Article(id=1198656223538151915, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1198656209390764948, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2023-0138, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1676217600000, receivedDateStr=2023-02-13, revisedDate=1678723200000, revisedDateStr=2023-03-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1763711513646, onlineDateStr=2025-11-21, pubDate=1697040000000, pubDateStr=2023-10-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1763711513646, onlineIssueDateStr=2025-11-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1763711513646, creator=13701087609, updateTime=1763711513646, updator=13701087609, issue=Issue{id=1198656209390764948, tenantId=1146029695717560320, journalId=1189982191388893191, year='2023', volume='58', issue='10', pageStart='2835', pageEnd='3150', issueExtLink='null', onlineDate='null', pubDate='1697040000000', pubDateStr='2023-10-12', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1763711510274, creator='13701087609', updateTime=1763711659007, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1198656833280897539, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1198656209390764948, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1198656833280897540, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1198656209390764948, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2909, endPage=2913, ext={EN=ArticleExt(id=1198656223890473478, articleId=1198656223538151915, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=A simulating method for dripping process of Ginkgo biloba leaf dripping pills based on computational fluid dynamics technology, columnId=null, journalTitle=Acta Pharmaceutica Sinica, columnName=null, runingTitle=null, highlight=null, articleAbstract=

A simulating method for dripping process of Ginkgo biloba leaf dripping pills based on computational fluid dynamics was constructed. Ginkgo biloba leaf dripping pills was explored as the experimental subject to simulate the dripping process based on FLOW-3D software. The dripping process was simulated through the derivation of the governing equations, the selection of the models, and simulation parameters. Firstly, the droplet morphologies and drop speeds under different liquid viscosity were simulated. It was found that with the increase of the liquid viscosity, the drop speed decreased and the difficulty of droplet preparation gradually increased. The simulation results