人工智能助力当代化学研究

以机器学习为代表的人工智能在当代的科学研究中正在发挥越来越重要的作用.不同于传统的计算机程序,机器学习人工智能可以通过对大量数据的反复分析和自身模型的优化,即“学习”过程,从而在大量的数据中寻找客观事物的相互联系,形成具有更好预测和决策能力的新模型,做出合理的判断.化学研究的特点恰恰是机器学习人工智能的强项.化学研究经常要面对十分复杂的物质体系和实验过程,从而很难通过化学物理原理进行精准的分析和判断.人工智能可以挖掘化学实验中产生的海量实验数据的相关性,帮助化学家做出合理分析预测,大大加速化学研发过程.本文介绍了当代人工智能方法及用其解决化学问题基本原理,并通过具体案例展示了人工智能辅助解决不同化学研发问题的方法以及对应的机器学习算法.将人工智能运用在化学科学的尝试正处于蓬勃上升期,人工智能已经初步展示出对化学研究的强大助力,希望本文能帮助更多的国内的化学工作者了解和运用这一有力的工具.     

比色法在爆炸物检测中的研究进展

爆炸物杀伤力大、隐蔽性强,被广泛应用于军事战争,也是恐怖分子施暴方式的首选。基于保护人民生命和财产安全的迫切需求,爆炸物检测技术受到世界公共安全领域的高度重视。相比于其它分析方法,比色法因操作简便、抗干扰性强,不依赖分析设备的优势成为最适用于现场快速检测爆炸物的方法之一。该文主要综述了比色法在爆炸物探测领域的研究进展,包括对比色探针和比色阵列研究进展的总结与前景分析,并重点讨论了比色人工嗅觉系统及其在爆炸物检测中的应用,展望了其未来的发展趋势和应用前景。     

外泌体分离与纯化技术研究进展

外泌体是所有真核细胞分泌到细胞外的直径介于30～150 nm的一种膜性纳米囊泡,参与细胞间生物信号的传递。大量实验证据表明,外泌体参与多种生物功能并发挥重要作用,包括蛋白质、RNA和脂质等生物分子的转移及多种疾病生理和病理过程的调节,被认为是疾病诊断、治疗和预后的重要的生物标志物和药物载体,因此发展简单、高效、经济的外泌体分离与纯化技术将有助于疾病的早期诊断和精准治疗。目前,利用外泌体的物理化学和生物化学特性已开发出多种分离外泌体的技术,但仍缺乏标准化和规模化临床级外泌体的分离方法,从而限制了其临床应用。另外,对分离出的外泌体的特征、纯度和数量的鉴定是判断外泌体分离纯化方法优劣的重要指标。本文综述了外泌体分离与纯化技术以及鉴定方法的研究进展,主要讨论分离技术的机制、性能、挑战和前景以及外泌体的鉴定方法,以期为外泌体的分离纯化提供新的思路和解决策略。     

Boosting Hydrogen Evolution at Visible Light Wavelengths by Using a Photocathode with Modal Strong Coupling between Plasmons and a Fabry-Pérot Nanocavity

Hot-hole injection from plasmonic metal nanoparticles to the valence band of p-type semiconductors and reduction by hot electrons should be improved for efficient and tuneable reduction to obtain beneficial chemical compounds. We employed the concept of modal strong coupling between plasmons and a Fabry-Pérot (FP) nanocavity to enhance the hot-hole injection efficiency. We fabricated a photocathode composed of gold nanoparticles (Au−NPs), p-type nickel oxide (NiO), and a platinum film (Pt film) (ANP). The ANP structure absorbs visible light over a broad wavelength range from 500 nm to 850 nm via hybrid modes based on the modal strong coupling between the plasmons of Au−NPs and the FP nanocavity of NiO on a Pt film. All wavelength regions of the hybrid modes of the modal strong coupling system promoted hot-hole injection from the Au−NPs to NiO and proton/water reduction by hot electrons. The incident photon-to-current efficiency based on H₂ evolution through water/proton reduction by hot electrons reached 0.2 % at 650 nm and 0.04 % at 800 nm.     

