Decade Milestone Advancement of Defect-Engineered g-C_(3)N_(4) for Solar Catalytic Applications

Over the past decade, graphitic carbon nitride(g-C_(3)N_(4)) has emerged as a universal photocatalyst toward various sustainable carbo-neutral technologies. Despite solar applications discrepancy, g-C_(3)N_(4) is still confronted with a general fatal issue of insufficient supply of thermodynamically active photocarriers due to its inferior solar harvesting ability and sluggish charge transfer dynamics. Fortunately, this could be significantly alleviated by the “all-in-one” defect engineering strategy, which enables a simultaneous amelioration of both textural uniqueness and intrinsic electronic band structures. To this end, we have summarized an unprecedently comprehensive discussion on defect controls including the vacancy/non-metallic dopant creation with optimized electronic band structure and electronic density, metallic doping with ultraactive coordinated environment(M–N_(x), M–C_(2)N_(2), M–O bonding), functional group grafting with optimized band structure, and promoted crystallinity with extended conjugation π system with weakened interlayered van der Waals interaction. Among them, the defect states induced by various defect types such as N vacancy, P/S/halogen dopants, and cyano group in boosting solar harvesting and accelerating photocarrier transfer have also been emphasized. More importantly, the shallow defect traps identified by femtosecond transient absorption spectra(fs-TAS) have also been highlighted. It is believed that this review would pave the way for future readers with a unique insight into a more precise defective g-C_(3)N_(4) “customization”, motivating more profound thinking and flourishing research outputs on g-C_(3)N_(4)-based photocatalysis.

The impact of demographic dynamics on food consumption and its environmental outcomes:Evidence from China

With increasing population and changing demographics,food consumption has experienced a significant transition in quantity and quality.However,a dearth of knowledge remains regarding its environmental impacts and how it responds to demographic dynamics,particularly in emerging economies like China.Using the two-stage Quadratic Almost Demand System(QUAIDS)model,this study empirically examines the impact of demographic dynamics on food consumption and its environmental outcomes based on the provincial data from 2000 to 2020 in China.Under various scenarios,according to changes in demographics,we extend our analysis to project the long-term trend of food consumption and its environmental impacts,including greenhouse gas(GHG)emissions,water footprint(WF),and land appropriation(LA).The results reveal that an increase in the proportion of senior people significantly decreases the consumption of grain and livestock meat and increases the consumption of poultry,egg,and aquatic products,particularly for urban residents.Moreover,an increase in the proportion of males in the population leads to higher consumption of poultry and aquatic products.Correspondingly,in the current scenario of an increased aging population and sex ratio,it is anticipated that GHG emissions,WF,and LA are likely to decrease by 1.37,2.52,and 3.56%,respectively.More importantly,in the scenario adhering to the standards of nutritional intake according to the Dietary Guidelines for Chinese Residents in 2022,GHG emissions,WF,and LA in urban areas would increase by 12.78,20.94,and 18.32%,respectively.Our findings suggest that changing demographics should be considered when designing policies to mitigate the diet-environment-health trilemma and achieve sustainable food consumption.

Heterointerface Engineering-Induced Oxygen Defects for the Manganese Dissolution Inhibition in Aqueous Zinc Ion Batteries

Manganese-based material is a prospective cathode material for aqueous zinc ion batteries(ZIBs)by virtue of its high theoretical capacity,high operating voltage,and low price.However,the manganese dissolution during the electrochemical reaction causes its electrochemical cycling stability to be undesirable.In this work,heterointerface engineering-induced oxygen defects are introduced into heterostructure MnO_(2)(δa-MnO_(2))by in situ electrochemical activation to inhibit manganese dissolution for aqueous zinc ion batteries.Meanwhile,the heterointerface between the disordered amorphous and the crystalline MnO_(2)ofδa-MnO_(2)is decisive for the formation of oxygen defects.And the experimental results indicate that the manganese dissolution ofδa-MnO_(2)is considerably inhibited during the charge/discharge cycle.Theoretical analysis indicates that the oxygen defect regulates the electronic and band structure and the Mn-O bonding state of the electrode material,thereby promoting electron transport kinetics as well as inhibiting Mn dissolution.Consequently,the capacity ofδa-MnO_(2)does not degrade after 100 cycles at a current density of 0.5 Ag^(-1)and also 91%capacity retention after 500cycles at 1 Ag^(-1).This study provides a promising insight into the development of high-performance manganese-based cathode materials through a facile and low-cost strategy.

