Toward understanding the microstructure characteristics,phase selection and magnetic properties of laser additive manufactured Nd-Fe-B permanent magnets

Nd-Fe-B permanent magnets play a crucial role in energy conversion and electronic devices.The essential magnetic properties of Nd-Fe-B magnets,particularly coercivity and remanent magnetization,are significantly infuenced by the phase characteristics and microstructure.In this work,Nd-Fe-B magnets were manufactured using vacuum induction melting(VIM),laser directed energy deposition(LDED)and laser powder bed fusion(LPBF)technologies.Themicrostructure evolution and phase selection of Nd-Fe-B magnets were then clarified in detail.The results indicated that the solidification velocity(V)and cooling rate(R)are key factors in the phase selection.In terms of the VIM-casting Nd-Fe-B magnet,a large volume fraction of theα-Fe soft magnetic phase(39.7 vol.%)and Nd2Fe17Bxmetastable phase(34.7 vol.%)areformed due to the low R(2.3×10-1?C s-1),whereas only a minor fraction of the Nd2Fe14B hard magnetic phase(5.15 vol.%)is presented.For the LDED-processed Nd-Fe-B deposit,although the Nd2Fe14B hard magnetic phase also had a low value(3.4 vol.%)as the values of V(<10-2m s-1)and R(5.06×103?C s-1)increased,part of theα-Fe soft magnetic phase(31.7vol.%)is suppressed,and a higher volume of Nd2Fe17Bxmetastable phases(47.5 vol.%)areformed.As a result,both the VIM-casting and LDED-processed Nd-Fe-B deposits exhibited poor magnetic properties.In contrast,employing the high values of V(>10-2m s-1)and R(1.45×106?C s-1)in the LPBF process resulted in the substantial formation of the Nd2Fe14B hard magnetic phase(55.8 vol.%)directly from the liquid,while theα-Fe soft magnetic phase and Nd2Fe17Bxmetastable phase precipitation are suppressed in the LPBF-processed Nd-Fe-B magnet.Additionally,crystallographic texture analysis reveals that the LPBF-processedNd-Fe-B magnets exhibit isotropic magnetic characteristics.Consequently,the LPBF-processed Nd-Fe-B deposit,exhibiting a coercivity of 656 k A m-1,remanence of 0.79 T and maximum energy product of 71.5 k J m-3,achieved an acceptable magnetic performance,comparable to other additive manufacturing processed Nd-Fe-B magnets from MQP(Nd-lean)Nd-Fe-Bpowder.

Additively manufactured Ti–Ta–Cu alloys for the next-generation load-bearing implants

Bacterial colonization of orthopedic implants is one of the leading causes of failure and clinical complexities for load-bearing metallic implants. Topical or systemic administration of antibiotics may not offer the most efficient defense against colonization, especially in the case of secondary infection, leading to surgical removal of implants and in some cases even limbs. In this study, laser powder bed fusion was implemented to fabricate Ti3Al2V alloy by a 1:1 weight mixture of CpTi and Ti6Al4V powders. Ti-Tantalum(Ta)–Copper(Cu) alloys were further analyzed by the addition of Ta and Cu into the Ti3Al2V custom alloy. The biological,mechanical, and tribo-biocorrosion properties of Ti3Al2V alloy were evaluated. A 10 wt.% Ta(10Ta) and 3 wt.% Cu(3Cu) were added to the Ti3Al2V alloy to enhance biocompatibility and impart inherent bacterial resistance. Additively manufactured implants were investigated for resistance against Pseudomonas aeruginosa and Staphylococcus aureus strains of bacteria for up to 48 h. A 3 wt.% Cu addition to Ti3Al2V displayed improved antibacterial efficacy, i.e.78%–86% with respect to CpTi. Mechanical properties for Ti3Al2V–10Ta–3Cu alloy were evaluated, demonstrating excellent fatigue resistance, exceptional shear strength, and improved tribological and tribo-biocorrosion characteristics when compared to Ti6Al4V. In vivo studies using a rat distal femur model revealed improved early-stage osseointegration for alloys with10 wt.% Ta addition compared to CpTi and Ti6Al4V. The 3 wt.% Cu-added compositions displayed biocompatibility and no adverse infammatory response in vivo. Our results establish the Ti3Al2V–10Ta–3Cu alloy’s synergistic effect on improving both in vivo biocompatibility and microbial resistance for the next generation of load-bearing metallic implants.

Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.

Automatic Fetal Segmentation Designed on Computer-Aided Detection with Ultrasound Images

In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of pregnancy.The HC might be utilized toward determining gestational age and tracking fetal development.This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited.The CAD system is divided into two steps:to begin,Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull.We identified the HC using dynamic programming,an elliptical fit,and a Hough transform.The computer-aided detection(CAD)program was well-trained on 999 pictures(HC18 challenge data source),and then verified on 335 photos from all trimesters in an independent test set.A skilled sonographer and an expert in medicine personally marked the test set.We used the crown-rump length(CRL)measurement to calculate the reference gestational age(GA).In the first,second,and third trimesters,the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7±2.7,0.0±4.5,and 2.0±12.0 days,respectively.The regular duration variance between the baseline GA and the health investigator’s GA remained 1.5±3.0,1.9±5.0,and 4.0±14 a couple of days.The mean variance between the standard GA and the CAD system’s GA remained between 0.5 and 5.0,with an additional variation of 2.9 to 12.5 days.The outcomes reveal that the computer-aided detection(CAD)program outperforms an expert sonographer.When paired with the classifications reported in the literature,the provided system achieves results that are comparable or even better.We have assessed and scheduled this computerized approach for HC evaluation,which includes information from all trimesters of gestation.

Post processing of additive manufactured Mg alloys:Current status,challenges,and opportunities

Magnesium(Mg)and its alloys are emerging as a structural material for the aerospace,automobile,and electronics industries,driven by the imperative of weight reduction.They are also drawing notable attention in the medical industries owing to their biodegradability and a lower elastic modulus comparable to bone.The ability to manufacture near-net shape products featuring intricate geometries has sparked huge interest in additive manufacturing(AM)of Mg alloys,reflecting a transformation in the manufacturing sectors.However,AM of Mg alloys presents more formidable challenges due to inherent properties,particularly susceptibility to oxidation,gas trapping,high thermal expansion coefficient,and low solidification temperature.This leads to defects such as porosity,lack of fusion,cracking,delamination,residual stresses,and inhomogeneity,ultimately influencing the mechanical,corrosion,and surface properties of AM Mg alloys.To address these issues,post-processing of AM Mg alloys are often needed to make them suitable for application.The present article reviews all post-processing techniques adapted for AM Mg alloys to date,including heat treatment,hot isostatic pressing,friction stir processing,and surface peening.The utilization of these methods within the hybrid AM process,employing interlayer post-processing,is also discussed.Optimal post-processing conditions are reported,and their influence on the microstructure,mechanical,and corrosion properties are detailed.Additionally,future prospects and research directions are proposed.

Quasi-static and dynamic compressive behaviour of additively manufactured Menger fractal cube structures

This paper presents the first-ever investigation of Menger fractal cubes'quasi-static compression and impact behaviour.Menger cubes with different void ratios were 3D printed using polylactic acid(PLA)with dimensions of 40 mm×40 mm×40 mm.Three different orders of Menger cubes with different void ratios were considered,namely M1 with a void ratio of 0.26,M2 with a void ratio of 0.45,and M3with a void ratio of 0.60.Quasi-static Compression tests were conducted using a universal testing machine,while the drop hammer was used to observe the behaviour under impact loading.The fracture mechanism,energy efficiency and force-time histories were studied.With the structured nature of the void formation and predictability of the failure modes,the Menger geometry showed some promise compared to other alternatives,such as foams and honeycombs.With the increasing void ratio,the Menger geometries show force-displacement behaviour similar to hyper-elastic materials such as rubber and polymers.The third-order Menger cubes showed the highest energy absorption efficiency compared to the other two geometries in this study.The findings of the present work reveal the possibility of using additively manufactured Menger geometries as an energy-efficient system capable of reducing the transmitting force in applications such as crash barriers.

