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1.
在经济发展新常态和建设“美丽中国”的要求下,科学合理地划定基本农田保护红线、城市开发边界和生态保护红线（“三线”）对统筹协调“生产-生活-生态”空间、高效配置土地资源、优化城市景观格局等具有积极意义。本文在对“三线”内涵解析的基础上,提出了“三线”协同划定的分析框架和协调策略,选取江苏省常州市金坛区为案例区,通过构建“生态-耕作-建设”三种利益导向的土地适宜性评价体系,分析了不同目标导向下各类土地利用适宜性程度,综合各适宜性评价结果识别土地利用冲突区,基于生态优先、集中紧凑、邻域和谐和空间识别的原则,对各类冲突区进行耦合协调,以期为“三线”划定提供新思路。研究结果表明：金坛区的生态保护红线范围主要包括西部的茅山片区、北部的天荒湖湿地以及钱资湖、长荡湖等区域,总面积260.63km2;基本农田主要分布在茅山片区及低山丘陵的东侧,中部冲积湖积圩田平原和东部的高亢平原,总面积303.57km2;城市开发边界圈定了中心城区可扩张的最大范围,总面积69.43km2。 相似文献
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基于“三生空间”的城市国土空间开发建设适宜性评价——以宁波市为例 总被引：1，自引：0，他引：1
《资源科学》2016,(11)
三生空间的合理布局与有序开发对城市实体空间布局、土地功能组合、国土空间秩序开发、引导人口资源环境与社会经济可持续发展具有重要意义。本文从土地资源的三生功能视角出发,以宁波市为例,提出了基于三生空间的城市国土空间开发建设适宜性评价框架;运用GIS技术,采用国土空间综合分区"千层饼"方法,以30m×30m栅格为基本单元,定量评估了宁波市国土空间开发建设适宜性;从分类到综合,多尺度揭示了宁波市国土开发的建设适宜规模与开发潜力。研究表明:(1)2012年宁波市生态空间面积为5872.33km2,占宁波市总面积的60.35%,主要分布在宁波南部宁象片区;生产空间面积为2442.34km2,占宁波市总面积的25.10%,集中分布在宁波市中部与北部平原;生活空间面积为1415.78km2,占宁波市总面积的14.55%,多集聚在宁波市核心六区;(2)宁波市不适宜城市建设空间居多,达5655.34km2,生态、生产与生活空间占比约为34∶9∶1;临界适宜城市建设空间居中,为2279.84km2,以生态空间与生产空间为主,二者占比约为2∶1;比较适宜空间为1526.71km2,以生产与生活空间为主,二者占比约为1∶2;一般适宜空间居后列,为268.56km2,生态空间、生产空间与生活空间比约为2∶1∶13;(3)宁波市北部余慈与南部宁象板块开发潜力较大,未来可有序拓展开发空间;中心城区国土空间开发建设适宜性较好,但开发历史悠久,开发潜力有限,未来开发宜以盘活存量,提高效率为主要途径。 相似文献
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地区国土空间格局演化不仅受自然资源禀赋等先天因素的影响,还受到集聚经济等后天因素的影响。区域发展战略通过改变当地的基础设施条件、交通通达性、功能布局等经济活动空间集聚的条件,重塑地区经济地理。资源环境承载力和国土开发适宜性评价（“双评价”）作为优化国土空间格局的重要基础和参照,过于强调资源本底,仅仅评估现状,也缺乏区域发展战略和重大工程项目对资源环境承载能力和国土空间开发适宜性产生的影响和变化的评估。因此,单纯依靠“双评价”对未来国土空间演化趋势和土地供需展开研判不够准确,对集聚经济的重视亦不足。本文首先构建一个空间经济学的分析框架,探讨和辨识国土空间演化过程的多种影响因素和机制,建立一个基于空间经济学理论的国土空间演化模拟分析平台,并通过人工神经网络（ANN）和元胞自动机（CA）整合实现机器学习和演化模拟。其次,在对比分析与校正的基础上,设置5种未来区域发展战略情景,对云南省全域进行高分辨率模拟分析,并据此对“双评价”结果进行修正。研究表明：①包含集聚经济因素的模拟结果更能反映空间经济活动的规律,且有更高的模拟精度。②各种区域战略的实施均有利于优化国土空间的开发格局,其中综合统筹和农业优先两种情景更有利于地区充分利用比较优势、因地制宜开展国土空间的开发工作。③基于综合统筹情景修正的“双评价”结果,进一步识别出了云南省国土空间适宜建设区、储备建设区、生态保护极重要区和不适宜建设区,在补充现有“双评价”结果的同时进一步对云南省现有国土空间格局进行优化。本文阐释了国土空间格局演化的空间经济机理,为新时代国土空间格局的优化以及“双评价”的修正,提供科学支撑和政策建议。 相似文献
