High adsorption selectivity of activated carbon and carbon molecular sieve boosting CO_(2)/N_(2) and CH_(4)/N_(2) separation

Flue gas and coal bed methane are two important sources of greenhouse gases.Pressure swing adsorption process has a wide range of application in the field of gas separation,and the selection of adsorbent is crucial.In this regard,in order to assess the better adsorbent for separating CO_(2) from flue gas and CH_(4) from coal bed methane,adsorption isotherms of CO_(2),CH_(4) and N_(2) on activated carbon and carbon molecular sieve are measured at 303.15,318.15 and 333.15 K,and up to 250 kPa.The experimental data fit better with Langmuir 2 compared to Langmuir 3 and Langmuir-Freundlich models,and Clausius-Clapeyron equation was used to calculate the isosteric heat.Both the order of the adsorbed amount and the adsorption heat on the two adsorbents are CO_(2)>CH_(4)>N_(2).The adsorption kinetics are calculated by the pseudo-first kinetic model,and the order of adsorption rates on activated carbon is N_(2)-CH_(4)>CO_(2),while on carbon molecular sieve,it is CO_(2)-N_(2)>CH_(4).It is shown that relative molecular mass and adsorption heat are the primary effect on kinetics for activated carbon,while kinetic diameter is the main resistance factor for carbon molecular sieve.Moreover,the adsorption selectivity of CH_(4)/N_(2) and CO_(2)/N_(2) were estimated with the ideal adsorption solution theory,and carbon molecular sieve performed best at 318.15 K for both CO_(2) and CH_(4) separation.The study suggested that activated carbon is a better choice for separating flue gas and carbon molecular sieve can be a strong candidate for separating coal bed methane.

Approaches to Improving Selectivity During Photoelectrochemical Transformation of Small Molecules

Photoelectrochemical (PEC) small-molecule oxidation can selectively transform substrates into high-value-added fine chemicals and increase the rate of cathode hydrogen evolution. Nevertheless, achieving high-selectivity PEC oxidation of small molecules to produce specific products is a very challenging task. In general, selectivity can be improved by changing the surface catalyticsites of the photoanode and modulating the interfacial environments of the reactions. Herein, recent advances in approaches to improving selective PEC oxidation of small molecules are introduced. We first briefly discuss the basic concept and fundamentals of small-molecule PEC oxidation. The reported approaches to improving the performance of selective PEC oxidation of small molecules are highlighted from two aspects: (1) changing the surface properties of photoanodes by selecting suitable materials or modifying the photoanodes and (2) mediating the oxidation reactions using redox mediators. The PEC oxidation mechanism of these studies is emphasized. We also discuss the challenges in this research direction and offer a perspective on the further development of selective PEC-based small-molecule transformation.

Enhancing selectivity in acidic CO_(2) electrolysis:Cation effects and catalyst innovation

The electrochemical reduction of CO_(2)(eCO_(2)R)under ambient conditions is crucial for reducing carbon emissions and achieving carbon neutrality.Despite progress with alkaline and neutral electrolytes,their efficiency is limited by(bi)carbonates formation.Acidic media have emerged as a solution,addressing the(bi)carbonates challenge but introducing the issue of the hydrogen evolu-tion reaction(HER),which reduces CO_(2) conversion efficiency in acidic environments.This review focuses on enhancing the selectivity of acidic CO_(2) electrolysis.It commences with an overview of the latest advancements in acidic CO_(2) electrolysis,focusing on product selectivity and electrocatalytic activity enhancements.It then delves into the critical factors shaping selectivity in acidic CO_(2) electrolysis,with a special emphasis on the influence of cations and catalyst design.Finally,the research challenges and personal perspectives of acidic CO_(2) electrolysis are suggested.

