Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fund...Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fundamental trade-off between haze and transparency,coupled with impractical thicknesses(≥1 mm).Inspired by squid’s skin-peeling mechanism,this work develops a peroxyformic acid(HCOOOH)-enabled precision peeling strategy to isolate intact 10-μm-thick bamboo green(BG)frameworks—100×thinner than wood-based counterparts while achieving an unprecedented optical performance(88%haze with 80%transparency).This performance surpasses delignified biomass(transparency<40%at 1 mm)and matches engineered cellulose composites,yet requires no energy-intensive nanofibrillation.The preserved native cellulose I crystalline structure(64.76%crystallinity)and wax-coated uniaxial fibril alignment(Hermans factor:0.23)contribute to high mechanical strength(903 MPa modulus)and broadband light scattering.As a light-management layer in polycrystalline silicon solar cells,the BG framework boosts photoelectric conversion efficiency by 0.41%absolute(18.74%→19.15%),outperforming synthetic anti-reflective coatings.The work establishes a scalable,waste-to-wealth route for optical-grade cellulose materials in next-generation optoelectronics.展开更多
实体识别是数据量质融合管理中的一项关键技术,对能否提高数据质量起着决定性作用.其目的在于识别出数据中表示同一对象的不同形式;以及同一形式所代表的不同对象.随着大数据研究技术的发展,大数据上的实体识别问题受到了广泛关注.因此...实体识别是数据量质融合管理中的一项关键技术,对能否提高数据质量起着决定性作用.其目的在于识别出数据中表示同一对象的不同形式;以及同一形式所代表的不同对象.随着大数据研究技术的发展,大数据上的实体识别问题受到了广泛关注.因此,在大数据的信息集成背景下,给出了一个基于Map-Reduce框架的大数据实体识别算法(entity identification in big data based on Map-Reduce,EIBM).该算法首先通过属性值计算记录间的相似程度,而后基于图聚类的方法进行实体识别从而输出得到最终结果.最后,在Hadoop平台上对真实数据集和人造数据集进行了多组实验,实验结果验证了算法的并行程度和对于处理大数据的有效性与高效性.展开更多
基金supported by National Natural Science Foundation of China(32494793).
文摘Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fundamental trade-off between haze and transparency,coupled with impractical thicknesses(≥1 mm).Inspired by squid’s skin-peeling mechanism,this work develops a peroxyformic acid(HCOOOH)-enabled precision peeling strategy to isolate intact 10-μm-thick bamboo green(BG)frameworks—100×thinner than wood-based counterparts while achieving an unprecedented optical performance(88%haze with 80%transparency).This performance surpasses delignified biomass(transparency<40%at 1 mm)and matches engineered cellulose composites,yet requires no energy-intensive nanofibrillation.The preserved native cellulose I crystalline structure(64.76%crystallinity)and wax-coated uniaxial fibril alignment(Hermans factor:0.23)contribute to high mechanical strength(903 MPa modulus)and broadband light scattering.As a light-management layer in polycrystalline silicon solar cells,the BG framework boosts photoelectric conversion efficiency by 0.41%absolute(18.74%→19.15%),outperforming synthetic anti-reflective coatings.The work establishes a scalable,waste-to-wealth route for optical-grade cellulose materials in next-generation optoelectronics.
文摘实体识别是数据量质融合管理中的一项关键技术,对能否提高数据质量起着决定性作用.其目的在于识别出数据中表示同一对象的不同形式;以及同一形式所代表的不同对象.随着大数据研究技术的发展,大数据上的实体识别问题受到了广泛关注.因此,在大数据的信息集成背景下,给出了一个基于Map-Reduce框架的大数据实体识别算法(entity identification in big data based on Map-Reduce,EIBM).该算法首先通过属性值计算记录间的相似程度,而后基于图聚类的方法进行实体识别从而输出得到最终结果.最后,在Hadoop平台上对真实数据集和人造数据集进行了多组实验,实验结果验证了算法的并行程度和对于处理大数据的有效性与高效性.