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城乡转型发展进程中的农村居民点用地调整应加强对其空间形态的系统认识与分类研究。本文从“点”-“面”耦合的角度构建农村居民点空间形态概念模型,基于“2+3”式抽样法选取山东省不同区域的5个典型乡镇及其内部的15个典型村庄作为研究对象,分别应用“点”属性的规模、形状和密度指标划分农村居民点类型,利用“面”属性的生产、生活和生态指标剖析农村居民点功能,进而通过对比分析揭示农村居民点“点-面”形态耦合特征。研究发现：①在“点状”形态上,典型乡镇的农村居民点规模差异较大,密度相对集聚,形状偏于不规则;经济落后地区和山区的农村居民点组合类型比较单一,经济发达和平原地区的农村居民点组合类型多样。②在“面状”形态上,农村居民点整体表现为“生产功能较好,生活功能差,生态功能一般”的特征;随着镇域内区位优势的衰减,典型村庄的生产功能逐渐降低并趋于均衡化,生活功能逐渐降低但分化程度更加剧烈,生态功能也逐渐降低但缩减幅度相对均衡。③在“点-面”形态耦合上,大规模-集聚型农村居民点的生活和生态功能偏强,小规模-分散型农村居民点的生活功能稍强,大规模-分散-不规则型农村居民点各项功能均较弱,这种组合类型和功能关系之间的关联性是外部性要素导向下农村居民点内部要素自组织演化的体现。 相似文献
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确定旅游承载力是处理自然保护地人地关系的重要手段,也是影响旅游地可持续发展的核心关键。以云南泸沽湖省级自然保护区为例,综合多源数据,利用“双评价”、容量转换等方法计算旅游承载力。结果发现：①泸沽湖具有“三区合一”的典型特征,由自然保护区、社区、景区3类空间叠加而成。自然保护区资源环境承载力等于社区人口负荷与景区旅游承载力之和。空间供给、资源消耗、设施提供和管理水平共同影响旅游承载力,其中空间供给是关键的制约因素;②依照“确定总量—减去存量—计算余量”的逻辑思路,计算出自然保护区适宜土地面积,获得特定建成情景下的资源环境承载力,减去社区人口负荷得到保护区承载力余量,可进一步转换为景区旅游承载力;③维持风景旅游型城镇建成环境条件下,泸沽湖高适宜的土地面积仅为298.54 hm2,占保护区总面积的3.67%;资源环境条件可支撑19920人,承载力余量为15918人,经过转换后可得旅游承载力为11938人/日;④针对短板的限制作用,提出采取倾斜木桶、延长长板等措施,为景区旅游承载力扩容提供思路。研究可为“三区合一”型自然保护地旅游承载力测算提供技术支撑,有利于保护地科学治理。 相似文献
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“一带一路”是中国政府实施的一项造福沿线地区人民的世纪合作倡议,涉及多个国家。明确“一带一路”主体水资源区的范围及其分区布局,对于分析和研究“一带一路”水安全保障具有重要意义。本文通过对大量文献资料的梳理,提出了“一带一路”主体路线和主体水资源区的概念和确定方法,绘制了其主体路线及主体水资源区图,并按照3级分区方法对确定的主体水资源区进行了分区。研究结果表明：① “一带一路”主体路线由中国陆上主体路线、中国海上主体路线、国外陆上主体路线和国外海上主体路线组成,各线之间互有连接,形成了一张覆盖亚、欧、非大陆的“三纵三横”网络;② “一带一路”主体水资源区横跨亚、欧、非大陆50个国家,总面积达1877.00万 km2;③ 研究区水资源一级分区、二级分区、三级分区的单元个数分别为11个、50个、1172个。本文为“一带一路”水资源研究圈定了主要研究区域,奠定了统一研究对象和基础,也可为“一带一路”相关其他研究提供参考和借鉴。 相似文献
7.
