Structural and microwave absorption properties of CoFe_(2)O_(4)/residual carbon composites

Electromagnetic interference,which necessitates the rapid advancement of substances with exceptional capabilities for bsorbing electromagnetic waves,is of urgent concern in contemporary society.In this work,CoFe_(2)O_(4)/residual carbon from coal gasification fine slag(CFO/RC)composites were created using a novel hydrothermal method.Various mechanisms for microwave absorption,including conductive loss,natural resonance,interfacial dipole polarization,and magnetic flux loss,are involved in these composites.Consequently,compared with pure residual carbon materials,this composite offers superior capabilities in microwave absorption.At 7.76GHz,the CFO/RC-2 composite achieves an impressive minimum reflection loss(RL_(min))of-43.99 dB with a thickness of 2.44 mm.Moreover,CFO/RC-3 demonstrates an effective absorption bandwidth(EAB)of up to 4.16 GHz,accompanied by a thickness of 1.18mm.This study revealed the remarkable capability of the composite to diminish electromagnetic waves,providing a new generation method for microwave absorbing materials of superior quality.

改进注意力机制嵌入PR-Net模型的水稻病害识别仿真

针对现有的CNN模型在水稻叶部病害的识别中准确率较低的问题,提出了一种结合并行结构和残差结构的混合卷积神经网络模型PRC-Net(parallel residual with coordinate attention network)。引入并行结构,提高卷积的感受野;结合残差结构,使特征信息完整的连续传递;在骨干模型PR-Net中嵌入改进的空间注意力机制,增强对不同尺度病斑特征信息的凝聚程度;为进一步提升病害识别的准确率,并减少模型的训练时间和推理时间,通过改变加权方式对模型结构进行优化。仿真结果表明:与InceptionResNetV2等分类模型相比,PRC-Net具有更少的训练参数、更短的训练时间和更高的识别精度,性能优于其他作物病害识别模型。

基于GRU-DRSN的双通道人体活动识别

人体活动识别(human activity recognizition, HAR)在医疗、军工、智能家居等领域有很大的应用空间。传统机器学习方法特征提取难度较大且精度不高。针对上述问题并结合传感器时序特性,提出了一种融合CBAM(convolutional block attention module)注意力机制的GRU-DRSN双通道并行模型,有效避免了传统串行模型因网络深度加深引起梯度爆炸和消失问题。同时并行结构使得两条支路具有相同的优先级,使用深度残差收缩网络(deep residual shrinkage network, DRSN)提取数据的深层空间特征,同时使用门控循环结构(gated recurrent unit, GRU)学习活动样本在时间序列上的特征,同时进行提取样本不同维度的特征,并通过CBAM模块进行特征的权重分配,最后通过Softmax层进行识别,实现了端对端的人体活动识别。使用公开数据集(wireless sensor data mining, WISDM)进行验证,模型平均精度达到了97.6%,与传统机器学习模型和前人所提神经网络模型相比,有更好的识别效果。

Structural properties of residual carbon in coal gasification fine slag and their influence on flotation separation and resource utilization:A review

Coal gasification fine slag(FS)is a typical solid waste generated in coal gasification.Its current disposal methods of stockpil-ing and landfilling have caused serious soil and ecological hazards.Separation recovery and the high-value utilization of residual carbon(RC)in FS are the keys to realizing the win-win situation of the coal chemical industry in terms of economic and environmental benefits.The structural properties,such as pore,surface functional group,and microcrystalline structures,of RC in FS(FS-RC)not only affect the flotation recovery efficiency of FS-RC but also form the basis for the high-value utilization of FS-RC.In this paper,the characteristics of FS-RC in terms of pore structure,surface functional groups,and microcrystalline structure are sorted out in accordance with gasification type and FS particle size.The reasons for the formation of the special structural properties of FS-RC are analyzed,and their influence on the flotation separation and high-value utilization of FS-RC is summarized.Separation methods based on the pore structural characterist-ics of FS-RC,such as ultrasonic pretreatment-pore-blocking flotation and pore breaking-flocculation flotation,are proposed to be the key development technologies for improving FS-RC recovery in the future.The design of low-cost,low-dose collectors containing polar bonds based on the surface and microcrystalline structures of FS-RC is proposed to be an important breakthrough point for strengthening the flotation efficiency of FS-RC in the future.The high-value utilization of FS should be based on the physicochemical structural proper-ties of FS-RC and should focus on the environmental impact of hazardous elements and the recyclability of chemical waste liquid to es-tablish an environmentally friendly utilization method.This review is of great theoretical importance for the comprehensive understand-ing of the unique structural properties of FS-RC,the breakthrough of the technological bottleneck in the efficient flotation separation of FS,and the expansion of the field of the high value-added utilization of FS-RC.