Laser-Constructing 3D Copper Current Collector with Crystalline Orientation Selectivity for Stable Lithium Metal Batteries

The practical application of lithium(Li)metal anodes in high-capacity batteries is impeded by the formation of hazardous Li dendrites.To address this challenge,this research presents a novel methodology that combines laser ablation and heat treatment to precisely induce controlled grain growth within laser-structured grooves on copper(Cu)current collectors.Specifically,this approach enhances the prevalence of Cu(100)facets within the grooves,effectively lowering the overpotential for Li nucleation and promoting preferential Li deposition.Unlike approaches that modify the entire surface of collectors,our work focuses on selectively enhancing lithiophilicity within the grooves to mitigate the formation of Li dendrites and exhibit exceptional performance metrics.The half-cell with these collectors maintains a remarkable Coulombic efficiency of 97.42%over 350 cycles at 1 mA cm^(−2).The symmetric cell can cycle stably for 1600 h at 0.5 mA cm^(−2).Furthermore,when integrated with LiFePO4 cathodes,the full-cell configuration demonstrates outstanding capacity retention of 92.39%after 400 cycles at a 1C discharge rate.This study introduces a novel technique for fabricating selective lithiophilic three-dimensional(3D)Cu current collectors,thereby enhancing the performance of Li metal batteries.The insights gained from this approach hold promise for enhancing the performance of all laser-processed 3D Cu current collectors by enabling precise lithiophilic modifications within complex structures.

Tuning the product selectivity of dimethyl oxalate hydrogenation over WO_(x) modified Cu/SiO_(2) catalysts

Product selectivity and reaction pathway are highly dependent on surface structure of heterogeneous catalysts.For vapor-phase hydrogenation of dimethyl oxalate(DMO),"EG route"(DMO→methyl glycolate(MG)ethylene glycol(EG)→ethanol(ET))and"MA route"(DMO→MG→methyl acetate(MA))were proposed over traditional Cu based catalysts and Mo-based or Fe-based catalysts,respectively.Herein,tunable yield of ET(93.7%)and MA(72.1%)were obtained through different reaction routes over WO_(x) modified Cu/SiO_(2) catalysts,and the corresponding reaction route was further proved by kinetic study and in-situ DRIFTS technology.Mechanistic studies demonstrated that H_(2) activation ability,acid density and Cu-WO_(x) interaction on the catalysts were tuned by regulating the surface W density,which resulted in the different reaction pathway and product selectivity.What's more,high yield of MA produced from DMO hydrogenation was firstly reported with the H_(2) pressure as low as 0.5 MPa.

An mmWave Dual-Band Integrated Substrate Gap Waveguide Single Cavity Filter with Frequency Selectivity

A novel dual-band ISGW cavity filter with enhanced frequency selectivity is proposed in this paper by utilizing a multi-mode coupling topology.Its cavity is designed to control the number of modes,and then the ports are determined by analyzing the coupling relationship between these selected modes.By synthesizing the coupling matrix of the filter,a nonresonating node(NRN)structure is introduced to flexibly tune the frequency of modes,which gets a dualband and quad-band filtering response from a tri-band filter no the NRN.Furthermore,a frequency selective surface(FSS)has been newly designed as the upper surface of the cavity,which significantly improves the bad out-of-band suppression and frequency selectivity that often exists in most traditional cavity filter designs and measurements.The results show that its two center frequencies are f01=27.50 GHz and f02=32.92GHz,respectively.Compared with the dual-band filter that there is no the FSS metasurface,the out-of-band suppression level is improved from measured 5 dB to18 dB,and its finite transmission zero(FTZ)numbers is increased from measured 1 to 4 between the two designed bands.Compared with the tri-band and quadband filter,its passband bandwidth is expanded from measured 1.17%,1.14%,and 1.13% or 1.31%,1.50%,0.56%,and 0.57% to 1.71% and 1.87%.In addition,the filter has compact,small,and lightweight characteristics.

An efficient strategy for the preparation of MIL-53(Al)-NH_(2)membranes with high ion selectivity and desalination performance

The efficient extraction of sodium(Na^(+))and lithium(Li^(+))from seawater and salt lakes is increasingly demanding due to their great application value in chemical industries.However,coexisting cations such as divalent calcium(Ca^(2+))and magnesium(Mg^(2+))ions are at the subnanometer scale in diameter,similar to target monovalent ions,making ion separation a great challenge.Here,we propose a simple and fast secondary growth method for the preparation of MIL-53(Al)-NH_(2)membranes on the surface of anodic aluminum oxide.Such membranes contain angstrom-scale(~7Å)channels for the entrance of small monovalent ions and water molecules,endowing the selectivities for monovalent cations over divalent cations and water over salt molecules.The resulting high-connectivity MIL-53(Al)-NH_(2)membranes exhibit excellent ion separation performance(a selectivity of 121.42 for Na^(+)/Ca^(2+)and 93.81 for Li^(+)/Mg^(2+))and desalination performance(a water/salt selectivity of up to 5196).This work highlights metal–organic framework membranes as potential candidates for realizing ion separation and desalination in liquid treatment.