江南四省耕地后备资源调查与评价 总被引：5，自引：0，他引：5
耕地后备资源储量调查及适宜性评价能够为耕地后备资源的合理开发利用提供科学依据。本文将3S技术支撑下的室内分析和野外科学考察相结合,从自然适宜性和生态安全性角度构建了江南四省(湖南、江西、浙江和福建)耕地后备资源的自然-生态适宜性指标体系,以“一票否决”和“设置容许度”两种方式对纳入的生态安全性指标进行区分,实现江南四省耕地后备资源的提取和评价。结果发现:江南四省共有未利用土地8 992.30km2,耕地后备资源总量为5 394.70km2,集中连片大面积的耕地后备资源基本上已开垦殆尽。储量少、质量低、分布零散是该地区耕地后备资源的基本特征。小面积耕地后备资源的充分利用已经成为江南四省维持耕地占补平衡的需要。 相似文献
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“一带一路”沿线国家的共同低碳转型对于全球应对气候变化意义重大,考察沿线国家碳强度收敛特征,并分析与中国开展经贸合作是否可以促进其碳强度收敛速度,对于客观评价中国在促进绿色“一带一路”建设中的作用具有重要意义。本文基于2003—2018年87个国家数据,运用包含空间效应的绝对和条件β收敛模型,从整体以及区域层面分析与中国开展投资和贸易合作对沿线国家碳强度收敛速度的影响。研究表明：①从整体层面来看,“一带一路”沿线国家碳强度存在空间β收敛趋势,并且条件β收敛速度大于绝对β收敛速度,反映了与中国开展经贸合作能够明显加速碳强度收敛。②从大洲层面来看,与中国开展经贸合作均对碳强度收敛速度起到促进作用。其中,对非洲地区的促进作用最大,其次是亚洲和拉丁美洲,最后是欧洲。③从大洲内部来看,与中国开展经贸合作对非洲撒哈拉沙漠以北地区碳强度收敛的促进作用大于以南地区;对亚洲各区域碳强度收敛的促进作用由大到小依次为中亚、西亚、南亚、东亚及东南亚。因此,“一带一路”沿线国家应加强区域交流沟通,共同应对气候变化,尤其是结合自身发展特点,深化与中国的经贸合作关系,有利于早日共同实现低碳转型。 相似文献
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研究城镇化与产业结构高级化对CO2排放的影响效应,对于促进CO2减排,实现经济可持续发展具有重要的现实意义。本文利用2000-2014年的中国省际面板数据,基于独立效应和联动效应双重视角,在对城镇化“质量效应”与“扩张效应”分析的基础上,结合由产业结构高级化的“结构红利”而产生的“经济服务化”趋势,实证分析了城镇化与产业结构高级化对CO2排放的影响机理。结果发现：①独立效应视角下,中国城镇化尚处于发展阶段,大规模的人口迁移带来基础设施的扩建,最终导致城镇化的“扩张效应”占主导地位,“质量效应”的CO2减排作用尚不明显,因此城镇化与CO2排放存在正相关关系。产业结构高级化所带来的“结构红利”促使经济发展表现出服务化特征,以及产生的技术进步与技术替代效应都有利于CO2减排,因此产业结构高级化与CO2排放存在负相关关系;②联动效应视角下,产业结构高级化提升了城镇吸纳能力与现代城镇化水平,促进了城镇化“质量效应”的发挥,因此产业结构高级化带来的这种“结构红利”有助于弱化城镇化“扩张效应”所带来的高CO2排放现象,但是弱化功能存在空间异质性。 相似文献
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建设高标准基本农田是保证中国粮食安全的重要举措,具有重要现实意义。选取重庆市垫江县为研究区,结合农用地质量分等、土地利用变更等多元数据,利用生态位（Niche）、局部莫兰指数（Local Moran’s I）、多元约束（Multi-constraints）方法进行高标准基本农田建设适宜性评价与区位选址,以期为垫江县及类似丘陵地区土地整治工作提供可参考的科学依据。研究表明：①评价指标空间离散性不一,地块连片度、田间道路通达度和地块到居民点距离变异性最强,成为高标准基本农田建设需要适当考虑的关键因子;②区位要素与综合适宜度空间分异明显,自然禀赋、基础设施和社会条件的适宜性表现为西部平坝、北部丘陵和中部河谷地带较高,而南部及“三山”（明月山、南华山和黄草山）地带较低,生态环境的适宜性空间格局反之,高标准基本农田综合适宜性格局亦表现为西部、北部和中部较强而南部较低的态势;③建设选址结果占基本农田的28.76%,同政府实际划定结果相比多出14.48%,说明垫江县高标准基本农田建设条件较好。上述研究表明,基于生态位、空间自相关和多元约束方法,能够较好地指导高标准基本农田建设与选址工作。 相似文献