Evolution of microstructure and properties of a novel Ni-based superalloy during stress relief annealing

We discussed the decrease in residual stress,precipitation evolution,and mechanical properties of GH4151 alloy in different annealing temperatures,which were studied by the scanning electron microscope(SEM),high-resolution transmission electron microscopy(HRTEM),and electron backscatter diffraction(EBSD).The findings reveal that annealing processing has a significant impact on diminishing residual stresses.As the annealing temperature rose from 950 to 1150℃,the majority of the residual stresses were relieved from 60.1 MPa down to 10.9 MPa.Moreover,the stress relaxation mechanism transitioned from being mainly controlled by dislocation slip to a combination of dislocation slip and grain boundary migration.Meanwhile,the annealing treatment promotes the decomposition of the Laves,accompanied by the precipitation ofμ-(Mo_(6)Co_(7))starting at 950℃ and reaching a maximum value at 1050℃.The tensile strength and plasticity of the annealing alloy at 1150℃ reached the maximum(1394 MPa,56.1%)which was 131%,200%fold than those of the as-cast alloy(1060 MPa,26.6%),but the oxidation process in the alloy was accelerated at 1150℃.The enhancement in durability and flexibility is primarily due to the dissolution of the brittle phase,along with the shape and dispersal of theγ′phase.

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content.The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge.The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets,with the dataset’s imbalance significantly affecting the model’s performance.In the present study,a new model,referred to as UD-VLD(Unbalanced Dataset-VAE-LSTM-DRN),was proposed to address above problem.The proposed model is an encrypted traffic identification model for handling unbalanced datasets.The encoder of the variational autoencoder(VAE)is combined with the decoder and Long-short term Memory(LSTM)in UD-VLD model to realize the data enhancement processing of the original unbalanced datasets.The enhanced data is processed by transforming the deep residual network(DRN)to address neural network gradient-related issues.Subsequently,the data is classified and recognized.The UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique,thereby solving the processing problem for unbalanced datasets.The UD-VLD model was tested using the publicly available Tor dataset and VPN dataset.The UD-VLD model is evaluated against other comparative models in terms of accuracy,loss rate,precision,recall,F1-score,total time,and ROC curve.The results reveal that the UD-VLD model exhibits better performance in both binary and multi classification,being higher than other encrypted traffic recognition models that exist for unbalanced datasets.Furthermore,the evaluation performance indicates that the UD-VLD model effectivelymitigates the impact of unbalanced data on traffic classification.and can serve as a novel solution for encrypted traffic identification.

Disturbance failure mechanism of highly stressed rock in deep excavation:Current status and prospects

This article reviews the current status on the dynamic behavior of highly stressed rocks under disturbances.Firstly,the experimental apparatus,methods,and theories related to the disturbance dynamics of deep,high-stress rock are reviewed,followed by the introduction of scholars’research on deep rock deformation and failure from an energy perspective.Subsequently,with a backdrop of highstress phenomena in deep hard rock,such as rock bursts and core disking,we delve into the current state of research on rock microstructure analysis and residual stresses from the perspective of studying the energy storage mechanisms in rocks.Thereafter,the current state of research on the mechanical response and the energy dissipation of highly stressed rock formations is briefly retrospected.Finally,the insufficient aspects in the current research on the disturbance and failure mechanisms in deep,highly stressed rock formations are summarized,and prospects for future research are provided.This work provides new avenues for the research on the mechanical response and damage-fracture mechanisms of rocks under high-stress conditions.

Possibilities for the synthesis of superheavy element Z=121 in fusion reactions

Based on the dinuclear system model,the calculated evaporation residue cross sections matched well with the current experimental results.The synthesis of superheavy elements Z=121 was systematically studied through combinations of stable projectiles with Z=21-30 and targets with half-lives exceeding 50 d.The influence of mass asymmetry and isotopic dependence on the projectile and target nuclei was investigated in detail.The reactions^(254)Es(^(46)Ti,3n)^(297)121 and^(252)Es(^(46)Ti,3n)^(295)121 were found to be experimentally feasible for synthesizing superheavy element Z=121,with maximal evaporation residue cross sections of 6.619 and 4.123 fb at 219.9 and 223.9 MeV,respectively.