were consistent with the experiment results. Secondly, the droplet morphologies at different drop speeds were investigated and verified by experiments. It was found that the simulation results had a good correlation with the experiment results. The results shown that the viscosity of the liquid was the critical material attribute, and the drop speed was the critical process parameter, according to the droplet morphology. The establishment of the simulation method can deepen the understanding of the dripping process and provide a reference for the selection of raw materials and process parameters.

, authors=null, authorsList=Xiao-ping WANG, Hai-bin QU, authorCompany=null, correspAuthors=Hai-bin QU, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2023 Acta Pharmaceutica Sinica. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1198656225618526885, articleId=1198656223538151915, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=一种基于计算流体动力学技术的银杏叶滴丸滴制过程仿真方法, columnId=1198656210498061206, journalTitle=药学学报, columnName=专题报道: 中药制造质量控制与智能制造, runingTitle=null, highlight=null, articleAbstract=

本文建立了一种基于计算流体动力学技术的银杏叶滴丸滴制过程仿真模拟方法。以银杏叶滴丸为研究对象, 采用FLOW-3D软件, 对滴制过程进行仿真计算。通过控制方程的推导、模型的选择及仿真参数的筛选, 模拟了液滴滴落过程。首先仿真计算不同药液黏度下液滴滴落的形态及滴速, 发现随着药液黏度的增加, 液滴速度减小, 滴制难度逐渐增加, 该仿真结果与实验趋势相符。其次考察了不同滴速下液滴滴落的形态, 并采用实验进行验证, 发现仿真计算结果与实验结果具有较好的相关性。结果表明, 黏度为影响滴制过程的关键药液性质, 滴速则为影响滴制过程的关键工艺参数。综上所述, 该仿真模拟方法的建立不仅可加深对滴丸滴制工艺的理解, 而且可为滴丸原料的选择及工艺参数的调节提供参考。

, authors=null, authorsList=王小平, 瞿海斌, authorCompany=null, correspAuthors=瞿海斌, authorNote=null, correspAuthorsNote=
*瞿海斌, Tel: 86-571-88208428, E-mail:
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一种基于计算流体动力学技术的银杏叶滴丸滴制过程仿真方法
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王小平 , 瞿海斌 *
药学学报 | 专题报道: 中药制造质量控制与智能制造 2023,58(10): 2909-2913
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药学学报 |专题报道: 中药制造质量控制与智能制造 2023 , 58 (10) : 2909 -2913
一种基于计算流体动力学技术的银杏叶滴丸滴制过程仿真方法
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王小平, 瞿海斌*
作者信息
  • 浙江大学药学院, 药物信息学研究所, 浙江 杭州 310058
通讯作者:
*瞿海斌, Tel: 86-571-88208428, E-mail:
A simulating method for dripping process of Ginkgo biloba leaf dripping pills based on computational fluid dynamics technology
Xiao-ping WANG, Hai-bin QU*
Affiliations
  • Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
出版时间: 2023-10-12 doi: 10.16438/j.0513-4870.2023-0138