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工业污染始终是人类工业化进程中面临的重要挑战,为解决中国工业污染与工业经济之间的协调问题,本文基于中国工业固体废物产生量增长率（solidt）、工业废水排放量增长率（liquidt）、工业废气排放量增长率（gast） 和工业经济增长率 （iavt） 构建“solidt-iavt”、“liquidt-iavt”和“gast-iavt”三种“工业污染与工业经济”系统,运用非线性MS-VAR模型,测度中国工业经济增长与工业污染内在关联机制的周期性。研究结果表明：①“工业污染与工业经济”系统潜存着在“低速增长区制”和“快速增长区制”之间相互转移的结构性突变迹象,并且具有非线性周期变化特征;②“工业污染与工业经济”系统处于“低 （快） 速增长区制”时,solidt与iavt呈负（正）相关关系,liquidt与iavt呈正（负）相关关系,gast与iavt呈正相关关系;③“solidt-iavt”系统和“gast-iavt”系统处于“低（快）速增长区制”时的可能性大（小）、持续性强（弱）,“liquidt-iavt”系统处于“快（低）速增长区制”的可能性大（小）、持续性强（弱）。 相似文献
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基于生态系统服务消费的京承生态补偿基金构建方式 总被引：1，自引：0，他引：1
生态系统服务的外部性是构成生态补偿的理论基础之一。不同类型的生态系统服务“溢出”的惠及范围不尽相同,生态补偿基金的构成比例应以生态系统服务的流动和消费为基础。本文以北京-承德生态补偿为例,根据2010年首都生态圈遥感影像和相关调研与问卷资料,引入物理学中的场强模型,计算了承德市主要生态系统服务的惠及区域,并提出京承生态补偿基金的构成比例。计算发现,2010年承德市主要生态系统服务的价值为76.45亿元/年。其中,26.76%被承德本地消费,28.22%被北京消费,18.74%为全国共享,其余被天津、河北等地消费。因此,若在北京-承德生态补偿的框架下,根据生态系统服务价值的消费量来构成生态补偿基金,则中央、北京和承德的出资比例为0.70
13.
团风县耕地资源价值及其空间分布 总被引：2，自引：0，他引：2
全面认知耕地资源价值的结构特征和空间分布规律是推进城乡一体化土地市场建设与城乡统筹发展的重要前提。本文在梳理当前耕地资源价值内涵的基础上,构建县域耕地资源价值测算体系,并以湖北省团风县为例,从村、镇和县不同地域尺度测算耕地资源经济、社会和生态价值,分析其空间分布规律。研究结果表明：团风县耕地资源生态、社会和经济价值的比例约为5
14.
针对WRF模式自带土地利用数据更新不及时及精度不高的现象,本文将国内外主要土地利用数据（MODIS2012、GLC2009、GLC2000）与WRF模式耦合进行土地利用数据的优选,并将优选数据初步应用于枣庄城市热岛模拟中。结果表明：①MODIS2012数据能够较好地反映研究区域土地利用类型的空间分布特征,有较好的模拟结果;②研究区10月份热岛强度最大为0.671℃,其次是1月份和7月份分别为0.570℃和0.550℃,而4月份热岛强度较小为0.467℃;城市热岛强度日最大值出现在22~23点,最小值出现在13~14点;③由于盛行风向的影响,滕州市与枣庄市中区城市热岛中心均存在春夏季北进、秋冬季南移的现象;④枣庄地区城市热岛主要受枣庄市中区的影响,各土地利用类型对城市热岛的贡献率大小排序为：城镇建设用地>农田>林地>未利用地>草地>水体。 相似文献
15.
北京城市绿地群落结构对降温增湿功能的影响 总被引：6，自引：0，他引：6
清晰揭示城市绿地的空间布局与景观结构特征对其生态服务功能的影响对指导城市规划设计具有重要参考价值。目前国内外已有较多城市绿地降温增湿及其影响因素的实证研究,但是定量解析绿地群落结构与降温增湿功能关系的研究并不多见。本文基于北京市24个典型绿地群落夏季降温增湿效果的实测,重点解析了绿地郁闭度和绿量对降温增湿功能的影响,并提出了绿地结构优化配置的最优阈值。研究结果表明：北京城市绿地夏季日均降温幅度0.2~2.0℃,日均增湿幅度0.20%~8.26%;不同群落结构绿地降温效果上,乔灌草型>乔草或乔木型>灌草型>草地型;在绿地增湿效果上,乔木型>乔灌草>乔草型>灌草型>草地型;郁闭度和绿量对绿地降温增湿功能均有明显影响,但郁闭度影响更大,冠层郁闭度介于0.60~0.85、三维绿量密度≥5m3/ m2的乔灌草或乔草型绿地具有最大降温增湿功能。 相似文献
16.
