Tracking direct and indirect impact on technology and policy of transformative research via ego citation network

Purpose:The disseminating of academic knowledge to nonacademic audiences partly relies on the transition of subsequent citing papers.This study aims to investigate direct and indirect impact on technology and policy originating from transformative research based on ego citation network.Design/methodology/approach:Key Nobel Prize-winning publications(NPs)in fields of gene engineering and astrophysics are regarded as a proxy for transformative research.In this contribution,we introduce a network-structural indicator of citing patents to measure technological impact of a target article and use policy citations as a preliminary tool for policy impact.Findings:The results show that the impact on technology and policy of NPs are higher than that of their subsequent citation generations in gene engineering but not in astrophysics.Research limitations:The selection of Nobel Prizes is not balanced and the database used in this study,Dimensions,suffers from incompleteness and inaccuracy of citation links.Practical implications:Our findings provide useful clues to better understand the characteristics of transformative research in technological and policy impact.Originality/value:This study proposes a new framework to explore the direct and indirect impact on technology and policy originating from transformative research.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

Cutting-Edge Challenges in Communication Technology and Computer Network Security

The rapid development of communication technology and computer networks has brought a lot of convenience to production and life,but it also increases the security problem.Information security has become one of the severe challenges faced by people in the digital age.Currently,the security problems facing the field of communication technology and computer networks in China mainly include the evolution of offensive technology,the risk of large-scale data transmission,the potential vulnerabilities introduced by emerging technology,and the dilemma of user identity verification.This paper analyzes the frontier challenges of communication technology and computer network security,and puts forward corresponding solutions,hoping to provide ideas for coping with the security challenges of communication technology and computer networks.

基于特征图像组合与改进ResNet-18的电能质量扰动识别方法

针对传统电能质量扰动(power quality disturbance,PQD)识别体系中单一图像特征信息受限与算法识别能力不足等问题,依据特征融合的思想,提出一种基于特征图像组合与改进ResNet-18的PQD识别方法。首先,对PQD信号进行变分模态分解(variational mode decomposition,VMD)得到一系列固有模态函数(intrinsic mode functions,IMFs)与残差分量;其次,将IMFs、残差分量、原始扰动信号与Subtract分量纵向拼接成分量矩阵,利用信号-图像转化方法生成特征分量彩色图;再次,对原始扰动信号进行连续小波变换(continuous wavelet transform,CWT)生成小波时-频图;最后,将特征分量彩色图与小波时-频图组合输入改进的六通道ResNet-18中训练学习并完成扰动识别。通过仿真对PQD识别方法进行分析并将其与目前常用识别体系进行比较。结果表明,所提方法具有较好的抗噪性能并且能够更好地提取PQD特征信息,达到更高的识别准确率。

Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.

基于SMOTE-IKPCA-SeNet深度迁移学习的小批量生产质量预测研究

随着智能制造技术的发展和客户个性化需求的增加,多品种小批量生产方式逐渐成为制造业的主流。面向大批量生产、以统计过程控制为核心的质量管理方式并不适用于小批量生产。针对复杂生产过程存在参数多、非线性和交互作用的问题,提出利用深度迁移学习的方式将历史生产数据作为源域迁移至小样本目标产品数据进行质量预测。首先,通过合成少数类过采样技术(synthetic minority over-sampling technique,SMOTE)和改进的核主成分分析(improved kernel principal component analysis,IKPCA)算法筛选源域和目标域的可迁移特征,这不仅兼顾了特征重要性和可迁移性,还减少了“负迁移”,提高了模型泛化能力;然后,采用结合通道注意力机制的卷积神经网络SeNet构建基于深度迁移学习的质量预测模型。仿真结果表明,随着目标域样本的增加,所提方法的预测准确性明显优于广泛采用的支持向量机建模方法。同时,所提可迁移特征筛选方法显著提高了深度迁移学习的质量预测效果,为复杂的小批量生产过程质量保证提供了新方法。