基于FS-SIA的毁伤预测神经网络超参数优化方法

针对毁伤预测中神经网络超参数设置及调试过程较为复杂的问题,提出一种基于特征选择结合群体智能(feature selection and swarm intelligence algorithm,FS-SIA)的超参数优化方法,用于在毁伤预测中对神经网络进行超参数的搜索和优化。首先,通过多种特征排序方法确定毁伤特征的重要性,选取公共的特征偏序子集用于模型训练。其次,针对具体的神经网络模型,分别采用多种群体智能算法进行超参数的搜索和优化。最后,得出特征集性能最优的超参数训练模型。实验结果表明,相较于未经特征排序而单纯采用群体智能算法的其他超参数优化模型,所提方法在毁伤预测中具有更快的收敛速度和更高的准确率。

基于YOLOv8改进的跌倒检测算法:CASL-YOLO

跌倒对老年人危害极大,是我国65岁以上老年人致残和伤害死亡的首要原因.然而,目前主流的跌倒检测技术受环境的干扰较大,在物体遮挡、光照变化等复杂场景下的检测准确率较低,且模型的参数量和计算量较高,导致成本居高不下,不能很好地部署应用于实际生活场景.针对上述问题,提出了一种在复杂环境下轻量级的基于YOLOv8模型改进的跌倒检测算法:CASL-YOLO.首先,该模型引入空间深度卷积(SPD-Conv)模块替代传统卷积模块,通过对每个特征映射进行卷积操作,保留通道维度中的全部信息,从而提高模型在低分辨率图像和小物体检测方面的性能;其次,引入基于位置信息的注意力机制,以捕获跨通道、方向和位置感知的信息,从而更准确地定位和识别人体目标;最后,在特征提取模块中引入选择性大卷积核(LSKNet)动态调整感受野,以有效处理跌倒检测场景中的复杂环境信息,提高网络的感知能力和检测精度.实验结果表明,在公开的Human Fall数据集上,CASL-YOLO的mAP@0.5达到96.8%,优于基线YOLOv8n,同时模型仅有3.4×MiB的参数量和11.7×10~9的计算量.相比其他检测算法,CASL-YOLO在参数量和计算量小幅增加的情况下,实现了更高的精度和性能,同时满足实际场景的部署要求.

基于Vision Transformer的车辆重识别模型优化

针对车辆重识别任务中样本类内差异性大和类间相似度高的问题,提出了一种Vision Transformer框架下的车辆重识别方法。设计一种关键区域选择模块,整合Transformer中注意力分数矩阵,加强车辆的具有辨别性区域的关注程度,减小局部区域过度集中的注意力权重;构建一种包含对比损失和中心损失的混合损失函数,对比损失函数的引入增强了模型捕捉和比较样本之间的差异的能力,中心损失使得同一类别的样本更加紧密地聚集在一起,增强类间样本的区分度。实验结果验证了其有效性。

基于数据挖掘技术的拖拉机发动机故障诊断

拖拉机是农业生产的重要工具,发动机是其核心部件,如发动机出现故障,将会直接影响农业生产效率和产量。为此,提出了一种使用数据挖掘技术进行拖拉机发动机故障诊断的方法。利用了机器学习技术和统计学,首先针对拖拉机田间运行信号噪音较大的问题,引入小波阈值去噪的方法;其次,基于卷积神经网络模型,引入一种注意力机制,提高故障诊断准确率,并通过对拖拉机传感器数据进行分析,可以帮助诊断和预测发动机故障;最后,通过实验结果验证了算法的有效性。研究结果不仅可以提高故障的准确性和效率,还能够节约维修成本和提高机器的利用率,具有较高的应用价值。

