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

The new possibility to perform operando X-ray absorption spectroscopy (XAS) in the laboratory expands the potential field of applications towards a broad research community. These applications are multidisciplinary at heart and benefit from joint expertise from different fields, most importantly chemistry, physics, geology, and instrumentation. Hence, a development of collaboration networks that combine skills and knowhow from different fields is highly beneficial in this endeavor. As operando laboratory-based XAS constitutes a highly interesting, advanced, and powerful characterization technique, we provide in this article practical guidelines for newcomers in the field, who would like to employ it. Here, we will describe ten important steps towards a successful operando laboratory-based XAS experiment, which are not only useful for the catalysis community, but for a much wider audience from other research fields, such as environmental chemistry as well as battery and fuel cell research.

The reduction of CO₂ in water can yield a variety of volatile products mixed with the starting material and often dinitrogen as an inert gas. While mass spectrometry is ideally suited to the quantitative analysis of gases in low concentrations, the simultaneous detection is usually performed with a preliminary chromatographic separation. In its absence, the mass spectrometric signal at m/z=28 can be due to CO, CO₂, and N₂. Here, we demonstrate that ionizing the mixture of reaction products under 16 eV results in the selective detection of CO at m/z=28, at the complete exclusion of CO₂ and N₂. This method is applicable to headspace analysis after a bulk electrolysis and delivers product compositions as they depend on catalyst and applied potential. Furthermore, its immediate nature also enables the experimentalist to perform, in real time, a direct monitoring of the reaction products generated during cyclic voltammetry.

Compound identification in complex mixtures by NMR and MS is best achieved through experimental databases (DB) mining. Experimental DB frequently show limitations regarding their completeness, availability or data quality, thus making predicted database of increasing common use. Querying large databases may lead to select unlikely structure candidates. Two approaches to dereplication are thus possible: filtering of a large DB before search or scoring of the results after a large scale search. The present work relies on the former approach. As far as we know, nmrshiftdb2 is the only open-source ¹³NMR chemical shift predictor that can be freely operated in batch mode. CFM-ID 4.0 is one of the best-performing open-source tools for ESI-MS/MS spectra prediction. LOTUS is a freely usable and comprehensive collection of secondary metabolites. Integrating the open source database and software LOTUS, CFM-ID, and nmrshiftdb2 in a dereplication workflow requires presently programming skills, owing to the diversity of data encoding and processing procedures. A graphical user interface that integrates seamlessly chemical structure collection, spectral data prediction and database building still does not exist, as far as we know. The present work proposes a stand–alone software tool that assists the identification of mixture components in a simple way.

Alkaline phosphatase (AP) enzymes are of broad interest in fields ranging from biochemistry and medicine to biotechnology and nanotechnology. Characterising the catalytic activity of AP is typically realised by either employing non-natural signal-generating substrates that are detectable by absorbance and fluorescence spectroscopy or by quantifying the release of inorganic phosphate by the classic malachite green assay. The latter method is often required for studying “spectroscopically silent” biomolecular substrates, but it does not enable continuous monitoring of kinetics in real-time. In recent years, newer techniques for studying AP function have been developed to circumvent this limitation, including fluorescent and colourimetric substrate-specific assays based on supramolecular chemistry, organic probes and nanomaterials, as well as other assays based on isothermal titration calorimetry, direct detection with infrared spectroscopy and mass spectrometry, and monitoring conformational change by fluorescent nanoantennas. Here, we review these strategies and comment on their strengths and weaknesses in the context of AP.

Reactor automation is revolutionising the way new chemical processes are discovered and developed. Assigning repetitive aspects of chemical synthesis to machines, such as experimental execution and data collection, provides more time for researchers to focus on critical interpretation and creative problem solving. The ability to autonomously prepare late-stage intermediates and complex products, rather than just simple starting materials, will play a central role in applications such as the efficient exploration of chemical space and responsive manufacturing. However, translating automated technologies from specific single-step tasks to more general multistep syntheses remains a significant challenge, owing to high structural diversity and chemical/physical interdependencies between the steps. Robotic batch and continuous flow platforms are gradually becoming more universal, providing access to a wider range of chemistries required to achieve autonomous multistep synthesis. Advances in process analytical technologies have enhanced our ability to monitor interconnected reactions in real-time, thus accelerating data collection and giving greater process control for ensuring a high standard of safety and product quality. Integration of these tools with control software creates a feedback loop, which can be harnessed for adaptive and flexible multistep screening or holistic self-optimisation. This review presents recent developments in the application of automated reactor technologies for multistep chemical synthesis, including batch and continuous flow platforms. Specifically, this review highlights how the integration of control software with advanced process analytical technologies and machine learning algorithms are accelerating the synthesis of complex molecules.

For the last fifteen years, photochemistry has known a renewal with the emergence of new photoredox approaches, along with the development of new powerful artificial sources of light. However, the described procedures often lack information about the characteristics of the setups (wavelength, actual received light power, distance from the source) even when commercial apparatuses are used. This lacunar information hampers the development of standardized procedures which would guarantee the reproducibility of the reactions. With the objective of furnishing a standardized setup, a lab-made reactor was designed. The use of 3D-printing technology makes it easily accessible to most laboratories. Its characterization showed the critical effect of the environmental conditions upon the light power received and how crucial they are for reproducibility of photochemical reactions. At last, the setup was evaluated against experiments taken from recent literature.

Biological apatites (main constituent of natural bones) correspond to non-stoichiometric hydroxyapatite HAp, presenting a large variety of ions as substituents (CO₃²⁻, F⁻, SiO₄⁴⁻, Mg²⁺, Na⁺…). The precise location and configuration of ionic substitutes in the HAp matrix are generally difficult to identify and characterize. This contribution details the structural characterization based on NMR data of a particular case of hydroxyapatite substitution by carbonates. For this purpose, all substitution mechanisms proposed to our knowledge in the literature are modeled by DFT and the corresponding calculated NMR parameters allowed to propose or confirm some interpretations of a certain number of experimental observations to rationalize the dependencies of the ¹³C chemical shift and energy on these structural parameters. The presented results open the way for a fast interpretation of ¹³C NMR experiments on defective HAp materials and will allow to predict the most stable arrangement of CO₃²⁻ for a given family of defects.