Additive manufactured osseointegrated screws with hierarchical design

Bone screws are devices used to fix implants or bones to bones.However,conventional screws are mechanically fixed with thread and often face long-term failure due to poor osseointegration.To improve osseointegration,screws are evolving from solid and smooth to porous and rough.Additive manufacturing(AM)offers a high degree of manufacturing freedom,enabling the preparation of predesigned screws that are porous and rough.This paper provides an overview of the problems currently faced by bone screws:long-term loosening and screw breakage.Next,advances in osseointegrated screws are summarized hierarchically(sub-micro,micro,and macro).At the sub-microscale level,we describe surface-modification techniques for enhancing osseointegration.At the micro level,we summarize the micro-design parameters that affect the mechanical and biological properties of porous osseointegrated screws,including porosity,pore size,and pore shape.In addition,we highlight three promising pore shapes:triply periodic minimal surface,auxetic structure with negative Poisson ratio,and the Voronoi structure.At the macro level,we outline the strategies of graded design,gradient design,and topology optimization design to improve the mechanical strength of porous osseointegrated screws.Simultaneously,this paper outlines advances in AM technology for enhancing the mechanical properties of porous osseointegrated screws.AM osseointegrated screws with hierarchical design are expected to provide excellent long-term fixation and the required mechanical strength.

Customized heat treatment process enabled excellent mechanical properties in wire arc additively manufactured Mg-RE-Zn-Zr alloys

Customized heat treatment is essential for enhancing the mechanical properties of additively manufactured metallic materials,especially for alloys with complex phase constituents and heterogenous microstructure.However,the interrelated evolutions of different microstructure features make it difficult to establish optimal heat treatment processes.Herein,we proposed a method for customized heat treatment process exploration and establishment to overcome this challenge for such kind of alloys,and a wire arc additively manufactured(WAAM)Mg-Gd-Y-Zn-Zr alloy with layered heterostructure was used for feasibility verification.Through this method,the optimal microstructures(fine grain,controllable amount of long period stacking ordered(LPSO)structure and nano-scaleβ'precipitates)and the corresponding customized heat treatment processes(520°C/30 min+200°C/48 h)were obtained to achieve a good combination of a high strength of 364 MPa and a considerable elongation of 6.2%,which surpassed those of other state-of-the-art WAAM-processed Mg alloys.Furthermore,we evidenced that the favorable effect of the undeformed LPSO structures on the mechanical properties was emphasized only when the nano-scaleβ'precipitates were present.It is believed that the findings promote the application of magnesium alloy workpieces and help to establish customized heat treatment processes for additively manufactured materials.

Characterization and Modeling of Mechanical Properties of Additively Manufactured Coconut Fiber-Reinforced Polypropylene Composites

In the face of the increased global campaign to minimize the emission of greenhouse gases and the need for sustainability in manufacturing, there is a great deal of research focusing on environmentally benign and renewable materials as a substitute for synthetic and petroleum-based products. Natural fiber-reinforced polymeric composites have recently been proposed as a viable alternative to synthetic materials. The current work investigates the suitability of coconut fiber-reinforced polypropylene as a structural material. The coconut fiber-reinforced polypropylene composites were developed. Samples of coconut fiber/polypropylene (PP) composites were prepared using Fused Filament Fabrication (FFF). Tests were then conducted on the mechanical properties of the composites for different proportions of coconut fibers. The results obtained indicate that the composites loaded with 2 wt% exhibited the highest tensile and flexural strength, while the ones loaded with 3 wt% had the highest compression strength. The ultimate tensile and flexural strength at 2 wt% were determined to be 34.13 MPa and 70.47 MPa respectively. The compression strength at 3 wt% was found to be 37.88 MPa. Compared to pure polypropylene, the addition of coconut fibers increased the tensile, flexural, and compression strength of the composite. In the study, an artificial neural network model was proposed to predict the mechanical properties of polymeric composites based on the proportion of fibers. The model was found to predict data with high accuracy.

Effect of thermo-mechanical treatment on microstructure and mechanical properties of wire-arc additively manufactured Al-Cu alloy

Wire-arc additive manufacture(WAAM)has great potential for manufacturing of Al-Cu components.However,inferior mechanical properties of WAAM deposited material restrict its industrial application.Inter-layer cold rolling and thermo-mechanical heat treatment(T8)with pre-stretching deformation between solution and aging treatment were adopted in this study.Their effects on hardness,mechanical properties and microstructure were analyzed and compared to the conventional heat treatment(T6).The results show that cold rolling increases the hardness and strengths,which further increase with T8 treatment.The ultimate tensile strength(UTS)of 513 MPa and yield stress(YS)of 413 MPa can be obtained in the inter-layer cold-rolled sample with T8 treatment,which is much higher than that in the as-deposited samples.The cold-rolled samples show higher elongation than that of as-deposited ones due to significant elimination of porosity in cold rolling;while both the T6 and T8 treatments decrease the elongation.The cold rolling and pre-stretching deformation both contribute to the formation of dense and dispersive precipitatedθ′phases,which inhibits the dislocation movement and enhances the strengths;as a result,T8 treatment shows better strengthening effect than the T6 treatment.The strengthening mechanism was analyzed and it was mainly related to work hardening and precipitation strengthening.