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.

Geophysical prediction of organic matter abundance in source rocks based on geochemical analysis:A case study of southwestern Bozhong Sag,Bohai Sea,China

The Bozhong Sag is the largest petroliferous sag in the Bohai Bay Basin,and the source rocks of Paleogene Dongying and Shahejie Formations were buried deeply.Most of the drillings were located at the structural high,and there were few wells that met good quality source rocks,so it is difficult to evaluate the source rocks in the study area precisely by geochemical analysis only.Based on the Rock-Eval pyrolysis,total organic carbon(TOC)testing,the organic matter(OM)abundance of Paleogene source rocks in the southwestern Bozhong Sag were evaluated,including the lower of second member of Dongying Formation(E_(3)d2L),the third member of Dongying Formation(E_(3)d_(3)),the first and second members of Shahejie Formation(E_(2)s_(1+2)),the third member of Shahejie Formation(E_(2)s_(3)).The results indicate that the E_(2)s_(1+2)and E_(2)s_(3)have better hydrocarbon generative potentials with the highest OM abundance,the E_(3)d_(3)are of the second good quality,and the E_(3)d2L have poor to fair hydrocarbon generative potential.Furthermore,the well logs were applied to predict TOC and residual hydrocarbon generation potential(S_(2))based on the sedimentary facies classification,usingΔlogR,generalizedΔlogR,logging multiple linear regression and BP neural network methods.The various methods were compared,and the BP neural network method have relatively better prediction accuracy.Based on the pre-stack simultaneous inversion(P-wave impedance,P-wave velocity and density inversion results)and the post-stack seismic attributes,the three-dimensional(3D)seismic prediction of TOC and S_(2)was carried out.The results show that the seismic near well prediction results of TOC and S_(2)based on seismic multi-attributes analysis correspond well with the results of well logging methods,and the plane prediction results are identical with the sedimentary facies map in the study area.The TOC and S_(2)values of E_(2)s_(1+2)and E_(2)s_(3)are higher than those in E_(3)d_(3)and E_(3)d_(2)L,basically consistent with the geochemical analysis results.This method makes up the deficiency of geochemical methods,establishing the connection between geophysical information and geochemical data,and it is helpful to the 3D quantitative prediction and the evaluation of high-quality source rocks in the areas where the drillings are limited.

Effect of integration of mechanical ball milling and flue gas desulfurization gypsum on dealkalization of bauxite residue

The synergistic impact of mechanical ball milling and flue gas desulfurization(FGD)gypsum on the dealkalization of bauxite residue was investigated through integrated analyses of solution chemistry,mineralogy,and microtopography.The results showed a significant decrease in Na_(2)O content(>30 wt.%)of FGD gypsum-treated bauxite residue after 30 min of mechanical ball milling.Mechanical ball milling resulted in differentiation of the elemental distribution,modification of the minerals in crystalline structure,and promotion in the dissolution of alkaline minerals,thus enhancing the acid neutralization capacity of bauxite residue.5 wt.%FGD gypsum combined with 30 min mechanical ball milling was optimal for the dealkalization of bauxite residue.

Corrosion behavior of co-gasification slag of furfural residue and coal on alumina-silica refractories

Gasification of furfural residue with coal can realize its efficient and clean utilization.But the high alkali metal content in furfural slag is easy to cause the corrosion of gasifier refractory.Two gasification coals with different silica alumina ratio and a furfural residue were selected in the study.The effects of furfural residue additions on corrosion of silica brick,corundum brick,high alumina brick and mullite brick were investigated by using XRD,SEM-EDS and Factsage Software,and the corrosion mechanism was analyzed.With increasing furfural residue addition,the permeability of the slags to high-aluminium-bearing refractories first decreases and then increases,while the permeability on silica brick shows a slight decrease trend.Leucite(KAlSi_(2)O_(6))with high-melting temperature is generated from the reaction of K_(2)O and SiO_(2)in slag with Al_(2)O_(3)in refractories after furfural residue is added,which hinders the infiltration of slag in refractories.Kaliophilite(KAlSiO_(4))of low-melting point is formed when K_(2)O content increases,and this contributes to the infiltration of slag in refractories.The acid-base reaction between slag and silica brick is distinctly occurred,more slag reacts with SiO_(2)in the silicon brick,resulting in a decrease in the amount of slag infiltrating into the silicon brick as furfural residue is added.The corrosion of silica brick is mainly caused by the acid-base reaction,while the corrosion of three alumina based refractory bricks of corundum,mullite and high alumina brick is determined by slag infiltration.A linear correlation between the percolation rate and slag viscosity is established,the slag permeability increases with decreasing viscosity,resulting in stronger permeability for the high Si/Al ratio slag with lower viscosity.

