is in situ protonated to form amino cations (-NH3⁺). The positively charged -NH3⁺ groups and hydrophobic molecules effectively disrupt the protonated water (H3O+)-rich network, inhibiting the invasion of H3O+ and thereby suppressing the hydrogen evolution reaction, while enhancing selectivity for acidic CO2RR. The catalyst achieves nearly 100% Faradaic efficiency for CO at current densities from 50 to 400 mA cm-2, with approximately 76% CO2 utilization efficiency in a flow cell, and sustains over 80% selectivity for more than 200 h in a self-designed PEM-porous solid electrolyte reactor. These findings highlight interfacial water management as a key design principle for efficient acidic CO2 electroreduction.

Wheat powdery mildew is a major fungal disease threatening global wheat production. To develop an effective and environmentally friendly control strategy, this study systematically evaluated the disease-suppressive efficacy of strong oxidative free radicals across a series of treatment parameters, including radical concentrations (3.0–8.0 mg/L), spraying durations (20–60 s), solution pH levels (5–8), spraying heights (0–20 cm), and treatment timings corresponding to different infection stages (0–120 h post-inoculation). Response surface methodology (RSM) was used to optimize these variables with the objective of maximizing disease control efficacy. The results showed that control efficacy increased with radical concentration up to 5.0 mg/L, beyond which a saturation effect was observed. The most effective conditions included a spraying duration of 50 s and a height of 6.5 cm. Maximum suppression was achieved when the treatment was applied within 0–12 h post-infection. Moreover, adjusting the solution pH to a range of 5–7 further enhanced the efficacy. The RSM-based predictive model demonstrated high accuracy (R2 = 0.9942), and the optimized parameters—6.65 mg/L radical concentration, 50.84 s spraying duration, and treatment at 15.67 h post-infection—yielded a predicted control efficacy of 97.64%, with a validation error below 0.5%. This study provides a quantitative basis for the precise and sustainable deployment of free radical-based treatments in wheat disease management.

Electrochemical CO2 reduction (CO2RR) to carbon monoxide (CO) offers significant economic and commercial potential. However, industrial-scale implementation of CO2RR for CO is presently impeded by high energy consumption, e.g., cell voltage >2.0 V at currents densities of 100 mA cm(-2), due to the low efficiency of existing catalysts, the high potential required for the anode reaction and the electrode/cell design. Herein, we developed a biomimetic bifunctional catalyst consisting of cobalt polyphthalocyanine-coated porous nitrogen-doped carbon aerogels (CoPPc/PNCA), featuring with a "coral-like" structure and stable single-molecular sites, which could drive CO2RR coupling with electrochemical sulfur oxidation reaction (SOR). Applied in the self-designed cathode gas electrode (CGE), the bifunctional catalyst achieves nearly 100 % Faradaic efficiency for CO production, and the stable SOR operation at the current density of similar to 100 mA cm(-2) in traditional H-cell. Furthermore, a CGE/flow membrane electrode assembly (CGE/FMA) electrolyzer was constructed with CoPPc/PNCA as the cathode and anode catalyst, achieving the industrial-level CO current density of 210 +/- 16 mA cm(-2) in an energy-saving CO2RR//SOR system at a cell voltage of -1.8 V, significantly reducing the energy consumption. Techno-economic analysis based on the CO2RR//SOR coupling system indicates profitability with the production cost of CO estimated to be $0.204 kg(-1) at similar to 110 mA cm(-2).

