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Analysis of Over 1,600 Chemistry YouTube Channels from 2005 to 2023
No full textChemistry – “the central science” – has found broad appeal on the freely available global video-sharing platform YouTube. Given that YouTube is now almost universally accessible and may be the first place people look to engage with science topics, and that chemistry YouTube videos are now even being cited in the peer-reviewed literature, it is important to know what sort of chemistry YouTube content is available, who is producing it, and who the target audiences are. By applying both manual and semi-automated search methods, we identified and analysed publicly available data for 1,619 chemistry YouTube channels that were available in 2023. We found that (1) the majority of chemistry YouTube channels (84%) were being produced by independent content creators with no clear affiliation to institutions, corporations, or any other body; (2) the background of the majority of chemistry channel creators was not readily apparent (57%); (3) that the majority of videos were aimed at students (71%); (4) that the majority of videos (71%) were focused on chemistry theory or exam revision. The USA, India, and the UK were the top three countries for producing English-language chemistry YouTube content (19%, 11%, and 5% respectively). 51% of chemistry YouTube channels had not posted a video in the 12 months prior to the sampling period in 2023. We also examined the number of videos produced, channel lifespans, the use of features such as playlists and short-form videos, apparent revenue streams (outside of default advertising), the use of other social media, and whether or not channels were exclusively producing chemistry content. We note that chemistry YouTube video production massively increased in 2020, concurrent with the outbreak of the COVID-19 global pandemic. This study and its associated dataset provide the first large-scale ‘census’ of how YouTube is being used for chemistry communication and education worldwide. We expect our findings to be of interest and use to policy makers, funding agencies, educators, content creators, and the public
Key Aspects in Designing High-Throughput Workflows in Electrocatalysis Research: A Case Study on IrCo Mixed-Metal Oxides
No full textWith the growing interest of the electrochemical community in high-throughput (HT) experimentation as a powerful tool in accelerating materials discovery, the implementation of HT methodologies and the design of HT workflows has gained traction. We identify 6 aspects essential to HT workflow design in electrochemistry and beyond to ease the incorporation of HT methods in the community’s research and to assist in their improvement. We study IrCo mixed-metal oxides (MMOs) for the oxygen evolution reaction (OER) in acidic media using the mentioned aspects to provide a practical example of possible workflow design pitfalls and strategies to counteract them
Hydrogen diffusion on Ni(100): A Combined Machine-Learning, Ring Polymer Molecular Dynamics, and Kinetic Monte Carlo Study
No full textWe introduce a methodological framework coupling machine-learning potentials, kinetic Monte Carlo (kMC), and ring polymer molecular dynamics (RPMD) to draw a comprehensive physical picture of the collective diffusion of hydrogen atoms on metal surfaces. For the benchmark case of hydrogen diffusion on a Ni(100) surface, the hydrogen adsorption and diffusion energetics and its dependence on the local coverage is described via a neural-network potential, where the training data is computed via periodic DFT and include all relevant optimized diffusion and desorption paths, sampled by nudged elastic band optimizations and molecular dynamics simulations. Nuclear quantum effects, being crucial for processes involving hydrogen at low temperatures, are treated by RPMD. The diffusion rate constants are calculated with a combination of umbrella samplings employed to map the free energy profile and separate samplings of recrossing trajectories to obtain the transmission coefficient. The calculated diffusion rates for different temperatures and local environments are then combined and fitted into a kMC model allowing to access larger time and length scales. Our results demonstrate an outstanding performance for the trained neural network potential in reproducing reference DFT energies and forces. We report the effective diffusion rates for different temperatures and hydrogen surface coverages obtained via this recipe in good agreement with the experimental results. The method combination proposed in this study can be instrumental for a wide range of applications in materials science
Direct coordination of phenol reductants to copper enables the Cu(II) reduction in Lytic polysaccharide monooxygenases