基于机器学习的镍基单晶高温合金蠕变寿命预测模型研究

构建合适的镍基单晶合金蠕变寿命预测模型,对于我国航空发动机叶片设计、强度分析和寿命预测具有重要意义。采用多项式回归、最近邻回归、支持向量机回归、决策树回归四种机器学习算法,建立镍基单晶高温合金蠕变寿命与合金成分、微观组织和蠕变工艺参数的关系模型,为镍基单晶高温合金的蠕变性能调控提供了新方法。基于蠕变寿命预测模型,系统地比较了四种算法和特征选择对模型性能的影响。结果表明,支持向量机回归模型的预测结果最优,相关性较高的四个特征依次为γ′固溶温度、Ta、W、Re。研究结果可为获得更有效的镍基单晶高温合金蠕变性能预测方法提供参考。

南美白对虾多密度高通量液相分型技术芯片设计与评估

针对南美白对虾育种研究中开展大规模全基因组重测序成本非常昂贵的问题,本研究对来自两个人工选择品种和四个市场领先公司的共180只南美白对虾进行全基因组重测序及全基因组单核苷酸多态性(Single nucleotide polymorphisms,SNPs)标记开发,并设计了12k、46k和92k三种密度规格的高通量液相分型技术芯片。对三款芯片性能进行评估的结果显示,三款芯片分别覆盖了11618、20055和20056个基因,基因覆盖度分别为43.50%、75.08%和75.09%;位于基因区位点分别为11618、35071和65138,占芯片总位点93.15%、76.24%和70.80%。次等位基因频率(Minor allele frequency,MAF)分别为0.27±0.11、0.34±0.12和0.27±0.12。三款芯片的位点在染色体上均匀分布,其中51.11%、76.63%和93.87%的位点平均间距范围为1~50 kB,均覆盖了MAPK、mTOR、Wnt和NOD-like受体等多个参与生长免疫功能的信号通路。本研究为南美白对虾HD-Marker体系建立提供了基础,从而为全基因组选择育种、种质资源评估、数量性状位点(Quantitative trait locus,QTL)定位和全基因组关联分析(Genome-wide association study,GAWS)提供技术支持。

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

Small heat shock protein B8:from cell functions to its involvement in diseases and potential therapeutic applications

Heat shock protein family B(small)member 8(HSPB8)is a 22 kDa ubiquitously expressed protein belonging to the family of small heat shock proteins.HSPB8 is involved in various cellular mechanisms mainly related to proteotoxic stress response and in other processes such as inflammation,cell division,and migration.HSPB8 binds misfolded clients to prevent their aggregation by assisting protein refolding or degradation through chaperone-assisted selective autophagy.In line with this function,the pro-degradative activity of HSPB8 has been found protective in several neurodegenerative and neuromuscular diseases characterized by protein misfolding and aggregation.In cancer,HSPB8 has a dual role being capable of exerting either a pro-or an anti-tumoral activity depending on the pathways and factors expressed by the model of cancer under investigation.Moreover,HSPB8 exerts a protective function in different diseases by modulating the inflammatory response,which characterizes not only neurodegenerative diseases,but also other chronic or acute conditions affecting the nervous system,such as multiple sclerosis and intracerebellar hemorrhage.Of note,HSPB8 modulation may represent a therapeutic approach in other neurological conditions that develop as a secondary consequence of other diseases.This is the case of cognitive impairment related to diabetes mellitus,in which HSPB8 exerts a protective activity by assuring mitochondrial homeostasis.This review aims to summarize the diverse and multiple functions of HSPB8 in different pathological conditions,focusing on the beneficial effects of its modulation.Drug-based and alternative therapeutic approaches targeting HSPB8 and its regulated pathways will be discussed,emphasizing how new strategies for cell and tissue-specific delivery represent an avenue to advance in disease treatments.