Preformed porous media (PPM) technology has emerged as a transformative approach to enhance heat and mass transfer in vacuum freeze-drying (VFD) of agricultural and food products. This review systematically analyzes recent advances in PPM research, with particular focus on spray freeze-drying (SFD) as the dominant technique for precision pore architecture control. Empirical studies confirm PPM’s efficacy: drying time reductions of 20–50% versus conventional VFD while improving product quality (e.g., 15% higher ginsenoside retention in ginseng, 90% enzyme activity preservation). Key innovations include gradient porous structures and multi-technology coupling strategies that fundamentally alter transfer mechanisms through: resistance mitigation via interconnected macropores (50–500 μm, 40–90% porosity), pseudo-convection effects enabling 30% faster vapor removal, and radiation enhancement boosting absorption by 40–60% and penetration depth 2–3 times. While inherent VFD limitations (e.g., low thermal conductivity) persist, we identify PPM-specific bottlenecks: precision regulation of pore structures (<5% size deviation), scalable fabrication of gradient architectures, synergy mechanisms in multi-field coupling (e.g., microwave-PPM interactions). The most promising advancements include 3D-printed gradient pores for customized transfer paths, intelligent monitoring-feedback systems, and multiscale modeling bridging pore-scale physics to macroscale kinetics. This review provides both a critical assessment of current progress and a forward-looking perspective to guide future research and industrial adoption of PPM-enhanced VFD.

With the widespread application of ozone technology in agricultural plant protection, developing an ozonated water atomizer that integrates efficient mixing and precise spraying has been recognized as a significant challenge. Swirling flow is considered a method to enhance hydrodynamics and mass transfer in gas–liquid mixing. This study innovatively combines an axial nozzle with a swirling mixing chamber, utilizing the negative pressure generated by the high-speed central airflow at the nozzle throat as the driving force for swirling mixing and initial atomization, completing mass transfer and preliminary atomization before the formation of the mist, thereby improving gas–liquid contact and mass transfer efficiency. Through numerical simulations, the impact of geometric parameters at key locations on the internal flow of the atomizer was analyzed. The optimized inlet diameter of the atomizer was found to be 9 mm, with a throat length of 3 mm and a self-priming hole diameter of 1.5 mm. Experimental results on droplet size and ozone droplet concentration verified that at the optimal spraying pressure of 0.6 MPa, a concentration of up to 3.73 mg·L−1 with an average droplet size of 102 µm, evenly distributed, could be generated at a distance of 40 cm from the target. This work provides a technological framework for advancing precision ozone-based plant protection, aligning with global efforts to reduce agrochemical footprints through innovative application systems. It offers theoretical guidance and data support for the development and design of high-efficiency ozone atomizers in agricultural applications, aiming to minimize the use of agricultural chemicals and promote the growth of green plant protection technologies.

Fusarium head blight (FHB), caused by Fusarium graminearum, is a significant fungal disease that adversely affects wheat production and food security. This study systematically evaluated the fungicidal efficacy of strong oxidative radicals (SORs) against F. graminearum and their effects on wheat growth and yield through a combination of in vitro and field experiments. In vitro experiments revealed that solutions containing different concentrations of radicals effectively suppressed the fungus. The results suggested that SOR solutions exhibited potent fungicidal activity against F. graminearum. At a concentration of 4.0 mg/L, the spore mortality rate was 96.8%, and at 5.0 mg/L, the rate reached 99.4%. The optimal concentration for the elimination of F. graminearum spores was determined to be 2.5 × 105 CFU/mL. The optimal treatment duration for SORs was 10 min. Furthermore, field trials investigated the effects of SORs on wheat growth, and agronomic traits were assessed, along with their efficacy in controlling FHB in field trials, both as a standalone treatment and in combination with commercial pesticides. The results indicated that the application of SORs alone achieved an 87.9% control efficacy, demonstrating significant potential for disease control. Furthermore, SORs positively influenced wheat agronomic traits such as plant height, spike length, grain weight per plant, grain number per plant and grain yield, providing a promising new approach for the green control of FHB.