Impact Analysis of Microscopic Defect Types on the Macroscopic Crack Propagation in Sintered Silver Nanoparticles

Sintered silver nanoparticles(AgNPs)arewidely used in high-power electronics due to their exceptional properties.However,the material reliability is significantly affected by various microscopic defects.In this work,the three primary micro-defect types at potential stress concentrations in sintered AgNPs are identified,categorized,and quantified.Molecular dynamics(MD)simulations are employed to observe the failure evolution of different microscopic defects.The dominant mechanisms responsible for this evolution are dislocation nucleation and dislocation motion.At the same time,this paper clarifies the quantitative relationship between the tensile strain amount and the failure mechanism transitions of the three defect types by defining key strain points.The impact of defect types on the failure process is also discussed.Furthermore,traction-separation curves extracted from microscopic defect evolutions serve as a bridge to connect the macro-scale model.The validity of the crack propagation model is confirmed through tensile tests.Finally,we thoroughly analyze how micro-defect types influence macro-crack propagation and attempt to find supporting evidence from the MD model.Our findings provide a multi-perspective reference for the reliability analysis of sintered AgNPs.

Resilience effects for household food expenditure and dietary diversity in rural western China

A more resilient livelihood is increasingly recognized as an efficient way to improve vulnerable households’food security and optimize their dietary decisions.This study quantifies rural household resilience in western China,identifies the three pillars(absorptive capacity,adaptive capacity,and transformative capacity)contribution to resilience,and then establishes the estimated Resilience Capacity Index(RCI)linked with food security and dietary diversity supported by the multiple indicator multiple cause(MIMIC)model.Results show that,despite geographical heterogeneity,the RCI consistently increased from 2015 to 2021.Households with a higher RCI inheriting better capacity to deal with risk and shocks are significantly and positively correlated with increasing food expenditure and diversifying food choices.It can be because resilient households will allocate more money to food expenditure instead of saving for livelihood uncertainty.Thus,policymakers can provide more incentives for rural households to adopt more dynamic and effective risk management strategies.This,in turn,could yield positive spillover effects by preventing human capital loss associated with dietary-related chronic diseases and mortality.

Trace Cobalt Doping and Defect Engineering of High Surface Area α-Ni(OH)_(2) for Electrocatalytic Urea Oxidation

Owing to the intrinsically sluggish kinetics of urea oxidation reaction(UOR)involving a six-electron transfer process,developing efficient UOR electrocatalyst is a great challenge remained to be overwhelmed.Herein,by taking advantage of 2-Methylimidazole,of which is a kind of alkali in water and owns strong coordination ability to Co^(2+)in methanol,trace Co(1.0 mol%)addition was found to induce defect engineering onα-Ni(OH)_(2)in a dual-solvent system of water and methanol.Physical characterization results revealed that the synthesized electrocatalyst(WM-Ni_(0.99)Co_(0.01)(OH)_(2))was a kind of defective nanosheet with thickness around 5-6 nm,attributing to the synergistic effect of Co doping and defect engineering,its electron structure was finely altered,and its specific surface a rea was tremendously enlarged from 68 to 172.3 m^(2)g^(-1).With all these merits,its overpotential to drive 10 mA cm^(-2)was reduced by 110 mV.Besides,the interfacial behavior of UOR was also well deciphered by operando electrochemical impedance spectroscopy.

YOLO-DD:Improved YOLOv5 for Defect Detection

As computer technology continues to advance,factories have increasingly higher demands for detecting defects.However,detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes.To address this issue,this paper proposes YOLO-DD,a defect detectionmodel based on YOLOv5 that is effective and robust.To improve the feature extraction process and better capture global information,the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer(RDAT).Additionally,an Information Gap Filling Strategy(IGFS)is proposed to improve the fusion of features at different scales.The classic lightweight attention mechanism Squeeze-and-Excitation(SE)module is also incorporated into the neck section to enhance feature expression and improve the model’s performance.Experimental results on the NEU-DET dataset demonstrate that YOLO-DDachieves competitive results compared to state-of-the-art methods,with a 2.0% increase in accuracy compared to the original YOLOv5,achieving 82.41% accuracy and38.25FPS(framesper second).Themodel is also testedon a self-constructed fabric defect dataset,and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5,demonstrating its stability and generalization ability.The high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection.