甲烷（CH4）是大气中重要的温室气体,其对全球气候变暖的增温贡献已达15%。受海洋和陆地双重影响的滨海湿地是CH4重要的自然来源。本文综述了滨海湿地CH4的产生过程、通量特征以及影响因素的研究动态。CH4产生过程的研究以分子生物学为主,在淡水湿地研究较多,因此在深度和广度上都有待突破。滨海湿地CH4通量特征具有较大的时空差异,这种差异性受到不同地域特殊的土壤理化性质、水文状况和植物群落等多种因素影响。水文条件是CH4产生和排放的决定性因素,温度和pH通过影响产CH4微生物活性来影响CH4排放。盐分对滨海湿地CH4影响的研究主要集中在浓度上,对盐分中
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科学制定生态补偿标准,引导企业合理规模种植,对于南方红壤丘陵山地生态脆弱区耕地生态修复具有重要意义。本文从企业微观经济行为出发,构建南方红壤丘陵山地新开垦耕地的生态补偿标准、新增生态系统服务价值、利用方式转化比例、土地面积的四维空间理论方法,提出以生态修复为导向的南方红壤丘陵山地生态脆弱区新开垦耕地补偿标准制定方法。并以浙江省松阳县为例进行了实证研究,研究结果表明：面向720 hm2待修复耕地,综合考虑当前生态补偿标准、县级政府财政能力,设定生态系统服务价值修复量为1000万元/a,补偿标准0.51万元/（hm2·a）,企业转换利用方式种植脐橙转换面积比例为50%,需补偿资金为368.49万元/a。建议面向南方红壤丘陵山地生态脆弱区新开垦耕地生态修复目标,出台空间定位准确和补偿标准细化的精准生态补偿政策。 相似文献
18.
海岸带蓝碳是介于海洋蓝碳和陆地绿碳之间的一种碳库,拥有巨大的碳汇潜能,对其固碳能力的研究具有重要的现实意义。本文选取1997年、2007年和2017年3个时间点的胶州湾遥感数据,在ArcGIS中解译获取胶州湾海岸带湿地数据,基于InVEST模型,对胶州湾海岸带蓝碳分布的时空格局演变及其服务价值进行评估。研究结果显示：①从时间上来看：1997—2007年和2007—2017年蓝碳总量分别是3.49亿t和2.32亿t,呈减少趋势。②从空间上来看：1997—2007年间蓝碳最大值主要分布在潮下带区域和河套、上马、棘洪滩、九龙街道的芦苇、碱蓬、大米草和养殖池区域,2007—2017年间主要分布在九龙、流亭、棘洪滩、上马街道的养殖池和芦苇区域。③1997—2017年20年间胶州湾蓝碳总价值为8522.13亿元,海岸带拥有巨大的碳汇潜能,保护海岸带生态系统刻不容缓。本文结果可为海岸带生态系统服务价值评估提供有益参考。 相似文献
19.
20.
We use a lattice-Boltzmann based Brownian dynamics simulation to investigate the separation of different lengths of DNA through the combination of a trapping force and the microflow created by counter-rotating vortices. We can separate most long DNA molecules from shorter chains that have lengths differing by as little as 30%. The sensitivity of this technique is determined by the flow rate, size of the trapping region, and the trapping strength. We expect that this technique can be used in microfluidic devices to separate long DNA fragments that result from techniques such as restriction enzyme digests of genomic DNA.The development of novel methods for manipulating biopolymers such as DNA is required for the continued advancement of microfluidic devices. Techniques such as restriction enzyme digests for genomic sequencing rely on the detection of DNA that differ in length by sometimes thousands of base pairs.1 Methods that separate DNA strands with resolutions on the order of kilobase pairs are required to analyze the products of this technique. To gain an insight into possible techniques to separate polymers, it can be helpful to review the methods to separate particles in microfluidic devices. Experimental work has shown how hydrodynamic mechanisms can lead to separation of particles based on size and deformability.2 Eddies, microvortices, and hydrodynamic tweezers have been used to trap and sort particles. The mechanism of the trapping and sorting arises from the differences between interactions of the particles with the fluid.2–8 In particular, counter-rotating vortices have been used to sort particles and manipulate biopolymers. They have been used to deposit DNA precisely across electrodes9 and trap DNA.10,11 Vortex flow may therefore be a good basis for a technique for sorting DNA by length.Streaming flow has been used in experiments to separate colloids of different size.3,4 Particles are passed through a channel with a flow field driven by oscillating bubbles and pressure. The flow field becomes a combination of closed and open streamlines. The vortex flow is controlled by the accoustic driving of the bubbles while pressure controls the net flow of the fluid. Larger particles are trapped in the closed vortex flow created by the bubbles, while smaller particles can escape the neighborhood of a bubble in the open streamlines. This leads to efficient separation of particles with size differences as small as 1 μm.Previous work on DNA has shown that counter-rotating vortices can be used to trap DNA dynamically. Long strands of DNA have been observed to stretch between the centers of two counter-rotating vortices. The polymer stays trapped in this state for significant amounts of time.12 In a different experiment, the vortices were used to thermally cycle the polymer and allow replication via the polymerase chain reaction (PCR). The DNA is also trapped against one wall by a thermophoretic force in these experiments.10 The strength of the trap is controlled by the gradient in temperature created by a focused infrared laser beam.Trapping DNA at one wall by counter-rotating vortices has also been explored in simulation and found to depend on the Peclet number, Pe = umaxL/Dm, where umax is the maximum speed of the vortex, L is the box size, and Dm is the diffusion coefficient of one bead in the polymer chain.11 The trapping rate of the DNA was shown to depend on the competition between the flow compressing the DNA into the trap region and the diffusion of the DNA out of the trap. For the work presented here, Pe ≅ 2000, similar to the previous work done with the same simulation.We extend the previous work to investigate if counter-rotating vortices can be used to separate DNA of different lengths. We use the same