超高效液相色谱-四极杆飞行时间质谱法快速筛查水产品中15种碱性合成色素

建立了超高效液相色谱-四极杆飞行时间质谱(UPLC-Q-TOF MS)快速检测水产品中15种碱性合成色素的方法。水产样品经乙腈(含10%(v/v)乙酸)提取,采用C₁₈复合硅胶基质分散管进行样品净化处理。目标物采用C₁₈色谱柱分离,以乙腈和0.1%(v/v)甲酸-5 mmol/L乙酸铵溶液为流动相,梯度洗脱,四极杆飞行时间串联质谱电喷雾正离子模式检测。结果表明:15种碱性色素的定量限(LOQs, S/N=10)为0.1~100 μg/kg,并在各自的线性范围内线性关系良好,相关系数(r)≥0.993。在3个加标水平下的平均回收率为80.60%~107.37%,测定结果的相对标准偏差为3.33%~6.69%(n=6)。该方法快速、简便、灵敏度高,适用于日常水产品中15种碱性合成色素的快速筛查。     

Water‐Splitting Catalysis and Solar Fuel Devices: Artificial Leaves on the Move

The development of new energy materials that can be utilized to make renewable and clean fuels from abundant and easily accessible resources is among the most challenging and demanding tasks in science today. Solar‐powered catalytic water‐splitting processes can be exploited as a source of electrons and protons to make clean renewable fuels, such as hydrogen, and in the sequestration of CO₂ and its conversion into low‐carbon energy carriers. Recently, there have been tremendous efforts to build up a stand‐alone solar‐to‐fuel conversion device, the “artificial leaf”, using light and water as raw materials. An overview of the recent progress in electrochemical and photo‐electrocatalytic water splitting devices is presented, using both molecular water oxidation complexes (WOCs) and nano‐structured assemblies to develop an artificial photosynthetic system.     

新型希夫碱型钳形人工受体的微波合成及其对中性分子的识别性能

以对苯二甲酸为起始原料,设计并合成了8个新型的芳环钳形人工受体(3a～3h),其结构经1H NMR,IR,ESI-MS和元素分析表征。利用UV-Vis滴定法初步考察了3a～3h对中性分子(邻苯二胺,间苯二胺,对苯二胺和二苯甲酮)的识别性能,结果表明3a～3h对其具有良好的识别性能。     

人工神经网络研究进展及其在光谱分析中的应用

介绍最常用的人工神经网络——BP神经网络的原理、结构及其研究进展,评述了人工神经网络在光谱分析中的应用。     

合成高密度烃类燃料研究进展

本文对合成高密度烃类燃料的进展进行了总结,分别对多环烃类燃料、高张力笼状烃类燃料和添加纳米级微粒的燃料进行了评述.以烃类物质为原料,通过聚合、加氢、异构等工艺合成的多环高密度燃料拥有较高的能量密度和较佳的稳定性能,是目前高密度燃 料的发展重点.高张力笼状烃类燃料和添加纳米级微粒的燃料拥有更大的密度(一般大于1g/ cm3)和燃烧热值,是极具发展前景的新一代燃料。     

Investigation of anthropic effects connected with metal ions concentration, organic matter and grain size in Bormida river sediments

An investigation on the sediment composition and grain size was carried out along the Bormida river (Piedmont, Northern Italy). The samples were taken both in the riverbed and on its banks. Multivariate statistical exploratory methods permitted to identify possible sources of primary pollution. In particular, principal component analysis (PCA) showed that there are probably at least three anthropogenic main causes of pollution, one connected with Cr, Ni and Sn, the second with cadmium and the last one with mercury. Some correlations were found between heavy metal ion concentrations and organic matter and/or the sediment grain size. Differences between the samples collected in the riverbed and on the banks were identified and the relationships between the principal components and the distance of the samples from the riverbed and from the hot spot represented by the ACNA industrial site were also analyzed.