Development and validation of an LC–MS/MS method for the quantification of artificial sweeteners in human matrices

Artificial sweeteners are widely used as substitutes for sugar. The sweeteners are generally considered safe, however their whereabouts during pregnancy and lactation and the effect on child development are poorly explored. There is a need for new tools to measure these substances during pregnancy and lactation. Here, we describe the development and validation of a sensitive liquid chromatography–tandem mass spectrometry method for the simultaneous quantification of acesulfame, cyclamate, saccharin and sucralose in human plasma, umbilical cord blood, amniotic fluid and breast milk. The samples were prepared by protein precipitation and separated on a Luna Omega Polar C₁₈ column (2.1 × 50 mm, 1.6 μm). Electrospray ionization in negative mode and multiple reaction monitoring were used to monitor the ion transitions. The validated concentration ranges were from 1 to 500 ng/ml (10–500 ng/ml for sucralose). Interassay precisions were all ≤15% and the accuracies were within ±15%. Stability, linearity, dilution integrity, carryover and recovery were also examined and satisfied the validation criteria. Finally, this analytical method was successfully applied on spiked samples of plasma, umbilical cord blood, amniotic fluid and breast milk, proving its suitability for use in clinical studies on artificial sweeteners, including during pregnancy and lactation.     

多组件学习器实现有机分子沸点的精准预测

沸点(BP)是有机分子液体的基本物理化学量, 也是化学工业生产中的重要参数. 有机分子的沸点由分子结构决定, 呈现复杂的结构-沸点关系, 函数法(Function Method)、基团贡献法(Group Contribution Method)等传统方法无法应对复杂多样有机分子结构的预测, 应用范围狭窄, 预测精度低. 本研究中, 我们利用基于人工神经网络(ANN)和支持向量机(SVM)的多组件学习器实现有机分子沸点的精准预测. 我们构建了基于可解释性描述符的ANN、基于相关性描述符的ANN及基于复合分子指纹的SVM三个异质模型, 并通过包含4550个各种类别的有机分子沸点的数据集进行训练得到了三个异质性学习器, 最后集成三个学习器对有机分子沸点进行预测. 相比于传统方法和此前的定量结构性质关系(QSPR)模型, 多组件模型结合了三种模型的优点, 展现出很好的预测精度和泛化能力以及低的过拟合, 实现了对多种类型有机分子的沸点的有效预测.     

Pyrimidoquinazolinophenanthroline Opens Next Chapter in Design of Bridging Ligands for Artificial Photosynthesis**

The synthesis and detailed characterization of a new Ru polypyridine complex containing a heteroditopic bridging ligand with previously unexplored metal-metal distances is presented. Due to the twisted geometry of the novel ligand, the resultant division of the ligand in two distinct subunits leads to steady state as well as excited state properties of the corresponding mononuclear Ru(II) polypyridine complex resembling those of prototype [Ru(bpy)₃]²⁺ (bpy=2,2'-bipyridine). The localization of the initially optically excited and the nature of the long-lived excited states on the Ru-facing ligand spheres is evaluated by resonance Raman and fs-TA spectroscopy, respectively, and supported by DFT and TDDFT calculations. Coordination of a second metal (Zn or Rh) to the available bis-pyrimidyl-like coordination sphere strongly influences the frontier orbitals, apparent by, for example, luminescence quenching. Thus, the new bridging ligand motif offers electronic properties, which can be adjusted by the nature of the second metal center. Using the heterodinuclear Ru−Rh complex, visible light-driven reduction of NAD⁺ to NADH was achieved, highlighting the potential of this system for photocatalytic applications.     

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.     

金属铁络合物光催化二氧化碳还原

二氧化碳(CO₂)光催化还原技术因兼具解决能源和全球变暖问题的潜力而受到关注。金属铁络合物作为分子型催化剂,具有价格低廉、量子效率高、结构可调控和选择性好等优势,表现出优异的CO₂光催化还原性能,成为CO₂光催化还原领域的研究热点。本文综述了近年来基于金属铁络合物光催化二氧化碳还原研究进展。介绍了铁金属络合物(如:铁卟啉、铁多吡啶、五齿铁配合物)CO₂均相光催化还原体系,总结了体系的构成以及作用机理等,着重关注了体系的催化效率和产物的选择性。此外,综述了以半导体纳米材料/量子点作为光敏剂,金属铁络合物作为催化剂的非均相催化体系的研究进展。最后,对该领域未来的研究方向和所面临的挑战做出展望。     