Repositioning fertilizer manufacturing subsidies for improving food security and reducing greenhouse gas emissions in China

China removed fertilizer manufacturing subsidies from 2015 to 2018 to bolster market-oriented reforms and foster environmentally sustainable practices.However,the impact of this policy reform on food security and the environment remains inadequately evaluated.Moreover,although green and low-carbon technologies offer environmental advantages,their widespread adoption is hindered by prohibitively high costs.This study analyzes the impact of removing fertilizer manufacturing subsidies and explores the potential feasibility of redirecting fertilizer manufacturing subsidies to invest in the diffusion of these technologies.Utilizing the China Agricultural University Agri-food Systems model,we analyzed the potential for achieving mutually beneficial outcomes regarding food security and environmental sustainability.The findings indicate that removing fertilizer manufacturing subsidies has reduced greenhouse gas(GHG)emissions from agricultural activities by 3.88 million metric tons,with minimal impact on food production.Redirecting fertilizer manufacturing subsidies to invest in green and low-carbon technologies,including slow and controlled-release fertilizer,organic-inorganic compound fertilizers,and machine deep placement of fertilizer,emerges as a strategy to concurrently curtail GHG emissions,ensure food security,and secure robust economic returns.Finally,we propose a comprehensive set of government interventions,including subsidies,field guidance,and improved extension systems,to promote the widespread adoption of these technologies.

Women’s empowerment and food consumption:Evidence from female-headed households in Tanzania

Despite the growing recognition of women’s increasing role in the household and corresponding empowerment programs in sub-Saharan Africa,intensive research on the relationship between women’s influence and household food consumption is minimal.Using the most recent(2017-2018)national household survey data from Tanzania,this study examined the influence of women’s empowerment on household food consumption.First,we compared the monthly consumption of eight food categories between female-headed households(FHHs)and male-headed households(MHHs)using both descriptive statistics and the propensity score matching(PSM)method.Furthermore,we adopted the two-stage Linear Expenditure System and Almost Ideal Demand System model(LES-AIDS)to estimate income and price elasticities for the two household types.The results show that FHHs consume bread and cereals,fish,oils and fats,vegetables,and confectionery(sugar,jam,honey,chocolate,etc.)more than MHHs.Moreover,FHHs have a significantly higher income elasticity of demand for all food groups than MHHs.They are also more price elastic than MHHs in meat,fish,oils,fats,sugar,jam,honey,chocolate,etc.

Immunoglobulin G-mediated food intolerance and metabolic syndrome influence the occurrence of reflux esophagitis in Helicobacter pylori-infected patients

BACKGROUND Reflux esophagitis has an increasing prevalence and complex and diverse symptoms.Identifying its risk factors is crucial to understanding the etiology,prevention,and management of the disease.The occurrence of reflux esophagitis may be associated with food reactions,Helicobacter pylori(H.pylori)infection,and metabolic syndromes.AIM To investigate the risk factors for reflux esophagitis and analyze the effects of immunoglobulin(Ig)G-mediated food intolerance,H.pylori infection,and metabolic syndrome on reflux esophagitis.METHODS Outpatients attending the Second Medical Center of the PLA General Hospital between 2017 and 2021 were retrospectively enrolled.The patients’basic information,test results,gastroscopy results,H.pylori test results,and IgG-mediated food intolerance results were collected.Multivariate logistic regression analysis was used to analyze risk factors for reflux esophagitis.Statistical mediation analysis was used to evaluate the effects of IgG-mediated food intolerance and metabolic syndrome on H.pylori infection affecting reflux esophagitis.RESULTS A total of 7954 outpatients were included;the prevalence of reflux esophagitis,IgG-mediated food intolerance,H.pylori infection,and metabolic syndrome were 20.84%,61.77%,35.91%,and 60.15%,respectively.Multivariate analysis showed that the independent risk factors for reflux esophagitis included IgG-mediated food intolerance(OR=1.688,95%CI:1.497-1.903,P<0.00001)and metabolic syndrome(OR=1.165,95%CI:1.030-1.317,P=0.01484),and the independent protective factor for reflux esophagitis was H.pylori infection(OR=0.400,95%CI:0.351-0.456,P<0.00001).IgG-mediated food intolerance had a partially positive mediating effect on H.pylori infection as it was associated with reduced occurrence of reflux esophagitis(P=0.0200).Metabolic syndrome had a partially negative mediating effect on H.pylori infection and reduced the occurrence of reflux esophagitis(P=0.0220).CONCLUSION Patients with IgG-mediated food intolerance and metabolic syndrome were at higher risk of developing reflux esophagitis,while patients with H.pylori infection were at lower risk.IgG-mediated food intolerance reduced the risk of reflux esophagitis pathogenesis in patients with H.pylori infection;however,metabolic syndrome increased the risk of patients with H.pylori infection developing reflux esophagitis.

Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

Software Defect Prediction Method Based on Stable Learning

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.

Attention-relation network for mobile phone screen defect classification via a few samples

How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens.An attention-relation network for the mobile phone screen defect classification is proposed in this paper.The architecture of the attention-relation network contains two modules:a feature extract module and a feature metric module.Different from other few-shot models,an attention mechanism is applied to metric learning in our model to measure the distance between features,so as to pay attention to the correlation between features and suppress unwanted information.Besides,we combine dilated convolution and skip connection to extract more feature information for follow-up processing.We validate attention-relation network on the mobile phone screen defect dataset.The experimental results show that the classification accuracy of the attentionrelation network is 0.9486 under the 5-way 1-shot training strategy and 0.9039 under the 5-way 5-shot setting.It achieves the excellent effect of classification for mobile phone screen defects and outperforms with dominant advantages.

Strip steel surface defect detection algorithm based on improved Faster R-CNN

To solve the problems of the low accuracy and poor real-time performance of traditional strip steel surface defect detection meth-ods,which are caused by the characteristics of many kinds,complex shapes,and different scales of strip surface defects,a strip steel surface defect detection algorithm based on improved Faster R-CNN is proposed.Firstly,the residual convolution module is inserted into the Swin Transformer network module to form the RC-Swin Transformer network module,and the RC-Swin Transformer module is introduced into the backbone network of the traditional Faster R-CNN to enhance the ability of the network to extract the global feature information of the image and adapt to the complex shape of the strip steel surface defect.To improve the attention of the network to defects in the image,a CBAM-BiFPN network module is designed,and then the backbone network is combined with the CBAM-BiFPN network to realize the de-tection and fusion of multi-scale features.The RoI align layer is used instead of the RoI pooling layer to improve the accuracy of defect loca-tion.Finally,Soft NMS is used to achieve non-maximum suppression and remove redundant boxes.In the comparative experiment on the NEU-DET dataset,the improved algorithm improves the mean average precision by 4.2%compared with the Faster R-CNN algorithm,and also improves the average precision by 6.1%and 6.7%for crazing defect and rolled-in scale defect,which are difficult to detect with the Faster R-CNN algorithm.The experiments show that the improvements proposed in the paper effectively improve the detection accuracy of the algorithm and have certain practical value.

High quality repair of osteochondral defects in rats using the extracellular matrix of antler stem cells

BACKGROUND Cartilage defects are some of the most common causes of arthritis.Cartilage lesions caused by inflammation,trauma or degenerative disease normally result in osteochondral defects.Previous studies have shown that decellularized extracellular matrix(ECM)derived from autologous,allogenic,or xenogeneic mesenchymal stromal cells(MSCs)can effectively restore osteochondral integrity.AIM To determine whether the decellularized ECM of antler reserve mesenchymal cells(RMCs),a xenogeneic material from antler stem cells,is superior to the currently available treatments for osteochondral defects.METHODS We isolated the RMCs from a 60-d-old sika deer antler and cultured them in vitro to 70%confluence;50 mg/mL L-ascorbic acid was then added to the medium to stimulate ECM deposition.Decellularized sheets of adipocyte-derived MSCs(aMSCs)and antlerogenic periosteal cells(another type of antler stem cells)were used as the controls.Three weeks after ascorbic acid stimulation,the ECM sheets were harvested and applied to the osteochondral defects in rat knee joints.RESULTS The defects were successfully repaired by applying the ECM-sheets.The highest quality of repair was achieved in the RMC-ECM group both in vitro(including cell attachment and proliferation),and in vivo(including the simultaneous regeneration of well-vascularized subchondral bone and avascular articular hyaline cartilage integrated with surrounding native tissues).Notably,the antler-stem-cell-derived ECM(xenogeneic)performed better than the aMSC-ECM(allogenic),while the ECM of the active antler stem cells was superior to that of the quiescent antler stem cells.CONCLUSION Decellularized xenogeneic ECM derived from the antler stem cell,particularly the active form(RMC-ECM),can achieve high quality repair/reconstruction of osteochondral defects,suggesting that selection of decellularized ECM for such repair should be focused more on bioactivity rather than kinship.