Rail Surface Defect Detection Based on Improved UPerNet and Connected Component Analysis

To guarantee the safety of railway operations,the swift detection of rail surface defects becomes imperative.Traditional methods of manual inspection and conventional nondestructive testing prove inefficient,especially when scaling to extensive railway networks.Moreover,the unpredictable and intricate nature of defect edge shapes further complicates detection efforts.Addressing these challenges,this paper introduces an enhanced Unified Perceptual Parsing for Scene Understanding Network(UPerNet)tailored for rail surface defect detection.Notably,the Swin Transformer Tiny version(Swin-T)network,underpinned by the Transformer architecture,is employed for adept feature extraction.This approach capitalizes on the global information present in the image and sidesteps the issue of inductive preference.The model’s efficiency is further amplified by the windowbased self-attention,which minimizes the model’s parameter count.We implement the cross-GPU synchronized batch normalization(SyncBN)for gradient optimization and integrate the Lovász-hinge loss function to leverage pixel dependency relationships.Experimental evaluations underscore the efficacy of our improved UPerNet,with results demonstrating Pixel Accuracy(PA)scores of 91.39%and 93.35%,Intersection over Union(IoU)values of 83.69%and 87.58%,Dice Coefficients of 91.12%and 93.38%,and Precision metrics of 90.85%and 93.41%across two distinct datasets.An increment in detection accuracy was discernible.For further practical applicability,we deploy semantic segmentation of rail surface defects,leveraging connected component processing techniques to distinguish varied defects within the same frame.By computing the actual defect length and area,our deep learning methodology presents results that offer intuitive insights for railway maintenance professionals.

Water source identification in mines combining LIF technology and ResNet

The problem of mine water source has always been an important hidden danger in mine safety production.The water source under the mine working face may lead to geological disasters,such as mine collapse and water disaster.The research background of mine water source identification involves many fields such as mining production,environmental protection,resource utilization and technological progress.It is a comprehensive and interdisciplinary subject,which helps to improve the safety and sustainability of mine production.Therefore,timely and accurate identification and control of mine water source is very important to ensure mine production safety.Laser-Induced Fluorescence(LIF)technology,characterized by high sensitivity,specificity,and spatial resolution,overcomes the time-consuming nature of traditional chemical methods.In this experiment,sandstone water and old air water were collected from the Huainan mining area as original samples.Five types of mixed water samples were prepared by varying their proportions,in addition to the two original water samples,resulting in a total of seven different water samples for testing.Four preprocessing methods,namely,MinMaxScaler,StandardScaler,Standard Normal Variate(SNV)transformation,and Centering Transformation(CT),were applied to preprocess the original spectral data to reduce noise and interference.CT was determined as the optimal preprocessing method based on class discrimination,data distribution,and data range.To maintain the original data features while reducing the data dimension,including the original spectral data,five sets of data were subjected to Principal Component Analysis(PCA)and Linear Discriminant Analysis(LDA)dimensionality reduction.Through comparing the clustering effect and Fisher's ratio of the first three dimensions,PCA was identified as the optimal dimensionality reduction method.Finally,two neural network models,CT+PCA+CNN and CT+PCA+ResNet,were constructed by combining Convolutional Neural Networks(CNN)and Residual Neural Networks(ResNet),respectively.When selecting the neural network models,the training time,number of iterative parameters,accuracy,and cross-entropy loss function in the classification problem were compared to determine the model best suited for water source data.The results indicated that CT+PCA+ResNet was the optimal approach for water source identification in this study.