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本文建立了一种基于计算流体动力学技术的银杏叶滴丸滴制过程仿真模拟方法。以银杏叶滴丸为研究对象, 采用FLOW-3D软件, 对滴制过程进行仿真计算。通过控制方程的推导、模型的选择及仿真参数的筛选, 模拟了液滴滴落过程。首先仿真计算不同药液黏度下液滴滴落的形态及滴速, 发现随着药液黏度的增加, 液滴速度减小, 滴制难度逐渐增加, 该仿真结果与实验趋势相符。其次考察了不同滴速下液滴滴落的形态, 并采用实验进行验证, 发现仿真计算结果与实验结果具有较好的相关性。结果表明, 黏度为影响滴制过程的关键药液性质, 滴速则为影响滴制过程的关键工艺参数。综上所述, 该仿真模拟方法的建立不仅可加深对滴丸滴制工艺的理解, 而且可为滴丸原料的选择及工艺参数的调节提供参考。

仿真技术  /  计算流体动力学技术  /  关键药液性质  /  关键工艺参数  /  银杏叶滴丸

A simulating method for dripping process of Ginkgo biloba leaf dripping pills based on computational fluid dynamics was constructed. Ginkgo biloba leaf dripping pills was explored as the experimental subject to simulate the dripping process based on FLOW-3D software. The dripping process was simulated through the derivation of the governing equations, the selection of the models, and simulation parameters. Firstly, the droplet morphologies and drop speeds under different liquid viscosity were simulated. It was found that with the increase of the liquid viscosity, the drop speed decreased and the difficulty of droplet preparation gradually increased. The simulation results were consistent with the experiment results. Secondly, the droplet morphologies at different drop speeds were investigated and verified by experiments. It was found that the simulation results had a good correlation with the experiment results. The results shown that the viscosity of the liquid was the critical material attribute, and the drop speed was the critical process parameter, according to the droplet morphology. The establishment of the simulation method can deepen the understanding of the dripping process and provide a reference for the selection of raw materials and process parameters.

simulating method  /  computational fluid dynamics technology  /  critical material attribute  /  critical process parameter  /  Ginkgo biloba leaf dripping pill
王小平, 瞿海斌. 一种基于计算流体动力学技术的银杏叶滴丸滴制过程仿真方法. 药学学报, 2023 , 58 (10) : 2909 -2913 . DOI: 10.16438/j.0513-4870.2023-0138
Xiao-ping WANG, Hai-bin QU. A simulating method for dripping process of Ginkgo biloba leaf dripping pills based on computational fluid dynamics technology[J]. Acta Pharmaceutica Sinica, 2023 , 58 (10) : 2909 -2913 . DOI: 10.16438/j.0513-4870.2023-0138
滴丸滴制过程是熔融药液由滴头处流出并自然滴落的过程。液滴成型及滴落具备流体动力学和流体热力学特性。现阶段尚无关于滴丸滴制过程的理论研究, 而关于液滴滴落的机制研究已初具雏形。流体细丝变薄、滴落过程中液滴变化的机制方程已逐渐被提出[1, 2], 且随着各种先进算法、流体模型、计算软件的发展, 结合液滴成型机制, 液滴滴落过程可通过计算进行仿真模拟。由于流体流动的复杂性和多样性, 关于高黏度非牛顿流体等复杂流体的研究仍较少, 大多是关于牛顿流体与低黏度非牛顿流体的研究。
随着信息技术的飞速发展, 仿真技术逐渐在各行业得到应用。计算流体动力学(computational fluid dynamics, CFD)[3]是把时间域与空间域上连续的物理量场用一系列离散点的变量值的集合来代替, 通过一定的原则和方式建立反映这些离散点上场变量之间关系的代数方程组, 求解代数方程组获得场变量的近似值。在流体动力学的研究中, 将CFD与理论分析、实验测量相结合, 从多角度对流体的流动过程进行全面的研究。