A short overview of the lead iodide residue impact and regulation strategies in perovskite solar cells

Lead iodide(PbI2) is a vital raw material for preparing perovskite solar cells(PSCs),and it not only takes part in forming the light absorption layer but also remains in the grain boundary as a passivator.In other words,the PbI2 content in the precursor and as formed film will affect the efficiency and stability of the PSCs.With moderate residual PbI2,it passivates the bulk/surface defects of perovskite,reduces the interfacial recombination,promotes the perovskite stability,minimizes the device hysteresis,and so on.Deficient PbI2 residue will reduce the interfacial passivation effect and device performance.In addition to facilitating the non-radiative recombination,over PbI2 residue can also lead to electronic insulation in the grain boundary and deteriorate the device performance.However,the impact and regulation of PbI2 residue on the device performance and stability is still not fully understood.Herein,a comprehensive and detailed review is presented by discussing the PbI2 residue impact and its regulation strategies(i.e., elimination,facilitation and conversion of the residue PbI2) to manipulate the PbI2 content,distribution and forms.Finally,we also show future outlooks in this field,with an aim to help further the progression of high-efficiency and stable PSCs.

A molecular insight into coke formation process of vacuum residue in thermal cracking reaction

Understanding the coking behaviors has been considered to be really essential for developing better vacuum residue processing technologies.A battery of thermal cracking tests of typical vacuum residue at 410℃ with various reaction time were performed to evaluate the coke formation process.The total yields of ideal components including naphtha,atmospheric gas oil(AGO)and vacuum gas oil(VGO)of thermal cracking reactions increased from 10.89%to 40.81%,and the conversion ratios increased from8.05%to 43.33%with increasing the reaction time from 10 to 70 min.The asphaltene content increased from 12.14%to a maximum of 22.39%and then decreased,and this maximum of asphaltene content occurred at the end of the coking induction period.The asphaltenes during the coking induction period,at the end and after coking induction period of those tested thermal cracking reactions were characterized to disclose the structure changing rules for coke formation process,and the coke formation pathways were discussed to reveal the coke formation process at molecular level.

Identifying the best common factor model via exploratory eactor analysis

Currently,there is no solid criterion for judging the quality of the estimators in factor analysis.This paper presents a new evaluation method for exploratory factor analysis that pinpoints an appropriate number of factors along with the best method for factor extraction.The proposed technique consists of two steps:testing the normality of the residuals from the fitted model via the Shapiro-Wilk test and using an empirical quantified index to judge the quality of the factor model.Examples are presented to demonstrate how the method is implemented and to verify its effectiveness.

Differential roles of seed and sprout regeneration in forest diversity and productivity after disturbance

Natural regeneration after disturbances is a key phase of forest development,which determines the trajectory of successional changes in tree species composition and diversity.Regenerating trees can originate from either seeds or sprouts produced by disturbed trees with sprouting ability.Although both regeneration strategies often develop and co-occur after a disturbance,they tend to affect forest development differently due to significant functional differences.However,the origin of tree regeneration is rarely distinguished in post-disturbance forest surveys and ecological studies,and the differential roles of seed and sprout regeneration in forest productivity and diversity remain poorly understood.To address these research gaps,we explored the role of sprout and seed regeneration in the formation of woody species diversity and above-ground biomass(AGB)productivity in early-stage forest development.Data were collected in two experimental forest stands in the Czech Republic,where trees were cut with varying intensities with the density of residual(uncut)trees ranging from 0 to 275 trees per hectare.All trees were mapped and their sizes were measured before cutting and then,either as a stump with sprouts or a residual tree,remeasured 11 years later.In addition,all tree saplings were mapped and measured 11 years after logging,and their origin(sprout or seed)was identified.To assess abundances and productivity,we estimated AGB of all2,685 sprouting stumps of 19 woody species and 504 generative(i.e.,seed origin)individuals of 16 woody species,using allometric equations.Mixed-effects models were used to analyze the effects of each regeneration strategy on woody species diversity and the total AGB under varying densities of residual trees.Nonmetric multidimensional scaling was used to evaluate the effect of regeneration strategies on species composition.AGB and diversity of sprouts were significantly higher than those of seed regeneration.Sprouts formed on average97.1%of the total regeneration AGB in H ady and 98.6%in Sobe s ice.The average species richness of sprouts was4.7 in H ady and 2.2 in Sob e sice,while the species richness of seed regeneration averaged 2.1 and 1.1 in H ady and Sob e sice,respectively.Increasing density of residual trees reduced AGB and diversity of both sprouts and seed regeneration,but seed regeneration was affected to a greater extent.Residual trees had an especially strong inhibitory effect on the establishment of seed regeneration.Consequently,seed-originated saplings were nearly absent in plots with high residual tree density,and abundant sprouts accounted for most of the AGB and diversity.However,unlike sprouts whose species composition resembled that of the original stand,seed regeneration brought in new species,enriching the stand?s overall species pool and beta diversity.Our results demonstrated differential roles of sprout and seed regeneration in the early stage of forest succession.Sprout regeneration was the main source of woody AGB productivity as well as species diversity,and its importance increased with the increasing density of standing mature trees.The results indicate the crucial yet previously underestimated role of sprout regeneration in post-disturbance forest dynamics.They suggest that the presence of residual mature trees,whether retained after partial cutting or undisturbed,can substantially suppress seed regeneration while the role of sprout regeneration in early succession becomes more distinctly evident.