Mechanical behavior and response mechanism of porous metal structures manufactured by laser powder bed fusion under compressive loading

Al Si10Mg porous protective structure often produces different damage forms under compressive loading,and these damage modes affect its protective function.In order to well meet the service requirements,there is an urgent need to comprehensively understand the mechanical behavior and response mechanism of AlSi10Mg porous structures under compressive loading.In this paper,Al Si10Mg porous structures with three kinds of volume fractions are designed and optimized to meet the requirements of high-impact,strong-energy absorption,and lightweight characteristics.The mechanical behaviors of AlSi10Mg porous structures,including the stress-strain relationship,structural bearing state,deformation and damage modes,and energy absorption characteristics,were obtained through experimental studies at different loading rates.The damage pattern of the damage section indicates that AlSi10Mg porous structures have both ductile and brittle mechanical properties.Numerical simulation studies show that the AlSi10Mg porous structure undergoes shear damage due to relative misalignment along the diagonal cross-section,and the damage location is almost at 45°to the load direction,which is the most direct cause of its structural damage,revealing the damage mechanism of AlSi10Mg porous structures under the compressive load.The normalized energy absorption model constructed in the paper well interprets the energy absorption state of Al Si10Mg porous structures and gives the sensitive location of the structures,and the results of this paper provide important references for peers in structural design and optimization.

UK manufacturers'sales decline 10%in Q12024

UK manufacturers experienced a challenging start to 2024,with sales in the first quarter(Q1)down 10 per cent on the previous quarter,according to a report by Unleashed.However,year-on-year growth showed a modest increase of 2 per cent,reflecting the Bank of England’s assessment of weak growth in the manufacturing sector.

Research on the Innovative Decisions of Supermarket Private Brands and Designated Manufacturers

One of the core competencies of a supermarket lies in its branding.With the continuous development of the market economy and the ongoing evolution of consumer demand,private brands have progressively emerged as significant contributors to supermarket growth.However,a pivotal developmental challenge for supermarkets is navigating the innovative decision-making process between private brands and designated manufacturers.This paper aims to investigate the innovative decisions between private brands and designated manufacturers,along with the relevant promotional strategies employed during entry into the United States market.

Effect of grain refinement induced by wire and arc additive manufacture (WAAM) on the corrosion behaviors of AZ31 magnesium alloy in NaCl solution

Additive manufacturing(AM)of Mg alloys has become a promising strategy for producing complex structures,but the corrosion performance of AM Mg components remains unexploited.In this study,wire and arc additive manufacturing(WAAM)was employed to produce single AZ31 layer.The results revealed that the WAAM AZ31 was characterized by significant grain refinement with non-textured crystallographic orientation,similar phase composition and stabilized corrosion performance comparing to the cast AZ31.These varied corrosion behaviors were principally ascribed to the size of grain,where cast AZ31 and WAAM AZ31 were featured by micro galvanic corrosion and intergranular corrosion,respectively.

Enhanced strength-ductility synergy in a wire and arc additively manufactured Mg alloy via tuning interlayer dwell time

Strength-ductility trade-off is a common issue in Mg alloys. This work proposed that a synergistic enhancement of strength and ductility could be achieved through tuning interlayer dwell time(IDT) in the wire and arc additive manufacturing(WAAM) process of Mg alloy.The thermal couples were used to monitor the thermal history during the WAAM process. Additionally, the effect of different IDTs on the microstructure characteristics and resultant mechanical properties of WAAM-processed Mg alloy thin-wall were investigated. The results showed that the stable temperature of the thin-wall component could reach 290 ℃ at IDT=0s, indicating that the thermal accumulation effect was remarkable. Consequently, unimodal coarse grains with an average size of 39.6 μm were generated, and the resultant room-temperature tensile property was poor. With the IDT extended to 60s, the thermal input and thermal dissipation reached a balance, and the stable temperature was only 170 ℃, closing to the initial temperature of the substrate. A refined grain structure with bimodal size distribution was obtained. The remelting zone had fine grains with the size of 15.2 μm, while the arc zone owned coarse grains with the size of 24.5 μm.The alternatively distributed coarse and fine grains lead to the elimination of strength-ductility trade-off. The ultimate tensile strength and elongation of the samples at IDT=60s are increased by 20.6 and 75.0% of those samples at IDT=0s, respectively. The findings will facilitate the development of additive manufacturing processes for advanced Mg alloys.