No full textCopper-dependent lytic polysaccharide monooxygenases (LPMOs) are key enzymes involved in the breakdown of recalcitrant polysaccharides such as cellulose and chitin. LPMOs require external electrons for the activations of either O2 or H2O2, which can be supplied by enzymatic electron donors or small molecule reductants. As quite abundant reductants in nature, phenolic compounds can serve as efficient reducing agents for reactions of LPMOs. Despite extensive studies, how phenolic compounds fuel the reactions of LPMOs is enigmatic. In this study, we report a novel mechanism for the reduction of LPMO-Cu(II) by the phenol reductants. Among various mechanisms investigated, we found the most favorable one involves the coordination re-placement of water by the phenol reductant. The coordination of pyrogallol (Pyr) to LPMO-Cu(II) can remarkably facilitate both the electron transfer from Pyr to Cu(II) and the proton transfer from the ligated OH group to the adjacent Glu148, thereby enhancing proton-coupled electron transfer process for the reduction of LPMO-Cu(II). Detailed comparisons and analysis have shown that the different ligand effects between LPMOs and the copper-dependent pMMO can result in the divergent mecha-nisms for Cu(II) reduction in two enzymes. These insights have greatly expanded our understanding on the interaction machin-ery of copper-dependent enzymes with phenol compounds in nature
Efficient Constitution of a Library of Rotenoid Analogs Active against Trypanosoma cruzi from a Digitalized Plant Extract Collection
No full textNatural products (NP) have proven to be a rich source of potentially bioactive compounds, and metabolomics is the current method of choice for characterizing natural extracts. To integrate the vast amount of data and information produced by modern metabolomics workflows, we recently developed a sample-centric approach for the semantic enrichment and alignment of metabolomics datasets. The resulting Experimental Natural Products Knowledge Graph (ENPKG) is queryable and integrates both newly acquired digitalized experimental data and information, and previously reported knowledge. It allows the highlighting of putative bioactive compounds at the extract level by comparing, for example, the occurrence of compounds of a given chemical class with bioactivity results. Using this approach, we recently described potent anti-Trypanosoma cruzi activity of two rotenoids, deguelin and rotenone. These compounds were identified in six active extracts from four plant species: Cnestis palala (Connaraceae), Chadsia grevei, Pachyrhizus erosus, and Desmodium heterophylum (Fabaceae). In this work, we present the results of the phytochemical investigation of four of these extracts and the establishment of a library of structural analogs for in vitro bioactivity testing. This work led to the isolation, characterization, and biological evaluation of the anti-T. cruzi potential of 41 compounds, including 11 rotenoids and seven compounds reported for the first time. Thanks to modern metabolite annotation and single-step isolation procedures, this work also demonstrates the possibility of considering natural extract libraries as a reservoir of rapidly accessible pure NPs. This perspective could increase the options for NP research and help accelerate NP drug discovery efforts
Microfluidic Sensors for the Detection of Motile Plant Zoospores
No full textPlant pathogen zoospores play a vital role in the transmission of several significant plant diseases, with their early detection being important for effective pathogen management. Current methods for pathogen detection involve labour-intensive specimen collection and laboratory testing, lacking real-time feedback capabilities. Methods that can be deployed in the field and remotely addressed are required. In this proof-of-concept study, we have developed an innovative zoospore-sensing device by combining a microfluidic system that interfaced a flow stream with a microfluidic cytometer, whilst incorporating a chemotactic response as a means to selectively detect motile spores. Spores of Phytophthora cactorum were captured in eddies in a stub at a right-angle bend in a flow channel from which they were able to swim up a detection channel following a gradient of attractant. They were then detected by a transient change in impedance, as measured with a single-chip lock-in amplifier, when they passed between a pair of electrodes. Such a sensing system has a great potential to be further developed into a portable, remotely addressable, low-cost sensing system, offering an important tool for field pathogen real-time detection applications
An integral activity-based protein profiling (IABPP) method for higher throughput determination of protein target sensitivity to small molecules