type of simulation outlined in Refs. 11 and 13–17, based on the lattice-Boltzmann method. The simulation method has successfully modeled systems as diverse as thermophoresis of DNA,14 migration of DNA in a microchannel,16 and translocation of DNA through a micropore.17,18 Using this method, the fluid is broken into a lattice with size, ΔL, chosen to be 0.5 μm, and is coupled to a worm-like chain model with Brownian dynamics for the polymer.19,20 The fluid velocity distribution function, ni(r, t), describes the fraction of fluid particles with a discretized velocity, ci, at each lattice site.21–24 A discrete velocity scheme with nineteen different velocities in three dimensions is used. The velocity distributions will evolve according to
(1)where L is a collision operator such that the fluid relaxes to the equilibrium distribution, given by a second-order expansion of the Maxwell-Boltzmann distribution
(2)where is the speed of sound. Δτ is the time step for the fluid in the simulation, Δτ = 8.8 × 10−5. The coefficients aci are determined by satisfying a local isotropy condition
(3)To simplify computation, the velocity distributions are transformed into moment space. The density ρ, momentum density j, and momentum flux density Π are some of the hydrodynamic moments of ni(r, t). The equilibrium conditions for these three moments are given by
(4)
(5)
(6)L has eigenvalues , which are the characteristic relaxation times of the moments. The Bhatanagar-Gross-Krook model is used to determine L:25 the non-conserved moments have a single relaxation time, τs = 1.0. The conserved moments are density and momentum; for these, τ−1 = 0. Fluctuations are added to the fluid stress as in the method of Ladd.24 We have also compared simulations with lattice sizes of 1 μm and 0.25 μm and found no significant differences in the results.The DNA used in the simulation is represented by a worm-like chain model parameterized to capture the dynamics of YOYO-stained λ DNA in bulk solution at room temperature.15,16,26 Long, flexible DNA is modeled since techniques to separate long DNA molecules with kilobase pair resolution are necessary to complete techniques such as genomic level sequencing using restriction enzyme digests.1 In addition, such DNA is often used in experiment. Its properties are similar to unstained DNA or DNA stained by other methods.27 Each molecule is represented by Nb beads and Nb − 1 springs. A chain composed of Nb − 1 springs will have a contour length of (Nb − 1) × 2.1 μm. The forces acting on each monomer include: an excluded volume force, a non-linear spring force, the viscous drag force, a random force that produces Brownian motion, a repulsive force from the container walls, and an attractive trapping force only at one wall as shown in Fig. Fig.11.13 The excluded volume interaction between beads i and j located at ri and rj is modeled using the following potential:
(7)where is the excluded volume parameter with σk = 0.105 μm, the length of one Kuhn segment, Nks = 19.8 is the number of Kuhn segments per spring, and is the characteristic size of the bead. This excluded volume potential reproduces self avoiding walk statistics. The non-linear spring force is based on force-extension curves from experiments and is given by
(8)which applies when Nks ≫ 1.Open in a separate windowFIG. 1.Simulation set-up. Arrows indicate direction of fluid flow. The region where the trapping force is active is shaded, and its width (Xstick) is shown. The region used to determine the trapping rate is indicated by the area labeled trap region. Figure is not to scale, the trap region and Xstick are smaller than shown.The beads are modeled as freely draining but subject to a drag force given by
Ff = ?6πηa(up ? uf).(9)The beads are also subjected to a random forcing term that is drawn from a Gaussian distribution with zero mean and a variance
σv = 2kBTζΔt.(10)The random force reproduces Brownian motion. To conserve total momentum, the momentum change imparted to the beads through their interactions with the fluid is balanced by a momentum change in the fluid. The momentum change is distributed to the three closest fluid lattice sites using a linear interpolation scheme based on the proximity of the lattice site to the polymer beads. Through this momentum transfer, hydrodynamic interactions between the beads occur.The beads are repelled from the walls with a force of magnitude
(11)where the repulsion range is 1ΔL. Each monomer will also be attracted to the top wall by a force with magnitude
