Ratiometric Detection of microRNA Using Hybridization Chain Reaction and Fluorogenic Silver Nanoclusters

miRNA (miR)-155 is a potential biomarker for breast cancers. We aimed at developing a nanosensor for miR-155 detection by integrating hybridization chain reaction (HCR) and silver nanoclusters (AgNCs). HCR serves as an enzyme-free and isothermal amplification method, whereas AgNCs provide a built-in fluorogenic detection probe that could simplify the downstream analysis. The two components were integrated by adding a nucleation sequence of AgNCs to the hairpin of HCR. The working principle was based on the influence of microenvironment towards the hosted AgNCs, whereby unfolding of hairpin upon HCR has manipulated the distance between the hosted AgNCs and cytosine-rich toehold region of hairpin. As such, the dominant emission of AgNCs changed from red to yellow in the absence and presence of miR-155, enabling a ratiometric measurement of miR with high sensitivity. The limit of detection (LOD) of our HCR-AgNCs nanosensor is 1.13 fM in buffered solution. We have also tested the assay in diluted serum samples, with comparable LOD of 1.58 fM obtained. This shows the great promise of our HCR-AgNCs nanosensor for clinical application.     

Chiral Interlocked Corrole Dimers Directly Linked at Inner Carbon Atoms of Confused Pyrrole Rings

A facile synthetic strategy towards conformationally stable chiral chromophores based on dimeric porphyrinoids has been established. A peculiar class of face-to-face intramolecularly interlocked corrole dimers were formed by the oxidative C−C coupling linked at the inner carbon sites upon simple treatment of copper(II) ions. Their intrinsic electronic structures were modulated by the peripheral corrole ring annulations, which lead to distinct optical properties and redox profiles. The stereogenic carbon centers implemented in the confused corrole skeleton provided a rationale for designing novel chiral materials.     

Hierarchical structure observation and nanoindentation size effect characterization for a limnetic shell

In the present research, hierarchical structure observation and mechanical property characterization for a type of biomaterial are carried out. The investigated biomaterial is Hyriopsis cumingii, a typical limnetic shell, which consists of two different structural layers, a prismatic "pillar"structure and a nacreous "brick and mortar" structure. The prismatic layer looks like a "pillar forest" with variationsection pillars sized on the order of several tens of microns.The nacreous material looks like a "brick wall" with bricks sized on the order of several microns. Both pillars and bricks are composed of nanoparticles. The mechanical properties of the hierarchical biomaterial are measured by using the nanoindentation test. Hardness and modulus are measured for both the nacre layer and the prismatic layer, respectively.The nanoindentation size effects for the hierarchical structural materials are investigated experimentally. The results show that the prismatic nanostructured material has a higher stiffness and hardness than the nacre nanostructured material.In addition, the nanoindentation size effects for the hierarchical structural materials are described theoretically, by using the trans-scale mechanics theory considering both strain gradient effect and the surface/interface effect. The modeling results are consistent with experimental ones.     

Polyvinylpyrrolidone and graphene-modified hematite nanoparticles for efficient electrocatalytic oxidation of p-nitrophenol

Journal of Solid State Electrochemistry - Polyvinylpyrrolidone (PVP) and graphene (G)-modified iron oxides (Fe2O3-PVP-G) are prepared by a simple hydrothermal reaction. Their morphology and...     

Investigations in the recrystallization of evolved gases from pyrolysis process of melamine

Journal of Thermal Analysis and Calorimetry - A complete analysis of the thermal process about melamine was presented, in which different methods were applied to determine the characteristic of the...     

紫色球杆菌视紫红质光谱特性的机器学习研究

近年来，机器学习等人工智能技术被应用于蛋白质工程，其在蛋白质结构、功能预测、催化活性等研究中具有独特优势。在未知蛋白质结构的情况下，将蛋白质序列和功能特性与机器学习相结合，基于序列-活性关系（innovative sequence-activity relationship，ISAR）算法，将蛋白质氨基酸序列数字化，用快速傅里叶变换（fast four transform，FFT）进行预处理，再进行偏最小二乘回归建模，可在数据集较少情况下拟合得到最佳模型。通过机器学习对紫色球杆菌视紫红质（gloeobacter violaceus rhodopsin，GR）的突变体蛋白质氨基酸序列与光谱最大吸收波长进行建模，获得了最佳模型。用最佳索引LEVM760106建模得到的确定系数R² 为0.944，均方误差E为11.64。用小波变换进行的预处理，其R² 虽也约为0.944，但E大于11.64，不及FFT进行的预处理。方法较好地解决了蛋白质序列与功能特性之间的数学建模问题，在蛋白质工程中可为预测更优的突变体提供支持。     

从废水到新能源:光催化燃料电池的优化与应用

随着经济的飞速发展,社会对能源的需求日益扩大,对工业废水的无害化处理也提出了更高的要求。光催化燃料电池 (photocatalytic fuel cell, PFC) 在燃料电池中引入半导体光催化材料作为电极,实现了有机污染物高效降解和同步对外产电的双重功能,在废水无害化与资源化利用方面具有潜在的应用价值。半导体光催化电极是PFC系统高效运行的核心组件,增强其可见光响应和光生载流子分离是提高PFC性能的关键策略。反应器结构设计和运行参数优化也有利于改善PFC性能。本文从PFC基本原理和应用入手,综述了PFC在环境污染物资源化处理中的研究进展,并详细阐述了提高PFC的污染控制性能和产电效率的优化手段,为进一步设计高效稳定的PFC系统并实现其在水污染控制和清洁能源生产中的应用提供理论指导。