Study on Quality Markers of Poria cocos Based on UPLC-Q-TOF-MS and Network Pharmacology Technology

[Objectives]To analyze the main chemical components of traditional Chinese medicine(TCM)Poria cocos by liquid chromatography-mass spectrometry,and explore the active components for P.cocos in the treatment of primary dysmenorrhea(PD)by network pharmacology to predict its quality markers(Q-marker).[Methods]Ultra performance liquid chromatography-quadrupole tandem time-of-flight mass spectrometry(UPLC-Q-TOF-MS)in positive and negative ion mode was used to collect high quality MS and MS/MS data of Poria cocos,and qualitative characterization of the components in Poria cocos was performed using Analyst TF 1.7.1 and PeakView 2.2 software with reference to internal databases and literature.Taking the above identified chemical components as the research object,we used network pharmacology to discover the potential effective components and their key targets of PD,and metabolic pathway enrichment analysis of the core targets was performed to screen the Q-marker of P.cocos based on the five principles of Q-marker of TCM.[Results]UPLC-Q-TOF-MS technique was used to identify 41 chemical components of P.cocos,including 3 amino acids,26 triterpenoids,4 lactones,7 organic acids and 1 adenosine.It was more likely to lose H 2O and CO 2 during cleavage and break at the carbonyl group.The triterpenoids were mainly in the form of[M-H]-peaks in negative ion mode,which was easy to lose some structural fragments such as H 2O,CH 3COOH,CH 4,CO 2,etc.Further network pharmacological analysis showed that 302 targets of chemical components of P.cocos,518 targets of PD,28 common targets of component and disease,and 27 core targets such as PTGS2,ESR1,TNF,IL1B were observed by PPI interactions network analysis.451 biological processes such as hormone response and inflammatory response regulation were obtained by GO enrichment analysis.KEGG enrichment analysis showed that 89 pathways including PI3K/Akt signaling pathway,IL-17 signaling pathway and TNF signaling pathway were obtained.The connectivity value of components was analyzed.The core components with the connectivity value greater than 10,including poricoic acid A,polyporenic acid,polyporenic acid C,and 25-hydroxy-3-epidehydrotumoric acid were selected,while the key targets with the connectivity value greater than 15 included TNF,PTGS2,IL1B and CASP3.Molecular docking between core components and key targets was performed,and most of the docking energy was less than-5 kcal/mol,indicating that the binding between the active components and target proteins of P.cocos was relatively stable,so 23 active components of P.cocos were determined.Following the five principles of Q-marker,four possible Q-markers of P.cocos were predicted,including poricoic acid A,pachymic acid,polyporenic acid C,and 25-hydroxy-3-epidehydrotumoric acid.[Conclusions]P.cocos was mainly composed of triterpenoids,its effect on the treatment of PD may be achieved mainly by poricoic acid A,pachymic acid,polyporenic acid C,and 25-hydroxy-3-epi-dehydrotumoric acid acting on PTGS2,ESR1,TNF,IL1B and other targets to regulate PI3K/Akt signaling pathway,IL-17 signaling pathway,TNF signaling pathway,etc.Based on these active components,poricoic acid A,pachymic acid,polyporenic acid C,and 25-hydroxy-3-epi-dehydrotumoric acid could be taken as Q-markers of P.cocos,which provided a solid basis for further improving the quality standard of P.cocos.

基于Pyramid-Attention-U-Net深度学习模型的实时拓扑优化设计

拓扑优化设计可为现代工程提供具有卓越热学、力学及声学等多学科性能的创新型结构,移动变形组件法(Moving Morphable Components,MMC)以其独特的显示拓扑优化方法备受青睐,MMC法通过一系列可移动变形的显示组件间的移动、变形、重叠实现边界演化以完成结构优化的目的。该文利用椭圆形初始组件替代原有直线型骨架厚度二次变化组件进行拓扑优化。在减少设计变量次数的同时,可缩短一定的计算时间,但在实际计算中,随着初始组件单元数增加,中间迭代计算过程时间依旧相对较多,为精确实时获取拓扑优化构型,该文引入Pyramid-Attention-U-Net(PA-U-Net)深度学习模型加速优化设计,避免中间迭代计算过程。研究结果表明:该方法不仅在可忽略的计算时间内准确实时获取不同参数下的初始组件拓扑构型,而且准确率可达90.89%,高于其他深度学习网络模型。同时这种将深度学习与拓扑优化方法有机结合的形式在大型工程结构优化设计中具有广阔的应用前景。