液滴滴落过程涉及液体与空气两种流体, 研究液滴滴落的过程可归属于多相流的计算。目前关于多相流等自由表面流体仿真计算可选择Eulerian法、Lagrangian法和Eulerian-Lagrangian法[4-6]。Eulerian法为体积追踪法, 将流体网格划分为无限个体积点, 计算某体积内的流体变化; Lagrangian法则着眼于流体的质点, 基本思想是跟踪每个流体质点在流动过程中的运动全过程, 记录每个质点在每一时刻、每一位置的各个物理量及变化。目前用于CFD的仿真软件多种多样, 如ANSYS Fluent、Phoenics、OpenFOAM、XFlow和FLOW-3D等[7-9]。ANSYS Fluent模型丰富, 可与ANSYS其他仿真模块进行多物理场仿真; Phoenics模型丰富, 开放程度高, 用户可任意修改和添加各种程序和模型; OpenFOAM由C++语言编写, 在linux下运行, 自2004年开始OpenFOAM一直作为免费使用的开源软件; XFlow无须划分网格, 是一种基于粒子无网格动力学求解器的计算软件; FLOW-3D是一种擅长于模拟自由表面流动的软件, 它基于流体体积法(volume of fluid, VOF) 原理, 开发了更精确的边界条件及表面追踪技术, 可对自由液面进行完整的描述。FLOW-3D采用的VOF法[10]属于Eulerian法, 且为有限差分法的一种, 其通过对网格内的流体情况进行分析计算, 模拟流体流动状态及轨迹。
银杏叶滴丸是我国临床应用较广的滴丸剂, 其对冠心病、脑梗塞等心脑血管疾病具有较好的疗效[11, 12]。制备银杏叶滴丸的熔融药液为高黏度非牛顿流体, 其滴落过程较牛顿流体更为复杂, 现阶段关于此类流体的研究较少。由于此流体黏度较高, 导致流动轨迹较长, 增加仿真计算量的同时, 给模型收敛带来较大困难。本研究综合实验室现有条件及各仿真软件的特点, 最终选择了擅长多相流模拟的FLOW-3D作为仿真软件, 在现有低黏度流体研究基础上, 对高黏度非牛顿流体的流动进行探讨, 建立了滴丸滴制仿真方法。通过研究液滴成型机制, 从流体动力学角度了解液滴的成型过程, 增加对滴丸滴制工艺的理解并给关键物料性质及工艺参数的辨识提供了参考。
仪器  高速工业相机(CMOS, 75FPS)、工业相机镜头、光源板及光源控制器(杭州微图视觉科技有限公司); 循环恒温槽(ZCY-15B)、低温循环槽(THYD-1030W) (宁波天恒仪器厂); 界面张力仪(DCAT21, 德国Dataphysics公司); 流变仪(RST, 美国Brookfield公司)。
材料  银杏叶提取物(批号160916, 浙江康恩贝制药股份有限公司); 聚乙二醇4000 (批号FS-160927276, 辽宁奥克医药辅料有限公司)。
仿真滴头模型  熔融药液自滴头处流出, 滴头的形状与大小直接影响液滴大小。滴头直径越大则形成的液滴越大, 滴头顶端具有一定角度, 可增加药液与滴头的接触面积, 增强表面张力对液滴滴落的影响, 增加滴落的液滴重量。本研究根据实验室滴丸制备过程中所用滴头实际大小和形状, 采用Auto-CAD进行滴头3D模型的绘制, 滴头示意图见图 1。其中, 滴头为顶端是45°倒角的中空圆柱体, R0为滴头内半径, 为0.14 cm, 材质为耐热的304不锈钢。
网格的划分  FLOW-3D软件附带的网格类型有Cartesian和Cylindrical, 由于滴头为中空圆柱体, 沿着中心轴圆周对称, 因此可选择Cylindrical网格, 该网格坐标系X轴为径向距离, Y轴为方位角, Z轴为高度位置。图 2为滴头网格划分图。本研究模拟的滴头长1 cm, 由于熔融药液黏度较高, 因此细丝拉伸至液滴滴落的行动轨迹较长。当网格数较高时, 相应的计算量也会增加, 为尽量减少计算量, 对滴头及滴制区域的网格进行分块绘制。如图 2所示, 滴头主区域以中心点为起点, 沿着滴头实体绘制, X方向的网格平面分别为0、0.14和0.215 5 cm (滴头内半径为0.14 cm, 外半径为0.22 cm, 为防止边缘仿真失真, X方向网格区域应略小于滴头实际尺寸), Z方向网格平面分别为-0.222 1和-1 cm, 液滴流出滴头时可能溢出滴头实体区域, 则滴制区域需进行一定的扩充, 因此, 滴制区域X方向的网格平面分别为0、0.14、0.215 5和0.4 cm, Z方向网格平面分别为-1、-1.222 1和-11 cm, 滴头及滴制区域Y方向网格平面均为0°和30°。网格的大小与计算精度有密切的关系, 网格过大则计算精度降低, 导致模拟不够准确, 网格过小则网格数增加, 会增加计算量与存储负担。网格的选择没有明确的标准, 可根据计算状态进行适当调整, Dinic等[9]采用FLOW-3D仿真液滴滴落时经筛选发现最优网格大小(0.005 cm) 与滴头半径比值为0.016, 但在考虑计算时间后, 最终选择了更大的网格(0.007 cm), 将计算时间缩短为原来的1/10。本研究的网格选择综合考虑计算时间及仿真效果, 且在多次优化实验后选择0.004 cm, Y方向cell数为1, 即仅进行XZ方向的2D平面模拟。
控制方程  流体的流动遵循三大守恒定律, 即质量守恒、动量守恒与热量守恒。考虑仿真过程的复杂性, 本研究暂不将能量守恒列入计算。质量守恒定义为一定时间内单位微元体质量的增加应等于同一时间间隔流入该微元体的质量, 质量守恒方程通常也称为连续方程, 其微分形式为公式(1):