人工智能在锂离子电池研发中的应用

锂离子电池已成为解决现代社会储能问题的最佳解决方案之一。然而,电池材料和器件开发都是复杂的多变量问题,传统的依赖研究人员进行实验的试错法在电池性能提升方面遇到了瓶颈。人工智能（AI）具有强大的高速、海量数据处理能力,是上述突破研究瓶颈的最具潜力的技术。其中,机器学习 (ML) 算法在评估多维数据变量和集合之间的组合关联方面的独特优势有望帮助研究人员发现不同因素之间的相互作用规律并阐明材料合成和设备制造的机制。本综述总结了锂离子电池传统研究方法遇到的各种挑战,并详细介绍了人工智能在电池材料研究、电池器件设计与制造、材料与器件表征、电池循环寿命与安全性评估等方面的应用。最重要的是,我们介绍了AI和ML在电池研究中面临的挑战,并讨论了它们应用的缺点和前景。我们相信,未来实验科学家、数学建模专家和AI专家之间更紧密的合作将极大地促进AI和ML方法用以解决传统方法难以克服的电池和材料问题。     

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。     

Photoinduced Water Oxidation in Chitosan Nanostructures Containing Covalently Linked RuII Chromophores and Encapsulated Iridium Oxide Nanoparticles

The luminophore Ru(bpy)₂(dcbpy)²⁺ (bpy=2,2’-bipyridine; dcbpy=4,4’-dicarboxy-2,2’-bipyridine) is covalently linked to a chitosan polymer; crosslinking by tripolyphosphate produced Ru-decorated chitosan fibers (NS-RuCh), with a 20 : 1 ratio between chitosan repeating units and Ru^II chromophores. The properties of the Ru^II compound are unperturbed by the chitosan structure, with NS-RuCh exhibiting the typical metal-to-ligand charge-transfer (MLCT) absorption and emission bands of Ru^II complexes. When crosslinks are made in the presence of IrO₂ nanoparticles, such species are encapsulated within the nanofibers, thus generating the IrO₂⊂NS-RuCh system, in which both Ru^II photosensitizers and IrO₂ water oxidation catalysts are within the nanofiber structures. NS-RuCh and IrO₂⊂NS-RuCh have been characterized by dynamic light scattering, scanning electronic microscopy, and energy-dispersive X-ray analysis, which indicated a 2 : 1 ratio between Ru^II chromophores and IrO₂ species. Photochemical water oxidation has been investigated by using IrO₂⊂NS-RuCh as the chromophore/catalyst assembly and persulfate anions as the sacrificial species: photochemical water oxidation yields O₂ with a quantum yield (Φ) of 0.21, definitely higher than the Φ obtained with a similar solution containing separated Ru(bpy)₃²⁺ and IrO₂ nanoparticles (0.05) or with respect to that obtained when using NS-RuCh and “free” IrO₂ nanoparticles (0.10). A fast hole-scavenging process (rate constant, 7×10⁴ s⁻¹) involving the oxidized photosensitizer and the IrO₂ catalyst within the IrO₂⊂NS-RuCh system is behind the improved photochemical quantum yield of IrO₂⊂NS-RuCh.     

糖厂澄清工段生产指标建模及操作参数优化

针对亚法糖厂澄清工段清汁色值和清汁残硫量难以在线测量的问题,提出了一种基于人工蜂群优化的在线极限学习机软测量方法。先用核主元分析法确定影响清汁质量的关键参数,建立基于在线极限学习机的软测量模型。同时利用人工蜂群算法对在线极限学习机的隐层参数进行寻优,优化所建模型。最后,使用带约束的粒子群对软测量模型进行优化求解,得到典型工况下的最优操作设定值,为后续工况操作提供参考依据。仿真结果表明,基于人工蜂群优化的在线极限学习机模型能够准确地预测清汁色值和残硫量,同时基于此模型优化的操作参数设定值能够达到期望的指标。     

Aqueous‐Solution Route to Zinc Telluride Films for Application to CO2 Reduction

As a photocathode for CO₂ reduction, zinc‐blende zinc telluride (ZnTe) was directly formed on a Zn/ZnO nanowire substrate by a simple dissolution–recrystallization mechanism without any surfactant. With the most negative conduction‐band edge among p‐type semiconductors, this new photocatalyst showed efficient and stable CO formation in photoelectrochemical CO₂ reduction at ?0.2–?0.7 V versus RHE without a sacrificial reagent.