New food sources and production systems:a comparison of international regulations and China’s advancements in novel foods with synthetic biology

The global shift towards sustainable food systems has sparked innovations in food sources and production systems,including cell-based meat,plant-based food products,precision fermentation,and 3D food printing.These advancements pose regulatory challenges and opportunities,with China emerging as a critical player in adopting and regulating new food technologies.This review explores the international landscape of new food sources and production systems(NFPS),focusing on China’s role and regulatory approaches compared to global practices.Through this comparative analysis,we aim to contribute to the ongoing dialogue on food safety regulation,offering insights and recommendations for policymakers,industry stakeholders,and researchers engaged in the global food system’s evolution.This comprehensive overview underscores the dynamic nature of regulatory frameworks governing NFPS,highlighting the international efforts to ensure food safety,consumer protection,and the sustainable evolution of the food industry.

Food nutrition and toxicology targeting on specific organs in the era of single-cell sequencing

Due to the complex natures of dietary food components,it is difficult to elucidate how the compounds affect host health.Dietary food often selectively presents its mechanism of action on different cell types,and participates in the modulation of targeted cells and their microenvironments within organs.However,the limitations of traditional in vitro assays or in vivo animal experiments cannot comprehensively examine cellular heterogeneity and the tissue-biased influences.Single-cell RNA sequencing(sc RNA-seq)has emerged as an indispensable methodology to decompose tissues into different cell types for the demonstration of transcriptional profiles of individual cells.Sc RNA-seq applications has been summarized on three typical organs(brain,liver,kidney),and two representative immune-and tumor related health problems.The everincreasing role of sc RNA-seq in dietary food research with further improvement can provide sub-cellular information and the coupling between other cellular modalities.In this review,we propose utilizing sc RNAseq to more effectively capture the subtle and complex effects of food chemicals,and how they may lead to health problems at single-cell resolution.This novel technique will be valuable to elucidate the underlying mechanism of both the health benefits of food nutrients and the detrimental consequences food toxicants at the cellular level.

Physico-Chemical and Nutritional Properties of a Food Broth Based on Nere (Parkia biglobosa)

Purpose: Diet and eating habits are major risk factors for the health and the development of disease, such as, for example, metabolic disorder leading to cardiovascular pathology and cancer, decreased immunity exposing to infections. This study of the physico-chemical and nutritional properties of a soumara-based food broth was carried out with the aim of promoting the consumption of organic broth made from nere seeds (soumara). That is to alleviate certain metabolic diseases, which is a matter of food safety, and also to limit the risk for the health about the consumption of some cooking stocks on the market. Methods: Several natural ingredients such as nere seeds (soumara), ginger, black pepper, parsley and garlic were used to create a nere-based stock. All these ingredients were freeze-dried and the powder obtained was used to make the broth, regarding their physical and chemical properties. Results: The broth had a good protein content of 17.41 ± 0.367 g/100g, a lipid content of 16.80 ± 0.08 g/100g and a fiber content of 8.66 ± 0.04 g/100g. In terms of nutritional values, the broth showed good levels of calcium 184.21 ± 0.09 mg/100g, potassium 50.04 ± 1.45 mg/100g and iron and zinc. In terms of antioxidant activity, the broth also showed good antioxidant activity. Conclusion: Regarding the properties of our food broth, whose composition is based on natural ingredients, could be recommended for consumption and, its properties, could play an important role in preventing and combating certain metabolic diseases.

Does Green Food Certification promote agri-food export quality?Evidence from China

The construction of a food certification system plays a vital role in upgrading export quality, which previous studies have largely overlooked. We match China's industry-level data of Green Food Certification with its HS6-digit export data of agri-food products to quantify the impact of Green Food Certification on export quality. We identify the significant and positive effect of Green Food Certification on export quality. The 2SLS estimation based on instrumental variables and a range of robustness checks confirm the validity and robustness of the benchmark conclusions. Further analysis discloses that Green Food Certification improves export quality by raising agricultural production efficiency and brand premiums. Heterogeneity analysis shows that the effect of Green Food Certification varies across regions, notably improving the quality of agri-food products exported to developed regions and regions with high levels of import supervision. Furthermore, among various product types, Green Food Certification significantly improves the export quality of primary products and products vulnerable to non-tariff measures. The above findings could guide the future development of agri-food quality certification systems, potentially leading to a transformation and promotion of the agri-food trade.