Based on the characteristics of small agricultural machinery in terms of flexibility and high efficiency when operating in small plots of hilly and mountainous areas, as well as the demand for improving the automation and intelligence levels of agricultural machinery, this paper conducted research on the path tracking control of the automatic navigation operation of a crawler sprayer. Based on the principles of the kinematic model and the position prediction model of the agricultural machinery chassis, a pure pursuit controller based on adaptive look-ahead distance was designed for the tracked motion chassis. Using a lightweight crawler sprayer as the research platform, integrating onboard industrial control computers, sensors, communication modules, and other hardware, an automatic navigation operation system was constructed, achieving precise control of the crawler sprayer during the path tracking process. Simulation test results show that the path tracking control method based on adaptive look-ahead distance has the characteristics of smooth control and small steady-state error. Field tests indicate that the crawler sprayer exhibits small deviations during path tracking, with an average absolute error of 2.15 cm and a maximum deviation of 4.08 cm when operating at a speed of 0.7 m/s. In the line-following test, with initial position deviations of 0.5 m, 1.0 m, and 1.5 m, the line-following times were 7.45 s, 11.91 s, and 13.66 s, respectively, and the line-following distances were 5.21 m, 8.34 m, and 9.56 m, respectively. The maximum overshoot values were 6.4%, 10.5%, and 12.6%, respectively. The autonomous navigation experiments showed a maximum deviation of 5.78 cm and a mean absolute error of 2.69 cm. The proportion of path deviations within ±5 cm and ±10 cm was 97.32% and 100%, respectively, confirming the feasibility of the proposed path tracking control method. This significantly enhanced the path tracking performance of the crawler sprayer while meeting the requirements for autonomous plant protection spraying operations.

Comprehensive growth index (CGI) more accurately reflects crop growth conditions than single indicators, which is crucial for precision irrigation, fertilization, and yield prediction. However, many current studies overlook the relationships between different growth parameters and their varying contributions to yield, leading to overlapping information and lower accuracy in monitoring crop growth. Therefore, this study focuses on winter wheat and constructs a comprehensive growth monitoring index (CGIac), based on adaptive weight allocation of growth parameters’ contribution to yield, using data such as leaf area index (LAI), soil plant analysis development (SPAD) values, plant height (PH), biomass (BM), and plant water content (PWC). Using UAV data on vegetation indices, feature selection was performed using the Elastic Net. The growth inversion model was then constructed using machine learning methods, including linear regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR). Based on the optimal growth inversion model for winter wheat, spatial distribution of wheat growth in the study area is obtained. The findings demonstrated that CGIac outperforms CGIav (constructed using equal weighting) and CGIcv (built using the coefficient of variation) in yield correlation and prediction accuracy. Specifically, the yield correlation of CGIac improved by up to 0.76 compared to individual indices, while yield prediction accuracy increased by up to 23.14%. Among the evaluated models, the RF model achieved the best performance, with a coefficient of determination (R2) of 0.895 and a root mean square error (RMSE) of 0.0058. A comparison with wheat orthophotos from the same period confirmed that the inversion results were highly consistent with actual growth conditions in the study area. The proposed method significantly improved the accuracy and applicability of winter wheat growth monitoring, overcoming the limitations of single parameters in growth prediction. Additionally, it provided new technological support and innovative solutions for regional crop monitoring and precision farming operations.

In this research, a new catalyst for activating persulfate was developed by loading iron and nickel ions onto powdered activated carbon (PAC) for treating methyl orange, and the preparation process was optimized and characterized. The efficacy of the treatment was evaluated using the Chemical Oxygen Demand (COD) removal rate, which reflects the impact of various process parameters, including catalyst dosage, sodium persulfate dosage, and reaction pH. Finally, the recovery and reuse performance of the catalyst were studied. The optimal conditions for preparing the activated sodium persulfate catalyst were determined to be as follows: a molar ratio of Fe3+ and Fe2+ to Ni of 4:1, a mass ratio of Fe3O4 to PAC of 1:4, a calcination temperature of 700 °C, and a calcination time of 4 h. This preparation led to an increase in surface porosity and the formation of a hollow structure within the catalyst. The active material on the surface was identified as nickel ferrite, comprising the elements C, O, Fe, and Ni. The magnetic property is beneficial to recycling. With the increase in catalyst and sodium persulfate dosage, the COD removal efficiency of the oxidation system increased first, and then, decreased. The catalyst showed good catalytic performance when the pH value was in the range of 3~11. Furthermore, Gas Chromatography–Mass Spectrometry (GC-MS) analysis indicated the complete oxidation of methyl orange dye molecules in the system. This result highlights the important role of the newly developed catalyst in activating persulfate.