Pancreatic stent improves the success rate of needle-knife papillotomy in patients with difficult biliary cannulation

BACKGROUND Needle-knife precut papillotomy(NKP)is typically performed freehand.However,it remains unclear whether pancreatic stent(PS)placement can improve the outcomes of NKP.AIM To explore whether PS placement improves the success rate of NKP in patients with difficult biliary cannulation.METHODS This single-center retrospective study included 190 patients who underwent NKP between January 2017 and December 2021 after failed conventional biliary cannulation.In cases with incidental pancreatic duct cannulation during conventional biliary cannulation,the decision for pre-NKP PS placement was made at the endoscopist's discretion.The primary outcome was the difference in the NKP success rate between patients with and without PS placement;the secondary outcome was the adverse event rate.RESULTS Among the 190 participants,82 received pre-NKP PS(PS-NKP group)whereas 108 did not[freehand or freehand NKP(FH-NKP)group].Post-NKP selective biliary cannulation was successful in 167(87.9%)patients,and the PS-NKP had a significantly higher success rate than the FH-NKP group(93.9%vs 83.3%,P=0.027).The overall adverse event rates were 7.3%and 11.1%in the PS-NKP and FH-NKP groups,respectively(P=0.493).A periampullary diverticulum(PAD)and significant intraoperative bleeding during NKP were independently associated with NKP failure;however,a pre-NKP PS was the only predictor of NKP success.Among the 44 participants with PADs,the PS-NKP group had a non-significantly higher NKP success rate than the FH-NKP group(87.5%and 65%,respectively;P=0.076).CONCLUSION PS significantly improved the success rate of NKP in patients with difficult biliary cannulation.

纤维肌痛综合征生物标记物的筛选及免疫细胞浸润分析

背景:纤维肌痛综合征作为常见风湿病,其发病与中枢敏化及免疫异常有关,但具体过程尚未阐明,缺乏特异性诊断标志物,不断探索该病的发病机制具有重要的临床意义。目的:基于加权基因共表达网络分析(WGCNA)等生物信息学方法和机器学习算法筛选纤维肌痛综合征潜在的诊断相关标志基因,并分析其免疫细胞浸润特征。方法:对来自基因表达综合数据库(GEO)的纤维肌痛综合征数据集转录谱进行差异分析和WGCNA分析,整合筛选出差异共表达基因,进一步采用机器学习套索回归(LASSO)算法、支持向量机递归特征消除(SVM-RFE)机器学习算法来识别核心生物标志物,并绘制受试者工作特征(ROC)曲线以评估诊断价值。最后,采用单样本基因集富集分析(ssGSEA)和基因集富集分析(GSEA)评估纤维肌痛综合征的免疫细胞浸润情况及通路富集。结果与结论:①对GSE67311数据集按照log2|(FC)|>0,P<0.05的条件进行差异分析后获得8个下调的差异表达基因;进行WGCNA分析后获得正相关性最高(r=0.22,P=0.04)的模块(MEdarkviolet)内含基因497个,负相关性最高(r=-0.41,P=6×10-5)的模块(MEsalmon2)内含基因19个;将差异表达基因与WGCNA的2个高相关性模块基因取交集,获得7个基因。②对上述7个基因进行LASSO回归算法筛选出4个基因,进行SVM-RFE机器学习算法筛选出5个基因,两者取交集后确定了3个核心基因,分别为重组1号染色体开放阅读框150蛋白(germinal center associated signaling and motility like,GCSAML)、整合素β8(Integrin beta-8,ITGB8)和羧肽酶A3(carboxypeptidase A3,CPA3);绘制3个核心基因的ROC曲线下面积分别为0.744,0.739,0.734,提示均具有很好的诊断价值,可作为纤维肌痛综合征的生物标志物。③免疫浸润分析结果显示,与对照组相比纤维肌痛综合征患者记忆B细胞、CD56 bright NK细胞和肥大细胞显著下调(P<0.05),且与上述3个生物标志物显著正相关(P<0.05)。④富集分析结果提示,纤维肌痛综合征的富集途径包括9条,主要与嗅觉传导、神经活性配体-受体相互作用及感染等通路密切相关。⑤上述结果显示,纤维肌痛综合征的发生发展与多基因参与、免疫调节异常及多个通路失调有关,但这些基因与免疫细胞之间的相互作用,以及它们与各通路之间的关系尚需进一步研究。