The design, manufacture and application of multistable mechanical metamaterials-a state-of-the-art review

Multistable mechanical metamaterials are a type of mechanical metamaterials with special features,such as reusability,energy storage and absorption capabilities,rapid deformation,and amplified output forces.These metamaterials are usually realized by series and/or parallel of bistable units.They can exhibit multiple stable configurations under external loads and can be switched reversely among each other,thereby realizing the reusability of mechanical metamaterials and offering broad engineering applications.This paper reviews the latest research progress in the design strategy,manufacture and application of multistable mechanical metamaterials.We divide bistable structures into three categories based on their basic element types and provide the criterion of their bistability.Various manufacturing techniques to fabricate these multistable mechanical metamaterials are introduced,including mold casting,cutting,folding and three-dimensional/4D printing.Furthermore,the prospects of multistable mechanical metamaterials for applications in soft driving,mechanical computing,energy absorption and wave controlling are discussed.Finally,this paper highlights possible challenges and opportunities for future investigations.The review aims to provide insights into the research and development of multistable mechanical metamaterials.

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign tumors.The benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in death.Initial recognition of BT is highly significant to protecting the patient’s life.Generally,the BT can be identified through the magnetic resonance imaging(MRI)scanning technique.But the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the brain.Recently,ML has prevailed against standard image processing techniques.Several studies denote the superiority of machine learning(ML)techniques over standard techniques.Therefore,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)model.To accomplish the detection of brain tumor effectively,a Computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research manuscript.Initially,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull stripping.Next,mayfly optimization with the Kapur’s thresholding based segmentation process takes place.For feature extraction proposes,local diagonal extreme patterns(LDEP)are exploited.At last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification process.The accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research work.The experimental validation of the proposed model demonstrates its promising performance over other existing methods.

Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screening and triage.At the same time,CXR interpretation is a time-consuming and subjective process.Furthermore,high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis.Therefore,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB accurately.With this motivation,this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on Chest X-rays(CXR).The presented WSODTL-TBC model aims to detect and classify TB on CXR images.Primarily,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation.Besides,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature vectors.In addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the work.The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many aspects.The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.

A facile strategy for tuning the density of surface-grafted biomolecules for melt extrusion-based additive manufacturing applications

Melt extrusion-based additive manufacturing(ME-AM)is a promising technique to fabricate porous scaffolds for tissue engi-neering applications.However,most synthetic semicrystalline polymers do not possess the intrinsic biological activity required to control cell fate.Grafting of biomolecules on polymeric surfaces of AM scaffolds enhances the bioactivity of a construct;however,there are limited strategies available to control the surface density.Here,we report a strategy to tune the surface density of bioactive groups by blending a low molecular weight poly(ε-caprolactone)5k(PCL5k)containing orthogonally reactive azide groups with an unfunctionalized high molecular weight PCL75k at different ratios.Stable porous three-dimensional(3D)scaf-folds were then fabricated using a high weight percentage(75 wt.%)of the low molecular weight PCL 5k.As a proof-of-concept test,we prepared films of three different mass ratios of low and high molecular weight polymers with a thermopress and reacted with an alkynated fluorescent model compound on the surface,yielding a density of 201-561 pmol/cm^(2).Subsequently,a bone morphogenetic protein 2(BMP-2)-derived peptide was grafted onto the films comprising different blend compositions,and the effect of peptide surface density on the osteogenic differentiation of human mesenchymal stromal cells(hMSCs)was assessed.After two weeks of culturing in a basic medium,cells expressed higher levels of BMP receptor II(BMPRII)on films with the conjugated peptide.In addition,we found that alkaline phosphatase activity was only significantly enhanced on films contain-ing the highest peptide density(i.e.,561 pmol/cm^(2)),indicating the importance of the surface density.Taken together,these results emphasize that the density of surface peptides on cell differentiation must be considered at the cell-material interface.Moreover,we have presented a viable strategy for ME-AM community that desires to tune the bulk and surface functionality via blending of(modified)polymers.Furthermore,the use of alkyne-azide“click”chemistry enables spatial control over bioconjugation of many tissue-specific moieties,making this approach a versatile strategy for tissue engineering applications.

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.