$ \frac{\partial \rho }{\partial t}+\frac{\partial \left(\rho u\right)}{\partial x}+\frac{\partial \left(\rho v\right)}{\partial y}+\frac{\partial \left(\rho w\right)}{\partial z}=0 $
其中, ρ为流体密度, xyz表示空间直角坐标, uvw分别为xyz方向的质点速度分量, 本研究的熔融药液为不可压缩流体, 则ρ为常数, 公式(1) 可改写为公式(2):
$ \frac{\partial \left(\rho u\right)}{\partial x}+\frac{\partial \left(\rho v\right)}{\partial y}+\frac{\partial \left(\rho w\right)}{\partial z}=0 $
若流动处于稳态, 则ρ不随时间而变化, 则表示为公式(3):
$ \frac{\partial \left(u\right)}{\partial x}+\frac{\partial \left(v\right)}{\partial y}+\frac{\partial \left(w\right)}{\partial z}=0 $
采用符号$ \nabla $表示散度, 则质量守恒方程可表示为公式(4)、(5):
$ \nabla \cdot \rho u=\mathrm{d}\mathrm{i}\mathrm{v}\left(\rho u\right)=\frac{\partial \left(\rho u\right)}{\partial x}+\frac{\partial \left(\rho v\right)}{\partial y}+\frac{\partial \left(\rho w\right)}{\partial z}=\mathrm{ }0 $
$ \nabla \cdot u=\mathrm{ }\mathrm{ }\mathrm{ }\mathrm{ }\mathrm{ }\mathrm{d}\mathrm{i}\mathrm{v}\left(u\right)=\frac{\partial \left(u\right)}{\partial x}+\frac{\partial \left(v\right)}{\partial y}+\frac{\partial \left(w\right)}{\partial z}=\mathrm{ }0 $
动量守恒定律是指一定时间内单位微元体的动量变化等于相同时间间隔内作用在该微元体上的合力。流体流动过程中主要受体积力与表面力作用, 体积力包括重力与电磁力, 表面力包括压力与黏性力, 黏性力又分为正应力与切应力。电磁力又称为静电力, 为电荷、电流在电磁场中受力总称, 本研究的流体可忽略其电磁力, 其主要受重力、压力与黏性力的作用。单位微元体的动量增长率等于微元体所受合力与通过控制面板流入微元体的动量之和, 其微分形式为公式(6):
$ \frac{\partial \rho u}{\partial t}={F}_{\mathrm{b}}+\nabla \cdot T-\nabla \left(\rho uu\right) $
其中, Fb为体积力, 即重力, $ \nabla \cdot T $为表面力, T为应力张量, 为同向性应力与偏向性应力之和, 计算公式(7) 为:
$ T=\mathrm{ }-pI+\tau $
其中, p为同向性应力, I为单元张量, τ为偏应力张量。
公式(6) 经展开得公式(8):
$ \rho \frac{\partial u}{\partial t}+u\frac{\partial \rho }{\partial t}={F}_{\mathrm{b}}+\nabla \cdot T-u\nabla \cdot \left(\rho u\right)-\rho u\cdot \nabla u $
其中, $ \frac{\partial \rho }{\partial t}+\nabla \cdot \left(\rho u\right) $项为连续性方程的微分形式[参考公式(1)], 则该项为0, 故公式(8) 可简化为公式(9)、(10):
$ \rho \frac{\partial u}{\partial t}+\rho u\cdot \nabla u={F}_{\mathrm{b}}+\nabla \cdot T $
即:
$ \rho \left(\frac{\partial u}{\partial t}+u\cdot \nabla u\right)=\nabla \cdot T+{F}_{\mathrm{b}} $
液滴滴落自由表面的边界条件为公式(11):
$ n\cdot T=\left(2H\sigma \right)n $
其中, σ为表面张力, n为法向量, H为自由表面的高斯曲率。
边界条件的选择  在FLOW-3D中, 边界条件有多种选择, 如Symmetry、Continuative、Wall、Periodic、Specified pressure、Volume flow rate、Outflow等。液滴滴落的入口条件可选择Specified pressure、Volume flow rate或Specified velocity, 考虑验证实验的可操作性, 本研究选择Volume flow rate作为入口条件, Outflow为出口条件, 其他面边界类型选择Symmetry。
模型的选择  FLOW-3D配备多种模型, 仿真过程中, 应根据实际物料性质及运动形式选择适宜的模块。制备银杏叶滴丸的熔融药液为黏性较大的非牛顿流体, 则应选择Viscosity and Turbulence模块, 药液滴落过程是由重力主导的下落过程, 且在下落过程中药液的表面张力对液滴的形成具有至关重要的作用, 因此Gravity and non-intertial reference frame与Surface tension模块也需选择。