Numerical modelling of post-failure behaviors of coal specimens

A modelling approach consisting of best-fit relations to estimate the post-yield strength parameters is presented for simulating post-peak behavior beyond the point of residual strength of coal pillars having different w/h ratios.The model was developed based on back-analysis of the complete stress-strain behavior of specimens belonging to six different Indian coal seams with different w/h ratios of 0.5 e13.5.It was found that the simultaneous degradation of the cohesion and friction angle of the Mohr-Coulomb rock material characterizes the post-peak strength behavior of the rock.The resulting expressions are simplistic as they require parameters that can be easily determined using uniaxial and triaxial compression results.Eventually,the developed model was validated by simulating the triaxial tests of coal specimens with different sizes under varying confining stresses and comparing its findings with the published test results.The study showed that its implementation in the numerical model could reproduce laboratory-observed mechanical response,deformation behavior,and failure mechanism very closely.

Constructing a stable interface on Ni-rich LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2) cathode via lactic acid-assisted engineering strategy

Ni-rich layered oxides are potential cathode materials for next-generation high energy density Li-ion batteries due to their high capacity and low cost.However,the inherently unstable surface properties,including high levels of residual Li compounds,dissolution of transition metal cations,and parasitic side reactions,have not been effectively addressed,leading to significant degradation in their electrochemical performance.In this study,we propose a simple and effective lactic acid-assisted interface engineering strategy to regulate the surface chemistry and properties of Ni-rich LiNi_(0.8)Co_(0.1)Mr_(0.1)O_(2) cathode.This novel surface treatment method successfully eliminates surface residual Li compounds,inhibits structural collapse,and mitigates cathode-electrolyte interface film growth.As a result,the lactic acidtreated LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2) achieved a remarkable capacity retention of 91.7% after 100 cycles at 0.5 C(25℃) and outstanding rate capability of 149.5 mA h g^(-1) at 10 C,significantly outperforming the pristine material.Furthermore,a pouch-type full cell incorporating the modified LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2) cathode demonstrates impressive long-term cycle life,retaining 81.5% of its capacity after 500 cycles at 1 C.More importantly,the thermal stability of the modified cathode is also dramatically improved.This study offers a valuable surface modification strategy for enhancing the overall performance of Ni-rich cathode materials.

Co-pyrolysis of Sewage Sludge with Distillation Residue: Kinetics Analysis via Iso-conversional Methods

This study explored the synergistic interaction of sewage sludge(SS)and distillation residue(DR)during co-pyrolysis for the optimized treatment of sewage sludge in cement kiln systems,utilizing thermogravimetric analysis(TGA)and thermogravimetric analysis with mass spectrometry(TGA-MS).The results reveal the coexisting synergistic and antagonistic effects in the co-pyrolysis of SS/DR.The synergistic effect arises from hydrogen free radicals in SS and catalytic components in ash fractions,while the antagonistic effect is mainly due to the melting of DR on the surface of SS particles during pyrolysis and the reaction of SS ash with alkali metals to form inert substances.SS/DR co-pyrolysis reduces the yielding of coke and gas while increasing tar production.This study will promote the reduction,recycling,and harmless treatment of hazardous solid waste.