Chemistry methods : new approaches to solving problems in chemistry最新文献

Disulfonated rhodamines are photostable and bright dyes widely used in life science and optical microscopy. However, di-sulfonated dyes were considered cell impermeable and not applicable in living cells. We challenged this assumption with 5 most popular rhodamines (Rho) having two carboxylic acid residues, versatile sulfonation patterns and emitting green (AS488), yellow (Rho530), orange (Rho565) and red (Rho590 and STAR RED) light. The probes comprising one rhodamine entity and a HaloTag^TM amine (O2) ligand (x) were prepared and applied for labeling of living, Vimentin-Halo (VIM-Halo) expressing U-2 OS cells. Surprisingly, we observed specific and bright staining with simplest compounds Rho590-x, Rho565-x and Rho530-x bearing two negative charges; they performed well also in stimulated emission depletion (STED) microscopy. Specific staining and red shifts in absorption and emission bands were observed with other probes having one negative charge; they were prepared by native chemical ligation and esterification.

An apparatus for real-time in-situ monitoring of electrochemical advanced oxidation processes using visible spectrophotometry has been developed. Central to the design is a 3D-printed sleeve that interfaces commercially available electrochemical and spectrophotometry units. Using the anodic oxidation of Acid Orange 7 as a test bed, the apparatus has been used for probing the impact of varying electrode composition, current density, electrolyte concentration, and stirring speed on the rate of decolorization. In addition, the unit was used to prove that decolorization can continue after electrolysis has been stopped, thereby showing the inherent value of real-time monitoring. Given that a significant challenge in the field of advanced oxidation processes is the inability to compare different reported systems, our approach, using commercially available equipment and a printable interface may open avenues for more standardized data collection.

Plastic is an important commodity that is used in several sectors. However, plastic waste generation is a pressing issue and needs attention as it risks the environment. While methods such as landfilling, incineration and recycling are known for handling plastic waste, they have their own limitations like generation of secondary pollutants and the low quality of the recycled plastic. In this scenario, new methods and technologies for efficiently handling plastic waste are the need of the hour as it is aggravating the concern of pollution and its health risks. This highlight article predominantly focuses on the recently reported combinatorial approach (Angew. Chem. Int. Ed. 2022, 61, e202212013), where it has been shown that integrating the magnetic property of bare Fe₃O₄ nanoparticles and nanozyme technology can be used for microplastic removal and degradation with nearly 100 % efficiency.

Automated prediction of reaction impurities is useful in early-stage reaction development, synthesis planning and optimization. Existing reaction predictors are catered towards main product prediction, and are often black-box, making it difficult to troubleshoot erroneous outcomes. This work aims to present an automated, interpretable impurity prediction workflow based on data mining large chemical reaction databases. A 14-step workflow was implemented in Python and RDKit using Reaxys® data. Evaluation of potential chemical reactions between functional groups present in the same reaction environment in the user-supplied query species can be accurately performed by directly mining the Reaxys® database for similar or ‘analogue’ reactions involving these functional groups. Reaction templates can then be extracted from analogue reactions and applied to the relevant species in the original query to return impurities and transformations of interest. Three proof-of-concept case studies (paracetamol, agomelatine and lersivirine) were conducted, with the workflow correctly suggesting impurities within the top two outcomes. At all stages, suggested impurities can be traced back to the originating template and analogue reaction in the literature, allowing for closer inspection and user validation. Ultimately, this work could be useful as a benchmark for more sophisticated algorithms or models since it is interpretable, as opposed to purely black-box solutions.

Measuring molecular binding kinetics represents one of the most important tasks in molecular interaction analysis. Surface plasmon resonance (SPR) is a popular tool for analyzing molecular binding. Plasmonic scattering microscopy (PSM) is a newly developed SPR imaging technology, which detects the out-of-plane scattering of surface plasmons by analytes and has pushed the detection limit of label-free SPR imaging down to a single-protein level. In addition, PSM also allows SPR imaging with high spatiotemporal resolution, making it possible to analyze cellular response to the molecular bindings. In this Mini Review, we present PSM as a method of choice for chemical and biological imaging, introduce its theoretical mechanism, present its experimental schemes, summarize its exciting applications, and discuss its challenges as well as the promising future.

Driven by the transition to a CO₂-neutral energy economy, research on polymer electrolyte fuel cells gained much interest during the last decade, with researchers trying to overcome the sluggish kinetics of the oxygen reduction reaction (ORR) limiting their performance. Modification of existing ORR catalysts with small amounts of ionic liquids (IL) represents an innovative approach to altering the catalytic activity and stability. ILs are supposed to take effect by modifying the local microenvironment at electrochemical interfaces. Nevertheless, a thorough understanding about the local distribution of ILs over solid catalysts is still lacking, hindering the IL modification strategy to be a generic approach to rationally modulating the catalytic performance of a catalyst. In this study we employed STEM-EDS spectral imaging to locate the IL distribution on the catalyst in presence of Nafion^TM. To overcome the difficulties associated with low energy STEM-EDS we setup a sophisticated data processing routine based on machine learning.