(12)and range Xstick (see Fig. Fig.1).1). The sticking force is turned off every one out of one hundred time steps of the polymer (1% of the simulation time steps). We vary both Xstick and Kstick to achieve separation of the polymers.In previous experiments, DNA has been trapped against one wall by using thermophoresis,10 dielectrophoresis,28 and nanoplasmonic tweezers.29 In the case of thermophoresis, the trap strength (Kstick) can be controlled by tuning the intensity of the temperature gradient and the trap extension (Xstick) can be controlled through the area over which the gradient extends. Both of these are set through focusing of the laser used to produce local heating. Similarly, the trap parameters can be controlled when using plasmonic tweezers by controlling the laser beam exciting the nanoplasmonic structures. In dielectrophoresis, the DNA is trapped by an AC electric field and can be controlled by tuning the frequency and amplitude of the field.In this work, the number of polymers, Np, is 10 unless otherwise noted, and the container size is 25 ΔL × 50 ΔL × 2 ΔL. The time step for the fluid is Δτ = 8.8 × 10−5 s, and for the polymer is Δt = 3.7 × 10−6 s. The total simulation time is over 100 chain relaxation times, allowing sufficient independent samples to perform statistical analysis.Two counter-rotating vortices, shown in 1, are produced by introducing external forces to the fluid bound by walls in the x-direction and periodic in the y and z. Two forces of equal magnitude push on the fluid in the upper x region (12ΔL < x < 25ΔL): one in the +y-direction along y = 10ΔL, and one in the –y-direction along y = 40ΔL. Such counter-rotating vortices can be produced in microfluidic channels using acoustically driven bubbles,3,4,30 local heating,10 or plasmonic nanostructures.5 The flow speed is controlled by very different external mechanisms in each case. We therefore choose a simple model to produce fluid flow that is not specific to one mechanism.The simulations are started using random initial conditions, and therefore, both lengths of polymer are dispersed throughout the channel. Within a few minutes, the steady state configurations pictured in Figs. Figs.22 and and33 are reached. We define the steady state as when the number of polymer chains in the trap changes by less than one chain (10 beads) per 1000 polymer time steps. Intermittently, some polymers may still escape and re-enter the trap even in the steady state. Three final configurations are possible: Both the lengths of DNA have become trapped, both lengths continue to rotate freely, or the shorter strand has become trapped while the longer rotates freely. Two of these states leave the polymers mixed; in the third, the strands have separated by size.Open in a separate windowFIG. 2.Snapshots at t = 0Δt (left) and t = 2500Δt (right) showing the separation of 15-bead strands (grey) from 10-bead strands (black) of DNA. For these simulations, Kstick = 55 and Ystick = 0.7ΔL.Open in a separate windowFIG. 3.Snapshots at t = 0Δt and t = 2500Δt showing the separation of 13-bead strands (grey) from 10-bead strands (black) of DNA. For these simulations, Kstick = 55 and Ystick = 0.7ΔL as in Fig. Fig.2.2. Note that one long polymer is trapped, as well as all of the shorter polymers.By tuning the attractive wall force parameters and fluid flow, the separated steady state can be realized. We first set the flow parameters that allow the larger chains to rotate freely at the center of the vortices while the shorter chains rotate closer to the wall. The trap strength, Kstick, and extension, Xstick, are changed until the shorter polymers do not leave the trap. The same parameters were used to separate 10-bead chains from 15-bead and 13-bead chains.As shown in Fig. Fig.2,2, we have been able to separate shorter 10-bead chains from longer 15-bead chains. In the steady state, 97% of the rotating polymers were long polymers averaged over twenty simulations initialized with different random starting conditions. For three simulations, one small polymer would intermittently leave the trap region. In two of these simulations, one long polymer became stably trapped in the steady state. In another simulation, one 15-bead chain was intermittently trapped. On average, the trapped polymers were 5% 15-bead chains and 95% 10-bead chains. Again, 97% of the rotating polymers were 15-bead chains.Simulations conducted with 10-bead and 13-bead chains also showed significant separation of the two sizes as can be seen in Fig. Fig.3.3. In the steady state, 30% of the trapped polymers are 13-bead chains and 70% are 10-bead chains, averaged over twenty different random initial starting conditions and 1000 polymer time steps. Only 14.8% of the shorter polymers were not trapped, leading to 85.2% of the freely rotating chains being 13-bead chains. This is therefore a viable test to detect the presence of these longer chains.We have also separated 20-bead chains from 10-bead chains with all of the shorter chains trapped and all of the longer chains freely rotating in the steady-state. These results do not change for twenty different random initial starting conditions and 1000 polymer time steps. None of the longer polymers intermittently enter