诱变技术选育高产麦角硫因的榆黄菇菌株

麦角硫因是食用菌中的天然活性物质,具有抗氧化和螯合金属等作用。为进一步提高生物发酵法生产麦角硫因的产量,本研究利用紫外诱变(UV)、甲基磺酸乙酯诱变(EMS)、航天诱变(SFM)技术对榆黄菇菌株进行诱变处理,以菌丝生长速度和液态发酵麦角硫因产量为评价指标,选育高产麦角硫因榆黄菇菌株。结果表明,UV诱变、EMS诱变和SFM诱变处理后分别得到了46、40、48株突变菌株,其中UV诱变产麦角硫因菌株的正突变率最高,为43.48%。UV诱变、EMS诱变和SFM诱变分别得到8、3、1株液态发酵麦角硫因产量极显著高于出发菌株的榆黄菇菌株(P<0.01)。在12株高产麦角硫因诱变菌株中,UV-30株产量最高,是出发菌株的2.16倍,UV-13的生长速度最快,是出发菌株的1.02倍。遗传距离分析表明SFM诱变菌株变异程度高于UV诱变菌株,且两者变异程度均高于EMS诱变菌株。本研究结果为麦角硫因生物合成途径基础研究提供了材料,同时为榆黄菇高产麦角硫因菌株诱变选育研究提供了参考依据。

基于数据挖掘研究针刺治疗偏头痛的选穴规律

目的基于数据挖掘研究近5年针刺治疗偏头痛的选穴规律。方法检索CNKI、VIP、WANFANG数据库2017年1月1日—2022年7月3日针灸(含中西医结合)治疗偏头痛的相关文献,提取其中的穴位组成。运用中医传承计算平台V3.0的频数统计、关联规则分析、聚类分析等功能,统计不同穴位使用频率并分析穴位配伍规律及关联、聚类效果。结果纳入文献共66篇,筛选穴位处方107首,涉及穴位114个。其中使用频次>20次的穴位共13个,包括风池、率谷、太阳等;使用频次>1次的穴位归经以足少阳胆经、手少阳三焦经为主,穴位分布以头颈部及四肢部为主。常用穴位配伍9个,以率谷-风池支持度最高,聚类为3时效果较好。结论偏头痛的配穴注重远近、阴阳、上下相结合,运用远近、表里经等经典配穴原则,达到缓解患者临床症状的作用。

Gyroid结构钛仿生骨支架修复下颌骨节段性缺损的生物力学性能

背景:三周期最小表面的多孔结构是最有前景的骨科生物结构之一,其中的Gyroid结构具有高比表面积、高渗透率、均曲率为零等特点。目的:通过有限元分析结合力学压缩实验测试,筛选出与下颌骨松质骨弹性模量范围相匹配的4 mm单胞Gyroid结构TC4仿生骨支架壁厚区间。方法:建立不同壁厚(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8 mm)4 mm单胞Gyroid结构有限元模型,分析Gyroid结构的等效弹性模量,筛选出与下颌骨松质骨弹性模量范围相匹配的Gyroid结构壁厚区间,壁厚分别为0.2,0.3,0.4,0.5,0.6,0.7 mm。根据有限元分析筛选结果,以Ti6Al4V为原料,采用选择性激光熔融技术制备不同壁厚的3D打印Gyroid结构试件,进行大颗粒喷砂酸蚀处理,通过力学压缩实验测试试件的弹性模量与抗压强度。结果与结论:(1)有限元分析结果显示,随着壁厚的增加,Gyroid结构的等效弹性模量升高,其中壁厚为0.2-0.7 mm Gyroid结构的等效弹性模量在下颌骨松质骨弹性模量范围内(1.5-4.0 GPa),用于3D打印Gyroid结构试件;(2)力学压缩实验结果显示,随着壁厚的增加,Gyroid结构试件的弹性模量与抗压强度均升高,其中0.3-0.5 mm壁厚Gyroid结构试件的弹性模量在下颌骨松质骨弹性模量范围内,0.3-0.7 mm壁厚Gyroid结构试件的抗压强度符合下颌骨的力学性能;(3)结果显示0.3-0.5 mm壁厚Gyroid结构与下颌骨的弹性模量范围相适应。