No full textActivity-based protein profiling (ABPP) is a chemoproteomic technique that uses chemical probes to label active enzymes selectively and covalently in complex proteomes. Competitive ABPP, which involves treatment of the active proteome with an analyte of interest, is especially powerful for profiling how small molecules impact specific protein activities. Advances in higher throughput workflows have made it possible to generate extensive competitive ABPP data across various biological systems and treatments, making this approach highly appealing for characterizing shared and unique proteins affected by perturbations such as drug or chemical exposures. To use the competitive ABPP approach effectively to understand potential adverse effects of chemicals of concern, a wide range of concentrations may be needed, particularly for chemicals that may lack toxicity data. In this work, we present an integral competitive ABPP method that enables target sensitivity differentiation across a wide range of concentrations for the model organophosphate (OP), paraoxon. Using previously developed OP-ABPs, we optimized conditions for tandem mass tag (TMT) multiplexing of ABPP samples and compared conventional competitive ABPP involving discrete samples at various paraoxon concentrations with pooling of samples across that same concentration range. The results show that small vs. large differences in integral intensities for the competitive sample can be used to distinguish low vs. high sensitivity proteins, respectively, without increasing the overall number of samples. We envision the integral ABPP method will provides a means to screen diverse chemicals more rapidly to identify both highly sensitive and less sensitive protein targets
EM-HyChem: Bridging molecular simulations and chemical reaction neural network-enabled approach to modelling energetic material chemistry
No full textThis study introduced a physics-inspired, top-down approach for modelling the reaction kinetics of energetic materials, based on observations of the time scale separation between pyrolysis and oxidation reactions. This modelling approach, named EM-HyChem, was developed with the inspiration of the original hybrid chemistry (HyChem) model, in which the reaction mechanism is divided into two submodels: pyrolysis and oxidation. In EM-HyChem, the key pyrolysis products and reaction mechanism are identified from the perspective of molecular fragments via geometry analysis, which is validated via neural network potential-enabled molecular dynamic simulations. A chemical reaction neural network (CRNN) model is applied to extract the rate parameters for the pyrolysis step from the reproduction of thermogravimetric experiments. An EM-HyChem model is later constructed by combining the pyrolysis step together with the oxidation models for the pyrolysis products. Two representative EMs, i.e., 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) and 1,3,5,7-tetranitro-1,3,5,7-tetrazocane (HMX), are considered here to evaluate the performance of the EM-HyChem model. The predicted burning rates across a wide range of pressure conditions (1–100 atm) are in good agreement with the experimental measurements and the results of other models. Further agreement among the temperate profile, melt layer thickness and surface temperatures support the EM-HyChem model
Electrostatic interaction between SARS-CoV-2 and charged surfaces: Spike protein evolution changed the game
No full textPrevious works show a key role of electrostatics for the SARS-CoV-2 virus in aspects such as virus-cell interactions or virus inactivation by ionic surfactants. Electrostatic
interactions depend strongly on the variant since the charge of the Spike protein (responsible for virus - environment interactions) evolved across the variants from the
highly negative Wild Type (WT) to the highly positive Omicron variant. The distribution of the charge also evolved from diffuse to highly localized. These facts suggest
that SARS-CoV-2 should interact strongly with charged surfaces in a way that changed during the virus evolution.
This question is studied here by computing the electrostatic interaction between WT, Delta and Omicron Spike proteins with charged surfaces using a new method (based on Debye-Huckel theory) that provides efficiently general results as a function of the surface charge density σ. We found that the interaction of the WT and Delta variant spikes with charged surfaces is dominated by repulsive image forces proportional to σ2 originated at the protein/water interface. On the contrary, the Omicron variant shows a distinct behaviour, being strongly attracted to negatively charged surfaces and repelled from positively charged ones. Therefore, the SARS-CoV-2 virus has evolved from being repelled by charged surfaces to being efficiently adsorbing to negatively charged ones
Organozinc reagents in solution: insights from ab initio molecular dynamics and X-ray absorption spectroscopy
No full textOrganozinc reagents play a critical role in synthesis, yet our comprehension of their structure-reactivity relationships is limited by a lack of information about their structures in solution. This study introduces a computational workflow, validated by X-ray absorption spectroscopy, to investigate organozinc reagents in solution. The solvation states of ZnCl2, ZnMeCl and ZnMe2 were explored using ab initio molecular dynamics (metadynamics and Blue Moon sampling) within an explicit solvent cage. The study revealed the existence of various solvation states at room temperature, providing clarity on the previously debated structure of ZnMe2 in THF solution. These findings were confirmed by near- edge X-ray absorption spectroscopy (XANES) interpreted using time-dependent density functional theory (TD-DFT) calculations