the trap region nor do any of the shorter polymers intermittently escape.The separation is achieved by tuning the trapping force and flow rate. Strong flows will push all the DNA molecules into the trap. The final state is mixed, with both short and long strands trapped. For flows that are too weak, the short molecules are not sufficiently compressed by the flow and therefore do not enter the trap region. The end state is mixed, with all polymers freely rotating. Separation is achieved when the flow rate is tuned so that the short strands are compressed against the channel wall while the long polymers rotate near the center of the vortices. The trap strength must then be set sufficiently high enough to prevent the short strands from being pulled by the hydrodynamic drag force out of the trap.The mechanism of the separation depends on the differences in the steady state configurations of the polymers and chances of a polymer escaping the trap. As shown in Fig. Fig.4,4, both longer and shorter chains are pulled into the trap region by the flow. However, the longer chains have a larger chance of a bead escaping into a region of the flow where the fluid velocity is sufficient to pull the entire strand out of the trap. As shown in Ref. 11, the trapping rate depends on diffusion in a polymer depleted region near the trap, in agreement with classical theory which neglects bead-wall interactions. In addition, the theory depends on the single bead diffusion rate and does not take into account the elastic force holding the beads together. Diffusion becomes as significant as convection in the polymer depleted region leading to dependence on the Peclet number. Since longer polymers have more beads; they have more chances of a single bead diffusing through this layer into the region where convection is again more important. Thus, they are pulled out of the trap at a faster rate than the shorter chains.Open in a separate windowFIG. 4.N, number of beads in the trap region, versus time for 15-bead DNA strands (solid line) and 10-bead DNA strands (dashed line). Here, ΔT = 10000Δt. The simulation parameters are the same as in Fig. Fig.22.In addition, longer chains have a second trap resulting from the microflow. As shown in Ref. 12, DNA in counter-rotating vortices can tumble at the center of one vortex or be stretched between the centers of the two vortices. We have observed both these conformations for the longer polymer strand. They are a stable trajectory for the longer polymer that remains outside of the trapping region. As seen in Fig. Fig.4,4, few monomers of the longer chains enter the trap region once the steady state has been reached. However, the shorter polymer rotates at a larger radius than the longer polymer as seen in Fig. Fig.5.5. The shorter polymers therefore are pushed back into the trap while the longer strands rotate stably outside the trapping region.Open in a separate windowFIG. 5.Trajectories of 15-bead DNA (grey) and 10-bead DNA (black). The position of each monomer is plotted for 100 consecutive time steps. Note that the longer polymers rotate in the center of the channel while the shorter polymers rotate at the edges. Simulation parameters are the same as in Fig. Fig.22.This mechanism is similar to the one proposed for the separation of colloids by size in Refs. 3 and 4. In that experimental work, the smaller colloidal particles rotated at larger radii. This allowed the smaller beads to be pushed out of the vicinity of the vortices by the streaming flow, while the larger beads continued to circle. However, in our simulations, we have the additional mechanism of separation based on the increased chance of a longer polymer escaping the trap region. This mechanism is important for maintaining the separation. Long polymers initially in the trap region or which diffuse into the trap would not be able to escape without it.We expect that this technique could be used to detect the sizes of DNA fragments on the order of thousands of base pairs. It relies on the flexibility of the molecule and its interaction with the flow. Common lab procedures such as restriction enzyme digests for DNA fingerprinting can produce these long fragments. Current techniques such as gel electrophoresis require significant time to separate the long strands that move more slowly through the matrix. This effect could therefore be a good candidate for developing a microfluidic analysis that is significantly faster than traditional procedures. Our separation occurs in minutes rather than in hours as for gel electrophoresis.As pointed out in Ref. 2, hydrodynamic effects have been shown to be important for microfluidic devices for separation. We have demonstrated, in simulation, a novel hydrodynamic mechanism for separating polymers by length. We hope that these promising calculations will inspire experiments to verify these results. 相似文献