In light of the environmental problems stemming from chemical pesticides, a preparation system for an ozone-induced free radical solution was developed to replace chemical pesticides for disease control. The effective synthesis process parameters for the solution under experimental conditions were determined through a single-factor experiment. The mechanism by which the solution eradicates pathogenic bacteria was investigated using electron microscopy, and a disease prevention and control experiment was conducted. Under slightly acidic conditions, the redox potential of the solution was observed to be high, with an air intake of 0.5 L/min and a liquid intake of 1.45 L/min, while the concentration decayed slowly, with a liquid intake of 0.98 L/min. The solution’s destructive effect on the bacteria’s internal and external structures intensified with prolonged action time and an increased number of free radicals. A 1.5 mg/L solution and 5% imidacloprid effectively reduced pest levels to grades 3 and 4, respectively. When the pH is 3, with air intake at 0.5 L/min and liquid intake at 0.98 L/min, the ozone-induced free radical solution exhibits strong oxidation and stability. At a concentration of 1.5 mg/L, the solution demonstrates a superior control effect on diseases and can partially replace chemical pesticides, offering a promising alternative for environmentally sustainable disease control.

Achieving high CO2 reduction reaction (CO2RR) current density in traditional H-cells presents significant challenges that require further efforts in both cathodic catalyst and electrolytic cells. This study presents a cathode-gas electrode (CGE) integrated by gas transmission channel and gas diffusion electrode and used in H-cells, named as "CGE/Hcell."Polymerized metal phthalocyanine (M = Co, Fe, Ni)-coated pore-rich nitrogen-doped hollow carbon spheres (NHCSs) were synthesized as cathode catalyst. The optimized polymerized cobalt phthalocyanine (CoPPc)/nitrogen-doped hollow carbon spheres (NHCSs) (COPPc/NHCSs) catalyst shows a moderate d-band center and barrier energy for *COOH intermediate formation and *CO desorption, thus displays the desirable activity of CO2RR. Owing to breaking the low CO2 solubility limit, CoPPc/NHCSs-based CGE/H-cell achieved a j CO of 206 mA cm- 2 and FECO of 85.49% at-1.5 V vs. RHE, and stable operation over 91 h. Using CoPPc/NHCSs as a bifunctional catalyst, the CO2RR coupling with SOR in CGE/H-cell theoretically saves 60% energy consumption and achieves j CO of 121 mA cm- 2 .

In this paper, degradation of ciprofloxacin (CIP) in a circulating dielectric barrier discharge (DBD) plasma system with Cu-CeO2@CA composite films addition was investigated. The Cu-CeO2@CA films were synthesized through hydrothermal method, and the samples were characterized by SEM, XRD, EDS, FT-IR and Raman spectra anal-ysis. The obtained results show that there was synergistic effect of the DBD plasma and the Cu-CeO2@CA films on the CIP degradation and mineralization. The optimal performance was obtained under the condition of 5 wt% Cu-CeO2 nanoparticles doping in the composite film. Besides the effect of the DBD plasma, the Cu-CeO2@CA films could drive the O3 decomposition, photocatalysis and then the more reactive oxygen species (ROS) for-mation. The presence of the main active species and their contributions to the CIP degradation were determined by electron spin resonance (ESR) analysis and radical quenching experiments. Methods of UV-vis spectrum, three-dimensional fluorescence spectrum and liquid chromatography mass spectrometry (LC-MS) were used to analyze the CIP decomposition process. The prepared Cu-CeO2@CA composite films had certain stability in the four cycles of application and the film catalyst was convenient for recycling. The toxicity evaluation results indicated the decline of the solution toxicity after treating in the Cu-CeO2@CA/DBD system.