Analysis of Intelligent Construction Technology of Building Prefabricated Components Based on BIM

In this paper,the intelligent construction of prefabricated components is analyzed based on building information modeling(BIM).It includes an overview of BIM-based prefabricated components and intelligent construction,intelligent production lines in BIM-based intelligent construction systems,and analysis of the application of intelligent manufacturing in BIM-based prefabricated components.It was found that the determination of construction goals,the establishment of intelligent construction systems,and the application of intelligent construction systems are all areas that need to be emphasized in producing prefabricated building components through intelligent construction.It is hoped that this analysis can provide some reference for the application of intelligent construction and the improvement of the quality of prefabricated building components.

Nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in aerospace community:a comparative analysis

The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,such as thin-walled structures,microchannels,and complex surfaces.Mechanical machining is the main material removal process for the vast majority of aerospace components.However,many problems exist,including severe and rapid tool wear,low machining efficiency,and poor surface integrity.Nontraditional energy-assisted mechanical machining is a hybrid process that uses nontraditional energies(vibration,laser,electricity,etc)to improve the machinability of local materials and decrease the burden of mechanical machining.This provides a feasible and promising method to improve the material removal rate and surface quality,reduce process forces,and prolong tool life.However,systematic reviews of this technology are lacking with respect to the current research status and development direction.This paper reviews the recent progress in the nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in the aerospace community.In addition,this paper focuses on the processing principles,material responses under nontraditional energy,resultant forces and temperatures,material removal mechanisms,and applications of these processes,including vibration-,laser-,electric-,magnetic-,chemical-,advanced coolant-,and hybrid nontraditional energy-assisted mechanical machining.Finally,a comprehensive summary of the principles,advantages,and limitations of each hybrid process is provided,and future perspectives on forward design,device development,and sustainability of nontraditional energy-assisted mechanical machining processes are discussed.

基于稠密连接的通道混合式PCANet的低分辨率有遮挡人脸识别

针对低分辨率有遮挡人脸识别问题提出了基于稠密连接的通道混合式主成分分析网络(DCH-PCANet)。现有的PCANet模型的卷积层只使用了通道无关式卷积(CIC)。通道无关式卷积由于未考虑特征图在通道方向上的相关性,可以更好地凸显单个特征图的局部纹理特征,对于补偿因低分辨率、遮挡等因素导致的特征损失具有重要意义,但也会强化遮挡区域的特征,从而放大坏特征的影响范围;而通道相关式卷积(CDC)由于充分考虑了各特征图在通道方向上的相关性,可以较好地抑制坏特征的作用,形成较为稀疏的特征图。在PCANet中添加了基于通道相关式卷积的特征图提取分支,形成了通道混合式PCANet;并且引入了稠密连接,以充分利用低阶特征提升有遮挡图像识别的鲁棒性。针对如下4种数据集进行了实验:受控环境、真实遮挡和模拟低分辨率的人脸数据集(AR人脸数据集),非受控环境、真实遮挡和模拟低分辨率的人脸数据集(MFR2和PKUMasked-Face),非受控环境、真实遮挡和真实低分辨率的人脸数据集(自建数据集)。实验结果表明,与现有方法相比,所提出的基于稠密连接的通道混合式PCANet具更好的遮挡鲁棒性和低分辨率鲁棒性,可以作为前沿方法的有效补充,提升其识别性能。

Winter wheat yield improvement by genetic gain across different provinces in China

