氮掺杂多孔石墨烯对硝基和偶氮化合物的催化还原性能——化学相关专业综合实验的设计与实践

针对溶有常见含硝基和偶氮化合物废水处理的问题,设计了无金属碳基催化剂制备及其对此类化合物催化还原的综合实验。实验以氧化石墨烯为原料,经水热法合成碳基催化剂氮掺杂多孔石墨烯或氮掺杂石墨烯,通过扫描电子显微镜、透射电子显微镜、比表面分析仪、X射线光电子能谱等对其结构、形貌和元素组成等进行表征,并研究其对硼氢化钠还原硝基和偶氮化合物的性能。该实验涉及碳基催化剂制备、表征和废水处理等多个实验环节,涵盖了物理化学、材料化学和环境化学等多个学科领域。通过该实验可以培养学生的科研思维,提高学生的科研动手能力和用化学知识解决实际问题的能力。

N-Doped graphene fiber anchored Pd nanoparticles as a fixed-bed catalyst for continuous-flow reduction of N-containing unsaturated compounds

Catalytic fixed-bed is an efficient and facile system for scalable organic synthesis due to its continuous and fast flow operation process.As a key unit in the fixed-bed system,catalytically active packing materials are required to possess some properties,such as high activity,excellent stability,and porous packing structure.Herein,we prepare a fibrous fixed-bed catalyst by anchoring Pd nanoparticles on N-doped graphene fiber(NHG)(Pd/NGF).Due to the porous and loose packing structure,the resultant Pd/NGF catalyst can be easily filled into the continuous-flow reactor to construct a fixed-bed system with low flow resistance.The corresponding catalytic fixed-bed system exhibits a favourable flow rate(8 mL/min)and excellent durability toward reduction reactions of N-containing unsaturated compounds to produce aromatic amines.This work provides a new design concept of fibrous fixed-bed catalysts with dual-active components(i.e.,graphene-derived active materials and metal nanoparticles)and catalytic organic synthesis in a continuous-flow process.

Single-atom catalysis for organic reactions

Metal-based catalysis,including homogeneous and heterogeneous catalysis,plays a significant role in the modern chemical industry.Heterogeneous catalysis is widely used due to the high efficiency,easy catalyst separation and recycling.However,the metal-utilization efficiency for conventional heterogeneous catalysts needs further improvement compared to homogeneous catalyst.To tackle this,the pursing of heterogenizing homogeneous catalysts has always been attractive but challenging.As a recently emerging class of catalytic material,single-atom catalysts(SACs)are expected to bridge homogeneous and heterogeneous catalytic process in organic reactions and have arguably become the most active new frontier in catalysis field.In this review,a brief introduction and development history of single-atom catalysis and SACs involved organic reactions are documented.In addition,recent advances in SACs and their practical applications in organic reactions such as oxidation,reduction,addition,coupling reaction,and other organic reactions are thoroughly reviewed.To understand structure-property relationships of single-atom catalysis in organic reactions,active sites or coordination structure,metal atom-utilization efficiency(e.g.,turnover frequency,TOF calculated based on active metal)and catalytic performance(e.g.,conversion and selectivity)of SACs are comprehensively summarized.Furthermore,the application limitations,development trends,future challenges and perspective of SAC for organic reaction are discussed.

石墨烯衍生物作为无金属碳基催化剂在有机催化中的应用

近年来,随着绿色化学和可持续发展等创新理念的提出,无金属催化剂逐渐成为催化领域的研究热点和前沿.石墨烯作为一种新型纳米碳材料,具有机械强度大、比表面积高、稳定性好、电学性质优异等特点,经过改性或功能化后的衍生物可以作为无金属碳基催化剂,在有机反应中展现了良好的应用前景.本文综述了石墨烯衍生物的结构和性质,探究了石墨烯基材料的结构与催化活性之间的关系,总结了此类材料作为无金属催化剂在氧化、还原/氢化、偶联、取代反应以及其他有机反应中的应用和反应机理.

Robust visual tracking via randomly projected instance learning

Purpose–The purpose of this paper is to design a robust tracking algorithm which is suitable for the real-time requirement and solves the mistake labeling issue in the appearance model of trackers with the spare features.Design/methodology/approach–This paper proposes a tracker to select the most discriminative randomly projected ferns and integrates a coarse-to-fine search strategy in this framework.First,the authors exploit multiple instance boosting learning to maximize the bag likelihood and select randomly projected fern from feature pool to degrade the effect of mistake labeling.Second,a coarse-to-fine search approach is first integrated into the framework of multiple instance learning(MIL)for less detections.Findings–The quantitative and qualitative experiments demonstrate that the tracker has shown favorable performance in efficiency and effective among the competitors of tracking algorithms.Originality/value–The proposed method selects the feature from the compressive domain by MIL AnyBoost and integrates the coarse-to-fine search strategy first to reduce the burden of detection.This paper designs a tracker with high speed and favorable results which is more suitable for real-time scene.