参数设置  模型、边界条件选择后, 需输入的参数主要有药液性质、边界参数、计算参数及输出参数。药液性质包括表面张力、接触角、黏度和密度。边界参数为Volume flow rate, 即流体流动参数; 计算参数包括计算步长和模拟时长, 本研究设置计算步长为1×e-5 s, 模拟时长为15 s; 同时以0.01 s为间隔输出仿真结果, 经计算之后的仿真结果可导出单张图片或制成滴制视频。
验证实验  验证实验均在实验室搭建的小试滴丸机上进行[13], 将滴头型号、物料以及工艺参数设置与仿真实验相同, 采用高速工业相机对滴制形态进行图像采集。将相机采集得到的液滴图像与仿真结果进行对比, 考察仿真结果与实验的吻合度。
本研究模拟的是液滴滴落的二维流动状态, 经模拟计算的液滴滴落过程可以用不同的流场形式表示, 如压力、流速等, 图 3为不同流场形式的液滴滴落图。在不同流场模式下, 均可清楚地表征液滴滴落过程中的形态, 且采用不同流场形式表征仿真结果并无差别, 因此, 本研究统一选择压力流场表征仿真结果。
本研究模拟的药液为银杏叶提取物(Ginkgo biloba leaf extract, GBE) 与聚乙二醇(polyethylene glycol, PEG) 4000的混合熔融液, PEG 4000常温下为固体, 凝点为50~56 ℃, GBE为固体细粉。GBE均匀分散在液态下的PEG 4000中形成熔融药液, 当药液温度不同时, 药液黏度相较于密度、表面张力等具有更为明显的改变。为更好地了解药液性质与液滴形态的关系, 本研究假定密度、接触角与表面张力固定不变, 仿真计算了不同药液黏度对液滴形态的影响。经计算, 随着药液黏度的增加, 液滴滴落速度减小, 滴制难度逐渐增加, 该仿真结果与实际实验趋势相符。因实际药液难以与仿真设置药液性质完全调节一致, 故该研究内容不予以实验验证。
本仿真研究设置的关键工艺参数为Volume flow rate, 该参数可理解为单位时间内的流体体积, 即体积流量。将体积流量分别设置为0.000 5、0.001、0.002和0.003 cm3·s-1, 考察各条件下模拟的液滴滴制情况, 为进一步验证该仿真结果的真实性, 本研究采用工业相机对不同体积流量下的液滴滴落情况进行拍摄。图 4为不同参数下模拟的液滴滴落图和工业相机采集的液滴图像, 当体积流量为0.002和0.003 cm3·s-1时, 仿真图中明显出现了小液滴(图 4EG中红色圆圈标记部分), 其主要是由于流体的体积流量增加, 导致滴落过程中药液迅速补充, 液滴滴落拉长形成的细丝体积相对较大, 因此细丝断裂过程中极易形成小液滴。而从相机拍摄的相应液滴图像可以明显看出, 随着体积流量的增加, 液滴滴落过程确实生成了小液滴, 仿真结果与实际实验结果相符。通过不同体积流量模拟结果, 表明了流体体积流量控制的必要性。在滴丸实际生产中, 体积流量测定较为困难, 则可衍生为同等意义的滴速, 即滴速过大时, 更容易生成小液滴, 增加了小丸生成的概率, 因此, 实际生产中滴速不宜过大, 应控制在合适范围内。
由上述仿真结果可知, 药液性质与工艺参数均为影响液滴滴制形态的重要参数, 为更好地保证滴丸质量, 可适当调节药液黏度, 并将滴速控制在适宜范围内。
本研究建立了基于CFD技术的滴丸滴制仿真方法。结果表明, 采用CFD技术, 可有效模拟出滴丸滴制过程的液滴形态, 且液滴药液性质与工艺参数均为影响液滴滴制形态的重要参数, 为更好地保证滴丸质量, 可适当调节药液黏度, 并将滴速控制在适宜范围内。后续研究可从提高药液参数测定的精度、提高仿真模拟精度等方面进一步优化仿真准确度。在深入了解滴丸滴制过程的基础上, CFD仿真技术的研究也可为滴丸的质量控制提供参考。
作者贡献: 王小平负责进行实验实施、数据收集、结果分析、撰写、修改文章; 瞿海斌负责指导整个实验实施以及指导文章的撰写和修改。
利益冲突: 所有作者均声明不存在任何利益冲突。
  • 国家中医药管理局创新团队与人才培养计划项目(ZYYCXTD-D-202002)
  • 浙江省科技计划项目(2018C03075)
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2023年第58卷第10期
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doi: 10.16438/j.0513-4870.2023-0138
  • 接收时间:2023-02-13
  • 首发时间:2025-11-21
  • 出版时间:2023-10-12
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  • 收稿日期:2023-02-13
  • 修回日期:2023-03-14
基金
国家中医药管理局创新团队与人才培养计划项目(ZYYCXTD-D-202002)
浙江省科技计划项目(2018C03075)
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    浙江大学药学院, 药物信息学研究所, 浙江 杭州 310058

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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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