The replacement of winter wheat varieties has contributed significantly to yield improvement worldwide,with remarkable progress in China.Drawing on two sets of data,production yield from the National Bureau of Statistics of China and experimental yield from literature,this study aims to(1)illustrate the increasing patterns of production yield among different provinces from 1978 to 2018 in China,(2)explore the genetic gain in yield and yield relevant traits through the variety replacement based on experimental yield from 1937 to 2016 in China,and(3)compare the yield gap between experimental yield and production yield.The results show that both the production and experimental yields significantly increased along with the variety replacement.The national annual yield increase ratio for the production yield was 1.67%from 1978 to 2018,varying from 0.96%in Sichuan Province to 2.78%in Hebei Province;such ratio for the experimental yield was 1.13%from 1937 to 2016.The yield gap between experimental and production yields decreased from the 1970s to the 2010s.This study reveals significant increases in some yield components consequent to variety replacement,including thousand-grain weight,kernel number per spike,and grain number per square meter;however,no change is shown in spike number per square meter.The biomass and harvest index consistently and significantly increased,whereas the plant height decreased significantly.

美国国家教育技术计划的政策主题、目标与工具变迁--基于NETP 1996-2024年的文本研究

教育数字化转型是推动未来教育发展与变革的关键。美国通过制定并调整教育技术政策以培养具有技术素养的创新人才提升国家竞争力。文章基于政策文本分析视角,构建了包括政策主题、政策目标与政策内容三个维度的分析框架,运用政策文献量化分析法对美国联邦教育部教育技术规划办公室于1996-2024年发布的7份关于教育技术与教育数字化发展的战略规划性文件进行整体分析,研究结论如下:在政策主题维度,政策主题的针对性与可操作性逐渐增强;政策目标维度,政策目标的内涵与协同性不断丰富与提升;政策工具维度,供给型、环境型和需求型政策工具使用较为均衡,在不同教育技术内容要素中的政策工具使用各有偏好与侧重。美国教育技术政策的变迁动向与行动逻辑为我国数字教育政策发展提供借鉴参考。

U-Net Models for Representing Wind Stress Anomalies over the Tropical Pacific and Their Integrations with an Intermediate Coupled Model for ENSO Studies

El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been developed to simulate and predict it.In some simplified coupled ocean-atmosphere models,the relationship between sea surface temperature(SST)anomalies and wind stress(τ)anomalies can be constructed by statistical methods,such as singular value decomposition(SVD).In recent years,the applications of artificial intelligence(AI)to climate modeling have shown promising prospects,and the integrations of AI-based models with dynamical models are active areas of research.This study constructs U-Net models for representing the relationship between SSTAs andτanomalies in the tropical Pacific;the UNet-derivedτmodel,denoted asτUNet,is then used to replace the original SVD-basedτmodel of an intermediate coupled model(ICM),forming a newly AI-integrated ICM,referred to as ICM-UNet.The simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific.In the ocean-only case study,theτUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM,the results of which also indicate reasonable simulations of typical ENSO events.These results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling studies.Furthermore,the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.

Advancing approach and toolbox in optimization of chloroplast genetic transformation technology

Chloroplast is a discrete,highly structured,and semi-autonomous cellular organelle.The small genome of chloroplast makes it an up-and-coming platform for synthetic biology.As a special means of synthetic biology,chloroplast genetic engineering shows excellent potential in reconstructing various sophisticated metabolic pathways within the plants for specific purposes,such as improving crop photosynthetic capacity,enhancing plant stress resistance,and synthesizing new drugs and vaccines.However,many plant species exhibit limited efficiency or inability in chloroplast genetic transformation.Hence,new transformation technologies and tools are being constantly developed.In order to further expand and facilitate the application of chloroplast genetic engineering,this review summarizes the new technologies in chloroplast genetic transformation in recent years and discusses the choice of appropriate synthetic biological elements for the construction of efficient chloroplast transformation vectors.

A frequency servo SoC with output power stabilization loop technology for miniaturized atomic clocks

A frequency servo system-on-chip(FS-SoC)featuring output power stabilization technology is introduced in this study for high-precision and miniaturized cesium(Cs)atomic clocks.The proposed power stabilization loop(PSL)technique,incorporating an off-chip power detector(PD),ensures that the output power of the FS-SoC remains stable,mitigating the impact of power fluctuations on the atomic clock's stability.Additionally,a one-pulse-per-second(1PPS)is employed to syn-chronize the clock with GPS.Fabricated using 65 nm CMOS technology,the measured phase noise of the FS-SoC stands at-69.5 dBc/Hz@100 Hz offset and-83.9 dBc/Hz@1 kHz offset,accompanied by a power dissipation of 19.7 mW.The Cs atomic clock employing the proposed FS-SoC and PSL obtains an Allan deviation of 1.7×10^(-11) with 1-s averaging time.

Active components of Bupleuri Radix in the treatment of schizophrenia analyzed by network pharmacology and clinical verification

Background:Bupleuri Radix is a common Chinese medicinal material in traditional Chinese medicine.Currently,the therapeutic effect of treating schizophrenia is relatively well understood.However,there are fewer studies examining the underlying mechanisms of its treatment.The objective of the study was to investigate the primary mechanisms of Bupleuri Radix in treating schizophrenia through network pharmacology and clinical validation.Method:Network pharmacology revealed possible molecular mechanisms,followed by clinical verification.Sixty-seven schizophrenia patients undergoing treatment at the Hunan Brain Hospital between October and November 2022 were recruited and randomly divided into the olanzapine group and the olanzapine+Bupleuri Radix group.Additionally,32 healthy people undergoing physical examinations during the same period were included as the control group.The patient’s positive and negative symptom scale scores were compared.qPCR was used to detect the mRNA expression levels of ESR1,mTOR,EIF4E,and SMAD4 in peripheral blood.Results:Through network pharmacological analysis,it was concluded in this study that Bupleuri Radix might regulate the mTOR,PI3K-Akt,and HIF-1 signaling pathways.Clinical experiments indicated that compared with before treatment,the positive and negative symptom scale scores and total scores of the two treatment groups were significantly decreased after treatment(P<0.01).In addition,the positive and negative symptom scale scores and total scores in the olanzapine+Bupleuri Radix group were significantly decreased(P<0.01)compared to the olanzapine group after treatment.Before treatment,ESR1 mRNA expression levels in peripheral blood were significantly higher in the two treatment groups than in the control group,whereas the mRNA expression levels of mTOR,EIF4E,and SMAD4 in peripheral blood were significantly lower(P<0.01).The mRNA expression levels of mTOR,EIF4E,and SMAD4 in peripheral blood were significantly higher after therapy than before treatment,whereas the mRNA expression levels of ESR1 in peripheral blood were significantly lower(P<0.01).After therapy,the olanzapine+Bupleuri Radix group’s mRNA expression levels of mTOR,EIF4E,and SMAD4 were significantly higher than those of the olanzapine group,whereas the mRNA expression levels of ESR1 were significantly lower(P<0.01).Conclusion:The mechanism of Bupleuri Radix’s therapeutic efficacy in schizophrenia may involve the up-regulation of mTOR,EIF4E,and SMAD4 mRNA expression and the down-regulation of ESR1 mRNA expression in peripheral blood.

Sodium Nitrate Passivation as a Novel Insulation Technology for Soft Magnetic Composites

Sodium nitrate passivation has been developed as a new insulation technology for the production of FeSiAl soft magnetic composites (SMCs). In this work, the evolution of coating layers grown at different pH values is investigated involving analyses on their composition and microstructure. An insulation coating obtained using an acidic NaNO_(3) solution is found to contain Fe2O_(3), SiO_(2), Al2O_(3), and AlO(OH). The Fe2O_(3) transforms into Fe3O4 with weakened oxidizability of the NO_(3)– at an elevated pH, whereas an alkaline NaNO_(3) solution leads to the production of Al2O_(3), AlO(OH), and SiO_(2). Such growth is explained from both thermodynamic and kinetic perspectives and is correlated to the soft magnetic properties of the FeSiAl SMCs. Under tuned passivation conditions, optimal performance with an effective permeability of 97.2 and a core loss of 296.4 mW∙cm−3 is achieved at 50 kHz and 100 mT.